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A Gene Gravity Model for the Evolution of Cancer Genomes: A Study of 3,000 Cancer Genomes across 9 Cancer Types

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Lin, Chen-Ching; Zhao, Junfei; Jia, Peilin; Li, Wen-Hsiung; Zhao, Zhongming

2015-01-01

Cancer development and progression result from somatic evolution by an accumulation of genomic alterations. The effects of those alterations on the fitness of somatic cells lead to evolutionary adaptations such as increased cell proliferation, angiogenesis, and altered anticancer drug responses. However, there are few general mathematical models to quantitatively examine how perturbations of a single gene shape subsequent evolution of the cancer genome. In this study, we proposed the gene gravity model to study the evolution of cancer genomes by incorporating the genome-wide transcription and somatic mutation profiles of ~3,000 tumors across 9 cancer types from The Cancer Genome Atlas into a broad gene network. We found that somatic mutations of a cancer driver gene may drive cancer genome evolution by inducing mutations in other genes. This functional consequence is often generated by the combined effect of genetic and epigenetic (e.g., chromatin regulation) alterations. By quantifying cancer genome evolution using the gene gravity model, we identified six putative cancer genes (AHNAK, COL11A1, DDX3X, FAT4, STAG2, and SYNE1). The tumor genomes harboring the nonsynonymous somatic mutations in these genes had a higher mutation density at the genome level compared to the wild-type groups. Furthermore, we provided statistical evidence that hypermutation of cancer driver genes on inactive X chromosomes is a general feature in female cancer genomes. In summary, this study sheds light on the functional consequences and evolutionary characteristics of somatic mutations during tumorigenesis by propelling adaptive cancer genome evolution, which would provide new perspectives for cancer research and therapeutics. PMID:26352260
Genome scale metabolic modeling of cancer

DEFF Research Database (Denmark)

Nilsson, Avlant; Nielsen, Jens

2017-01-01

of metabolism which allows simulation and hypotheses testing of metabolic strategies. It has successfully been applied to many microorganisms and is now used to study cancer metabolism. Generic models of human metabolism have been reconstructed based on the existence of metabolic genes in the human genome......Cancer cells reprogram metabolism to support rapid proliferation and survival. Energy metabolism is particularly important for growth and genes encoding enzymes involved in energy metabolism are frequently altered in cancer cells. A genome scale metabolic model (GEM) is a mathematical formalization...
Human Cancer Models Initiative | Office of Cancer Genomics

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The Human Cancer Models Initiative (HCMI) is an international consortium that is generating novel human tumor-derived culture models, which are annotated with genomic and clinical data. In an effort to advance cancer research and more fully understand how in vitro findings are related to clinical biology, HCMI-developed models and related data will be available as a community resource for cancer research.
Punctuated evolution of prostate cancer genomes.

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Baca, Sylvan C; Prandi, Davide; Lawrence, Michael S; Mosquera, Juan Miguel; Romanel, Alessandro; Drier, Yotam; Park, Kyung; Kitabayashi, Naoki; MacDonald, Theresa Y; Ghandi, Mahmoud; Van Allen, Eliezer; Kryukov, Gregory V; Sboner, Andrea; Theurillat, Jean-Philippe; Soong, T David; Nickerson, Elizabeth; Auclair, Daniel; Tewari, Ashutosh; Beltran, Himisha; Onofrio, Robert C; Boysen, Gunther; Guiducci, Candace; Barbieri, Christopher E; Cibulskis, Kristian; Sivachenko, Andrey; Carter, Scott L; Saksena, Gordon; Voet, Douglas; Ramos, Alex H; Winckler, Wendy; Cipicchio, Michelle; Ardlie, Kristin; Kantoff, Philip W; Berger, Michael F; Gabriel, Stacey B; Golub, Todd R; Meyerson, Matthew; Lander, Eric S; Elemento, Olivier; Getz, Gad; Demichelis, Francesca; Rubin, Mark A; Garraway, Levi A

2013-04-25

The analysis of exonic DNA from prostate cancers has identified recurrently mutated genes, but the spectrum of genome-wide alterations has not been profiled extensively in this disease. We sequenced the genomes of 57 prostate tumors and matched normal tissues to characterize somatic alterations and to study how they accumulate during oncogenesis and progression. By modeling the genesis of genomic rearrangements, we identified abundant DNA translocations and deletions that arise in a highly interdependent manner. This phenomenon, which we term "chromoplexy," frequently accounts for the dysregulation of prostate cancer genes and appears to disrupt multiple cancer genes coordinately. Our modeling suggests that chromoplexy may induce considerable genomic derangement over relatively few events in prostate cancer and other neoplasms, supporting a model of punctuated cancer evolution. By characterizing the clonal hierarchy of genomic lesions in prostate tumors, we charted a path of oncogenic events along which chromoplexy may drive prostate carcinogenesis. Copyright © 2013 Elsevier Inc. All rights reserved.
A site specific model and analysis of the neutral somatic mutation rate in whole-genome cancer data.

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Bertl, Johanna; Guo, Qianyun; Juul, Malene; Besenbacher, Søren; Nielsen, Morten Muhlig; Hornshøj, Henrik; Pedersen, Jakob Skou; Hobolth, Asger

2018-04-19

Detailed modelling of the neutral mutational process in cancer cells is crucial for identifying driver mutations and understanding the mutational mechanisms that act during cancer development. The neutral mutational process is very complex: whole-genome analyses have revealed that the mutation rate differs between cancer types, between patients and along the genome depending on the genetic and epigenetic context. Therefore, methods that predict the number of different types of mutations in regions or specific genomic elements must consider local genomic explanatory variables. A major drawback of most methods is the need to average the explanatory variables across the entire region or genomic element. This procedure is particularly problematic if the explanatory variable varies dramatically in the element under consideration. To take into account the fine scale of the explanatory variables, we model the probabilities of different types of mutations for each position in the genome by multinomial logistic regression. We analyse 505 cancer genomes from 14 different cancer types and compare the performance in predicting mutation rate for both regional based models and site-specific models. We show that for 1000 randomly selected genomic positions, the site-specific model predicts the mutation rate much better than regional based models. We use a forward selection procedure to identify the most important explanatory variables. The procedure identifies site-specific conservation (phyloP), replication timing, and expression level as the best predictors for the mutation rate. Finally, our model confirms and quantifies certain well-known mutational signatures. We find that our site-specific multinomial regression model outperforms the regional based models. The possibility of including genomic variables on different scales and patient specific variables makes it a versatile framework for studying different mutational mechanisms. Our model can serve as the neutral null model
Building a semantic web-based metadata repository for facilitating detailed clinical modeling in cancer genome studies.

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Sharma, Deepak K; Solbrig, Harold R; Tao, Cui; Weng, Chunhua; Chute, Christopher G; Jiang, Guoqian

2017-06-05

Detailed Clinical Models (DCMs) have been regarded as the basis for retaining computable meaning when data are exchanged between heterogeneous computer systems. To better support clinical cancer data capturing and reporting, there is an emerging need to develop informatics solutions for standards-based clinical models in cancer study domains. The objective of the study is to develop and evaluate a cancer genome study metadata management system that serves as a key infrastructure in supporting clinical information modeling in cancer genome study domains. We leveraged a Semantic Web-based metadata repository enhanced with both ISO11179 metadata standard and Clinical Information Modeling Initiative (CIMI) Reference Model. We used the common data elements (CDEs) defined in The Cancer Genome Atlas (TCGA) data dictionary, and extracted the metadata of the CDEs using the NCI Cancer Data Standards Repository (caDSR) CDE dataset rendered in the Resource Description Framework (RDF). The ITEM/ITEM_GROUP pattern defined in the latest CIMI Reference Model is used to represent reusable model elements (mini-Archetypes). We produced a metadata repository with 38 clinical cancer genome study domains, comprising a rich collection of mini-Archetype pattern instances. We performed a case study of the domain "clinical pharmaceutical" in the TCGA data dictionary and demonstrated enriched data elements in the metadata repository are very useful in support of building detailed clinical models. Our informatics approach leveraging Semantic Web technologies provides an effective way to build a CIMI-compliant metadata repository that would facilitate the detailed clinical modeling to support use cases beyond TCGA in clinical cancer study domains.
Identifying anti-growth factors for human cancer cell lines through genome-scale metabolic modeling

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Ghaffari, Pouyan; Mardinoglu, Adil; Asplund, Anna

2015-01-01

Human cancer cell lines are used as important model systems to study molecular mechanisms associated with tumor growth, hereunder how genomic and biological heterogeneity found in primary tumors affect cellular phenotypes. We reconstructed Genome scale metabolic models (GEMs) for eleven cell lines...... based on RNA-Seq data and validated the functionality of these models with data from metabolite profiling. We used cell line-specific GEMs to analyze the differences in the metabolism of cancer cell lines, and to explore the heterogeneous expression of the metabolic subsystems. Furthermore, we predicted...... for inhibition of cell growth may provide leads for the development of efficient cancer treatment strategies....
Genomic instability--an evolving hallmark of cancer.

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Negrini, Simona; Gorgoulis, Vassilis G; Halazonetis, Thanos D

2010-03-01

Genomic instability is a characteristic of most cancers. In hereditary cancers, genomic instability results from mutations in DNA repair genes and drives cancer development, as predicted by the mutator hypothesis. In sporadic (non-hereditary) cancers the molecular basis of genomic instability remains unclear, but recent high-throughput sequencing studies suggest that mutations in DNA repair genes are infrequent before therapy, arguing against the mutator hypothesis for these cancers. Instead, the mutation patterns of the tumour suppressor TP53 (which encodes p53), ataxia telangiectasia mutated (ATM) and cyclin-dependent kinase inhibitor 2A (CDKN2A; which encodes p16INK4A and p14ARF) support the oncogene-induced DNA replication stress model, which attributes genomic instability and TP53 and ATM mutations to oncogene-induced DNA damage.
Warburg effect and translocation-induced genomic instability: two yeast models for cancer cells

International Nuclear Information System (INIS)

Tosato, Valentina; Grüning, Nana-Maria; Breitenbach, Michael; Arnak, Remigiusz; Ralser, Markus; Bruschi, Carlo V.

2013-01-01

Yeast has been established as an efficient model system to study biological principles underpinning human health. In this review we focus on yeast models covering two aspects of cancer formation and progression (i) the activity of pyruvate kinase (PK), which recapitulates metabolic features of cancer cells, including the Warburg effect, and (ii) chromosome bridge-induced translocation (BIT) mimiking genome instability in cancer. Saccharomyces cerevisiae is an excellent model to study cancer cell metabolism, as exponentially growing yeast cells exhibit many metabolic similarities with rapidly proliferating cancer cells. The metabolic reconfiguration includes an increase in glucose uptake and fermentation, at the expense of respiration and oxidative phosphorylation (the Warburg effect), and involves a broad reconfiguration of nucleotide and amino acid metabolism. Both in yeast and humans, the regulation of this process seems to have a central player, PK, which is up-regulated in cancer, and to occur mostly on a post-transcriptional and post-translational basis. Furthermore, BIT allows to generate selectable translocation-derived recombinants (“translocants”), between any two desired chromosomal locations, in wild-type yeast strains transformed with a linear DNA cassette carrying a selectable marker flanked by two DNA sequences homologous to different chromosomes. Using the BIT system, targeted non-reciprocal translocations in mitosis are easily inducible. An extensive collection of different yeast translocants exhibiting genome instability and aberrant phenotypes similar to cancer cells has been produced and subjected to analysis. In this review, we hence provide an overview upon two yeast cancer models, and extrapolate general principles for mimicking human disease mechanisms in yeast.
Warburg effect and translocation-induced genomic instability: two yeast models for cancer cells

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Tosato, Valentina [International Centre for Genetic Engineering and Biotechnology, Trieste (Italy); Grüning, Nana-Maria [Cambridge System Biology Center, Department of Biochemistry, University of Cambridge, Cambridge (United Kingdom); Breitenbach, Michael [Division of Genetics, Department of Cell Biology, University of Salzburg, Salzburg (Austria); Arnak, Remigiusz [International Centre for Genetic Engineering and Biotechnology, Trieste (Italy); Ralser, Markus [Cambridge System Biology Center, Department of Biochemistry, University of Cambridge, Cambridge (United Kingdom); Bruschi, Carlo V., E-mail: bruschi@icgeb.org [International Centre for Genetic Engineering and Biotechnology, Trieste (Italy)

2013-01-18

Yeast has been established as an efficient model system to study biological principles underpinning human health. In this review we focus on yeast models covering two aspects of cancer formation and progression (i) the activity of pyruvate kinase (PK), which recapitulates metabolic features of cancer cells, including the Warburg effect, and (ii) chromosome bridge-induced translocation (BIT) mimiking genome instability in cancer. Saccharomyces cerevisiae is an excellent model to study cancer cell metabolism, as exponentially growing yeast cells exhibit many metabolic similarities with rapidly proliferating cancer cells. The metabolic reconfiguration includes an increase in glucose uptake and fermentation, at the expense of respiration and oxidative phosphorylation (the Warburg effect), and involves a broad reconfiguration of nucleotide and amino acid metabolism. Both in yeast and humans, the regulation of this process seems to have a central player, PK, which is up-regulated in cancer, and to occur mostly on a post-transcriptional and post-translational basis. Furthermore, BIT allows to generate selectable translocation-derived recombinants (“translocants”), between any two desired chromosomal locations, in wild-type yeast strains transformed with a linear DNA cassette carrying a selectable marker flanked by two DNA sequences homologous to different chromosomes. Using the BIT system, targeted non-reciprocal translocations in mitosis are easily inducible. An extensive collection of different yeast translocants exhibiting genome instability and aberrant phenotypes similar to cancer cells has been produced and subjected to analysis. In this review, we hence provide an overview upon two yeast cancer models, and extrapolate general principles for mimicking human disease mechanisms in yeast.
WARBURG EFFECT AND TRANSLOCATION-INDUCED GENOMIC INSTABILITY: TWO YEAST MODELS FOR CANCER CELLS

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Valentina eTosato

2013-01-01

Full Text Available Yeast has been established as an efficient model system to study biological principles underpinning human health. In this review we focus on yeast models covering two aspects of cancer formation and progression i the activity of pyruvate kinase (PK, which recapitulates metabolic features of cancer cells, including the Warburg effect, and ii Bridge-Induced chromosome Translocation (BIT mimicking genome instability in cancer. Saccharomyces cerevisiae is an excellent model to study cancer cell metabolism, as exponentially growing yeast cells exhibit many metabolic similarities with rapidly proliferating cancer cells. The metabolic reconfiguration includes an increase in glucose uptake and fermentation, at the expense of respiration and oxidative phosphorylation (the Warburg effect, and involves a broad reconfiguration of nucleotide and amino acid metabolism. Both in yeast and humans, the regulation of this process seems to have a central player, pyruvate kinase, which is up-regulated in cancer, and to occur mostly on a post-transcriptional and posttranslational basis. Furthermore, BIT allows to generate selectable translocation-derived recombinants (translocants, between any two desired chromosomal locations, in wild-type yeast strains transformed with a linear DNA cassette carrying a selectable marker flanked by two DNA sequences homologous to different chromosomes. Using the Bridge-Induced Translocation system, targeted non-reciprocal translocations in mitosis are easily inducible. An extensive collection of different yeast translocants exhibiting genome instability and aberrant phenotypes similar to cancer cells has been produced and subjected to analysis. In this review, we hence provide an overview upon two yeast cancer models, and extrapolate general principles for mimicking human disease mechanisms in yeast.
Modeling the integration of bacterial rRNA fragments into the human cancer genome.

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Sieber, Karsten B; Gajer, Pawel; Dunning Hotopp, Julie C

2016-03-21

Cancer is a disease driven by the accumulation of genomic alterations, including the integration of exogenous DNA into the human somatic genome. We previously identified in silico evidence of DNA fragments from a Pseudomonas-like bacteria integrating into the 5'-UTR of four proto-oncogenes in stomach cancer sequencing data. The functional and biological consequences of these bacterial DNA integrations remain unknown. Modeling of these integrations suggests that the previously identified sequences cover most of the sequence flanking the junction between the bacterial and human DNA. Further examination of these reads reveals that these integrations are rich in guanine nucleotides and the integrated bacterial DNA may have complex transcript secondary structures. The models presented here lay the foundation for future experiments to test if bacterial DNA integrations alter the transcription of the human genes.
Predictive genomics: A cancer hallmark network framework for predicting tumor clinical phenotypes using genome sequencing data

OpenAIRE

Wang, Edwin; Zaman, Naif; Mcgee, Shauna; Milanese, Jean-Sébastien; Masoudi-Nejad, Ali; O'Connor, Maureen

2014-01-01

We discuss a cancer hallmark network framework for modelling genome-sequencing data to predict cancer clonal evolution and associated clinical phenotypes. Strategies of using this framework in conjunction with genome sequencing data in an attempt to predict personalized drug targets, drug resistance, and metastasis for a cancer patient, as well as cancer risks for a healthy individual are discussed. Accurate prediction of cancer clonal evolution and clinical phenotypes will have substantial i...
Cancer 2015: a longitudinal whole-of-system study of genomic cancer medicine.

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Thomas, David M; Fox, Stephen; Lorgelly, Paula K; Ashley, David; Richardson, Gary; Lipton, Lara; Parisot, John P; Lucas, Mark; McNeil, John; Wright, Michael

2015-12-01

Genomic cancer medicine promises revolutionary change in oncology. The impacts of 'personalized medicine', based upon a molecular classification of cancer and linked to targeted therapies, will extend from individual patient outcomes to the health economy at large. To address the 'whole-of-system' impact of genomic cancer medicine, we have established a prospective cohort of patients with newly diagnosed cancer in the state of Victoria, Australia, about whom we have collected a broad range of clinical, demographic, molecular, and patient-reported data, as well as data on health resource utilization. Our goal is to create a model for investigating public investment in genomic medicine that maximizes the cost:benefit ratio for the Australian community at large. Copyright © 2015 Elsevier Ltd. All rights reserved.
Genome Stability Pathways in Head and Neck Cancers

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Glenn Jenkins

2013-01-01

Full Text Available Genomic instability underlies the transformation of host cells toward malignancy, promotes development of invasion and metastasis and shapes the response of established cancer to treatment. In this review, we discuss recent advances in our understanding of genomic stability in squamous cell carcinoma of the head and neck (HNSCC, with an emphasis on DNA repair pathways. HNSCC is characterized by distinct profiles in genome stability between similarly staged cancers that are reflected in risk, treatment response and outcomes. Defective DNA repair generates chromosomal derangement that can cause subsequent alterations in gene expression, and is a hallmark of progression toward carcinoma. Variable functionality of an increasing spectrum of repair gene polymorphisms is associated with increased cancer risk, while aetiological factors such as human papillomavirus, tobacco and alcohol induce significantly different behaviour in induced malignancy, underpinned by differences in genomic stability. Targeted inhibition of signalling receptors has proven to be a clinically-validated therapy, and protein expression of other DNA repair and signalling molecules associated with cancer behaviour could potentially provide a more refined clinical model for prognosis and treatment prediction. Development and expansion of current genomic stability models is furthering our understanding of HNSCC pathophysiology and uncovering new, promising treatment strategies.
Genome Stability Pathways in Head and Neck Cancers

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O'Byrne, Kenneth J.; Panizza, Benedict; Richard, Derek J.

2013-01-01

Genomic instability underlies the transformation of host cells toward malignancy, promotes development of invasion and metastasis and shapes the response of established cancer to treatment. In this review, we discuss recent advances in our understanding of genomic stability in squamous cell carcinoma of the head and neck (HNSCC), with an emphasis on DNA repair pathways. HNSCC is characterized by distinct profiles in genome stability between similarly staged cancers that are reflected in risk, treatment response and outcomes. Defective DNA repair generates chromosomal derangement that can cause subsequent alterations in gene expression, and is a hallmark of progression toward carcinoma. Variable functionality of an increasing spectrum of repair gene polymorphisms is associated with increased cancer risk, while aetiological factors such as human papillomavirus, tobacco and alcohol induce significantly different behaviour in induced malignancy, underpinned by differences in genomic stability. Targeted inhibition of signalling receptors has proven to be a clinically-validated therapy, and protein expression of other DNA repair and signalling molecules associated with cancer behaviour could potentially provide a more refined clinical model for prognosis and treatment prediction. Development and expansion of current genomic stability models is furthering our understanding of HNSCC pathophysiology and uncovering new, promising treatment strategies. PMID:24364026
A Site Specific Model And Analysis Of The Neutral Somatic Mutation Rate In Whole-Genome Cancer Data

DEFF Research Database (Denmark)

Bertl, Johanna; Guo, Qianyun; Rasmussen, Malene Juul

2017-01-01

Detailed modelling of the neutral mutational process in cancer cells is crucial for identifying driver mutations and understanding the mutational mechanisms that act during cancer development. The neutral mutational process is very complex: whole-genome analyses have revealed that the mutation ra...
The Pediatric Cancer Genome Project

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Downing, James R; Wilson, Richard K; Zhang, Jinghui; Mardis, Elaine R; Pui, Ching-Hon; Ding, Li; Ley, Timothy J; Evans, William E

2013-01-01

The St. Jude Children’s Research Hospital–Washington University Pediatric Cancer Genome Project (PCGP) is participating in the international effort to identify somatic mutations that drive cancer. These cancer genome sequencing efforts will not only yield an unparalleled view of the altered signaling pathways in cancer but should also identify new targets against which novel therapeutics can be developed. Although these projects are still deep in the phase of generating primary DNA sequence data, important results are emerging and valuable community resources are being generated that should catalyze future cancer research. We describe here the rationale for conducting the PCGP, present some of the early results of this project and discuss the major lessons learned and how these will affect the application of genomic sequencing in the clinic. PMID:22641210
Mining genome sequencing data to identify the genomic features linked to breast cancer histopathology

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Ping, Zheng; Siegal, Gene P.; Almeida, Jonas S.; Schnitt, Stuart J.; Shen, Dejun

2014-01-01

Background: Genetics and genomics have radically altered our understanding of breast cancer progression. However, the genomic basis of various histopathologic features of breast cancer is not yet well-defined. Materials and Methods: The Cancer Genome Atlas (TCGA) is an international database containing a large collection of human cancer genome sequencing data. cBioPortal is a web tool developed for mining these sequencing data. We performed mining of TCGA sequencing data in an attempt to characterize the genomic features correlated with breast cancer histopathology. We first assessed the quality of the TCGA data using a group of genes with known alterations in various cancers. Both genome-wide gene mutation and copy number changes as well as a group of genes with a high frequency of genetic changes were then correlated with various histopathologic features of invasive breast cancer. Results: Validation of TCGA data using a group of genes with known alterations in breast cancer suggests that the TCGA has accurately documented the genomic abnormalities of multiple malignancies. Further analysis of TCGA breast cancer sequencing data shows that accumulation of specific genomic defects is associated with higher tumor grade, larger tumor size and receptor negativity. Distinct groups of genomic changes were found to be associated with the different grades of invasive ductal carcinoma. The mutator role of the TP53 gene was validated by genomic sequencing data of invasive breast cancer and TP53 mutation was found to play a critical role in defining high tumor grade. Conclusions: Data mining of the TCGA genome sequencing data is an innovative and reliable method to help characterize the genomic abnormalities associated with histopathologic features of invasive breast cancer. PMID:24672738
Mining genome sequencing data to identify the genomic features linked to breast cancer histopathology

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Zheng Ping

2014-01-01

Full Text Available Background: Genetics and genomics have radically altered our understanding of breast cancer progression. However, the genomic basis of various histopathologic features of breast cancer is not yet well-defined. Materials and Methods: The Cancer Genome Atlas (TCGA is an international database containing a large collection of human cancer genome sequencing data. cBioPortal is a web tool developed for mining these sequencing data. We performed mining of TCGA sequencing data in an attempt to characterize the genomic features correlated with breast cancer histopathology. We first assessed the quality of the TCGA data using a group of genes with known alterations in various cancers. Both genome-wide gene mutation and copy number changes as well as a group of genes with a high frequency of genetic changes were then correlated with various histopathologic features of invasive breast cancer. Results: Validation of TCGA data using a group of genes with known alterations in breast cancer suggests that the TCGA has accurately documented the genomic abnormalities of multiple malignancies. Further analysis of TCGA breast cancer sequencing data shows that accumulation of specific genomic defects is associated with higher tumor grade, larger tumor size and receptor negativity. Distinct groups of genomic changes were found to be associated with the different grades of invasive ductal carcinoma. The mutator role of the TP53 gene was validated by genomic sequencing data of invasive breast cancer and TP53 mutation was found to play a critical role in defining high tumor grade. Conclusions: Data mining of the TCGA genome sequencing data is an innovative and reliable method to help characterize the genomic abnormalities associated with histopathologic features of invasive breast cancer.

Genomic and Epigenomic Alterations in Cancer.

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Chakravarthi, Balabhadrapatruni V S K; Nepal, Saroj; Varambally, Sooryanarayana

2016-07-01

Multiple genetic and epigenetic events characterize tumor progression and define the identity of the tumors. Advances in high-throughput technologies, like gene expression profiling, next-generation sequencing, proteomics, and metabolomics, have enabled detailed molecular characterization of various tumors. The integration and analyses of these high-throughput data have unraveled many novel molecular aberrations and network alterations in tumors. These molecular alterations include multiple cancer-driving mutations, gene fusions, amplification, deletion, and post-translational modifications, among others. Many of these genomic events are being used in cancer diagnosis, whereas others are therapeutically targeted with small-molecule inhibitors. Multiple genes/enzymes that play a role in DNA and histone modifications are also altered in various cancers, changing the epigenomic landscape during cancer initiation and progression. Apart from protein-coding genes, studies are uncovering the critical regulatory roles played by noncoding RNAs and noncoding regions of the genome during cancer progression. Many of these genomic and epigenetic events function in tandem to drive tumor development and metastasis. Concurrent advances in genome-modulating technologies, like gene silencing and genome editing, are providing ability to understand in detail the process of cancer initiation, progression, and signaling as well as opening up avenues for therapeutic targeting. In this review, we discuss some of the recent advances in cancer genomic and epigenomic research. Copyright © 2016 American Society for Investigative Pathology. Published by Elsevier Inc. All rights reserved.
SIGMA: A System for Integrative Genomic Microarray Analysis of Cancer Genomes

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Davies Jonathan J

2006-12-01

Full Text Available Abstract Background The prevalence of high resolution profiling of genomes has created a need for the integrative analysis of information generated from multiple methodologies and platforms. Although the majority of data in the public domain are gene expression profiles, and expression analysis software are available, the increase of array CGH studies has enabled integration of high throughput genomic and gene expression datasets. However, tools for direct mining and analysis of array CGH data are limited. Hence, there is a great need for analytical and display software tailored to cross platform integrative analysis of cancer genomes. Results We have created a user-friendly java application to facilitate sophisticated visualization and analysis such as cross-tumor and cross-platform comparisons. To demonstrate the utility of this software, we assembled array CGH data representing Affymetrix SNP chip, Stanford cDNA arrays and whole genome tiling path array platforms for cross comparison. This cancer genome database contains 267 profiles from commonly used cancer cell lines representing 14 different tissue types. Conclusion In this study we have developed an application for the visualization and analysis of data from high resolution array CGH platforms that can be adapted for analysis of multiple types of high throughput genomic datasets. Furthermore, we invite researchers using array CGH technology to deposit both their raw and processed data, as this will be a continually expanding database of cancer genomes. This publicly available resource, the System for Integrative Genomic Microarray Analysis (SIGMA of cancer genomes, can be accessed at http://sigma.bccrc.ca.
Chapter 27 -- Breast Cancer Genomics, Section VI, Pathology and Biological Markers of Invasive Breast Cancer

Energy Technology Data Exchange (ETDEWEB)

Spellman, Paul T.; Heiser, Laura; Gray, Joe W.

2009-06-18

Breast cancer is predominantly a disease of the genome with cancers arising and progressing through accumulation of aberrations that alter the genome - by changing DNA sequence, copy number, and structure in ways that that contribute to diverse aspects of cancer pathophysiology. Classic examples of genomic events that contribute to breast cancer pathophysiology include inherited mutations in BRCA1, BRCA2, TP53, and CHK2 that contribute to the initiation of breast cancer, amplification of ERBB2 (formerly HER2) and mutations of elements of the PI3-kinase pathway that activate aspects of epidermal growth factor receptor (EGFR) signaling and deletion of CDKN2A/B that contributes to cell cycle deregulation and genome instability. It is now apparent that accumulation of these aberrations is a time-dependent process that accelerates with age. Although American women living to an age of 85 have a 1 in 8 chance of developing breast cancer, the incidence of cancer in women younger than 30 years is uncommon. This is consistent with a multistep cancer progression model whereby mutation and selection drive the tumor's development, analogous to traditional Darwinian evolution. In the case of cancer, the driving events are changes in sequence, copy number, and structure of DNA and alterations in chromatin structure or other epigenetic marks. Our understanding of the genetic, genomic, and epigenomic events that influence the development and progression of breast cancer is increasing at a remarkable rate through application of powerful analysis tools that enable genome-wide analysis of DNA sequence and structure, copy number, allelic loss, and epigenomic modification. Application of these techniques to elucidation of the nature and timing of these events is enriching our understanding of mechanisms that increase breast cancer susceptibility, enable tumor initiation and progression to metastatic disease, and determine therapeutic response or resistance. These studies also
Interest in genomic SNP testing for prostate cancer risk: a pilot survey.

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Hall, Michael J; Ruth, Karen J; Chen, David Yt; Gross, Laura M; Giri, Veda N

2015-01-01

Advancements in genomic testing have led to the identification of single nucleotide polymorphisms (SNPs) associated with prostate cancer. The clinical utility of SNP tests to evaluate prostate cancer risk is unclear. Studies have not examined predictors of interest in novel genomic SNP tests for prostate cancer risk in a diverse population. Consecutive participants in the Fox Chase Prostate Cancer Risk Assessment Program (PRAP) (n = 40) and unselected men from surgical urology clinics (n = 40) completed a one-time survey. Items examined interest in genomic SNP testing for prostate cancer risk, knowledge, impact of unsolicited findings, and psychosocial factors including health literacy. Knowledge of genomic SNP tests was low in both groups, but interest was higher among PRAP men (p testing in both groups. Multivariable modeling identified several predictors of higher interest in a genomic SNP test including higher perceived risk (p = 0.025), indicating zero reasons for not wanting testing (vs ≥1 reason) (p = 0.013), and higher health literacy (p = 0.016). Knowledge of genomic SNP testing was low in this sample, but higher among high-risk men. High-risk status may increase interest in novel genomic tests, while low literacy may lessen interest.
Endometrial and acute myeloid leukemia cancer genomes characterized

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Two studies from The Cancer Genome Atlas (TCGA) program reveal details about the genomic landscapes of acute myeloid leukemia (AML) and endometrial cancer. Both provide new insights into the molecular underpinnings of these cancers.
Genomic and Functional Approaches to Understanding Cancer Aneuploidy.

Science.gov (United States)

Taylor, Alison M; Shih, Juliann; Ha, Gavin; Gao, Galen F; Zhang, Xiaoyang; Berger, Ashton C; Schumacher, Steven E; Wang, Chen; Hu, Hai; Liu, Jianfang; Lazar, Alexander J; Cherniack, Andrew D; Beroukhim, Rameen; Meyerson, Matthew

2018-04-09

Aneuploidy, whole chromosome or chromosome arm imbalance, is a near-universal characteristic of human cancers. In 10,522 cancer genomes from The Cancer Genome Atlas, aneuploidy was correlated with TP53 mutation, somatic mutation rate, and expression of proliferation genes. Aneuploidy was anti-correlated with expression of immune signaling genes, due to decreased leukocyte infiltrates in high-aneuploidy samples. Chromosome arm-level alterations show cancer-specific patterns, including loss of chromosome arm 3p in squamous cancers. We applied genome engineering to delete 3p in lung cells, causing decreased proliferation rescued in part by chromosome 3 duplication. This study defines genomic and phenotypic correlates of cancer aneuploidy and provides an experimental approach to study chromosome arm aneuploidy. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.
Predictive genomics: a cancer hallmark network framework for predicting tumor clinical phenotypes using genome sequencing data.

Science.gov (United States)

Wang, Edwin; Zaman, Naif; Mcgee, Shauna; Milanese, Jean-Sébastien; Masoudi-Nejad, Ali; O'Connor-McCourt, Maureen

2015-02-01

Tumor genome sequencing leads to documenting thousands of DNA mutations and other genomic alterations. At present, these data cannot be analyzed adequately to aid in the understanding of tumorigenesis and its evolution. Moreover, we have little insight into how to use these data to predict clinical phenotypes and tumor progression to better design patient treatment. To meet these challenges, we discuss a cancer hallmark network framework for modeling genome sequencing data to predict cancer clonal evolution and associated clinical phenotypes. The framework includes: (1) cancer hallmarks that can be represented by a few molecular/signaling networks. 'Network operational signatures' which represent gene regulatory logics/strengths enable to quantify state transitions and measures of hallmark traits. Thus, sets of genomic alterations which are associated with network operational signatures could be linked to the state/measure of hallmark traits. The network operational signature transforms genotypic data (i.e., genomic alterations) to regulatory phenotypic profiles (i.e., regulatory logics/strengths), to cellular phenotypic profiles (i.e., hallmark traits) which lead to clinical phenotypic profiles (i.e., a collection of hallmark traits). Furthermore, the framework considers regulatory logics of the hallmark networks under tumor evolutionary dynamics and therefore also includes: (2) a self-promoting positive feedback loop that is dominated by a genomic instability network and a cell survival/proliferation network is the main driver of tumor clonal evolution. Surrounding tumor stroma and its host immune systems shape the evolutionary paths; (3) cell motility initiating metastasis is a byproduct of the above self-promoting loop activity during tumorigenesis; (4) an emerging hallmark network which triggers genome duplication dominates a feed-forward loop which in turn could act as a rate-limiting step for tumor formation; (5) mutations and other genomic alterations have
Genomic and Functional Approaches to Understanding Cancer Aneuploidy

NARCIS (Netherlands)

Taylor, Alison M.; Shih, Juliann; Ha, Gavin; Gao, Galen F.; Zhang, Xiaoyang; Berger, Ashton C.; Schumacher, Steven E.; Wang, Chen; Hu, Hai; Liu, Jianfang; Lazar, Alexander J.; Caesar-Johnson, Samantha J.; Demchok, John A.; Felau, Ina; Kasapi, Melpomeni; Ferguson, Martin L.; Hutter, Carolyn M.; Sofia, Heidi J.; Tarnuzzer, Roy; Wang, Zhining; Yang, Liming; Zenklusen, Jean C.; Zhang, Jiashan (Julia); Chudamani, Sudha; Liu, Jia; Lolla, Laxmi; Naresh, Rashi; Pihl, Todd; Sun, Qiang; Wan, Yunhu; Wu, Ye; Cho, Juok; DeFreitas, Timothy; Frazer, Scott; Gehlenborg, Nils; Getz, Gad; Heiman, David I.; Kim, Jaegil; Lawrence, Michael S.; Lin, Pei; Meier, Sam; Noble, Michael S.; Saksena, Gordon; Voet, Doug; Zhang, Hailei; Bernard, Brady; Chambwe, Nyasha; Dhankani, Varsha; Knijnenburg, Theo; Kramer, Roger; Leinonen, Kalle; Liu, Yuexin; Miller, Michael; Reynolds, Sheila; Shmulevich, Ilya; Thorsson, Vesteinn; Zhang, Wei; Akbani, Rehan; Broom, Bradley M.; Hegde, Apurva M.; Ju, Zhenlin; Kanchi, Rupa S.; Korkut, Anil; Li, Jun; Liang, Han; Ling, Shiyun; Liu, Wenbin; Lu, Yiling; Mills, Gordon B.; Ng, Kwok Shing; Rao, Arvind; Ryan, Michael; Wang, Jing; Weinstein, John N.; Zhang, Jiexin; Abeshouse, Adam; Armenia, Joshua; Chakravarty, Debyani; Chatila, Walid K.; de Bruijn, Ino; Gao, Jianjiong; Gross, Benjamin E.; Heins, Zachary J.; Kundra, Ritika; La, Konnor; Ladanyi, Marc; Luna, Augustin; Nissan, Moriah G.; Ochoa, Angelica; Phillips, Sarah M.; Reznik, Ed; Sanchez-Vega, Francisco; Sander, Chris; Schultz, Nikolaus; Sheridan, Robert; Sumer, S. Onur; Sun, Yichao; Taylor, Barry S.; Wang, Jioajiao; Zhang, Hongxin; Anur, Pavana; Peto, Myron; Spellman, Paul; Benz, Christopher; Stuart, Joshua M.; Wong, Christopher K.; Yau, Christina; Hayes, D. Neil; Parker, Joel S.; Wilkerson, Matthew D.; Ally, Adrian; Balasundaram, Miruna; Bowlby, Reanne; Brooks, Denise; Carlsen, Rebecca; Chuah, Eric; Dhalla, Noreen; Holt, Robert; Jones, Steven J.M.; Kasaian, Katayoon; Lee, Darlene; Ma, Yussanne; Marra, Marco A.; Mayo, Michael; Moore, Richard A.; Mungall, Andrew J.; Mungall, Karen; Robertson, A. Gordon; Sadeghi, Sara; Schein, Jacqueline E.; Sipahimalani, Payal; Tam, Angela; Thiessen, Nina; Tse, Kane; Wong, Tina; Berger, Ashton C.; Beroukhim, Rameen; Cherniack, Andrew D.; Cibulskis, Carrie; Gabriel, Stacey B.; Gao, Galen F.; Ha, Gavin; Meyerson, Matthew; Schumacher, Steven E.; Shih, Juliann; Kucherlapati, Melanie H.; Kucherlapati, Raju S.; Baylin, Stephen; Cope, Leslie; Danilova, Ludmila; Bootwalla, Moiz S.; Lai, Phillip H.; Maglinte, Dennis T.; Van Den Berg, David J.; Weisenberger, Daniel J.; Auman, J. Todd; Balu, Saianand; Bodenheimer, Tom; Fan, Cheng; Hoadley, Katherine A.; Hoyle, Alan P.; Jefferys, Stuart R.; Jones, Corbin D.; Meng, Shaowu; Mieczkowski, Piotr A.; Mose, Lisle E.; Perou, Amy H.; Perou, Charles M.; Roach, Jeffrey; Shi, Yan; Simons, Janae V.; Skelly, Tara; Soloway, Matthew G.; Tan, Donghui; Veluvolu, Umadevi; Fan, Huihui; Hinoue, Toshinori; Laird, Peter W.; Shen, Hui; Zhou, Wanding; Bellair, Michelle; Chang, Kyle; Covington, Kyle; Creighton, Chad J.; Dinh, Huyen; Doddapaneni, Harsha Vardhan; Donehower, Lawrence A.; Drummond, Jennifer; Gibbs, Richard A.; Glenn, Robert; Hale, Walker; Han, Yi; Hu, Jianhong; Korchina, Viktoriya; Lee, Sandra; Lewis, Lora; Li, Wei; Liu, Xiuping; Morgan, Margaret; Morton, Donna; Muzny, Donna; Santibanez, Jireh; Sheth, Margi; Shinbrot, Eve; Wang, Linghua; Wang, Min; Wheeler, David A.; Xi, Liu; Zhao, Fengmei; Hess, Julian; Appelbaum, Elizabeth L.; Bailey, Matthew; Cordes, Matthew G.; Ding, Li; Fronick, Catrina C.; Fulton, Lucinda A.; Fulton, Robert S.; Kandoth, Cyriac; Mardis, Elaine R.; McLellan, Michael D.; Miller, Christopher A.; Schmidt, Heather K.; Wilson, Richard K.; Crain, Daniel; Curley, Erin; Gardner, Johanna; Lau, Kevin; Mallery, David; Morris, Scott; Paulauskis, Joseph; Penny, Robert; Shelton, Candace; Shelton, Troy; Sherman, Mark; Thompson, Eric; Yena, Peggy; Bowen, Jay; Gastier-Foster, Julie M.; Gerken, Mark; Leraas, Kristen M.; Lichtenberg, Tara M.; Ramirez, Nilsa C.; Wise, Lisa; Zmuda, Erik; Corcoran, Niall; Costello, Tony; Hovens, Christopher; Carvalho, Andre L.; de Carvalho, Ana C.; Fregnani, José H.; Longatto-Filho, Adhemar; Reis, Rui M.; Scapulatempo-Neto, Cristovam; Silveira, Henrique C.S.; Vidal, Daniel O.; Burnette, Andrew; Eschbacher, Jennifer; Hermes, Beth; Noss, Ardene; Singh, Rosy; Anderson, Matthew L.; Castro, Patricia D.; Ittmann, Michael; Huntsman, David; Kohl, Bernard; Le, Xuan; Thorp, Richard; Andry, Chris; Duffy, Elizabeth R.; Lyadov, Vladimir; Paklina, Oxana; Setdikova, Galiya; Shabunin, Alexey; Tavobilov, Mikhail; McPherson, Christopher; Warnick, Ronald; Berkowitz, Ross; Cramer, Daniel; Feltmate, Colleen; Horowitz, Neil; Kibel, Adam; Muto, Michael; Raut, Chandrajit P.; Malykh, Andrei; Barnholtz-Sloan, Jill S.; Barrett, Wendi; Devine, Karen; Fulop, Jordonna; Ostrom, Quinn T.; Shimmel, Kristen; Wolinsky, Yingli; Sloan, Andrew E.; De Rose, Agostino; Giuliante, Felice; Goodman, Marc; Karlan, Beth Y.; Hagedorn, Curt H.; Eckman, John; Harr, Jodi; Myers, Jerome; Tucker, Kelinda; Zach, Leigh Anne; Deyarmin, Brenda; Hu, Hai; Kvecher, Leonid; Larson, Caroline; Mural, Richard J.; Somiari, Stella; Vicha, Ales; Zelinka, Tomas; Bennett, Joseph; Iacocca, Mary; Rabeno, Brenda; Swanson, Patricia; Latour, Mathieu; Lacombe, Louis; Têtu, Bernard; Bergeron, Alain; McGraw, Mary; Staugaitis, Susan M.; Chabot, John; Hibshoosh, Hanina; Sepulveda, Antonia; Su, Tao; Wang, Timothy; Potapova, Olga; Voronina, Olga; Desjardins, Laurence; Mariani, Odette; Roman-Roman, Sergio; Sastre, Xavier; Stern, Marc Henri; Cheng, Feixiong; Signoretti, Sabina; Berchuck, Andrew; Bigner, Darell; Lipp, Eric; Marks, Jeffrey; McCall, Shannon; McLendon, Roger; Secord, Angeles; Sharp, Alexis; Behera, Madhusmita; Brat, Daniel J.; Chen, Amy; Delman, Keith; Force, Seth; Khuri, Fadlo; Magliocca, Kelly; Maithel, Shishir; Olson, Jeffrey J.; Owonikoko, Taofeek; Pickens, Alan; Ramalingam, Suresh; Shin, Dong M.; Sica, Gabriel; Van Meir, Erwin G.; Zhang, Hongzheng; Eijckenboom, Wil; Gillis, Ad; Korpershoek, Esther; Looijenga, Leendert; Oosterhuis, Wolter; Stoop, Hans; van Kessel, Kim E.; Zwarthoff, Ellen C.; Calatozzolo, Chiara; Cuppini, Lucia; Cuzzubbo, Stefania; DiMeco, Francesco; Finocchiaro, Gaetano; Mattei, Luca; Perin, Alessandro; Pollo, Bianca; Chen, Chu; Houck, John; Lohavanichbutr, Pawadee; Hartmann, Arndt; Stoehr, Christine; Stoehr, Robert; Taubert, Helge; Wach, Sven; Wullich, Bernd; Kycler, Witold; Murawa, Dawid; Wiznerowicz, Maciej; Chung, Ki; Edenfield, W. Jeffrey; Martin, Julie; Baudin, Eric; Bubley, Glenn; Bueno, Raphael; De Rienzo, Assunta; Richards, William G.; Kalkanis, Steven; Mikkelsen, Tom; Noushmehr, Houtan; Scarpace, Lisa; Girard, Nicolas; Aymerich, Marta; Campo, Elias; Giné, Eva; Guillermo, Armando López; Van Bang, Nguyen; Hanh, Phan Thi; Phu, Bui Duc; Tang, Yufang; Colman, Howard; Evason, Kimberley; Dottino, Peter R.; Martignetti, John A.; Gabra, Hani; Juhl, Hartmut; Akeredolu, Teniola; Stepa, Serghei; Hoon, Dave; Ahn, Keunsoo; Kang, Koo Jeong; Beuschlein, Felix; Breggia, Anne; Birrer, Michael; Bell, Debra; Borad, Mitesh; Bryce, Alan H.; Castle, Erik; Chandan, Vishal; Cheville, John; Copland, John A.; Farnell, Michael; Flotte, Thomas; Giama, Nasra; Ho, Thai; Kendrick, Michael; Kocher, Jean Pierre; Kopp, Karla; Moser, Catherine; Nagorney, David; O'Brien, Daniel; O'Neill, Brian Patrick; Patel, Tushar; Petersen, Gloria; Que, Florencia; Rivera, Michael; Roberts, Lewis; Smallridge, Robert; Smyrk, Thomas; Stanton, Melissa; Thompson, R. Houston; Torbenson, Michael; Yang, Ju Dong; Zhang, Lizhi; Brimo, Fadi; Ajani, Jaffer A.; Angulo Gonzalez, Ana Maria; Behrens, Carmen; Bondaruk, Jolanta; Broaddus, Russell; Czerniak, Bogdan; Esmaeli, Bita; Fujimoto, Junya; Gershenwald, Jeffrey; Guo, Charles; Lazar, Alexander J.; Logothetis, Christopher; Meric-Bernstam, Funda; Moran, Cesar; Ramondetta, Lois; Rice, David; Sood, Anil; Tamboli, Pheroze; Thompson, Timothy; Troncoso, Patricia; Tsao, Anne; Wistuba, Ignacio; Carter, Candace; Haydu, Lauren; Hersey, Peter; Jakrot, Valerie; Kakavand, Hojabr; Kefford, Richard; Lee, Kenneth; Long, Georgina; Mann, Graham; Quinn, Michael; Saw, Robyn; Scolyer, Richard; Shannon, Kerwin; Spillane, Andrew; Stretch, Jonathan; Synott, Maria; Thompson, John; Wilmott, James; Al-Ahmadie, Hikmat; Chan, Timothy A.; Ghossein, Ronald; Gopalan, Anuradha; Levine, Douglas A.; Reuter, Victor; Singer, Samuel; Singh, Bhuvanesh; Tien, Nguyen Viet; Broudy, Thomas; Mirsaidi, Cyrus; Nair, Praveen; Drwiega, Paul; Miller, Judy; Smith, Jennifer; Zaren, Howard; Park, Joong Won; Hung, Nguyen Phi; Kebebew, Electron; Linehan, W. Marston; Metwalli, Adam R.; Pacak, Karel; Pinto, Peter A.; Schiffman, Mark; Schmidt, Laura S.; Vocke, Cathy D.; Wentzensen, Nicolas; Worrell, Robert; Yang, Hannah; Moncrieff, Marc; Goparaju, Chandra; Melamed, Jonathan; Pass, Harvey; Botnariuc, Natalia; Caraman, Irina; Cernat, Mircea; Chemencedji, Inga; Clipca, Adrian; Doruc, Serghei; Gorincioi, Ghenadie; Mura, Sergiu; Pirtac, Maria; Stancul, Irina; Tcaciuc, Diana; Albert, Monique; Alexopoulou, Iakovina; Arnaout, Angel; Bartlett, John; Engel, Jay; Gilbert, Sebastien; Parfitt, Jeremy; Sekhon, Harman; Thomas, George; Rassl, Doris M.; Rintoul, Robert C.; Bifulco, Carlo; Tamakawa, Raina; Urba, Walter; Hayward, Nicholas; Timmers, Henri; Antenucci, Anna; Facciolo, Francesco; Grazi, Gianluca; Marino, Mirella; Merola, Roberta; de Krijger, Ronald; Gimenez-Roqueplo, Anne Paule; Piché, Alain; Chevalier, Simone; McKercher, Ginette; Birsoy, Kivanc; Barnett, Gene; Brewer, Cathy; Farver, Carol; Naska, Theresa; Pennell, Nathan A.; Raymond, Daniel; Schilero, Cathy; Smolenski, Kathy; Williams, Felicia; Morrison, Carl; Borgia, Jeffrey A.; Liptay, Michael J.; Pool, Mark; Seder, Christopher W.; Junker, Kerstin; Omberg, Larsson; Dinkin, Mikhail; Manikhas, George; Alvaro, Domenico; Bragazzi, Maria Consiglia; Cardinale, Vincenzo; Carpino, Guido; Gaudio, Eugenio; Chesla, David; Cottingham, Sandra; Dubina, Michael; Moiseenko, Fedor; Dhanasekaran, Renumathy; Becker, Karl Friedrich; Janssen, Klaus Peter; Slotta-Huspenina, Julia; Abdel-Rahman, Mohamed H.; Aziz, Dina; Bell, Sue; Cebulla, Colleen M.; Davis, Amy; Duell, Rebecca; Elder, J. Bradley; Hilty, Joe; Kumar, Bahavna; Lang, James; Lehman, Norman L.; Mandt, Randy; Nguyen, Phuong; Pilarski, Robert; Rai, Karan; Schoenfield, Lynn; Senecal, Kelly; Wakely, Paul; Hansen, Paul; Lechan, Ronald; Powers, James; Tischler, Arthur; Grizzle, William E.; Sexton, Katherine C.; Kastl, Alison; Henderson, Joel; Porten, Sima; Waldmann, Jens; Fassnacht, Martin; Asa, Sylvia L.; Schadendorf, Dirk; Couce, Marta; Graefen, Markus; Huland, Hartwig; Sauter, Guido; Schlomm, Thorsten; Simon, Ronald; Tennstedt, Pierre; Olabode, Oluwole; Nelson, Mark; Bathe, Oliver; Carroll, Peter R.; Chan, June M.; Disaia, Philip; Glenn, Pat; Kelley, Robin K.; Landen, Charles N.; Phillips, Joanna; Prados, Michael; Simko, Jeffry; Smith-McCune, Karen; VandenBerg, Scott; Roggin, Kevin; Fehrenbach, Ashley; Kendler, Ady; Sifri, Suzanne; Steele, Ruth; Jimeno, Antonio; Carey, Francis; Forgie, Ian; Mannelli, Massimo; Carney, Michael; Hernandez, Brenda; Campos, Benito; Herold-Mende, Christel; Jungk, Christin; Unterberg, Andreas; von Deimling, Andreas; Bossler, Aaron; Galbraith, Joseph; Jacobus, Laura; Knudson, Michael; Knutson, Tina; Ma, Deqin; Milhem, Mohammed; Sigmund, Rita; Godwin, Andrew K.; Madan, Rashna; Rosenthal, Howard G.; Adebamowo, Clement; Adebamowo, Sally N.; Boussioutas, Alex; Beer, David; Giordano, Thomas; Mes-Masson, Anne Marie; Saad, Fred; Bocklage, Therese; Landrum, Lisa; Mannel, Robert; Moore, Kathleen; Moxley, Katherine; Postier, Russel; Walker, Joan; Zuna, Rosemary; Feldman, Michael; Valdivieso, Federico; Dhir, Rajiv; Luketich, James; Mora Pinero, Edna M.; Quintero-Aguilo, Mario; Carlotti, Carlos Gilberto; Dos Santos, Jose Sebastião; Kemp, Rafael; Sankarankuty, Ajith; Tirapelli, Daniela; Catto, James; Agnew, Kathy; Swisher, Elizabeth; Creaney, Jenette; Robinson, Bruce; Shelley, Carl Simon; Godwin, Eryn M.; Kendall, Sara; Shipman, Cassaundra; Bradford, Carol; Carey, Thomas; Haddad, Andrea; Moyer, Jeffey; Peterson, Lisa; Prince, Mark; Rozek, Laura; Wolf, Gregory; Bowman, Rayleen; Fong, Kwun M.; Yang, Ian; Korst, Robert; Rathmell, W. Kimryn; Fantacone-Campbell, J. Leigh; Hooke, Jeffrey A.; Kovatich, Albert J.; Shriver, Craig D.; DiPersio, John; Drake, Bettina; Govindan, Ramaswamy; Heath, Sharon; Ley, Timothy; Van Tine, Brian; Westervelt, Peter; Rubin, Mark A.; Lee, Jung Il; Aredes, Natália D.; Mariamidze, Armaz; Cherniack, Andrew D.; Beroukhim, Rameen; Meyerson, Matthew

2018-01-01

Aneuploidy, whole chromosome or chromosome arm imbalance, is a near-universal characteristic of human cancers. In 10,522 cancer genomes from The Cancer Genome Atlas, aneuploidy was correlated with TP53 mutation, somatic mutation rate, and expression of proliferation genes. Aneuploidy was
Genomic and Molecular Landscape of DNA Damage Repair Deficiency across The Cancer Genome Atlas

Directory of Open Access Journals (Sweden)

Theo A. Knijnenburg

2018-04-01

Full Text Available Summary: DNA damage repair (DDR pathways modulate cancer risk, progression, and therapeutic response. We systematically analyzed somatic alterations to provide a comprehensive view of DDR deficiency across 33 cancer types. Mutations with accompanying loss of heterozygosity were observed in over 1/3 of DDR genes, including TP53 and BRCA1/2. Other prevalent alterations included epigenetic silencing of the direct repair genes EXO5, MGMT, and ALKBH3 in ∼20% of samples. Homologous recombination deficiency (HRD was present at varying frequency in many cancer types, most notably ovarian cancer. However, in contrast to ovarian cancer, HRD was associated with worse outcomes in several other cancers. Protein structure-based analyses allowed us to predict functional consequences of rare, recurrent DDR mutations. A new machine-learning-based classifier developed from gene expression data allowed us to identify alterations that phenocopy deleterious TP53 mutations. These frequent DDR gene alterations in many human cancers have functional consequences that may determine cancer progression and guide therapy. : Knijnenburg et al. present The Cancer Genome Atlas (TCGA Pan-Cancer analysis of DNA damage repair (DDR deficiency in cancer. They use integrative genomic and molecular analyses to identify frequent DDR alterations across 33 cancer types, correlate gene- and pathway-level alterations with genome-wide measures of genome instability and impaired function, and demonstrate the prognostic utility of DDR deficiency scores. Keywords: The Cancer Genome Atlas PanCanAtlas project, DNA damage repair, somatic mutations, somatic copy-number alterations, epigenetic silencing, DNA damage footprints, mutational signatures, integrative statistical analysis, protein structure analysis
Translating the cancer genome: Going beyond p values

Energy Technology Data Exchange (ETDEWEB)

Chin, Lynda; Chin, Lynda; Gray, Joe W.

2008-04-03

Cancer cells are endowed with diverse biological capabilities driven by myriad inherited and somatic genetic and epigenetic aberrations that commandeer key cancer-relevant pathways. Efforts to elucidate these aberrations began with Boveri's hypothesis of aberrant mitoses causing cancer and continue today with a suite of powerful high-resolution technologies that enable detailed catalogues of genomic aberrations and epigenomic modifications. Tomorrow will likely bring the complete atlas of reversible and irreversible alteration in individual cancers. The challenge now is to discern causal molecular abnormalities from genomic and epigenomic 'noise', to understand how the ensemble of these aberrations collaborate to drive cancer pathophysiology. Here, we highlight lessons learned from now classical examples of successful translation of genomic discoveries into clinical practice, lessons that may be used to guide and accelerate translation of emerging genomic insights into practical clinical endpoints that can impact on practice of cancer medicine.
Genome-wide identification of significant aberrations in cancer genome.

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Yuan, Xiguo; Yu, Guoqiang; Hou, Xuchu; Shih, Ie-Ming; Clarke, Robert; Zhang, Junying; Hoffman, Eric P; Wang, Roger R; Zhang, Zhen; Wang, Yue

2012-07-27

Somatic Copy Number Alterations (CNAs) in human genomes are present in almost all human cancers. Systematic efforts to characterize such structural variants must effectively distinguish significant consensus events from random background aberrations. Here we introduce Significant Aberration in Cancer (SAIC), a new method for characterizing and assessing the statistical significance of recurrent CNA units. Three main features of SAIC include: (1) exploiting the intrinsic correlation among consecutive probes to assign a score to each CNA unit instead of single probes; (2) performing permutations on CNA units that preserve correlations inherent in the copy number data; and (3) iteratively detecting Significant Copy Number Aberrations (SCAs) and estimating an unbiased null distribution by applying an SCA-exclusive permutation scheme. We test and compare the performance of SAIC against four peer methods (GISTIC, STAC, KC-SMART, CMDS) on a large number of simulation datasets. Experimental results show that SAIC outperforms peer methods in terms of larger area under the Receiver Operating Characteristics curve and increased detection power. We then apply SAIC to analyze structural genomic aberrations acquired in four real cancer genome-wide copy number data sets (ovarian cancer, metastatic prostate cancer, lung adenocarcinoma, glioblastoma). When compared with previously reported results, SAIC successfully identifies most SCAs known to be of biological significance and associated with oncogenes (e.g., KRAS, CCNE1, and MYC) or tumor suppressor genes (e.g., CDKN2A/B). Furthermore, SAIC identifies a number of novel SCAs in these copy number data that encompass tumor related genes and may warrant further studies. Supported by a well-grounded theoretical framework, SAIC has been developed and used to identify SCAs in various cancer copy number data sets, providing useful information to study the landscape of cancer genomes. Open-source and platform-independent SAIC software is
Pathway and network analysis of cancer genomes

DEFF Research Database (Denmark)

Creixell, Pau; Reimand, Jueri; Haider, Syed

2015-01-01

Genomic information on tumors from 50 cancer types cataloged by the International Cancer Genome Consortium (ICGC) shows that only a few well-studied driver genes are frequently mutated, in contrast to many infrequently mutated genes that may also contribute to tumor biology. Hence there has been...
Computational approaches to identify functional genetic variants in cancer genomes

DEFF Research Database (Denmark)

Gonzalez-Perez, Abel; Mustonen, Ville; Reva, Boris

2013-01-01

The International Cancer Genome Consortium (ICGC) aims to catalog genomic abnormalities in tumors from 50 different cancer types. Genome sequencing reveals hundreds to thousands of somatic mutations in each tumor but only a minority of these drive tumor progression. We present the result of discu......The International Cancer Genome Consortium (ICGC) aims to catalog genomic abnormalities in tumors from 50 different cancer types. Genome sequencing reveals hundreds to thousands of somatic mutations in each tumor but only a minority of these drive tumor progression. We present the result...... of discussions within the ICGC on how to address the challenge of identifying mutations that contribute to oncogenesis, tumor maintenance or response to therapy, and recommend computational techniques to annotate somatic variants and predict their impact on cancer phenotype....
Break Breast Cancer Addiction by CRISPR/Cas9 Genome Editing.

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Yang, Haitao; Jaeger, MariaLynn; Walker, Averi; Wei, Daniel; Leiker, Katie; Weitao, Tao

2018-01-01

Breast cancer is the leading diagnosed cancer for women globally. Evolution of breast cancer in tumorigenesis, metastasis and treatment resistance appears to be driven by the aberrant gene expression and protein degradation encoded by the cancer genomes. The uncontrolled cancer growth relies on these cellular events, thus constituting the cancerous programs and rendering the addiction towards them. These programs are likely the potential anticancer biomarkers for Personalized Medicine of breast cancer. This review intends to delineate the impact of the CRSPR/Cas-mediated genome editing in identification and validation of these anticancer biomarkers. It reviews the progress in three aspects of CRISPR/Cas9-mediated editing of the breast cancer genomes: Somatic genome editing, transcription and protein degradation addictions.
Understanding intratumor heterogeneity by combining genome analysis and mathematical modeling.

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Niida, Atsushi; Nagayama, Satoshi; Miyano, Satoru; Mimori, Koshi

2018-04-01

Cancer is composed of multiple cell populations with different genomes. This phenomenon called intratumor heterogeneity (ITH) is supposed to be a fundamental cause of therapeutic failure. Therefore, its principle-level understanding is a clinically important issue. To achieve this goal, an interdisciplinary approach combining genome analysis and mathematical modeling is essential. For example, we have recently performed multiregion sequencing to unveil extensive ITH in colorectal cancer. Moreover, by employing mathematical modeling of cancer evolution, we demonstrated that it is possible that this ITH is generated by neutral evolution. In this review, we introduce recent advances in a research field related to ITH and also discuss strategies for exploiting novel findings on ITH in a clinical setting. © 2018 The Authors. Cancer Science published by John Wiley & Sons Australia, Ltd on behalf of Japanese Cancer Association.
Functional annotation of rare gene aberration drivers of pancreatic cancer | Office of Cancer Genomics

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As we enter the era of precision medicine, characterization of cancer genomes will directly influence therapeutic decisions in the clinic. Here we describe a platform enabling functionalization of rare gene mutations through their high-throughput construction, molecular barcoding and delivery to cancer models for in vivo tumour driver screens. We apply these technologies to identify oncogenic drivers of pancreatic ductal adenocarcinoma (PDAC).
Childhood Cancer Genomics Gaps and Opportunities - Workshop Summary

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NCI convened a workshop of representative research teams that have been leaders in defining the genomic landscape of childhood cancers to discuss the influence of genomic discoveries on the future of childhood cancer research.
Characterizing genomic alterations in cancer by complementary functional associations.

Science.gov (United States)

Kim, Jong Wook; Botvinnik, Olga B; Abudayyeh, Omar; Birger, Chet; Rosenbluh, Joseph; Shrestha, Yashaswi; Abazeed, Mohamed E; Hammerman, Peter S; DiCara, Daniel; Konieczkowski, David J; Johannessen, Cory M; Liberzon, Arthur; Alizad-Rahvar, Amir Reza; Alexe, Gabriela; Aguirre, Andrew; Ghandi, Mahmoud; Greulich, Heidi; Vazquez, Francisca; Weir, Barbara A; Van Allen, Eliezer M; Tsherniak, Aviad; Shao, Diane D; Zack, Travis I; Noble, Michael; Getz, Gad; Beroukhim, Rameen; Garraway, Levi A; Ardakani, Masoud; Romualdi, Chiara; Sales, Gabriele; Barbie, David A; Boehm, Jesse S; Hahn, William C; Mesirov, Jill P; Tamayo, Pablo

2016-05-01

Systematic efforts to sequence the cancer genome have identified large numbers of mutations and copy number alterations in human cancers. However, elucidating the functional consequences of these variants, and their interactions to drive or maintain oncogenic states, remains a challenge in cancer research. We developed REVEALER, a computational method that identifies combinations of mutually exclusive genomic alterations correlated with functional phenotypes, such as the activation or gene dependency of oncogenic pathways or sensitivity to a drug treatment. We used REVEALER to uncover complementary genomic alterations associated with the transcriptional activation of β-catenin and NRF2, MEK-inhibitor sensitivity, and KRAS dependency. REVEALER successfully identified both known and new associations, demonstrating the power of combining functional profiles with extensive characterization of genomic alterations in cancer genomes.
Modeling the Aneuploidy Control of Cancer

Directory of Open Access Journals (Sweden)

Wang Zhong

2010-07-01

Full Text Available Abstract Background Aneuploidy has long been recognized to be associated with cancer. A growing body of evidence suggests that tumorigenesis, the formation of new tumors, can be attributed to some extent to errors occurring at the mitotic checkpoint, a major cell cycle control mechanism that acts to prevent chromosome missegregation. However, so far no statistical model has been available quantify the role aneuploidy plays in determining cancer. Methods We develop a statistical model for testing the association between aneuploidy loci and cancer risk in a genome-wide association study. The model incorporates quantitative genetic principles into a mixture-model framework in which various genetic effects, including additive, dominant, imprinting, and their interactions, are estimated by implementing the EM algorithm. Results Under the new model, a series of hypotheses tests are formulated to explain the pattern of the genetic control of cancer through aneuploid loci. Simulation studies were performed to investigate the statistical behavior of the model. Conclusions The model will provide a tool for estimating the effects of genetic loci on aneuploidy abnormality in genome-wide studies of cancer cells.
Xenopatients 2.0: reprogramming the epigenetic landscapes of patient-derived cancer genomes.

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Menendez, Javier A; Alarcón, Tomás; Corominas-Faja, Bruna; Cuyàs, Elisabet; López-Bonet, Eugeni; Martin, Angel G; Vellon, Luciano

2014-01-01

In the science-fiction thriller film Minority Report, a specialized police department called "PreCrime" apprehends criminals identified in advance based on foreknowledge provided by 3 genetically altered humans called "PreCogs". We propose that Yamanaka stem cell technology can be similarly used to (epi)genetically reprogram tumor cells obtained directly from cancer patients and create self-evolving personalized translational platforms to foresee the evolutionary trajectory of individual tumors. This strategy yields a large stem cell population and captures the cancer genome of an affected individual, i.e., the PreCog-induced pluripotent stem (iPS) cancer cells, which are immediately available for experimental manipulation, including pharmacological screening for personalized "stemotoxic" cancer drugs. The PreCog-iPS cancer cells will re-differentiate upon orthotopic injection into the corresponding target tissues of immunodeficient mice (i.e., the PreCrime-iPS mouse avatars), and this in vivo model will run through specific cancer stages to directly explore their biological properties for drug screening, diagnosis, and personalized treatment in individual patients. The PreCog/PreCrime-iPS approach can perform sets of comparisons to directly observe changes in the cancer-iPS cell line vs. a normal iPS cell line derived from the same human genetic background. Genome editing of PreCog-iPS cells could create translational platforms to directly investigate the link between genomic expression changes and cellular malignization that is largely free from genetic and epigenetic noise and provide proof-of-principle evidence for cutting-edge "chromosome therapies" aimed against cancer aneuploidy. We might infer the epigenetic marks that correct the tumorigenic nature of the reprogrammed cancer cell population and normalize the malignant phenotype in vivo. Genetically engineered models of conditionally reprogrammable mice to transiently express the Yamanaka stemness factors

Genome-wide identification of significant aberrations in cancer genome

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Yuan Xiguo

2012-07-01

Full Text Available Abstract Background Somatic Copy Number Alterations (CNAs in human genomes are present in almost all human cancers. Systematic efforts to characterize such structural variants must effectively distinguish significant consensus events from random background aberrations. Here we introduce Significant Aberration in Cancer (SAIC, a new method for characterizing and assessing the statistical significance of recurrent CNA units. Three main features of SAIC include: (1 exploiting the intrinsic correlation among consecutive probes to assign a score to each CNA unit instead of single probes; (2 performing permutations on CNA units that preserve correlations inherent in the copy number data; and (3 iteratively detecting Significant Copy Number Aberrations (SCAs and estimating an unbiased null distribution by applying an SCA-exclusive permutation scheme. Results We test and compare the performance of SAIC against four peer methods (GISTIC, STAC, KC-SMART, CMDS on a large number of simulation datasets. Experimental results show that SAIC outperforms peer methods in terms of larger area under the Receiver Operating Characteristics curve and increased detection power. We then apply SAIC to analyze structural genomic aberrations acquired in four real cancer genome-wide copy number data sets (ovarian cancer, metastatic prostate cancer, lung adenocarcinoma, glioblastoma. When compared with previously reported results, SAIC successfully identifies most SCAs known to be of biological significance and associated with oncogenes (e.g., KRAS, CCNE1, and MYC or tumor suppressor genes (e.g., CDKN2A/B. Furthermore, SAIC identifies a number of novel SCAs in these copy number data that encompass tumor related genes and may warrant further studies. Conclusions Supported by a well-grounded theoretical framework, SAIC has been developed and used to identify SCAs in various cancer copy number data sets, providing useful information to study the landscape of cancer genomes
Applications of Support Vector Machine (SVM) Learning in Cancer Genomics.

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Huang, Shujun; Cai, Nianguang; Pacheco, Pedro Penzuti; Narrandes, Shavira; Wang, Yang; Xu, Wayne

2018-01-01

Machine learning with maximization (support) of separating margin (vector), called support vector machine (SVM) learning, is a powerful classification tool that has been used for cancer genomic classification or subtyping. Today, as advancements in high-throughput technologies lead to production of large amounts of genomic and epigenomic data, the classification feature of SVMs is expanding its use in cancer genomics, leading to the discovery of new biomarkers, new drug targets, and a better understanding of cancer driver genes. Herein we reviewed the recent progress of SVMs in cancer genomic studies. We intend to comprehend the strength of the SVM learning and its future perspective in cancer genomic applications. Copyright© 2018, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.
TCGA study identifies genomic features of cervical cancer

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Investigators with The Cancer Genome Atlas (TCGA) Research Network have identified novel genomic and molecular characteristics of cervical cancer that will aid in subclassification of the disease and may help target therapies that are most appropriate for each patient.
Understanding Cancer Genome and Its Evolution by Next Generation Sequencing

DEFF Research Database (Denmark)

Hou, Yong

Cancer will cause 13 million deaths by the year of 2030, ranking the second leading cause of death worldwide. Previous studies indicate that most of the cancers originate from cells that acquired somatic mutations and evolved as Darwin Theory. Ten biological insights of cancer have been summarized...... recently. Cutting-age technologies like next generation sequencing (NGS) enable exploring cancer genome and evolution much more efficiently. However, integrated cancer genome sequencing studies showed great inter-/intra-tumoral heterogeneity (ITH) and complex evolution patterns beyond the cancer biological...... knowledge we previously know. There is very limited knowledge of East Asia lung cancer genome except enrichment of EGFR mutations and lack of KRAS mutations. We carried out integrated genomic, transcriptomic and methylomic analysis of 335 primary Chinese lung adenocarcinomas (LUAD) and 35 corresponding...
Genome Modeling System: A Knowledge Management Platform for Genomics.

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Malachi Griffith

2015-07-01

Full Text Available In this work, we present the Genome Modeling System (GMS, an analysis information management system capable of executing automated genome analysis pipelines at a massive scale. The GMS framework provides detailed tracking of samples and data coupled with reliable and repeatable analysis pipelines. The GMS also serves as a platform for bioinformatics development, allowing a large team to collaborate on data analysis, or an individual researcher to leverage the work of others effectively within its data management system. Rather than separating ad-hoc analysis from rigorous, reproducible pipelines, the GMS promotes systematic integration between the two. As a demonstration of the GMS, we performed an integrated analysis of whole genome, exome and transcriptome sequencing data from a breast cancer cell line (HCC1395 and matched lymphoblastoid line (HCC1395BL. These data are available for users to test the software, complete tutorials and develop novel GMS pipeline configurations. The GMS is available at https://github.com/genome/gms.
Characterization of genomic alterations in radiation-associated breast cancer among childhood cancer survivors, using comparative genomic hybridization (CGH arrays.

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Xiaohong R Yang

Full Text Available Ionizing radiation is an established risk factor for breast cancer. Epidemiologic studies of radiation-exposed cohorts have been primarily descriptive; molecular events responsible for the development of radiation-associated breast cancer have not been elucidated. In this study, we used array comparative genomic hybridization (array-CGH to characterize genome-wide copy number changes in breast tumors collected in the Childhood Cancer Survivor Study (CCSS. Array-CGH data were obtained from 32 cases who developed a second primary breast cancer following chest irradiation at early ages for the treatment of their first cancers, mostly Hodgkin lymphoma. The majority of these cases developed breast cancer before age 45 (91%, n = 29, had invasive ductal tumors (81%, n = 26, estrogen receptor (ER-positive staining (68%, n = 19 out of 28, and high proliferation as indicated by high Ki-67 staining (77%, n = 17 out of 22. Genomic regions with low-copy number gains and losses and high-level amplifications were similar to what has been reported in sporadic breast tumors, however, the frequency of amplifications of the 17q12 region containing human epidermal growth factor receptor 2 (HER2 was much higher among CCSS cases (38%, n = 12. Our findings suggest that second primary breast cancers in CCSS were enriched for an "amplifier" genomic subgroup with highly proliferative breast tumors. Future investigation in a larger irradiated cohort will be needed to confirm our findings.
Genome organization, instabilities, stem cells, and cancer

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Senthil Kumar Pazhanisamy

2009-01-01

Full Text Available It is now widely recognized that advances in exploring genome organization provide remarkable insights on the induction and progression of chromosome abnormalities. Much of what we know about how mutations evolve and consequently transform into genome instabilities has been characterized in the spatial organization context of chromatin. Nevertheless, many underlying concepts of impact of the chromatin organization on perpetuation of multiple mutations and on propagation of chromosomal aberrations remain to be investigated in detail. Genesis of genome instabilities from accumulation of multiple mutations that drive tumorigenesis is increasingly becoming a focal theme in cancer studies. This review focuses on structural alterations evolve to raise a variety of genome instabilities that are manifested at the nucleotide, gene or sub-chromosomal, and whole chromosome level of genome. Here we explore an underlying connection between genome instability and cancer in the light of genome architecture. This review is limited to studies directed towards spatial organizational aspects of origin and propagation of aberrations into genetically unstable tumors.
Conditional Selection of Genomic Alterations Dictates Cancer Evolution and Oncogenic Dependencies.

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Mina, Marco; Raynaud, Franck; Tavernari, Daniele; Battistello, Elena; Sungalee, Stephanie; Saghafinia, Sadegh; Laessle, Titouan; Sanchez-Vega, Francisco; Schultz, Nikolaus; Oricchio, Elisa; Ciriello, Giovanni

2017-08-14

Cancer evolves through the emergence and selection of molecular alterations. Cancer genome profiling has revealed that specific events are more or less likely to be co-selected, suggesting that the selection of one event depends on the others. However, the nature of these evolutionary dependencies and their impact remain unclear. Here, we designed SELECT, an algorithmic approach to systematically identify evolutionary dependencies from alteration patterns. By analyzing 6,456 genomes from multiple tumor types, we constructed a map of oncogenic dependencies associated with cellular pathways, transcriptional readouts, and therapeutic response. Finally, modeling of cancer evolution shows that alteration dependencies emerge only under conditional selection. These results provide a framework for the design of strategies to predict cancer progression and therapeutic response. Copyright © 2017 Elsevier Inc. All rights reserved.
Identification of coding and non-coding mutational hotspots in cancer genomes.

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Piraino, Scott W; Furney, Simon J

2017-01-05

The identification of mutations that play a causal role in tumour development, so called "driver" mutations, is of critical importance for understanding how cancers form and how they might be treated. Several large cancer sequencing projects have identified genes that are recurrently mutated in cancer patients, suggesting a role in tumourigenesis. While the landscape of coding drivers has been extensively studied and many of the most prominent driver genes are well characterised, comparatively less is known about the role of mutations in the non-coding regions of the genome in cancer development. The continuing fall in genome sequencing costs has resulted in a concomitant increase in the number of cancer whole genome sequences being produced, facilitating systematic interrogation of both the coding and non-coding regions of cancer genomes. To examine the mutational landscapes of tumour genomes we have developed a novel method to identify mutational hotspots in tumour genomes using both mutational data and information on evolutionary conservation. We have applied our methodology to over 1300 whole cancer genomes and show that it identifies prominent coding and non-coding regions that are known or highly suspected to play a role in cancer. Importantly, we applied our method to the entire genome, rather than relying on predefined annotations (e.g. promoter regions) and we highlight recurrently mutated regions that may have resulted from increased exposure to mutational processes rather than selection, some of which have been identified previously as targets of selection. Finally, we implicate several pan-cancer and cancer-specific candidate non-coding regions, which could be involved in tumourigenesis. We have developed a framework to identify mutational hotspots in cancer genomes, which is applicable to the entire genome. This framework identifies known and novel coding and non-coding mutional hotspots and can be used to differentiate candidate driver regions from
Perspectives of Integrative Cancer Genomics in Next Generation Sequencing Era

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So Mee Kwon

2012-06-01

Full Text Available The explosive development of genomics technologies including microarrays and next generation sequencing (NGS has provided comprehensive maps of cancer genomes, including the expression of mRNAs and microRNAs, DNA copy numbers, sequence variations, and epigenetic changes. These genome-wide profiles of the genetic aberrations could reveal the candidates for diagnostic and/or prognostic biomarkers as well as mechanistic insights into tumor development and progression. Recent efforts to establish the huge cancer genome compendium and integrative omics analyses, so-called "integromics", have extended our understanding on the cancer genome, showing its daunting complexity and heterogeneity. However, the challenges of the structured integration, sharing, and interpretation of the big omics data still remain to be resolved. Here, we review several issues raised in cancer omics data analysis, including NGS, focusing particularly on the study design and analysis strategies. This might be helpful to understand the current trends and strategies of the rapidly evolving cancer genomics research.
Cancer Genomics: Diversity and Disparity Across Ethnicity and Geography.

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Tan, Daniel S W; Mok, Tony S K; Rebbeck, Timothy R

2016-01-01

Ethnic and geographic differences in cancer incidence, prognosis, and treatment outcomes can be attributed to diversity in the inherited (germline) and somatic genome. Although international large-scale sequencing efforts are beginning to unravel the genomic underpinnings of cancer traits, much remains to be known about the underlying mechanisms and determinants of genomic diversity. Carcinogenesis is a dynamic, complex phenomenon representing the interplay between genetic and environmental factors that results in divergent phenotypes across ethnicities and geography. For example, compared with whites, there is a higher incidence of prostate cancer among Africans and African Americans, and the disease is generally more aggressive and fatal. Genome-wide association studies have identified germline susceptibility loci that may account for differences between the African and non-African patients, but the lack of availability of appropriate cohorts for replication studies and the incomplete understanding of genomic architecture across populations pose major limitations. We further discuss the transformative potential of routine diagnostic evaluation for actionable somatic alterations, using lung cancer as an example, highlighting implications of population disparities, current hurdles in implementation, and the far-reaching potential of clinical genomics in enhancing cancer prevention, diagnosis, and treatment. As we enter the era of precision cancer medicine, a concerted multinational effort is key to addressing population and genomic diversity as well as overcoming barriers and geographical disparities in research and health care delivery. © 2015 by American Society of Clinical Oncology.
Genomic rearrangements of PTEN in prostate cancer

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Sopheap ePhin

2013-09-01

Full Text Available The phosphatase and tensin homolog gene on chromosome 10q23.3 (PTEN is a negative regulator of the PIK3/Akt survival pathway and is the most frequently deleted tumor suppressor gene in prostate cancer. Monoallelic loss of PTEN is present in up to 60% of localized prostate cancers and complete loss of PTEN in prostate cancer is linked to metastasis and androgen independent progression. Studies on the genomic status of PTEN in prostate cancer initially used a two-color fluorescence in-situ hybridization (FISH assay for PTEN copy number detection in formalin fixed paraffin embedded tissue preparations. More recently, a four-color FISH assay containing two additional control probes flanking the PTEN locus with a lower false-positive rate was reported. Combined with the detection of other critical genomic biomarkers for prostate cancer such as ERG, AR, and MYC, the evaluation of PTEN genomic status has proven to be invaluable for patient stratification and management. Although less frequent than allelic deletions, point mutations in the gene and epigenetic silencing are also known to contribute to loss of PTEN function, and ultimately to prostate cancer initiation. Overall, it is clear that PTEN is a powerful biomarker for prostate cancer. Used as a companion diagnostic for emerging therapeutic drugs, FISH analysis of PTEN is promisingly moving human prostate cancer closer to more effective cancer management and therapies.
Context Sensitive Modeling of Cancer Drug Sensitivity.

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Bo-Juen Chen

Full Text Available Recent screening of drug sensitivity in large panels of cancer cell lines provides a valuable resource towards developing algorithms that predict drug response. Since more samples provide increased statistical power, most approaches to prediction of drug sensitivity pool multiple cancer types together without distinction. However, pan-cancer results can be misleading due to the confounding effects of tissues or cancer subtypes. On the other hand, independent analysis for each cancer-type is hampered by small sample size. To balance this trade-off, we present CHER (Contextual Heterogeneity Enabled Regression, an algorithm that builds predictive models for drug sensitivity by selecting predictive genomic features and deciding which ones should-and should not-be shared across different cancers, tissues and drugs. CHER provides significantly more accurate models of drug sensitivity than comparable elastic-net-based models. Moreover, CHER provides better insight into the underlying biological processes by finding a sparse set of shared and type-specific genomic features.
Tolerance of Whole-Genome Doubling Propagates Chromosomal Instability and Accelerates Cancer Genome Evolution

DEFF Research Database (Denmark)

Dewhurst, Sally M.; McGranahan, Nicholas; Burrell, Rebecca A.

2014-01-01

The contribution of whole-genome doubling to chromosomal instability (CIN) and tumor evolution is unclear. We use long-term culture of isogenic tetraploid cells from a stable diploid colon cancer progenitor to investigate how a genome-doubling event affects genome stability over time. Rare cells...
Oncogenomic portals for the visualization and analysis of genome-wide cancer data.

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Klonowska, Katarzyna; Czubak, Karol; Wojciechowska, Marzena; Handschuh, Luiza; Zmienko, Agnieszka; Figlerowicz, Marek; Dams-Kozlowska, Hanna; Kozlowski, Piotr

2016-01-05

Somatically acquired genomic alterations that drive oncogenic cellular processes are of great scientific and clinical interest. Since the initiation of large-scale cancer genomic projects (e.g., the Cancer Genome Project, The Cancer Genome Atlas, and the International Cancer Genome Consortium cancer genome projects), a number of web-based portals have been created to facilitate access to multidimensional oncogenomic data and assist with the interpretation of the data. The portals provide the visualization of small-size mutations, copy number variations, methylation, and gene/protein expression data that can be correlated with the available clinical, epidemiological, and molecular features. Additionally, the portals enable to analyze the gathered data with the use of various user-friendly statistical tools. Herein, we present a highly illustrated review of seven portals, i.e., Tumorscape, UCSC Cancer Genomics Browser, ICGC Data Portal, COSMIC, cBioPortal, IntOGen, and BioProfiling.de. All of the selected portals are user-friendly and can be exploited by scientists from different cancer-associated fields, including those without bioinformatics background. It is expected that the use of the portals will contribute to a better understanding of cancer molecular etiology and will ultimately accelerate the translation of genomic knowledge into clinical practice.
Tumor Genomic Profiling in Breast Cancer Patients Using Targeted Massively Parallel Sequencing

Science.gov (United States)

2016-03-01

2015 “Cancer Care as a Model for Precision Medicine” MIT Collaborative Series Massachusetts Institute of Technology Invited Talk 2016 “Cancer...Precision Medicine” MIT -CHIEF Series Massachusetts Institute of Technology Invited Talk National 2013 “CanSeq: The Use of Whole Exome Sequencing To...Pennsylvania Philadelphia, PA Invited Talk 2014 “Clinical Genomics and Precision Cancer Medicine” Center for Molecular Oncology Memorial Sloan
Childhood Cancer Genomics (PDQ®)—Health Professional Version

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Genomic findings have been useful in the identification of subsets of patients that have distinct biological features and clinical characteristics (such as prognosis) for some pediatric cancers. Learn about the genomic alterations associated with central nervous system, leukemia, lymphoma, liver, sarcoma, neuroblastoma, retinoblastoma, melanoma, kidney, and thyroid cancers in children in this comprehensive summary for clinicians.
Pathways to Genome-targeted Therapies in Serous Ovarian Cancer.

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Axelrod, Joshua; Delaney, Joe

2017-07-01

Genome sequencing technologies and corresponding oncology publications have generated enormous publicly available datasets for many cancer types. While this has enabled new treatments, and in some limited cases lifetime management of the disease, the treatment options for serous ovarian cancer remain dismal. This review summarizes recent advances in our understanding of ovarian cancer, with a focus on heterogeneity, functional genomics, and actionable data.
Microenvironmental Heterogeneity Parallels Breast Cancer Progression: A Histology-Genomic Integration Analysis.

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Rachael Natrajan

2016-02-01

Full Text Available The intra-tumor diversity of cancer cells is under intense investigation; however, little is known about the heterogeneity of the tumor microenvironment that is key to cancer progression and evolution. We aimed to assess the degree of microenvironmental heterogeneity in breast cancer and correlate this with genomic and clinical parameters.We developed a quantitative measure of microenvironmental heterogeneity along three spatial dimensions (3-D in solid tumors, termed the tumor ecosystem diversity index (EDI, using fully automated histology image analysis coupled with statistical measures commonly used in ecology. This measure was compared with disease-specific survival, key mutations, genome-wide copy number, and expression profiling data in a retrospective study of 510 breast cancer patients as a test set and 516 breast cancer patients as an independent validation set. In high-grade (grade 3 breast cancers, we uncovered a striking link between high microenvironmental heterogeneity measured by EDI and a poor prognosis that cannot be explained by tumor size, genomics, or any other data types. However, this association was not observed in low-grade (grade 1 and 2 breast cancers. The prognostic value of EDI was superior to known prognostic factors and was enhanced with the addition of TP53 mutation status (multivariate analysis test set, p = 9 × 10-4, hazard ratio = 1.47, 95% CI 1.17-1.84; validation set, p = 0.0011, hazard ratio = 1.78, 95% CI 1.26-2.52. Integration with genome-wide profiling data identified losses of specific genes on 4p14 and 5q13 that were enriched in grade 3 tumors with high microenvironmental diversity that also substratified patients into poor prognostic groups. Limitations of this study include the number of cell types included in the model, that EDI has prognostic value only in grade 3 tumors, and that our spatial heterogeneity measure was dependent on spatial scale and tumor size.To our knowledge, this is the first
The Genomic Evolution of Prostate Cancer

Science.gov (United States)

2017-06-01

the proposed project : 1. To continue to acquire a comprehensive understanding of prostate cancer genomics . 2. To develop an understanding of... Genetics I • ECEV 35901 Evolutionary Genomics • Fundamentals of Clinical Research • HGEN 47400 Introduction to Probability and Statistics for Geneticists...Marc Gillard,2 David M. Hatcher,5 Westin R. Tom,5 Walter M. Stadler2 and Kevin P. White1,2,3 1Institute for Genomics and Systems Biology , Departments of

Genomics of Colorectal Cancer in African Americans

OpenAIRE

Brim, Hassan; Ashktorab, Hassan

2016-01-01

Genome-wide studies are increasingly becoming a must, especially for complex diseases such as cancer where multiple genes and diverse molecular mechanisms are known to be involved in genes’ function alteration. In this review, we report our latest genomic and epigenomic findings in African-American colorectal cancer patients. This population suffers a higher burden of the disease and most investigators in this field are looking for the underlying genetic and epigenetic targets that might be r...
Genomic Resources for Cancer Epidemiology

Science.gov (United States)

This page provides links to research resources, complied by the Epidemiology and Genomics Research Program, that may be of interest to genetic epidemiologists conducting cancer research, but is not exhaustive.
Next-generation sequence analysis of cancer xenograft models.

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Fernando J Rossello

Full Text Available Next-generation sequencing (NGS studies in cancer are limited by the amount, quality and purity of tissue samples. In this situation, primary xenografts have proven useful preclinical models. However, the presence of mouse-derived stromal cells represents a technical challenge to their use in NGS studies. We examined this problem in an established primary xenograft model of small cell lung cancer (SCLC, a malignancy often diagnosed from small biopsy or needle aspirate samples. Using an in silico strategy that assign reads according to species-of-origin, we prospectively compared NGS data from primary xenograft models with matched cell lines and with published datasets. We show here that low-coverage whole-genome analysis demonstrated remarkable concordance between published genome data and internal controls, despite the presence of mouse genomic DNA. Exome capture sequencing revealed that this enrichment procedure was highly species-specific, with less than 4% of reads aligning to the mouse genome. Human-specific expression profiling with RNA-Seq replicated array-based gene expression experiments, whereas mouse-specific transcript profiles correlated with published datasets from human cancer stroma. We conclude that primary xenografts represent a useful platform for complex NGS analysis in cancer research for tumours with limited sample resources, or those with prominent stromal cell populations.
Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas

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Gregory P. Way

2018-04-01

Full Text Available Summary: Precision oncology uses genomic evidence to match patients with treatment but often fails to identify all patients who may respond. The transcriptome of these “hidden responders” may reveal responsive molecular states. We describe and evaluate a machine-learning approach to classify aberrant pathway activity in tumors, which may aid in hidden responder identification. The algorithm integrates RNA-seq, copy number, and mutations from 33 different cancer types across The Cancer Genome Atlas (TCGA PanCanAtlas project to predict aberrant molecular states in tumors. Applied to the Ras pathway, the method detects Ras activation across cancer types and identifies phenocopying variants. The model, trained on human tumors, can predict response to MEK inhibitors in wild-type Ras cell lines. We also present data that suggest that multiple hits in the Ras pathway confer increased Ras activity. The transcriptome is underused in precision oncology and, combined with machine learning, can aid in the identification of hidden responders. : Way et al. develop a machine-learning approach using PanCanAtlas data to detect Ras activation in cancer. Integrating mutation, copy number, and expression data, the authors show that their method detects Ras-activating variants in tumors and sensitivity to MEK inhibitors in cell lines. Keywords: Gene expression, machine learning, Ras, NF1, KRAS, NRAS, HRAS, pan-cancer, TCGA, drug sensitivity
Integrated analysis of whole genome and transcriptome sequencing reveals diverse transcriptomic aberrations driven by somatic genomic changes in liver cancers.

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Yuichi Shiraishi

Full Text Available Recent studies applying high-throughput sequencing technologies have identified several recurrently mutated genes and pathways in multiple cancer genomes. However, transcriptional consequences from these genomic alterations in cancer genome remain unclear. In this study, we performed integrated and comparative analyses of whole genomes and transcriptomes of 22 hepatitis B virus (HBV-related hepatocellular carcinomas (HCCs and their matched controls. Comparison of whole genome sequence (WGS and RNA-Seq revealed much evidence that various types of genomic mutations triggered diverse transcriptional changes. Not only splice-site mutations, but also silent mutations in coding regions, deep intronic mutations and structural changes caused splicing aberrations. HBV integrations generated diverse patterns of virus-human fusion transcripts depending on affected gene, such as TERT, CDK15, FN1 and MLL4. Structural variations could drive over-expression of genes such as WNT ligands, with/without creating gene fusions. Furthermore, by taking account of genomic mutations causing transcriptional aberrations, we could improve the sensitivity of deleterious mutation detection in known cancer driver genes (TP53, AXIN1, ARID2, RPS6KA3, and identified recurrent disruptions in putative cancer driver genes such as HNF4A, CPS1, TSC1 and THRAP3 in HCCs. These findings indicate genomic alterations in cancer genome have diverse transcriptomic effects, and integrated analysis of WGS and RNA-Seq can facilitate the interpretation of a large number of genomic alterations detected in cancer genome.
A Genome-Wide Breast Cancer Scan in African Americans

Science.gov (United States)

2010-06-01

SNPs from the African American breast cancer scan to COGs , a European collaborative study which is has designed a SNP array with that will be genotyped...Award Number: W81XWH-08-1-0383 TITLE: A Genome-wide Breast Cancer Scan in African Americans PRINCIPAL INVESTIGATOR: Christopher A...SUBTITLE A Genome-wide Breast Cancer Scan in African Americans 5a. CONTRACT NUMBER 5b. GRANT NUMBER W81XWH-08-1-0383 5c. PROGRAM
Providing guidance for genomics-based cancer treatment decisions: insights from stakeholder engagement for post-prostatectomy radiation therapy.

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Abe, James; Lobo, Jennifer M; Trifiletti, Daniel M; Showalter, Timothy N

2017-08-24

Despite the emergence of genomics-based risk prediction tools in oncology, there is not yet an established framework for communication of test results to cancer patients to support shared decision-making. We report findings from a stakeholder engagement program that aimed to develop a framework for using Markov models with individualized model inputs, including genomics-based estimates of cancer recurrence probability, to generate personalized decision aids for prostate cancer patients faced with radiation therapy treatment decisions after prostatectomy. We engaged a total of 22 stakeholders, including: prostate cancer patients, urological surgeons, radiation oncologists, genomic testing industry representatives, and biomedical informatics faculty. Slides were at each meeting to provide background information regarding the analytical framework. Participants were invited to provide feedback during the meeting, including revising the overall project aims. Stakeholder meeting content was reviewed and summarized by stakeholder group and by theme. The majority of stakeholder suggestions focused on aspects of decision aid design and formatting. Stakeholders were enthusiastic about the potential value of using decision analysis modeling with personalized model inputs for cancer recurrence risk, as well as competing risks from age and comorbidities, to generate a patient-centered tool to assist decision-making. Stakeholders did not view privacy considerations as a major barrier to the proposed decision aid program. A common theme was that decision aids should be portable across multiple platforms (electronic and paper), should allow for interaction by the user to adjust model inputs iteratively, and available to patients both before and during consult appointments. Emphasis was placed on the challenge of explaining the model's composite result of quality-adjusted life years. A range of stakeholders provided valuable insights regarding the design of a personalized decision
Perspective on Oncogenic Processes at the End of the Beginning of Cancer Genomics

NARCIS (Netherlands)

Ding, Li; Bailey, Matthew H.; Porta-Pardo, Eduard; Thorsson, Vesteinn; Colaprico, Antonio; Bertrand, Denis; Gibbs, David L.; Weerasinghe, Amila; Huang, Kuan lin; Tokheim, Collin; Cortés-Ciriano, Isidro; Jayasinghe, Reyka; Chen, Feng; Yu, Lihua; Sun, Sam; Olsen, Catharina; Kim, Jaegil; Taylor, Alison M.; Cherniack, Andrew D.; Akbani, Rehan; Suphavilai, Chayaporn; Nagarajan, Niranjan; Stuart, Joshua M.; Mills, Gordon B.; Wyczalkowski, Matthew A.; Vincent, Benjamin G.; Hutter, Carolyn M.; Zenklusen, Jean Claude; Hoadley, Katherine A.; Wendl, Michael C.; Shmulevich, llya; Lazar, Alexander J.; Wheeler, David A.; Getz, Gad; Caesar-Johnson, Samantha J.; Demchok, John A.; Felau, Ina; Kasapi, Melpomeni; Ferguson, Martin L.; Hutter, Carolyn M.; Sofia, Heidi J.; Tarnuzzer, Roy; Wang, Zhining; Yang, Liming; Zenklusen, Jean C.; Zhang, Jiashan (Julia); Chudamani, Sudha; Liu, Jia; Lolla, Laxmi; Naresh, Rashi; Pihl, Todd; Sun, Qiang; Wan, Yunhu; Wu, Ye; Cho, Juok; DeFreitas, Timothy; Frazer, Scott; Gehlenborg, Nils; Getz, Gad; Heiman, David I.; Kim, Jaegil; Lawrence, Michael S.; Lin, Pei; Meier, Sam; Noble, Michael S.; Saksena, Gordon; Voet, Doug; Zhang, Hailei; Bernard, Brady; Chambwe, Nyasha; Dhankani, Varsha; Knijnenburg, Theo; Kramer, Roger; Leinonen, Kalle; Liu, Yuexin; Miller, Michael; Reynolds, Sheila; Shmulevich, Ilya; Thorsson, Vesteinn; Zhang, Wei; Akbani, Rehan; Broom, Bradley M.; Hegde, Apurva M.; Ju, Zhenlin; Kanchi, Rupa S.; Korkut, Anil; Li, Jun; Liang, Han; Ling, Shiyun; Liu, Wenbin; Lu, Yiling; Mills, Gordon B.; Ng, Kwok Shing; Rao, Arvind; Ryan, Michael; Wang, Jing; Weinstein, John N.; Zhang, Jiexin; Abeshouse, Adam; Armenia, Joshua; Chakravarty, Debyani; Chatila, Walid K.; de Bruijn, Ino; Gao, Jianjiong; Gross, Benjamin E.; Heins, Zachary J.; Kundra, Ritika; La, Konnor; Ladanyi, Marc; Luna, Augustin; Nissan, Moriah G.; Ochoa, Angelica; Phillips, Sarah M.; Reznik, Ed; Sanchez-Vega, Francisco; Sander, Chris; Schultz, Nikolaus; Sheridan, Robert; Sumer, S. Onur; Sun, Yichao; Taylor, Barry S.; Wang, Jioajiao; Zhang, Hongxin; Anur, Pavana; Peto, Myron; Spellman, Paul; Benz, Christopher; Stuart, Joshua M.; Wong, Christopher K.; Yau, Christina; Hayes, D. Neil; Parker, Joel S.; Wilkerson, Matthew D.; Ally, Adrian; Balasundaram, Miruna; Bowlby, Reanne; Brooks, Denise; Carlsen, Rebecca; Chuah, Eric; Dhalla, Noreen; Holt, Robert; Jones, Steven J.M.; Kasaian, Katayoon; Lee, Darlene; Ma, Yussanne; Marra, Marco A.; Mayo, Michael; Moore, Richard A.; Mungall, Andrew J.; Mungall, Karen; Robertson, A. Gordon; Sadeghi, Sara; Schein, Jacqueline E.; Sipahimalani, Payal; Tam, Angela; Thiessen, Nina; Tse, Kane; Wong, Tina; Berger, Ashton C.; Beroukhim, Rameen; Cherniack, Andrew D.; Cibulskis, Carrie; Gabriel, Stacey B.; Gao, Galen F.; Ha, Gavin; Meyerson, Matthew; Schumacher, Steven E.; Shih, Juliann; Kucherlapati, Melanie H.; Kucherlapati, Raju S.; Baylin, Stephen; Cope, Leslie; Danilova, Ludmila; Bootwalla, Moiz S.; Lai, Phillip H.; Maglinte, Dennis T.; Van Den Berg, David J.; Weisenberger, Daniel J.; Auman, J. Todd; Balu, Saianand; Bodenheimer, Tom; Fan, Cheng; Hoadley, Katherine A.; Hoyle, Alan P.; Jefferys, Stuart R.; Jones, Corbin D.; Meng, Shaowu; Mieczkowski, Piotr A.; Mose, Lisle E.; Perou, Amy H.; Perou, Charles M.; Roach, Jeffrey; Shi, Yan; Simons, Janae V.; Skelly, Tara; Soloway, Matthew G.; Tan, Donghui; Veluvolu, Umadevi; Fan, Huihui; Hinoue, Toshinori; Laird, Peter W.; Shen, Hui; Zhou, Wanding; Bellair, Michelle; Chang, Kyle; Covington, Kyle; Creighton, Chad J.; Dinh, Huyen; Doddapaneni, Harsha Vardhan; Donehower, Lawrence A.; Drummond, Jennifer; Gibbs, Richard A.; Glenn, Robert; Hale, Walker; Han, Yi; Hu, Jianhong; Korchina, Viktoriya; Lee, Sandra; Lewis, Lora; Li, Wei; Liu, Xiuping; Morgan, Margaret; Morton, Donna; Muzny, Donna; Santibanez, Jireh; Sheth, Margi; Shinbrot, Eve; Wang, Linghua; Wang, Min; Wheeler, David A.; Xi, Liu; Zhao, Fengmei; Hess, Julian; Appelbaum, Elizabeth L.; Bailey, Matthew; Cordes, Matthew G.; Ding, Li; Fronick, Catrina C.; Fulton, Lucinda A.; Fulton, Robert S.; Kandoth, Cyriac; Mardis, Elaine R.; McLellan, Michael D.; Miller, Christopher A.; Schmidt, Heather K.; Wilson, Richard K.; Crain, Daniel; Curley, Erin; Gardner, Johanna; Lau, Kevin; Mallery, David; Morris, Scott; Paulauskis, Joseph; Penny, Robert; Shelton, Candace; Shelton, Troy; Sherman, Mark; Thompson, Eric; Yena, Peggy; Bowen, Jay; Gastier-Foster, Julie M.; Gerken, Mark; Leraas, Kristen M.; Lichtenberg, Tara M.; Ramirez, Nilsa C.; Wise, Lisa; Zmuda, Erik; Corcoran, Niall; Costello, Tony; Hovens, Christopher; Carvalho, Andre L.; de Carvalho, Ana C.; Fregnani, José H.; Longatto-Filho, Adhemar; Reis, Rui M.; Scapulatempo-Neto, Cristovam; Silveira, Henrique C.S.; Vidal, Daniel O.; Burnette, Andrew; Eschbacher, Jennifer; Hermes, Beth; Noss, Ardene; Singh, Rosy; Anderson, Matthew L.; Castro, Patricia D.; Ittmann, Michael; Huntsman, David; Kohl, Bernard; Le, Xuan; Thorp, Richard; Andry, Chris; Duffy, Elizabeth R.; Lyadov, Vladimir; Paklina, Oxana; Setdikova, Galiya; Shabunin, Alexey; Tavobilov, Mikhail; McPherson, Christopher; Warnick, Ronald; Berkowitz, Ross; Cramer, Daniel; Feltmate, Colleen; Horowitz, Neil; Kibel, Adam; Muto, Michael; Raut, Chandrajit P.; Malykh, Andrei; Barnholtz-Sloan, Jill S.; Barrett, Wendi; Devine, Karen; Fulop, Jordonna; Ostrom, Quinn T.; Shimmel, Kristen; Wolinsky, Yingli; Sloan, Andrew E.; De Rose, Agostino; Giuliante, Felice; Goodman, Marc; Karlan, Beth Y.; Hagedorn, Curt H.; Eckman, John; Harr, Jodi; Myers, Jerome; Tucker, Kelinda; Zach, Leigh Anne; Deyarmin, Brenda; Hu, Hai; Kvecher, Leonid; Larson, Caroline; Mural, Richard J.; Somiari, Stella; Vicha, Ales; Zelinka, Tomas; Bennett, Joseph; Iacocca, Mary; Rabeno, Brenda; Swanson, Patricia; Latour, Mathieu; Lacombe, Louis; Têtu, Bernard; Bergeron, Alain; McGraw, Mary; Staugaitis, Susan M.; Chabot, John; Hibshoosh, Hanina; Sepulveda, Antonia; Su, Tao; Wang, Timothy; Potapova, Olga; Voronina, Olga; Desjardins, Laurence; Mariani, Odette; Roman-Roman, Sergio; Sastre, Xavier; Stern, Marc Henri; Cheng, Feixiong; Signoretti, Sabina; Berchuck, Andrew; Bigner, Darell; Lipp, Eric; Marks, Jeffrey; McCall, Shannon; McLendon, Roger; Secord, Angeles; Sharp, Alexis; Behera, Madhusmita; Brat, Daniel J.; Chen, Amy; Delman, Keith; Force, Seth; Khuri, Fadlo; Magliocca, Kelly; Maithel, Shishir; Olson, Jeffrey J.; Owonikoko, Taofeek; Pickens, Alan; Ramalingam, Suresh; Shin, Dong M.; Sica, Gabriel; Van Meir, Erwin G.; Zhang, Hongzheng; Eijckenboom, Wil; Gillis, Ad; Korpershoek, Esther; Looijenga, Leendert; Oosterhuis, Wolter; Stoop, Hans; van Kessel, Kim E.; Zwarthoff, Ellen C.; Calatozzolo, Chiara; Cuppini, Lucia; Cuzzubbo, Stefania; DiMeco, Francesco; Finocchiaro, Gaetano; Mattei, Luca; Perin, Alessandro; Pollo, Bianca; Chen, Chu; Houck, John; Lohavanichbutr, Pawadee; Hartmann, Arndt; Stoehr, Christine; Stoehr, Robert; Taubert, Helge; Wach, Sven; Wullich, Bernd; Kycler, Witold; Murawa, Dawid; Wiznerowicz, Maciej; Chung, Ki; Edenfield, W. Jeffrey; Martin, Julie; Baudin, Eric; Bubley, Glenn; Bueno, Raphael; De Rienzo, Assunta; Richards, William G.; Kalkanis, Steven; Mikkelsen, Tom; Noushmehr, Houtan; Scarpace, Lisa; Girard, Nicolas; Aymerich, Marta; Campo, Elias; Giné, Eva; Guillermo, Armando López; Van Bang, Nguyen; Hanh, Phan Thi; Phu, Bui Duc; Tang, Yufang; Colman, Howard; Evason, Kimberley; Dottino, Peter R.; Martignetti, John A.; Gabra, Hani; Juhl, Hartmut; Akeredolu, Teniola; Stepa, Serghei; Hoon, Dave; Ahn, Keunsoo; Kang, Koo Jeong; Beuschlein, Felix; Breggia, Anne; Birrer, Michael; Bell, Debra; Borad, Mitesh; Bryce, Alan H.; Castle, Erik; Chandan, Vishal; Cheville, John; Copland, John A.; Farnell, Michael; Flotte, Thomas; Giama, Nasra; Ho, Thai; Kendrick, Michael; Kocher, Jean Pierre; Kopp, Karla; Moser, Catherine; Nagorney, David; O'Brien, Daniel; O'Neill, Brian Patrick; Patel, Tushar; Petersen, Gloria; Que, Florencia; Rivera, Michael; Roberts, Lewis; Smallridge, Robert; Smyrk, Thomas; Stanton, Melissa; Thompson, R. Houston; Torbenson, Michael; Yang, Ju Dong; Zhang, Lizhi; Brimo, Fadi; Ajani, Jaffer A.; Gonzalez, Ana Maria Angulo; Behrens, Carmen; Bondaruk, Jolanta; Broaddus, Russell; Czerniak, Bogdan; Esmaeli, Bita; Fujimoto, Junya; Gershenwald, Jeffrey; Guo, Charles; Lazar, Alexander J.; Logothetis, Christopher; Meric-Bernstam, Funda; Moran, Cesar; Ramondetta, Lois; Rice, David; Sood, Anil; Tamboli, Pheroze; Thompson, Timothy; Troncoso, Patricia; Tsao, Anne; Wistuba, Ignacio; Carter, Candace; Haydu, Lauren; Hersey, Peter; Jakrot, Valerie; Kakavand, Hojabr; Kefford, Richard; Lee, Kenneth; Long, Georgina; Mann, Graham; Quinn, Michael; Saw, Robyn; Scolyer, Richard; Shannon, Kerwin; Spillane, Andrew; Stretch, Jonathan; Synott, Maria; Thompson, John; Wilmott, James; Al-Ahmadie, Hikmat; Chan, Timothy A.; Ghossein, Ronald; Gopalan, Anuradha; Levine, Douglas A.; Reuter, Victor; Singer, Samuel; Singh, Bhuvanesh; Tien, Nguyen Viet; Broudy, Thomas; Mirsaidi, Cyrus; Nair, Praveen; Drwiega, Paul; Miller, Judy; Smith, Jennifer; Zaren, Howard; Park, Joong Won; Hung, Nguyen Phi; Kebebew, Electron; Linehan, W. Marston; Metwalli, Adam R.; Pacak, Karel; Pinto, Peter A.; Schiffman, Mark; Schmidt, Laura S.; Vocke, Cathy D.; Wentzensen, Nicolas; Worrell, Robert; Yang, Hannah; Moncrieff, Marc; Goparaju, Chandra; Melamed, Jonathan; Pass, Harvey; Botnariuc, Natalia; Caraman, Irina; Cernat, Mircea; Chemencedji, Inga; Clipca, Adrian; Doruc, Serghei; Gorincioi, Ghenadie; Mura, Sergiu; Pirtac, Maria; Stancul, Irina; Tcaciuc, Diana; Albert, Monique; Alexopoulou, Iakovina; Arnaout, Angel; Bartlett, John; Engel, Jay; Gilbert, Sebastien; Parfitt, Jeremy; Sekhon, Harman; Thomas, George; Rassl, Doris M.; Rintoul, Robert C.; Bifulco, Carlo; Tamakawa, Raina; Urba, Walter; Hayward, Nicholas; Timmers, Henri; Antenucci, Anna; Facciolo, Francesco; Grazi, Gianluca; Marino, Mirella; Merola, Roberta; de Krijger, Ronald; Gimenez-Roqueplo, Anne Paule; Piché, Alain; Chevalier, Simone; McKercher, Ginette; Birsoy, Kivanc; Barnett, Gene; Brewer, Cathy; Farver, Carol; Naska, Theresa; Pennell, Nathan A.; Raymond, Daniel; Schilero, Cathy; Smolenski, Kathy; Williams, Felicia; Morrison, Carl; Borgia, Jeffrey A.; Liptay, Michael J.; Pool, Mark; Seder, Christopher W.; Junker, Kerstin; Omberg, Larsson; Dinkin, Mikhail; Manikhas, George; Alvaro, Domenico; Bragazzi, Maria Consiglia; Cardinale, Vincenzo; Carpino, Guido; Gaudio, Eugenio; Chesla, David; Cottingham, Sandra; Dubina, Michael; Moiseenko, Fedor; Dhanasekaran, Renumathy; Becker, Karl Friedrich; Janssen, Klaus Peter; Slotta-Huspenina, Julia; Abdel-Rahman, Mohamed H.; Aziz, Dina; Bell, Sue; Cebulla, Colleen M.; Davis, Amy; Duell, Rebecca; Elder, J. Bradley; Hilty, Joe; Kumar, Bahavna; Lang, James; Lehman, Norman L.; Mandt, Randy; Nguyen, Phuong; Pilarski, Robert; Rai, Karan; Schoenfield, Lynn; Senecal, Kelly; Wakely, Paul; Hansen, Paul; Lechan, Ronald; Powers, James; Tischler, Arthur; Grizzle, William E.; Sexton, Katherine C.; Kastl, Alison; Henderson, Joel; Porten, Sima; Waldmann, Jens; Fassnacht, Martin; Asa, Sylvia L.; Schadendorf, Dirk; Couce, Marta; Graefen, Markus; Huland, Hartwig; Sauter, Guido; Schlomm, Thorsten; Simon, Ronald; Tennstedt, Pierre; Olabode, Oluwole; Nelson, Mark; Bathe, Oliver; Carroll, Peter R.; Chan, June M.; Disaia, Philip; Glenn, Pat; Kelley, Robin K.; Landen, Charles N.; Phillips, Joanna; Prados, Michael; Simko, Jeffry; Smith-McCune, Karen; VandenBerg, Scott; Roggin, Kevin; Fehrenbach, Ashley; Kendler, Ady; Sifri, Suzanne; Steele, Ruth; Jimeno, Antonio; Carey, Francis; Forgie, Ian; Mannelli, Massimo; Carney, Michael; Hernandez, Brenda; Campos, Benito; Herold-Mende, Christel; Jungk, Christin; Unterberg, Andreas; von Deimling, Andreas; Bossler, Aaron; Galbraith, Joseph; Jacobus, Laura; Knudson, Michael; Knutson, Tina; Ma, Deqin; Milhem, Mohammed; Sigmund, Rita; Godwin, Andrew K.; Madan, Rashna; Rosenthal, Howard G.; Adebamowo, Clement; Adebamowo, Sally N.; Boussioutas, Alex; Beer, David; Giordano, Thomas; Mes-Masson, Anne Marie; Saad, Fred; Bocklage, Therese; Landrum, Lisa; Mannel, Robert; Moore, Kathleen; Moxley, Katherine; Postier, Russel; Walker, Joan; Zuna, Rosemary; Feldman, Michael; Valdivieso, Federico; Dhir, Rajiv; Luketich, James; Pinero, Edna M.Mora; Quintero-Aguilo, Mario; Carlotti, Carlos Gilberto; Dos Santos, Jose Sebastião; Kemp, Rafael; Sankarankuty, Ajith; Tirapelli, Daniela; Catto, James; Agnew, Kathy; Swisher, Elizabeth; Creaney, Jenette; Robinson, Bruce; Shelley, Carl Simon; Godwin, Eryn M.; Kendall, Sara; Shipman, Cassaundra; Bradford, Carol; Carey, Thomas; Haddad, Andrea; Moyer, Jeffey; Peterson, Lisa; Prince, Mark; Rozek, Laura; Wolf, Gregory; Bowman, Rayleen; Fong, Kwun M.; Yang, Ian; Korst, Robert; Rathmell, W. Kimryn; Fantacone-Campbell, J. Leigh; Hooke, Jeffrey A.; Kovatich, Albert J.; Shriver, Craig D.; DiPersio, John; Drake, Bettina; Govindan, Ramaswamy; Heath, Sharon; Ley, Timothy; Van Tine, Brian; Westervelt, Peter; Rubin, Mark A.; Lee, Jung Il; Aredes, Natália D.; Mariamidze, Armaz

2018-01-01

The Cancer Genome Atlas (TCGA) has catalyzed systematic characterization of diverse genomic alterations underlying human cancers. At this historic junction marking the completion of genomic characterization of over 11,000 tumors from 33 cancer types, we present our current understanding of the
Genome evolution during progression to breast cancer

KAUST Repository

Newburger, D. E.; Kashef-Haghighi, D.; Weng, Z.; Salari, R.; Sweeney, R. T.; Brunner, A. L.; Zhu, S. X.; Guo, X.; Varma, S.; Troxell, M. L.; West, R. B.; Batzoglou, S.; Sidow, A.

2013-01-01

Cancer evolution involves cycles of genomic damage, epigenetic deregulation, and increased cellular proliferation that eventually culminate in the carcinoma phenotype. Early neoplasias, which are often found concurrently with carcinomas and are histologically distinguishable from normal breast tissue, are less advanced in phenotype than carcinomas and are thought to represent precursor stages. To elucidate their role in cancer evolution we performed comparative whole-genome sequencing of early neoplasias, matched normal tissue, and carcinomas from six patients, for a total of 31 samples. By using somatic mutations as lineage markers we built trees that relate the tissue samples within each patient. On the basis of these lineage trees we inferred the order, timing, and rates of genomic events. In four out of six cases, an early neoplasia and the carcinoma share a mutated common ancestor with recurring aneuploidies, and in all six cases evolution accelerated in the carcinoma lineage. Transition spectra of somatic mutations are stable and consistent across cases, suggesting that accumulation of somatic mutations is a result of increased ancestral cell division rather than specific mutational mechanisms. In contrast to highly advanced tumors that are the focus of much of the current cancer genome sequencing, neither the early neoplasia genomes nor the carcinomas are enriched with potentially functional somatic point mutations. Aneuploidies that occur in common ancestors of neoplastic and tumor cells are the earliest events that affect a large number of genes and may predispose breast tissue to eventual development of invasive carcinoma.
Genome evolution during progression to breast cancer

KAUST Repository

Newburger, D. E.

2013-04-08

Cancer evolution involves cycles of genomic damage, epigenetic deregulation, and increased cellular proliferation that eventually culminate in the carcinoma phenotype. Early neoplasias, which are often found concurrently with carcinomas and are histologically distinguishable from normal breast tissue, are less advanced in phenotype than carcinomas and are thought to represent precursor stages. To elucidate their role in cancer evolution we performed comparative whole-genome sequencing of early neoplasias, matched normal tissue, and carcinomas from six patients, for a total of 31 samples. By using somatic mutations as lineage markers we built trees that relate the tissue samples within each patient. On the basis of these lineage trees we inferred the order, timing, and rates of genomic events. In four out of six cases, an early neoplasia and the carcinoma share a mutated common ancestor with recurring aneuploidies, and in all six cases evolution accelerated in the carcinoma lineage. Transition spectra of somatic mutations are stable and consistent across cases, suggesting that accumulation of somatic mutations is a result of increased ancestral cell division rather than specific mutational mechanisms. In contrast to highly advanced tumors that are the focus of much of the current cancer genome sequencing, neither the early neoplasia genomes nor the carcinomas are enriched with potentially functional somatic point mutations. Aneuploidies that occur in common ancestors of neoplastic and tumor cells are the earliest events that affect a large number of genes and may predispose breast tissue to eventual development of invasive carcinoma.
A Million Cancer Genome Warehouse

Science.gov (United States)

2012-11-20

of a national program for Cancer Information Donors, the American Society for Clinical Oncology (ASCO) has proposed a rapid learning system for...or Scala and Spark; “scrum” organization of small programming teams; calculating “velocity” to predict time to develop new features; and Agile...2012 to 00-00-2012 4. TITLE AND SUBTITLE A Million Cancer Genome Warehouse 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6
Genomic analysis and selected molecular pathways in rare cancers

International Nuclear Information System (INIS)

Liu, Stephen V; Lenkiewicz, Elizabeth; Evers, Lisa; Holley, Tara; Kiefer, Jeffrey; Demeure, Michael J; Ramanathan, Ramesh K; Von Hoff, Daniel D; Barrett, Michael T; Ruiz, Christian; Glatz, Katharina; Bubendorf, Lukas; Eng, Cathy

2012-01-01

It is widely accepted that many cancers arise as a result of an acquired genomic instability and the subsequent evolution of tumor cells with variable patterns of selected and background aberrations. The presence and behaviors of distinct neoplastic cell populations within a patient's tumor may underlie multiple clinical phenotypes in cancers. A goal of many current cancer genome studies is the identification of recurring selected driver events that can be advanced for the development of personalized therapies. Unfortunately, in the majority of rare tumors, this type of analysis can be particularly challenging. Large series of specimens for analysis are simply not available, allowing recurring patterns to remain hidden. In this paper, we highlight the use of DNA content-based flow sorting to identify and isolate DNA-diploid and DNA-aneuploid populations from tumor biopsies as a strategy to comprehensively study the genomic composition and behaviors of individual cancers in a series of rare solid tumors: intrahepatic cholangiocarcinoma, anal carcinoma, adrenal leiomyosarcoma, and pancreatic neuroendocrine tumors. We propose that the identification of highly selected genomic events in distinct tumor populations within each tumor can identify candidate driver events that can facilitate the development of novel, personalized treatment strategies for patients with cancer. (paper)
An expanding universe of the non-coding genome in cancer biology.

Science.gov (United States)

Xue, Bin; He, Lin

2014-06-01

Neoplastic transformation is caused by accumulation of genetic and epigenetic alterations that ultimately convert normal cells into tumor cells with uncontrolled proliferation and survival, unlimited replicative potential and invasive growth [Hanahan,D. et al. (2011) Hallmarks of cancer: the next generation. Cell, 144, 646-674]. Although the majority of the cancer studies have focused on the functions of protein-coding genes, emerging evidence has started to reveal the importance of the vast non-coding genome, which constitutes more than 98% of the human genome. A number of non-coding RNAs (ncRNAs) derived from the 'dark matter' of the human genome exhibit cancer-specific differential expression and/or genomic alterations, and it is increasingly clear that ncRNAs, including small ncRNAs and long ncRNAs (lncRNAs), play an important role in cancer development by regulating protein-coding gene expression through diverse mechanisms. In addition to ncRNAs, nearly half of the mammalian genomes consist of transposable elements, particularly retrotransposons. Once depicted as selfish genomic parasites that propagate at the expense of host fitness, retrotransposon elements could also confer regulatory complexity to the host genomes during development and disease. Reactivation of retrotransposons in cancer, while capable of causing insertional mutagenesis and genome rearrangements to promote oncogenesis, could also alter host gene expression networks to favor tumor development. Taken together, the functional significance of non-coding genome in tumorigenesis has been previously underestimated, and diverse transcripts derived from the non-coding genome could act as integral functional components of the oncogene and tumor suppressor network. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
Applications of Support Vector Machine (SVM) Learning in Cancer Genomics

OpenAIRE

HUANG, SHUJUN; CAI, NIANGUANG; PACHECO, PEDRO PENZUTI; NARANDES, SHAVIRA; WANG, YANG; XU, WAYNE

2017-01-01

Machine learning with maximization (support) of separating margin (vector), called support vector machine (SVM) learning, is a powerful classification tool that has been used for cancer genomic classification or subtyping. Today, as advancements in high-throughput technologies lead to production of large amounts of genomic and epigenomic data, the classification feature of SVMs is expanding its use in cancer genomics, leading to the discovery of new biomarkers, new drug targets, and a better ...
Databases and web tools for cancer genomics study.

Science.gov (United States)

Yang, Yadong; Dong, Xunong; Xie, Bingbing; Ding, Nan; Chen, Juan; Li, Yongjun; Zhang, Qian; Qu, Hongzhu; Fang, Xiangdong

2015-02-01

Publicly-accessible resources have promoted the advance of scientific discovery. The era of genomics and big data has brought the need for collaboration and data sharing in order to make effective use of this new knowledge. Here, we describe the web resources for cancer genomics research and rate them on the basis of the diversity of cancer types, sample size, omics data comprehensiveness, and user experience. The resources reviewed include data repository and analysis tools; and we hope such introduction will promote the awareness and facilitate the usage of these resources in the cancer research community. Copyright © 2015 The Authors. Production and hosting by Elsevier Ltd.. All rights reserved.
Integrating cancer genomic data into electronic health records

Directory of Open Access Journals (Sweden)

Jeremy L. Warner

2016-10-01

Full Text Available Abstract The rise of genomically targeted therapies and immunotherapy has revolutionized the practice of oncology in the last 10–15 years. At the same time, new technologies and the electronic health record (EHR in particular have permeated the oncology clinic. Initially designed as billing and clinical documentation systems, EHR systems have not anticipated the complexity and variety of genomic information that needs to be reviewed, interpreted, and acted upon on a daily basis. Improved integration of cancer genomic data with EHR systems will help guide clinician decision making, support secondary uses, and ultimately improve patient care within oncology clinics. Some of the key factors relating to the challenge of integrating cancer genomic data into EHRs include: the bioinformatics pipelines that translate raw genomic data into meaningful, actionable results; the role of human curation in the interpretation of variant calls; and the need for consistent standards with regard to genomic and clinical data. Several emerging paradigms for integration are discussed in this review, including: non-standardized efforts between individual institutions and genomic testing laboratories; “middleware” products that portray genomic information, albeit outside of the clinical workflow; and application programming interfaces that have the potential to work within clinical workflow. The critical need for clinical-genomic knowledge bases, which can be independent or integrated into the aforementioned solutions, is also discussed.
Mechanisms of Base Substitution Mutagenesis in Cancer Genomes

Directory of Open Access Journals (Sweden)

Albino Bacolla

2014-03-01

Full Text Available Cancer genome sequence data provide an invaluable resource for inferring the key mechanisms by which mutations arise in cancer cells, favoring their survival, proliferation and invasiveness. Here we examine recent advances in understanding the molecular mechanisms responsible for the predominant type of genetic alteration found in cancer cells, somatic single base substitutions (SBSs. Cytosine methylation, demethylation and deamination, charge transfer reactions in DNA, DNA replication timing, chromatin status and altered DNA proofreading activities are all now known to contribute to the mechanisms leading to base substitution mutagenesis. We review current hypotheses as to the major processes that give rise to SBSs and evaluate their relative relevance in the light of knowledge acquired from cancer genome sequencing projects and the study of base modifications, DNA repair and lesion bypass. Although gene expression data on APOBEC3B enzymes provide support for a role in cancer mutagenesis through U:G mismatch intermediates, the enzyme preference for single-stranded DNA may limit its activity genome-wide. For SBSs at both CG:CG and YC:GR sites, we outline evidence for a prominent role of damage by charge transfer reactions that follow interactions of the DNA with reactive oxygen species (ROS and other endogenous or exogenous electron-abstracting molecules.
Mechanisms of base substitution mutagenesis in cancer genomes.

Science.gov (United States)

Bacolla, Albino; Cooper, David N; Vasquez, Karen M

2014-03-05

Cancer genome sequence data provide an invaluable resource for inferring the key mechanisms by which mutations arise in cancer cells, favoring their survival, proliferation and invasiveness. Here we examine recent advances in understanding the molecular mechanisms responsible for the predominant type of genetic alteration found in cancer cells, somatic single base substitutions (SBSs). Cytosine methylation, demethylation and deamination, charge transfer reactions in DNA, DNA replication timing, chromatin status and altered DNA proofreading activities are all now known to contribute to the mechanisms leading to base substitution mutagenesis. We review current hypotheses as to the major processes that give rise to SBSs and evaluate their relative relevance in the light of knowledge acquired from cancer genome sequencing projects and the study of base modifications, DNA repair and lesion bypass. Although gene expression data on APOBEC3B enzymes provide support for a role in cancer mutagenesis through U:G mismatch intermediates, the enzyme preference for single-stranded DNA may limit its activity genome-wide. For SBSs at both CG:CG and YC:GR sites, we outline evidence for a prominent role of damage by charge transfer reactions that follow interactions of the DNA with reactive oxygen species (ROS) and other endogenous or exogenous electron-abstracting molecules.
Genomics, Endoscopy, and Control of Gastroesophageal Cancers: A PerspectiveSummary

Directory of Open Access Journals (Sweden)

Brian J. Reid

2017-05-01

Full Text Available In The Cancer Genome Atlas the goals were to define how to treat advanced cancers with targeted therapy. However, the challenges facing cancer interception for early detection and prevention include length bias in which current screening and surveillance approaches frequently miss rapidly progressing cancers that then present at advanced stages in the clinic with symptoms (underdiagnosis. In contrast, many early detection strategies detect benign conditions that may never progress to cancer during a lifetime, and the patient dies of unrelated causes (overdiagnosis. This challenge to cancer interception is believed to be due to the speed at which the neoplasm evolves, called length bias sampling; rapidly progressing cancers are missed by current early detection strategies. In contrast, slowly or non-progressing cancers or their precursors are selectively detected. This has led to the concept of cancer interception, which can be defined as active interception of a biological process that drives cancer development before the patient presents in the clinic with an advanced, symptomatic cancer. The solutions needed to advance strategies for cancer interception require assessing the rate at which the cancer evolves over time and space. This is an essential challenge that needs to be addressed by robust study designs including normal and non-progressing controls when known to be appropriate. Keywords: Barrett's Esophagus, Biomarkers, Chromosome Aberrations, Esophageal Neoplasms, Gastroesophageal Reflux, Genomic Instability, Genomics, Stomach
Cross Cancer Genomic Investigation of Inflammation Pathway for Five Common Cancers: Lung, Ovary, Prostate, Breast, and Colorectal Cancer.

Science.gov (United States)

Hung, Rayjean J; Ulrich, Cornelia M; Goode, Ellen L; Brhane, Yonathan; Muir, Kenneth; Chan, Andrew T; Marchand, Loic Le; Schildkraut, Joellen; Witte, John S; Eeles, Rosalind; Boffetta, Paolo; Spitz, Margaret R; Poirier, Julia G; Rider, David N; Fridley, Brooke L; Chen, Zhihua; Haiman, Christopher; Schumacher, Fredrick; Easton, Douglas F; Landi, Maria Teresa; Brennan, Paul; Houlston, Richard; Christiani, David C; Field, John K; Bickeböller, Heike; Risch, Angela; Kote-Jarai, Zsofia; Wiklund, Fredrik; Grönberg, Henrik; Chanock, Stephen; Berndt, Sonja I; Kraft, Peter; Lindström, Sara; Al Olama, Ali Amin; Song, Honglin; Phelan, Catherine; Wentzensen, Nicholas; Peters, Ulrike; Slattery, Martha L; Sellers, Thomas A; Casey, Graham; Gruber, Stephen B; Hunter, David J; Amos, Christopher I; Henderson, Brian

2015-11-01

Inflammation has been hypothesized to increase the risk of cancer development as an initiator or promoter, yet no large-scale study of inherited variation across cancer sites has been conducted. We conducted a cross-cancer genomic analysis for the inflammation pathway based on 48 genome-wide association studies within the National Cancer Institute GAME-ON Network across five common cancer sites, with a total of 64 591 cancer patients and 74 467 control patients. Subset-based meta-analysis was used to account for possible disease heterogeneity, and hierarchical modeling was employed to estimate the effect of the subcomponents within the inflammation pathway. The network was visualized by enrichment map. All statistical tests were two-sided. We identified three pleiotropic loci within the inflammation pathway, including one novel locus in Ch12q24 encoding SH2B3 (rs3184504), which reached GWAS significance with a P value of 1.78 x 10(-8), and it showed an association with lung cancer (P = 2.01 x 10(-6)), colorectal cancer (GECCO P = 6.72x10(-6); CORECT P = 3.32x10(-5)), and breast cancer (P = .009). We also identified five key subpathway components with genetic variants that are relevant for the risk of these five cancer sites: inflammatory response for colorectal cancer (P = .006), inflammation related cell cycle gene for lung cancer (P = 1.35x10(-6)), and activation of immune response for ovarian cancer (P = .009). In addition, sequence variations in immune system development played a role in breast cancer etiology (P = .001) and innate immune response was involved in the risk of both colorectal (P = .022) and ovarian cancer (P = .003). Genetic variations in inflammation and its related subpathway components are keys to the development of lung, colorectal, ovary, and breast cancer, including SH2B3, which is associated with lung, colorectal, and breast cancer. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e

Defining a Cancer Dependency Map | Office of Cancer Genomics

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Most human epithelial tumors harbor numerous alterations, making it difficult to predict which genes are required for tumor survival. To systematically identify cancer dependencies, we analyzed 501 genome-scale loss-of-function screens performed in diverse human cancer cell lines. We developed DEMETER, an analytical framework that segregates on- from off-target effects of RNAi. 769 genes were differentially required in subsets of these cell lines at a threshold of six SDs from the mean.
Genomic instability and radiation risk in molecular pathways to colon cancer.

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Jan Christian Kaiser

Full Text Available Colon cancer is caused by multiple genomic alterations which lead to genomic instability (GI. GI appears in molecular pathways of microsatellite instability (MSI and chromosomal instability (CIN with clinically observed case shares of about 15-20% and 80-85%. Radiation enhances the colon cancer risk by inducing GI, but little is known about different outcomes for MSI and CIN. Computer-based modelling can facilitate the understanding of the phenomena named above. Comprehensive biological models, which combine the two main molecular pathways to colon cancer, are fitted to incidence data of Japanese a-bomb survivors. The preferred model is selected according to statistical criteria and biological plausibility. Imprints of cell-based processes in the succession from adenoma to carcinoma are identified by the model from age dependences and secular trends of the incidence data. Model parameters show remarkable compliance with mutation rates and growth rates for adenoma, which has been reported over the last fifteen years. Model results suggest that CIN begins during fission of intestinal crypts. Chromosomal aberrations are generated at a markedly elevated rate which favors the accelerated growth of premalignant adenoma. Possibly driven by a trend of Westernization in the Japanese diet, incidence rates for the CIN pathway increased notably in subsequent birth cohorts, whereas rates pertaining to MSI remained constant. An imbalance between number of CIN and MSI cases began to emerge in the 1980s, whereas in previous decades the number of cases was almost equal. The CIN pathway exhibits a strong radio-sensitivity, probably more intensive in men. Among young birth cohorts of both sexes the excess absolute radiation risk related to CIN is larger by an order of magnitude compared to the MSI-related risk. Observance of pathway-specific risks improves the determination of the probability of causation for radiation-induced colon cancer in individual patients
Identification of mutated driver pathways in cancer using a multi-objective optimization model.

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Zheng, Chun-Hou; Yang, Wu; Chong, Yan-Wen; Xia, Jun-Feng

2016-05-01

New-generation high-throughput technologies, including next-generation sequencing technology, have been extensively applied to solve biological problems. As a result, large cancer genomics projects such as the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium are producing large amount of rich and diverse data in multiple cancer types. The identification of mutated driver genes and driver pathways from these data is a significant challenge. Genome aberrations in cancer cells can be divided into two types: random 'passenger mutation' and functional 'driver mutation'. In this paper, we introduced a Multi-objective Optimization model based on a Genetic Algorithm (MOGA) to solve the maximum weight submatrix problem, which can be employed to identify driver genes and driver pathways promoting cancer proliferation. The maximum weight submatrix problem defined to find mutated driver pathways is based on two specific properties, i.e., high coverage and high exclusivity. The multi-objective optimization model can adjust the trade-off between high coverage and high exclusivity. We proposed an integrative model by combining gene expression data and mutation data to improve the performance of the MOGA algorithm in a biological context. Copyright © 2016 Elsevier Ltd. All rights reserved.
Breast Cancer in Africa: Limitations and Opportunities for Application of Genomic Medicine

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Allison Silverstein

2016-01-01

Full Text Available As genomic medicine gains clinical applicability across a spectrum of diseases, insufficient application in low-income settings stands to increase health disparity. Breast cancer screening, diagnosis, and treatment have benefited greatly from genomic medicine in high-income settings. As breast cancer is a leading cause of both cancer incidence and mortality in Africa, attention and resources must be applied to research and clinical initiatives to integrate genomic medicine into breast cancer care. In terms of research, there is a paucity of investigations into genetic determinants of breast cancer specific to African populations, despite consensus in the literature that predisposition and susceptibility genes vary between populations. Therefore, we need targeted strengthening of existing research efforts and support of new initiatives. Results will improve clinical care through screening and diagnosis with genetic testing specific to breast cancer in African populations. Clinically, genomic medicine can provide information capable of improving resource allocation to the population which most stands to benefit from increased screening or tailored treatment modalities. In situations where mammography or chemotherapy options are limited, this information will allow for the greatest impact. Implementation of genomic medicine will face numerous systemic barriers but is essential to improve breast cancer outcomes and survival.
Genomes of early onset prostate cancer

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Weischenfeldt, Joachim; Korbel, Jan O.

2017-01-01

Purpose of review Prostate cancer is a disease of the elderly but a clinically relevant subset occurs early in life. In the current review, we discuss recent findings and the current understanding of the molecular underpinnings associated with early-onset prostate cancer (PCa) and the evidence...... supporting age-specific differences in the cancer genomes. Recent findings Recent surveys of PCa patient cohorts have provided novel age-dependent links between germline and somatic aberrations which points to differences in the molecular cause and treatment options. Summary Identifying the earliest...... receptor pathway....
Causation of cancer by ionizing radiation and genomic instability

International Nuclear Information System (INIS)

Streffer, Christian

2013-01-01

The causation of cancer by ionizing radiation has been shown in many epidemiological (with exposed humans) as well as experimental studies with mammals especially mice but also rats, dogs and monkeys. Risk values have been determined in medium radiation dose ranges (∼100 to 2,000 mSv). However, in the low dose range (<100 mSv) the situation is unclear and unsolved up to now. A better knowledge of the mechanisms for the development of cancer in humans over decades after low to medium radiation exposures is necessary for the understanding of the open questions. An increase of chromosomal aberrations and other genetic changes have been frequently observed directly after radiation exposures in many cell systems including human cells. However, in 1989 it was found that an increase of genomic instability occurred after irradiation of mouse zygotes in the fibroblasts of the neonates developing from the irradiated zygotes. That means genomic instability developed many cell generations later in cells which never had been exposed to various qualities of ionizing radiations in vivo and any treatment and secondary cancers developed in photon irradiated M.Hodgkin patients preferentially in those patients who showed a comparatively high genomic instability in their lymphocytes. Since several decades it has been experienced that certain cancer patients show an extremely high radio-sensitivity. This clinical observation has been confirmed by experimental investigations with cells of such patients. It has been proven that this increased radio-sensitivity is due to genetic mutations. A number of syndromes could be defined on such a genetic basis like ataxia telangiectasia, bloom's syndrome, fanconi anemia, retinoblasoma and others. In all these syndromes mutations occur in genes which are to regulation of the cell cycle or DNA repair (preferentially repair of DSBs). These patients with an increased radio-sensitivity frequently develop cancer - very often lymphoma - and they also
Focusing on function to mine cancer genome data | Center for Cancer Research

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CCR scientists have devised a strategy to sift through the tens of thousands of mutations in cancer genome data to find mutations that actually drive the disease. They have used the method to discover that the JNK signaling pathway, which in different contexts can either spur cancerous growth or rein it in, acts as a tumor suppressor in gastric cancers.
Fenton reaction induced cancer in wild type rats recapitulates genomic alterations observed in human cancer.

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Shinya Akatsuka

Full Text Available Iron overload has been associated with carcinogenesis in humans. Intraperitoneal administration of ferric nitrilotriacetate initiates a Fenton reaction in renal proximal tubules of rodents that ultimately leads to a high incidence of renal cell carcinoma (RCC after repeated treatments. We performed high-resolution microarray comparative genomic hybridization to identify characteristics in the genomic profiles of this oxidative stress-induced rat RCCs. The results revealed extensive large-scale genomic alterations with a preference for deletions. Deletions and amplifications were numerous and sometimes fragmented, demonstrating that a Fenton reaction is a cause of such genomic alterations in vivo. Frequency plotting indicated that two of the most commonly altered loci corresponded to a Cdkn2a/2b deletion and a Met amplification. Tumor sizes were proportionally associated with Met expression and/or amplification, and clustering analysis confirmed our results. Furthermore, we developed a procedure to compare whole genomic patterns of the copy number alterations among different species based on chromosomal syntenic relationship. Patterns of the rat RCCs showed the strongest similarity to the human RCCs among five types of human cancers, followed by human malignant mesothelioma, an iron overload-associated cancer. Therefore, an iron-dependent Fenton chemical reaction causes large-scale genomic alterations during carcinogenesis, which may result in distinct genomic profiles. Based on the characteristics of extensive genome alterations in human cancer, our results suggest that this chemical reaction may play a major role during human carcinogenesis.
Transcription Restores DNA Repair to Heterochromatin, Determining Regional Mutation Rates in Cancer Genomes

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Christina L. Zheng

2014-11-01

Full Text Available Somatic mutations in cancer are more frequent in heterochromatic and late-replicating regions of the genome. We report that regional disparities in mutation density are virtually abolished within transcriptionally silent genomic regions of cutaneous squamous cell carcinomas (cSCCs arising in an XPC−/− background. XPC−/− cells lack global genome nucleotide excision repair (GG-NER, thus establishing differential access of DNA repair machinery within chromatin-rich regions of the genome as the primary cause for the regional disparity. Strikingly, we find that increasing levels of transcription reduce mutation prevalence on both strands of gene bodies embedded within H3K9me3-dense regions, and only to those levels observed in H3K9me3-sparse regions, also in an XPC-dependent manner. Therefore, transcription appears to reduce mutation prevalence specifically by relieving the constraints imposed by chromatin structure on DNA repair. We model this relationship among transcription, chromatin state, and DNA repair, revealing a new, personalized determinant of cancer risk.
Emerging technologies to create inducible and genetically defined porcine cancer models

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Lawrence B Schook

2016-02-01

Full Text Available There is an emerging need for new animal models that address unmet translational cancer research requirements. Transgenic porcine models provide an exceptional opportunity due to their genetic, anatomic and physiological similarities with humans. Due to recent advances in the sequencing of domestic animal genomes and the development of new organism cloning technologies, it is now very feasible to utilize pigs as a malleable species, with similar anatomic and physiological features with humans, in which to develop cancer models. In this review, we discuss genetic modification technologies successfully used to produce porcine biomedical models, in particular the Cre-loxP System as well as major advances and perspectives the CRISPR/Cas9 System. Recent advancements in porcine tumor modeling and genome editing will bring porcine models to the forefront of translational cancer research.
Emerging Technologies to Create Inducible and Genetically Defined Porcine Cancer Models.

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Schook, Lawrence B; Rund, Laurie; Begnini, Karine R; Remião, Mariana H; Seixas, Fabiana K; Collares, Tiago

2016-01-01

There is an emerging need for new animal models that address unmet translational cancer research requirements. Transgenic porcine models provide an exceptional opportunity due to their genetic, anatomic, and physiological similarities with humans. Due to recent advances in the sequencing of domestic animal genomes and the development of new organism cloning technologies, it is now very feasible to utilize pigs as a malleable species, with similar anatomic and physiological features with humans, in which to develop cancer models. In this review, we discuss genetic modification technologies successfully used to produce porcine biomedical models, in particular the Cre-loxP System as well as major advances and perspectives the CRISPR/Cas9 System. Recent advancements in porcine tumor modeling and genome editing will bring porcine models to the forefront of translational cancer research.
The mediator complex in genomic and non-genomic signaling in cancer.

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Weber, Hannah; Garabedian, Michael J

2018-05-01

Mediator is a conserved, multi-subunit macromolecular machine divided structurally into head, middle, and tail modules, along with a transiently associating kinase module. Mediator functions as an integrator of transcriptional regulatory activity by interacting with DNA-bound transcription factors and with RNA polymerase II (Pol II) to both activate and repress gene expression. Mediator has been shown to affect multiple steps in transcription, including chromatin looping between enhancers and promoters, pre-initiation complex formation, transcriptional elongation, and mRNA splicing. Individual Mediator subunits participate in regulation of gene expression by the estrogen and androgen receptors and are altered in a number of endocrine cancers, including breast and prostate cancer. In addition to its role in genomic signaling, MED12 has been implicated in non-genomic signaling by interacting with and activating TGF-beta receptor 2 in the cytoplasm. Recent structural studies have revealed extensive inter-domain interactions and complex architecture of the Mediator-Pol II complex, suggesting that Mediator is capable of reorganizing its conformation and composition to fit cellular needs. We propose that alterations in Mediator subunit expression that occur in various cancers could impact the organization and function of Mediator, resulting in changes in gene expression that promote malignancy. A better understanding of the role of Mediator in cancer could reveal new approaches to the diagnosis and treatment of Mediator-dependent endocrine cancers, especially in settings of therapy resistance. Copyright © 2017 Elsevier Inc. All rights reserved.
Organoids as Models for Neoplastic Transformation | Office of Cancer Genomics

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Cancer models strive to recapitulate the incredible diversity inherent in human tumors. A key challenge in accurate tumor modeling lies in capturing the panoply of homo- and heterotypic cellular interactions within the context of a three-dimensional tissue microenvironment. To address this challenge, researchers have developed organotypic cancer models (organoids) that combine the 3D architecture of in vivo tissues with the experimental facility of 2D cell lines.
Understanding the development of human bladder cancer by using a whole-organ genomic mapping strategy.

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Majewski, Tadeusz; Lee, Sangkyou; Jeong, Joon; Yoon, Dong-Sup; Kram, Andrzej; Kim, Mi-Sook; Tuziak, Tomasz; Bondaruk, Jolanta; Lee, Sooyong; Park, Weon-Seo; Tang, Kuang S; Chung, Woonbok; Shen, Lanlan; Ahmed, Saira S; Johnston, Dennis A; Grossman, H Barton; Dinney, Colin P; Zhou, Jain-Hua; Harris, R Alan; Snyder, Carrie; Filipek, Slawomir; Narod, Steven A; Watson, Patrice; Lynch, Henry T; Gazdar, Adi; Bar-Eli, Menashe; Wu, Xifeng F; McConkey, David J; Baggerly, Keith; Issa, Jean-Pierre; Benedict, William F; Scherer, Steven E; Czerniak, Bogdan

2008-07-01

The search for the genomic sequences involved in human cancers can be greatly facilitated by maps of genomic imbalances identifying the involved chromosomal regions, particularly those that participate in the development of occult preneoplastic conditions that progress to clinically aggressive invasive cancer. The integration of such regions with human genome sequence variation may provide valuable clues about their overall structure and gene content. By extension, such knowledge may help us understand the underlying genetic components involved in the initiation and progression of these cancers. We describe the development of a genome-wide map of human bladder cancer that tracks its progression from in situ precursor conditions to invasive disease. Testing for allelic losses using a genome-wide panel of 787 microsatellite markers was performed on multiple DNA samples, extracted from the entire mucosal surface of the bladder and corresponding to normal urothelium, in situ preneoplastic lesions, and invasive carcinoma. Using this approach, we matched the clonal allelic losses in distinct chromosomal regions to specific phases of bladder neoplasia and produced a detailed genetic map of bladder cancer development. These analyses revealed three major waves of genetic changes associated with growth advantages of successive clones and reflecting a stepwise conversion of normal urothelial cells into cancer cells. The genetic changes map to six regions at 3q22-q24, 5q22-q31, 9q21-q22, 10q26, 13q14, and 17p13, which may represent critical hits driving the development of bladder cancer. Finally, we performed high-resolution mapping using single nucleotide polymorphism markers within one region on chromosome 13q14, containing the model tumor suppressor gene RB1, and defined a minimal deleted region associated with clonal expansion of in situ neoplasia. These analyses provided new insights on the involvement of several non-coding sequences mapping to the region and identified
Population-based statistical inference for temporal sequence of somatic mutations in cancer genomes.

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Rhee, Je-Keun; Kim, Tae-Min

2018-04-20

It is well recognized that accumulation of somatic mutations in cancer genomes plays a role in carcinogenesis; however, the temporal sequence and evolutionary relationship of somatic mutations remain largely unknown. In this study, we built a population-based statistical framework to infer the temporal sequence of acquisition of somatic mutations. Using the model, we analyzed the mutation profiles of 1954 tumor specimens across eight tumor types. As a result, we identified tumor type-specific directed networks composed of 2-15 cancer-related genes (nodes) and their mutational orders (edges). The most common ancestors identified in pairwise comparison of somatic mutations were TP53 mutations in breast, head/neck, and lung cancers. The known relationship of KRAS to TP53 mutations in colorectal cancers was identified, as well as potential ancestors of TP53 mutation such as NOTCH1, EGFR, and PTEN mutations in head/neck, lung and endometrial cancers, respectively. We also identified apoptosis-related genes enriched with ancestor mutations in lung cancers and a relationship between APC hotspot mutations and TP53 mutations in colorectal cancers. While evolutionary analysis of cancers has focused on clonal versus subclonal mutations identified in individual genomes, our analysis aims to further discriminate ancestor versus descendant mutations in population-scale mutation profiles that may help select cancer drivers with clinical relevance.
Carcinogen susceptibility is regulated by genome architecture and predicts cancer mutagenesis.

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García-Nieto, Pablo E; Schwartz, Erin K; King, Devin A; Paulsen, Jonas; Collas, Philippe; Herrera, Rafael E; Morrison, Ashby J

2017-10-02

The development of many sporadic cancers is directly initiated by carcinogen exposure. Carcinogens induce malignancies by creating DNA lesions (i.e., adducts) that can result in mutations if left unrepaired. Despite this knowledge, there has been remarkably little investigation into the regulation of susceptibility to acquire DNA lesions. In this study, we present the first quantitative human genome-wide map of DNA lesions induced by ultraviolet (UV) radiation, the ubiquitous carcinogen in sunlight that causes skin cancer. Remarkably, the pattern of carcinogen susceptibility across the genome of primary cells significantly reflects mutation frequency in malignant melanoma. Surprisingly, DNase-accessible euchromatin is protected from UV, while lamina-associated heterochromatin at the nuclear periphery is vulnerable. Many cancer driver genes have an intrinsic increase in carcinogen susceptibility, including the BRAF oncogene that has the highest mutation frequency in melanoma. These findings provide a genome-wide snapshot of DNA injuries at the earliest stage of carcinogenesis. Furthermore, they identify carcinogen susceptibility as an origin of genome instability that is regulated by nuclear architecture and mirrors mutagenesis in cancer. © 2017 The Authors.
Ectopic Expression of Testis Germ Cell Proteins in Cancer and Its Potential Role in Genomic Instability

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Aaraby Yoheswaran Nielsen

2016-06-01

Full Text Available Genomic instability is a hallmark of human cancer and an enabling factor for the genetic alterations that drive cancer development. The processes involved in genomic instability resemble those of meiosis, where genetic material is interchanged between homologous chromosomes. In most types of human cancer, epigenetic changes, including hypomethylation of gene promoters, lead to the ectopic expression of a large number of proteins normally restricted to the germ cells of the testis. Due to the similarities between meiosis and genomic instability, it has been proposed that activation of meiotic programs may drive genomic instability in cancer cells. Some germ cell proteins with ectopic expression in cancer cells indeed seem to promote genomic instability, while others reduce polyploidy and maintain mitotic fidelity. Furthermore, oncogenic germ cell proteins may indirectly contribute to genomic instability through induction of replication stress, similar to classic oncogenes. Thus, current evidence suggests that testis germ cell proteins are implicated in cancer development by regulating genomic instability during tumorigenesis, and these proteins therefore represent promising targets for novel therapeutic strategies.
Quantitative high-resolution genomic analysis of single cancer cells.

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Hannemann, Juliane; Meyer-Staeckling, Sönke; Kemming, Dirk; Alpers, Iris; Joosse, Simon A; Pospisil, Heike; Kurtz, Stefan; Görndt, Jennifer; Püschel, Klaus; Riethdorf, Sabine; Pantel, Klaus; Brandt, Burkhard

2011-01-01

During cancer progression, specific genomic aberrations arise that can determine the scope of the disease and can be used as predictive or prognostic markers. The detection of specific gene amplifications or deletions in single blood-borne or disseminated tumour cells that may give rise to the development of metastases is of great clinical interest but technically challenging. In this study, we present a method for quantitative high-resolution genomic analysis of single cells. Cells were isolated under permanent microscopic control followed by high-fidelity whole genome amplification and subsequent analyses by fine tiling array-CGH and qPCR. The assay was applied to single breast cancer cells to analyze the chromosomal region centred by the therapeutical relevant EGFR gene. This method allows precise quantitative analysis of copy number variations in single cell diagnostics.
Crossing the LINE toward genomic instability: LINE-1 retrotransposition in cancer

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Kemp, Jacqueline; Longworth, Michelle

2015-12-01

Retrotransposons are repetitive DNA sequences that are positioned throughout the human genome. Retrotransposons are capable of copying themselves and mobilizing new copies to novel genomic locations in a process called retrotransposition. While most retrotransposon sequences in the human genome are incomplete and incapable of mobilization, the LINE-1 retrotransposon, which comprises approximately 17% of the human genome, remains active. The disruption of cellular mechanisms that suppress retrotransposon activity is linked to the generation of aneuploidy, a potential driver of tumor development. When retrotransposons insert into a novel genomic region, they have the potential to disrupt the coding sequence of endogenous genes and alter gene expression, which can lead to deleterious consequences for the organism. Additionally, increased LINE-1 copy numbers provide more chances for recombination events to occur between retrotransposons, which can lead to chromosomal breaks and rearrangements. LINE-1 activity is increased in various cancer cell lines and in patient tissues resected from primary tumors. LINE-1 activity also correlates with increased cancer metastasis. This review aims to give a brief overview of the connections between LINE-1 retrotransposition and the loss of genome stability. We will also discuss the mechanisms that repress retrotransposition in human cells and their links to cancer.
Dana-Farber Cancer Institute: Identification of Therapeutic Targets Across Cancer Types | Office of Cancer Genomics

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The Dana Farber Cancer Institute CTD2 Center focuses on the use of high-throughput genetic and bioinformatic approaches to identify and credential oncogenes and co-dependencies in cancers. This Center aims to provide the cancer research community with information that will facilitate the prioritization of targets based on both genomic and functional evidence, inform the most appropriate genetic context for downstream mechanistic and validation studies, and enable the translation of this information into therapeutics and diagnostics.

Quo natas, Danio?—Recent Progress in Modeling Cancer in Zebrafish

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Stefanie Kirchberger

2017-08-01

Full Text Available Over the last decade, zebrafish has proven to be a powerful model in cancer research. Zebrafish form tumors that histologically and genetically resemble human cancers. The live imaging and cost-effective compound screening possible with zebrafish especially complement classic mouse cancer models. Here, we report recent progress in the field, including genetically engineered zebrafish cancer models, xenotransplantation of human cancer cells into zebrafish, promising approaches toward live investigation of the tumor microenvironment, and identification of therapeutic strategies by performing compound screens on zebrafish cancer models. Given the recent advances in genome editing, personalized zebrafish cancer models are now a realistic possibility. In addition, ongoing automation will soon allow high-throughput compound screening using zebrafish cancer models to be part of preclinical precision medicine approaches.
Break Breast Cancer Addiction by CRISPR/Cas9 Genome Editing

OpenAIRE

Yang, Haitao; Jaeger, MariaLynn; Walker, Averi; Wei, Daniel; Leiker, Katie; Weitao, Tao

2018-01-01

Breast cancer is the leading diagnosed cancer for women globally. Evolution of breast cancer in tumorigenesis, metastasis and treatment resistance appears to be driven by the aberrant gene expression and protein degradation encoded by the cancer genomes. The uncontrolled cancer growth relies on these cellular events, thus constituting the cancerous programs and rendering the addiction towards them. These programs are likely the potential anticancer biomarkers for Personalized Medicine of brea...
Genome-wide association scan for variants associated with early-onset prostate cancer.

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Ethan M Lange

Full Text Available Prostate cancer is the most common non-skin cancer and the second leading cause of cancer related mortality for men in the United States. There is strong empirical and epidemiological evidence supporting a stronger role of genetics in early-onset prostate cancer. We performed a genome-wide association scan for early-onset prostate cancer. Novel aspects of this study include the focus on early-onset disease (defined as men with prostate cancer diagnosed before age 56 years and use of publically available control genotype data from previous genome-wide association studies. We found genome-wide significant (p<5×10(-8 evidence for variants at 8q24 and 11p15 and strong supportive evidence for a number of previously reported loci. We found little evidence for individual or systematic inflated association findings resulting from using public controls, demonstrating the utility of using public control data in large-scale genetic association studies of common variants. Taken together, these results demonstrate the importance of established common genetic variants for early-onset prostate cancer and the power of including early-onset prostate cancer cases in genetic association studies.
Identification of candidate new cancer susceptibility genes using yeast genomics

International Nuclear Information System (INIS)

Brown, M.; Brown, J.A.; Game, J.C.

2003-01-01

A large proportion of cancer susceptibility syndromes are the result of mutations in genes in DNA repair or in cell-cycle checkpoints in response to DNA damage, such as ataxia telangiectasia (AT), Fanconi's anemia (FA), Bloom's syndrome (BS), Nijmegen breakage syndrome (NBS), and xeroderma pigmentosum (XP). Mutations in these genes often cause gross chromosomal instability leading to an increased mutation rate of all genes including those directly responsible for cancer. We have proposed that because the orthologs of these genes in budding yeast, S. cerevisiae, confer protection against killing by DNA damaging agents it should be possible to identify new cancer susceptibility genes by identifying yeast genes whose deletion causes sensitivity to DNA damage. We therefore screened the recently completed collection of individual gene deletion mutants to identify genes that affect sensitivity to DNA-damaging agents. Screening for sensitivity in this obtained up to now with the F98 glioma model othe fact that each deleted gene is replaced by a cassette containing two molecular 'barcodes', or 20-mers, that uniquely identify the strain when DNA from a pool of strains is hybridized to an oligonucleotide array containing the complementary sequences of the barcodes. We performed the screen with UV, IR, H 2 0 2 and other DNA damaging agents. In addition to identifying genes already known to confer resistance to DNA damaging agents we have identified, and individually confirmed, several genes not previously associated with resistance. Several of these are of unknown function. We have also examined the chromosomal stability of selected strains and found that IR sensitive strains often but not always exhibit genomic instability. We are presently constructing a yeast artificial chromosome to globally interrogate all the genes in the deletion pool for their involvement in genomic stability. This work shows that budding yeast is a valuable eukaryotic model organism to identify
A new generation of cancer genome diagnostics for routine clinical use: overcoming the roadblocks to personalized cancer medicine.

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Heuckmann, J M; Thomas, R K

2015-09-01

The identification of 'druggable' kinase gene alterations has revolutionized cancer treatment in the last decade by providing new and successfully targetable drug targets. Thus, genotyping tumors for matching the right patients with the right drugs have become a clinical routine. Today, advances in sequencing technology and computational genome analyses enable the discovery of a constantly growing number of genome alterations relevant for clinical decision making. As a consequence, several technological approaches have emerged in order to deal with these rapidly increasing demands for clinical cancer genome analyses. Here, we describe challenges on the path to the broad introduction of diagnostic cancer genome analyses and the technologies that can be applied to overcome them. We define three generations of molecular diagnostics that are in clinical use. The latest generation of these approaches involves deep and thus, highly sensitive sequencing of all therapeutically relevant types of genome alterations-mutations, copy number alterations and rearrangements/fusions-in a single assay. Such approaches therefore have substantial advantages (less time and less tissue required) over PCR-based methods that typically have to be combined with fluorescence in situ hybridization for detection of gene amplifications and fusions. Since these new technologies work reliably on routine diagnostic formalin-fixed, paraffin-embedded specimens, they can help expedite the broad introduction of personalized cancer therapy into the clinic by providing comprehensive, sensitive and accurate cancer genome diagnoses in 'real-time'. © The Author 2015. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved. For permissions, please email: journals.permissions@oup.com.
Characterization of HPV and host genome interactions in primary head and neck cancers

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Parfenov, Michael; Pedamallu, Chandra Sekhar; Gehlenborg, Nils; Freeman, Samuel S.; Danilova, Ludmila; Bristow, Christopher A.; Lee, Semin; Hadjipanayis, Angela G.; Ivanova, Elena V.; Wilkerson, Matthew D.; Protopopov, Alexei; Yang, Lixing; Seth, Sahil; Song, Xingzhi; Tang, Jiabin; Ren, Xiaojia; Zhang, Jianhua; Pantazi, Angeliki; Santoso, Netty; Xu, Andrew W.; Mahadeshwar, Harshad; Wheeler, David A.; Haddad, Robert I.; Jung, Joonil; Ojesina, Akinyemi I.; Issaeva, Natalia; Yarbrough, Wendell G.; Hayes, D. Neil; Grandis, Jennifer R.; El-Naggar, Adel K.; Meyerson, Matthew; Park, Peter J.; Chin, Lynda; Seidman, J. G.; Hammerman, Peter S.; Kucherlapati, Raju; Ally, Adrian; Balasundaram, Miruna; Birol, Inanc; Bowlby, Reanne; Butterfield, Yaron S.N.; Carlsen, Rebecca; Cheng, Dean; Chu, Andy; Dhalla, Noreen; Guin, Ranabir; Holt, Robert A.; Jones, Steven J.M.; Lee, Darlene; Li, Haiyan I.; Marra, Marco A.; Mayo, Michael; Moore, Richard A.; Mungall, Andrew J.; Robertson, A. Gordon; Schein, Jacqueline E.; Sipahimalani, Payal; Tam, Angela; Thiessen, Nina; Wong, Tina; Protopopov, Alexei; Santoso, Netty; Lee, Semin; Parfenov, Michael; Zhang, Jianhua; Mahadeshwar, Harshad S.; Tang, Jiabin; Ren, Xiaojia; Seth, Sahil; Haseley, Psalm; Zeng, Dong; Yang, Lixing; Xu, Andrew W.; Song, Xingzhi; Pantazi, Angeliki; Bristow, Christopher; Hadjipanayis, Angela; Seidman, Jonathan; Chin, Lynda; Park, Peter J.; Kucherlapati, Raju; Akbani, Rehan; Casasent, Tod; Liu, Wenbin; Lu, Yiling; Mills, Gordon; Motter, Thomas; Weinstein, John; Diao, Lixia; Wang, Jing; Fan, You Hong; Liu, Jinze; Wang, Kai; Auman, J. Todd; Balu, Saianand; Bodenheimer, Tom; Buda, Elizabeth; Hayes, D. Neil; Hoadley, Katherine A.; Hoyle, Alan P.; Jefferys, Stuart R.; Jones, Corbin D.; Kimes, Patrick K.; Marron, J.S.; Meng, Shaowu; Mieczkowski, Piotr A.; Mose, Lisle E.; Parker, Joel S.; Perou, Charles M.; Prins, Jan F.; Roach, Jeffrey; Shi, Yan; Simons, Janae V.; Singh, Darshan; Soloway, Mathew G.; Tan, Donghui; Veluvolu, Umadevi; Walter, Vonn; Waring, Scot; Wilkerson, Matthew D.; Wu, Junyuan; Zhao, Ni; Cherniack, Andrew D.; Hammerman, Peter S.; Tward, Aaron D.; Pedamallu, Chandra Sekhar; Saksena, Gordon; Jung, Joonil; Ojesina, Akinyemi I.; Carter, Scott L.; Zack, Travis I.; Schumacher, Steven E.; Beroukhim, Rameen; Freeman, Samuel S.; Meyerson, Matthew; Cho, Juok; Chin, Lynda; Getz, Gad; Noble, Michael S.; DiCara, Daniel; Zhang, Hailei; Heiman, David I.; Gehlenborg, Nils; Voet, Doug; Lin, Pei; Frazer, Scott; Stojanov, Petar; Liu, Yingchun; Zou, Lihua; Kim, Jaegil; Lawrence, Michael S.; Sougnez, Carrie; Lichtenstein, Lee; Cibulskis, Kristian; Lander, Eric; Gabriel, Stacey B.; Muzny, Donna; Doddapaneni, HarshaVardhan; Kovar, Christie; Reid, Jeff; Morton, Donna; Han, Yi; Hale, Walker; Chao, Hsu; Chang, Kyle; Drummond, Jennifer A.; Gibbs, Richard A.; Kakkar, Nipun; Wheeler, David; Xi, Liu; Ciriello, Giovanni; Ladanyi, Marc; Lee, William; Ramirez, Ricardo; Sander, Chris; Shen, Ronglai; Sinha, Rileen; Weinhold, Nils; Taylor, Barry S.; Aksoy, B. Arman; Dresdner, Gideon; Gao, Jianjiong; Gross, Benjamin; Jacobsen, Anders; Reva, Boris; Schultz, Nikolaus; Sumer, S. Onur; Sun, Yichao; Chan, Timothy; Morris, Luc; Stuart, Joshua; Benz, Stephen; Ng, Sam; Benz, Christopher; Yau, Christina; Baylin, Stephen B.; Cope, Leslie; Danilova, Ludmila; Herman, James G.; Bootwalla, Moiz; Maglinte, Dennis T.; Laird, Peter W.; Triche, Timothy; Weisenberger, Daniel J.; Van Den Berg, David J.; Agrawal, Nishant; Bishop, Justin; Boutros, Paul C.; Bruce, Jeff P; Byers, Lauren Averett; Califano, Joseph; Carey, Thomas E.; Chen, Zhong; Cheng, Hui; Chiosea, Simion I.; Cohen, Ezra; Diergaarde, Brenda; Egloff, Ann Marie; El-Naggar, Adel K.; Ferris, Robert L.; Frederick, Mitchell J.; Grandis, Jennifer R.; Guo, Yan; Haddad, Robert I.; Hammerman, Peter S.; Harris, Thomas; Hayes, D. Neil; Hui, Angela BY; Lee, J. Jack; Lippman, Scott M.; Liu, Fei-Fei; McHugh, Jonathan B.; Myers, Jeff; Ng, Patrick Kwok Shing; Perez-Ordonez, Bayardo; Pickering, Curtis R.; Prystowsky, Michael; Romkes, Marjorie; Saleh, Anthony D.; Sartor, Maureen A.; Seethala, Raja; Seiwert, Tanguy Y.; Si, Han; Tward, Aaron D.; Van Waes, Carter; Waggott, Daryl M.; Wiznerowicz, Maciej; Yarbrough, Wendell; Zhang, Jiexin; Zuo, Zhixiang; Burnett, Ken; Crain, Daniel; Gardner, Johanna; Lau, Kevin; Mallery, David; Morris, Scott; Paulauskis, Joseph; Penny, Robert; Shelton, Candance; Shelton, Troy; Sherman, Mark; Yena, Peggy; Black, Aaron D.; Bowen, Jay; Frick, Jessica; Gastier-Foster, Julie M.; Harper, Hollie A.; Lichtenberg, Tara M.; Ramirez, Nilsa C.; Wise, Lisa; Zmuda, Erik; Baboud, Julien; Jensen, Mark A.; Kahn, Ari B.; Pihl, Todd D.; Pot, David A.; Srinivasan, Deepak; Walton, Jessica S.; Wan, Yunhu; Burton, Robert; Davidsen, Tanja; Demchok, John A.; Eley, Greg; Ferguson, Martin L.; Shaw, Kenna R. Mills; Ozenberger, Bradley A.; Sheth, Margi; Sofia, Heidi J.; Tarnuzzer, Roy; Wang, Zhining; Yang, Liming; Zenklusen, Jean Claude; Saller, Charles; Tarvin, Katherine; Chen, Chu; Bollag, Roni; Weinberger, Paul; Golusiński, Wojciech; Golusiński, Paweł; Ibbs, Matthiew; Korski, Konstanty; Mackiewicz, Andrzej; Suchorska, Wiktoria; Szybiak, Bartosz; Wiznerowicz, Maciej; Burnett, Ken; Curley, Erin; Gardner, Johanna; Mallery, David; Penny, Robert; Shelton, Troy; Yena, Peggy; Beard, Christina; Mitchell, Colleen; Sandusky, George; Agrawal, Nishant; Ahn, Julie; Bishop, Justin; Califano, Joseph; Khan, Zubair; Bruce, Jeff P; Hui, Angela BY; Irish, Jonathan; Liu, Fei-Fei; Perez-Ordonez, Bayardo; Waldron, John; Boutros, Paul C.; Waggott, Daryl M.; Myers, Jeff; Lippman, Scott M.; Egea, Sophie; Gomez-Fernandez, Carmen; Herbert, Lynn; Bradford, Carol R.; Carey, Thomas E.; Chepeha, Douglas B.; Haddad, Andrea S.; Jones, Tamara R.; Komarck, Christine M.; Malakh, Mayya; McHugh, Jonathan B.; Moyer, Jeffrey S.; Nguyen, Ariane; Peterson, Lisa A.; Prince, Mark E.; Rozek, Laura S.; Sartor, Maureen A.; Taylor, Evan G.; Walline, Heather M.; Wolf, Gregory T.; Boice, Lori; Chera, Bhishamjit S.; Funkhouser, William K.; Gulley, Margaret L.; Hackman, Trevor G.; Hayes, D. Neil; Hayward, Michele C.; Huang, Mei; Rathmell, W. Kimryn; Salazar, Ashley H.; Shockley, William W.; Shores, Carol G.; Thorne, Leigh; Weissler, Mark C.; Wrenn, Sylvia; Zanation, Adam M.; Chiosea, Simion I.; Diergaarde, Brenda; Egloff, Ann Marie; Ferris, Robert L.; Romkes, Marjorie; Seethala, Raja; Brown, Brandee T.; Guo, Yan; Pham, Michelle; Yarbrough, Wendell G.

2014-01-01

Previous studies have established that a subset of head and neck tumors contains human papillomavirus (HPV) sequences and that HPV-driven head and neck cancers display distinct biological and clinical features. HPV is known to drive cancer by the actions of the E6 and E7 oncoproteins, but the molecular architecture of HPV infection and its interaction with the host genome in head and neck cancers have not been comprehensively described. We profiled a cohort of 279 head and neck cancers with next generation RNA and DNA sequencing and show that 35 (12.5%) tumors displayed evidence of high-risk HPV types 16, 33, or 35. Twenty-five cases had integration of the viral genome into one or more locations in the human genome with statistical enrichment for genic regions. Integrations had a marked impact on the human genome and were associated with alterations in DNA copy number, mRNA transcript abundance and splicing, and both inter- and intrachromosomal rearrangements. Many of these events involved genes with documented roles in cancer. Cancers with integrated vs. nonintegrated HPV displayed different patterns of DNA methylation and both human and viral gene expressions. Together, these data provide insight into the mechanisms by which HPV interacts with the human genome beyond expression of viral oncoproteins and suggest that specific integration events are an integral component of viral oncogenesis. PMID:25313082
Genome-wide association study identifies new prostate cancer susceptibility loci

DEFF Research Database (Denmark)

Schumacher, Fredrick R.; Berndt, Sonja I.; Siddiq, Afshan

2011-01-01

Prostate cancer (PrCa) is the most common non-skin cancer diagnosed among males in developed countries and the second leading cause of cancer mortality, yet little is known regarding its etiology and factors that influence clinical outcome. Genome-wide association studies (GWAS) of PrCa have iden...
Integrative Analysis of Complex Cancer Genomics and Clinical Profiles Using the cBioPortal

Science.gov (United States)

Gao, Jianjiong; Aksoy, Bülent Arman; Dogrusoz, Ugur; Dresdner, Gideon; Gross, Benjamin; Sumer, S. Onur; Sun, Yichao; Jacobsen, Anders; Sinha, Rileen; Larsson, Erik; Cerami, Ethan; Sander, Chris; Schultz, Nikolaus

2014-01-01

The cBioPortal for Cancer Genomics (http://cbioportal.org) provides a Web resource for exploring, visualizing, and analyzing multidimensional cancer genomics data. The portal reduces molecular profiling data from cancer tissues and cell lines into readily understandable genetic, epigenetic, gene expression, and proteomic events. The query interface combined with customized data storage enables researchers to interactively explore genetic alterations across samples, genes, and pathways and, when available in the underlying data, to link these to clinical outcomes. The portal provides graphical summaries of gene-level data from multiple platforms, network visualization and analysis, survival analysis, patient-centric queries, and software programmatic access. The intuitive Web interface of the portal makes complex cancer genomics profiles accessible to researchers and clinicians without requiring bioinformatics expertise, thus facilitating biological discoveries. Here, we provide a practical guide to the analysis and visualization features of the cBioPortal for Cancer Genomics. PMID:23550210
Contributing to Tumor Molecular Characterization Projects with a Global Impact | Office of Cancer Genomics

Science.gov (United States)

My name is Nicholas Griner and I am the Scientific Program Manager for the Cancer Genome Characterization Initiative (CGCI) in the Office of Cancer Genomics (OCG). Until recently, I spent most of my scientific career working in a cancer research laboratory. In my postdoctoral training, my research focused on identifying novel pathways that contribute to both prostate and breast cancers and studying proteins within these pathways that may be targeted with cancer drugs.
Quantitative high-resolution genomic analysis of single cancer cells.

Directory of Open Access Journals (Sweden)

Juliane Hannemann

Full Text Available During cancer progression, specific genomic aberrations arise that can determine the scope of the disease and can be used as predictive or prognostic markers. The detection of specific gene amplifications or deletions in single blood-borne or disseminated tumour cells that may give rise to the development of metastases is of great clinical interest but technically challenging. In this study, we present a method for quantitative high-resolution genomic analysis of single cells. Cells were isolated under permanent microscopic control followed by high-fidelity whole genome amplification and subsequent analyses by fine tiling array-CGH and qPCR. The assay was applied to single breast cancer cells to analyze the chromosomal region centred by the therapeutical relevant EGFR gene. This method allows precise quantitative analysis of copy number variations in single cell diagnostics.
ATM-deficiency increases genomic instability and metastatic potential in a mouse model of pancreatic cancer.

Science.gov (United States)

Drosos, Yiannis; Escobar, David; Chiang, Ming-Yi; Roys, Kathryn; Valentine, Virginia; Valentine, Marc B; Rehg, Jerold E; Sahai, Vaibhav; Begley, Lesa A; Ye, Jianming; Paul, Leena; McKinnon, Peter J; Sosa-Pineda, Beatriz

2017-09-11

Germline mutations in ATM (encoding the DNA-damage signaling kinase, ataxia-telangiectasia-mutated) increase Familial Pancreatic Cancer (FPC) susceptibility, and ATM somatic mutations have been identified in resected human pancreatic tumors. Here we investigated how Atm contributes to pancreatic cancer by deleting this gene in a murine model of the disease expressing oncogenic Kras (Kras G12D ). We show that partial or total ATM deficiency cooperates with Kras G12D to promote highly metastatic pancreatic cancer. We also reveal that ATM is activated in pancreatic precancerous lesions in the context of DNA damage and cell proliferation, and demonstrate that ATM deficiency leads to persistent DNA damage in both precancerous lesions and primary tumors. Using low passage cultures from primary tumors and liver metastases we show that ATM loss accelerates Kras-induced carcinogenesis without conferring a specific phenotype to pancreatic tumors or changing the status of the tumor suppressors p53, p16 Ink4a and p19 Arf . However, ATM deficiency markedly increases the proportion of chromosomal alterations in pancreatic primary tumors and liver metastases. More importantly, ATM deficiency also renders murine pancreatic tumors highly sensitive to radiation. These and other findings in our study conclusively establish that ATM activity poses a major barrier to oncogenic transformation in the pancreas via maintaining genomic stability.
Cloud Based Resource for Data Hosting, Visualization and Analysis Using UCSC Cancer Genomics Browser | Informatics Technology for Cancer Research (ITCR)

Science.gov (United States)

The Cancer Analysis Virtual Machine (CAVM) project will leverage cloud technology, the UCSC Cancer Genomics Browser, and the Galaxy analysis workflow system to provide investigators with a flexible, scalable platform for hosting, visualizing and analyzing their own genomic data.
Managing the genomic revolution in cancer diagnostics.

Science.gov (United States)

Nguyen, Doreen; Gocke, Christopher D

2017-08-01

Molecular tumor profiling is now a routine part of patient care, revealing targetable genomic alterations and molecularly distinct tumor subtypes with therapeutic and prognostic implications. The widespread adoption of next-generation sequencing technologies has greatly facilitated clinical implementation of genomic data and opened the door for high-throughput multigene-targeted sequencing. Herein, we discuss the variability of cancer genetic profiling currently offered by clinical laboratories, the challenges of applying rapidly evolving medical knowledge to individual patients, and the need for more standardized population-based molecular profiling.
Human Papillomavirus Genome Integration and Head and Neck Cancer.

Science.gov (United States)

Pinatti, L M; Walline, H M; Carey, T E

2018-06-01

We conducted a critical review of human papillomavirus (HPV) integration into the host genome in oral/oropharyngeal cancer, reviewed the literature for HPV-induced cancers, and obtained current data for HPV-related oral and oropharyngeal cancers. In addition, we performed studies to identify HPV integration sites and the relationship of integration to viral-host fusion transcripts and whether integration is required for HPV-associated oncogenesis. Viral integration of HPV into the host genome is not required for the viral life cycle and might not be necessary for cellular transformation, yet HPV integration is frequently reported in cervical and head and neck cancer specimens. Studies of large numbers of early cervical lesions revealed frequent viral integration into gene-poor regions of the host genome with comparatively rare integration into cellular genes, suggesting that integration is a stochastic event and that site of integration may be largely a function of chance. However, more recent studies of head and neck squamous cell carcinomas (HNSCCs) suggest that integration may represent an additional oncogenic mechanism through direct effects on cancer-related gene expression and generation of hybrid viral-host fusion transcripts. In HNSCC cell lines as well as primary tumors, integration into cancer-related genes leading to gene disruption has been reported. The studies have shown that integration-induced altered gene expression may be associated with tumor recurrence. Evidence from several studies indicates that viral integration into genic regions is accompanied by local amplification, increased expression in some cases, interruption of gene expression, and likely additional oncogenic effects. Similarly, reported examples of viral integration near microRNAs suggest that altered expression of these regulatory molecules may also contribute to oncogenesis. Future work is indicated to identify the mechanisms of these events on cancer cell behavior.
Noncoding Genomics in Gastric Cancer and the Gastric Precancerous Cascade: Pathogenesis and Biomarkers

Directory of Open Access Journals (Sweden)

Alejandra Sandoval-Bórquez

2015-01-01

Full Text Available Gastric cancer is the fifth most common cancer and the third leading cause of cancer-related death, whose patterns vary among geographical regions and ethnicities. It is a multifactorial disease, and its development depends on infection by Helicobacter pylori (H. pylori and Epstein-Barr virus (EBV, host genetic factors, and environmental factors. The heterogeneity of the disease has begun to be unraveled by a comprehensive mutational evaluation of primary tumors. The low-abundance of mutations suggests that other mechanisms participate in the evolution of the disease, such as those found through analyses of noncoding genomics. Noncoding genomics includes single nucleotide polymorphisms (SNPs, regulation of gene expression through DNA methylation of promoter sites, miRNAs, other noncoding RNAs in regulatory regions, and other topics. These processes and molecules ultimately control gene expression. Potential biomarkers are appearing from analyses of noncoding genomics. This review focuses on noncoding genomics and potential biomarkers in the context of gastric cancer and the gastric precancerous cascade.
Early Onset Malignancies - Genomic Study of Cancer Disparities

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The Early Onset Malignancies Initiative studies the genomic basis of six cancers that develop at an earlier age, occur in higher rates, and are typically more aggressive in certain minority populations.
Genome-wide association study of susceptibility loci for breast cancer in Sardinian population.

Science.gov (United States)

Palomba, Grazia; Loi, Angela; Porcu, Eleonora; Cossu, Antonio; Zara, Ilenia; Budroni, Mario; Dei, Mariano; Lai, Sandra; Mulas, Antonella; Olmeo, Nina; Ionta, Maria Teresa; Atzori, Francesco; Cuccuru, Gianmauro; Pitzalis, Maristella; Zoledziewska, Magdalena; Olla, Nazario; Lovicu, Mario; Pisano, Marina; Abecasis, Gonçalo R; Uda, Manuela; Tanda, Francesco; Michailidou, Kyriaki; Easton, Douglas F; Chanock, Stephen J; Hoover, Robert N; Hunter, David J; Schlessinger, David; Sanna, Serena; Crisponi, Laura; Palmieri, Giuseppe

2015-05-10

Despite progress in identifying genes associated with breast cancer, many more risk loci exist. Genome-wide association analyses in genetically-homogeneous populations, such as that of Sardinia (Italy), could represent an additional approach to detect low penetrance alleles. We performed a genome-wide association study comparing 1431 Sardinian patients with non-familial, BRCA1/2-mutation-negative breast cancer to 2171 healthy Sardinian blood donors. DNA was genotyped using GeneChip Human Mapping 500 K Arrays or Genome-Wide Human SNP Arrays 6.0. To increase genomic coverage, genotypes of additional SNPs were imputed using data from HapMap Phase II. After quality control filtering of genotype data, 1367 cases (9 men) and 1658 controls (1156 men) were analyzed on a total of 2,067,645 SNPs. Overall, 33 genomic regions (67 candidate SNPs) were associated with breast cancer risk at the p < 0(-6) level. Twenty of these regions contained defined genes, including one already associated with breast cancer risk: TOX3. With a lower threshold for preliminary significance to p < 10(-5), we identified 11 additional SNPs in FGFR2, a well-established breast cancer-associated gene. Ten candidate SNPs were selected, excluding those already associated with breast cancer, for technical validation as well as replication in 1668 samples from the same population. Only SNP rs345299, located in intron 1 of VAV3, remained suggestively associated (p-value, 1.16 x 10(-5)), but it did not associate with breast cancer risk in pooled data from two large, mixed-population cohorts. This study indicated the role of TOX3 and FGFR2 as breast cancer susceptibility genes in BRCA1/2-wild-type breast cancer patients from Sardinian population.
Genome-wide association study of susceptibility loci for breast cancer in Sardinian population

International Nuclear Information System (INIS)

Palomba, Grazia; Loi, Angela; Porcu, Eleonora; Cossu, Antonio; Zara, Ilenia

2015-01-01

Despite progress in identifying genes associated with breast cancer, many more risk loci exist. Genome-wide association analyses in genetically-homogeneous populations, such as that of Sardinia (Italy), could represent an additional approach to detect low penetrance alleles. We performed a genome-wide association study comparing 1431 Sardinian patients with non-familial, BRCA1/2-mutation-negative breast cancer to 2171 healthy Sardinian blood donors. DNA was genotyped using GeneChip Human Mapping 500 K Arrays or Genome-Wide Human SNP Arrays 6.0. To increase genomic coverage, genotypes of additional SNPs were imputed using data from HapMap Phase II. After quality control filtering of genotype data, 1367 cases (9 men) and 1658 controls (1156 men) were analyzed on a total of 2,067,645 SNPs. Overall, 33 genomic regions (67 candidate SNPs) were associated with breast cancer risk at the p < 10 −6 level. Twenty of these regions contained defined genes, including one already associated with breast cancer risk: TOX3. With a lower threshold for preliminary significance to p < 10 −5 , we identified 11 additional SNPs in FGFR2, a well-established breast cancer-associated gene. Ten candidate SNPs were selected, excluding those already associated with breast cancer, for technical validation as well as replication in 1668 samples from the same population. Only SNP rs345299, located in intron 1 of VAV3, remained suggestively associated (p-value, 1.16x10 −5 ), but it did not associate with breast cancer risk in pooled data from two large, mixed-population cohorts. This study indicated the role of TOX3 and FGFR2 as breast cancer susceptibility genes in BRCA1/2-wild-type breast cancer patients from Sardinian population. The online version of this article (doi:10.1186/s12885-015-1392-9) contains supplementary material, which is available to authorized users
Distinct p53 genomic binding patterns in normal and cancer-derived human cells

Energy Technology Data Exchange (ETDEWEB)

Botcheva K.; McCorkle S. R.; McCombie W. R.; Dunn J. J.; Anderson C. W.

2011-12-15

We report here genome-wide analysis of the tumor suppressor p53 binding sites in normal human cells. 743 high-confidence ChIP-seq peaks representing putative genomic binding sites were identified in normal IMR90 fibroblasts using a reference chromatin sample. More than 40% were located within 2 kb of a transcription start site (TSS), a distribution similar to that documented for individually studied, functional p53 binding sites and, to date, not observed by previous p53 genome-wide studies. Nearly half of the high-confidence binding sites in the IMR90 cells reside in CpG islands, in marked contrast to sites reported in cancer-derived cells. The distinct genomic features of the IMR90 binding sites do not reflect a distinct preference for specific sequences, since the de novo developed p53 motif based on our study is similar to those reported by genome-wide studies of cancer cells. More likely, the different chromatin landscape in normal, compared with cancer-derived cells, influences p53 binding via modulating availability of the sites. We compared the IMR90 ChIPseq peaks to the recently published IMR90 methylome1 and demonstrated that they are enriched at hypomethylated DNA. Our study represents the first genome-wide, de novo mapping of p53 binding sites in normal human cells and reveals that p53 binding sites reside in distinct genomic landscapes in normal and cancer-derived human cells.
Genome-wide association analysis identifies three new breast cancer susceptibility loci

DEFF Research Database (Denmark)

Ghoussaini, Maya; Fletcher, Olivia; Michailidou, Kyriaki

2012-01-01

Breast cancer is the most common cancer among women. To date, 22 common breast cancer susceptibility loci have been identified accounting for ∼8% of the heritability of the disease. We attempted to replicate 72 promising associations from two independent genome-wide association studies (GWAS...

Genomic Instability Promoted by Overexpression of Mismatch Repair Factors in Yeast: A Model for Understanding Cancer Progression.

Science.gov (United States)

Chakraborty, Ujani; Dinh, Timothy A; Alani, Eric

2018-04-13

Mismatch repair (MMR) proteins act in spellchecker roles to excise misincorporation errors that occur during DNA replication. Curiously, large-scale analyses of a variety of cancers showed that increased expression of MMR proteins often correlated with tumor aggressiveness, metastasis, and early recurrence. To better understand these observations, we used the TCGA and GENT databases to analyze MMR protein expression in cancers. We found that the MMR genes MSH2 and MSH6 are overexpressed more frequently than MSH3 , and that MSH2 and MSH6 are often co-overexpressed as a result of copy number amplifications of these genes. These observations encouraged us to test the effects of upregulating MMR protein levels in baker's yeast, where we can sensitively monitor genome instability phenotypes associated with cancer initiation and progression. Msh6 overexpression (2 to 4-fold) almost completely disrupted mechanisms that prevent recombination between divergent DNA sequences by interacting with the DNA polymerase processivity clamp PCNA and by sequestering the Sgs1 helicase. Importantly, co-overexpression of Msh2 and Msh6 (∼8-fold) conferred, in a PCNA interaction dependent manner, several genome instability phenotypes including increased mutation rate, increased sensitivity to the DNA replication inhibitor hydroxyurea and the DNA damaging agents methyl methanesulfonate and 4-nitroquinoline N-oxide, and elevated loss of heterozygosity. Msh2 and Msh6 co-overexpression also altered the cell cycle distribution of exponentially growing cells, resulting in an increased fraction of unbudded cells, consistent with a larger percentage of cells in G1. These novel observations suggested that overexpression of MSH factors affected the integrity of the DNA replication fork, causing genome instability phenotypes that could be important for promoting cancer progression. Copyright © 2018, Genetics.
Association between genomic recurrence risk and well-being among breast cancer patients

International Nuclear Information System (INIS)

Retèl, Valesca P; Groothuis-Oudshoorn, Catharina GM; Aaronson, Neil K; Brewer, Noel T; Rutgers, Emiel JT; Harten, Wim H van

2013-01-01

Gene expression profiling (GEP) is increasingly used in the rapidly evolving field of personalized medicine. We sought to evaluate the association between GEP-assessed of breast cancer recurrence risk and patients’ well-being. Participants were Dutch women from 10 hospitals being treated for early stage breast cancer who were enrolled in the MINDACT trial (Microarray In Node-negative and 1 to 3 positive lymph node Disease may Avoid ChemoTherapy). As part of the trial, they received a disease recurrence risk estimate based on a 70-gene signature and on standard clinical criteria as scored via a modified version of Adjuvant! Online. /Women completed a questionnaire 6–8 weeks after surgery and after their decision regarding adjuvant chemotherapy. The questionnaire assessed perceived understanding, knowledge, risk perception, satisfaction, distress, cancer worry and health-related quality of life (HRQoL), 6–8 weeks after surgery and decision regarding adjuvant chemotherapy. Women (n = 347, response rate 62%) reported high satisfaction with and a good understanding of the GEP information they received. Women with low risk estimates from both the standard and genomic tests reported the lowest distress levels. Distress was higher predominately among patients who had received high genomic risk estimates, who did not receive genomic risk estimates, or who received conflicting estimates based on genomic and clinical criteria. Cancer worry was highest for patients with higher risk perceptions and lower satisfaction. Patients with concordant high-risk profiles and those for whom such profiles were not available reported lower quality of life. Patients were generally satisfied with the information they received about recurrence risk based on genomic testing. Some types of genomic test results were associated with greater distress levels, but not with cancer worry or HRQoL. ISRCTN: http://www.controlled-trials.com/ISRCTN18543567/MINDACT
Actomyosin drives cancer cell nuclear dysmorphia and threatens genome stability.

Science.gov (United States)

Takaki, Tohru; Montagner, Marco; Serres, Murielle P; Le Berre, Maël; Russell, Matt; Collinson, Lucy; Szuhai, Karoly; Howell, Michael; Boulton, Simon J; Sahai, Erik; Petronczki, Mark

2017-07-24

Altered nuclear shape is a defining feature of cancer cells. The mechanisms underlying nuclear dysmorphia in cancer remain poorly understood. Here we identify PPP1R12A and PPP1CB, two subunits of the myosin phosphatase complex that antagonizes actomyosin contractility, as proteins safeguarding nuclear integrity. Loss of PPP1R12A or PPP1CB causes nuclear fragmentation, nuclear envelope rupture, nuclear compartment breakdown and genome instability. Pharmacological or genetic inhibition of actomyosin contractility restores nuclear architecture and genome integrity in cells lacking PPP1R12A or PPP1CB. We detect actin filaments at nuclear envelope rupture sites and define the Rho-ROCK pathway as the driver of nuclear damage. Lamin A protects nuclei from the impact of actomyosin activity. Blocking contractility increases nuclear circularity in cultured cancer cells and suppresses deformations of xenograft nuclei in vivo. We conclude that actomyosin contractility is a major determinant of nuclear shape and that unrestrained contractility causes nuclear dysmorphia, nuclear envelope rupture and genome instability.
Health psychology and translational genomic research: bringing innovation to cancer-related behavioral interventions.

Science.gov (United States)

McBride, Colleen M; Birmingham, Wendy C; Kinney, Anita Y

2015-01-01

The past decade has witnessed rapid advances in human genome sequencing technology and in the understanding of the role of genetic and epigenetic alterations in cancer development. These advances have raised hopes that such knowledge could lead to improvements in behavioral risk reduction interventions, tailored screening recommendations, and treatment matching that together could accelerate the war on cancer. Despite this optimism, translation of genomic discovery for clinical and public health applications has moved relatively slowly. To date, health psychologists and the behavioral sciences generally have played a very limited role in translation research. In this report we discuss what we mean by genomic translational research and consider the social forces that have slowed translational research, including normative assumptions that translation research must occur downstream of basic science, thus relegating health psychology and other behavioral sciences to a distal role. We then outline two broad priority areas in cancer prevention, detection, and treatment where evidence will be needed to guide evaluation and implementation of personalized genomics: (a) effective communication, to broaden dissemination of genomic discovery, including patient-provider communication and familial communication, and (b) the need to improve the motivational impact of behavior change interventions, including those aimed at altering lifestyle choices and those focusing on decision making regarding targeted cancer treatments and chemopreventive adherence. We further discuss the role that health psychologists can play in interdisciplinary teams to shape translational research priorities and to evaluate the utility of emerging genomic discoveries for cancer prevention and control. PsycINFO Database Record (c) 2015 APA, all rights reserved.
The Front Line of Genomic Translation

International Nuclear Information System (INIS)

O'Neill, C. S.; McBride, C. M.; Koehly, L. M.; Bryan, A. D.; Wideroff, L.

2012-01-01

Cancer prevention, detection, and treatment represent the front line of genomic translation. Increasingly, new genomic knowledge is being used to inform personalized cancer prevention recommendations and treatment [1-3]. Genomic applications proposed and realized span the full cancer continuum, from cancer prevention and early detection vis a vis genomic risk profiles to motivate behavioral risk reduction and adherence [4] to screening and prophylactic prevention recommendations for high-risk families [5-7], to enhancing cancer survivorship by using genomic tumor profiles to inform treatment decisions and targeted cancer therapies [8, 9]. Yet the utility for many of these applications is as yet unclear and will be influenced heavily by the public’s, patients’, and health care providers’ responses and in numerous other factors, such as health care delivery models [3]. The contributors to this special issue consider various target groups’ responses and contextual factors. To reflect the cancer continuum, the special issue is divided into three broad, overlapping themes-primary prevention, high risk families and family communication and clinical translation.
Personal Genomic Testing for Cancer Risk: Results From the Impact of Personal Genomics Study.

Science.gov (United States)

Gray, Stacy W; Gollust, Sarah E; Carere, Deanna Alexis; Chen, Clara A; Cronin, Angel; Kalia, Sarah S; Rana, Huma Q; Ruffin, Mack T; Wang, Catharine; Roberts, J Scott; Green, Robert C

2017-02-20

Purpose Significant concerns exist regarding the potential for unwarranted behavior changes and the overuse of health care resources in response to direct-to-consumer personal genomic testing (PGT). However, little is known about customers' behaviors after PGT. Methods Longitudinal surveys were given to new customers of 23andMe (Mountain View, CA) and Pathway Genomics (San Diego, CA). Survey data were linked to individual-level PGT results through a secure data transfer process. Results Of the 1,042 customers who completed baseline and 6-month surveys (response rate, 71.2%), 762 had complete cancer-related data and were analyzed. Most customers reported that learning about their genetic risk of cancers was a motivation for testing (colorectal, 88%; prostate, 95%; breast, 94%). No customers tested positive for pathogenic mutations in highly penetrant cancer susceptibility genes. A minority of individuals received elevated single nucleotide polymorphism-based PGT cancer risk estimates (colorectal, 24%; prostate, 24%; breast, 12%). At 6 months, customers who received elevated PGT cancer risk estimates were not significantly more likely to change their diet, exercise, or advanced planning behaviors or engage in cancer screening, compared with individuals at average or reduced risk. Men who received elevated PGT prostate cancer risk estimates changed their vitamin and supplement use more than those at average or reduced risk (22% v 7.6%, respectively; adjusted odds ratio, 3.41; 95% CI, 1.44 to 8.18). Predictors of 6-month behavior include baseline behavior (exercise, vitamin or supplement use, and screening), worse health status (diet and vitamin or supplement use), and older age (advanced planning, screening). Conclusion Most adults receiving elevated direct-to-consumer PGT single nucleotide polymorphism-based cancer risk estimates did not significantly change their diet, exercise, advanced care planning, or cancer screening behaviors.
Cancer Genome Interpreter annotates the biological and clinical relevance of tumor alterations.

Science.gov (United States)

Tamborero, David; Rubio-Perez, Carlota; Deu-Pons, Jordi; Schroeder, Michael P; Vivancos, Ana; Rovira, Ana; Tusquets, Ignasi; Albanell, Joan; Rodon, Jordi; Tabernero, Josep; de Torres, Carmen; Dienstmann, Rodrigo; Gonzalez-Perez, Abel; Lopez-Bigas, Nuria

2018-03-28

While tumor genome sequencing has become widely available in clinical and research settings, the interpretation of tumor somatic variants remains an important bottleneck. Here we present the Cancer Genome Interpreter, a versatile platform that automates the interpretation of newly sequenced cancer genomes, annotating the potential of alterations detected in tumors to act as drivers and their possible effect on treatment response. The results are organized in different levels of evidence according to current knowledge, which we envision can support a broad range of oncology use cases. The resource is publicly available at http://www.cancergenomeinterpreter.org .
Inferring causal genomic alterations in breast cancer using gene expression data

Science.gov (United States)

2011-01-01

Background One of the primary objectives in cancer research is to identify causal genomic alterations, such as somatic copy number variation (CNV) and somatic mutations, during tumor development. Many valuable studies lack genomic data to detect CNV; therefore, methods that are able to infer CNVs from gene expression data would help maximize the value of these studies. Results We developed a framework for identifying recurrent regions of CNV and distinguishing the cancer driver genes from the passenger genes in the regions. By inferring CNV regions across many datasets we were able to identify 109 recurrent amplified/deleted CNV regions. Many of these regions are enriched for genes involved in many important processes associated with tumorigenesis and cancer progression. Genes in these recurrent CNV regions were then examined in the context of gene regulatory networks to prioritize putative cancer driver genes. The cancer driver genes uncovered by the framework include not only well-known oncogenes but also a number of novel cancer susceptibility genes validated via siRNA experiments. Conclusions To our knowledge, this is the first effort to systematically identify and validate drivers for expression based CNV regions in breast cancer. The framework where the wavelet analysis of copy number alteration based on expression coupled with the gene regulatory network analysis, provides a blueprint for leveraging genomic data to identify key regulatory components and gene targets. This integrative approach can be applied to many other large-scale gene expression studies and other novel types of cancer data such as next-generation sequencing based expression (RNA-Seq) as well as CNV data. PMID:21806811
Whole-genome and Transcriptome Sequencing of Prostate Cancer Identify New Genetic Alterations Driving Disease Progression

DEFF Research Database (Denmark)

Ren, Shancheng; Wei, Gong-Hong; Liu, Dongbing

2018-01-01

BACKGROUND: Global disparities in prostate cancer (PCa) incidence highlight the urgent need to identify genomic abnormalities in prostate tumors in different ethnic populations including Asian men. OBJECTIVE: To systematically explore the genomic complexity and define disease-driven genetic......-scale and comprehensive genomic data of prostate cancer from Asian population. Identification of these genetic alterations may help advance prostate cancer diagnosis, prognosis, and treatment....... alterations in PCa. DESIGN, SETTING, AND PARTICIPANTS: The study sequenced whole-genome and transcriptome of tumor-benign paired tissues from 65 treatment-naive Chinese PCa patients. Subsequent targeted deep sequencing of 293 PCa-relevant genes was performed in another cohort of 145 prostate tumors. OUTCOME...
Development of a Method to Implement Whole-Genome Bisulfite Sequencing of cfDNA from Cancer Patients and a Mouse Tumor Model

Directory of Open Access Journals (Sweden)

Elaine C. Maggi

2018-01-01

Full Text Available The goal of this study was to develop a method for whole genome cell-free DNA (cfDNA methylation analysis in humans and mice with the ultimate goal to facilitate the identification of tumor derived DNA methylation changes in the blood. Plasma or serum from patients with pancreatic neuroendocrine tumors or lung cancer, and plasma from a murine model of pancreatic adenocarcinoma was used to develop a protocol for cfDNA isolation, library preparation and whole-genome bisulfite sequencing of ultra low quantities of cfDNA, including tumor-specific DNA. The protocol developed produced high quality libraries consistently generating a conversion rate >98% that will be applicable for the analysis of human and mouse plasma or serum to detect tumor-derived changes in DNA methylation.
Altered mitochondrial genome content signals worse pathology and prognosis in prostate cancer.

Science.gov (United States)

Kalsbeek, Anton M F; Chan, Eva K F; Grogan, Judith; Petersen, Desiree C; Jaratlerdsiri, Weerachai; Gupta, Ruta; Lyons, Ruth J; Haynes, Anne-Maree; Horvath, Lisa G; Kench, James G; Stricker, Phillip D; Hayes, Vanessa M

2018-01-01

Mitochondrial genome (mtDNA) content is depleted in many cancers. In prostate cancer, there is intra-glandular as well as inter-patient mtDNA copy number variation. In this study, we determine if mtDNA content can be used as a predictor for prostate cancer staging and outcomes. Fresh prostate cancer biopsies from 115 patients were obtained at time of surgery. All cores underwent pathological review, followed by isolation of cancer and normal tissue. DNA was extracted and qPCR performed to quantify the total amount of mtDNA as a ratio to genomic DNA. Differences in mtDNA content were compared for prostate cancer pathology features and disease outcomes. We showed a significantly reduced mtDNA content in prostate cancer compared with normal adjacent prostate tissue (mean difference 1.73-fold, P-value Prostate cancer with increased mtDNA content showed unfavorable pathologic characteristics including, higher disease stage (PT2 vs PT3 P-value = 0.018), extracapsular extension (P-value = 0.02) and a trend toward an increased Gleason score (P-value = 0.064). No significant association was observed between changes in mtDNA content and biochemical recurrence (median follow up of 107 months). Contrary to other cancer types, prostate cancer tissue shows no universally depleted mtDNA content. Rather, the change in mtDNA content is highly variable, mirroring known prostate cancer genome heterogeneity. Patients with high mtDNA content have an unfavorable pathology, while a high mtDNA content in normal adjacent prostate tissue is associated with worse prognosis. © 2017 Wiley Periodicals, Inc.
Enhancing knowledge discovery from cancer genomics data with Galaxy.

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Albuquerque, Marco A; Grande, Bruno M; Ritch, Elie J; Pararajalingam, Prasath; Jessa, Selin; Krzywinski, Martin; Grewal, Jasleen K; Shah, Sohrab P; Boutros, Paul C; Morin, Ryan D

2017-05-01

The field of cancer genomics has demonstrated the power of massively parallel sequencing techniques to inform on the genes and specific alterations that drive tumor onset and progression. Although large comprehensive sequence data sets continue to be made increasingly available, data analysis remains an ongoing challenge, particularly for laboratories lacking dedicated resources and bioinformatics expertise. To address this, we have produced a collection of Galaxy tools that represent many popular algorithms for detecting somatic genetic alterations from cancer genome and exome data. We developed new methods for parallelization of these tools within Galaxy to accelerate runtime and have demonstrated their usability and summarized their runtimes on multiple cloud service providers. Some tools represent extensions or refinement of existing toolkits to yield visualizations suited to cohort-wide cancer genomic analysis. For example, we present Oncocircos and Oncoprintplus, which generate data-rich summaries of exome-derived somatic mutation. Workflows that integrate these to achieve data integration and visualizations are demonstrated on a cohort of 96 diffuse large B-cell lymphomas and enabled the discovery of multiple candidate lymphoma-related genes. Our toolkit is available from our GitHub repository as Galaxy tool and dependency definitions and has been deployed using virtualization on multiple platforms including Docker. © The Author 2017. Published by Oxford University Press.
Dana-Farber Cancer Institute: Identification of Therapeutic Targets in KRAS Driven Lung Cancer | Office of Cancer Genomics

Science.gov (United States)

The CTD2 Center at Dana Farber Cancer Institute focuses on the use of high-throughput genetic and bioinformatic approaches to identify and credential oncogenes and co-dependencies in cancers. This Center aims to provide the cancer research community with information that will facilitate the prioritization of targets based on both genomic and functional evidence, inform the most appropriate genetic context for downstream mechanistic and validation studies, and enable the translation of this information into therapeutics and diagnostics.
The Genomic and Proteomic Content of Cancer Cell-Derived Exosomes

International Nuclear Information System (INIS)

Henderson, Meredith C.; Azorsa, David O.

2012-01-01

Exosomes are secreted membrane vesicles that have been proposed as an effective means to detect a variety of disease states, including cancer. The properties of exosomes, including stability in biological fluids, allow for their efficient isolation and make them an ideal vehicle for studies on early disease detection and evaluation. Much data has been collected over recent years regarding the messenger RNA, microRNA, and protein contents of exosomes. In addition, many studies have described the functional role that exosomes play in disease initiation and progression. Tumor cells have been shown to secrete exosomes, often in increased amounts compared to normal cells, and these exosomes can carry the genomic and proteomic signatures characteristic of the tumor cells from which they were derived. While these unique signatures make exosomes ideal for cancer detection, exosomes derived from cancer cells have also been shown to play a functional role in cancer progression. Here, we review the unique genomic and proteomic contents of exosomes originating from cancer cells as well as their functional effects to promote tumor progression.
Centrosome Dysfunction Contributes To Chromosome Instability, Chromoanagenesis And Genome Reprograming In Cancer.

Directory of Open Access Journals (Sweden)

German A Pihan

2013-11-01

Full Text Available The unique ability of centrosomes to nucleate and organize microtubules makes them unrivaled conductors of important interphase processes, such as intracellular payload traffic, cell polarity, cell locomotion, and organization of the immunologic synapse. But it is in mitosis that centrosomes loom large, for they orchestrate, with clockmaker’s precision, the assembly and functioning of the mitotic spindle, ensuring the equal partitioning of the replicated genome into daughter cells. Centrosome dysfunction is inextricably linked to aneuploidy and chromosome instability, both hallmarks of cancer cells. Several aspects of centrosome function in normal and cancer cells have been molecularly characterized during the last two decades, greatly enhancing our mechanistic understanding of this tiny organelle. Whether centrosome defects alone can cause cancer, remains unanswered. Until recently, the aggregate of the evidence had suggested that centrosome dysfunction, by deregulating the fidelity of chromosome segregation, promotes and accelerates the characteristic Darwinian evolution of the cancer genome enabled by increased mutational load and/or decreased DNA repair. Very recent experimental work has shown that missegreated chromosomes resulting from centrosome dysfunction may experience extensive DNA damage, suggesting additional dimensions to the role of centrosomes in cancer. Centrosome dysfunction is particularly prevalent in tumors in which the genome has undergone extensive structural rearrangements and chromosome domain reshuffling. Ongoing gene reshuffling reprograms the genome for continuous growth, survival, and evasion of the immune system. Manipulation of molecular networks controlling centrosome function may soon become a viable target for specific therapeutic intervention in cancer, particularly since normal cells, which lack centrosome alterations, may be spared the toxicity of such therapies.
Integrated Genomic Analysis of the Ubiquitin Pathway across Cancer Types

Directory of Open Access Journals (Sweden)

Zhongqi Ge

2018-04-01

Full Text Available Summary: Protein ubiquitination is a dynamic and reversible process of adding single ubiquitin molecules or various ubiquitin chains to target proteins. Here, using multidimensional omic data of 9,125 tumor samples across 33 cancer types from The Cancer Genome Atlas, we perform comprehensive molecular characterization of 929 ubiquitin-related genes and 95 deubiquitinase genes. Among them, we systematically identify top somatic driver candidates, including mutated FBXW7 with cancer-type-specific patterns and amplified MDM2 showing a mutually exclusive pattern with BRAF mutations. Ubiquitin pathway genes tend to be upregulated in cancer mediated by diverse mechanisms. By integrating pan-cancer multiomic data, we identify a group of tumor samples that exhibit worse prognosis. These samples are consistently associated with the upregulation of cell-cycle and DNA repair pathways, characterized by mutated TP53, MYC/TERT amplification, and APC/PTEN deletion. Our analysis highlights the importance of the ubiquitin pathway in cancer development and lays a foundation for developing relevant therapeutic strategies. : Ge et al. analyze a cohort of 9,125 TCGA samples across 33 cancer types to provide a comprehensive characterization of the ubiquitin pathway. They detect somatic driver candidates in the ubiquitin pathway and identify a cluster of patients with poor survival, highlighting the importance of this pathway in cancer development. Keywords: ubiquitin pathway, pan-cancer analysis, The Cancer Genome Atlas, tumor subtype, cancer prognosis, therapeutic targets, biomarker, FBXW7
An integrated clinical and genomic information system for cancer precision medicine.

Science.gov (United States)

Jang, Yeongjun; Choi, Taekjin; Kim, Jongho; Park, Jisub; Seo, Jihae; Kim, Sangok; Kwon, Yeajee; Lee, Seungjae; Lee, Sanghyuk

2018-04-20

Increasing affordability of next-generation sequencing (NGS) has created an opportunity for realizing genomically-informed personalized cancer therapy as a path to precision oncology. However, the complex nature of genomic information presents a huge challenge for clinicians in interpreting the patient's genomic alterations and selecting the optimum approved or investigational therapy. An elaborate and practical information system is urgently needed to support clinical decision as well as to test clinical hypotheses quickly. Here, we present an integrated clinical and genomic information system (CGIS) based on NGS data analyses. Major components include modules for handling clinical data, NGS data processing, variant annotation and prioritization, drug-target-pathway analysis, and population cohort explorer. We built a comprehensive knowledgebase of genes, variants, drugs by collecting annotated information from public and in-house resources. Structured reports for molecular pathology are generated using standardized terminology in order to help clinicians interpret genomic variants and utilize them for targeted cancer therapy. We also implemented many features useful for testing hypotheses to develop prognostic markers from mutation and gene expression data. Our CGIS software is an attempt to provide useful information for both clinicians and scientists who want to explore genomic information for precision oncology.
Whole-genome sequencing analysis of phenotypic heterogeneity and anticipation in Li-Fraumeni cancer predisposition syndrome.

Science.gov (United States)

Ariffin, Hany; Hainaut, Pierre; Puzio-Kuter, Anna; Choong, Soo Sin; Chan, Adelyne Sue Li; Tolkunov, Denis; Rajagopal, Gunaretnam; Kang, Wenfeng; Lim, Leon Li Wen; Krishnan, Shekhar; Chen, Kok-Siong; Achatz, Maria Isabel; Karsa, Mawar; Shamsani, Jannah; Levine, Arnold J; Chan, Chang S

2014-10-28

The Li-Fraumeni syndrome (LFS) and its variant form (LFL) is a familial predisposition to multiple forms of childhood, adolescent, and adult cancers associated with germ-line mutation in the TP53 tumor suppressor gene. Individual disparities in tumor patterns are compounded by acceleration of cancer onset with successive generations. It has been suggested that this apparent anticipation pattern may result from germ-line genomic instability in TP53 mutation carriers, causing increased DNA copy-number variations (CNVs) with successive generations. To address the genetic basis of phenotypic disparities of LFS/LFL, we performed whole-genome sequencing (WGS) of 13 subjects from two generations of an LFS kindred. Neither de novo CNV nor significant difference in total CNV was detected in relation with successive generations or with age at cancer onset. These observations were consistent with an experimental mouse model system showing that trp53 deficiency in the germ line of father or mother did not increase CNV occurrence in the offspring. On the other hand, individual records on 1,771 TP53 mutation carriers from 294 pedigrees were compiled to assess genetic anticipation patterns (International Agency for Research on Cancer TP53 database). No strictly defined anticipation pattern was observed. Rather, in multigeneration families, cancer onset was delayed in older compared with recent generations. These observations support an alternative model for apparent anticipation in which rare variants from noncarrier parents may attenuate constitutive resistance to tumorigenesis in the offspring of TP53 mutation carriers with late cancer onset.
Characterizing the cancer genome in lung adenocarcinoma

Science.gov (United States)

Weir, Barbara A.; Woo, Michele S.; Getz, Gad; Perner, Sven; Ding, Li; Beroukhim, Rameen; Lin, William M.; Province, Michael A.; Kraja, Aldi; Johnson, Laura A.; Shah, Kinjal; Sato, Mitsuo; Thomas, Roman K.; Barletta, Justine A.; Borecki, Ingrid B.; Broderick, Stephen; Chang, Andrew C.; Chiang, Derek Y.; Chirieac, Lucian R.; Cho, Jeonghee; Fujii, Yoshitaka; Gazdar, Adi F.; Giordano, Thomas; Greulich, Heidi; Hanna, Megan; Johnson, Bruce E.; Kris, Mark G.; Lash, Alex; Lin, Ling; Lindeman, Neal; Mardis, Elaine R.; McPherson, John D.; Minna, John D.; Morgan, Margaret B.; Nadel, Mark; Orringer, Mark B.; Osborne, John R.; Ozenberger, Brad; Ramos, Alex H.; Robinson, James; Roth, Jack A.; Rusch, Valerie; Sasaki, Hidefumi; Shepherd, Frances; Sougnez, Carrie; Spitz, Margaret R.; Tsao, Ming-Sound; Twomey, David; Verhaak, Roel G. W.; Weinstock, George M.; Wheeler, David A.; Winckler, Wendy; Yoshizawa, Akihiko; Yu, Soyoung; Zakowski, Maureen F.; Zhang, Qunyuan; Beer, David G.; Wistuba, Ignacio I.; Watson, Mark A.; Garraway, Levi A.; Ladanyi, Marc; Travis, William D.; Pao, William; Rubin, Mark A.; Gabriel, Stacey B.; Gibbs, Richard A.; Varmus, Harold E.; Wilson, Richard K.; Lander, Eric S.; Meyerson, Matthew

2008-01-01

Somatic alterations in cellular DNA underlie almost all human cancers1. The prospect of targeted therapies2 and the development of high-resolution, genome-wide approaches3–8 are now spurring systematic efforts to characterize cancer genomes. Here we report a large-scale project to characterize copy-number alterations in primary lung adenocarcinomas. By analysis of a large collection of tumors (n = 371) using dense single nucleotide polymorphism arrays, we identify a total of 57 significantly recurrent events. We find that 26 of 39 autosomal chromosome arms show consistent large-scale copy-number gain or loss, of which only a handful have been linked to a specific gene. We also identify 31 recurrent focal events, including 24 amplifications and 7 homozygous deletions. Only six of these focal events are currently associated with known mutations in lung carcinomas. The most common event, amplification of chromosome 14q13.3, is found in ~12% of samples. On the basis of genomic and functional analyses, we identify NKX2-1 (NK2 homeobox 1, also called TITF1), which lies in the minimal 14q13.3 amplification interval and encodes a lineage-specific transcription factor, as a novel candidate proto-oncogene involved in a significant fraction of lung adenocarcinomas. More generally, our results indicate that many of the genes that are involved in lung adenocarcinoma remain to be discovered. PMID:17982442
Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

NARCIS (Netherlands)

Schaub, Franz X.; Dhankani, Varsha; Berger, Ashton C.; Trivedi, Mihir; Richardson, Anne B.; Shaw, Reid; Zhao, Wei; Zhang, Xiaoyang; Ventura, Andrea; Liu, Yuexin; Ayer, Donald E.; Hurlin, Peter J.; Cherniack, Andrew D.; Eisenman, Robert N.; Bernard, Brady; Grandori, Carla; Caesar-Johnson, Samantha J.; Demchok, John A.; Felau, Ina; Kasapi, Melpomeni; Ferguson, Martin L.; Hutter, Carolyn M.; Sofia, Heidi J.; Tarnuzzer, Roy; Wang, Zhining; Yang, Liming; Zenklusen, Jean C.; Zhang, Jiashan (Julia); Chudamani, Sudha; Liu, Jia; Lolla, Laxmi; Naresh, Rashi; Pihl, Todd; Sun, Qiang; Wan, Yunhu; Wu, Ye; Cho, Juok; DeFreitas, Timothy; Frazer, Scott; Gehlenborg, Nils; Getz, Gad; Heiman, David I.; Kim, Jaegil; Lawrence, Michael S.; Lin, Pei; Meier, Sam; Noble, Michael S.; Saksena, Gordon; Voet, Doug; Zhang, Hailei; Bernard, Brady; Chambwe, Nyasha; Dhankani, Varsha; Knijnenburg, Theo; Kramer, Roger; Leinonen, Kalle; Liu, Yuexin; Miller, Michael; Reynolds, Sheila; Shmulevich, Ilya; Thorsson, Vesteinn; Zhang, Wei; Akbani, Rehan; Broom, Bradley M.; Hegde, Apurva M.; Ju, Zhenlin; Kanchi, Rupa S.; Korkut, Anil; Li, Jun; Liang, Han; Ling, Shiyun; Liu, Wenbin; Lu, Yiling; Mills, Gordon B.; Ng, Kwok Shing; Rao, Arvind; Ryan, Michael; Wang, Jing; Weinstein, John N.; Zhang, Jiexin; Abeshouse, Adam; Armenia, Joshua; Chakravarty, Debyani; Chatila, Walid K.; de Bruijn, Ino; Gao, Jianjiong; Gross, Benjamin E.; Heins, Zachary J.; Kundra, Ritika; La, Konnor; Ladanyi, Marc; Luna, Augustin; Nissan, Moriah G.; Ochoa, Angelica; Phillips, Sarah M.; Reznik, Ed; Sanchez-Vega, Francisco; Sander, Chris; Schultz, Nikolaus; Sheridan, Robert; Sumer, S. Onur; Sun, Yichao; Taylor, Barry S.; Wang, Jioajiao; Zhang, Hongxin; Anur, Pavana; Peto, Myron; Spellman, Paul; Benz, Christopher; Stuart, Joshua M.; Wong, Christopher K.; Yau, Christina; Hayes, D. Neil; Parker, Joel S.; Wilkerson, Matthew D.; Ally, Adrian; Balasundaram, Miruna; Bowlby, Reanne; Brooks, Denise; Carlsen, Rebecca; Chuah, Eric; Dhalla, Noreen; Holt, Robert; Jones, Steven J.M.; Kasaian, Katayoon; Lee, Darlene; Ma, Yussanne; Marra, Marco A.; Mayo, Michael; Moore, Richard A.; Mungall, Andrew J.; Mungall, Karen; Robertson, A. Gordon; Sadeghi, Sara; Schein, Jacqueline E.; Sipahimalani, Payal; Tam, Angela; Thiessen, Nina; Tse, Kane; Wong, Tina; Berger, Ashton C.; Beroukhim, Rameen; Cherniack, Andrew D.; Cibulskis, Carrie; Gabriel, Stacey B.; Gao, Galen F.; Ha, Gavin; Meyerson, Matthew; Schumacher, Steven E.; Shih, Juliann; Kucherlapati, Melanie H.; Kucherlapati, Raju S.; Baylin, Stephen; Cope, Leslie; Danilova, Ludmila; Bootwalla, Moiz S.; Lai, Phillip H.; Maglinte, Dennis T.; Van Den Berg, David J.; Weisenberger, Daniel J.; Auman, J. Todd; Balu, Saianand; Bodenheimer, Tom; Fan, Cheng; Hoadley, Katherine A.; Hoyle, Alan P.; Jefferys, Stuart R.; Jones, Corbin D.; Meng, Shaowu; Mieczkowski, Piotr A.; Mose, Lisle E.; Perou, Amy H.; Perou, Charles M.; Roach, Jeffrey; Shi, Yan; Simons, Janae V.; Skelly, Tara; Soloway, Matthew G.; Tan, Donghui; Veluvolu, Umadevi; Fan, Huihui; Hinoue, Toshinori; Laird, Peter W.; Shen, Hui; Zhou, Wanding; Bellair, Michelle; Chang, Kyle; Covington, Kyle; Creighton, Chad J.; Dinh, Huyen; Doddapaneni, Harsha Vardhan; Donehower, Lawrence A.; Drummond, Jennifer; Gibbs, Richard A.; Glenn, Robert; Hale, Walker; Han, Yi; Hu, Jianhong; Korchina, Viktoriya; Lee, Sandra; Lewis, Lora; Li, Wei; Liu, Xiuping; Morgan, Margaret; Morton, Donna; Muzny, Donna; Santibanez, Jireh; Sheth, Margi; Shinbrot, Eve; Wang, Linghua; Wang, Min; Wheeler, David A.; Xi, Liu; Zhao, Fengmei; Hess, Julian; Appelbaum, Elizabeth L.; Bailey, Matthew; Cordes, Matthew G.; Ding, Li; Fronick, Catrina C.; Fulton, Lucinda A.; Fulton, Robert S.; Kandoth, Cyriac; Mardis, Elaine R.; McLellan, Michael D.; Miller, Christopher A.; Schmidt, Heather K.; Wilson, Richard K.; Crain, Daniel; Curley, Erin; Gardner, Johanna; Lau, Kevin; Mallery, David; Morris, Scott; Paulauskis, Joseph; Penny, Robert; Shelton, Candace; Shelton, Troy; Sherman, Mark; Thompson, Eric; Yena, Peggy; Bowen, Jay; Gastier-Foster, Julie M.; Gerken, Mark; Leraas, Kristen M.; Lichtenberg, Tara M.; Ramirez, Nilsa C.; Wise, Lisa; Zmuda, Erik; Corcoran, Niall; Costello, Tony; Hovens, Christopher; Carvalho, Andre L.; de Carvalho, Ana C.; Fregnani, José H.; Longatto-Filho, Adhemar; Reis, Rui M.; Scapulatempo-Neto, Cristovam; Silveira, Henrique C.S.; Vidal, Daniel O.; Burnette, Andrew; Eschbacher, Jennifer; Hermes, Beth; Noss, Ardene; Singh, Rosy; Anderson, Matthew L.; Castro, Patricia D.; Ittmann, Michael; Huntsman, David; Kohl, Bernard; Le, Xuan; Thorp, Richard; Andry, Chris; Duffy, Elizabeth R.; Lyadov, Vladimir; Paklina, Oxana; Setdikova, Galiya; Shabunin, Alexey; Tavobilov, Mikhail; McPherson, Christopher; Warnick, Ronald; Berkowitz, Ross; Cramer, Daniel; Feltmate, Colleen; Horowitz, Neil; Kibel, Adam; Muto, Michael; Raut, Chandrajit P.; Malykh, Andrei; Barnholtz-Sloan, Jill S.; Barrett, Wendi; Devine, Karen; Fulop, Jordonna; Ostrom, Quinn T.; Shimmel, Kristen; Wolinsky, Yingli; Sloan, Andrew E.; De Rose, Agostino; Giuliante, Felice; Goodman, Marc; Karlan, Beth Y.; Hagedorn, Curt H.; Eckman, John; Harr, Jodi; Myers, Jerome; Tucker, Kelinda; Zach, Leigh Anne; Deyarmin, Brenda; Hu, Hai; Kvecher, Leonid; Larson, Caroline; Mural, Richard J.; Somiari, Stella; Vicha, Ales; Zelinka, Tomas; Bennett, Joseph; Iacocca, Mary; Rabeno, Brenda; Swanson, Patricia; Latour, Mathieu; Lacombe, Louis; Têtu, Bernard; Bergeron, Alain; McGraw, Mary; Staugaitis, Susan M.; Chabot, John; Hibshoosh, Hanina; Sepulveda, Antonia; Su, Tao; Wang, Timothy; Potapova, Olga; Voronina, Olga; Desjardins, Laurence; Mariani, Odette; Roman-Roman, Sergio; Sastre, Xavier; Stern, Marc Henri; Cheng, Feixiong; Signoretti, Sabina; Berchuck, Andrew; Bigner, Darell; Lipp, Eric; Marks, Jeffrey; McCall, Shannon; McLendon, Roger; Secord, Angeles; Sharp, Alexis; Behera, Madhusmita; Brat, Daniel J.; Chen, Amy; Delman, Keith; Force, Seth; Khuri, Fadlo; Magliocca, Kelly; Maithel, Shishir; Olson, Jeffrey J.; Owonikoko, Taofeek; Pickens, Alan; Ramalingam, Suresh; Shin, Dong M.; Sica, Gabriel; Van Meir, Erwin G.; Zhang, Hongzheng; Eijckenboom, Wil; Gillis, Ad; Korpershoek, Esther; Looijenga, Leendert; Oosterhuis, Wolter; Stoop, Hans; van Kessel, Kim E.; Zwarthoff, Ellen C.; Calatozzolo, Chiara; Cuppini, Lucia; Cuzzubbo, Stefania; DiMeco, Francesco; Finocchiaro, Gaetano; Mattei, Luca; Perin, Alessandro; Pollo, Bianca; Chen, Chu; Houck, John; Lohavanichbutr, Pawadee; Hartmann, Arndt; Stoehr, Christine; Stoehr, Robert; Taubert, Helge; Wach, Sven; Wullich, Bernd; Kycler, Witold; Murawa, Dawid; Wiznerowicz, Maciej; Chung, Ki; Edenfield, W. Jeffrey; Martin, Julie; Baudin, Eric; Bubley, Glenn; Bueno, Raphael; De Rienzo, Assunta; Richards, William G.; Kalkanis, Steven; Mikkelsen, Tom; Noushmehr, Houtan; Scarpace, Lisa; Girard, Nicolas; Aymerich, Marta; Campo, Elias; Giné, Eva; Guillermo, Armando López; Van Bang, Nguyen; Hanh, Phan Thi; Phu, Bui Duc; Tang, Yufang; Colman, Howard; Evason, Kimberley; Dottino, Peter R.; Martignetti, John A.; Gabra, Hani; Juhl, Hartmut; Akeredolu, Teniola; Stepa, Serghei; Hoon, Dave; Ahn, Keunsoo; Kang, Koo Jeong; Beuschlein, Felix; Breggia, Anne; Birrer, Michael; Bell, Debra; Borad, Mitesh; Bryce, Alan H.; Castle, Erik; Chandan, Vishal; Cheville, John; Copland, John A.; Farnell, Michael; Flotte, Thomas; Giama, Nasra; Ho, Thai; Kendrick, Michael; Kocher, Jean Pierre; Kopp, Karla; Moser, Catherine; Nagorney, David; O'Brien, Daniel; O'Neill, Brian Patrick; Patel, Tushar; Petersen, Gloria; Que, Florencia; Rivera, Michael; Roberts, Lewis; Smallridge, Robert; Smyrk, Thomas; Stanton, Melissa; Thompson, R. Houston; Torbenson, Michael; Yang, Ju Dong; Zhang, Lizhi; Brimo, Fadi; Ajani, Jaffer A.; Angulo Gonzalez, Ana Maria; Behrens, Carmen; Bondaruk, Jolanta; Broaddus, Russell; Czerniak, Bogdan; Esmaeli, Bita; Fujimoto, Junya; Gershenwald, Jeffrey; Guo, Charles; Lazar, Alexander J.; Logothetis, Christopher; Meric-Bernstam, Funda; Moran, Cesar; Ramondetta, Lois; Rice, David; Sood, Anil; Tamboli, Pheroze; Thompson, Timothy; Troncoso, Patricia; Tsao, Anne; Wistuba, Ignacio; Carter, Candace; Haydu, Lauren; Hersey, Peter; Jakrot, Valerie; Kakavand, Hojabr; Kefford, Richard; Lee, Kenneth; Long, Georgina; Mann, Graham; Quinn, Michael; Saw, Robyn; Scolyer, Richard; Shannon, Kerwin; Spillane, Andrew; Stretch, Jonathan; Synott, Maria; Thompson, John; Wilmott, James; Al-Ahmadie, Hikmat; Chan, Timothy A.; Ghossein, Ronald; Gopalan, Anuradha; Levine, Douglas A.; Reuter, Victor; Singer, Samuel; Singh, Bhuvanesh; Tien, Nguyen Viet; Broudy, Thomas; Mirsaidi, Cyrus; Nair, Praveen; Drwiega, Paul; Miller, Judy; Smith, Jennifer; Zaren, Howard; Park, Joong Won; Hung, Nguyen Phi; Kebebew, Electron; Linehan, W. Marston; Metwalli, Adam R.; Pacak, Karel; Pinto, Peter A.; Schiffman, Mark; Schmidt, Laura S.; Vocke, Cathy D.; Wentzensen, Nicolas; Worrell, Robert; Yang, Hannah; Moncrieff, Marc; Goparaju, Chandra; Melamed, Jonathan; Pass, Harvey; Botnariuc, Natalia; Caraman, Irina; Cernat, Mircea; Chemencedji, Inga; Clipca, Adrian; Doruc, Serghei; Gorincioi, Ghenadie; Mura, Sergiu; Pirtac, Maria; Stancul, Irina; Tcaciuc, Diana; Albert, Monique; Alexopoulou, Iakovina; Arnaout, Angel; Bartlett, John; Engel, Jay; Gilbert, Sebastien; Parfitt, Jeremy; Sekhon, Harman; Thomas, George; Rassl, Doris M.; Rintoul, Robert C.; Bifulco, Carlo; Tamakawa, Raina; Urba, Walter; Hayward, Nicholas; Timmers, Henri; Antenucci, Anna; Facciolo, Francesco; Grazi, Gianluca; Marino, Mirella; Merola, Roberta; de Krijger, Ronald; Gimenez-Roqueplo, Anne Paule; Piché, Alain; Chevalier, Simone; McKercher, Ginette; Birsoy, Kivanc; Barnett, Gene; Brewer, Cathy; Farver, Carol; Naska, Theresa; Pennell, Nathan A.; Raymond, Daniel; Schilero, Cathy; Smolenski, Kathy; Williams, Felicia; Morrison, Carl; Borgia, Jeffrey A.; Liptay, Michael J.; Pool, Mark; Seder, Christopher W.; Junker, Kerstin; Omberg, Larsson; Dinkin, Mikhail; Manikhas, George; Alvaro, Domenico; Bragazzi, Maria Consiglia; Cardinale, Vincenzo; Carpino, Guido; Gaudio, Eugenio; Chesla, David; Cottingham, Sandra; Dubina, Michael; Moiseenko, Fedor; Dhanasekaran, Renumathy; Becker, Karl Friedrich; Janssen, Klaus Peter; Slotta-Huspenina, Julia; Abdel-Rahman, Mohamed H.; Aziz, Dina; Bell, Sue; Cebulla, Colleen M.; Davis, Amy; Duell, Rebecca; Elder, J. Bradley; Hilty, Joe; Kumar, Bahavna; Lang, James; Lehman, Norman L.; Mandt, Randy; Nguyen, Phuong; Pilarski, Robert; Rai, Karan; Schoenfield, Lynn; Senecal, Kelly; Wakely, Paul; Hansen, Paul; Lechan, Ronald; Powers, James; Tischler, Arthur; Grizzle, William E.; Sexton, Katherine C.; Kastl, Alison; Henderson, Joel; Porten, Sima; Waldmann, Jens; Fassnacht, Martin; Asa, Sylvia L.; Schadendorf, Dirk; Couce, Marta; Graefen, Markus; Huland, Hartwig; Sauter, Guido; Schlomm, Thorsten; Simon, Ronald; Tennstedt, Pierre; Olabode, Oluwole; Nelson, Mark; Bathe, Oliver; Carroll, Peter R.; Chan, June M.; Disaia, Philip; Glenn, Pat; Kelley, Robin K.; Landen, Charles N.; Phillips, Joanna; Prados, Michael; Simko, Jeffry; Smith-McCune, Karen; VandenBerg, Scott; Roggin, Kevin; Fehrenbach, Ashley; Kendler, Ady; Sifri, Suzanne; Steele, Ruth; Jimeno, Antonio; Carey, Francis; Forgie, Ian; Mannelli, Massimo; Carney, Michael; Hernandez, Brenda; Campos, Benito; Herold-Mende, Christel; Jungk, Christin; Unterberg, Andreas; von Deimling, Andreas; Bossler, Aaron; Galbraith, Joseph; Jacobus, Laura; Knudson, Michael; Knutson, Tina; Ma, Deqin; Milhem, Mohammed; Sigmund, Rita; Godwin, Andrew K.; Madan, Rashna; Rosenthal, Howard G.; Adebamowo, Clement; Adebamowo, Sally N.; Boussioutas, Alex; Beer, David; Giordano, Thomas; Mes-Masson, Anne Marie; Saad, Fred; Bocklage, Therese; Landrum, Lisa; Mannel, Robert; Moore, Kathleen; Moxley, Katherine; Postier, Russel; Walker, Joan; Zuna, Rosemary; Feldman, Michael; Valdivieso, Federico; Dhir, Rajiv; Luketich, James; Mora Pinero, Edna M.; Quintero-Aguilo, Mario; Carlotti, Carlos Gilberto; Dos Santos, Jose Sebastião; Kemp, Rafael; Sankarankuty, Ajith; Tirapelli, Daniela; Catto, James; Agnew, Kathy; Swisher, Elizabeth; Creaney, Jenette; Robinson, Bruce; Shelley, Carl Simon; Godwin, Eryn M.; Kendall, Sara; Shipman, Cassaundra; Bradford, Carol; Carey, Thomas; Haddad, Andrea; Moyer, Jeffey; Peterson, Lisa; Prince, Mark; Rozek, Laura; Wolf, Gregory; Bowman, Rayleen; Fong, Kwun M.; Yang, Ian; Korst, Robert; Rathmell, W. Kimryn; Fantacone-Campbell, J. Leigh; Hooke, Jeffrey A.; Kovatich, Albert J.; Shriver, Craig D.; DiPersio, John; Drake, Bettina; Govindan, Ramaswamy; Heath, Sharon; Ley, Timothy; Van Tine, Brian; Westervelt, Peter; Rubin, Mark A.; Lee, Jung Il; Aredes, Natália D.; Mariamidze, Armaz

2018-01-01

Although the MYC oncogene has been implicated in cancer, a systematic assessment of alterations of MYC, related transcription factors, and co-regulatory proteins, forming the proximal MYC network (PMN), across human cancers is lacking. Using computational approaches, we define genomic and proteomic

Alternate service delivery models in cancer genetic counseling: a mini-review

Directory of Open Access Journals (Sweden)

Adam Hudson Buchanan

2016-05-01

Full Text Available Demand for cancer genetic counseling has grown rapidly in recent years as germline genomic information has become increasingly incorporated into cancer care and the field has entered the public consciousness through high-profile celebrity publications. Increased demand and existing variability in the availability of trained cancer genetics clinicians place a priority on developing and evaluating alternate service delivery models for genetic counseling. This mini-review summarizes the state of science regarding service delivery models such as telephone counseling, telegenetics and group counseling. Research on comparative effectiveness of these models in traditional individual, in-person genetic counseling has been promising for improving access to care in a manner acceptable to patients. Yet, it has not fully evaluated the short- and long-term patient- and system-level outcomes that will help answer the question of whether these models achieve the same beneficial psychosocial and behavioral outcomes as traditional cancer genetic counseling. We propose a research agenda focused on comparative effectiveness of available service delivery models and how to match models to patients and practice settings. Only through this rigorous research can clinicians and systems find the optimal balance of clinical quality, ready and secure access to care, and financial sustainability. Such research will be integral to achieving the promise of genomic medicine in oncology.
Genetic Alterations in the Molecular Subtypes of Bladder Cancer: Illustration in the Cancer Genome Atlas Dataset.

Science.gov (United States)

Choi, Woonyoung; Ochoa, Andrea; McConkey, David J; Aine, Mattias; Höglund, Mattias; Kim, William Y; Real, Francisco X; Kiltie, Anne E; Milsom, Ian; Dyrskjøt, Lars; Lerner, Seth P

2017-09-01

Recent whole genome mRNA expression profiling studies revealed that bladder cancers can be grouped into molecular subtypes, some of which share clinical properties and gene expression patterns with the intrinsic subtypes of breast cancer and the molecular subtypes found in other solid tumors. The molecular subtypes in other solid tumors are enriched with specific mutations and copy number aberrations that are thought to underlie their distinct progression patterns, and biological and clinical properties. The availability of comprehensive genomic data from The Cancer Genome Atlas (TCGA) and other large projects made it possible to correlate the presence of DNA alterations with tumor molecular subtype membership. Our overall goal was to determine whether specific DNA mutations and/or copy number variations are enriched in specific molecular subtypes. We used the complete TCGA RNA-seq dataset and three different published classifiers developed by our groups to assign TCGA's bladder cancers to molecular subtypes, and examined the prevalence of the most common DNA alterations within them. We interpreted the results against the background of what was known from the published literature about the prevalence of these alterations in nonmuscle-invasive and muscle-invasive bladder cancers. The results confirmed that alterations involving RB1 and NFE2L2 were enriched in basal cancers, whereas alterations involving FGFR3 and KDM6A were enriched in luminal tumors. The results further reinforce the conclusion that the molecular subtypes of bladder cancer are distinct disease entities with specific genetic alterations. Our observation showed that some of subtype-enriched mutations and copy number aberrations are clinically actionable, which has direct implications for the clinical management of patients with bladder cancer. Copyright © 2017 European Association of Urology. Published by Elsevier B.V. All rights reserved.
PBX1 Genomic Pioneer Function Drives ERα Signaling Underlying Progression in Breast Cancer

Science.gov (United States)

Magnani, Luca; Ballantyne, Elizabeth B.; Zhang, Xiaoyang; Lupien, Mathieu

2011-01-01

Altered transcriptional programs are a hallmark of diseases, yet how these are established is still ill-defined. PBX1 is a TALE homeodomain protein involved in the development of different types of cancers. The estrogen receptor alpha (ERα) is central to the development of two-thirds of all breast cancers. Here we demonstrate that PBX1 acts as a pioneer factor and is essential for the ERα-mediated transcriptional response driving aggressive tumors in breast cancer. Indeed, PBX1 expression correlates with ERα in primary breast tumors, and breast cancer cells depleted of PBX1 no longer proliferate following estrogen stimulation. Profiling PBX1 recruitment and chromatin accessibility across the genome of breast cancer cells through ChIP-seq and FAIRE-seq reveals that PBX1 is loaded and promotes chromatin openness at specific genomic locations through its capacity to read specific epigenetic signatures. Accordingly, PBX1 guides ERα recruitment to a specific subset of sites. Expression profiling studies demonstrate that PBX1 controls over 70% of the estrogen response. More importantly, the PBX1-dependent transcriptional program is associated with poor-outcome in breast cancer patients. Correspondingly, PBX1 expression alone can discriminate a priori the outcome in ERα-positive breast cancer patients. These features are markedly different from the previously characterized ERα-associated pioneer factor FoxA1. Indeed, PBX1 is the only pioneer factor identified to date that discriminates outcome such as metastasis in ERα-positive breast cancer patients. Together our results reveal that PBX1 is a novel pioneer factor defining aggressive ERα-positive breast tumors, as it guides ERα genomic activity to unique genomic regions promoting a transcriptional program favorable to breast cancer progression. PMID:22125492
Integration and comparison of different genomic data for outcome prediction in cancer

OpenAIRE

Gomez Rueda, Hugo; Martínez Ledesma, Emmanuel; Martínez Torteya, Antonio; Palacios Corona, Rebeca; Treviño, Victor

2005-01-01

Background In cancer, large-scale technologies such as next-generation sequencing and microarrays have produced a wide number of genomic features such as DNA copy number alterations (CNA), mRNA expression (EXPR), microRNA expression (MIRNA), and DNA somatic mutations (MUT), among others. Several analyses of a specific type of these genomic data have generated many prognostic biomarkers in cancer. However, it is uncertain which of these data is more powerful and whether the best data-type is c...
The national cancer institute (NCI) and cancer biology in a 'post genome world'

International Nuclear Information System (INIS)

Klausner, Richard D.

1996-01-01

The National Cancer Institute (NCI) exists to reduce the burden of all cancers through research and discovery. Extensive restructuring of the NCI over the past year has been aimed at assuring that the institution functions in all ways to promote opportunities for discovery in the laboratory, in the clinic, and in the community. To do this well requires the difficult and almost paradoxical problem of planning for scientific discovery which, in turn is based on the freedom to pursue the unanticipated. The intellectual and structural landscape of science is changing and it places new challenges, new demands and new opportunities for facilitating discovery. The nature of cancer as a disease of genomic instability and of accumulated genetic change, coupled with a possibility of the development of new technologies for reading, utilizing, interpreting and manipulating the genome of single cells, provides unprecedented opportunities for a new type of high through-put biology that will change the nature of discovery, cancer detection, diagnosis, prognosis, therapeutic decision-making and therapeutic discovery. To capture these new opportunities will require attention to be paid to integrate the development of technology and new scientific discoveries with the ability to apply advances rapidly and efficiently through clinical trials
Differentiation among prostate cancer patients with Gleason score of 7 using histopathology whole-slide image and genomic data

Science.gov (United States)

Ren, Jian; Karagoz, Kubra; Gatza, Michael; Foran, David J.; Qi, Xin

2018-03-01

Prostate cancer is the most common non-skin related cancer affecting 1 in 7 men in the United States. Treatment of patients with prostate cancer still remains a difficult decision-making process that requires physicians to balance clinical benefits, life expectancy, comorbidities, and treatment-related side effects. Gleason score (a sum of the primary and secondary Gleason patterns) solely based on morphological prostate glandular architecture has shown as one of the best predictors of prostate cancer outcome. Significant progress has been made on molecular subtyping prostate cancer delineated through the increasing use of gene sequencing. Prostate cancer patients with Gleason score of 7 show heterogeneity in recurrence and survival outcomes. Therefore, we propose to assess the correlation between histopathology images and genomic data with disease recurrence in prostate tumors with a Gleason 7 score to identify prognostic markers. In the study, we identify image biomarkers within tissue WSIs by modeling the spatial relationship from automatically created patches as a sequence within WSI by adopting a recurrence network model, namely long short-term memory (LSTM). Our preliminary results demonstrate that integrating image biomarkers from CNN with LSTM and genomic pathway scores, is more strongly correlated with patients recurrence of disease compared to standard clinical markers and engineered image texture features. The study further demonstrates that prostate cancer patients with Gleason score of 4+3 have a higher risk of disease progression and recurrence compared to prostate cancer patients with Gleason score of 3+4.
Understanding aneuploidy in cancer through the lens of system inheritance, fuzzy inheritance and emergence of new genome systems.

Science.gov (United States)

Ye, Christine J; Regan, Sarah; Liu, Guo; Alemara, Sarah; Heng, Henry H

2018-01-01

In the past 15 years, impressive progress has been made to understand the molecular mechanism behind aneuploidy, largely due to the effort of using various -omics approaches to study model systems (e.g. yeast and mouse models) and patient samples, as well as the new realization that chromosome alteration-mediated genome instability plays the key role in cancer. As the molecular characterization of the causes and effects of aneuploidy progresses, the search for the general mechanism of how aneuploidy contributes to cancer becomes increasingly challenging: since aneuploidy can be linked to diverse molecular pathways (in regards to both cause and effect), the chances of it being cancerous is highly context-dependent, making it more difficult to study than individual molecular mechanisms. When so many genomic and environmental factors can be linked to aneuploidy, and most of them not commonly shared among patients, the practical value of characterizing additional genetic/epigenetic factors contributing to aneuploidy decreases. Based on the fact that cancer typically represents a complex adaptive system, where there is no linear relationship between lower-level agents (such as each individual gene mutation) and emergent properties (such as cancer phenotypes), we call for a new strategy based on the evolutionary mechanism of aneuploidy in cancer, rather than continuous analysis of various individual molecular mechanisms. To illustrate our viewpoint, we have briefly reviewed both the progress and challenges in this field, suggesting the incorporation of an evolutionary-based mechanism to unify diverse molecular mechanisms. To further clarify this rationale, we will discuss some key concepts of the genome theory of cancer evolution, including system inheritance, fuzzy inheritance, and cancer as a newly emergent cellular system. Illustrating how aneuploidy impacts system inheritance, fuzzy inheritance and the emergence of new systems is of great importance. Such synthesis
Association between invasive ovarian cancer susceptibility and 11 best candidate SNPs from breast cancer genome-wide association study

DEFF Research Database (Denmark)

Song, Honglin; Ramus, Susan J; Kjaer, Susanne Krüger

2009-01-01

Because both ovarian and breast cancer are hormone-related and are known to have some predisposition genes in common, we evaluated 11 of the most significant hits (six with confirmed associations with breast cancer) from the breast cancer genome-wide association study for association with invasiv...
Translational and functional oncogenomics. From cancer-oriented genomic screenings to new diagnostic tools and improved cancer treatment.

Science.gov (United States)

Medico, Enzo

2008-01-01

We present here an experimental pipeline for the systematic identification and functional characterization of genes with high potential diagnostic and therapeutic value in human cancer. Complementary competences and resources have been brought together in the TRANSFOG Consortium to reach the following integrated research objectives: 1) execution of cancer-oriented genomic screenings on tumor tissues and experimental models and merging of the results to generate a prioritized panel of candidate genes involved in cancer progression and metastasis; 2) setup of systems for high-throughput delivery of full-length cDNAs, for gain-of-function analysis of the prioritized candidate genes; 3) collection of vectors and oligonucleotides for systematic, RNA interference-mediated down-regulation of the candidate genes; 4) adaptation of existing cell-based and model organism assays to a systematic analysis of gain and loss of function of the candidate genes, for identification and preliminary validation of novel potential therapeutic targets; 5) proteomic analysis of signal transduction and protein-protein interaction for better dissection of aberrant cancer signaling pathways; 6) validation of the diagnostic potential of the identified cancer genes towards the clinical use of diagnostic molecular signatures; 7) generation of a shared informatics platform for data handling and gene functional annotation. The results of the first three years of activity of the TRANSFOG Consortium are also briefly presented and discussed.
Cross-cancer genome-wide analysis of lung, ovary, breast, prostate, and colorectal cancer reveals novel pleiotropic associations

NARCIS (Netherlands)

Fehringer, G. (Gordon); P. Kraft (Peter); P.D.P. Pharoah (Paul); R. Eeles (Rosalind); Chatterjee, N. (Nilanjan); F.R. Schumacher (Fredrick R); J.M. Schildkraut (Joellen); S. Lindstrom (Stephen); P. Brennan (Paul); H. Bickeböller (Heike); R. Houlston (Richard); M.T. Landi (Maria Teresa); N.E. Caporaso (Neil); Risch, A. (Angela); A.A. Al Olama (Ali Amin); S.I. Berndt (Sonja); Giovannucci, E.L. (Edward L.); H. Grönberg (Henrik); Z. Kote-Jarai; Ma, J. (Jing); K.R. Muir (K.); M.J. Stampfer (Meir J.); Stevens, V.L. (Victoria L.); F. Wiklund (Fredrik); W.C. Willett (Walter C.); E.L. Goode (Ellen); Permuth, J.B. (Jennifer B.); H. Risch (Harvey); Reid, B.M. (Brett M.); Bezieau, S. (Stephane); H. Brenner (Hermann); Chan, A.T. (Andrew T.); J. Chang-Claude (Jenny); T.J. Hudson (Thomas); Kocarnik, J.K. (Jonathan K.); P. Newcomb (Polly); Schoen, R.E. (Robert E.); Slattery, M.L. (Martha L.); White, E. (Emily); M.A. Adank (Muriel); H. Ahsan (Habibul); K. Aittomäki (Kristiina); Baglietto, L. (Laura); Blomquist, C. (Carl); F. Canzian (Federico); K. Czene (Kamila); I. dos Santos Silva (Isabel); Eliassen, A.H. (A. Heather); J.D. Figueroa (Jonine); D. Flesch-Janys (Dieter); O. Fletcher (Olivia); M. García-Closas (Montserrat); M.M. Gaudet (Mia); Johnson, N. (Nichola); P. Hall (Per); A. Hazra (Aditi); R. Hein (Rebecca); Hofman, A. (Albert); J.L. Hopper (John); A. Irwanto (Astrid); M. Johansson (Mattias); R. Kaaks (Rudolf); M.G. Kibriya (Muhammad); P. Lichtner (Peter); J. Liu (Jianjun); E. Lund (Eiliv); Makalic, E. (Enes); A. Meindl (Alfons); B. Müller-Myhsok (B.); Muranen, T.A. (Taru A.); H. Nevanlinna (Heli); P.H.M. Peeters; J. Peto (Julian); R. Prentice (Ross); N. Rahman (Nazneen); M.-J. Sanchez (Maria-Jose); D.F. Schmidt (Daniel); R.K. Schmutzler (Rita); M.C. Southey (Melissa); Tamimi, R. (Rulla); S.P.L. Travis (Simon); C. Turnbull (Clare); Uitterlinden, A.G. (Andre G.); Z. Wang (Zhaoming); A.S. Whittemore (Alice); X.R. Yang (Xiaohong); W. Zheng (Wei); D. Buchanan (Daniel); G. Casey (Graham); G. Conti (Giario); C.K. Edlund (Christopher); S. Gallinger (Steve); R. Haile (Robert); M. Jenkins (Mark); Marchand, L. (Loïcle); Li, L. (Li); N.M. Lindor (Noralane); Schmit, S.L. (Stephanie L.); S.N. Thibodeau (Stephen); M.O. Woods (Michael); T. Rafnar (Thorunn); J. Gudmundsson (Julius); S.N. Stacey (Simon); Stefansson, K. (Kari); P. Sulem (Patrick); Chen, Y.A. (Y. Ann); J.P. Tyrer (Jonathan); Christiani, D.C. (David C.); Wei, Y. (Yongyue); H. Shen (Hongbing); Z. Hu (Zhibin); X.-O. Shu (Xiao-Ou); Shiraishi, K. (Kouya); A. Takahashi (Atsushi); Y. Bossé (Yohan); M. Obeidat (Ma'en); D.C. Nickle (David); W. Timens (Wim); M. Freedman (Matthew); Li, Q. (Qiyuan); D. Seminara (Daniela); S.J. Chanock (Stephen); Gong, J. (Jian); U. Peters (Ulrike); S.B. Gruber (Stephen); Amos, C.I. (Christopher I.); T.A. Sellers (Thomas A.); D.F. Easton (Douglas F.); D. Hunter (David); C.A. Haiman (Christopher A.); B.E. Henderson (Brian); R.J. Hung (Rayjean)

2016-01-01

textabstractIdentifying genetic variants with pleiotropic associations can uncover common pathways influencing multiple cancers. We took a two-stage approach to conduct genome-wide association studies for lung, ovary, breast, prostate, and colorectal cancer from the GAME-ON/GECCO Network (61,851
Provision of personalized genomic diagnostic technologies for breast and colorectal cancer: an analysis of patient needs, expectations and priorities.

Science.gov (United States)

Issa, Amalia M; Hutchinson, Janis F; Tufail, Waqas; Fletcher, Erica; Ajike, Roseline; Tenorio, Jose

2011-07-01

Several novel pharmacogenomic diagnostic tests are commercially available for breast and colorectal cancer, and are increasingly being used in clinical practice for improving treatment decisions. However, there is little evidence evaluating the value of these new genomic technologies from the perspective of patients. As part of an ongoing effort to understand the continuum of the process of adoption of genomic diagnostics, our aim in this study was to examine the value of genomic diagnostics to breast and colorectal cancer patients, and their willingness to adopt and use genomic diagnostics. We conducted six focus groups of breast and colorectal cancer patients from the oncology clinics at The Methodist Hospital, Houston, TX, USA. An adapted Q-sort instrument was also administered to focus group participants. The majority of breast and colorectal cancer patients are interested in using novel genomic diagnostics for deciding about treatment options. Most participants in our study expressed a willingness to pay out-of-pocket for genomic testing (z = 0.736). Reliability and validity of genomic testing were of significant concern (z = 1.32) for the majority of breast and colorectal cancer patients. Participants identified several facilitators and barriers within health systems that might either facilitate or impede the widespread adoption and use of genomic diagnostics in healthcare delivery. This study demonstrates breast and colorectal cancer patients' willingness to adopt and pay for novel genomic diagnostics, as well as identifies several salient factors associated with patient preferences for genomic diagnostics.
Multi-region and single-cell sequencing reveal variable genomic heterogeneity in rectal cancer.

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Liu, Mingshan; Liu, Yang; Di, Jiabo; Su, Zhe; Yang, Hong; Jiang, Beihai; Wang, Zaozao; Zhuang, Meng; Bai, Fan; Su, Xiangqian

2017-11-23

Colorectal cancer is a heterogeneous group of malignancies with complex molecular subtypes. While colon cancer has been widely investigated, studies on rectal cancer are very limited. Here, we performed multi-region whole-exome sequencing and single-cell whole-genome sequencing to examine the genomic intratumor heterogeneity (ITH) of rectal tumors. We sequenced nine tumor regions and 88 single cells from two rectal cancer patients with tumors of the same molecular classification and characterized their mutation profiles and somatic copy number alterations (SCNAs) at the multi-region and the single-cell levels. A variable extent of genomic heterogeneity was observed between the two patients, and the degree of ITH increased when analyzed on the single-cell level. We found that major SCNAs were early events in cancer development and inherited steadily. Single-cell sequencing revealed mutations and SCNAs which were hidden in bulk sequencing. In summary, we studied the ITH of rectal cancer at regional and single-cell resolution and demonstrated that variable heterogeneity existed in two patients. The mutational scenarios and SCNA profiles of two patients with treatment naïve from the same molecular subtype are quite different. Our results suggest each tumor possesses its own architecture, which may result in different diagnosis, prognosis, and drug responses. Remarkable ITH exists in the two patients we have studied, providing a preliminary impression of ITH in rectal cancer.
dbEM: A database of epigenetic modifiers curated from cancerous and normal genomes

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Singh Nanda, Jagpreet; Kumar, Rahul; Raghava, Gajendra P. S.

2016-01-01

We have developed a database called dbEM (database of Epigenetic Modifiers) to maintain the genomic information of about 167 epigenetic modifiers/proteins, which are considered as potential cancer targets. In dbEM, modifiers are classified on functional basis and comprise of 48 histone methyl transferases, 33 chromatin remodelers and 31 histone demethylases. dbEM maintains the genomic information like mutations, copy number variation and gene expression in thousands of tumor samples, cancer cell lines and healthy samples. This information is obtained from public resources viz. COSMIC, CCLE and 1000-genome project. Gene essentiality data retrieved from COLT database further highlights the importance of various epigenetic proteins for cancer survival. We have also reported the sequence profiles, tertiary structures and post-translational modifications of these epigenetic proteins in cancer. It also contains information of 54 drug molecules against different epigenetic proteins. A wide range of tools have been integrated in dbEM e.g. Search, BLAST, Alignment and Profile based prediction. In our analysis, we found that epigenetic proteins DNMT3A, HDAC2, KDM6A, and TET2 are highly mutated in variety of cancers. We are confident that dbEM will be very useful in cancer research particularly in the field of epigenetic proteins based cancer therapeutics. This database is available for public at URL: http://crdd.osdd.net/raghava/dbem.
A national study of breast and colorectal cancer patients' decision-making for novel personalized medicine genomic diagnostics.

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Issa, Amalia M; Tufail, Waqas; Atehortua, Nelson; McKeever, John

2013-05-01

Molecular diagnostics are increasingly being used to help guide decision-making for personalized medical treatment of breast and colorectal cancer patients. The main aim of this study was to better understand and determine breast and colorectal cancer patients' decision-making strategies and the trade-offs they make in deciding about characteristics of molecular genomic diagnostics for breast and colorectal cancer. We surveyed a nationally representative sample of 300 breast and colorectal cancer patients using a previously developed web-administered instrument. Eligibility criteria included patients aged 18 years and older with either breast or colorectal cancer. We explored several attributes and attribute levels of molecular genomic diagnostics in 20 scenarios. Our analysis revealed that both breast and colorectal cancer patients weighted the capability of molecular genomic diagnostics to determine the probability of treatment efficacy as being of greater importance than information provided to detect adverse events. The probability of either false-positive or -negative results was ranked highly as a potential barrier by both breast and colorectal patients. However, 78.6% of breast cancer patients ranked the possibility of a 'false-negative test result leading to undertreatment' higher than the 'chance of a false positive, which may lead to overtreatment' (68%). This finding contrasted with the views of colorectal cancer patients who ranked the chance of a false positive as being of greater concern than a false negative (72.8 vs 63%). Overall, cancer patients exhibited a high willingness to accept and pay for genomic diagnostic tests, especially among breast cancer patients. Cancer patients seek a test accuracy rate of 90% or higher. Breast and colorectal cancer patients' decisions about genomic diagnostics are influenced more by the probability of being cured than by avoiding potential severe adverse events. This study provides insights into the relative weight
Genetic basis of kidney cancer: Role of genomics for the development of disease-based therapeutics

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Linehan, W. Marston

2012-01-01

Kidney cancer is not a single disease; it is made up of a number of different types of cancer, including clear cell, type 1 papillary, type 2 papillary, chromophobe, TFE3, TFEB, and oncocytoma. Sporadic, nonfamilial kidney cancer includes clear cell kidney cancer (75%), type 1 papillary kidney cancer (10%), papillary type 2 kidney cancer (including collecting duct and medullary RCC) (5%), the microphalmia-associated transcription (MiT) family translocation kidney cancers (TFE3, TFEB, and MITF), chromophobe kidney cancer (5%), and oncocytoma (5%). Each has a distinct histology, a different clinical course, responds differently to therapy, and is caused by mutation in a different gene. Genomic studies identifying the genes for kidney cancer, including the VHL, MET, FLCN, fumarate hydratase, succinate dehydrogenase, TSC1, TSC2, and TFE3 genes, have significantly altered the ways in which patients with kidney cancer are managed. While seven FDA-approved agents that target the VHL pathway have been approved for the treatment of patients with advanced kidney cancer, further genomic studies, such as whole genome sequencing, gene expression patterns, and gene copy number, will be required to gain a complete understanding of the genetic basis of kidney cancer and of the kidney cancer gene pathways and, most importantly, to provide the foundation for the development of effective forms of therapy for patients with this disease. PMID:23038766
Naturally Occurring Canine Invasive Urinary Bladder Cancer: A Complementary Animal Model to Improve the Success Rate in Human Clinical Trials of New Cancer Drugs

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Christopher M. Fulkerson

2017-01-01

Full Text Available Genomic analyses are defining numerous new targets for cancer therapy. Therapies aimed at specific genetic and epigenetic targets in cancer cells as well as expanded development of immunotherapies are placing increased demands on animal models. Traditional experimental models do not possess the collective features (cancer heterogeneity, molecular complexity, invasion, metastasis, and immune cell response critical to predict success or failure of emerging therapies in humans. There is growing evidence, however, that dogs with specific forms of naturally occurring cancer can serve as highly relevant animal models to complement traditional models. Invasive urinary bladder cancer (invasive urothelial carcinoma (InvUC in dogs, for example, closely mimics the cancer in humans in pathology, molecular features, biological behavior including sites and frequency of distant metastasis, and response to chemotherapy. Genomic analyses are defining further intriguing similarities between InvUC in dogs and that in humans. Multiple canine clinical trials have been completed, and others are in progress with the aim of translating important findings into humans to increase the success rate of human trials, as well as helping pet dogs. Examples of successful targeted therapy studies and the challenges to be met to fully utilize naturally occurring dog models of cancer will be reviewed.
Naturally Occurring Canine Invasive Urinary Bladder Cancer: A Complementary Animal Model to Improve the Success Rate in Human Clinical Trials of New Cancer Drugs.

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Fulkerson, Christopher M; Dhawan, Deepika; Ratliff, Timothy L; Hahn, Noah M; Knapp, Deborah W

2017-01-01

Genomic analyses are defining numerous new targets for cancer therapy. Therapies aimed at specific genetic and epigenetic targets in cancer cells as well as expanded development of immunotherapies are placing increased demands on animal models. Traditional experimental models do not possess the collective features (cancer heterogeneity, molecular complexity, invasion, metastasis, and immune cell response) critical to predict success or failure of emerging therapies in humans. There is growing evidence, however, that dogs with specific forms of naturally occurring cancer can serve as highly relevant animal models to complement traditional models. Invasive urinary bladder cancer (invasive urothelial carcinoma (InvUC)) in dogs, for example, closely mimics the cancer in humans in pathology, molecular features, biological behavior including sites and frequency of distant metastasis, and response to chemotherapy. Genomic analyses are defining further intriguing similarities between InvUC in dogs and that in humans. Multiple canine clinical trials have been completed, and others are in progress with the aim of translating important findings into humans to increase the success rate of human trials, as well as helping pet dogs. Examples of successful targeted therapy studies and the challenges to be met to fully utilize naturally occurring dog models of cancer will be reviewed.
Cross-Cancer Genome-Wide Analysis of Lung, Ovary, Breast, Prostate, and Colorectal Cancer Reveals Novel Pleiotropic Associations

NARCIS (Netherlands)

Fehringer, Gordon; Kraft, Peter; Pharoah, Paul D.; Eeles, Rosalind A.; Chatterjee, Nilanjan; Schumacher, Fredrick R.; Schildkraut, Joellen M.; Lindstrom, Sara; Brennan, Paul; Bickeboller, Heike; Houlston, Richard S.; Landi, Maria Teresa; Caporaso, Neil; Risch, Angela; Al Olama, Ali Amin; Berndt, Sonja I.; Giovannucci, Edward L.; Gronberg, Henrik; Kote-Jarai, Zsofia; Ma, Jing; Muir, Kenneth; Stampfer, Meir J.; Stevens, Victoria L.; Wiklund, Fredrik; Willett, Walter C.; Goode, Ellen L.; Permuth, Jennifer B.; Risch, Harvey A.; Reid, Brett M.; Bezieau, Stephane; Brenner, Hermann; Chan, Andrew T.; Chang-Claude, Jenny; Hudson, Thomas J.; Kocarnik, Jonathan K.; Newcomb, Polly A.; Schoen, Robert E.; Slattery, Martha L.; White, Emily; Adank, Muriel A.; Ahsan, Habibul; Aittomaki, Kristiina; Baglietto, Laura; Blomquist, Carl; Canzian, Federico; Czene, Kamila; dos-Santos-Silva, Isabel; Eliassen, A. Heather; Figueroa, Jonine D.; Timens, Wim

2016-01-01

Identifying genetic variants with pleiotropic associations can uncover common pathways influencing multiple cancers. We took a two-stage approach to conduct genome-wide association studies for lung, ovary, breast, prostate, and colorectal cancer from the GAME-ON/GECCO Network (61,851 cases, 61,820
Cross-cancer genome-wide analysis of lung, ovary, breast, prostate, and colorectal cancer reveals novel pleiotropic associations

NARCIS (Netherlands)

Fehringer, Gordon; Kraft, Peter; Pharoah, Paul D.; Eeles, Rosalind A.; Chatterjee, Nilanjan; Schumacher, Fredrick R.; Schildkraut, Joellen M.; Lindström, Sara; Brennan, Paul; Bickeböller, Heike; Houlston, Richard S.; Landi, Maria Teresa; Caporaso, Neil; Risch, Angela; Al Olama, Ali Amin; Berndt, Sonja I.; Giovannucci, Edward L.; Grönberg, Henrik; Kote-Jarai, Zsofia; Ma, Jing; Muir, Kenneth; Stampfer, Meir J.; Stevens, Victoria L.; Wiklund, Fredrik; Willett, Walter C.; Goode, Ellen L.; Permuth, Jennifer B.; Risch, Harvey A.; Reid, Brett M.; Bezieau, Stephane; Brenner, Hermann; Chan, Andrew T.; Chang-Claude, Jenny; Hudson, Thomas J.; Kocarnik, Jonathan K.; Newcomb, Polly A.; Schoen, Robert E.; Slattery, Martha L.; White, Emily; Adank, Muriel A.; Ahsan, Habibul; Aittomäki, Kristiina; Baglietto, Laura; Blomquist, Carl; Canzian, Federico; Czene, Kamila; Dos-Santos-silva, Isabel; Eliassen, A. Heather; Figueroa, Jonine D.; Flesch-Janys, Dieter; Fletcher, Olivia; Garcia-Closas, Montserrat; Gaudet, Mia M.; Johnson, Nichola; Hall, Per; Hazra, Aditi; Hein, Rebecca; Hofman, Albert; Hopper, John L.; Irwanto, Astrid; Johansson, Mattias; Kaaks, Rudolf; Kibriya, Muhammad G.; Lichtner, Peter; Liu, Jianjun; Lund, Eiliv; Makalic, Enes; Meindl, Alfons; Müller-Myhsok, Bertram; Muranen, Taru A.; Nevanlinna, Heli; Peeters, Petra H.; Peto, Julian; Prentice, Ross L.; Rahman, Nazneen; Sanchez, Maria Jose; Schmidt, Daniel F.; Schmutzler, Rita K.; Southey, Melissa C.; Tamimi, Rulla; Travis, Ruth C.; Turnbull, Clare; Uitterlinden, Andre G.; Wang, Zhaoming; Whittemore, Alice S.; Yang, Xiaohong R.; Zheng, Wei; Buchanan, Daniel D.; Casey, Graham; Conti, David V.; Edlund, Christopher K.; Gallinger, Steven; Haile, Robert W.; Jenkins, Mark; Marchand, Loïcle; Li, Li; Lindor, Noralene M.; Schmit, Stephanie L.; Thibodeau, Stephen N.; Woods, Michael O.; Rafnar, Thorunn; Gudmundsson, Julius; Stacey, Simon N.; Stefansson, Kari; Sulem, Patrick; Chen, Y. Ann; Tyrer, Jonathan P.; Christiani, David C.; Wei, Yongyue; Shen, Hongbing; Hu, Zhibin; Shu, Xiao Ou; Shiraishi, Kouya; Takahashi, Atsushi; Bossé, Yohan; Obeidat, Ma'en; Nickle, David; Timens, Wim; Freedman, Matthew L.; Li, Qiyuan; Seminara, Daniela; Chanock, Stephen J.; Gong, Jian; Peters, Ulrike; Gruber, Stephen B.; Amos, Christopher I.; Sellers, Thomas A.; Easton, Douglas F.; Hunter, David J.; Haiman, Christopher A.; Henderson, Brian E.; Hung, Rayjean J.

2016-01-01

Identifying genetic variants with pleiotropic associations can uncover common pathways influencing multiple cancers. We took a two-stage approach to conduct genome-wide association studies for lung, ovary, breast, prostate, and colorectal cancer from the GAME-ON/GECCO Network (61,851 cases, 61,820
Cancer genomics

DEFF Research Database (Denmark)

Norrild, Bodil; Guldberg, Per; Ralfkiær, Elisabeth Methner

2007-01-01

Almost all cells in the human body contain a complete copy of the genome with an estimated number of 25,000 genes. The sequences of these genes make up about three percent of the genome and comprise the inherited set of genetic information. The genome also contains information that determines whe...

Data Mining Approaches for Genomic Biomarker Development: Applications Using Drug Screening Data from the Cancer Genome Project and the Cancer Cell Line Encyclopedia.

Directory of Open Access Journals (Sweden)

David G Covell

Full Text Available Developing reliable biomarkers of tumor cell drug sensitivity and resistance can guide hypothesis-driven basic science research and influence pre-therapy clinical decisions. A popular strategy for developing biomarkers uses characterizations of human tumor samples against a range of cancer drug responses that correlate with genomic change; developed largely from the efforts of the Cancer Cell Line Encyclopedia (CCLE and Sanger Cancer Genome Project (CGP. The purpose of this study is to provide an independent analysis of this data that aims to vet existing and add novel perspectives to biomarker discoveries and applications. Existing and alternative data mining and statistical methods will be used to a evaluate drug responses of compounds with similar mechanism of action (MOA, b examine measures of gene expression (GE, copy number (CN and mutation status (MUT biomarkers, combined with gene set enrichment analysis (GSEA, for hypothesizing biological processes important for drug response, c conduct global comparisons of GE, CN and MUT as biomarkers across all drugs screened in the CGP dataset, and d assess the positive predictive power of CGP-derived GE biomarkers as predictors of drug response in CCLE tumor cells. The perspectives derived from individual and global examinations of GEs, MUTs and CNs confirm existing and reveal unique and shared roles for these biomarkers in tumor cell drug sensitivity and resistance. Applications of CGP-derived genomic biomarkers to predict the drug response of CCLE tumor cells finds a highly significant ROC, with a positive predictive power of 0.78. The results of this study expand the available data mining and analysis methods for genomic biomarker development and provide additional support for using biomarkers to guide hypothesis-driven basic science research and pre-therapy clinical decisions.
Genomic profiling toward precision medicine in non-small cell lung cancer: getting beyond EGFR

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Richer AL

2015-02-01

Full Text Available Amanda L Richer,1 Jacqueline M Friel,1 Vashti M Carson,2 Landon J Inge,1 Timothy G Whitsett2 1Norton Thoracic Institute, St Joseph’s Hospital and Medical Center, 2Cancer and Cell Biology Division, Translational Genomics Research Institute, Phoenix, AZ, USA Abstract: Lung cancer remains the leading cause of cancer-related mortality worldwide. The application of next-generation genomic technologies has offered a more comprehensive look at the mutational landscape across the different subtypes of non-small cell lung cancer (NSCLC. A number of recurrent mutations such as TP53, KRAS, and epidermal growth factor receptor (EGFR have been identified in NSCLC. While targeted therapeutic successes have been demonstrated in the therapeutic targeting of EGFR and ALK, the majority of NSCLC tumors do not harbor these genomic events. This review looks at the current treatment paradigms for lung adenocarcinomas and squamous cell carcinomas, examining genomic aberrations that dictate therapy selection, as well as novel therapeutic strategies for tumors harboring mutations in KRAS, TP53, and LKB1 which, to date, have been considered “undruggable”. A more thorough understanding of the molecular alterations that govern NSCLC tumorigenesis, aided by next-generation sequencing, will lead to targeted therapeutic options expected to dramatically reduce the high mortality rate observed in lung cancer. Keywords: non-small cell lung cancer, precision medicine, epidermal growth factor receptor, Kirsten rat sarcoma viral oncogene homolog, serine/threonine kinase 11, tumor protein p53
Funding Opportunity: Genomic Data Centers

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Funding Opportunity CCG, Funding Opportunity Center for Cancer Genomics, CCG, Center for Cancer Genomics, CCG RFA, Center for cancer genomics rfa, genomic data analysis network, genomic data analysis network centers,
Pan-cancer analysis of genomic scar signatures associated with homologous recombination deficiency suggests novel indications for existing cancer drugs

DEFF Research Database (Denmark)

Marquard, Andrea Marion; Eklund, Aron Charles; Joshi, Tejal

2015-01-01

Ovarian and triple-negative breast cancers with BRCA1 or BRCA2 loss are highly sensitive to treatment with PARP inhibitors and platinum-based cytotoxic agents and show an accumulation of genomic scars in the form of gross DNA copy number aberrations. Cancers without BRCA1 or BRCA2 loss...... but with accumulation of similar genomic scars also show increased sensitivity to platinum-based chemotherapy. Therefore, reliable biomarkers to identify DNA repair-deficient cancers prior to treatment may be useful for directing patients to platinum chemotherapy and possibly PARP inhibitors. Recently, three SNP array...... may be strong candidates for clinical trials with PARP inhibitors or platinum-based chemotherapeutic regimens....
University of Texas MD Anderson Cancer Center: High-Throughput Screening Identifying Driving Mutations in Endometrial Cancer | Office of Cancer Genomics

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Recent advances in next-generation sequencing technology have enabled the unprecedented characterization of a full spectrum of somatic alterations in cancer genomes. Given the large numbers of somatic mutations typically detected by this approach, a key challenge in the downstream analysis is to distinguish “drivers” that functionally contribute to tumorigenesis from “passengers” that occur as the consequence of genomic instability.
Genome-wide association study of prostate cancer-specific survival

DEFF Research Database (Denmark)

Szulkin, Robert; Karlsson, Robert; Whitington, Thomas

2015-01-01

BACKGROUND: Unnecessary intervention and overtreatment of indolent disease are common challenges in clinical management of prostate cancer. Improved tools to distinguish lethal from indolent disease are critical. METHODS: We performed a genome-wide survival analysis of cause-specific death in 24,...
Genomic Copy Number Dictates a Gene-Independent Cell Response to CRISPR/Cas9 Targeting | Office of Cancer Genomics

Science.gov (United States)

The CRISPR/Cas9 system enables genome editing and somatic cell genetic screens in mammalian cells. We performed genome-scale loss-of-function screens in 33 cancer cell lines to identify genes essential for proliferation/survival and found a strong correlation between increased gene copy number and decreased cell viability after genome editing. Within regions of copy-number gain, CRISPR/Cas9 targeting of both expressed and unexpressed genes, as well as intergenic loci, led to significantly decreased cell proliferation through induction of a G2 cell-cycle arrest.
Whole genomes redefine the mutational landscape of pancreatic cancer

OpenAIRE

Waddell, Nicola; Pajic, Marina; Patch, Ann-Marie; Chang, David K.; Kassahn, Karin S.; Bailey, Peter; Johns, Amber L.; Miller, David; Nones, Katia; Quek, Kelly; Quinn, Michael C. J.; Robertson, Alan J.; Fadlullah, Muhammad Z. H.; Bruxner, Tim J. C.; Christ, Angelika N.

2015-01-01

Pancreatic cancer remains one of the most lethal of malignancies and a major health burden. We performed whole-genome sequencing and copy number variation (CNV) analysis of 100 pancreatic ductal adenocarcinomas (PDACs). Chromosomal rearrangements leading to gene disruption were prevalent, affecting genes known to be important in pancreatic cancer (TP53, SMAD4, CDKN2A, ARID1A and ROBO2) and new candidate drivers of pancreatic carcinogenesis (KDM6A and PREX2). Patterns of structural variation (...
Network Biomarkers of Bladder Cancer Based on a Genome-Wide Genetic and Epigenetic Network Derived from Next-Generation Sequencing Data.

Science.gov (United States)

Li, Cheng-Wei; Chen, Bor-Sen

2016-01-01

Epigenetic and microRNA (miRNA) regulation are associated with carcinogenesis and the development of cancer. By using the available omics data, including those from next-generation sequencing (NGS), genome-wide methylation profiling, candidate integrated genetic and epigenetic network (IGEN) analysis, and drug response genome-wide microarray analysis, we constructed an IGEN system based on three coupling regression models that characterize protein-protein interaction networks (PPINs), gene regulatory networks (GRNs), miRNA regulatory networks (MRNs), and epigenetic regulatory networks (ERNs). By applying system identification method and principal genome-wide network projection (PGNP) to IGEN analysis, we identified the core network biomarkers to investigate bladder carcinogenic mechanisms and design multiple drug combinations for treating bladder cancer with minimal side-effects. The progression of DNA repair and cell proliferation in stage 1 bladder cancer ultimately results not only in the derepression of miR-200a and miR-200b but also in the regulation of the TNF pathway to metastasis-related genes or proteins, cell proliferation, and DNA repair in stage 4 bladder cancer. We designed a multiple drug combination comprising gefitinib, estradiol, yohimbine, and fulvestrant for treating stage 1 bladder cancer with minimal side-effects, and another multiple drug combination comprising gefitinib, estradiol, chlorpromazine, and LY294002 for treating stage 4 bladder cancer with minimal side-effects.
Molecular targets in urothelial cancer: detection, treatment, and animal models of bladder cancer

Science.gov (United States)

Smolensky, Dmitriy; Rathore, Kusum; Cekanova, Maria

2016-01-01

Bladder cancer remains one of the most expensive cancers to treat in the United States due to the length of required treatment and degree of recurrence. In order to treat bladder cancer more effectively, targeted therapies are being investigated. In order to use targeted therapy in a patient, it is important to provide a genetic background of the patient. Recent advances in genome sequencing, as well as transcriptome analysis, have identified major pathway components altered in bladder cancer. The purpose of this review is to provide a broad background on bladder cancer, including its causes, diagnosis, stages, treatments, animal models, as well as signaling pathways in bladder cancer. The major focus is given to the PI3K/AKT pathway, p53/pRb signaling pathways, and the histone modification machinery. Because several promising immunological therapies are also emerging in the treatment of bladder cancer, focus is also given on general activation of the immune system for the treatment of bladder cancer. PMID:27784990
A novel model to combine clinical and pathway-based transcriptomic information for the prognosis prediction of breast cancer.

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Sijia Huang

2014-09-01

Full Text Available Breast cancer is the most common malignancy in women worldwide. With the increasing awareness of heterogeneity in breast cancers, better prediction of breast cancer prognosis is much needed for more personalized treatment and disease management. Towards this goal, we have developed a novel computational model for breast cancer prognosis by combining the Pathway Deregulation Score (PDS based pathifier algorithm, Cox regression and L1-LASSO penalization method. We trained the model on a set of 236 patients with gene expression data and clinical information, and validated the performance on three diversified testing data sets of 606 patients. To evaluate the performance of the model, we conducted survival analysis of the dichotomized groups, and compared the areas under the curve based on the binary classification. The resulting prognosis genomic model is composed of fifteen pathways (e.g., P53 pathway that had previously reported cancer relevance, and it successfully differentiated relapse in the training set (log rank p-value = 6.25e-12 and three testing data sets (log rank p-value < 0.0005. Moreover, the pathway-based genomic models consistently performed better than gene-based models on all four data sets. We also find strong evidence that combining genomic information with clinical information improved the p-values of prognosis prediction by at least three orders of magnitude in comparison to using either genomic or clinical information alone. In summary, we propose a novel prognosis model that harnesses the pathway-based dysregulation as well as valuable clinical information. The selected pathways in our prognosis model are promising targets for therapeutic intervention.
Telomere Length Dynamics and the Evolution of Cancer Genome Architecture

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Kez Cleal

2018-02-01

Full Text Available Telomeres are progressively eroded during repeated rounds of cell division due to the end replication problem but also undergo additional more substantial stochastic shortening events. In most cases, shortened telomeres induce a cell-cycle arrest or trigger apoptosis, although for those cells that bypass such signals during tumour progression, a critical length threshold is reached at which telomere dysfunction may ensue. Dysfunction of the telomere nucleoprotein complex can expose free chromosome ends to the DNA double-strand break (DSB repair machinery, leading to telomere fusion with both telomeric and non-telomeric loci. The consequences of telomere fusions in promoting genome instability have long been appreciated through the breakage–fusion–bridge (BFB cycle mechanism, although recent studies using high-throughput sequencing technologies have uncovered evidence of involvement in a wider spectrum of genomic rearrangements including chromothripsis. A critical step in cancer progression is the transition of a clone to immortality, through the stabilisation of the telomere repeat array. This can be achieved via the reactivation of telomerase, or the induction of the alternative lengthening of telomeres (ALT pathway. Whilst telomere dysfunction may promote genome instability and tumour progression, by limiting the replicative potential of a cell and enforcing senescence, telomere shortening can act as a tumour suppressor mechanism. However, the burden of senescent cells has also been implicated as a driver of ageing and age-related pathology, and in the promotion of cancer through inflammatory signalling. Considering the critical role of telomere length in governing cancer biology, we review questions related to the prognostic value of studying the dynamics of telomere shortening and fusion, and discuss mechanisms and consequences of telomere-induced genome rearrangements.
Musa sebagai Model Genom

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RITA MEGIA

2005-12-01

Full Text Available During the meeting in Arlington, USA in 2001, the scientists grouped in PROMUSA agreed with the launching of the Global Musa Genomics Consortium. The Consortium aims to apply genomics technologies to the improvement of this important crop. These genome projects put banana as the third model species after Arabidopsis and rice that will be analyzed and sequenced. Comparing to Arabidopsis and rice, banana genome provides a unique and powerful insight into structural and in functional genomics that could not be found in those two species. This paper discussed these subjects-including the importance of banana as the fourth main food in the world, the evolution and biodiversity of this genetic resource and its parasite.
Specific genomic regions are differentially affected by copy number alterations across distinct cancer types, in aggregated cytogenetic data.

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Kumar, Nitin; Cai, Haoyang; von Mering, Christian; Baudis, Michael

2012-01-01

Regional genomic copy number alterations (CNA) are observed in the vast majority of cancers. Besides specifically targeting well-known, canonical oncogenes, CNAs may also play more subtle roles in terms of modulating genetic potential and broad gene expression patterns of developing tumors. Any significant differences in the overall CNA patterns between different cancer types may thus point towards specific biological mechanisms acting in those cancers. In addition, differences among CNA profiles may prove valuable for cancer classifications beyond existing annotation systems. We have analyzed molecular-cytogenetic data from 25579 tumors samples, which were classified into 160 cancer types according to the International Classification of Disease (ICD) coding system. When correcting for differences in the overall CNA frequencies between cancer types, related cancers were often found to cluster together according to similarities in their CNA profiles. Based on a randomization approach, distance measures from the cluster dendrograms were used to identify those specific genomic regions that contributed significantly to this signal. This approach identified 43 non-neutral genomic regions whose propensity for the occurrence of copy number alterations varied with the type of cancer at hand. Only a subset of these identified loci overlapped with previously implied, highly recurrent (hot-spot) cytogenetic imbalance regions. Thus, for many genomic regions, a simple null-hypothesis of independence between cancer type and relative copy number alteration frequency can be rejected. Since a subset of these regions display relatively low overall CNA frequencies, they may point towards second-tier genomic targets that are adaptively relevant but not necessarily essential for cancer development.
NCC-AUC: an AUC optimization method to identify multi-biomarker panel for cancer prognosis from genomic and clinical data.

Science.gov (United States)

Zou, Meng; Liu, Zhaoqi; Zhang, Xiang-Sun; Wang, Yong

2015-10-15

In prognosis and survival studies, an important goal is to identify multi-biomarker panels with predictive power using molecular characteristics or clinical observations. Such analysis is often challenged by censored, small-sample-size, but high-dimensional genomic profiles or clinical data. Therefore, sophisticated models and algorithms are in pressing need. In this study, we propose a novel Area Under Curve (AUC) optimization method for multi-biomarker panel identification named Nearest Centroid Classifier for AUC optimization (NCC-AUC). Our method is motived by the connection between AUC score for classification accuracy evaluation and Harrell's concordance index in survival analysis. This connection allows us to convert the survival time regression problem to a binary classification problem. Then an optimization model is formulated to directly maximize AUC and meanwhile minimize the number of selected features to construct a predictor in the nearest centroid classifier framework. NCC-AUC shows its great performance by validating both in genomic data of breast cancer and clinical data of stage IB Non-Small-Cell Lung Cancer (NSCLC). For the genomic data, NCC-AUC outperforms Support Vector Machine (SVM) and Support Vector Machine-based Recursive Feature Elimination (SVM-RFE) in classification accuracy. It tends to select a multi-biomarker panel with low average redundancy and enriched biological meanings. Also NCC-AUC is more significant in separation of low and high risk cohorts than widely used Cox model (Cox proportional-hazards regression model) and L1-Cox model (L1 penalized in Cox model). These performance gains of NCC-AUC are quite robust across 5 subtypes of breast cancer. Further in an independent clinical data, NCC-AUC outperforms SVM and SVM-RFE in predictive accuracy and is consistently better than Cox model and L1-Cox model in grouping patients into high and low risk categories. In summary, NCC-AUC provides a rigorous optimization framework to
Genetics and Genomics: Discovery, Validation, and Utility of Novel Tools for management of Prostate Cancer

Directory of Open Access Journals (Sweden)

Alan W. Shindel

2017-01-01

Full Text Available Genomics is the science of how genes influence human health and disease states. It differs from traditional genetic screening in that the transcriptional activity (or other markers in full panels of related genes are studied. Compared to simple genetic testing, assessment of expression levels in a panel of genes provides a more nuanced and holistic understanding of genetic modulation of human disease. Genomic testing may be used to great effect in resolving controversial questions on detection and treatment of prostate cancer. Genomic tests are currently in use for numerous facets of prostate cancer care, including screening, biopsy, and treatment planning. The clinical validity (predictive capacity of these assays has been well established; studies on clinical utility (i.e. usefulness of these tests in guiding patient/provider decisions have shown promising results. Men’s health specialists should be familiar with the role genomic testing will play in contemporary management of prostate cancer.
Genomic Data Commons and Genomic Cloud Pilots - Google Hangout

Science.gov (United States)

Join us for a live, moderated discussion about two NCI efforts to expand access to cancer genomics data: the Genomic Data Commons and Genomic Cloud Pilots. NCI subject matters experts will include Louis M. Staudt, M.D., Ph.D., Director Center for Cancer Genomics, Warren Kibbe, Ph.D., Director, NCI Center for Biomedical Informatics and Information Technology, and moderated by Anthony Kerlavage, Ph.D., Chief, Cancer Informatics Branch, Center for Biomedical Informatics and Information Technology. We welcome your questions before and during the Hangout on Twitter using the hashtag #AskNCI.
Use of genome editing tools in human stem cell-based disease modeling and precision medicine.

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Wei, Yu-da; Li, Shuang; Liu, Gai-gai; Zhang, Yong-xian; Ding, Qiu-rong

2015-10-01

Precision medicine emerges as a new approach that takes into account individual variability. The successful conduct of precision medicine requires the use of precise disease models. Human pluripotent stem cells (hPSCs), as well as adult stem cells, can be differentiated into a variety of human somatic cell types that can be used for research and drug screening. The development of genome editing technology over the past few years, especially the CRISPR/Cas system, has made it feasible to precisely and efficiently edit the genetic background. Therefore, disease modeling by using a combination of human stem cells and genome editing technology has offered a new platform to generate " personalized " disease models, which allow the study of the contribution of individual genetic variabilities to disease progression and the development of precise treatments. In this review, recent advances in the use of genome editing in human stem cells and the generation of stem cell models for rare diseases and cancers are discussed.
Preferences for learning different types of genome sequencing results among young breast cancer patients: Role of psychological and clinical factors.

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Kaphingst, Kimberly A; Ivanovich, Jennifer; Lyons, Sarah; Biesecker, Barbara; Dresser, Rebecca; Elrick, Ashley; Matsen, Cindy; Goodman, Melody

2018-01-29

The growing importance of genome sequencing means that patients will increasingly face decisions regarding what results they would like to learn. The present study examined psychological and clinical factors that might affect these preferences. 1,080 women diagnosed with breast cancer at age 40 or younger completed an online survey. We assessed their interest in learning various types of genome sequencing results: risk of preventable disease or unpreventable disease, cancer treatment response, uncertain meaning, risk to relatives' health, and ancestry/physical traits. Multivariable logistic regression was used to examine whether being "very" interested in each result type was associated with clinical factors: BRCA1/2 mutation status, prior genetic testing, family history of breast cancer, and psychological factors: cancer recurrence worry, genetic risk worry, future orientation, health information orientation, and genome sequencing knowledge. The proportion of respondents who were very interested in learning each type of result ranged from 16% to 77%. In all multivariable models, those who were very interested in learning a result type had significantly higher knowledge about sequencing benefits, greater genetic risks worry, and stronger health information orientation compared to those with less interest (p-values psychological factors. Shared decision-making approaches that increase knowledge about genome sequencing and incorporate patient preferences for health information and learning about genetic risks may help support patients' informed choices about learning different types of sequencing results. © Society of Behavioral Medicine 2018.
Genome-wide association studies in bladder cancer: first results and potential relevance.

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Kiemeney, Lambertus A; Grotenhuis, Anne J; Vermeulen, Sita H; Wu, Xifeng

2009-09-01

The role of genetic susceptibility in the development of urinary bladder cancer is unclear, as it is in many other types of cancer. Since 2007, however, an innovative research approach (i.e. genome-wide association studies or GWASs) has led to the identification of numerous genomic loci that harbor susceptibility factors for one or more cancer sites. All GWASs have been published in high-impact journals and the strengths of the design are acknowledged by all experts, but there is criticism about the relevance of the results. Late 2008, the first GWAS in bladder cancer was published. In this review, the principles of GWASs are explained, as well as their strengths and limitations. The study in bladder cancer among 4000 cases and 38,000 controls identified three new susceptibility loci at 8q24, 3q28, and 5p15 that increase the risk of bladder cancer by 22, 19, and 16%, respectively. The results of two other GWASs in bladder cancer are expected to appear this year. Joint analysis of the three studies will probably identify additional susceptibility loci. The results of bladder cancer GWASs may point the way to yet unknown disease mechanisms. So far, the findings are not sufficiently discriminative for risk predictions to be used in clinical care or public health.

Multi-platform whole-genome microarray analyses refine the epigenetic signature of breast cancer metastasis with gene expression and copy number.

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Joseph Andrews

2010-01-01

Full Text Available We have previously identified genome-wide DNA methylation changes in a cell line model of breast cancer metastasis. These complex epigenetic changes that we observed, along with concurrent karyotype analyses, have led us to hypothesize that complex genomic alterations in cancer cells (deletions, translocations and ploidy are superimposed over promoter-specific methylation events that are responsible for gene-specific expression changes observed in breast cancer metastasis.We undertook simultaneous high-resolution, whole-genome analyses of MDA-MB-468GFP and MDA-MB-468GFP-LN human breast cancer cell lines (an isogenic, paired lymphatic metastasis cell line model using Affymetrix gene expression (U133, promoter (1.0R, and SNP/CNV (SNP 6.0 microarray platforms to correlate data from gene expression, epigenetic (DNA methylation, and combination copy number variant/single nucleotide polymorphism microarrays. Using Partek Software and Ingenuity Pathway Analysis we integrated datasets from these three platforms and detected multiple hypomethylation and hypermethylation events. Many of these epigenetic alterations correlated with gene expression changes. In addition, gene dosage events correlated with the karyotypic differences observed between the cell lines and were reflected in specific promoter methylation patterns. Gene subsets were identified that correlated hyper (and hypo methylation with the loss (or gain of gene expression and in parallel, with gene dosage losses and gains, respectively. Individual gene targets from these subsets were also validated for their methylation, expression and copy number status, and susceptible gene pathways were identified that may indicate how selective advantage drives the processes of tumourigenesis and metastasis.Our approach allows more precisely profiling of functionally relevant epigenetic signatures that are associated with cancer progression and metastasis.
CMS: a web-based system for visualization and analysis of genome-wide methylation data of human cancers.

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Gu, Fei; Doderer, Mark S; Huang, Yi-Wen; Roa, Juan C; Goodfellow, Paul J; Kizer, E Lynette; Huang, Tim H M; Chen, Yidong

2013-01-01

DNA methylation of promoter CpG islands is associated with gene suppression, and its unique genome-wide profiles have been linked to tumor progression. Coupled with high-throughput sequencing technologies, it can now efficiently determine genome-wide methylation profiles in cancer cells. Also, experimental and computational technologies make it possible to find the functional relationship between cancer-specific methylation patterns and their clinicopathological parameters. Cancer methylome system (CMS) is a web-based database application designed for the visualization, comparison and statistical analysis of human cancer-specific DNA methylation. Methylation intensities were obtained from MBDCap-sequencing, pre-processed and stored in the database. 191 patient samples (169 tumor and 22 normal specimen) and 41 breast cancer cell-lines are deposited in the database, comprising about 6.6 billion uniquely mapped sequence reads. This provides comprehensive and genome-wide epigenetic portraits of human breast cancer and endometrial cancer to date. Two views are proposed for users to better understand methylation structure at the genomic level or systemic methylation alteration at the gene level. In addition, a variety of annotation tracks are provided to cover genomic information. CMS includes important analytic functions for interpretation of methylation data, such as the detection of differentially methylated regions, statistical calculation of global methylation intensities, multiple gene sets of biologically significant categories, interactivity with UCSC via custom-track data. We also present examples of discoveries utilizing the framework. CMS provides visualization and analytic functions for cancer methylome datasets. A comprehensive collection of datasets, a variety of embedded analytic functions and extensive applications with biological and translational significance make this system powerful and unique in cancer methylation research. CMS is freely accessible
Sparse multivariate factor analysis regression models and its applications to integrative genomics analysis.

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Zhou, Yan; Wang, Pei; Wang, Xianlong; Zhu, Ji; Song, Peter X-K

2017-01-01

The multivariate regression model is a useful tool to explore complex associations between two kinds of molecular markers, which enables the understanding of the biological pathways underlying disease etiology. For a set of correlated response variables, accounting for such dependency can increase statistical power. Motivated by integrative genomic data analyses, we propose a new methodology-sparse multivariate factor analysis regression model (smFARM), in which correlations of response variables are assumed to follow a factor analysis model with latent factors. This proposed method not only allows us to address the challenge that the number of association parameters is larger than the sample size, but also to adjust for unobserved genetic and/or nongenetic factors that potentially conceal the underlying response-predictor associations. The proposed smFARM is implemented by the EM algorithm and the blockwise coordinate descent algorithm. The proposed methodology is evaluated and compared to the existing methods through extensive simulation studies. Our results show that accounting for latent factors through the proposed smFARM can improve sensitivity of signal detection and accuracy of sparse association map estimation. We illustrate smFARM by two integrative genomics analysis examples, a breast cancer dataset, and an ovarian cancer dataset, to assess the relationship between DNA copy numbers and gene expression arrays to understand genetic regulatory patterns relevant to the disease. We identify two trans-hub regions: one in cytoband 17q12 whose amplification influences the RNA expression levels of important breast cancer genes, and the other in cytoband 9q21.32-33, which is associated with chemoresistance in ovarian cancer. © 2016 WILEY PERIODICALS, INC.
Classification of human cancers based on DNA copy number amplification modeling

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Knuutila Sakari

2008-05-01

Full Text Available Abstract Background DNA amplifications alter gene dosage in cancer genomes by multiplying the gene copy number. Amplifications are quintessential in a considerable number of advanced cancers of various anatomical locations. The aims of this study were to classify human cancers based on their amplification patterns, explore the biological and clinical fundamentals behind their amplification-pattern based classification, and understand the characteristics in human genomic architecture that associate with amplification mechanisms. Methods We applied a machine learning approach to model DNA copy number amplifications using a data set of binary amplification records at chromosome sub-band resolution from 4400 cases that represent 82 cancer types. Amplification data was fused with background data: clinical, histological and biological classifications, and cytogenetic annotations. Statistical hypothesis testing was used to mine associations between the data sets. Results Probabilistic clustering of each chromosome identified 111 amplification models and divided the cancer cases into clusters. The distribution of classification terms in the amplification-model based clustering of cancer cases revealed cancer classes that were associated with specific DNA copy number amplification models. Amplification patterns – finite or bounded descriptions of the ranges of the amplifications in the chromosome – were extracted from the clustered data and expressed according to the original cytogenetic nomenclature. This was achieved by maximal frequent itemset mining using the cluster-specific data sets. The boundaries of amplification patterns were shown to be enriched with fragile sites, telomeres, centromeres, and light chromosome bands. Conclusions Our results demonstrate that amplifications are non-random chromosomal changes and specifically selected in tumor tissue microenvironment. Furthermore, statistical evidence showed that specific chromosomal features
Genomic Alterations Observed in Colitis-Associated Cancers Are Distinct From Those Found in Sporadic Colorectal Cancers and Vary by Type of Inflammatory Bowel Disease.

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Yaeger, Rona; Shah, Manish A; Miller, Vincent A; Kelsen, Judith R; Wang, Kai; Heins, Zachary J; Ross, Jeffrey S; He, Yuting; Sanford, Eric; Yantiss, Rhonda K; Balasubramanian, Sohail; Stephens, Philip J; Schultz, Nikolaus; Oren, Moshe; Tang, Laura; Kelsen, David

2016-08-01

Patients with inflammatory bowel diseases, such as Crohn's disease (CD) and ulcerative colitis (UC), are at increased risk for small bowel or colorectal cancers (colitis-associated cancers [CACs]). We compared the spectrum of genomic alterations in CACs with those of sporadic colorectal cancers (CRCs) and investigated differences between CACs from patients with CD vs UC. We studied tumor tissues from patients with CACs treated at Memorial Sloan Kettering Cancer Center or Weill Cornell Medical College from 2003 through 2015. We performed hybrid capture-based next-generation sequencing analysis of >300 cancer-related genes to comprehensively characterize genomic alterations. We performed genomic analyses of 47 CACs (from 29 patients with UC and 18 with CD; 43 primary tumors and 4 metastases). Primary tumors developed in the ileum (n = 2), right colon (n = 18), left colon (n = 6), and rectosigmoid or rectum (n = 21). We found genomic alterations in TP53, IDH1, and MYC to be significantly more frequent, and mutations in APC to be significantly less frequent, than those reported in sporadic CRCs by The Cancer Genome Atlas or Foundation Medicine. We identified genomic alterations that might be targeted by a therapeutic agent in 17 of 47 (36%) CACs. These included the mutation encoding IDH1 R132; amplification of FGFR1, FGFR2, and ERBB2; and mutations encoding BRAF V600E and an EML4-ALK fusion protein. Alterations in IDH1 and APC were significantly more common in CACs from patients with CD than UC. In an analysis of CACs from 47 patients, we found significant differences in the spectrum of genomic alterations in CACs compared with sporadic CRCs. We observed a high frequency of IDH1 R132 mutations in patients with CD but not UC, as well as a high frequency of MYC amplification in CACs. Many genetic alterations observed in CACs could serve as therapeutic targets. Copyright © 2016 AGA Institute. Published by Elsevier Inc. All rights reserved.
Cancer prevention, the need to preserve the integrity of the genome at all cost.

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Okafor, M T; Nwagha, T U; Anusiem, C; Okoli, U A; Nubila, N I; Al-Alloosh, F; Udenyia, I J

2018-05-01

The entire genetic information carried by an organism makes up its genome. Genes have a diverse number of functions. They code different proteins for normal proliferation of cells. However, changes in the base sequence of genes affect their protein by-products which act as messengers for normal cellular functions such as proliferation and repairs. Salient processes for maintaining the integrity of the genome are hinged on intricate mechanisms put in place for the evolution to tackle genomic stresses. To discuss how cells sense and repair damage to their deoxyribonucleic acid (DNA) as well as to highlight how defects in the genes involved in DNA repair contribute to cancer development. Methodology: Online searches on the following databases such as Google Scholar, PubMed, Biomed Central, and SciELO were done. Attempt was made to review articles with keywords such as cancer, cell cycle, tumor suppressor genes, and DNA repair. The cell cycle, tumor suppression genes, DNA repair mechanism, as well as their contribution to cancer development, were discussed and reviewed. Knowledge on how cells detect and repair DNA damage through an array of mechanisms should allay our anxiety as regards cancer development. More studies on DNA damage detection and repair processes are important toward a holistic approach to cancer treatment.
Significance of genomic instability in breast cancer in atomic bomb survivors: analysis of microarray-comparative genomic hybridization

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Oikawa Masahiro

2011-12-01

Full Text Available Abstract Background It has been postulated that ionizing radiation induces breast cancers among atomic bomb (A-bomb survivors. We have reported a higher incidence of HER2 and C-MYC oncogene amplification in breast cancers from A-bomb survivors. The purpose of this study was to clarify the effect of A-bomb radiation exposure on genomic instability (GIN, which is an important hallmark of carcinogenesis, in archival formalin-fixed paraffin-embedded (FFPE tissues of breast cancer by using microarray-comparative genomic hybridization (aCGH. Methods Tumor DNA was extracted from FFPE tissues of invasive ductal cancers from 15 survivors who were exposed at 1.5 km or less from the hypocenter and 13 calendar year-matched non-exposed patients followed by aCGH analysis using a high-density oligonucleotide microarray. The total length of copy number aberrations (CNA was used as an indicator of GIN, and correlation with clinicopathological factors were statistically tested. Results The mean of the derivative log ratio spread (DLRSpread, which estimates the noise by calculating the spread of log ratio differences between consecutive probes for all chromosomes, was 0.54 (range, 0.26 to 1.05. The concordance of results between aCGH and fluorescence in situ hybridization (FISH for HER2 gene amplification was 88%. The incidence of HER2 amplification and histological grade was significantly higher in the A-bomb survivors than control group (P = 0.04, respectively. The total length of CNA tended to be larger in the A-bomb survivors (P = 0.15. Correlation analysis of CNA and clinicopathological factors revealed that DLRSpread was negatively correlated with that significantly (P = 0.034, r = -0.40. Multivariate analysis with covariance revealed that the exposure to A-bomb was a significant (P = 0.005 independent factor which was associated with larger total length of CNA of breast cancers. Conclusions Thus, archival FFPE tissues from A-bomb survivors are useful for
Significance of genomic instability in breast cancer in atomic bomb survivors: analysis of microarray-comparative genomic hybridization

International Nuclear Information System (INIS)

Oikawa, Masahiro; Yoshiura, Koh-ichiro; Kondo, Hisayoshi; Miura, Shiro; Nagayasu, Takeshi; Nakashima, Masahiro

2011-01-01

It has been postulated that ionizing radiation induces breast cancers among atomic bomb (A-bomb) survivors. We have reported a higher incidence of HER2 and C-MYC oncogene amplification in breast cancers from A-bomb survivors. The purpose of this study was to clarify the effect of A-bomb radiation exposure on genomic instability (GIN), which is an important hallmark of carcinogenesis, in archival formalin-fixed paraffin-embedded (FFPE) tissues of breast cancer by using microarray-comparative genomic hybridization (aCGH). Tumor DNA was extracted from FFPE tissues of invasive ductal cancers from 15 survivors who were exposed at 1.5 km or less from the hypocenter and 13 calendar year-matched non-exposed patients followed by aCGH analysis using a high-density oligonucleotide microarray. The total length of copy number aberrations (CNA) was used as an indicator of GIN, and correlation with clinicopathological factors were statistically tested. The mean of the derivative log ratio spread (DLRSpread), which estimates the noise by calculating the spread of log ratio differences between consecutive probes for all chromosomes, was 0.54 (range, 0.26 to 1.05). The concordance of results between aCGH and fluorescence in situ hybridization (FISH) for HER2 gene amplification was 88%. The incidence of HER2 amplification and histological grade was significantly higher in the A-bomb survivors than control group (P = 0.04, respectively). The total length of CNA tended to be larger in the A-bomb survivors (P = 0.15). Correlation analysis of CNA and clinicopathological factors revealed that DLRSpread was negatively correlated with that significantly (P = 0.034, r = -0.40). Multivariate analysis with covariance revealed that the exposure to A-bomb was a significant (P = 0.005) independent factor which was associated with larger total length of CNA of breast cancers. Thus, archival FFPE tissues from A-bomb survivors are useful for genome-wide aCGH analysis. Our results suggested that A
Significance of genomic instability in breast cancer in atomic bomb survivors: analysis of microarray-comparative genomic hybridization.

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Oikawa, Masahiro; Yoshiura, Koh-ichiro; Kondo, Hisayoshi; Miura, Shiro; Nagayasu, Takeshi; Nakashima, Masahiro

2011-12-07

It has been postulated that ionizing radiation induces breast cancers among atomic bomb (A-bomb) survivors. We have reported a higher incidence of HER2 and C-MYC oncogene amplification in breast cancers from A-bomb survivors. The purpose of this study was to clarify the effect of A-bomb radiation exposure on genomic instability (GIN), which is an important hallmark of carcinogenesis, in archival formalin-fixed paraffin-embedded (FFPE) tissues of breast cancer by using microarray-comparative genomic hybridization (aCGH). Tumor DNA was extracted from FFPE tissues of invasive ductal cancers from 15 survivors who were exposed at 1.5 km or less from the hypocenter and 13 calendar year-matched non-exposed patients followed by aCGH analysis using a high-density oligonucleotide microarray. The total length of copy number aberrations (CNA) was used as an indicator of GIN, and correlation with clinicopathological factors were statistically tested. The mean of the derivative log ratio spread (DLRSpread), which estimates the noise by calculating the spread of log ratio differences between consecutive probes for all chromosomes, was 0.54 (range, 0.26 to 1.05). The concordance of results between aCGH and fluorescence in situ hybridization (FISH) for HER2 gene amplification was 88%. The incidence of HER2 amplification and histological grade was significantly higher in the A-bomb survivors than control group (P = 0.04, respectively). The total length of CNA tended to be larger in the A-bomb survivors (P = 0.15). Correlation analysis of CNA and clinicopathological factors revealed that DLRSpread was negatively correlated with that significantly (P = 0.034, r = -0.40). Multivariate analysis with covariance revealed that the exposure to A-bomb was a significant (P = 0.005) independent factor which was associated with larger total length of CNA of breast cancers. Thus, archival FFPE tissues from A-bomb survivors are useful for genome-wide aCGH analysis. Our results suggested that A
Analysis of The Cancer Genome Atlas sequencing data reveals novel properties of the human papillomavirus 16 genome in head and neck squamous cell carcinoma.

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Nulton, Tara J; Olex, Amy L; Dozmorov, Mikhail; Morgan, Iain M; Windle, Brad

2017-03-14

Human papillomavirus (HPV) DNA is detected in up to 80% of oropharyngeal carcinomas (OPC) and this HPV positive disease has reached epidemic proportions. To increase our understanding of the disease, we investigated the status of the HPV16 genome in HPV-positive head and neck cancers (HNC). Raw RNA-Seq and Whole Genome Sequence data from The Cancer Genome Atlas HNC samples were analyzed to gain a full understanding of the HPV genome status for these tumors. Several remarkable and novel observations were made following this analysis. Firstly, there are three main HPV genome states in these tumors that are split relatively evenly: An episomal only state, an integrated state, and a state in which the viral genome exists as a hybrid episome with human DNA. Secondly, none of the tumors expressed high levels of E6; E6*I is the dominant variant expressed in all tumors. The most striking conclusion from this study is that around three quarters of HPV16 positive HNC contain episomal versions of the viral genome that are likely replicating in an E1-E2 dependent manner. The clinical and therapeutic implications of these observations are discussed.
Novel mouse model recapitulates genome and transcriptome alterations in human colorectal carcinomas.

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McNeil, Nicole E; Padilla-Nash, Hesed M; Buishand, Floryne O; Hue, Yue; Ried, Thomas

2017-03-01

Human colorectal carcinomas are defined by a nonrandom distribution of genomic imbalances that are characteristic for this disease. Often, these imbalances affect entire chromosomes. Understanding the role of these aneuploidies for carcinogenesis is of utmost importance. Currently, established transgenic mice do not recapitulate the pathognonomic genome aberration profile of human colorectal carcinomas. We have developed a novel model based on the spontaneous transformation of murine colon epithelial cells. During this process, cells progress through stages of pre-immortalization, immortalization and, finally, transformation, and result in tumors when injected into immunocompromised mice. We analyzed our model for genome and transcriptome alterations using ArrayCGH, spectral karyotyping (SKY), and array based gene expression profiling. ArrayCGH revealed a recurrent pattern of genomic imbalances. These results were confirmed by SKY. Comparing these imbalances with orthologous maps of human chromosomes revealed a remarkable overlap. We observed focal deletions of the tumor suppressor genes Trp53 and Cdkn2a/p16. High-level focal genomic amplification included the locus harboring the oncogene Mdm2, which was confirmed by FISH in the form of double minute chromosomes. Array-based global gene expression revealed distinct differences between the sequential steps of spontaneous transformation. Gene expression changes showed significant similarities with human colorectal carcinomas. Pathways most prominently affected included genes involved in chromosomal instability and in epithelial to mesenchymal transition. Our novel mouse model therefore recapitulates the most prominent genome and transcriptome alterations in human colorectal cancer, and might serve as a valuable tool for understanding the dynamic process of tumorigenesis, and for preclinical drug testing. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.
Emory University: High-Throughput Protein-Protein Interaction Dataset for Lung Cancer-Associated Genes | Office of Cancer Genomics

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To discover novel PPI signaling hubs for lung cancer, CTD2 Center at Emory utilized large-scale genomics datasets and literature to compile a set of lung cancer-associated genes. A library of expression vectors were generated for these genes and utilized for detecting pairwise PPIs with cell lysate-based TR-FRET assays in high-throughput screening format. Read the abstract.
CTD² Dashboard: a searchable web interface to connect validated results from the Cancer Target Discovery and Development Network* | Office of Cancer Genomics

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The Cancer Target Discovery and Development (CTD2) Network aims to use functional genomics to accelerate the translation of high-throughput and high-content genomic and small-molecule data towards use in precision oncology.
Profiles of Genomic Instability in High-Grade Serous Ovarian Cancer Predict Treatment Outcome

DEFF Research Database (Denmark)

Wang, Zhigang C.; Birkbak, Nicolai Juul; Culhane, Aedín C.

2012-01-01

Purpose: High-grade serous cancer (HGSC) is the most common cancer of the ovary and is characterized by chromosomal instability. Defects in homologous recombination repair (HRR) are associated with genomic instability in HGSC, and are exploited by therapy targeting DNA repair. Defective HRR cause...
Whole-genome sequencing and comprehensive molecular profiling identify new driver mutations in gastric cancer

NARCIS (Netherlands)

Wang, Kai; Yuen, Siu Tsan; Xu, Jiangchun; Lee, Siu Po; Yan, Helen H N; Shi, Stephanie T; Siu, Hoi Cheong; Deng, Shibing; Chu, Kent Man; Law, Simon; Chan, Kok Hoe; Chan, Annie S Y; Tsui, Wai Yin; Ho, Siu Lun; Chan, Anthony K W; Man, Jonathan L K; Foglizzo, Valentina; Ng, Man Kin; Chan, April S; Ching, Yick Pang; Cheng, Grace H W; Xie, Tao; Fernandez, Julio; Li, Vivian S W; Clevers, Hans; Rejto, Paul A; Mao, Mao; Leung, Suet Yi

Gastric cancer is a heterogeneous disease with diverse molecular and histological subtypes. We performed whole-genome sequencing in 100 tumor-normal pairs, along with DNA copy number, gene expression and methylation profiling, for integrative genomic analysis. We found subtype-specific genetic and
Oral cancer prognosis based on clinicopathologic and genomic markers using a hybrid of feature selection and machine learning methods

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2013-01-01

Background Machine learning techniques are becoming useful as an alternative approach to conventional medical diagnosis or prognosis as they are good for handling noisy and incomplete data, and significant results can be attained despite a small sample size. Traditionally, clinicians make prognostic decisions based on clinicopathologic markers. However, it is not easy for the most skilful clinician to come out with an accurate prognosis by using these markers alone. Thus, there is a need to use genomic markers to improve the accuracy of prognosis. The main aim of this research is to apply a hybrid of feature selection and machine learning methods in oral cancer prognosis based on the parameters of the correlation of clinicopathologic and genomic markers. Results In the first stage of this research, five feature selection methods have been proposed and experimented on the oral cancer prognosis dataset. In the second stage, the model with the features selected from each feature selection methods are tested on the proposed classifiers. Four types of classifiers are chosen; these are namely, ANFIS, artificial neural network, support vector machine and logistic regression. A k-fold cross-validation is implemented on all types of classifiers due to the small sample size. The hybrid model of ReliefF-GA-ANFIS with 3-input features of drink, invasion and p63 achieved the best accuracy (accuracy = 93.81%; AUC = 0.90) for the oral cancer prognosis. Conclusions The results revealed that the prognosis is superior with the presence of both clinicopathologic and genomic markers. The selected features can be investigated further to validate the potential of becoming as significant prognostic signature in the oral cancer studies. PMID:23725313
Systematic Functional Interrogation of Rare Cancer Variants Identifies Oncogenic Alleles | Office of Cancer Genomics

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Cancer genome characterization efforts now provide an initial view of the somatic alterations in primary tumors. However, most point mutations occur at low frequency, and the function of these alleles remains undefined. We have developed a scalable systematic approach to interrogate the function of cancer-associated gene variants. We subjected 474 mutant alleles curated from 5,338 tumors to pooled in vivo tumor formation assays and gene expression profiling. We identified 12 transforming alleles, including two in genes (PIK3CB, POT1) that have not been shown to be tumorigenic.
Distinct Mechanisms of Nuclease-Directed DNA-Structure-Induced Genetic Instability in Cancer Genomes.

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Zhao, Junhua; Wang, Guliang; Del Mundo, Imee M; McKinney, Jennifer A; Lu, Xiuli; Bacolla, Albino; Boulware, Stephen B; Zhang, Changsheng; Zhang, Haihua; Ren, Pengyu; Freudenreich, Catherine H; Vasquez, Karen M

2018-01-30

Sequences with the capacity to adopt alternative DNA structures have been implicated in cancer etiology; however, the mechanisms are unclear. For example, H-DNA-forming sequences within oncogenes have been shown to stimulate genetic instability in mammals. Here, we report that H-DNA-forming sequences are enriched at translocation breakpoints in human cancer genomes, further implicating them in cancer etiology. H-DNA-induced mutations were suppressed in human cells deficient in the nucleotide excision repair nucleases, ERCC1-XPF and XPG, but were stimulated in cells deficient in FEN1, a replication-related endonuclease. Further, we found that these nucleases cleaved H-DNA conformations, and the interactions of modeled H-DNA with ERCC1-XPF, XPG, and FEN1 proteins were explored at the sub-molecular level. The results suggest mechanisms of genetic instability triggered by H-DNA through distinct structure-specific, cleavage-based replication-independent and replication-dependent pathways, providing critical evidence for a role of the DNA structure itself in the etiology of cancer and other human diseases. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.
Biosemiotic Entropy of the Genome: Mutations and Epigenetic Imbalances Resulting in Cancer

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Samuel S. Shepard

2013-01-01

Full Text Available Biosemiotic entropy involves the deterioration of biological sign systems. The genome is a coded sign system that is connected to phenotypic outputs through the interpretive functions of the tRNA/ribosome machinery. This symbolic sign system (semiosis at the core of all biology has been termed “biosemiosis”. Layers of biosemiosis and cellular information management are analogous in varying degrees to the semiotics of computer programming, spoken, and written human languages. Biosemiotic entropy — an error or deviation from a healthy state — results from errors in copying functional information (mutations and errors in the appropriate context or quantity of gene expression (epigenetic imbalance. The concept of biosemiotic entropy is a deeply imbedded assumption in the study of cancer biology. Cells have a homeostatic, preprogrammed, ideal or healthy state that is rooted in genomics, strictly orchestrated by epigenetic regulation, and maintained by DNA repair mechanisms. Cancer is an eminent illustration of biosemiotic entropy, in which the corrosion of genetic information via substitutions, deletions, insertions, fusions, and aberrant regulation results in malignant phenotypes. However, little attention has been given to explicitly outlining the paradigm of biosemiotic entropy in the context of cancer. Herein we distill semiotic theory (from the familiar and well understood spheres of human language and computer code to draw analogies useful for understanding the operation of biological semiosis at the genetic level. We propose that the myriad checkpoints, error correcting mechanisms, and immunities are all systems whose primary role is to defend against the constant pressure of biosemiotic entropy, which malignancy must shut down in order to achieve advanced stages. In lieu of the narrower tumor suppressor/oncogene model, characterization of oncogenesis into the biosemiotic framework of sign, index, or object entropy may allow for more
Correction for Measurement Error from Genotyping-by-Sequencing in Genomic Variance and Genomic Prediction Models

DEFF Research Database (Denmark)

Ashraf, Bilal; Janss, Luc; Jensen, Just

sample). The GBSeq data can be used directly in genomic models in the form of individual SNP allele-frequency estimates (e.g., reference reads/total reads per polymorphic site per individual), but is subject to measurement error due to the low sequencing depth per individual. Due to technical reasons....... In the current work we show how the correction for measurement error in GBSeq can also be applied in whole genome genomic variance and genomic prediction models. Bayesian whole-genome random regression models are proposed to allow implementation of large-scale SNP-based models with a per-SNP correction...... for measurement error. We show correct retrieval of genomic explained variance, and improved genomic prediction when accounting for the measurement error in GBSeq data...

NMD Microarray Analysis for Rapid Genome-Wide Screen of Mutated Genes in Cancer

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Maija Wolf

2005-01-01

Full Text Available Gene mutations play a critical role in cancer development and progression, and their identification offers possibilities for accurate diagnostics and therapeutic targeting. Finding genes undergoing mutations is challenging and slow, even in the post-genomic era. A new approach was recently developed by Noensie and Dietz to prioritize and focus the search, making use of nonsense-mediated mRNA decay (NMD inhibition and microarray analysis (NMD microarrays in the identification of transcripts containing nonsense mutations. We combined NMD microarrays with array-based CGH (comparative genomic hybridization in order to identify inactivation of tumor suppressor genes in cancer. Such a “mutatomics” screening of prostate cancer cell lines led to the identification of inactivating mutations in the EPHB2 gene. Up to 8% of metastatic uncultured prostate cancers also showed mutations of this gene whose loss of function may confer loss of tissue architecture. NMD microarray analysis could turn out to be a powerful research method to identify novel mutated genes in cancer cell lines, providing targets that could then be further investigated for their clinical relevance and therapeutic potential.
Clonal expansion and linear genome evolution through breast cancer progression from pre-invasive stages to asynchronous metastasis

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Krøigård, Anne Bruun; Larsen, Martin Jakob; Lænkholm, Anne Vibeke

2015-01-01

Evolution of the breast cancer genome from pre-invasive stages to asynchronous metastasis is complex and mostly unexplored, but highly demanded as it may provide novel markers for and mechanistic insights in cancer progression. The increasing use of personalized therapy of breast cancer necessita......Evolution of the breast cancer genome from pre-invasive stages to asynchronous metastasis is complex and mostly unexplored, but highly demanded as it may provide novel markers for and mechanistic insights in cancer progression. The increasing use of personalized therapy of breast cancer...... progression from one breast cancer patient, including two different regions of Ductal Carcinoma In Situ (DCIS), primary tumor and an asynchronous metastasis. We identify a remarkable landscape of somatic mutations, retained throughout breast cancer progression and with new mutational events emerging at each...
Open reading frames associated with cancer in the dark matter of the human genome.

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Delgado, Ana Paula; Brandao, Pamela; Chapado, Maria Julia; Hamid, Sheilin; Narayanan, Ramaswamy

2014-01-01

The uncharacterized proteins (open reading frames, ORFs) in the human genome offer an opportunity to discover novel targets for cancer. A systematic analysis of the dark matter of the human proteome for druggability and biomarker discovery is crucial to mining the genome. Numerous data mining tools are available to mine these ORFs to develop a comprehensive knowledge base for future target discovery and validation. Using the Genetic Association Database, the ORFs of the human dark matter proteome were screened for evidence of association with neoplasms. The Phenome-Genome Integrator tool was used to establish phenotypic association with disease traits including cancer. Batch analysis of the tools for protein expression analysis, gene ontology and motifs and domains was used to characterize the ORFs. Sixty-two ORFs were identified for neoplasm association. The expression Quantitative Trait Loci (eQTL) analysis identified thirteen ORFs related to cancer traits. Protein expression, motifs and domain analysis and genome-wide association studies verified the relevance of these OncoORFs in diverse tumors. The OncoORFs are also associated with a wide variety of human diseases and disorders. Our results link the OncoORFs to diverse diseases and disorders. This suggests a complex landscape of the uncharacterized proteome in human diseases. These results open the dark matter of the proteome to novel cancer target research. Copyright© 2014, International Institute of Anticancer Research (Dr. John G. Delinasios), All rights reserved.
Whole genomes redefine the mutational landscape of pancreatic cancer.

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Waddell, Nicola; Pajic, Marina; Patch, Ann-Marie; Chang, David K; Kassahn, Karin S; Bailey, Peter; Johns, Amber L; Miller, David; Nones, Katia; Quek, Kelly; Quinn, Michael C J; Robertson, Alan J; Fadlullah, Muhammad Z H; Bruxner, Tim J C; Christ, Angelika N; Harliwong, Ivon; Idrisoglu, Senel; Manning, Suzanne; Nourse, Craig; Nourbakhsh, Ehsan; Wani, Shivangi; Wilson, Peter J; Markham, Emma; Cloonan, Nicole; Anderson, Matthew J; Fink, J Lynn; Holmes, Oliver; Kazakoff, Stephen H; Leonard, Conrad; Newell, Felicity; Poudel, Barsha; Song, Sarah; Taylor, Darrin; Waddell, Nick; Wood, Scott; Xu, Qinying; Wu, Jianmin; Pinese, Mark; Cowley, Mark J; Lee, Hong C; Jones, Marc D; Nagrial, Adnan M; Humphris, Jeremy; Chantrill, Lorraine A; Chin, Venessa; Steinmann, Angela M; Mawson, Amanda; Humphrey, Emily S; Colvin, Emily K; Chou, Angela; Scarlett, Christopher J; Pinho, Andreia V; Giry-Laterriere, Marc; Rooman, Ilse; Samra, Jaswinder S; Kench, James G; Pettitt, Jessica A; Merrett, Neil D; Toon, Christopher; Epari, Krishna; Nguyen, Nam Q; Barbour, Andrew; Zeps, Nikolajs; Jamieson, Nigel B; Graham, Janet S; Niclou, Simone P; Bjerkvig, Rolf; Grützmann, Robert; Aust, Daniela; Hruban, Ralph H; Maitra, Anirban; Iacobuzio-Donahue, Christine A; Wolfgang, Christopher L; Morgan, Richard A; Lawlor, Rita T; Corbo, Vincenzo; Bassi, Claudio; Falconi, Massimo; Zamboni, Giuseppe; Tortora, Giampaolo; Tempero, Margaret A; Gill, Anthony J; Eshleman, James R; Pilarsky, Christian; Scarpa, Aldo; Musgrove, Elizabeth A; Pearson, John V; Biankin, Andrew V; Grimmond, Sean M

2015-02-26

Pancreatic cancer remains one of the most lethal of malignancies and a major health burden. We performed whole-genome sequencing and copy number variation (CNV) analysis of 100 pancreatic ductal adenocarcinomas (PDACs). Chromosomal rearrangements leading to gene disruption were prevalent, affecting genes known to be important in pancreatic cancer (TP53, SMAD4, CDKN2A, ARID1A and ROBO2) and new candidate drivers of pancreatic carcinogenesis (KDM6A and PREX2). Patterns of structural variation (variation in chromosomal structure) classified PDACs into 4 subtypes with potential clinical utility: the subtypes were termed stable, locally rearranged, scattered and unstable. A significant proportion harboured focal amplifications, many of which contained druggable oncogenes (ERBB2, MET, FGFR1, CDK6, PIK3R3 and PIK3CA), but at low individual patient prevalence. Genomic instability co-segregated with inactivation of DNA maintenance genes (BRCA1, BRCA2 or PALB2) and a mutational signature of DNA damage repair deficiency. Of 8 patients who received platinum therapy, 4 of 5 individuals with these measures of defective DNA maintenance responded.
Whole genomes redefine the mutational landscape of pancreatic cancer

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Waddell, Nicola; Pajic, Marina; Patch, Ann-Marie; Chang, David K.; Kassahn, Karin S.; Bailey, Peter; Johns, Amber L.; Miller, David; Nones, Katia; Quek, Kelly; Quinn, Michael C. J.; Robertson, Alan J.; Fadlullah, Muhammad Z. H.; Bruxner, Tim J. C.; Christ, Angelika N.; Harliwong, Ivon; Idrisoglu, Senel; Manning, Suzanne; Nourse, Craig; Nourbakhsh, Ehsan; Wani, Shivangi; Wilson, Peter J; Markham, Emma; Cloonan, Nicole; Anderson, Matthew J.; Fink, J. Lynn; Holmes, Oliver; Kazakoff, Stephen H.; Leonard, Conrad; Newell, Felicity; Poudel, Barsha; Song, Sarah; Taylor, Darrin; Waddell, Nick; Wood, Scott; Xu, Qinying; Wu, Jianmin; Pinese, Mark; Cowley, Mark J.; Lee, Hong C.; Jones, Marc D.; Nagrial, Adnan M.; Humphris, Jeremy; Chantrill, Lorraine A.; Chin, Venessa; Steinmann, Angela M.; Mawson, Amanda; Humphrey, Emily S.; Colvin, Emily K.; Chou, Angela; Scarlett, Christopher J.; Pinho, Andreia V.; Giry-Laterriere, Marc; Rooman, Ilse; Samra, Jaswinder S.; Kench, James G.; Pettitt, Jessica A.; Merrett, Neil D.; Toon, Christopher; Epari, Krishna; Nguyen, Nam Q.; Barbour, Andrew; Zeps, Nikolajs; Jamieson, Nigel B.; Graham, Janet S.; Niclou, Simone P.; Bjerkvig, Rolf; Grützmann, Robert; Aust, Daniela; Hruban, Ralph H.; Maitra, Anirban; Iacobuzio-Donahue, Christine A.; Wolfgang, Christopher L.; Morgan, Richard A.; Lawlor, Rita T.; Corbo, Vincenzo; Bassi, Claudio; Falconi, Massimo; Zamboni, Giuseppe; Tortora, Giampaolo; Tempero, Margaret A.; Gill, Anthony J.; Eshleman, James R.; Pilarsky, Christian; Scarpa, Aldo; Musgrove, Elizabeth A.; Pearson, John V.; Biankin, Andrew V.; Grimmond, Sean M.

2015-01-01

Pancreatic cancer remains one of the most lethal of malignancies and a major health burden. We performed whole-genome sequencing and copy number variation (CNV) analysis of 100 pancreatic ductal adenocarcinomas (PDACs). Chromosomal rearrangements leading to gene disruption were prevalent, affecting genes known to be important in pancreatic cancer (TP53, SMAD4, CDKN2A, ARID1A and ROBO2) and new candidate drivers of pancreatic carcinogenesis (KDM6A and PREX2). Patterns of structural variation (variation in chromosomal structure) classified PDACs into 4 subtypes with potential clinical utility: the subtypes were termed stable, locally rearranged, scattered and unstable. A significant proportion harboured focal amplifications, many of which contained druggable oncogenes (ERBB2, MET, FGFR1, CDK6, PIK3R3 and PIK3CA), but at low individual patient prevalence. Genomic instability co-segregated with inactivation of DNA maintenance genes (BRCA1, BRCA2 or PALB2) and a mutational signature of DNA damage repair deficiency. Of 8 patients who received platinum therapy, 4 of 5 individuals with these measures of defective DNA maintenance responded. PMID:25719666
Integrative Annotation of Variants from 1092 Humans: Application to Cancer Genomics

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Khurana, Ekta; Fu, Yao; Colonna, Vincenza

2013-01-01

Identifying Important Identifiers Each of us has millions of sequence variations in our genomes. Signatures of purifying or negative selection should help identify which of those variations is functionally important. Khurana et al. (1235587) used sequence polymorphisms from 1092 humans across 14...... sites tended to occur in network hub promoters. Many recurrent somatic cancer variants occurred in noncoding regulatory regions and thus might indicate mutations that drive cancer....
“A rising tide lifts all boats”: establishing a multidisciplinary genomic tumor board for breast cancer patients with advanced disease

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Michelle L. McGowan

2016-11-01

Full Text Available Abstract Background Research suggests that multidisciplinary genomic tumor boards (MGTB can inform cancer patient care, though little is known about factors influencing how MGTBs interpret genomic test results, make recommendations, and perceive the utility of this approach. This study’s objective was to observe, describe, and assess the establishment of the Breast Multidisciplinary Genomic Tumor Board, the first MGTB focused on interpreting genomic test results for breast cancer patients with advanced disease. Methods We conducted a qualitative case study involving participant observation at monthly MGTB meetings from October 2013 through November 2014 and interviews with 12 MGTB members. We analyzed social dynamics and interactions within the MGTB regarding interpretation of genomic findings and participants’ views on effectiveness of the MGTB in using genomics to inform patient care. Results Twenty-two physicians, physician-scientists, basic scientists, bioethicists, and allied care professionals comprised the MGTB. The MGTB reviewed FoundationOne™ results for 40 metastatic breast cancer patients. Based on findings, the board mostly recommended referring patients to clinical trials (34 and medical genetics (15, and Food and Drug Administration-approved (FDA breast cancer therapies (13. Though multidisciplinary, recommendations were driven by medical oncologists. Interviewees described providing more precise care recommendations and professional development as advantages and the limited actionability of genomic test results as a challenge for the MGTB. Conclusions Findings suggest both feasibility and desirability of pooling professional expertise in genomically-guided breast cancer care and challenges to institutionalizing a Breast MGTB, specifically in promoting interdisciplinary contributions and managing limited actionability of genomic test results for patients with advanced disease.
Building prognostic models for breast cancer patients using clinical variables and hundreds of gene expression signatures

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Liu Yufeng

2011-01-01

Full Text Available Abstract Background Multiple breast cancer gene expression profiles have been developed that appear to provide similar abilities to predict outcome and may outperform clinical-pathologic criteria; however, the extent to which seemingly disparate profiles provide additive prognostic information is not known, nor do we know whether prognostic profiles perform equally across clinically defined breast cancer subtypes. We evaluated whether combining the prognostic powers of standard breast cancer clinical variables with a large set of gene expression signatures could improve on our ability to predict patient outcomes. Methods Using clinical-pathological variables and a collection of 323 gene expression "modules", including 115 previously published signatures, we build multivariate Cox proportional hazards models using a dataset of 550 node-negative systemically untreated breast cancer patients. Models predictive of pathological complete response (pCR to neoadjuvant chemotherapy were also built using this approach. Results We identified statistically significant prognostic models for relapse-free survival (RFS at 7 years for the entire population, and for the subgroups of patients with ER-positive, or Luminal tumors. Furthermore, we found that combined models that included both clinical and genomic parameters improved prognostication compared with models with either clinical or genomic variables alone. Finally, we were able to build statistically significant combined models for pathological complete response (pCR predictions for the entire population. Conclusions Integration of gene expression signatures and clinical-pathological factors is an improved method over either variable type alone. Highly prognostic models could be created when using all patients, and for the subset of patients with lymph node-negative and ER-positive breast cancers. Other variables beyond gene expression and clinical-pathological variables, like gene mutation status or DNA
In silico modeling predicts drug sensitivity of patient-derived cancer cells.

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Pingle, Sandeep C; Sultana, Zeba; Pastorino, Sandra; Jiang, Pengfei; Mukthavaram, Rajesh; Chao, Ying; Bharati, Ila Sri; Nomura, Natsuko; Makale, Milan; Abbasi, Taher; Kapoor, Shweta; Kumar, Ansu; Usmani, Shahabuddin; Agrawal, Ashish; Vali, Shireen; Kesari, Santosh

2014-05-21

Glioblastoma (GBM) is an aggressive disease associated with poor survival. It is essential to account for the complexity of GBM biology to improve diagnostic and therapeutic strategies. This complexity is best represented by the increasing amounts of profiling ("omics") data available due to advances in biotechnology. The challenge of integrating these vast genomic and proteomic data can be addressed by a comprehensive systems modeling approach. Here, we present an in silico model, where we simulate GBM tumor cells using genomic profiling data. We use this in silico tumor model to predict responses of cancer cells to targeted drugs. Initially, we probed the results from a recent hypothesis-independent, empirical study by Garnett and co-workers that analyzed the sensitivity of hundreds of profiled cancer cell lines to 130 different anticancer agents. We then used the tumor model to predict sensitivity of patient-derived GBM cell lines to different targeted therapeutic agents. Among the drug-mutation associations reported in the Garnett study, our in silico model accurately predicted ~85% of the associations. While testing the model in a prospective manner using simulations of patient-derived GBM cell lines, we compared our simulation predictions with experimental data using the same cells in vitro. This analysis yielded a ~75% agreement of in silico drug sensitivity with in vitro experimental findings. These results demonstrate a strong predictability of our simulation approach using the in silico tumor model presented here. Our ultimate goal is to use this model to stratify patients for clinical trials. By accurately predicting responses of cancer cells to targeted agents a priori, this in silico tumor model provides an innovative approach to personalizing therapy and promises to improve clinical management of cancer.
Genomic profiling identifies GATA6 as a candidate oncogene amplified in pancreatobiliary cancer.

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Kevin A Kwei

2008-05-01

Full Text Available Pancreatobiliary cancers have among the highest mortality rates of any cancer type. Discovering the full spectrum of molecular genetic alterations may suggest new avenues for therapy. To catalogue genomic alterations, we carried out array-based genomic profiling of 31 exocrine pancreatic cancers and 6 distal bile duct cancers, expanded as xenografts to enrich the tumor cell fraction. We identified numerous focal DNA amplifications and deletions, including in 19% of pancreatobiliary cases gain at cytoband 18q11.2, a locus uncommonly amplified in other tumor types. The smallest shared amplification at 18q11.2 included GATA6, a transcriptional regulator previously linked to normal pancreas development. When amplified, GATA6 was overexpressed at both the mRNA and protein levels, and strong immunostaining was observed in 25 of 54 (46% primary pancreatic cancers compared to 0 of 33 normal pancreas specimens surveyed. GATA6 expression in xenografts was associated with specific microarray gene-expression patterns, enriched for GATA binding sites and mitochondrial oxidative phosphorylation activity. siRNA mediated knockdown of GATA6 in pancreatic cancer cell lines with amplification led to reduced cell proliferation, cell cycle progression, and colony formation. Our findings indicate that GATA6 amplification and overexpression contribute to the oncogenic phenotypes of pancreatic cancer cells, and identify GATA6 as a candidate lineage-specific oncogene in pancreatobiliary cancer, with implications for novel treatment strategies.
Endocrine-Therapy-Resistant ESR1 Variants Revealed by Genomic Characterization of Breast-Cancer-Derived Xenografts

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Shunqiang Li

2013-09-01

Full Text Available To characterize patient-derived xenografts (PDXs for functional studies, we made whole-genome comparisons with originating breast cancers representative of the major intrinsic subtypes. Structural and copy number aberrations were found to be retained with high fidelity. However, at the single-nucleotide level, variable numbers of PDX-specific somatic events were documented, although they were only rarely functionally significant. Variant allele frequencies were often preserved in the PDXs, demonstrating that clonal representation can be transplantable. Estrogen-receptor-positive PDXs were associated with ESR1 ligand-binding-domain mutations, gene amplification, or an ESR1/YAP1 translocation. These events produced different endocrine-therapy-response phenotypes in human, cell line, and PDX endocrine-response studies. Hence, deeply sequenced PDX models are an important resource for the search for genome-forward treatment options and capture endocrine-drug-resistance etiologies that are not observed in standard cell lines. The originating tumor genome provides a benchmark for assessing genetic drift and clonal representation after transplantation.
Genome-wide association study in discordant sibships identifies multiple inherited susceptibility alleles linked to lung cancer.

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Galvan, Antonella; Falvella, Felicia S; Frullanti, Elisa; Spinola, Monica; Incarbone, Matteo; Nosotti, Mario; Santambrogio, Luigi; Conti, Barbara; Pastorino, Ugo; Gonzalez-Neira, Anna; Dragani, Tommaso A

2010-03-01

We analyzed a series of young (median age = 52 years) non-smoker lung cancer patients and their unaffected siblings as controls, using a genome-wide 620 901 single-nucleotide polymorphism (SNP) array analysis and a case-control DNA pooling approach. We identified 82 putatively associated SNPs that were retested by individual genotyping followed by use of the sib transmission disequilibrium test, pointing to 36 SNPs associated with lung cancer risk in the discordant sibs series. Analysis of these 36 SNPs in a polygenic model characterized by additive and interchangeable effects of rare alleles revealed a highly statistically significant dosage-dependent association between risk allele carrier status and proportion of cancer cases. Replication of the same 36 SNPs in a population-based series confirmed the association with lung cancer for three SNPs, suggesting that phenocopies and genetic heterogeneity can play a major role in the complex genetics of lung cancer risk in the general population.
FISH Oracle 2: a web server for integrative visualization of genomic data in cancer research.

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Mader, Malte; Simon, Ronald; Kurtz, Stefan

2014-03-31

A comprehensive view on all relevant genomic data is instrumental for understanding the complex patterns of molecular alterations typically found in cancer cells. One of the most effective ways to rapidly obtain an overview of genomic alterations in large amounts of genomic data is the integrative visualization of genomic events. We developed FISH Oracle 2, a web server for the interactive visualization of different kinds of downstream processed genomics data typically available in cancer research. A powerful search interface and a fast visualization engine provide a highly interactive visualization for such data. High quality image export enables the life scientist to easily communicate their results. A comprehensive data administration allows to keep track of the available data sets. We applied FISH Oracle 2 to published data and found evidence that, in colorectal cancer cells, the gene TTC28 may be inactivated in two different ways, a fact that has not been published before. The interactive nature of FISH Oracle 2 and the possibility to store, select and visualize large amounts of downstream processed data support life scientists in generating hypotheses. The export of high quality images supports explanatory data visualization, simplifying the communication of new biological findings. A FISH Oracle 2 demo server and the software is available at http://www.zbh.uni-hamburg.de/fishoracle.
Do online prognostication tools represent a valid alternative to genomic profiling in the context of adjuvant treatment of early breast cancer? A systematic review of the literature.

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El Hage Chehade, Hiba; Wazir, Umar; Mokbel, Kinan; Kasem, Abdul; Mokbel, Kefah

2018-01-01

Decision-making regarding adjuvant chemotherapy has been based on clinical and pathological features. However, such decisions are seldom consistent. Web-based predictive models have been developed using data from cancer registries to help determine the need for adjuvant therapy. More recently, with the recognition of the heterogenous nature of breast cancer, genomic assays have been developed to aid in the therapeutic decision-making. We have carried out a comprehensive literature review regarding online prognostication tools and genomic assays to assess whether online tools could be used as valid alternatives to genomic profiling in decision-making regarding adjuvant therapy in early breast cancer. Breast cancer has been recently recognized as a heterogenous disease based on variations in molecular characteristics. Online tools are valuable in guiding adjuvant treatment, especially in resource constrained countries. However, in the era of personalized therapy, molecular profiling appears to be superior in predicting clinical outcome and guiding therapy. Copyright © 2017 Elsevier Inc. All rights reserved.
MTAP deletion confers enhanced dependency on the PRMT5 arginine methyltransferase in cancer cells | Office of Cancer Genomics

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The discovery of cancer dependencies has the potential to inform therapeutic strategies and to identify putative drug targets. Integrating data from comprehensive genomic profiling of cancer cell lines and from functional characterization of cancer cell dependencies, we discovered that loss of the enzyme methylthioadenosine phosphorylase (MTAP) confers a selective dependence on protein arginine methyltransferase 5 (PRMT5) and its binding partner WDR77. MTAP is frequently lost due to its proximity to the commonly deleted tumor suppressor gene, CDKN2A.
HCMI Organization | Office of Cancer Genomics

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Consortium The Human Cancer Models Initiative (HCMI) was created and funded by the US National Cancer Institute, Cancer Research UK, the foundation Hubrecht Organoid Technology, and the Wellcome Sanger Institute. Together, these organizations develop policy and make programmatic decisions to contribute to the function of the HCMI. National Cancer Institute
Genetic and genomic analysis modeling of germline c-MYC overexpression and cancer susceptibility

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Nunes Virginia

2008-01-01

Full Text Available Abstract Background Germline genetic variation is associated with the differential expression of many human genes. The phenotypic effects of this type of variation may be important when considering susceptibility to common genetic diseases. Three regions at 8q24 have recently been identified to independently confer risk of prostate cancer. Variation at 8q24 has also recently been associated with risk of breast and colorectal cancer. However, none of the risk variants map at or relatively close to known genes, with c-MYC mapping a few hundred kilobases distally. Results This study identifies cis-regulators of germline c-MYC expression in immortalized lymphocytes of HapMap individuals. Quantitative analysis of c-MYC expression in normal prostate tissues suggests an association between overexpression and variants in Region 1 of prostate cancer risk. Somatic c-MYC overexpression correlates with prostate cancer progression and more aggressive tumor forms, which was also a pathological variable associated with Region 1. Expression profiling analysis and modeling of transcriptional regulatory networks predicts a functional association between MYC and the prostate tumor suppressor KLF6. Analysis of MYC/Myc-driven cell transformation and tumorigenesis substantiates a model in which MYC overexpression promotes transformation by down-regulating KLF6. In this model, a feedback loop through E-cadherin down-regulation causes further transactivation of c-MYC. Conclusion This study proposes that variation at putative 8q24 cis-regulator(s of transcription can significantly alter germline c-MYC expression levels and, thus, contribute to prostate cancer susceptibility by down-regulating the prostate tumor suppressor KLF6 gene.
Genome-Wide Interaction Analyses between Genetic Variants and Alcohol Consumption and Smoking for Risk of Colorectal Cancer.

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Jian Gong

2016-10-01

Full Text Available Genome-wide association studies (GWAS have identified many genetic susceptibility loci for colorectal cancer (CRC. However, variants in these loci explain only a small proportion of familial aggregation, and there are likely additional variants that are associated with CRC susceptibility. Genome-wide studies of gene-environment interactions may identify variants that are not detected in GWAS of marginal gene effects. To study this, we conducted a genome-wide analysis for interaction between genetic variants and alcohol consumption and cigarette smoking using data from the Colon Cancer Family Registry (CCFR and the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO. Interactions were tested using logistic regression. We identified interaction between CRC risk and alcohol consumption and variants in the 9q22.32/HIATL1 (Pinteraction = 1.76×10-8; permuted p-value 3.51x10-8 region. Compared to non-/occasional drinking light to moderate alcohol consumption was associated with a lower risk of colorectal cancer among individuals with rs9409565 CT genotype (OR, 0.82 [95% CI, 0.74-0.91]; P = 2.1×10-4 and TT genotypes (OR,0.62 [95% CI, 0.51-0.75]; P = 1.3×10-6 but not associated among those with the CC genotype (p = 0.059. No genome-wide statistically significant interactions were observed for smoking. If replicated our suggestive finding of a genome-wide significant interaction between genetic variants and alcohol consumption might contribute to understanding colorectal cancer etiology and identifying subpopulations with differential susceptibility to the effect of alcohol on CRC risk.
GENOMIC PREDICTOR OF RESPONSE AND SURVIVAL FOLLOWING TAXANE-ANTHRACYCLINE CHEMOTHERAPY FOR INVASIVE BREAST CANCER

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Hatzis, Christos; Pusztai, Lajos; Valero, Vicente; Booser, Daniel J.; Esserman, Laura; Lluch, Ana; Vidaurre, Tatiana; Holmes, Frankie; Souchon, Eduardo; Martin, Miguel; Cotrina, José; Gomez, Henry; Hubbard, Rebekah; Chacón, J. Ignacio; Ferrer-Lozano, Jaime; Dyer, Richard; Buxton, Meredith; Gong, Yun; Wu, Yun; Ibrahim, Nuhad; Andreopoulou, Eleni; Ueno, Naoto T.; Hunt, Kelly; Yang, Wei; Nazario, Arlene; DeMichele, Angela; O’Shaughnessy, Joyce; Hortobagyi, Gabriel N.; Symmans, W. Fraser

2017-01-01

CONTEXT Accurate prediction of who will (or won’t) have high probability of survival benefit from standard treatments is fundamental for individualized cancer treatment strategies. OBJECTIVE To develop a predictor of response and survival from chemotherapy for newly diagnosed invasive breast cancer. DESIGN Development of different predictive signatures for resistance and response to neoadjuvant chemotherapy (stratified according to estrogen receptor (ER) status) from gene expression microarrays of newly diagnosed breast cancer (310 patients). Then prediction of breast cancer treatment-sensitivity using the combination of signatures for: 1) sensitivity to endocrine therapy, 2) chemo-resistance, and 3) chemo-sensitivity. Independent validation (198 patients) and comparison with other reported genomic predictors of chemotherapy response. SETTING Prospective multicenter study to develop and test genomic predictors for neoadjuvant chemotherapy. PATIENTS Newly diagnosed HER2-negative breast cancer treated with chemotherapy containing sequential taxane and anthracycline-based regimens then endocrine therapy (if hormone receptor-positive). MAIN OUTCOME MEASURES Distant relapse-free survival (DRFS) if predicted treatment-sensitive and absolute risk reduction (ARR, difference in DRFS of the two predicted groups) at median follow-up (3 years), and their 95% confidence intervals (CI). RESULTS Patients in the independent validation cohort (99% clinical Stage II–III) who were predicted to be treatment-sensitive (28% of total) had DRFS of 92% (CI 85–100) and survival benefit compared to others (absolute risk reduction (ARR) 18%; CI 6–28). Predictions were accurate if breast cancer was ER-positive (30% predicted sensitive, DRFS 97%, CI 91–100; ARR 11%, CI 0.1–21) or ER-negative (26% predicted sensitive, DRFS 83%, CI 68–100; ARR 26%, CI 4–28), and were significant in multivariate analysis after adjusting for relevant clinical-pathologic characteristics. Other
Epigenome remodelling in breast cancer: insights from an early in vitro model of carcinogenesis.

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Locke, Warwick J; Clark, Susan J

2012-11-15

Epigenetic gene regulation has influence over a diverse range of cellular functions, including the maintenance of pluripotency, differentiation, and cellular identity, and is deregulated in many diseases, including cancer. Whereas the involvement of epigenetic dysregulation in cancer is well documented, much of the mechanistic detail involved in triggering these changes remains unclear. In the current age of genomics, the development of new sequencing technologies has seen an influx of genomic and epigenomic data and drastic improvements in both resolution and coverage. Studies in cancer cell lines and clinical samples using next-generation sequencing are rapidly delivering spectacular insights into the nature of the cancer genome and epigenome. Despite these improvements in technology, the timing and relationship between genetic and epigenetic changes that occur during the process of carcinogenesis are still unclear. In particular, what changes to the epigenome are playing a driving role during carcinogenesis and what influence the temporal nature of these changes has on cancer progression are not known. Understanding the early epigenetic changes driving breast cancer has the exciting potential to provide a novel set of therapeutic targets or early-disease biomarkers or both. Therefore, it is important to find novel systems that permit the study of initial epigenetic events that potentially occur during the first stages of breast cancer. Non-malignant human mammary epithelial cells (HMECs) provide an exciting in vitro model of very early breast carcinogenesis. When grown in culture, HMECs are able to temporarily escape senescence and acquire a pre-malignant breast cancer-like phenotype (variant HMECs, or vHMECs). Cultured HMECs are composed mainly of cells from the basal breast epithelial layer. Therefore, vHMECs are considered to represent the basal-like subtype of breast cancer. The transition from HMECs to vHMECs in culture recapitulates the epigenomic

Improving breast cancer survival analysis through competition-based multidimensional modeling.

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Erhan Bilal

Full Text Available Breast cancer is the most common malignancy in women and is responsible for hundreds of thousands of deaths annually. As with most cancers, it is a heterogeneous disease and different breast cancer subtypes are treated differently. Understanding the difference in prognosis for breast cancer based on its molecular and phenotypic features is one avenue for improving treatment by matching the proper treatment with molecular subtypes of the disease. In this work, we employed a competition-based approach to modeling breast cancer prognosis using large datasets containing genomic and clinical information and an online real-time leaderboard program used to speed feedback to the modeling team and to encourage each modeler to work towards achieving a higher ranked submission. We find that machine learning methods combined with molecular features selected based on expert prior knowledge can improve survival predictions compared to current best-in-class methodologies and that ensemble models trained across multiple user submissions systematically outperform individual models within the ensemble. We also find that model scores are highly consistent across multiple independent evaluations. This study serves as the pilot phase of a much larger competition open to the whole research community, with the goal of understanding general strategies for model optimization using clinical and molecular profiling data and providing an objective, transparent system for assessing prognostic models.
Use of deep whole-genome sequencing data to identify structure risk variants in breast cancer susceptibility genes.

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Guo, Xingyi; Shi, Jiajun; Cai, Qiuyin; Shu, Xiao-Ou; He, Jing; Wen, Wanqing; Allen, Jamie; Pharoah, Paul; Dunning, Alison; Hunter, David J; Kraft, Peter; Easton, Douglas F; Zheng, Wei; Long, Jirong

2018-03-01

Functional disruptions of susceptibility genes by large genomic structure variant (SV) deletions in germlines are known to be associated with cancer risk. However, few studies have been conducted to systematically search for SV deletions in breast cancer susceptibility genes. We analysed deep (> 30x) whole-genome sequencing (WGS) data generated in blood samples from 128 breast cancer patients of Asian and European descent with either a strong family history of breast cancer or early cancer onset disease. To identify SV deletions in known or suspected breast cancer susceptibility genes, we used multiple SV calling tools including Genome STRiP, Delly, Manta, BreakDancer and Pindel. SV deletions were detected by at least three of these bioinformatics tools in five genes. Specifically, we identified heterozygous deletions covering a fraction of the coding regions of BRCA1 (with approximately 80kb in two patients), and TP53 genes (with ∼1.6 kb in two patients), and of intronic regions (∼1 kb) of the PALB2 (one patient), PTEN (three patients) and RAD51C genes (one patient). We confirmed the presence of these deletions using real-time quantitative PCR (qPCR). Our study identified novel SV deletions in breast cancer susceptibility genes and the identification of such SV deletions may improve clinical testing.
Evolution and clinical impact of co-occurring genetic alterations in advanced-stage EGFR-mutant lung cancers. | Office of Cancer Genomics

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A widespread approach to modern cancer therapy is to identify a single oncogenic driver gene and target its mutant-protein product (for example, EGFR-inhibitor treatment in EGFR-mutant lung cancers). However, genetically driven resistance to targeted therapy limits patient survival. Through genomic analysis of 1,122 EGFR-mutant lung cancer cell-free DNA samples and whole-exome analysis of seven longitudinally collected tumor samples from a patient with EGFR-mutant lung cancer, we identified critical co-occurring oncogenic events present in most advanced-stage EGFR-mutant lung cancers.
Copy-number and gene dependency analysis reveals partial copy loss of wild-type SF3B1 as a novel cancer vulnerability. | Office of Cancer Genomics

Science.gov (United States)

Genomic instability is a hallmark of human cancer, and results in widespread somatic copy number alterations. We used a genome-scale shRNA viability screen in human cancer cell lines to systematically identify genes that are essential in the context of particular copy-number alterations (copy-number associated gene dependencies). The most enriched class of copy-number associated gene dependencies was CYCLOPS (Copy-number alterations Yielding Cancer Liabilities Owing to Partial losS) genes, and spliceosome components were the most prevalent.
Genome-wide association study for ovarian cancer susceptibility using pooled DNA.

NARCIS (Netherlands)

Lu, Y.; Chen, X.; Beesley, J.; Johnatty, S.E.; Defazio, A.; Lambrechts, S.; Lambrechts, D.; Despierre, E.; Vergotes, I.; Chang-Claude, J.; Hein, R.; Nickels, S.; Wang-Gohrke, S.; Dork, T.; Durst, M.; Antonenkova, N.; Bogdanova, N.; Goodman, M.T.; Lurie, G.; Wilkens, L.R.; Carney, M.E.; Butzow, R.; Nevanlinna, H.; Heikkinen, T.; Leminen, A.; Kiemeney, L.A.L.M.; Massuger, L.F.A.G.; Altena, A.M. van; Aben, K.K.H.; Kjaer, S.K.; Hogdall, E.; Jensen, A.; Brooks-Wilson, A.; Le, N.; Cook, L.; Earp, M.; Kelemen, L.; Easton, D.; Pharoah, P.; Song, H.; Tyrer, J.; Ramus, S.; Menon, U.; Gentry-Maharaj, A.; Gayther, S.A.; Bandera, E.V.; Olson, S.H.; Orlow, I.; Rodriguez-Rodriguez, L.; MacGregor, S.; Chenevix-Trench, G.

2012-01-01

Recent Genome-Wide Association Studies (GWAS) have identified four low-penetrance ovarian cancer susceptibility loci. We hypothesized that further moderate- or low-penetrance variants exist among the subset of single-nucleotide polymorphisms (SNPs) not well tagged by the genotyping arrays used in
CRISPR/Cas9: Transcending the Reality of Genome Editing.

Science.gov (United States)

Chira, Sergiu; Gulei, Diana; Hajitou, Amin; Zimta, Alina-Andreea; Cordelier, Pierre; Berindan-Neagoe, Ioana

2017-06-16

With the expansion of the microbiology field of research, a new genome editing tool arises from the biology of bacteria that holds the promise of achieving precise modifications in the genome with a simplicity and versatility that surpasses previous genome editing methods. This new technique, commonly named CRISPR/Cas9, led to a rapid expansion of the biomedical field; more specifically, cancer characterization and modeling have benefitted greatly from the genome editing capabilities of CRISPR/Cas9. In this paper, we briefly summarize recent improvements in CRISPR/Cas9 design meant to overcome the limitations that have arisen from the nuclease activity of Cas9 and the influence of this technology in cancer research. In addition, we present challenges that might impede the clinical applicability of CRISPR/Cas9 for cancer therapy and highlight future directions for designing CRISPR/Cas9 delivery systems that might prove useful for cancer therapeutics. Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.
Common structural and epigenetic changes in the genome of castration-resistant prostate cancer.

Science.gov (United States)

Friedlander, Terence W; Roy, Ritu; Tomlins, Scott A; Ngo, Vy T; Kobayashi, Yasuko; Azameera, Aruna; Rubin, Mark A; Pienta, Kenneth J; Chinnaiyan, Arul; Ittmann, Michael M; Ryan, Charles J; Paris, Pamela L

2012-02-01

Progression of primary prostate cancer to castration-resistant prostate cancer (CRPC) is associated with numerous genetic and epigenetic alterations that are thought to promote survival at metastatic sites. In this study, we investigated gene copy number and CpG methylation status in CRPC to gain insight into specific pathophysiologic pathways that are active in this advanced form of prostate cancer. Our analysis defined and validated 495 genes exhibiting significant differences in CRPC in gene copy number, including gains in androgen receptor (AR) and losses of PTEN and retinoblastoma 1 (RB1). Significant copy number differences existed between tumors with or without AR gene amplification, including a common loss of AR repressors in AR-unamplified tumors. Simultaneous gene methylation and allelic deletion occurred frequently in RB1 and HSD17B2, the latter of which is involved in testosterone metabolism. Lastly, genomic DNA from most CRPC was hypermethylated compared with benign prostate tissue. Our findings establish a comprehensive methylation signature that couples epigenomic and structural analyses, thereby offering insights into the genomic alterations in CRPC that are associated with a circumvention of hormonal therapy. Genes identified in this integrated genomic study point to new drug targets in CRPC, an incurable disease state which remains the chief therapeutic challenge. ©2012 AACR.
Digestive tumor bank protocol: from surgical specimens to genomic studies of digestive cancers.

Science.gov (United States)

Popescu, I; Stroescu, C; Dumitrascu, T; Herlea, V; Paslaru, Liliana; Lazar, V; Boissin, H; Taieb, J; Horeanga, Ionela

2006-01-01

Cancer is a complex polygenic and multifactorial disease, resulting from successive dynamic changes in the genome of somatic cells and from the accumulation of molecular alterations in both tumour cells and host cells. For the majority of cancers, including many malignancies of the gastrointestinal tract, our current means of diagnosis and treatment of the tumors are grossly insufficient. In recent years the development of several gene expression profiling methods such as comparative genomic hybridization (CGH), differential display, serial analysis of gene expression (SAGE) and DNA arrays, together with the sequencing of the human genome, has provided an opportunity to monitor and investigate the complete cascade of molecular events leading to tumor development and progression. Given the central role played by surgeons in the current management of patients with solid cancers, it is of paramount importance for them to know the principles characterizing this laboratory tools to critically assess the results originating from this biotechnology. We describe in this article the scientific partnership between Fundeni Clinical Institute Bucharest, Romania and RNtech Company, Paris, France for the development of a center of biological resources (Biobank) as well as the standardized protocol of working with the biological samples, the ongoing projects and the future perspectives.
The expanding universe of cohesin functions: a new genome stability caretaker involved in human disease and cancer.

Science.gov (United States)

Mannini, Linda; Menga, Stefania; Musio, Antonio

2010-06-01

Cohesin is responsible for sister chromatid cohesion, ensuring the correct chromosome segregation. Beyond this role, cohesin and regulatory cohesin genes seem to play a role in preserving genome stability and gene transcription regulation. DNA damage is thought to be a major culprit for many human diseases, including cancer. Our present knowledge of the molecular basis underlying genome instability is extremely limited. Mutations in cohesin genes cause human diseases such as Cornelia de Lange syndrome and Roberts syndrome/SC phocomelia, and all the cell lines derived from affected patients show genome instability. Cohesin mutations have also been identified in colorectal cancer. Here, we will discuss the human disorders caused by alterations of cohesin function, with emphasis on the emerging role of cohesin as a genome stability caretaker.
Gastric cancers of Western European and African patients show different patterns of genomic instability

Directory of Open Access Journals (Sweden)

Mulder Chris JJ

2011-01-01

Full Text Available Abstract Background Infection with H. pylori is important in the etiology of gastric cancer. Gastric cancer is infrequent in Africa, despite high frequencies of H. pylori infection, referred to as the African enigma. Variation in environmental and host factors influencing gastric cancer risk between different populations have been reported but little is known about the biological differences between gastric cancers from different geographic locations. We aim to study genomic instability patterns of gastric cancers obtained from patients from United Kingdom (UK and South Africa (SA, in an attempt to support the African enigma hypothesis at the biological level. Methods DNA was isolated from 67 gastric adenocarcinomas, 33 UK patients, 9 Caucasian SA patients and 25 native SA patients. Microsatellite instability and chromosomal instability were analyzed by PCR and microarray comparative genomic hybridization, respectively. Data was analyzed by supervised univariate and multivariate analyses as well as unsupervised hierarchical cluster analysis. Results Tumors from Caucasian and native SA patients showed significantly more microsatellite instable tumors (p Conclusions Gastric cancers from SA and UK patients show differences in genetic instability patterns, indicating possible different biological mechanisms in patients from different geographical origin. This is of future clinical relevance for stratification of gastric cancer therapy.
An update of the classical Bokhman’s dualistic model of endometrial cancer

Directory of Open Access Journals (Sweden)

Miłosz Wilczyński

2016-07-01

Full Text Available According to the classical dualistic model introduced by Bokhman in 1983, endometrial cancer (EC is divided into two basic types. The prototypical histological type for type I and type II of EC is endometrioid carcinoma and serous carcinoma, respectively. The traditional classification is based on clinical, endocrine and histopathological features, however, it sometimes does not reflect the full heterogeneity of EC. New molecular evidence, supported by clinical diversity of the cancer, indicates that the classical dualistic model is valid only to some extent. The review updates a mutational diversity of EC, introducing a new molecular classification of the tumour in regard to data presented by The Cancer Genome Atlas Research Network (TGCA.
Integrative Genomics Reveals Mechanisms of Copy Number Alterations Responsible for Transcriptional Deregulation in Colorectal Cancer

Science.gov (United States)

Camps, Jordi; Nguyen, Quang Tri; Padilla-Nash, Hesed M.; Knutsen, Turid; McNeil, Nicole E.; Wangsa, Danny; Hummon, Amanda B.; Grade, Marian; Ried, Thomas; Difilippantonio, Michael J.

2016-01-01

To evaluate the mechanisms and consequences of chromosomal aberrations in colorectal cancer (CRC), we used a combination of spectral karyotyping, array comparative genomic hybridization (aCGH), and array-based global gene expression profiling on 31 primary carcinomas and 15 established cell lines. Importantly, aCGH showed that the genomic profiles of primary tumors are recapitulated in the cell lines. We revealed a preponderance of chromosome breakpoints at sites of copy number variants (CNVs) in the CRC cell lines, a novel mechanism of DNA breakage in cancer. The integration of gene expression and aCGH led to the identification of 157 genes localized within high-level copy number changes whose transcriptional deregulation was significantly affected across all of the samples, thereby suggesting that these genes play a functional role in CRC. Genomic amplification at 8q24 was the most recurrent event and led to the overexpression of MYC and FAM84B. Copy number dependent gene expression resulted in deregulation of known cancer genes such as APC, FGFR2, and ERBB2. The identification of only 36 genes whose localization near a breakpoint could account for their observed deregulated expression demonstrates that the major mechanism for transcriptional deregulation in CRC is genomic copy number changes resulting from chromosomal aberrations. PMID:19691111
The genomic analysis of lactic acidosis and acidosis response in human cancers.

Directory of Open Access Journals (Sweden)

Julia Ling-Yu Chen

2008-12-01

Full Text Available The tumor microenvironment has a significant impact on tumor development. Two important determinants in this environment are hypoxia and lactic acidosis. Although lactic acidosis has long been recognized as an important factor in cancer, relatively little is known about how cells respond to lactic acidosis and how that response relates to cancer phenotypes. We develop genome-scale gene expression studies to dissect transcriptional responses of primary human mammary epithelial cells to lactic acidosis and hypoxia in vitro and to explore how they are linked to clinical tumor phenotypes in vivo. The resulting experimental signatures of responses to lactic acidosis and hypoxia are evaluated in a heterogeneous set of breast cancer datasets. A strong lactic acidosis response signature identifies a subgroup of low-risk breast cancer patients having distinct metabolic profiles suggestive of a preference for aerobic respiration. The association of lactic acidosis response with good survival outcomes may relate to the role of lactic acidosis in directing energy generation toward aerobic respiration and utilization of other energy sources via inhibition of glycolysis. This "inhibition of glycolysis" phenotype in tumors is likely caused by the repression of glycolysis gene expression and Akt inhibition. Our study presents a genomic evaluation of the prognostic information of a lactic acidosis response independent of the hypoxic response. Our results identify causal roles of lactic acidosis in metabolic reprogramming, and the direct functional consequence of lactic acidosis pathway activity on cellular responses and tumor development. The study also demonstrates the utility of genomic analysis that maps expression-based findings from in vitro experiments to human samples to assess links to in vivo clinical phenotypes.
Pancreatic cancer genomes reveal aberrations in axon guidance pathway genes.

Science.gov (United States)

Biankin, Andrew V; Waddell, Nicola; Kassahn, Karin S; Gingras, Marie-Claude; Muthuswamy, Lakshmi B; Johns, Amber L; Miller, David K; Wilson, Peter J; Patch, Ann-Marie; Wu, Jianmin; Chang, David K; Cowley, Mark J; Gardiner, Brooke B; Song, Sarah; Harliwong, Ivon; Idrisoglu, Senel; Nourse, Craig; Nourbakhsh, Ehsan; Manning, Suzanne; Wani, Shivangi; Gongora, Milena; Pajic, Marina; Scarlett, Christopher J; Gill, Anthony J; Pinho, Andreia V; Rooman, Ilse; Anderson, Matthew; Holmes, Oliver; Leonard, Conrad; Taylor, Darrin; Wood, Scott; Xu, Qinying; Nones, Katia; Fink, J Lynn; Christ, Angelika; Bruxner, Tim; Cloonan, Nicole; Kolle, Gabriel; Newell, Felicity; Pinese, Mark; Mead, R Scott; Humphris, Jeremy L; Kaplan, Warren; Jones, Marc D; Colvin, Emily K; Nagrial, Adnan M; Humphrey, Emily S; Chou, Angela; Chin, Venessa T; Chantrill, Lorraine A; Mawson, Amanda; Samra, Jaswinder S; Kench, James G; Lovell, Jessica A; Daly, Roger J; Merrett, Neil D; Toon, Christopher; Epari, Krishna; Nguyen, Nam Q; Barbour, Andrew; Zeps, Nikolajs; Kakkar, Nipun; Zhao, Fengmei; Wu, Yuan Qing; Wang, Min; Muzny, Donna M; Fisher, William E; Brunicardi, F Charles; Hodges, Sally E; Reid, Jeffrey G; Drummond, Jennifer; Chang, Kyle; Han, Yi; Lewis, Lora R; Dinh, Huyen; Buhay, Christian J; Beck, Timothy; Timms, Lee; Sam, Michelle; Begley, Kimberly; Brown, Andrew; Pai, Deepa; Panchal, Ami; Buchner, Nicholas; De Borja, Richard; Denroche, Robert E; Yung, Christina K; Serra, Stefano; Onetto, Nicole; Mukhopadhyay, Debabrata; Tsao, Ming-Sound; Shaw, Patricia A; Petersen, Gloria M; Gallinger, Steven; Hruban, Ralph H; Maitra, Anirban; Iacobuzio-Donahue, Christine A; Schulick, Richard D; Wolfgang, Christopher L; Morgan, Richard A; Lawlor, Rita T; Capelli, Paola; Corbo, Vincenzo; Scardoni, Maria; Tortora, Giampaolo; Tempero, Margaret A; Mann, Karen M; Jenkins, Nancy A; Perez-Mancera, Pedro A; Adams, David J; Largaespada, David A; Wessels, Lodewyk F A; Rust, Alistair G; Stein, Lincoln D; Tuveson, David A; Copeland, Neal G; Musgrove, Elizabeth A; Scarpa, Aldo; Eshleman, James R; Hudson, Thomas J; Sutherland, Robert L; Wheeler, David A; Pearson, John V; McPherson, John D; Gibbs, Richard A; Grimmond, Sean M

2012-11-15

Pancreatic cancer is a highly lethal malignancy with few effective therapies. We performed exome sequencing and copy number analysis to define genomic aberrations in a prospectively accrued clinical cohort (n = 142) of early (stage I and II) sporadic pancreatic ductal adenocarcinoma. Detailed analysis of 99 informative tumours identified substantial heterogeneity with 2,016 non-silent mutations and 1,628 copy-number variations. We define 16 significantly mutated genes, reaffirming known mutations (KRAS, TP53, CDKN2A, SMAD4, MLL3, TGFBR2, ARID1A and SF3B1), and uncover novel mutated genes including additional genes involved in chromatin modification (EPC1 and ARID2), DNA damage repair (ATM) and other mechanisms (ZIM2, MAP2K4, NALCN, SLC16A4 and MAGEA6). Integrative analysis with in vitro functional data and animal models provided supportive evidence for potential roles for these genetic aberrations in carcinogenesis. Pathway-based analysis of recurrently mutated genes recapitulated clustering in core signalling pathways in pancreatic ductal adenocarcinoma, and identified new mutated genes in each pathway. We also identified frequent and diverse somatic aberrations in genes described traditionally as embryonic regulators of axon guidance, particularly SLIT/ROBO signalling, which was also evident in murine Sleeping Beauty transposon-mediated somatic mutagenesis models of pancreatic cancer, providing further supportive evidence for the potential involvement of axon guidance genes in pancreatic carcinogenesis.
Three-dimensional models of cancer for pharmacology and cancer cell biology: capturing tumor complexity in vitro/ex vivo.

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Hickman, John A; Graeser, Ralph; de Hoogt, Ronald; Vidic, Suzana; Brito, Catarina; Gutekunst, Matthias; van der Kuip, Heiko

2014-09-01

Cancers are complex and heterogeneous pathological "organs" in a dynamic interplay with their host. Models of human cancer in vitro, used in cancer biology and drug discovery, are generally highly reductionist. These cancer models do not incorporate complexity or heterogeneity. This raises the question as to whether the cancer models' biochemical circuitry (not their genome) represents, with sufficient fidelity, a tumor in situ. Around 95% of new anticancer drugs eventually fail in clinical trial, despite robust indications of activity in existing in vitro pre-clinical models. Innovative models are required that better capture tumor biology. An important feature of all tissues, and tumors, is that cells grow in three dimensions. Advances in generating and characterizing simple and complex (with added stromal components) three-dimensional in vitro models (3D models) are reviewed in this article. The application of stirred bioreactors to permit both scale-up/scale-down of these cancer models and, importantly, methods to permit controlled changes in environment (pH, nutrients, and oxygen) are also described. The challenges of generating thin tumor slices, their utility, and potential advantages and disadvantages are discussed. These in vitro/ex vivo models represent a distinct move to capture the realities of tumor biology in situ, but significant characterization work still remains to be done in order to show that their biochemical circuitry accurately reflects that of a tumor. Copyright © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity.

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Barretina, Jordi; Caponigro, Giordano; Stransky, Nicolas; Venkatesan, Kavitha; Margolin, Adam A; Kim, Sungjoon; Wilson, Christopher J; Lehár, Joseph; Kryukov, Gregory V; Sonkin, Dmitriy; Reddy, Anupama; Liu, Manway; Murray, Lauren; Berger, Michael F; Monahan, John E; Morais, Paula; Meltzer, Jodi; Korejwa, Adam; Jané-Valbuena, Judit; Mapa, Felipa A; Thibault, Joseph; Bric-Furlong, Eva; Raman, Pichai; Shipway, Aaron; Engels, Ingo H; Cheng, Jill; Yu, Guoying K; Yu, Jianjun; Aspesi, Peter; de Silva, Melanie; Jagtap, Kalpana; Jones, Michael D; Wang, Li; Hatton, Charles; Palescandolo, Emanuele; Gupta, Supriya; Mahan, Scott; Sougnez, Carrie; Onofrio, Robert C; Liefeld, Ted; MacConaill, Laura; Winckler, Wendy; Reich, Michael; Li, Nanxin; Mesirov, Jill P; Gabriel, Stacey B; Getz, Gad; Ardlie, Kristin; Chan, Vivien; Myer, Vic E; Weber, Barbara L; Porter, Jeff; Warmuth, Markus; Finan, Peter; Harris, Jennifer L; Meyerson, Matthew; Golub, Todd R; Morrissey, Michael P; Sellers, William R; Schlegel, Robert; Garraway, Levi A

2012-03-28

The systematic translation of cancer genomic data into knowledge of tumour biology and therapeutic possibilities remains challenging. Such efforts should be greatly aided by robust preclinical model systems that reflect the genomic diversity of human cancers and for which detailed genetic and pharmacological annotation is available. Here we describe the Cancer Cell Line Encyclopedia (CCLE): a compilation of gene expression, chromosomal copy number and massively parallel sequencing data from 947 human cancer cell lines. When coupled with pharmacological profiles for 24 anticancer drugs across 479 of the cell lines, this collection allowed identification of genetic, lineage, and gene-expression-based predictors of drug sensitivity. In addition to known predictors, we found that plasma cell lineage correlated with sensitivity to IGF1 receptor inhibitors; AHR expression was associated with MEK inhibitor efficacy in NRAS-mutant lines; and SLFN11 expression predicted sensitivity to topoisomerase inhibitors. Together, our results indicate that large, annotated cell-line collections may help to enable preclinical stratification schemata for anticancer agents. The generation of genetic predictions of drug response in the preclinical setting and their incorporation into cancer clinical trial design could speed the emergence of 'personalized' therapeutic regimens.
The Cancer Cell Line Encyclopedia enables predictive modeling of anticancer drug sensitivity

Science.gov (United States)

Barretina, Jordi; Caponigro, Giordano; Stransky, Nicolas; Venkatesan, Kavitha; Margolin, Adam A.; Kim, Sungjoon; Wilson, Christopher J.; Lehár, Joseph; Kryukov, Gregory V.; Sonkin, Dmitriy; Reddy, Anupama; Liu, Manway; Murray, Lauren; Berger, Michael F.; Monahan, John E.; Morais, Paula; Meltzer, Jodi; Korejwa, Adam; Jané-Valbuena, Judit; Mapa, Felipa A.; Thibault, Joseph; Bric-Furlong, Eva; Raman, Pichai; Shipway, Aaron; Engels, Ingo H.; Cheng, Jill; Yu, Guoying K.; Yu, Jianjun; Aspesi, Peter; de Silva, Melanie; Jagtap, Kalpana; Jones, Michael D.; Wang, Li; Hatton, Charles; Palescandolo, Emanuele; Gupta, Supriya; Mahan, Scott; Sougnez, Carrie; Onofrio, Robert C.; Liefeld, Ted; MacConaill, Laura; Winckler, Wendy; Reich, Michael; Li, Nanxin; Mesirov, Jill P.; Gabriel, Stacey B.; Getz, Gad; Ardlie, Kristin; Chan, Vivien; Myer, Vic E.; Weber, Barbara L.; Porter, Jeff; Warmuth, Markus; Finan, Peter; Harris, Jennifer L.; Meyerson, Matthew; Golub, Todd R.; Morrissey, Michael P.; Sellers, William R.; Schlegel, Robert; Garraway, Levi A.

2012-01-01

The systematic translation of cancer genomic data into knowledge of tumor biology and therapeutic avenues remains challenging. Such efforts should be greatly aided by robust preclinical model systems that reflect the genomic diversity of human cancers and for which detailed genetic and pharmacologic annotation is available1. Here we describe the Cancer Cell Line Encyclopedia (CCLE): a compilation of gene expression, chromosomal copy number, and massively parallel sequencing data from 947 human cancer cell lines. When coupled with pharmacologic profiles for 24 anticancer drugs across 479 of the lines, this collection allowed identification of genetic, lineage, and gene expression-based predictors of drug sensitivity. In addition to known predictors, we found that plasma cell lineage correlated with sensitivity to IGF1 receptor inhibitors; AHR expression was associated with MEK inhibitor efficacy in NRAS-mutant lines; and SLFN11 expression predicted sensitivity to topoisomerase inhibitors. Altogether, our results suggest that large, annotated cell line collections may help to enable preclinical stratification schemata for anticancer agents. The generation of genetic predictions of drug response in the preclinical setting and their incorporation into cancer clinical trial design could speed the emergence of “personalized” therapeutic regimens2. PMID:22460905
Genome-wide alterations of the DNA replication program during tumor progression

Science.gov (United States)

Arneodo, A.; Goldar, A.; Argoul, F.; Hyrien, O.; Audit, B.

2016-08-01

Oncogenic stress is a major driving force in the early stages of cancer development. Recent experimental findings reveal that, in precancerous lesions and cancers, activated oncogenes may induce stalling and dissociation of DNA replication forks resulting in DNA damage. Replication timing is emerging as an important epigenetic feature that recapitulates several genomic, epigenetic and functional specificities of even closely related cell types. There is increasing evidence that chromosome rearrangements, the hallmark of many cancer genomes, are intimately associated with the DNA replication program and that epigenetic replication timing changes often precede chromosomic rearrangements. The recent development of a novel methodology to map replication fork polarity using deep sequencing of Okazaki fragments has provided new and complementary genome-wide replication profiling data. We review the results of a wavelet-based multi-scale analysis of genomic and epigenetic data including replication profiles along human chromosomes. These results provide new insight into the spatio-temporal replication program and its dynamics during differentiation. Here our goal is to bring to cancer research, the experimental protocols and computational methodologies for replication program profiling, and also the modeling of the spatio-temporal replication program. To illustrate our purpose, we report very preliminary results obtained for the chronic myelogeneous leukemia, the archetype model of cancer. Finally, we discuss promising perspectives on using genome-wide DNA replication profiling as a novel efficient tool for cancer diagnosis, prognosis and personalized treatment.
Genome-Wide Interaction Analyses between Genetic Variants and Alcohol Consumption and Smoking for Risk of Colorectal Cancer

Science.gov (United States)

Newcomb, Polly A.; Campbell, Peter T.; Baron, John A.; Berndt, Sonja I.; Bezieau, Stephane; Brenner, Hermann; Casey, Graham; Chan, Andrew T.; Chang-Claude, Jenny; Du, Mengmeng; Figueiredo, Jane C.; Gallinger, Steven; Giovannucci, Edward L.; Haile, Robert W.; Harrison, Tabitha A.; Hayes, Richard B.; Hoffmeister, Michael; Hopper, John L.; Hudson, Thomas J.; Jeon, Jihyoun; Jenkins, Mark A.; Küry, Sébastien; Le Marchand, Loic; Lin, Yi; Lindor, Noralane M.; Nishihara, Reiko; Ogino, Shuji; Potter, John D.; Rudolph, Anja; Schoen, Robert E.; Seminara, Daniela; Slattery, Martha L.; Thibodeau, Stephen N.; Thornquist, Mark; Toth, Reka; Wallace, Robert; White, Emily; Jiao, Shuo; Lemire, Mathieu; Hsu, Li; Peters, Ulrike

2016-01-01

Genome-wide association studies (GWAS) have identified many genetic susceptibility loci for colorectal cancer (CRC). However, variants in these loci explain only a small proportion of familial aggregation, and there are likely additional variants that are associated with CRC susceptibility. Genome-wide studies of gene-environment interactions may identify variants that are not detected in GWAS of marginal gene effects. To study this, we conducted a genome-wide analysis for interaction between genetic variants and alcohol consumption and cigarette smoking using data from the Colon Cancer Family Registry (CCFR) and the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO). Interactions were tested using logistic regression. We identified interaction between CRC risk and alcohol consumption and variants in the 9q22.32/HIATL1 (Pinteraction = 1.76×10−8; permuted p-value 3.51x10-8) region. Compared to non-/occasional drinking light to moderate alcohol consumption was associated with a lower risk of colorectal cancer among individuals with rs9409565 CT genotype (OR, 0.82 [95% CI, 0.74–0.91]; P = 2.1×10−4) and TT genotypes (OR,0.62 [95% CI, 0.51–0.75]; P = 1.3×10−6) but not associated among those with the CC genotype (p = 0.059). No genome-wide statistically significant interactions were observed for smoking. If replicated our suggestive finding of a genome-wide significant interaction between genetic variants and alcohol consumption might contribute to understanding colorectal cancer etiology and identifying subpopulations with differential susceptibility to the effect of alcohol on CRC risk. PMID:27723779
Genome-wide association analysis identifies new lung cancer susceptibility loci in never-smoking women in Asia.

NARCIS (Netherlands)

Lan, Q.; Hsiung, C.A.; Matsuo, K.; Hong, Y.C.; Seow, A.; Wang, Z.; Hosgood, H.D.; Chen, K.; Wang, J.C.; Chatterjee, N.; Hu, W.; Wong, M.P.; Zheng, W.; Caporaso, N.; Park, J.Y.; Chen, C.J.; Kim, Y.H.; Kim, Y.T.; Landi, M.T.; Shen, H.; Lawrence, C.; Burdett, L.; Yeager, M.; Yuenger, J.; Jacobs, K.B.; Chang, I.S.; Mitsudomi, T.; Kim, H.N.; Chang, G.C.; Bassig, B.A.; Tucker, M.; Wei, F.; Yin, Y.; Wu, C.; An, S.J.; Qian, B.; Lee, V.H.; Lu, D.; Liu, J.; Jeon, H.S.; Hsiao, C.F.; Sung, J.S.; Kim, J.H.; Gao, Y.T.; Tsai, Y.H.; Jung, Y.J.; Guo, H.; Hu, Z.; Hutchinson, A.; Wang, W.C.; Klein, R.; Chung, C.C.; Oh, I.J.; Chen, K.Y.; Berndt, S.I.; He, X.; Wu, W.; Chang, J.; Zhang, X.C.; Huang, M.S.; Zheng, H.; Wang, J.; Zhao, X.|info:eu-repo/dai/nl/413577805; Li, Y.; Choi, J.E.; Su, W.C.; Park, K.H.; Sung, S.W.; Shu, X.O.; Chen, Y.M.; Liu, L.; Kang, C.H.; Hu, L.; Chen, C.H.; Pao, W.; Kim, Y.C.; Yang, T.Y.; Xu, J.; Guan, P.; Tan, W.; Su, J.; Wang, C.L.; Li, H.; Sihoe, A.D.; Zhao, Z.|info:eu-repo/dai/nl/304120995; Chen, Y.; Choi, Y.Y.; Hung, J.Y.; Kim, J.S.; Yoon, H.I.; Cai, Q.; Lin, C.C.; Park, I.K.; Xu, P.; Dong, J.; Kim, C.; He, Q; Perng, R.P.; Kohno, T.; Kweon, S.S.; Chen, C.Y.; Vermeulen, R.|info:eu-repo/dai/nl/216532620; Wu, J.; Lim, W.Y.; Chen, K.C.; Chow, W.H.; Ji, B.T.; Chan, J.K.; Chu, M.; Li, Y.J.; Yokota, J.; Li, J.; Chen, H.; Xiang, Y.B.; Yu, C.J.; Kunitoh, H.; Wu, G.; Jin, L.; Lo, Y.L.; Shiraishi, K.; Chen, Y.H.; Lin, H.C.; Wu, T.; WU, Y.; Yang, P.C.; Zhou, B.; Shin, M.H.; Fraumeni, J.F.; Lin, D.; Chanock, S.J.; Rothman, N.

2012-01-01

To identify common genetic variants that contribute to lung cancer susceptibility, we conducted a multistage genome-wide association study of lung cancer in Asian women who never smoked. We scanned 5,510 never-smoking female lung cancer cases and 4,544 controls drawn from 14 studies from mainland

Seven prostate cancer susceptibility loci identified by a multi-stage genome-wide association study

DEFF Research Database (Denmark)

Kote-Jarai, Zsofia; Olama, Ali Amin Al; Giles, Graham G

2011-01-01

Prostate cancer (PrCa) is the most frequently diagnosed male cancer in developed countries. We conducted a multi-stage genome-wide association study for PrCa and previously reported the results of the first two stages, which identified 16 PrCa susceptibility loci. We report here the results of st...
A Probabilistic Genome-Wide Gene Reading Frame Sequence Model

DEFF Research Database (Denmark)

Have, Christian Theil; Mørk, Søren

We introduce a new type of probabilistic sequence model, that model the sequential composition of reading frames of genes in a genome. Our approach extends gene finders with a model of the sequential composition of genes at the genome-level -- effectively producing a sequential genome annotation...... as output. The model can be used to obtain the most probable genome annotation based on a combination of i: a gene finder score of each gene candidate and ii: the sequence of the reading frames of gene candidates through a genome. The model --- as well as a higher order variant --- is developed and tested...... and are evaluated by the effect on prediction performance. Since bacterial gene finding to a large extent is a solved problem it forms an ideal proving ground for evaluating the explicit modeling of larger scale gene sequence composition of genomes. We conclude that the sequential composition of gene reading frames...
Genome-Wide Association Study to Identify Susceptibility Loci That Modify Radiation-Related Risk for Breast Cancer After Childhood Cancer.

Science.gov (United States)

Morton, Lindsay M; Sampson, Joshua N; Armstrong, Gregory T; Chen, Ting-Huei; Hudson, Melissa M; Karlins, Eric; Dagnall, Casey L; Li, Shengchao Alfred; Wilson, Carmen L; Srivastava, Deo Kumar; Liu, Wei; Kang, Guolian; Oeffinger, Kevin C; Henderson, Tara O; Moskowitz, Chaya S; Gibson, Todd M; Merino, Diana M; Wong, Jeannette R; Hammond, Sue; Neglia, Joseph P; Turcotte, Lucie M; Miller, Jeremy; Bowen, Laura; Wheeler, William A; Leisenring, Wendy M; Whitton, John A; Burdette, Laurie; Chung, Charles; Hicks, Belynda D; Jones, Kristine; Machiela, Mitchell J; Vogt, Aurelie; Wang, Zhaoming; Yeager, Meredith; Neale, Geoffrey; Lear, Matthew; Strong, Louise C; Yasui, Yutaka; Stovall, Marilyn; Weathers, Rita E; Smith, Susan A; Howell, Rebecca; Davies, Stella M; Radloff, Gretchen A; Onel, Kenan; Berrington de González, Amy; Inskip, Peter D; Rajaraman, Preetha; Fraumeni, Joseph F; Bhatia, Smita; Chanock, Stephen J; Tucker, Margaret A; Robison, Leslie L

2017-11-01

Childhood cancer survivors treated with chest-directed radiotherapy have substantially elevated risk for developing breast cancer. Although genetic susceptibility to breast cancer in the general population is well studied, large-scale evaluation of breast cancer susceptibility after chest-directed radiotherapy for childhood cancer is lacking. We conducted a genome-wide association study of breast cancer in female survivors of childhood cancer, pooling two cohorts with detailed treatment data and systematic, long-term follow-up: the Childhood Cancer Survivor Study and St. Jude Lifetime Cohort. The study population comprised 207 survivors who developed breast cancer and 2774 who had not developed any subsequent neoplasm as of last follow-up. Genotyping and subsequent imputation yielded 16 958 466 high-quality variants for analysis. We tested associations in the overall population and in subgroups stratified by receipt of lower than 10 and 10 or higher gray breast radiation exposure. We report P values and pooled per-allele risk estimates from Cox proportional hazards regression models. All statistical tests were two-sided. Among survivors who received 10 or higher gray breast radiation exposure, a locus on 1q41 was associated with subsequent breast cancer risk (rs4342822, nearest gene PROX1 , risk allele frequency in control subjects [RAF controls ] = 0.46, hazard ratio = 1.92, 95% confidence interval = 1.49 to 2.44, P = 7.09 × 10 -9 ). Two rare variants also showed potentially promising associations (breast radiation ≥10 gray: rs74949440, 11q23, TAGLN , RAF controls = 0.02, P = 5.84 × 10 -8 ; breast cancer risk after childhood cancer. Published by Oxford University Press 2017. This work is written by US Government employees and is in the public domain in the US.
Evaluation of K-ras and p53 expression in pancreatic adenocarcinoma using the cancer genome atlas.

Directory of Open Access Journals (Sweden)

Liming Lu

Full Text Available Genetic alterations in K-ras and p53 are thought to be critical in pancreatic cancer development and progression. However, K-ras and p53 expression in pancreatic adenocarcinoma have not been systematically examined in The Cancer Genome Atlas (TCGA Data Portal. Information regarding K-ras and p53 alterations, mRNA expression data, and protein/protein phosphorylation abundance was retrieved from The Cancer Genome Atlas (TCGA databases, and analyses were performed by the cBioPortal for Cancer Genomics. The mutual exclusivity analysis showed that events in K-ras and p53 were likely to co-occur in pancreatic adenocarcinoma (Log odds ratio = 1.599, P = 0.006. The graphical summary of the mutations showed that there were hotspots for protein activation. In the network analysis, no solid association between K-ras and p53 was observed in pancreatic adenocarcinoma. In the survival analysis, neither K-ras nor p53 were associated with both survival events. As in the data mining study in the TCGA databases, our study provides a new perspective to understand the genetic features of K-ras and p53 in pancreatic adenocarcinoma.
Associations between circulating carotenoids, genomic instability and the risk of high-grade prostate cancer.

Science.gov (United States)

Nordström, Tobias; Van Blarigan, Erin L; Ngo, Vy; Roy, Ritu; Weinberg, Vivian; Song, Xiaoling; Simko, Jeffry; Carroll, Peter R; Chan, June M; Paris, Pamela L

2016-03-01

Carotenoids are a class of nutrients with antioxidant properties that have been purported to protect against cancer. However, the reported associations between carotenoids and prostate cancer have been heterogeneous and lacking data on interactions with nucleotide sequence variations and genomic biomarkers. To examine the associations between carotenoid levels and the risk of high-grade prostate cancer, also considering antioxidant-related genes and tumor instability. We measured plasma levels of carotenoids and genotyped 20 single nucleotide polymorphisms (SNP) in SOD1, SOD2, SOD3, XRCC1, and OGG1 among 559 men with non-metastatic prostate cancer undergoing radical prostatectomy. We performed copy number analysis in a subset of these men (n = 67) to study tumor instability assessed as Fraction of the Genome Altered (FGA). We examined associations between carotenoids, genotypes, tumor instability and risk of high-grade prostate cancer (Gleason grade ≥ 4 + 3) using logistic and linear regression. Circulating carotenoid levels were inversely associated with the risk of high-grade prostate cancer; odds ratios (OR) and 95% confidence intervals (CI) comparing highest versus lowest quartiles were: 0.34 (95% CI: 0.18-0.66) for α-carotene, 0.31 (95% CI: 0.15-0.63) for β-carotene, 0.55 (0.28-1.08) for lycopene and 0.37 (0.18-0.75) for total carotenoids. SNPs rs25489 in XRCC1, rs699473 in SOD3 and rs1052133 in OGG1 modified these associations for α-carotene, β-carotene and lycopene, respectively (P ≤ 0.05). The proportion of men with a high degree of FGA increased with Gleason Score (P carotenoids at diagnosis, particularly among men carrying specific somatic variations, were inversely associated with risk of high-grade prostate cancer. In exploratory analyses, higher lycopene level was associated with less genomic instability among men with low-grade disease which is novel and supports the hypothesis that lycopene may inhibit progression of
Technical note: Equivalent genomic models with a residual polygenic effect.

Science.gov (United States)

Liu, Z; Goddard, M E; Hayes, B J; Reinhardt, F; Reents, R

2016-03-01

Routine genomic evaluations in animal breeding are usually based on either a BLUP with genomic relationship matrix (GBLUP) or single nucleotide polymorphism (SNP) BLUP model. For a multi-step genomic evaluation, these 2 alternative genomic models were proven to give equivalent predictions for genomic reference animals. The model equivalence was verified also for young genotyped animals without phenotypes. Due to incomplete linkage disequilibrium of SNP markers to genes or causal mutations responsible for genetic inheritance of quantitative traits, SNP markers cannot explain all the genetic variance. A residual polygenic effect is normally fitted in the genomic model to account for the incomplete linkage disequilibrium. In this study, we start by showing the proof that the multi-step GBLUP and SNP BLUP models are equivalent for the reference animals, when they have a residual polygenic effect included. Second, the equivalence of both multi-step genomic models with a residual polygenic effect was also verified for young genotyped animals without phenotypes. Additionally, we derived formulas to convert genomic estimated breeding values of the GBLUP model to its components, direct genomic values and residual polygenic effect. Third, we made a proof that the equivalence of these 2 genomic models with a residual polygenic effect holds also for single-step genomic evaluation. Both the single-step GBLUP and SNP BLUP models lead to equal prediction for genotyped animals with phenotypes (e.g., reference animals), as well as for (young) genotyped animals without phenotypes. Finally, these 2 single-step genomic models with a residual polygenic effect were proven to be equivalent for estimation of SNP effects, too. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.
Cross-cancer genome-wide analysis of lung, ovary, breast, prostate and colorectal cancer reveals novel pleiotropic associations

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Fehringer, Gordon; Kraft, Peter; Pharoah, Paul D.; Eeles, Rosalind A.; Chatterjee, Nilanjan; Schumacher, Fred; Schildkraut, Joellen; Lindström, Sara; Brennan, Paul; Bickeböller, Heike; Houlston, Richard S.; Landi, Maria Teresa; Caporaso, Neil; Risch, Angela; Olama, Ali Amin Al; Berndt, Sonja I; Giovannucci, Edward; Grönberg, Henrik; Kote-Jarai, Zsofia; Ma, Jing; Muir, Kenneth; Stampfer, Meir; Stevens, Victoria L.; Wiklund, Fredrik; Willett, Walter; Goode, Ellen L.; Permuth, Jennifer; Risch, Harvey A.; Reid, Brett M.; Bezieau, Stephane; Brenner, Hermann; Chan, Andrew T.; Chang-Claude, Jenny; Hudson, Thomas J.; Kocarnik, Jonathan K.; Newcomb, Polly A.; Schoen, Robert E.; Slattery, Martha L.; White, Emily; Adank, Muriel A.; Ahsan, Habibul; Aittomäki, Kristiina; Baglietto, Laura; Blomquist, Carl; Canzian, Federico; Czene, Kamila; dos-Santos-Silva, Isabel; Eliassen, A. Heather; Figueroa, Jonine; Flesch-Janys, Dieter; Fletcher, Olivia; Garcia-Closas, Montserrat; Gaudet, Mia M.; Johnson, Nichola; Hall, Per; Hazra, Aditi; Hein, Rebecca; Hofman, Albert; Hopper, John L.; Irwanto, Astrid; Johansson, Mattias; Kaaks, Rudolf; Kibriya, Muhammad G.; Lichtner, Peter; Liu, Jianjun; Lund, Eiliv; Makalic, Enes; Meindl, Alfons; Müller-Myhsok, Bertram; Muranen, Taru A.; Nevanlinna, Heli; Peeters, Petra H.; Peto, Julian; Prentice, Ross L.; Rahman, Nazneen; Sanchez, Maria Jose; Schmidt, Daniel F.; Schmutzler, Rita K.; Southey, Melissa C.; Tamimi, Rulla; Travis, Ruth C.; Turnbull, Clare; Uitterlinden, Andre G.; Wang, Zhaoming; Whittemore, Alice S.; Yang, Xiaohong R.; Zheng, Wei; Rafnar, Thorunn; Gudmundsson, Julius; Stacey, Simon N.; Stefansson, Kari; Sulem, Patrick; Chen, Y. Ann; Tyrer, Jonathan P.; Christiani, David C.; Wei, Yongyue; Shen, Hongbing; Hu, Zhibin; Shu, Xiao-Ou; Shiraishi, Kouya; Takahashi, Atsushi; Bossé, Yohan; Obeidat, Ma’en; Nickle, David; Timens, Wim; Freedman, Matthew L.; Li, Qiyuan; Seminara, Daniela; Chanock, Stephen J.; Gong, Jian; Peters, Ulrike; Gruber, Stephen B.; Amos, Christopher I.; Sellers, Thomas A.; Easton, Douglas F.; Hunter, David J.; Haiman, Christopher A.; Henderson, Brian E.; Hung, Rayjean J.

2016-01-01

Identifying genetic variants with pleiotropic associations can uncover common pathways influencing multiple cancers. We took a two-staged approach to conduct genome-wide association studies for lung, ovary, breast, prostate and colorectal cancer from the GAME-ON/GECCO Network (61,851 cases, 61,820 controls) to identify pleiotropic loci. Findings were replicated in independent association studies (55,789 cases, 330,490 controls). We identified a novel pleiotropic association at 1q22 involving breast and lung squamous cell carcinoma, with eQTL analysis showing an association with ADAM15/THBS3 gene expression in lung. We also identified a known breast cancer locus CASP8/ALS2CR12 associated with prostate cancer, a known cancer locus at CDKN2B-AS1 with different variants associated with lung adenocarcinoma and prostate cancer and confirmed the associations of a breast BRCA2 locus with lung and serous ovarian cancer. This is the largest study to date examining pleiotropy across multiple cancer-associated loci, identifying common mechanisms of cancer development and progression. PMID:27197191
Cost-effectiveness of the Decipher Genomic Classifier to Guide Individualized Decisions for Early Radiation Therapy After Prostatectomy for Prostate Cancer.

Science.gov (United States)

Lobo, Jennifer M; Trifiletti, Daniel M; Sturz, Vanessa N; Dicker, Adam P; Buerki, Christine; Davicioni, Elai; Cooperberg, Matthew R; Karnes, R Jeffrey; Jenkins, Robert B; Den, Robert B; Showalter, Timothy N

2017-06-01

Controversy exists regarding the effectiveness of early adjuvant versus salvage radiation therapy after prostatectomy for prostate cancer. Estimates of prostate cancer progression from the Decipher genomic classifier (GC) could guide informed decision-making and improve the outcomes for patients. We developed a Markov model to compare the costs and quality-adjusted life years (QALYs) associated with GC-based treatment decisions regarding adjuvant therapy after prostatectomy with those of 2 control strategies: usual care (determined from patterns of care studies) and the alternative of 100% adjuvant radiation therapy. Using the bootstrapping method of sampling with replacement, the cases of 10,000 patients were simulated during a 10-year time horizon, with each subject having individual estimates for cancer progression (according to GC findings) and noncancer mortality (according to age). GC-based care was more effective and less costly than 100% adjuvant radiation therapy and resulted in cost savings up to an assay cost of $11,402. Compared with usual care, GC-based care resulted in more QALYs. Assuming a $4000 assay cost, the incremental cost-effectiveness ratio was $90,833 per QALY, assuming a 7% usage rate of adjuvant radiation therapy. GC-based care was also associated with a 16% reduction in the percentage of patients with distant metastasis at 5 years compared with usual care. The Decipher GC could be a cost-effective approach for genomics-driven cancer treatment decisions after prostatectomy, with improvements in estimated clinical outcomes compared with usual care. The individualized decision analytic framework applied in the present study offers a flexible approach to estimate the potential utility of genomic assays for personalized cancer medicine. Copyright © 2016 Elsevier Inc. All rights reserved.
Genomic Characterization of Primary Invasive Lobular Breast Cancer.

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Desmedt, Christine; Zoppoli, Gabriele; Gundem, Gunes; Pruneri, Giancarlo; Larsimont, Denis; Fornili, Marco; Fumagalli, Debora; Brown, David; Rothé, Françoise; Vincent, Delphine; Kheddoumi, Naima; Rouas, Ghizlane; Majjaj, Samira; Brohée, Sylvain; Van Loo, Peter; Maisonneuve, Patrick; Salgado, Roberto; Van Brussel, Thomas; Lambrechts, Diether; Bose, Ron; Metzger, Otto; Galant, Christine; Bertucci, François; Piccart-Gebhart, Martine; Viale, Giuseppe; Biganzoli, Elia; Campbell, Peter J; Sotiriou, Christos

2016-06-01

Invasive lobular breast cancer (ILBC) is the second most common histologic subtype after invasive ductal breast cancer (IDBC). Despite clinical and pathologic differences, ILBC is still treated as IDBC. We aimed to identify genomic alterations in ILBC with potential clinical implications. From an initial 630 ILBC primary tumors, we interrogated oncogenic substitutions and insertions and deletions of 360 cancer genes and genome-wide copy number aberrations in 413 and 170 ILBC samples, respectively, and correlated those findings with clinicopathologic and outcome features. Besides the high mutation frequency of CDH1 in 65% of tumors, alterations in one of the three key genes of the phosphatidylinositol 3-kinase pathway, PIK3CA, PTEN, and AKT1, were present in more than one-half of the cases. HER2 and HER3 were mutated in 5.1% and 3.6% of the tumors, with most of these mutations having a proven role in activating the human epidermal growth factor receptor/ERBB pathway. Mutations in FOXA1 and ESR1 copy number gains were detected in 9% and 25% of the samples. All these alterations were more frequent in ILBC than in IDBC. The histologic diversity of ILBC was associated with specific alterations, such as enrichment for HER2 mutations in the mixed, nonclassic, and ESR1 gains in the solid subtype. Survival analyses revealed that chromosome 1q and 11p gains showed independent prognostic value in ILBC and that HER2 and AKT1 mutations were associated with increased risk of early relapse. This study demonstrates that we can now begin to individualize the treatment of ILBC, with HER2, HER3, and AKT1 mutations representing high-prevalence therapeutic targets and FOXA1 mutations and ESR1 gains deserving urgent dedicated clinical investigation, especially in the context of endocrine treatment. © 2016 by American Society of Clinical Oncology.
Genomic agonism and phenotypic antagonism between estrogen and progesterone receptors in breast cancer

OpenAIRE

Singhal, Hari; Greene, Marianne E.; Tarulli, Gerard; Zarnke, Allison L.; Bourgo, Ryan J.; Laine, Muriel; Chang, Ya-Fang; Ma, Shihong; Dembo, Anna G.; Raj, Ganesh V.; Hickey, Theresa E.; Tilley, Wayne D.; Greene, Geoffrey L.

2016-01-01

The functional role of progesterone receptor (PR) and its impact on estrogen signaling in breast cancer remain controversial. In primary ER+ (estrogen receptor?positive)/PR+ human tumors, we report that PR reprograms estrogen signaling as a genomic agonist and a phenotypic antagonist. In isolation, estrogen and progestin act as genomic agonists by regulating the expression of common target genes in similar directions, but at different levels. Similarly, in isolation, progestin is also a weak ...
Genomic outlier profile analysis: mixture models, null hypotheses, and nonparametric estimation.

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Ghosh, Debashis; Chinnaiyan, Arul M

2009-01-01

In most analyses of large-scale genomic data sets, differential expression analysis is typically assessed by testing for differences in the mean of the distributions between 2 groups. A recent finding by Tomlins and others (2005) is of a different type of pattern of differential expression in which a fraction of samples in one group have overexpression relative to samples in the other group. In this work, we describe a general mixture model framework for the assessment of this type of expression, called outlier profile analysis. We start by considering the single-gene situation and establishing results on identifiability. We propose 2 nonparametric estimation procedures that have natural links to familiar multiple testing procedures. We then develop multivariate extensions of this methodology to handle genome-wide measurements. The proposed methodologies are compared using simulation studies as well as data from a prostate cancer gene expression study.
Development of A Mouse Model of Menopausal Ovarian Cancer

Directory of Open Access Journals (Sweden)

Elizabeth R. Smith

2014-02-01

Full Text Available Despite significant understanding of the genetic mutations involved in ovarian epithelial cancer and advances in genomic approaches for expression and mutation profiling of tumor tissues, several key questions in ovarian cancer biology remain enigmatic: the mechanism for the well-established impact of reproductive factors on ovarian cancer risk remains obscure; questions of the cell of origin of ovarian cancer continue to be debated; and the precursor lesion, sequence, or events in progression remain to be defined. Suitable mouse models should complement the analysis of human tumor tissues and may provide clues to these questions currently perplexing ovarian cancer biology.A potentially useful model is the germ cell-deficient Wv (white spotting variant mutant mouse line, which may be used to study the impact of menopausal physiology on the increased risk of ovarian cancer. The Wv mice harbor a point mutation in c-Kit that reduces the receptor tyrosine kinase activity to about 1-5% (it is not a null mutation. Homozygous Wv mutant females have a reduced ovarian germ cell reservoir at birth and the follicles are rapidly depleted upon reaching reproductive maturity, but other biological phenotypes are minimal and the mice have a normal life span. The loss of ovarian function precipitates changes in hormonal and metabolic activity that model features of menopause in humans. As a consequence of follicle depletion, the Wv ovaries develop ovarian tubular adenomas, a benign epithelial tumor corresponding to surface epithelial invaginations and papillomatosis that mark human ovarian aging. Ongoing work will test the possibility of converting the benign epithelial tubular adenomas into neoplastic tumors by addition of an oncogenic mutation, such as of Tp53, to model the genotype and biology of serous ovarian cancer.Model based on the Wv mice may have the potential to gain biological and etiological insights into ovarian cancer development and prevention.
Cross-Cancer Genome-Wide Analysis of Lung, Ovary, Breast, Prostate, and Colorectal Cancer Reveals Novel Pleiotropic Associations.

Science.gov (United States)

Fehringer, Gordon; Kraft, Peter; Pharoah, Paul D; Eeles, Rosalind A; Chatterjee, Nilanjan; Schumacher, Fredrick R; Schildkraut, Joellen M; Lindström, Sara; Brennan, Paul; Bickeböller, Heike; Houlston, Richard S; Landi, Maria Teresa; Caporaso, Neil; Risch, Angela; Amin Al Olama, Ali; Berndt, Sonja I; Giovannucci, Edward L; Grönberg, Henrik; Kote-Jarai, Zsofia; Ma, Jing; Muir, Kenneth; Stampfer, Meir J; Stevens, Victoria L; Wiklund, Fredrik; Willett, Walter C; Goode, Ellen L; Permuth, Jennifer B; Risch, Harvey A; Reid, Brett M; Bezieau, Stephane; Brenner, Hermann; Chan, Andrew T; Chang-Claude, Jenny; Hudson, Thomas J; Kocarnik, Jonathan K; Newcomb, Polly A; Schoen, Robert E; Slattery, Martha L; White, Emily; Adank, Muriel A; Ahsan, Habibul; Aittomäki, Kristiina; Baglietto, Laura; Blomquist, Carl; Canzian, Federico; Czene, Kamila; Dos-Santos-Silva, Isabel; Eliassen, A Heather; Figueroa, Jonine D; Flesch-Janys, Dieter; Fletcher, Olivia; Garcia-Closas, Montserrat; Gaudet, Mia M; Johnson, Nichola; Hall, Per; Hazra, Aditi; Hein, Rebecca; Hofman, Albert; Hopper, John L; Irwanto, Astrid; Johansson, Mattias; Kaaks, Rudolf; Kibriya, Muhammad G; Lichtner, Peter; Liu, Jianjun; Lund, Eiliv; Makalic, Enes; Meindl, Alfons; Müller-Myhsok, Bertram; Muranen, Taru A; Nevanlinna, Heli; Peeters, Petra H; Peto, Julian; Prentice, Ross L; Rahman, Nazneen; Sanchez, Maria Jose; Schmidt, Daniel F; Schmutzler, Rita K; Southey, Melissa C; Tamimi, Rulla; Travis, Ruth C; Turnbull, Clare; Uitterlinden, Andre G; Wang, Zhaoming; Whittemore, Alice S; Yang, Xiaohong R; Zheng, Wei; Buchanan, Daniel D; Casey, Graham; Conti, David V; Edlund, Christopher K; Gallinger, Steven; Haile, Robert W; Jenkins, Mark; Le Marchand, Loïc; Li, Li; Lindor, Noralene M; Schmit, Stephanie L; Thibodeau, Stephen N; Woods, Michael O; Rafnar, Thorunn; Gudmundsson, Julius; Stacey, Simon N; Stefansson, Kari; Sulem, Patrick; Chen, Y Ann; Tyrer, Jonathan P; Christiani, David C; Wei, Yongyue; Shen, Hongbing; Hu, Zhibin; Shu, Xiao-Ou; Shiraishi, Kouya; Takahashi, Atsushi; Bossé, Yohan; Obeidat, Ma'en; Nickle, David; Timens, Wim; Freedman, Matthew L; Li, Qiyuan; Seminara, Daniela; Chanock, Stephen J; Gong, Jian; Peters, Ulrike; Gruber, Stephen B; Amos, Christopher I; Sellers, Thomas A; Easton, Douglas F; Hunter, David J; Haiman, Christopher A; Henderson, Brian E; Hung, Rayjean J

2016-09-01

Identifying genetic variants with pleiotropic associations can uncover common pathways influencing multiple cancers. We took a two-stage approach to conduct genome-wide association studies for lung, ovary, breast, prostate, and colorectal cancer from the GAME-ON/GECCO Network (61,851 cases, 61,820 controls) to identify pleiotropic loci. Findings were replicated in independent association studies (55,789 cases, 330,490 controls). We identified a novel pleiotropic association at 1q22 involving breast and lung squamous cell carcinoma, with eQTL analysis showing an association with ADAM15/THBS3 gene expression in lung. We also identified a known breast cancer locus CASP8/ALS2CR12 associated with prostate cancer, a known cancer locus at CDKN2B-AS1 with different variants associated with lung adenocarcinoma and prostate cancer, and confirmed the associations of a breast BRCA2 locus with lung and serous ovarian cancer. This is the largest study to date examining pleiotropy across multiple cancer-associated loci, identifying common mechanisms of cancer development and progression. Cancer Res; 76(17); 5103-14. ©2016 AACR. ©2016 American Association for Cancer Research.
Exposing the cancer genome atlas as a SPARQL endpoint.

Science.gov (United States)

Deus, Helena F; Veiga, Diogo F; Freire, Pablo R; Weinstein, John N; Mills, Gordon B; Almeida, Jonas S

2010-12-01

The Cancer Genome Atlas (TCGA) is a multidisciplinary, multi-institutional effort to characterize several types of cancer. Datasets from biomedical domains such as TCGA present a particularly challenging task for those interested in dynamically aggregating its results because the data sources are typically both heterogeneous and distributed. The Linked Data best practices offer a solution to integrate and discover data with those characteristics, namely through exposure of data as Web services supporting SPARQL, the Resource Description Framework query language. Most SPARQL endpoints, however, cannot easily be queried by data experts. Furthermore, exposing experimental data as SPARQL endpoints remains a challenging task because, in most cases, data must first be converted to Resource Description Framework triples. In line with those requirements, we have developed an infrastructure to expose clinical, demographic and molecular data elements generated by TCGA as a SPARQL endpoint by assigning elements to entities of the Simple Sloppy Semantic Database (S3DB) management model. All components of the infrastructure are available as independent Representational State Transfer (REST) Web services to encourage reusability, and a simple interface was developed to automatically assemble SPARQL queries by navigating a representation of the TCGA domain. A key feature of the proposed solution that greatly facilitates assembly of SPARQL queries is the distinction between the TCGA domain descriptors and data elements. Furthermore, the use of the S3DB management model as a mediator enables queries to both public and protected data without the need for prior submission to a single data source. Copyright © 2010 Elsevier Inc. All rights reserved.
Exposing the cancer genome atlas as a SPARQL endpoint

Science.gov (United States)

Deus, Helena F.; Veiga, Diogo F.; Freire, Pablo R.; Weinstein, John N.; Mills, Gordon B.; Almeida, Jonas S.

2011-01-01

The Cancer Genome Atlas (TCGA) is a multidisciplinary, multi-institutional effort to characterize several types of cancer. Datasets from biomedical domains such as TCGA present a particularly challenging task for those interested in dynamically aggregating its results because the data sources are typically both heterogeneous and distributed. The Linked Data best practices offer a solution to integrate and discover data with those characteristics, namely through exposure of data as Web services supporting SPARQL, the Resource Description Framework query language. Most SPARQL endpoints, however, cannot easily be queried by data experts. Furthermore, exposing experimental data as SPARQL endpoints remains a challenging task because, in most cases, data must first be converted to Resource Description Framework triples. In line with those requirements, we have developed an infrastructure to expose clinical, demographic and molecular data elements generated by TCGA as a SPARQL endpoint by assigning elements to entities of the Simple Sloppy Semantic Database (S3DB) management model. All components of the infrastructure are available as independent Representational State Transfer (REST) Web services to encourage reusability, and a simple interface was developed to automatically assemble SPARQL queries by navigating a representation of the TCGA domain. A key feature of the proposed solution that greatly facilitates assembly of SPARQL queries is the distinction between the TCGA domain descriptors and data elements. Furthermore, the use of the S3DB management model as a mediator enables queries to both public and protected data without the need for prior submission to a single data source. PMID:20851208
Whole-genome sequencing identifies genomic heterogeneity at a nucleotide and chromosomal level in bladder cancer

Science.gov (United States)

Morrison, Carl D.; Liu, Pengyuan; Woloszynska-Read, Anna; Zhang, Jianmin; Luo, Wei; Qin, Maochun; Bshara, Wiam; Conroy, Jeffrey M.; Sabatini, Linda; Vedell, Peter; Xiong, Donghai; Liu, Song; Wang, Jianmin; Shen, He; Li, Yinwei; Omilian, Angela R.; Hill, Annette; Head, Karen; Guru, Khurshid; Kunnev, Dimiter; Leach, Robert; Eng, Kevin H.; Darlak, Christopher; Hoeflich, Christopher; Veeranki, Srividya; Glenn, Sean; You, Ming; Pruitt, Steven C.; Johnson, Candace S.; Trump, Donald L.

2014-01-01

Using complete genome analysis, we sequenced five bladder tumors accrued from patients with muscle-invasive transitional cell carcinoma of the urinary bladder (TCC-UB) and identified a spectrum of genomic aberrations. In three tumors, complex genotype changes were noted. All three had tumor protein p53 mutations and a relatively large number of single-nucleotide variants (SNVs; average of 11.2 per megabase), structural variants (SVs; average of 46), or both. This group was best characterized by chromothripsis and the presence of subclonal populations of neoplastic cells or intratumoral mutational heterogeneity. Here, we provide evidence that the process of chromothripsis in TCC-UB is mediated by nonhomologous end-joining using kilobase, rather than megabase, fragments of DNA, which we refer to as “stitchers,” to repair this process. We postulate that a potential unifying theme among tumors with the more complex genotype group is a defective replication–licensing complex. A second group (two bladder tumors) had no chromothripsis, and a simpler genotype, WT tumor protein p53, had relatively few SNVs (average of 5.9 per megabase) and only a single SV. There was no evidence of a subclonal population of neoplastic cells. In this group, we used a preclinical model of bladder carcinoma cell lines to study a unique SV (translocation and amplification) of the gene glutamate receptor ionotropic N-methyl D-aspertate as a potential new therapeutic target in bladder cancer. PMID:24469795
Identification of somatic mutations in cancer through Bayesian-based analysis of sequenced genome pairs.

Science.gov (United States)

Christoforides, Alexis; Carpten, John D; Weiss, Glen J; Demeure, Michael J; Von Hoff, Daniel D; Craig, David W

2013-05-04

The field of cancer genomics has rapidly adopted next-generation sequencing (NGS) in order to study and characterize malignant tumors with unprecedented resolution. In particular for cancer, one is often trying to identify somatic mutations--changes specific to a tumor and not within an individual's germline. However, false positive and false negative detections often result from lack of sufficient variant evidence, contamination of the biopsy by stromal tissue, sequencing errors, and the erroneous classification of germline variation as tumor-specific. We have developed a generalized Bayesian analysis framework for matched tumor/normal samples with the purpose of identifying tumor-specific alterations such as single nucleotide mutations, small insertions/deletions, and structural variation. We describe our methodology, and discuss its application to other types of paired-tissue analysis such as the detection of loss of heterozygosity as well as allelic imbalance. We also demonstrate the high level of sensitivity and specificity in discovering simulated somatic mutations, for various combinations of a) genomic coverage and b) emulated heterogeneity. We present a Java-based implementation of our methods named Seurat, which is made available for free academic use. We have demonstrated and reported on the discovery of different types of somatic change by applying Seurat to an experimentally-derived cancer dataset using our methods; and have discussed considerations and practices regarding the accurate detection of somatic events in cancer genomes. Seurat is available at https://sites.google.com/site/seuratsomatic.
A novel genomic alteration of LSAMP associates with aggressive prostate cancer in African American men

Directory of Open Access Journals (Sweden)

Gyorgy Petrovics

2015-12-01

Full Text Available Evaluation of cancer genomes in global context is of great interest in light of changing ethnic distribution of the world population. We focused our study on men of African ancestry because of their disproportionately higher rate of prostate cancer (CaP incidence and mortality. We present a systematic whole genome analyses, revealing alterations that differentiate African American (AA and Caucasian American (CA CaP genomes. We discovered a recurrent deletion on chromosome 3q13.31 centering on the LSAMP locus that was prevalent in tumors from AA men (cumulative analyses of 435 patients: whole genome sequence, 14; FISH evaluations, 101; and SNP array, 320 patients. Notably, carriers of this deletion experienced more rapid disease progression. In contrast, PTEN and ERG common driver alterations in CaP were significantly lower in AA prostate tumors compared to prostate tumors from CA. Moreover, the frequency of inter-chromosomal rearrangements was significantly higher in AA than CA tumors. These findings reveal differentially distributed somatic mutations in CaP across ancestral groups, which have implications for precision medicine strategies.
Identification of genomic copy number variations associated with specific clinical features of head and neck cancer.

Science.gov (United States)

Zagradišnik, Boris; Krgović, Danijela; Herodež, Špela Stangler; Zagorac, Andreja; Ćižmarević, Bogdan; Vokač, Nadja Kokalj

2018-01-01

Copy number variations (CNSs) of large genomic regions are an important mechanism implicated in the development of head and neck cancer, however, for most changes their exact role is not well understood. The aim of this study was to find possible associations between gains/losses of genomic regions and clinically distinct subgroups of head and neck cancer patients. Array comparative genomic hybridization (aCGH) analysis was performed on DNA samples in 64 patients with cancer in oral cavity, oropharynx or hypopharynx. Overlapping genomic regions created from gains and losses were used for statistical analysis. Following regions were overrepresented: in tumors with stage I or II a gain of 2.98 Mb on 6p21.2-p11 and a gain of 7.4 Mb on 8q11.1-q11.23; in tumors with grade I histology a gain of 1.1 Mb on 8q24.13, a loss of a large part of p arm of chromosome 3, a loss of a 1.24 Mb on 6q14.3, and a loss of terminal 32 Mb region of 8p23.3; in cases with affected lymph nodes a gain of 0.75 Mb on 3q24, and a gain of 0.9 Mb on 3q26.32-q26.33; in cases with unaffected lymph nodes a gain of 1.1 Mb on 8q23.3, in patients not treated with surgery a gain of 12.2 Mb on 7q21.3-q22.3 and a gain of 0.33 Mb on 20q11.22. Our study identified several genomic regions of interest which appear to be associated with various clinically distinct subgroups of head and neck cancer. They represent a potentially important source of biomarkers useful for the clinical management of head and neck cancer. In particular, the PIK3CA and AGTR1 genes could be singled out to predict the lymph node involvement.
Genomic Profiling of Prostate Cancers from African American Men

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Patricia Castro

2009-03-01

Full Text Available African American (AA men have a higher incidence and significantly higher mortality rates from prostate cancer than white men, but the biological basis for these differences are poorly understood. Few studies have been carried out to determine whether there are areas of allelic loss or gain in prostate cancers from AA men that are over-represented in or specific to this group. To better understand the molecular mechanisms of prostate cancer in AA men, we have analyzed 20 prostate cancers from AA men with high-density single-nucleotide polymorphism arrays to detect genomic copy number alterations. We identified 17 regions showing significant loss and 4 regions with significant gains. Most of these regions had been linked to prostate cancer by previous studies of copy number alterations of predominantly white patients. We identified a novel region of loss at 4p16.3, which has been shown to be lost in breast, colon, and bladder cancers. Comparison of our primary tumors with tumors from white patients from a previously published cohort with similar pathological characteristics showed higher frequency of loss of at numerous loci including 6q13-22, 8p21, 13q13-14, and 16q11-24 and gains of 7p21 and 8q24, all of which had higher frequencies in metastatic lesions in this previously published cohort. Thus, the clinically localized cancers from AA men more closely resembled metastatic cancers from white men. This difference may in part explain the more aggressive clinical behavior of prostate cancer in AA men.

Identification of a Genomic Signature Predicting for Recurrence in Early Stage Ovarian Cancer

Science.gov (United States)

2015-12-01

do it. Thus, instead of simply sequencing all the FFPE samples, we used 10 tumor samples (5 recurrent and 5 non recurrent ) to test sequencing and...Award Number: W81XWH-12-1-0521 TITLE: Identification of a Genomic Signature Predicting for Recurrence in Early-Stage Ovarian Cancer PRINCIPAL...4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER 5b. GRANT NUMBER W81XWH-12-1-0521 Identification of a Genomic Signature Predicting for Recurrence in
Genomic Feature Models

DEFF Research Database (Denmark)

Sørensen, Peter; Edwards, Stefan McKinnon; Rohde, Palle Duun

-additive genetic mechanisms. These modeling approaches have proven to be highly useful to determine population genetic parameters as well as prediction of genetic risk or value. We present a series of statistical modelling approaches that use prior biological information for evaluating the collective action......Whole-genome sequences and multiple trait phenotypes from large numbers of individuals will soon be available in many populations. Well established statistical modeling approaches enable the genetic analyses of complex trait phenotypes while accounting for a variety of additive and non...... regions and gene ontologies) that provide better model fit and increase predictive ability of the statistical model for this trait....
Spectrum of mitochondrial genomic variation and associated clinical presentation of prostate cancer in South African men.

Science.gov (United States)

McCrow, John P; Petersen, Desiree C; Louw, Melanie; Chan, Eva K F; Harmeyer, Katherine; Vecchiarelli, Stefano; Lyons, Ruth J; Bornman, M S Riana; Hayes, Vanessa M

2016-03-01

Prostate cancer incidence and mortality rates are significantly increased in African-American men, but limited studies have been performed within Sub-Saharan African populations. As mitochondria control energy metabolism and apoptosis we speculate that somatic mutations within mitochondrial genomes are candidate drivers of aggressive prostate carcinogenesis. We used matched blood and prostate tissue samples from 87 South African men (77 with African ancestry) to perform deep sequencing of complete mitochondrial genomes. Clinical presentation was biased toward aggressive disease (Gleason score >7, 64%), and compared with men without prostate cancer either with or without benign prostatic hyperplasia. We identified 144 somatic mtDNA single nucleotide variants (SNVs), of which 80 were observed in 39 men presenting with aggressive disease. Both the number and frequency of somatic mtDNA SNVs were associated with higher pathological stage. Besides doubling the total number of somatic PCa-associated mitochondrial genome mutations identified to date, we associate mutational load with aggressive prostate cancer status in men of African ancestry. © 2015 The Authors. The Prostate published by Wiley Periodicals, Inc.
Developing Cancer Informatics Applications and Tools Using the NCI Genomic Data Commons API.

Science.gov (United States)

Wilson, Shane; Fitzsimons, Michael; Ferguson, Martin; Heath, Allison; Jensen, Mark; Miller, Josh; Murphy, Mark W; Porter, James; Sahni, Himanso; Staudt, Louis; Tang, Yajing; Wang, Zhining; Yu, Christine; Zhang, Junjun; Ferretti, Vincent; Grossman, Robert L

2017-11-01

The NCI Genomic Data Commons (GDC) was launched in 2016 and makes available over 4 petabytes (PB) of cancer genomic and associated clinical data to the research community. This dataset continues to grow and currently includes over 14,500 patients. The GDC is an example of a biomedical data commons, which collocates biomedical data with storage and computing infrastructure and commonly used web services, software applications, and tools to create a secure, interoperable, and extensible resource for researchers. The GDC is (i) a data repository for downloading data that have been submitted to it, and also a system that (ii) applies a common set of bioinformatics pipelines to submitted data; (iii) reanalyzes existing data when new pipelines are developed; and (iv) allows users to build their own applications and systems that interoperate with the GDC using the GDC Application Programming Interface (API). We describe the GDC API and how it has been used both by the GDC itself and by third parties. Cancer Res; 77(21); e15-18. ©2017 AACR . ©2017 American Association for Cancer Research.
The Genomic Grade Assay Compared With Ki67 to Determine Risk of Distant Breast Cancer Recurrence

DEFF Research Database (Denmark)

Ignatiadis, Michail; Azim, Hatem A; Desmedt, Christine

2016-01-01

Importance: The Genomic Grade Index (GGI) was previously developed, evaluated on frozen tissue, and shown to be prognostic in early breast cancer. To test the GGI in formalin-fixed, paraffin-embedded breast cancer tumors, a quantitative reverse transcriptase polymerase chain reaction assay...
TMEPAI genome editing in triple negative breast cancer cells

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Bantari W.K. Wardhani

2017-05-01

Full Text Available Background: Clustered regularly interspaced short palindromic repeats/CRISPR-associated 9 (CRISPR/Cas9 is a powerful genome editing technique. It consists of RNA-guided DNA endonuclease Cas9 and single guide RNA (gRNA. By combining their expressions, high efficiency cleavage of the target gene can be achieved, leading to the formation of DNA double-strand break (DSB at the genomic locus of interest which will be repaired via NHEJ (non-homologous end joining or HDR (homology-directed repair and mediate DNA alteration. We aimed to apply the CRISPR/Cas9 technique to knock-out the transmembrane prostate androgen-induced protein (TMEPAI gene in the triple negative breast cancer cell line.Methods: Designed gRNA which targets the TMEPAI gene was synthesized, annealed, and cloned into gRNA expression vector. It was co-transfected into the TNBC cell line using polyethylenimine (PEI together with Cas9-GFP and puromycin resistant gene vector. At 24-hours post-transfection, cells were selected by puromycin for 3 days before they were cloned. Selected knock-out clones were subsequently checked on their protein levels by western blotting.Results: CRISPR/Cas9, a genome engineering technique successfully knocked-out TMEPAI in the Hs578T TNBC cell line. Sequencing shows a frameshift mutation in TMEPAI. Western blot shows the absence of TMEPAI band on Hs578T KO cells.Conclusion: TMEPAI gene was deleted in the TNBC cell line using the genomic editing technique CRISPR/Cas9. The deletion was confirmed by genome and protein analysis.
Large BRCA1 and BRCA2 genomic rearrangements in Danish high risk breast-ovarian cancer families

DEFF Research Database (Denmark)

Hansen, Thomas v O; Jønson, Lars; Albrechtsen, Anders

2009-01-01

BRCA1 and BRCA2 germ-line mutations predispose to breast and ovarian cancer. Large genomic rearrangements of BRCA1 account for 0-36% of all disease causing mutations in various populations, while large genomic rearrangements in BRCA2 are more rare. We examined 642 East Danish breast and/or ovaria...
A network-based drug repositioning infrastructure for precision cancer medicine through targeting significantly mutated genes in the human cancer genomes.

Science.gov (United States)

Cheng, Feixiong; Zhao, Junfei; Fooksa, Michaela; Zhao, Zhongming

2016-07-01

Development of computational approaches and tools to effectively integrate multidomain data is urgently needed for the development of newly targeted cancer therapeutics. We proposed an integrative network-based infrastructure to identify new druggable targets and anticancer indications for existing drugs through targeting significantly mutated genes (SMGs) discovered in the human cancer genomes. The underlying assumption is that a drug would have a high potential for anticancer indication if its up-/down-regulated genes from the Connectivity Map tended to be SMGs or their neighbors in the human protein interaction network. We assembled and curated 693 SMGs in 29 cancer types and found 121 proteins currently targeted by known anticancer or noncancer (repurposed) drugs. We found that the approved or experimental cancer drugs could potentially target these SMGs in 33.3% of the mutated cancer samples, and this number increased to 68.0% by drug repositioning through surveying exome-sequencing data in approximately 5000 normal-tumor pairs from The Cancer Genome Atlas. Furthermore, we identified 284 potential new indications connecting 28 cancer types and 48 existing drugs (adjusted P < .05), with a 66.7% success rate validated by literature data. Several existing drugs (e.g., niclosamide, valproic acid, captopril, and resveratrol) were predicted to have potential indications for multiple cancer types. Finally, we used integrative analysis to showcase a potential mechanism-of-action for resveratrol in breast and lung cancer treatment whereby it targets several SMGs (ARNTL, ASPM, CTTN, EIF4G1, FOXP1, and STIP1). In summary, we demonstrated that our integrative network-based infrastructure is a promising strategy to identify potential druggable targets and uncover new indications for existing drugs to speed up molecularly targeted cancer therapeutics. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All
Multiscale modeling of three-dimensional genome

Science.gov (United States)

Zhang, Bin; Wolynes, Peter

The genome, the blueprint of life, contains nearly all the information needed to build and maintain an entire organism. A comprehensive understanding of the genome is of paramount interest to human health and will advance progress in many areas, including life sciences, medicine, and biotechnology. The overarching goal of my research is to understand the structure-dynamics-function relationships of the human genome. In this talk, I will be presenting our efforts in moving towards that goal, with a particular emphasis on studying the three-dimensional organization, the structure of the genome with multi-scale approaches. Specifically, I will discuss the reconstruction of genome structures at both interphase and metaphase by making use of data from chromosome conformation capture experiments. Computationally modeling of chromatin fiber at atomistic level from first principles will also be presented as our effort for studying the genome structure from bottom up.
Genomic and oncoproteomic advances in detection and treatment of colorectal cancer.

LENUS (Irish Health Repository)

McHugh, Seamus M

2012-02-01

AIMS: We will examine the latest advances in genomic and proteomic laboratory technology. Through an extensive literature review we aim to critically appraise those studies which have utilized these latest technologies and ascertain their potential to identify clinically useful biomarkers. METHODS: An extensive review of the literature was carried out in both online medical journals and through the Royal College of Surgeons in Ireland library. RESULTS: Laboratory technology has advanced in the fields of genomics and oncoproteomics. Gene expression profiling with DNA microarray technology has allowed us to begin genetic profiling of colorectal cancer tissue. The response to chemotherapy can differ amongst individual tumors. For the first time researchers have begun to isolate and identify the genes responsible. New laboratory techniques allow us to isolate proteins preferentially expressed in colorectal cancer tissue. This could potentially lead to identification of a clinically useful protein biomarker in colorectal cancer screening and treatment. CONCLUSION: If a set of discriminating genes could be used for characterization and prediction of chemotherapeutic response, an individualized tailored therapeutic regime could become the standard of care for those undergoing systemic treatment for colorectal cancer. New laboratory techniques of protein identification may eventually allow identification of a clinically useful biomarker that could be used for screening and treatment. At present however, both expression of different gene signatures and isolation of various protein peaks has been limited by study size. Independent multi-centre correlation of results with larger sample sizes is needed to allow translation into clinical practice.
Genomic and oncoproteomic advances in detection and treatment of colorectal cancer.

LENUS (Irish Health Repository)

McHugh, Seamus M

2009-01-01

AIMS: We will examine the latest advances in genomic and proteomic laboratory technology. Through an extensive literature review we aim to critically appraise those studies which have utilized these latest technologies and ascertain their potential to identify clinically useful biomarkers. METHODS: An extensive review of the literature was carried out in both online medical journals and through the Royal College of Surgeons in Ireland library. RESULTS: Laboratory technology has advanced in the fields of genomics and oncoproteomics. Gene expression profiling with DNA microarray technology has allowed us to begin genetic profiling of colorectal cancer tissue. The response to chemotherapy can differ amongst individual tumors. For the first time researchers have begun to isolate and identify the genes responsible. New laboratory techniques allow us to isolate proteins preferentially expressed in colorectal cancer tissue. This could potentially lead to identification of a clinically useful protein biomarker in colorectal cancer screening and treatment. CONCLUSION: If a set of discriminating genes could be used for characterization and prediction of chemotherapeutic response, an individualized tailored therapeutic regime could become the standard of care for those undergoing systemic treatment for colorectal cancer. New laboratory techniques of protein identification may eventually allow identification of a clinically useful biomarker that could be used for screening and treatment. At present however, both expression of different gene signatures and isolation of various protein peaks has been limited by study size. Independent multi-centre correlation of results with larger sample sizes is needed to allow translation into clinical practice.
Genome-scale biological models for industrial microbial systems.

Science.gov (United States)

Xu, Nan; Ye, Chao; Liu, Liming

2018-04-01

The primary aims and challenges associated with microbial fermentation include achieving faster cell growth, higher productivity, and more robust production processes. Genome-scale biological models, predicting the formation of an interaction among genetic materials, enzymes, and metabolites, constitute a systematic and comprehensive platform to analyze and optimize the microbial growth and production of biological products. Genome-scale biological models can help optimize microbial growth-associated traits by simulating biomass formation, predicting growth rates, and identifying the requirements for cell growth. With regard to microbial product biosynthesis, genome-scale biological models can be used to design product biosynthetic pathways, accelerate production efficiency, and reduce metabolic side effects, leading to improved production performance. The present review discusses the development of microbial genome-scale biological models since their emergence and emphasizes their pertinent application in improving industrial microbial fermentation of biological products.
A genome-wide association study identifies susceptibility loci for ovarian cancer at 2q31 and 8q24

DEFF Research Database (Denmark)

Goode, Ellen L; Chenevix-Trench, Georgia; Song, Honglin

2010-01-01

Ovarian cancer accounts for more deaths than all other gynecological cancers combined. To identify common low-penetrance ovarian cancer susceptibility genes, we conducted a genome-wide association study of 507,094 SNPs in 1,768 individuals with ovarian cancer (cases) and 2,354 controls, with foll...
Racial and Ethnic Differences in the Epidemiology and Genomics of Lung Cancer.

Science.gov (United States)

Schabath, Matthew B; Cress, Douglas; Munoz-Antonia, Teresita

2016-10-01

Lung cancer is the most common cancer in the world. In addition to the geographical and sex-specific differences in the incidence, mortality, and survival rates of lung cancer, growing evidence suggests that racial and ethnic differences exist. We reviewed published data related to racial and ethnic differences in lung cancer. Current knowledge and substantive findings related to racial and ethnic differences in lung cancer were summarized, focusing on incidence, mortality, survival, cigarette smoking, prevention and early detection, and genomics. Systems-level and health care professional-related issues likely to contribute to specific racial and ethnic health disparities were also reviewed to provide possible suggestions for future strategies to reduce the disproportionate burden of lung cancer. Although lung carcinogenesis is a multifactorial process driven by exogenous exposures, genetic variations, and an accumulation of somatic genetic events, it appears to have racial and ethnic differences that in turn impact the observed epidemiological differences in rates of incidence, mortality, and survival.
The Naked Mole Rat Genome Resource : facilitating analyses of cancer and longevity-related adaptations

OpenAIRE

Keane, Michael; Craig, Thomas; Alfoldi, Jessica; Berlin, Aaron M; Johnson, Jeremy; Seluanov, Andrei; Gorbunova, Vera; Di Palma, Federica; Lindblad-Toh, Kerstin; Church, George M; de Magalhaes, Joao Pedro

2014-01-01

MOTIVATION: The naked mole rat (Heterocephalus glaber) is an exceptionally long-lived and cancer-resistant rodent native to East Africa. Although its genome was previously sequenced, here we report a new assembly sequenced by us with substantially higher N50 values for scaffolds and contigs. RESULTS: We analyzed the annotation of this new improved assembly and identified candidate genomic adaptations which may have contributed to the evolution of the naked mole rat's extraordinary traits, inc...
Genome-wide association study of pancreatic cancer in Japanese population.

Directory of Open Access Journals (Sweden)

Siew-Kee Low

Full Text Available Pancreatic cancer shows very poor prognosis and is the fifth leading cause of cancer death in Japan. Previous studies indicated some genetic factors contributing to the development and progression of pancreatic cancer; however, there are limited reports for common genetic variants to be associated with this disease, especially in the Asian population. We have conducted a genome-wide association study (GWAS using 991 invasive pancreatic ductal adenocarcinoma cases and 5,209 controls, and identified three loci showing significant association (P-value<5x10(-7 with susceptibility to pancreatic cancer. The SNPs that showed significant association carried estimated odds ratios of 1.29, 1.32, and 3.73 with 95% confidence intervals of 1.17-1.43, 1.19-1.47, and 2.24-6.21; P-value of 3.30x10(-7, 3.30x10(-7, and 4.41x10(-7; located on chromosomes 6p25.3, 12p11.21 and 7q36.2, respectively. These associated SNPs are located within linkage disequilibrium blocks containing genes that have been implicated some roles in the oncogenesis of pancreatic cancer.
A computational approach to distinguish somatic vs. germline origin of genomic alterations from deep sequencing of cancer specimens without a matched normal.

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James X Sun

2018-02-01

Full Text Available A key constraint in genomic testing in oncology is that matched normal specimens are not commonly obtained in clinical practice. Thus, while well-characterized genomic alterations do not require normal tissue for interpretation, a significant number of alterations will be unknown in whether they are germline or somatic, in the absence of a matched normal control. We introduce SGZ (somatic-germline-zygosity, a computational method for predicting somatic vs. germline origin and homozygous vs. heterozygous or sub-clonal state of variants identified from deep massively parallel sequencing (MPS of cancer specimens. The method does not require a patient matched normal control, enabling broad application in clinical research. SGZ predicts the somatic vs. germline status of each alteration identified by modeling the alteration's allele frequency (AF, taking into account the tumor content, tumor ploidy, and the local copy number. Accuracy of the prediction depends on the depth of sequencing and copy number model fit, which are achieved in our clinical assay by sequencing to high depth (>500x using MPS, covering 394 cancer-related genes and over 3,500 genome-wide single nucleotide polymorphisms (SNPs. Calls are made using a statistic based on read depth and local variability of SNP AF. To validate the method, we first evaluated performance on samples from 30 lung and colon cancer patients, where we sequenced tumors and matched normal tissue. We examined predictions for 17 somatic hotspot mutations and 20 common germline SNPs in 20,182 clinical cancer specimens. To assess the impact of stromal admixture, we examined three cell lines, which were titrated with their matched normal to six levels (10-75%. Overall, predictions were made in 85% of cases, with 95-99% of variants predicted correctly, a significantly superior performance compared to a basic approach based on AF alone. We then applied the SGZ method to the COSMIC database of known somatic variants
Single-molecule optical genome mapping of a human HapMap and a colorectal cancer cell line.

Science.gov (United States)

Teo, Audrey S M; Verzotto, Davide; Yao, Fei; Nagarajan, Niranjan; Hillmer, Axel M

2015-01-01

Next-generation sequencing (NGS) technologies have changed our understanding of the variability of the human genome. However, the identification of genome structural variations based on NGS approaches with read lengths of 35-300 bases remains a challenge. Single-molecule optical mapping technologies allow the analysis of DNA molecules of up to 2 Mb and as such are suitable for the identification of large-scale genome structural variations, and for de novo genome assemblies when combined with short-read NGS data. Here we present optical mapping data for two human genomes: the HapMap cell line GM12878 and the colorectal cancer cell line HCT116. High molecular weight DNA was obtained by embedding GM12878 and HCT116 cells, respectively, in agarose plugs, followed by DNA extraction under mild conditions. Genomic DNA was digested with KpnI and 310,000 and 296,000 DNA molecules (≥ 150 kb and 10 restriction fragments), respectively, were analyzed per cell line using the Argus optical mapping system. Maps were aligned to the human reference by OPTIMA, a new glocal alignment method. Genome coverage of 6.8× and 5.7× was obtained, respectively; 2.9× and 1.7× more than the coverage obtained with previously available software. Optical mapping allows the resolution of large-scale structural variations of the genome, and the scaffold extension of NGS-based de novo assemblies. OPTIMA is an efficient new alignment method; our optical mapping data provide a resource for genome structure analyses of the human HapMap reference cell line GM12878, and the colorectal cancer cell line HCT116.
biomvRhsmm: Genomic Segmentation with Hidden Semi-Markov Model

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Yang Du

2014-01-01

Full Text Available High-throughput technologies like tiling array and next-generation sequencing (NGS generate continuous homogeneous segments or signal peaks in the genome that represent transcripts and transcript variants (transcript mapping and quantification, regions of deletion and amplification (copy number variation, or regions characterized by particular common features like chromatin state or DNA methylation ratio (epigenetic modifications. However, the volume and output of data produced by these technologies present challenges in analysis. Here, a hidden semi-Markov model (HSMM is implemented and tailored to handle multiple genomic profile, to better facilitate genome annotation by assisting in the detection of transcripts, regulatory regions, and copy number variation by holistic microarray or NGS. With support for various data distributions, instead of limiting itself to one specific application, the proposed hidden semi-Markov model is designed to allow modeling options to accommodate different types of genomic data and to serve as a general segmentation engine. By incorporating genomic positions into the sojourn distribution of HSMM, with optional prior learning using annotation or previous studies, the modeling output is more biologically sensible. The proposed model has been compared with several other state-of-the-art segmentation models through simulation benchmarking, which shows that our efficient implementation achieves comparable or better sensitivity and specificity in genomic segmentation.
Identification of chromosome aberrations in sporadic microsatellite stable and unstable colorectal cancers using array comparative genomic hybridization

DEFF Research Database (Denmark)

Jensen, Thomas Dyrsø; Li, Jian; Wang, Kai

2011-01-01

cancers constitute approximately 85% of sporadic cases, whereas microsatellite unstable (MSI) cases constitute the remaining 15%. In this study, we used array comparative genomic hybridization (aCGH) to identify genomic hotspot regions that harbor recurrent copy number changes. The study material...

Ocean biogeochemistry modeled with emergent trait-based genomics

Science.gov (United States)

Coles, V. J.; Stukel, M. R.; Brooks, M. T.; Burd, A.; Crump, B. C.; Moran, M. A.; Paul, J. H.; Satinsky, B. M.; Yager, P. L.; Zielinski, B. L.; Hood, R. R.

2017-12-01

Marine ecosystem models have advanced to incorporate metabolic pathways discovered with genomic sequencing, but direct comparisons between models and “omics” data are lacking. We developed a model that directly simulates metagenomes and metatranscriptomes for comparison with observations. Model microbes were randomly assigned genes for specialized functions, and communities of 68 species were simulated in the Atlantic Ocean. Unfit organisms were replaced, and the model self-organized to develop community genomes and transcriptomes. Emergent communities from simulations that were initialized with different cohorts of randomly generated microbes all produced realistic vertical and horizontal ocean nutrient, genome, and transcriptome gradients. Thus, the library of gene functions available to the community, rather than the distribution of functions among specific organisms, drove community assembly and biogeochemical gradients in the model ocean.
Plantagora: modeling whole genome sequencing and assembly of plant genomes.

Directory of Open Access Journals (Sweden)

Roger Barthelson

Full Text Available BACKGROUND: Genomics studies are being revolutionized by the next generation sequencing technologies, which have made whole genome sequencing much more accessible to the average researcher. Whole genome sequencing with the new technologies is a developing art that, despite the large volumes of data that can be produced, may still fail to provide a clear and thorough map of a genome. The Plantagora project was conceived to address specifically the gap between having the technical tools for genome sequencing and knowing precisely the best way to use them. METHODOLOGY/PRINCIPAL FINDINGS: For Plantagora, a platform was created for generating simulated reads from several different plant genomes of different sizes. The resulting read files mimicked either 454 or Illumina reads, with varying paired end spacing. Thousands of datasets of reads were created, most derived from our primary model genome, rice chromosome one. All reads were assembled with different software assemblers, including Newbler, Abyss, and SOAPdenovo, and the resulting assemblies were evaluated by an extensive battery of metrics chosen for these studies. The metrics included both statistics of the assembly sequences and fidelity-related measures derived by alignment of the assemblies to the original genome source for the reads. The results were presented in a website, which includes a data graphing tool, all created to help the user compare rapidly the feasibility and effectiveness of different sequencing and assembly strategies prior to testing an approach in the lab. Some of our own conclusions regarding the different strategies were also recorded on the website. CONCLUSIONS/SIGNIFICANCE: Plantagora provides a substantial body of information for comparing different approaches to sequencing a plant genome, and some conclusions regarding some of the specific approaches. Plantagora also provides a platform of metrics and tools for studying the process of sequencing and assembly
The infinite sites model of genome evolution.

Science.gov (United States)

Ma, Jian; Ratan, Aakrosh; Raney, Brian J; Suh, Bernard B; Miller, Webb; Haussler, David

2008-09-23

We formalize the problem of recovering the evolutionary history of a set of genomes that are related to an unseen common ancestor genome by operations of speciation, deletion, insertion, duplication, and rearrangement of segments of bases. The problem is examined in the limit as the number of bases in each genome goes to infinity. In this limit, the chromosomes are represented by continuous circles or line segments. For such an infinite-sites model, we present a polynomial-time algorithm to find the most parsimonious evolutionary history of any set of related present-day genomes.
GENOME-BASED MODELING AND DESIGN OF METABOLIC INTERACTIONS IN MICROBIAL COMMUNITIES

Directory of Open Access Journals (Sweden)

Radhakrishnan Mahadevan

2012-10-01

With the advent of genome sequencing, omics technologies, bioinformatics and genome-scale modeling, researchers now have unprecedented capabilities to analyze and engineer the metabolism of microbial communities. The goal of this review is to summarize recent applications of genome-scale metabolic modeling to microbial communities. A brief introduction to lumped community models is used to motivate the need for genome-level descriptions of individual species and their metabolic interactions. The review of genome-scale models begins with static modeling approaches, which are appropriate for communities where the extracellular environment can be assumed to be time invariant or slowly varying. Dynamic extensions of the static modeling approach are described, and then applications of genome-scale models for design of synthetic microbial communities are reviewed. The review concludes with a summary of metagenomic tools for analyzing community metabolism and an outlook for future research.
Causes of genome instability

DEFF Research Database (Denmark)

Langie, Sabine A S; Koppen, Gudrun; Desaulniers, Daniel

2015-01-01

function, chromosome segregation, telomere length). The purpose of this review is to describe the crucial aspects of genome instability, to outline the ways in which environmental chemicals can affect this cancer hallmark and to identify candidate chemicals for further study. The overall aim is to make......Genome instability is a prerequisite for the development of cancer. It occurs when genome maintenance systems fail to safeguard the genome's integrity, whether as a consequence of inherited defects or induced via exposure to environmental agents (chemicals, biological agents and radiation). Thus...
Common variants associated with breast cancer in genome-wide association studies are modifiers of breast cancer risk in BRCA1 and BRCA2 mutation carriers

NARCIS (Netherlands)

Wang, Xianshu; Pankratz, V. Shane; Fredericksen, Zachary; Tarrell, Robert; Karaus, Mary; McGuffog, Lesley; Pharaoh, Paul D. P.; Ponder, Bruce A. J.; Dunning, Alison M.; Peock, Susan; Cook, Margaret; Oliver, Clare; Frost, Debra; Sinilnikova, Olga M.; Stoppa-Lyonnet, Dominique; Mazoyer, Sylvie; Houdayer, Claude; Hogervorst, Frans B. L.; Hooning, Maartje J.; Ligtenberg, Marjolijn J.; Spurdle, Amanda; Chenevix-Trench, Georgia; Schmutzler, Rita K.; Wappenschmidt, Barbara; Engel, Christoph; Meindl, Alfons; Domchek, Susan M.; Nathanson, Katherine L.; Rebbeck, Timothy R.; Singer, Christian F.; Gschwantler-Kaulich, Daphne; Dressler, Catherina; Fink, Anneliese; Szabo, Csilla I.; Zikan, Michal; Foretova, Lenka; Claes, Kathleen; Thomas, Gilles; Hoover, Robert N.; Hunter, David J.; Chanock, Stephen J.; Easton, Douglas F.; Antoniou, Antonis C.; Couch, Fergus J.; Gregory, Helen; Miedzybrodzka, Zosia; Morrison, Patrick; Cole, Trevor; McKeown, Carole; Taylor, Amy; Donaldson, Alan; Paterson, Joan; Murray, Alexandra; Rogers, Mark; McCann, Emma; Kennedy, John; Barton, David; Porteous, Mary; Brewer, Carole; Kivuva, Emma; Searle, Anne; Goodman, Selina; Davidson, Rosemarie; Murday, Victoria; Bradshaw, Nicola; Snadden, Lesley; Longmuir, Mark; Watt, Catherine; Izatt, Louise; Pichert, Gabriella; Langman, Caroline; Dorkins, Huw; Barwell, Julian; Chu, Carol; Bishop, Tim; Miller, Julie; Ellis, Ian; Evans, D. Gareth; Lalloo, Fiona; Holt, Felicity; Male, Alison; Robinson, Anne; Gardiner, Carol; Douglas, Fiona; Claber, Oonagh; Walker, Lisa; McLeod, Diane; Eeles, Ros; Shanley, Susan; Rahman, Nazneen; Houlston, Richard; Bancroft, Elizabeth; D'Mello, Lucia; Page, Elizabeth; Ardern-Jones, Audrey; Mitra, Anita; Cook, Jackie; Quarrell, Oliver; Bardsley, Cathryn; Hodgson, Shirley; Goff, Sheila; Brice, Glen; Winchester, Lizzie; Eccles, Diana; Lucassen, Anneke; Crawford, Gillian; Tyler, Emma; McBride, Donna; Bérard, Léon; Sinilnikova, Olga; Barjhoux, Laure; Giraud, Sophie; Léone, Mélanie; Gauthier-Villars, Marion; Moncoutier, Virginie; Belotti, Muriel; de Pauw, Antoine; Bressac-de-Paillerets, Brigitte; Remenieras, Audrey; Byrde, Véronique; Caron, Olivier; Lenoir, Gilbert; Bignon, Yves-Jean; Uhrhammer, Nancy; Lasset, Christine; Bonadona, Valérie; Hardouin, Agnès; Berthet, Pascaline; Sobol, Hagay; Bourdon, Violaine; Eisinger, Françoise; Coulet, Florence; Colas, Chrystelle; Soubrier, Florent; Coupier, Isabelle; Payrat, Jean-Philippe; Fournier, Joëlle; Révillion, Françoise; Vennin, Philippe; Adenis, Claude; Rouleau, Etienne; Lidereau, Rosette; Demange, Liliane; Nogues, Catherine; Muller, Danièle; Fricker, Jean-Pierre; Longy, Michel; Sevenet, Nicolas; Toulas, Christine; Guimbaud, Rosine; Gladieff, Laurence; Feillel, Viviane; Leroux, Dominique; Dreyfus, Hélèn; Rebischung, Christine; Cassini, Cécile; Olivier-Faivre, Laurence; Prieur, Fabienne; Ferrer, Sandra Fert; Frénay, Marc; Vénat-Bouvet, Laurence; Lynch, Henry T.; Hogervorst, Frans; Vernhoef, Senno; Pijpe, Anouk; van 't Veer, Laura; van Leeuwen, Flora; Rookus, Matti; Collée, Margriet; van den Ouweland, Ans; Kriege, Mieke; Schutte, Mieke; Hooning, Maartje; Seynaeve, Caroline; van Asperen, Christi; Wijnen, Juul; Vreeswijk, Maaike; Tollenaar, Rob; Devilee, Peter; Ligtenberg, Marjolijn; Hoogerbrugge, Nicoline; Ausems, Margreet; van der Luijt, Rob; Aalfs, Cora; van Os, Theo; Gille, Hans; Waisfisz, Quinten; Meijers-Heijboer, Hanne; Gomez-Garcia, Encarna; van Roozendaal, Kees; Blok, Marinus; Oosterwijk, Jan; van der Hout, Annemieke; Mourits, Marian; Vasen, Hans; Szabo, Csilla; Pohlreich, Petr; Kleibl, Zdenek; Machackova, Eva; Lukesova, Miroslava; de Leeneer, Kim; Poppe, Bruce; de Paepe, Anne

2010-01-01

Recent studies have identified single nucleotide polymorphisms (SNPs) that significantly modify breast cancer risk in BRCA1 and BRCA2 mutation carriers. Since these risk modifiers were originally identified as genetic risk factors for breast cancer in genome-wide association studies (GWASs),
Genome-wide retroviral insertional tagging of genes involved in cancer in Cdkn2a-deficient mice

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Lund, Anders H; Turner, Geoffrey; Trubetskoy, Alla

2002-01-01

We have used large-scale insertional mutagenesis to identify functional landmarks relevant to cancer in the recently completed mouse genome sequence. We infected Cdkn2a(-/-) mice with Moloney murine leukemia virus (MoMuLV) to screen for loci that can participate in tumorigenesis in collaboration...... retroviral integration sites and mapped them against the mouse genome sequence databases from Celera and Ensembl. In addition to 17 insertions targeting gene loci known to be cancer-related, we identified a total of 37 new common insertion sites (CISs), of which 8 encode components of signaling pathways...... that are involved in cancer. The effectiveness of large-scale insertional mutagenesis in a sensitized genetic background is demonstrated by the preference for activation of MAP kinase signaling, collaborating with Cdkn2a loss in generating the lymphoid and myeloid tumors. Collectively, our results show that large...
Data Mining Supercomputing with SAS JMP® Genomics

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Richard S. Segall

2011-02-01

Full Text Available JMP® Genomics is statistical discovery software that can uncover meaningful patterns in high-throughput genomics and proteomics data. JMP® Genomics is designed for biologists, biostatisticians, statistical geneticists, and those engaged in analyzing the vast stores of data that are common in genomic research (SAS, 2009. Data mining was performed using JMP® Genomics on the two collections of microarray databases available from National Center for Biotechnology Information (NCBI for lung cancer and breast cancer. The Gene Expression Omnibus (GEO of NCBI serves as a public repository for a wide range of highthroughput experimental data, including the two collections of lung cancer and breast cancer that were used for this research. The results for applying data mining using software JMP® Genomics are shown in this paper with numerous screen shots.
Functional Genomics Uncover the Biology behind the Responsiveness of Head and Neck Squamous Cell Cancer Patients to Cetuximab.

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Bossi, Paolo; Bergamini, Cristiana; Siano, Marco; Cossu Rocca, Maria; Sponghini, Andrea P; Favales, Federica; Giannoccaro, Marco; Marchesi, Edoardo; Cortelazzi, Barbara; Perrone, Federica; Pilotti, Silvana; Locati, Laura D; Licitra, Lisa; Canevari, Silvana; De Cecco, Loris

2016-08-01

To identify the tumor portrait of the minority of head and neck squamous cell carcinoma (HNSCC) patients with recurrent-metastatic (RM) disease who upon treatment with platinum-based chemotherapy plus cetuximab present a long-lasting response. The gene expression of pretreatment samples from 40 HNSCC-RM patients, divided in two groups [14 long-progression-free survival (PFS) and 26 short-PFS (median = 19 and 3 months, respectively)], was associated with PFS and was challenged against a dataset from metastatic colon cancer patients treated with cetuximab. For biologic analysis, we performed functional and subtype association using gene set enrichment analysis, associated biology across all currently available HNSCC signatures, and inferred drug sensitivity using data from the Cancer Genomic Project. The identified genomic profile exhibited a significant predictive value that was essentially confirmed in the single publicly available dataset of cetuximab-treated patients. The main divergence between long- and short-PFS groups was based on developmental/differentiation status. The long-PFS patients are characterized by basal subtype traits such as strong EGFR signaling phenotype and hypoxic differentiation, further validated by the significantly higher association with the hypoxia metagene. The short-PFS patients presented a strong activation of RAS signaling confirmed in an in vitro model of two isogenic HNSCC cell lines sensitive or resistant to cetuximab. The predicted drug sensitivity for all four EGFR inhibitors was higher in long- versus short-PFS patients (P range: biology behind response to platinum-based chemotherapy plus cetuximab in RM-HNSCC cancer and may have translational implications improving treatment selection. Clin Cancer Res; 22(15); 3961-70. ©2016 AACRSee related commentary by Chau and Hammerman, p. 3710. ©2016 American Association for Cancer Research.
An information-theoretic approach to the modeling and analysis of whole-genome bisulfite sequencing data.

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Jenkinson, Garrett; Abante, Jordi; Feinberg, Andrew P; Goutsias, John

2018-03-07

DNA methylation is a stable form of epigenetic memory used by cells to control gene expression. Whole genome bisulfite sequencing (WGBS) has emerged as a gold-standard experimental technique for studying DNA methylation by producing high resolution genome-wide methylation profiles. Statistical modeling and analysis is employed to computationally extract and quantify information from these profiles in an effort to identify regions of the genome that demonstrate crucial or aberrant epigenetic behavior. However, the performance of most currently available methods for methylation analysis is hampered by their inability to directly account for statistical dependencies between neighboring methylation sites, thus ignoring significant information available in WGBS reads. We present a powerful information-theoretic approach for genome-wide modeling and analysis of WGBS data based on the 1D Ising model of statistical physics. This approach takes into account correlations in methylation by utilizing a joint probability model that encapsulates all information available in WGBS methylation reads and produces accurate results even when applied on single WGBS samples with low coverage. Using the Shannon entropy, our approach provides a rigorous quantification of methylation stochasticity in individual WGBS samples genome-wide. Furthermore, it utilizes the Jensen-Shannon distance to evaluate differences in methylation distributions between a test and a reference sample. Differential performance assessment using simulated and real human lung normal/cancer data demonstrate a clear superiority of our approach over DSS, a recently proposed method for WGBS data analysis. Critically, these results demonstrate that marginal methods become statistically invalid when correlations are present in the data. This contribution demonstrates clear benefits and the necessity of modeling joint probability distributions of methylation using the 1D Ising model of statistical physics and of
[Current advances and future prospects of genome editing technology in the field of biomedicine.

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Sakuma, Tetsushi

Genome editing technology can alter the genomic sequence at will, contributing the creation of cellular and animal models of human diseases including hereditary disorders and cancers, and the generation of the mutation-corrected human induced pluripotent stem cells for ex vivo regenerative medicine. In addition, novel approaches such as drug development using genome-wide CRISPR screening and cancer suppression using epigenome editing technology, which can change the epigenetic modifications in a site-specific manner, have also been conducted. In this article, I summarize the current advances and future prospects of genome editing technology in the field of biomedicine.
Inferences from Genomic Models in Stratified Populations

DEFF Research Database (Denmark)

Janss, Luc; de los Campos, Gustavo; Sheehan, Nuala

2012-01-01

Unaccounted population stratification can lead to spurious associations in genome-wide association studies (GWAS) and in this context several methods have been proposed to deal with this problem. An alternative line of research uses whole-genome random regression (WGRR) models that fit all marker...
Clinical application of genomic profiling to find druggable targets for adolescent and young adult (AYA) cancer patients with metastasis

International Nuclear Information System (INIS)

Cha, Soojin; Lee, Jeongeun; Shin, Jong-Yeon; Kim, Ji-Yeon; Sim, Sung Hoon; Keam, Bhumsuk; Kim, Tae Min; Kim, Dong-Wan; Heo, Dae Seog; Lee, Se-Hoon; Kim, Jong-Il

2016-01-01

Although adolescent and young adult (AYA) cancers are characterized by biological features and clinical outcomes distinct from those of other age groups, the molecular profile of AYA cancers has not been well defined. In this study, we analyzed cancer genomes from rare types of metastatic AYA cancers to identify driving and/or druggable genetic alterations. Prospectively collected AYA tumor samples from seven different patients were analyzed using three different genomics platforms (whole-exome sequencing, whole-transcriptome sequencing or OncoScan™). Using well-known bioinformatics tools (bwa, Picard, GATK, MuTect, and Somatic Indel Detector) and our annotation approach with open access databases (DAVID and DGIdb), we processed sequencing data and identified driving genetic alterations and their druggability. The mutation frequencies of AYA cancers were lower than those of other adult cancers (median = 0.56), except for a germ cell tumor with hypermutation. We identified patient-specific genetic alterations in candidate driving genes: RASA2 and NF1 (prostate cancer), TP53 and CDKN2C (olfactory neuroblastoma), FAT1, NOTCH1, and SMAD4 (head and neck cancer), KRAS (urachal carcinoma), EML4-ALK (lung cancer), and MDM2 and PTEN (liposarcoma). We then suggested potential drugs for each patient according to his or her altered genes and related pathways. By comparing candidate driving genes between AYA cancers and those from all age groups for the same type of cancer, we identified different driving genes in prostate cancer and a germ cell tumor in AYAs compared with all age groups, whereas three common alterations (TP53, FAT1, and NOTCH1) in head and neck cancer were identified in both groups. We identified the patient-specific genetic alterations and druggability of seven rare types of AYA cancers using three genomics platforms. Additionally, genetic alterations in cancers from AYA and those from all age groups varied by cancer type. The online version of this article
Genome-wide Analysis Identifies Novel Loci Associated with Ovarian Cancer Outcomes

DEFF Research Database (Denmark)

Johnatty, Sharon E; Tyrer, Jonathan P; Kar, Siddhartha

2015-01-01

PURPOSE: Chemotherapy resistance remains a major challenge in the treatment of ovarian cancer. We hypothesize that germline polymorphisms might be associated with clinical outcome. EXPERIMENTAL DESIGN: We analyzed approximately 2.8 million genotyped and imputed SNPs from the iCOGS experiment...... for progression-free survival (PFS) and overall survival (OS) in 2,901 European epithelial ovarian cancer (EOC) patients who underwent first-line treatment of cytoreductive surgery and chemotherapy regardless of regimen, and in a subset of 1,098 patients treated with ≥ 4 cycles of paclitaxel and carboplatin...... at standard doses. We evaluated the top SNPs in 4,434 EOC patients, including patients from The Cancer Genome Atlas. In addition, we conducted pathway analysis of all intragenic SNPs and tested their association with PFS and OS using gene set enrichment analysis. RESULTS: Five SNPs were significantly...
Integrating proteomic and functional genomic technologies in discovery-driven translational breast cancer research

DEFF Research Database (Denmark)

Celis, Julio E; Gromov, Pavel; Gromova, Irina

2003-01-01

The application of state-of-the-art proteomics and functional genomics technologies to the study of cancer is rapidly shifting toward the analysis of clinically relevant samples derived from patients, as the ultimate aim of translational research is to bring basic discoveries closer to the bedside...
Interactive or static reports to guide clinical interpretation of cancer genomics.

Science.gov (United States)

Gray, Stacy W; Gagan, Jeffrey; Cerami, Ethan; Cronin, Angel M; Uno, Hajime; Oliver, Nelly; Lowenstein, Carol; Lederman, Ruth; Revette, Anna; Suarez, Aaron; Lee, Charlotte; Bryan, Jordan; Sholl, Lynette; Van Allen, Eliezer M

2018-05-01

Misinterpretation of complex genomic data presents a major challenge in the implementation of precision oncology. We sought to determine whether interactive genomic reports with embedded clinician education and optimized data visualization improved genomic data interpretation. We conducted a randomized, vignette-based survey study to determine whether exposure to interactive reports for a somatic gene panel, as compared to static reports, improves physicians' genomic comprehension and report-related satisfaction (overall scores calculated across 3 vignettes, range 0-18 and 1-4, respectively, higher score corresponding with improved endpoints). One hundred and five physicians at a tertiary cancer center participated (29% participation rate): 67% medical, 20% pediatric, 7% radiation, and 7% surgical oncology; 37% female. Prior to viewing the case-based vignettes, 34% of the physicians reported difficulty making treatment recommendations based on the standard static report. After vignette/report exposure, physicians' overall comprehension scores did not differ by report type (mean score: interactive 11.6 vs static 10.5, difference = 1.1, 95% CI, -0.3, 2.5, P = .13). However, physicians exposed to the interactive report were more likely to correctly assess sequencing quality (P < .001) and understand when reports needed to be interpreted with caution (eg, low tumor purity; P = .02). Overall satisfaction scores were higher in the interactive group (mean score 2.5 vs 2.1, difference = 0.4, 95% CI, 0.2-0.7, P = .001). Interactive genomic reports may improve physicians' ability to accurately assess genomic data and increase report-related satisfaction. Additional research in users' genomic needs and efforts to integrate interactive reports into electronic health records may facilitate the implementation of precision oncology.
Passage relevance models for genomics search

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Frieder Ophir

2009-03-01

Full Text Available Abstract We present a passage relevance model for integrating syntactic and semantic evidence of biomedical concepts and topics using a probabilistic graphical model. Component models of topics, concepts, terms, and document are represented as potential functions within a Markov Random Field. The probability of a passage being relevant to a biologist's information need is represented as the joint distribution across all potential functions. Relevance model feedback of top ranked passages is used to improve distributional estimates of query concepts and topics in context, and a dimensional indexing strategy is used for efficient aggregation of concept and term statistics. By integrating multiple sources of evidence including dependencies between topics, concepts, and terms, we seek to improve genomics literature passage retrieval precision. Using this model, we are able to demonstrate statistically significant improvements in retrieval precision using a large genomics literature corpus.
Use of genome-wide association studies for cancer research and drug repositioning.

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Jizhun Zhang

Full Text Available Although genome-wide association studies have identified many risk loci associated with colorectal cancer, the molecular basis of these associations are still unclear. We aimed to infer biological insights and highlight candidate genes of interest within GWAS risk loci. We used an in silico pipeline based on functional annotation, quantitative trait loci mapping of cis-acting gene, PubMed text-mining, protein-protein interaction studies, genetic overlaps with cancer somatic mutations and knockout mouse phenotypes, and functional enrichment analysis to prioritize the candidate genes at the colorectal cancer risk loci. Based on these analyses, we observed that these genes were the targets of approved therapies for colorectal cancer, and suggested that drugs approved for other indications may be repurposed for the treatment of colorectal cancer. This study highlights the use of publicly available data as a cost effective solution to derive biological insights, and provides an empirical evidence that the molecular basis of colorectal cancer can provide important leads for the discovery of new drugs.
BYSTANDER EFFECTS GENOMIC INSTABILITY, ADAPTIVE RESPONSE AND CANCER RISK ASSESSMENT FOR RADIAION AND CHEMICAL EXPOSURES

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BYSTANDER EFFECTS, GENOMIC INSTABILITY, ADAPTIVE RESPONSE AND CANCER RISK ASSESSMENT FOR RADIATION AND CHEMICAL EXPOSURESR. Julian PrestonEnvironmental Carcinogenesis Division, U.S. Environmental Protection Agency, Research Triangle Park, N.C. 27711, USAThere ...
Microbial comparative pan-genomics using binomial mixture models

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Ussery David W

2009-08-01

Full Text Available Abstract Background The size of the core- and pan-genome of bacterial species is a topic of increasing interest due to the growing number of sequenced prokaryote genomes, many from the same species. Attempts to estimate these quantities have been made, using regression methods or mixture models. We extend the latter approach by using statistical ideas developed for capture-recapture problems in ecology and epidemiology. Results We estimate core- and pan-genome sizes for 16 different bacterial species. The results reveal a complex dependency structure for most species, manifested as heterogeneous detection probabilities. Estimated pan-genome sizes range from small (around 2600 gene families in Buchnera aphidicola to large (around 43000 gene families in Escherichia coli. Results for Echerichia coli show that as more data become available, a larger diversity is estimated, indicating an extensive pool of rarely occurring genes in the population. Conclusion Analyzing pan-genomics data with binomial mixture models is a way to handle dependencies between genomes, which we find is always present. A bottleneck in the estimation procedure is the annotation of rarely occurring genes.

Genomic instability and radiation effects

International Nuclear Information System (INIS)

Christian Streffer

2007-01-01

Complete text of publication follows. Cancer, genetic mutations and developmental abnormalities are apparently associated with an increased genomic instability. Such phenomena have been frequently shown in human cancer cells in vitro and in situ. It is also well-known that individuals with a genetic predisposition for cancer proneness, such as ataxia telangiectesia, Fanconi anaemia etc. demonstrate a general high genomic instability e.g. in peripheral lymphocytes before a cancer has developed. Analogous data have been found in mice which develop a specific congenital malformation which has a genetic background. Under these aspects it is of high interest that ionising radiation can increase the genomic instability of mammalian cells after exposures in vitro an in vivo. This phenomenon is expressed 20 to 40 cell cycles after the exposure e.g. by de novo chromosomal aberrations. Such effects have been observed with high and low LET radiation, high LET radiation is more efficient. With low LET radiation a good dose response is observed in the dose range 0.2 to 2.0 Gy, Recently it has been reported that senescence and genomic instability was induced in human fibroblasts after 1 mGy carbon ions (1 in 18 cells are hit), apparently bystander effects also occurred under these conditions. The instability has been shown with DNA damage, chromosomal aberrations, gene mutation and cell death. It is also transferred to the next generation of mice with respect to gene mutations, chromosomal aberrations and congenital malformations. Several mechanisms have been discussed. The involvement of telomeres has gained interest. Genomic instability seems to be induced by a general lesion to the whole genome. The transmission of one chromosome from an irradiated cell to an non-irradiated cell leads to genomic instability in the untreated cells. Genomic instability increases mutation rates in the affected cells in general. As radiation late effects (cancer, gene mutations and congenital
Sorting cancer karyotypes using double-cut-and-joins, duplications and deletions.

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Zeira, Ron; Shamir, Ron

2018-05-03

Problems of genome rearrangement are central in both evolution and cancer research. Most genome rearrangement models assume that the genome contains a single copy of each gene and the only changes in the genome are structural, i.e., reordering of segments. In contrast, tumor genomes also undergo numerical changes such as deletions and duplications, and thus the number of copies of genes varies. Dealing with unequal gene content is a very challenging task, addressed by few algorithms to date. More realistic models are needed to help trace genome evolution during tumorigenesis. Here we present a model for the evolution of genomes with multiple gene copies using the operation types double-cut-and-joins, duplications and deletions. The events supported by the model are reversals, translocations, tandem duplications, segmental deletions, and chromosomal amplifications and deletions, covering most types of structural and numerical changes observed in tumor samples. Our goal is to find a series of operations of minimum length that transform one karyotype into the other. We show that the problem is NP-hard and give an integer linear programming formulation that solves the problem exactly under some mild assumptions. We test our method on simulated genomes and on ovarian cancer genomes. Our study advances the state of the art in two ways: It allows a broader set of operations than extant models, thus being more realistic, and it is the first study attempting to reconstruct the full sequence of structural and numerical events during cancer evolution. Code and data are available in https://github.com/Shamir-Lab/Sorting-Cancer-Karyotypes. ronzeira@post.tau.ac.il, rshamir@tau.ac.il. Supplementary data are available at Bioinformatics online.
Cancer associated epigenetic transitions identified by genome-wide histone methylation binding profiles in human colorectal cancer samples and paired normal mucosa

International Nuclear Information System (INIS)

Enroth, Stefan; Rada-Iglesisas, Alvaro; Andersson, Robin; Wallerman, Ola; Wanders, Alkwin; Påhlman, Lars; Komorowski, Jan; Wadelius, Claes

2011-01-01

Despite their well-established functional roles, histone modifications have received less attention than DNA methylation in the cancer field. In order to evaluate their importance in colorectal cancer (CRC), we generated the first genome-wide histone modification profiles in paired normal colon mucosa and tumor samples. Chromatin immunoprecipitation and microarray hybridization (ChIP-chip) was used to identify promoters enriched for histone H3 trimethylated on lysine 4 (H3K4me3) and lysine 27 (H3K27me3) in paired normal colon mucosa and tumor samples from two CRC patients and for the CRC cell line HT29. By comparing histone modification patterns in normal mucosa and tumors, we found that alterations predicted to have major functional consequences were quite rare. Furthermore, when normal or tumor tissue samples were compared to HT29, high similarities were observed for H3K4me3. However, the differences found for H3K27me3, which is important in determining cellular identity, indicates that cell lines do not represent optimal tissue models. Finally, using public expression data, we uncovered previously unknown changes in CRC expression patterns. Genes positive for H3K4me3 in normal and/or tumor samples, which are typically already active in normal mucosa, became hyperactivated in tumors, while genes with H3K27me3 in normal and/or tumor samples and which are expressed at low levels in normal mucosa, became hypersilenced in tumors. Genome wide histone modification profiles can be used to find epigenetic aberrations in genes associated with cancer. This strategy gives further insights into the epigenetic contribution to the oncogenic process and may identify new biomarkers
Evaluating genome-wide association study-identified breast cancer risk variants in African-American women.

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Jirong Long

Full Text Available Genome-wide association studies (GWAS, conducted mostly in European or Asian descendants, have identified approximately 67 genetic susceptibility loci for breast cancer. Given the large differences in genetic architecture between the African-ancestry genome and genomes of Asians and Europeans, it is important to investigate these loci in African-ancestry populations. We evaluated index SNPs in all 67 breast cancer susceptibility loci identified to date in our study including up to 3,300 African-American women (1,231 cases and 2,069 controls, recruited in the Southern Community Cohort Study (SCCS and the Nashville Breast Health Study (NBHS. Seven SNPs were statistically significant (P ≤ 0.05 with the risk of overall breast cancer in the same direction as previously reported: rs10069690 (5p15/TERT, rs999737 (14q24/RAD51L1, rs13387042 (2q35/TNP1, rs1219648 (10q26/FGFR2, rs8170 (19p13/BABAM1, rs17817449 (16q12/FTO, and rs13329835 (16q23/DYL2. A marginally significant association (P<0.10 was found for three additional SNPs: rs1045485 (2q33/CASP8, rs4849887 (2q14/INHBB, and rs4808801 (19p13/ELL. Three additional SNPs, including rs1011970 (9p21/CDKN2A/2B, rs941764 (14q32/CCDC88C, and rs17529111 (6q14/FAM46A, showed a significant association in analyses conducted by breast cancer subtype. The risk of breast cancer was elevated with an increasing number of risk variants, as measured by quintile of the genetic risk score, from 1.00 (reference, to 1.75 (1.30-2.37, 1.56 (1.15-2.11, 2.02 (1.50-2.74 and 2.63 (1.96-3.52, respectively, (P = 7.8 × 10(-10. Results from this study highlight the need for large genetic studies in AAs to identify risk variants impacting this population.
Genome-wide association study identifies novel breast cancer susceptibility loci

Science.gov (United States)

Easton, Douglas F.; Pooley, Karen A.; Dunning, Alison M.; Pharoah, Paul D. P.; Thompson, Deborah; Ballinger, Dennis G.; Struewing, Jeffery P.; Morrison, Jonathan; Field, Helen; Luben, Robert; Wareham, Nicholas; Ahmed, Shahana; Healey, Catherine S.; Bowman, Richard; Meyer, Kerstin B.; Haiman, Christopher A.; Kolonel, Laurence K.; Henderson, Brian E.; Marchand, Loic Le; Brennan, Paul; Sangrajrang, Suleeporn; Gaborieau, Valerie; Odefrey, Fabrice; Shen, Chen-Yang; Wu, Pei-Ei; Wang, Hui-Chun; Eccles, Diana; Evans, D. Gareth; Peto, Julian; Fletcher, Olivia; Johnson, Nichola; Seal, Sheila; Stratton, Michael R.; Rahman, Nazneen; Chenevix-Trench, Georgia; Bojesen, Stig E.; Nordestgaard, Børge G.; Axelsson, Christen K.; Garcia-Closas, Montserrat; Brinton, Louise; Chanock, Stephen; Lissowska, Jolanta; Peplonska, Beata; Nevanlinna, Heli; Fagerholm, Rainer; Eerola, Hannaleena; Kang, Daehee; Yoo, Keun-Young; Noh, Dong-Young; Ahn, Sei-Hyun; Hunter, David J.; Hankinson, Susan E.; Cox, David G.; Hall, Per; Wedren, Sara; Liu, Jianjun; Low, Yen-Ling; Bogdanova, Natalia; Schürmann, Peter; Dörk, Thilo; Tollenaar, Rob A. E. M.; Jacobi, Catharina E.; Devilee, Peter; Klijn, Jan G. M.; Sigurdson, Alice J.; Doody, Michele M.; Alexander, Bruce H.; Zhang, Jinghui; Cox, Angela; Brock, Ian W.; MacPherson, Gordon; Reed, Malcolm W. R.; Couch, Fergus J.; Goode, Ellen L.; Olson, Janet E.; Meijers-Heijboer, Hanne; van den Ouweland, Ans; Uitterlinden, André; Rivadeneira, Fernando; Milne, Roger L.; Ribas, Gloria; Gonzalez-Neira, Anna; Benitez, Javier; Hopper, John L.; McCredie, Margaret; Southey, Melissa; Giles, Graham G.; Schroen, Chris; Justenhoven, Christina; Brauch, Hiltrud; Hamann, Ute; Ko, Yon-Dschun; Spurdle, Amanda B.; Beesley, Jonathan; Chen, Xiaoqing; Mannermaa, Arto; Kosma, Veli-Matti; Kataja, Vesa; Hartikainen, Jaana; Day, Nicholas E.; Cox, David R.; Ponder, Bruce A. J.; Luccarini, Craig; Conroy, Don; Shah, Mitul; Munday, Hannah; Jordan, Clare; Perkins, Barbara; West, Judy; Redman, Karen; Driver, Kristy; Aghmesheh, Morteza; Amor, David; Andrews, Lesley; Antill, Yoland; Armes, Jane; Armitage, Shane; Arnold, Leanne; Balleine, Rosemary; Begley, Glenn; Beilby, John; Bennett, Ian; Bennett, Barbara; Berry, Geoffrey; Blackburn, Anneke; Brennan, Meagan; Brown, Melissa; Buckley, Michael; Burke, Jo; Butow, Phyllis; Byron, Keith; Callen, David; Campbell, Ian; Chenevix-Trench, Georgia; Clarke, Christine; Colley, Alison; Cotton, Dick; Cui, Jisheng; Culling, Bronwyn; Cummings, Margaret; Dawson, Sarah-Jane; Dixon, Joanne; Dobrovic, Alexander; Dudding, Tracy; Edkins, Ted; Eisenbruch, Maurice; Farshid, Gelareh; Fawcett, Susan; Field, Michael; Firgaira, Frank; Fleming, Jean; Forbes, John; Friedlander, Michael; Gaff, Clara; Gardner, Mac; Gattas, Mike; George, Peter; Giles, Graham; Gill, Grantley; Goldblatt, Jack; Greening, Sian; Grist, Scott; Haan, Eric; Harris, Marion; Hart, Stewart; Hayward, Nick; Hopper, John; Humphrey, Evelyn; Jenkins, Mark; Jones, Alison; Kefford, Rick; Kirk, Judy; Kollias, James; Kovalenko, Sergey; Lakhani, Sunil; Leary, Jennifer; Lim, Jacqueline; Lindeman, Geoff; Lipton, Lara; Lobb, Liz; Maclurcan, Mariette; Mann, Graham; Marsh, Deborah; McCredie, Margaret; McKay, Michael; McLachlan, Sue Anne; Meiser, Bettina; Milne, Roger; Mitchell, Gillian; Newman, Beth; O'Loughlin, Imelda; Osborne, Richard; Peters, Lester; Phillips, Kelly; Price, Melanie; Reeve, Jeanne; Reeve, Tony; Richards, Robert; Rinehart, Gina; Robinson, Bridget; Rudzki, Barney; Salisbury, Elizabeth; Sambrook, Joe; Saunders, Christobel; Scott, Clare; Scott, Elizabeth; Scott, Rodney; Seshadri, Ram; Shelling, Andrew; Southey, Melissa; Spurdle, Amanda; Suthers, Graeme; Taylor, Donna; Tennant, Christopher; Thorne, Heather; Townshend, Sharron; Tucker, Kathy; Tyler, Janet; Venter, Deon; Visvader, Jane; Walpole, Ian; Ward, Robin; Waring, Paul; Warner, Bev; Warren, Graham; Watson, Elizabeth; Williams, Rachael; Wilson, Judy; Winship, Ingrid; Young, Mary Ann; Bowtell, David; Green, Adele; deFazio, Anna; Chenevix-Trench, Georgia; Gertig, Dorota; Webb, Penny

2009-01-01

Breast cancer exhibits familial aggregation, consistent with variation in genetic susceptibility to the disease. Known susceptibility genes account for less than 25% of the familial risk of breast cancer, and the residual genetic variance is likely to be due to variants conferring more moderate risks. To identify further susceptibility alleles, we conducted a two-stage genome-wide association study in 4,398 breast cancer cases and 4,316 controls, followed by a third stage in which 30 single nucleotide polymorphisms (SNPs) were tested for confirmation in 21,860 cases and 22,578 controls from 22 studies. We used 227,876 SNPs that were estimated to correlate with 77% of known common SNPs in Europeans at r2>0.5. SNPs in five novel independent loci exhibited strong and consistent evidence of association with breast cancer (P<10−7). Four of these contain plausible causative genes (FGFR2, TNRC9, MAP3K1 and LSP1). At the second stage, 1,792 SNPs were significant at the P<0.05 level compared with an estimated 1,343 that would be expected by chance, indicating that many additional common susceptibility alleles may be identifiable by this approach. PMID:17529967
Multiscale Cancer Modeling

Science.gov (United States)

Macklin, Paul; Cristini, Vittorio

2013-01-01

Simulating cancer behavior across multiple biological scales in space and time, i.e., multiscale cancer modeling, is increasingly being recognized as a powerful tool to refine hypotheses, focus experiments, and enable more accurate predictions. A growing number of examples illustrate the value of this approach in providing quantitative insight on the initiation, progression, and treatment of cancer. In this review, we introduce the most recent and important multiscale cancer modeling works that have successfully established a mechanistic link between different biological scales. Biophysical, biochemical, and biomechanical factors are considered in these models. We also discuss innovative, cutting-edge modeling methods that are moving predictive multiscale cancer modeling toward clinical application. Furthermore, because the development of multiscale cancer models requires a new level of collaboration among scientists from a variety of fields such as biology, medicine, physics, mathematics, engineering, and computer science, an innovative Web-based infrastructure is needed to support this growing community. PMID:21529163
*K-means and Cluster Models for Cancer Signatures

OpenAIRE

Kakushadze, Zura; Yu, Willie

2017-01-01

We present *K-means clustering algorithm and source code by expanding statistical clustering methods applied in https://ssrn.com/abstract=2802753 to quantitative finance. *K-means is statistically deterministic without specifying initial centers, etc. We apply *K-means to extracting cancer signatures from genome data without using nonnegative matrix factorization (NMF). *K-means’ computational cost is a fraction of NMF’s. Using 1389 published samples for 14 cancer types, we find that 3 cancer...
Integrative genome analyses identify key somatic driver mutations of small-cell lung cancer

NARCIS (Netherlands)

Peifer, Martin; Fernandez-Cuesta, Lynnette; Sos, Martin L.; George, Julie; Seidel, Danila; Kasper, Lawryn H.; Plenker, Dennis; Leenders, Frauke; Sun, Ruping; Zander, Thomas; Menon, Roopika; Koker, Mirjam; Dahmen, Ilona; Mueller, Christian; Di Cerbo, Vincenzo; Schildhaus, Hans-Ulrich; Altmueller, Janine; Baessmann, Ingelore; Becker, Christian; de Wilde, Bram; Vandesompele, Jo; Boehm, Diana; Ansen, Sascha; Gabler, Franziska; Wilkening, Ines; Heynck, Stefanie; Heuckmann, Johannes M.; Lu, Xin; Carter, Scott L.; Cibulskis, Kristian; Banerji, Shantanu; Getz, Gad; Park, Kwon-Sik; Rauh, Daniel; Gruetter, Christian; Fischer, Matthias; Pasqualucci, Laura; Wright, Gavin; Wainer, Zoe; Russell, Prudence; Petersen, Iver; Chen, Yuan; Stoelben, Erich; Ludwig, Corinna; Schnabel, Philipp; Hoffmann, Hans; Muley, Thomas; Brockmann, Michael; Engel-Riedel, Walburga; Muscarella, Lucia A.; Fazio, Vito M.; Groen, Harry; Timens, Wim; Sietsma, Hannie; Thunnissen, Erik; Smit, Egbert; Heideman, Danielle A. M.; Snijders, Peter J. F.; Cappuzzo, Federico; Ligorio, Claudia; Damiani, Stefania; Field, John; Solberg, Steinar; Brustugun, Odd Terje; Lund-Iversen, Marius; Saenger, Joerg; Clement, Joachim H.; Soltermann, Alex; Moch, Holger; Weder, Walter; Solomon, Benjamin; Soria, Jean-Charles; Validire, Pierre; Besse, Benjamin; Brambilla, Elisabeth; Brambilla, Christian; Lantuejoul, Sylvie; Lorimier, Philippe; Schneider, Peter M.; Hallek, Michael; Pao, William; Meyerson, Matthew; Sage, Julien; Shendure, Jay; Schneider, Robert; Buettner, Reinhard; Wolf, Juergen; Nuernberg, Peter; Perner, Sven; Heukamp, Lukas C.; Brindle, Paul K.; Haas, Stefan; Thomas, Roman K.

2012-01-01

Small-cell lung cancer (SCLC) is an aggressive lung tumor subtype with poor prognosis(1-3). We sequenced 29 SCLC exomes, 2 genomes and 15 transcriptomes and found an extremely high mutation rate of 7.4 +/- 1 protein-changing mutations per million base pairs. Therefore, we conducted integrated
Genome-scale analysis of aberrant DNA methylation in colorectal cancer

Science.gov (United States)

Hinoue, Toshinori; Weisenberger, Daniel J.; Lange, Christopher P.E.; Shen, Hui; Byun, Hyang-Min; Van Den Berg, David; Malik, Simeen; Pan, Fei; Noushmehr, Houtan; van Dijk, Cornelis M.; Tollenaar, Rob A.E.M.; Laird, Peter W.

2012-01-01

Colorectal cancer (CRC) is a heterogeneous disease in which unique subtypes are characterized by distinct genetic and epigenetic alterations. Here we performed comprehensive genome-scale DNA methylation profiling of 125 colorectal tumors and 29 adjacent normal tissues. We identified four DNA methylation–based subgroups of CRC using model-based cluster analyses. Each subtype shows characteristic genetic and clinical features, indicating that they represent biologically distinct subgroups. A CIMP-high (CIMP-H) subgroup, which exhibits an exceptionally high frequency of cancer-specific DNA hypermethylation, is strongly associated with MLH1 DNA hypermethylation and the BRAFV600E mutation. A CIMP-low (CIMP-L) subgroup is enriched for KRAS mutations and characterized by DNA hypermethylation of a subset of CIMP-H-associated markers rather than a unique group of CpG islands. Non-CIMP tumors are separated into two distinct clusters. One non-CIMP subgroup is distinguished by a significantly higher frequency of TP53 mutations and frequent occurrence in the distal colon, while the tumors that belong to the fourth group exhibit a low frequency of both cancer-specific DNA hypermethylation and gene mutations and are significantly enriched for rectal tumors. Furthermore, we identified 112 genes that were down-regulated more than twofold in CIMP-H tumors together with promoter DNA hypermethylation. These represent ∼7% of genes that acquired promoter DNA methylation in CIMP-H tumors. Intriguingly, 48/112 genes were also transcriptionally down-regulated in non-CIMP subgroups, but this was not attributable to promoter DNA hypermethylation. Together, we identified four distinct DNA methylation subgroups of CRC and provided novel insight regarding the role of CIMP-specific DNA hypermethylation in gene silencing. PMID:21659424
Molecular analysis of urothelial cancer cell lines for modeling tumor biology and drug response.

Science.gov (United States)

Nickerson, M L; Witte, N; Im, K M; Turan, S; Owens, C; Misner, K; Tsang, S X; Cai, Z; Wu, S; Dean, M; Costello, J C; Theodorescu, D

2017-01-05

The utility of tumor-derived cell lines is dependent on their ability to recapitulate underlying genomic aberrations and primary tumor biology. Here, we sequenced the exomes of 25 bladder cancer (BCa) cell lines and compared mutations, copy number alterations (CNAs), gene expression and drug response to BCa patient profiles in The Cancer Genome Atlas (TCGA). We observed a mutation pattern associated with altered CpGs and APOBEC-family cytosine deaminases similar to mutation signatures derived from somatic alterations in muscle-invasive (MI) primary tumors, highlighting a major mechanism(s) contributing to cancer-associated alterations in the BCa cell line exomes. Non-silent sequence alterations were confirmed in 76 cancer-associated genes, including mutations that likely activate oncogenes TERT and PIK3CA, and alter chromatin-associated proteins (MLL3, ARID1A, CHD6 and KDM6A) and established BCa genes (TP53, RB1, CDKN2A and TSC1). We identified alterations in signaling pathways and proteins with related functions, including the PI3K/mTOR pathway, altered in 60% of lines; BRCA DNA repair, 44%; and SYNE1-SYNE2, 60%. Homozygous deletions of chromosome 9p21 are known to target the cell cycle regulators CDKN2A and CDKN2B. This loci was commonly lost in BCa cell lines and we show the deletions extended to the polyamine enzyme methylthioadenosine (MTA) phosphorylase (MTAP) in 36% of lines, transcription factor DMRTA1 (27%) and antiviral interferon epsilon (IFNE, 19%). Overall, the BCa cell line genomic aberrations were concordant with those found in BCa patient tumors. We used gene expression and copy number data to infer pathway activities for cell lines, then used the inferred pathway activities to build a predictive model of cisplatin response. When applied to platinum-treated patients gathered from TCGA, the model predicted treatment-specific response. Together, these data and analysis represent a valuable community resource to model basic tumor biology and to study
Novel approach to cancer therapeutics using comparative cancer biology

OpenAIRE

Revi, Bhindu

2018-01-01

Developing personalized cancer therapies based on cancer genomics methodologies forms the basis for future cancer therapeutics. A genomics platform was developed based on canine cancer to produce a proof-of-concept for personalized genomics led therapeutic choices but also developing personalized therapeutics for canine cancer patients themselves. The platform identified the genetic state of a canine cancer patient within two drugable pathways; p53 and HSP90/IRF1. The former ge...
Incorporating Protein Biosynthesis into the Saccharomyces cerevisiae Genome-scale Metabolic Model

DEFF Research Database (Denmark)

Olivares Hernandez, Roberto

Based on stoichiometric biochemical equations that occur into the cell, the genome-scale metabolic models can quantify the metabolic fluxes, which are regarded as the final representation of the physiological state of the cell. For Saccharomyces Cerevisiae the genome scale model has been construc......Based on stoichiometric biochemical equations that occur into the cell, the genome-scale metabolic models can quantify the metabolic fluxes, which are regarded as the final representation of the physiological state of the cell. For Saccharomyces Cerevisiae the genome scale model has been...
Transcriptomic and genomic features of invasive lobular breast cancer.

Science.gov (United States)

Desmedt, Christine; Zoppoli, Gabriele; Sotiriou, Christos; Salgado, Roberto

2017-06-01

Accounting for 10-15% of all breast neoplasms, invasive lobular breast cancer (ILC) is the second most common histological subtype of breast cancer after invasive ductal breast cancer (IDC). Understanding ILC biology, which differs from IDC in terms of clinical presentation, treatment response, relapse timing and patterns, is essential in order to adopt novel, disease-specific management strategies. While the contribution of the histological subtypes to tumour biology has been poorly investigated and acknowledged in the past, recently several major, independent efforts have led to the assembly and molecular characterization of well-annotated ILC case sets. In this review, we provide a critical overview of the literature exploring ILC, through comprehensive and multiomic methods. The first part specifically focuses on ILC transcriptomic features by reviewing the intrinsic molecular subtypes, the application of gene expression scores for the prediction of recurrence, and the identification of gene expression subtypes. The second part describes the main research efforts that lead to the identification of the genomic landscape of ILC, with a special focus to findings that differentiate ILC from IDC and carry potential clinical relevance. Copyright © 2017 Elsevier Ltd. All rights reserved.
Virtual Genome Walking across the 32 Gb Ambystoma mexicanum genome; assembling gene models and intronic sequence.

Science.gov (United States)

Evans, Teri; Johnson, Andrew D; Loose, Matthew

2018-01-12

Large repeat rich genomes present challenges for assembly using short read technologies. The 32 Gb axolotl genome is estimated to contain ~19 Gb of repetitive DNA making an assembly from short reads alone effectively impossible. Indeed, this model species has been sequenced to 20× coverage but the reads could not be conventionally assembled. Using an alternative strategy, we have assembled subsets of these reads into scaffolds describing over 19,000 gene models. We call this method Virtual Genome Walking as it locally assembles whole genome reads based on a reference transcriptome, identifying exons and iteratively extending them into surrounding genomic sequence. These assemblies are then linked and refined to generate gene models including upstream and downstream genomic, and intronic, sequence. Our assemblies are validated by comparison with previously published axolotl bacterial artificial chromosome (BAC) sequences. Our analyses of axolotl intron length, intron-exon structure, repeat content and synteny provide novel insights into the genic structure of this model species. This resource will enable new experimental approaches in axolotl, such as ChIP-Seq and CRISPR and aid in future whole genome sequencing efforts. The assembled sequences and annotations presented here are freely available for download from https://tinyurl.com/y8gydc6n . The software pipeline is available from https://github.com/LooseLab/iterassemble .
Understanding Cancer Prognosis

Medline Plus

Full Text Available ... Cancer Biology Research Cancer Genomics Research Research on Causes of Cancer Cancer Diagnosis Research Cancer Prevention Research Screening & Early ... Scientists NCI Research Areas Cancer Biology Cancer Genomics Causes of Cancer Diagnosis Prevention Screening & Early Detection Treatment Cancer & Public ...
A genomic and transcriptomic approach for a differential diagnosis between primary and secondary ovarian carcinomas in patients with a previous history of breast cancer

International Nuclear Information System (INIS)

Meyniel, Jean-Philippe; Alran, Séverine; Rapinat, Audrey; Gentien, David; Roman-Roman, Sergio; Mignot, Laurent; Sastre-Garau, Xavier; Cottu, Paul H; Decraene, Charles; Stern, Marc-Henri; Couturier, Jérôme; Lebigot, Ingrid; Nicolas, André; Weber, Nina; Fourchotte, Virginie

2010-01-01

The distinction between primary and secondary ovarian tumors may be challenging for pathologists. The purpose of the present work was to develop genomic and transcriptomic tools to further refine the pathological diagnosis of ovarian tumors after a previous history of breast cancer. Sixteen paired breast-ovary tumors from patients with a former diagnosis of breast cancer were collected. The genomic profiles of paired tumors were analyzed using the Affymetrix GeneChip ® Mapping 50 K Xba Array or Genome-Wide Human SNP Array 6.0 (for one pair), and the data were normalized with ITALICS (ITerative and Alternative normaLIzation and Copy number calling for affymetrix Snp arrays) algorithm or Partek Genomic Suite, respectively. The transcriptome of paired samples was analyzed using Affymetrix GeneChip ® Human Genome U133 Plus 2.0 Arrays, and the data were normalized with gc-Robust Multi-array Average (gcRMA) algorithm. A hierarchical clustering of these samples was performed, combined with a dataset of well-identified primary and secondary ovarian tumors. In 12 of the 16 paired tumors analyzed, the comparison of genomic profiles confirmed the pathological diagnosis of primary ovarian tumor (n = 5) or metastasis of breast cancer (n = 7). Among four cases with uncertain pathological diagnosis, genomic profiles were clearly distinct between the ovarian and breast tumors in two pairs, thus indicating primary ovarian carcinomas, and showed common patterns in the two others, indicating metastases from breast cancer. In all pairs, the result of the transcriptomic analysis was concordant with that of the genomic analysis. In patients with ovarian carcinoma and a previous history of breast cancer, SNP array analysis can be used to distinguish primary and secondary ovarian tumors. Transcriptomic analysis may be used when primary breast tissue specimen is not available
Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models

Science.gov (United States)

Cuevas, Jaime; Crossa, José; Montesinos-López, Osval A.; Burgueño, Juan; Pérez-Rodríguez, Paulino; de los Campos, Gustavo

2016-01-01

The phenomenon of genotype × environment (G × E) interaction in plant breeding decreases selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction models incorporating G × E have been recently developed and used in genomic selection of plant breeding programs. Genomic prediction models for assessing multi-environment G × E interaction are extensions of a single-environment model, and have advantages and limitations. In this study, we propose two multi-environment Bayesian genomic models: the first model considers genetic effects (u) that can be assessed by the Kronecker product of variance–covariance matrices of genetic correlations between environments and genomic kernels through markers under two linear kernel methods, linear (genomic best linear unbiased predictors, GBLUP) and Gaussian (Gaussian kernel, GK). The other model has the same genetic component as the first model (u) plus an extra component, f, that captures random effects between environments that were not captured by the random effects u. We used five CIMMYT data sets (one maize and four wheat) that were previously used in different studies. Results show that models with G × E always have superior prediction ability than single-environment models, and the higher prediction ability of multi-environment models with u and f over the multi-environment model with only u occurred 85% of the time with GBLUP and 45% of the time with GK across the five data sets. The latter result indicated that including the random effect f is still beneficial for increasing prediction ability after adjusting by the random effect u. PMID:27793970
Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models

Directory of Open Access Journals (Sweden)

Jaime Cuevas

2017-01-01

Full Text Available The phenomenon of genotype × environment (G × E interaction in plant breeding decreases selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction models incorporating G × E have been recently developed and used in genomic selection of plant breeding programs. Genomic prediction models for assessing multi-environment G × E interaction are extensions of a single-environment model, and have advantages and limitations. In this study, we propose two multi-environment Bayesian genomic models: the first model considers genetic effects ( u that can be assessed by the Kronecker product of variance–covariance matrices of genetic correlations between environments and genomic kernels through markers under two linear kernel methods, linear (genomic best linear unbiased predictors, GBLUP and Gaussian (Gaussian kernel, GK. The other model has the same genetic component as the first model ( u plus an extra component, f, that captures random effects between environments that were not captured by the random effects u . We used five CIMMYT data sets (one maize and four wheat that were previously used in different studies. Results show that models with G × E always have superior prediction ability than single-environment models, and the higher prediction ability of multi-environment models with u and f over the multi-environment model with only u occurred 85% of the time with GBLUP and 45% of the time with GK across the five data sets. The latter result indicated that including the random effect f is still beneficial for increasing prediction ability after adjusting by the random effect u .
Genetic profiles of gastroesophageal cancer: combined analysis using expression array and tiling array--comparative genomic hybridization

DEFF Research Database (Denmark)

Isinger-Ekstrand, Anna; Johansson, Jan; Ohlsson, Mattias

2010-01-01

15, 13q34, and 12q13, whereas different profiles with gains at 5p15, 7p22, 2q35, and 13q34 characterized gastric cancers. CDK6 and EGFR were identified as putative target genes in cancers of the esophagus and the gastroesophageal junction, with upregulation in one quarter of the tumors. Gains......We aimed to characterize the genomic profiles of adenocarcinomas in the gastroesophageal junction in relation to cancers in the esophagus and the stomach. Profiles of gains/losses as well as gene expression profiles were obtained from 27 gastroesophageal adenocarcinomas by means of 32k high......-resolution array-based comparative genomic hybridization and 27k oligo gene expression arrays, and putative target genes were validated in an extended series. Adenocarcinomas in the distal esophagus and the gastroesophageal junction showed strong similarities with the most common gains at 20q13, 8q24, 1q21-23, 5p...
Genome-wide assessment of the association of rare and common copy number variations to testicular germ cell cancer

DEFF Research Database (Denmark)

Edsgard, Stefan Daniel; Dalgaard, Marlene Danner; Weinhold, Nils

2013-01-01

Testicular germ cell cancer (TGCC) is one of the most heritable forms of cancer. Previous genome-wide association studies have focused on single nucleotide polymorphisms, largely ignoring the influence of copy number variants (CNVs). Here we present a genome-wide study of CNV on a cohort of 212...... of rare CNVs related to cell migration (false-discovery rate = 0.021, 1.8% of cases and 1.1% of controls). Dysregulation during migration of primordial germ cells has previously been suspected to be a part of TGCC development and this set of multiple rare variants may thereby have a minor contribution...

Structural variation discovery in the cancer genome using next generation sequencing: Computational solutions and perspectives

Science.gov (United States)

Liu, Biao; Conroy, Jeffrey M.; Morrison, Carl D.; Odunsi, Adekunle O.; Qin, Maochun; Wei, Lei; Trump, Donald L.; Johnson, Candace S.; Liu, Song; Wang, Jianmin

2015-01-01

Somatic Structural Variations (SVs) are a complex collection of chromosomal mutations that could directly contribute to carcinogenesis. Next Generation Sequencing (NGS) technology has emerged as the primary means of interrogating the SVs of the cancer genome in recent investigations. Sophisticated computational methods are required to accurately identify the SV events and delineate their breakpoints from the massive amounts of reads generated by a NGS experiment. In this review, we provide an overview of current analytic tools used for SV detection in NGS-based cancer studies. We summarize the features of common SV groups and the primary types of NGS signatures that can be used in SV detection methods. We discuss the principles and key similarities and differences of existing computational programs and comment on unresolved issues related to this research field. The aim of this article is to provide a practical guide of relevant concepts, computational methods, software tools and important factors for analyzing and interpreting NGS data for the detection of SVs in the cancer genome. PMID:25849937
The Naked Mole Rat Genome Resource: facilitating analyses of cancer and longevity-related adaptations.

Science.gov (United States)

Keane, Michael; Craig, Thomas; Alföldi, Jessica; Berlin, Aaron M; Johnson, Jeremy; Seluanov, Andrei; Gorbunova, Vera; Di Palma, Federica; Lindblad-Toh, Kerstin; Church, George M; de Magalhães, João Pedro

2014-12-15

The naked mole rat (Heterocephalus glaber) is an exceptionally long-lived and cancer-resistant rodent native to East Africa. Although its genome was previously sequenced, here we report a new assembly sequenced by us with substantially higher N50 values for scaffolds and contigs. We analyzed the annotation of this new improved assembly and identified candidate genomic adaptations which may have contributed to the evolution of the naked mole rat's extraordinary traits, including in regions of p53, and the hyaluronan receptors CD44 and HMMR (RHAMM). Furthermore, we developed a freely available web portal, the Naked Mole Rat Genome Resource (http://www.naked-mole-rat.org), featuring the data and results of our analysis, to assist researchers interested in the genome and genes of the naked mole rat, and also to facilitate further studies on this fascinating species. © The Author 2014. Published by Oxford University Press.
The NCI Genomic Data Commons as an engine for precision medicine.

Science.gov (United States)

Jensen, Mark A; Ferretti, Vincent; Grossman, Robert L; Staudt, Louis M

2017-07-27

The National Cancer Institute Genomic Data Commons (GDC) is an information system for storing, analyzing, and sharing genomic and clinical data from patients with cancer. The recent high-throughput sequencing of cancer genomes and transcriptomes has produced a big data problem that precludes many cancer biologists and oncologists from gleaning knowledge from these data regarding the nature of malignant processes and the relationship between tumor genomic profiles and treatment response. The GDC aims to democratize access to cancer genomic data and to foster the sharing of these data to promote precision medicine approaches to the diagnosis and treatment of cancer.
Collaborative Genomics Study Advances Precision Oncology

Science.gov (United States)

A collaborative study conducted by two Office of Cancer Genomics (OCG) initiatives highlights the importance of integrating structural and functional genomics programs to improve cancer therapies, and more specifically, contribute to precision oncology treatments for children.
Gross genomic damage measured by DNA image cytometry independently predicts gastric cancer patient survival

NARCIS (Netherlands)

Belien, J.A.M.; Buffart, T.E.; Gill, A.; Broeckaert, M.A.M.; Quirke, P.; Meijer, G.A.; Grabsch, H.

2009-01-01

BACKGROUND: DNA aneuploidy reflects gross genomic changes. It can be measured by flow cytometry (FCM-DNA) or image cytometry (ICM-DNA). In gastric cancer, the prevalence of DNA aneuploidy has been reported to range from 27 to 100%, with conflicting associations with clinicopathological variables.
Use of genome-scale microbial models for metabolic engineering

DEFF Research Database (Denmark)

Patil, Kiran Raosaheb; Åkesson, M.; Nielsen, Jens

2004-01-01

Metabolic engineering serves as an integrated approach to design new cell factories by providing rational design procedures and valuable mathematical and experimental tools. Mathematical models have an important role for phenotypic analysis, but can also be used for the design of optimal metaboli...... network structures. The major challenge for metabolic engineering in the post-genomic era is to broaden its design methodologies to incorporate genome-scale biological data. Genome-scale stoichiometric models of microorganisms represent a first step in this direction....
The Cancer Genome Atlas Comprehensive Molecular Characterization of Renal Cell Carcinoma

OpenAIRE

Christopher J. Ricketts; Aguirre A. De Cubas; Huihui Fan; Christof C. Smith; Martin Lang; Ed Reznik; Reanne Bowlby; Ewan A. Gibb; Rehan Akbani; Rameen Beroukhim; Donald P. Bottaro; Toni K. Choueiri; Richard A. Gibbs; Andrew K. Godwin; Scott Haake

2018-01-01

Summary: Renal cell carcinoma (RCC) is not a single disease, but several histologically defined cancers with different genetic drivers, clinical courses, and therapeutic responses. The current study evaluated 843 RCC from the three major histologic subtypes, including 488 clear cell RCC, 274 papillary RCC, and 81 chromophobe RCC. Comprehensive genomic and phenotypic analysis of the RCC subtypes reveals distinctive features of each subtype that provide the foundation for the development of sub...
Synergistic Interactions with PI3K Inhibition that Induce Apoptosis. | Office of Cancer Genomics

Science.gov (United States)

Activating mutations involving the PI3K pathway occur frequently in human cancers. However, PI3K inhibitors primarily induce cell cycle arrest, leaving a significant reservoir of tumor cells that may acquire or exhibit resistance. We searched for genes that are required for the survival of PI3K mutant cancer cells in the presence of PI3K inhibition by conducting a genome scale shRNA-based apoptosis screen in a PIK3CA mutant human breast cancer cell. We identified 5 genes (PIM2, ZAK, TACC1, ZFR, ZNF565) whose suppression induced cell death upon PI3K inhibition.
Simultaneous Structural Variation Discovery in Multiple Paired-End Sequenced Genomes

Science.gov (United States)

Hormozdiari, Fereydoun; Hajirasouliha, Iman; McPherson, Andrew; Eichler, Evan E.; Sahinalp, S. Cenk

Next generation sequencing technologies have been decreasing the costs and increasing the world-wide capacity for sequence production at an unprecedented rate, making the initiation of large scale projects aiming to sequence almost 2000 genomes [1]. Structural variation detection promises to be one of the key diagnostic tools for cancer and other diseases with genomic origin. In this paper, we study the problem of detecting structural variation events in two or more sequenced genomes through high throughput sequencing . We propose to move from the current model of (1) detecting genomic variations in single next generation sequenced (NGS) donor genomes independently, and (2) checking whether two or more donor genomes indeed agree or disagree on the variations (in this paper we name this framework Independent Structural Variation Discovery and Merging - ISV&M), to a new model in which we detect structural variation events among multiple genomes simultaneously.
Exploiting linkage disequilibrium in statistical modelling in quantitative genomics

DEFF Research Database (Denmark)

Wang, Lei

Alleles at two loci are said to be in linkage disequilibrium (LD) when they are correlated or statistically dependent. Genomic prediction and gene mapping rely on the existence of LD between gentic markers and causul variants of complex traits. In the first part of the thesis, a novel method...... to quantify and visualize local variation in LD along chromosomes in describet, and applied to characterize LD patters at the local and genome-wide scale in three Danish pig breeds. In the second part, different ways of taking LD into account in genomic prediction models are studied. One approach is to use...... the recently proposed antedependence models, which treat neighbouring marker effects as correlated; another approach involves use of haplotype block information derived using the program Beagle. The overall conclusion is that taking LD information into account in genomic prediction models potentially improves...
Genome-wide association study identifies multiple loci associated with both mammographic density and breast cancer risk

Science.gov (United States)

Lindström, Sara; Thompson, Deborah J.; Paterson, Andrew D.; Li, Jingmei; Gierach, Gretchen L.; Scott, Christopher; Stone, Jennifer; Douglas, Julie A.; dos-Santos-Silva, Isabel; Fernandez-Navarro, Pablo; Verghase, Jajini; Smith, Paula; Brown, Judith; Luben, Robert; Wareham, Nicholas J.; Loos, Ruth J.F.; Heit, John A.; Pankratz, V. Shane; Norman, Aaron; Goode, Ellen L.; Cunningham, Julie M.; deAndrade, Mariza; Vierkant, Robert A.; Czene, Kamila; Fasching, Peter A.; Baglietto, Laura; Southey, Melissa C.; Giles, Graham G.; Shah, Kaanan P.; Chan, Heang-Ping; Helvie, Mark A.; Beck, Andrew H.; Knoblauch, Nicholas W.; Hazra, Aditi; Hunter, David J.; Kraft, Peter; Pollan, Marina; Figueroa, Jonine D.; Couch, Fergus J.; Hopper, John L.; Hall, Per; Easton, Douglas F.; Boyd, Norman F.; Vachon, Celine M.; Tamimi, Rulla M.

2015-01-01

Mammographic density reflects the amount of stromal and epithelial tissues in relation to adipose tissue in the breast and is a strong risk factor for breast cancer. Here we report the results from meta-analysis of genome-wide association studies (GWAS) of three mammographic density phenotypes: dense area, non-dense area and percent density in up to 7,916 women in stage 1 and an additional 10,379 women in stage 2. We identify genome-wide significant (P<5×10−8) loci for dense area (AREG, ESR1, ZNF365, LSP1/TNNT3, IGF1, TMEM184B, SGSM3/MKL1), non-dense area (8p11.23) and percent density (PRDM6, 8p11.23, TMEM184B). Four of these regions are known breast cancer susceptibility loci, and four additional regions were found to be associated with breast cancer (P<0.05) in a large meta-analysis. These results provide further evidence of a shared genetic basis between mammographic density and breast cancer and illustrate the power of studying intermediate quantitative phenotypes to identify putative disease susceptibility loci. PMID:25342443
Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models.

Science.gov (United States)

Cuevas, Jaime; Crossa, José; Montesinos-López, Osval A; Burgueño, Juan; Pérez-Rodríguez, Paulino; de Los Campos, Gustavo

2017-01-05

The phenomenon of genotype × environment (G × E) interaction in plant breeding decreases selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction models incorporating G × E have been recently developed and used in genomic selection of plant breeding programs. Genomic prediction models for assessing multi-environment G × E interaction are extensions of a single-environment model, and have advantages and limitations. In this study, we propose two multi-environment Bayesian genomic models: the first model considers genetic effects [Formula: see text] that can be assessed by the Kronecker product of variance-covariance matrices of genetic correlations between environments and genomic kernels through markers under two linear kernel methods, linear (genomic best linear unbiased predictors, GBLUP) and Gaussian (Gaussian kernel, GK). The other model has the same genetic component as the first model [Formula: see text] plus an extra component, F: , that captures random effects between environments that were not captured by the random effects [Formula: see text] We used five CIMMYT data sets (one maize and four wheat) that were previously used in different studies. Results show that models with G × E always have superior prediction ability than single-environment models, and the higher prediction ability of multi-environment models with [Formula: see text] over the multi-environment model with only u occurred 85% of the time with GBLUP and 45% of the time with GK across the five data sets. The latter result indicated that including the random effect f is still beneficial for increasing prediction ability after adjusting by the random effect [Formula: see text]. Copyright © 2017 Cuevas et al.
Epidemiology & Genomics Research Program

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The Epidemiology and Genomics Research Program, in the National Cancer Institute's Division of Cancer Control and Population Sciences, funds research in human populations to understand the determinants of cancer occurrence and outcomes.
Genome-wide linkage scan for colorectal cancer susceptibility genes supports linkage to chromosome 3q

Directory of Open Access Journals (Sweden)

Velculescu Victor E

2008-04-01

Full Text Available Abstract Background Colorectal cancer is one of the most common causes of cancer-related mortality. The disease is clinically and genetically heterogeneous though a strong hereditary component has been identified. However, only a small proportion of the inherited susceptibility can be ascribed to dominant syndromes, such as Hereditary Non-Polyposis Colorectal Cancer (HNPCC or Familial Adenomatous Polyposis (FAP. In an attempt to identify novel colorectal cancer predisposing genes, we have performed a genome-wide linkage analysis in 30 Swedish non-FAP/non-HNPCC families with a strong family history of colorectal cancer. Methods Statistical analysis was performed using multipoint parametric and nonparametric linkage. Results Parametric analysis under the assumption of locus homogeneity excluded any common susceptibility regions harbouring a predisposing gene for colorectal cancer. However, several loci on chromosomes 2q, 3q, 6q, and 7q with suggestive linkage were detected in the parametric analysis under the assumption of locus heterogeneity as well as in the nonparametric analysis. Among these loci, the locus on chromosome 3q21.1-q26.2 was the most consistent finding providing positive results in both parametric and nonparametric analyses Heterogeneity LOD score (HLOD = 1.90, alpha = 0.45, Non-Parametric LOD score (NPL = 2.1. Conclusion The strongest evidence of linkage was seen for the region on chromosome 3. Interestingly, the same region has recently been reported as the most significant finding in a genome-wide analysis performed with SNP arrays; thus our results independently support the finding on chromosome 3q.
A genome-wide association study of upper aerodigestive tract cancers conducted within the INHANCE consortium.

LENUS (Irish Health Repository)

McKay, James D

2011-03-01

Genome-wide association studies (GWAS) have been successful in identifying common genetic variation involved in susceptibility to etiologically complex disease. We conducted a GWAS to identify common genetic variation involved in susceptibility to upper aero-digestive tract (UADT) cancers. Genome-wide genotyping was carried out using the Illumina HumanHap300 beadchips in 2,091 UADT cancer cases and 3,513 controls from two large European multi-centre UADT cancer studies, as well as 4,821 generic controls. The 19 top-ranked variants were investigated further in an additional 6,514 UADT cancer cases and 7,892 controls of European descent from an additional 13 UADT cancer studies participating in the INHANCE consortium. Five common variants presented evidence for significant association in the combined analysis (p ≤ 5 × 10⁻⁷). Two novel variants were identified, a 4q21 variant (rs1494961, p = 1×10⁻⁸) located near DNA repair related genes HEL308 and FAM175A (or Abraxas) and a 12q24 variant (rs4767364, p =2 × 10⁻⁸) located in an extended linkage disequilibrium region that contains multiple genes including the aldehyde dehydrogenase 2 (ALDH2) gene. Three remaining variants are located in the ADH gene cluster and were identified previously in a candidate gene study involving some of these samples. The association between these three variants and UADT cancers was independently replicated in 5,092 UADT cancer cases and 6,794 controls non-overlapping samples presented here (rs1573496-ADH7, p = 5 × 10⁻⁸); rs1229984-ADH1B, p = 7 × 10⁻⁹; and rs698-ADH1C, p = 0.02). These results implicate two variants at 4q21 and 12q24 and further highlight three ADH variants in UADT cancer susceptibility.
A genome-wide association study of upper aerodigestive tract cancers conducted within the INHANCE consortium.

Directory of Open Access Journals (Sweden)

James D McKay

2011-03-01

Full Text Available Genome-wide association studies (GWAS have been successful in identifying common genetic variation involved in susceptibility to etiologically complex disease. We conducted a GWAS to identify common genetic variation involved in susceptibility to upper aero-digestive tract (UADT cancers. Genome-wide genotyping was carried out using the Illumina HumanHap300 beadchips in 2,091 UADT cancer cases and 3,513 controls from two large European multi-centre UADT cancer studies, as well as 4,821 generic controls. The 19 top-ranked variants were investigated further in an additional 6,514 UADT cancer cases and 7,892 controls of European descent from an additional 13 UADT cancer studies participating in the INHANCE consortium. Five common variants presented evidence for significant association in the combined analysis (p ≤ 5 × 10⁻⁷. Two novel variants were identified, a 4q21 variant (rs1494961, p = 1×10⁻⁸ located near DNA repair related genes HEL308 and FAM175A (or Abraxas and a 12q24 variant (rs4767364, p =2 × 10⁻⁸ located in an extended linkage disequilibrium region that contains multiple genes including the aldehyde dehydrogenase 2 (ALDH2 gene. Three remaining variants are located in the ADH gene cluster and were identified previously in a candidate gene study involving some of these samples. The association between these three variants and UADT cancers was independently replicated in 5,092 UADT cancer cases and 6,794 controls non-overlapping samples presented here (rs1573496-ADH7, p = 5 × 10⁻⁸; rs1229984-ADH1B, p = 7 × 10⁻⁹; and rs698-ADH1C, p = 0.02. These results implicate two variants at 4q21 and 12q24 and further highlight three ADH variants in UADT cancer susceptibility.
Cross-species genomics matches driver mutations and cell compartments to model ependymoma

Science.gov (United States)

Johnson, Robert A.; Wright, Karen D.; Poppleton, Helen; Mohankumar, Kumarasamypet M.; Finkelstein, David; Pounds, Stanley B.; Rand, Vikki; Leary, Sarah E.S.; White, Elsie; Eden, Christopher; Hogg, Twala; Northcott, Paul; Mack, Stephen; Neale, Geoffrey; Wang, Yong-Dong; Coyle, Beth; Atkinson, Jennifer; DeWire, Mariko; Kranenburg, Tanya A.; Gillespie, Yancey; Allen, Jeffrey C.; Merchant, Thomas; Boop, Fredrick A.; Sanford, Robert. A.; Gajjar, Amar; Ellison, David W.; Taylor, Michael D.; Grundy, Richard G.; Gilbertson, Richard J.

2010-01-01

Understanding the biology that underlies histologically similar but molecularly distinct subgroups of cancer has proven difficult since their defining genetic alterations are often numerous, and the cellular origins of most cancers remain unknown1–3. We sought to decipher this heterogeneity by integrating matched genetic alterations and candidate cells of origin to generate accurate disease models. First, we identified subgroups of human ependymoma, a form of neural tumor that arises throughout the central nervous system (CNS). Subgroup specific alterations included amplifications and homozygous deletions of genes not yet implicated in ependymoma. To select cellular compartments most likely to give rise to subgroups of ependymoma, we matched the transcriptomes of human tumors to those of mouse neural stem cells (NSCs), isolated from different regions of the CNS at different developmental stages, with an intact or deleted Ink4a/Arf locus. The transcriptome of human cerebral ependymomas with amplified EPHB2 and deleted INK4A/ARF matched only that of embryonic cerebral Ink4a/Arf−/− NSCs. Remarkably, activation of Ephb2 signaling in these, but not other NSCs, generated the first mouse model of ependymoma, which is highly penetrant and accurately models the histology and transcriptome of one subgroup of human cerebral tumor. Further comparative analysis of matched mouse and human tumors revealed selective deregulation in the expression and copy number of genes that control synaptogenesis, pinpointing disruption of this pathway as a critical event in the production of this ependymoma subgroup. Our data demonstrate the power of cross-species genomics to meticulously match subgroup specific driver mutations with cellular compartments to model and interrogate cancer subgroups. PMID:20639864
Personalized In Vitro and In Vivo Cancer Models to Guide Precision Medicine | Office of Cancer Genomics

Science.gov (United States)

Precision medicine is an approach that takes into account the influence of individuals' genes, environment, and lifestyle exposures to tailor interventions. Here, we describe the development of a robust precision cancer care platform that integrates whole-exome sequencing with a living biobank that enables high-throughput drug screens on patient-derived tumor organoids. To date, 56 tumor-derived organoid cultures and 19 patient-derived xenograft (PDX) models have been established from the 769 patients enrolled in an Institutional Review Board-approved clinical trial.
Genome chaos: survival strategy during crisis.

Science.gov (United States)

Liu, Guo; Stevens, Joshua B; Horne, Steven D; Abdallah, Batoul Y; Ye, Karen J; Bremer, Steven W; Ye, Christine J; Chen, David J; Heng, Henry H

2014-01-01

Genome chaos, a process of complex, rapid genome re-organization, results in the formation of chaotic genomes, which is followed by the potential to establish stable genomes. It was initially detected through cytogenetic analyses, and recently confirmed by whole-genome sequencing efforts which identified multiple subtypes including "chromothripsis", "chromoplexy", "chromoanasynthesis", and "chromoanagenesis". Although genome chaos occurs commonly in tumors, both the mechanism and detailed aspects of the process are unknown due to the inability of observing its evolution over time in clinical samples. Here, an experimental system to monitor the evolutionary process of genome chaos was developed to elucidate its mechanisms. Genome chaos occurs following exposure to chemotherapeutics with different mechanisms, which act collectively as stressors. Characterization of the karyotype and its dynamic changes prior to, during, and after induction of genome chaos demonstrates that chromosome fragmentation (C-Frag) occurs just prior to chaotic genome formation. Chaotic genomes seem to form by random rejoining of chromosomal fragments, in part through non-homologous end joining (NHEJ). Stress induced genome chaos results in increased karyotypic heterogeneity. Such increased evolutionary potential is demonstrated by the identification of increased transcriptome dynamics associated with high levels of karyotypic variance. In contrast to impacting on a limited number of cancer genes, re-organized genomes lead to new system dynamics essential for cancer evolution. Genome chaos acts as a mechanism of rapid, adaptive, genome-based evolution that plays an essential role in promoting rapid macroevolution of new genome-defined systems during crisis, which may explain some unwanted consequences of cancer treatment.
Complete genome sequence of the myxobacterium Sorangium cellulosum

DEFF Research Database (Denmark)

Schneiker, S; Perlova, O; Kaiser, O

2007-01-01

The genus Sorangium synthesizes approximately half of the secondary metabolites isolated from myxobacteria, including the anti-cancer metabolite epothilone. We report the complete genome sequence of the model Sorangium strain S. cellulosum Soce56, which produces several natural products and has...... morphological and physiological properties typical of the genus. The circular genome, comprising 13,033,779 base pairs, is the largest bacterial genome sequenced to date. No global synteny with the genome of Myxococcus xanthus is apparent, revealing an unanticipated level of divergence between...... these myxobacteria. A large percentage of the genome is devoted to regulation, particularly post-translational phosphorylation, which probably supports the strain's complex, social lifestyle. This regulatory network includes the highest number of eukaryotic protein kinase-like kinases discovered in any organism...

DNA replication stress as a hallmark of cancer.

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Macheret, Morgane; Halazonetis, Thanos D

2015-01-01

Human cancers share properties referred to as hallmarks, among which sustained proliferation, escape from apoptosis, and genomic instability are the most pervasive. The sustained proliferation hallmark can be explained by mutations in oncogenes and tumor suppressors that regulate cell growth, whereas the escape from apoptosis hallmark can be explained by mutations in the TP53, ATM, or MDM2 genes. A model to explain the presence of the three hallmarks listed above, as well as the patterns of genomic instability observed in human cancers, proposes that the genes driving cell proliferation induce DNA replication stress, which, in turn, generates genomic instability and selects for escape from apoptosis. Here, we review the data that support this model, as well as the mechanisms by which oncogenes induce replication stress. Further, we argue that DNA replication stress should be considered as a hallmark of cancer because it likely drives cancer development and is very prevalent.
Understanding Cancer Prognosis

Medline Plus

Full Text Available ... Partners & Collaborators Spotlight on Scientists Research Areas Cancer Biology Research Cancer Genomics Research Research on Causes of ... Collaborators Spotlight on Scientists NCI Research Areas Cancer Biology Cancer Genomics Causes of Cancer Diagnosis Prevention Screening & ...
Understanding Cancer Prognosis

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Full Text Available ... Laboratory for Cancer Research Partners & Collaborators Spotlight on Scientists Research Areas Cancer Biology Research Cancer Genomics Research ... Centers Frederick National Lab Partners & Collaborators Spotlight on Scientists NCI Research Areas Cancer Biology Cancer Genomics Causes ...
ScreenBEAM: a novel meta-analysis algorithm for functional genomics screens via Bayesian hierarchical modeling | Office of Cancer Genomics

Science.gov (United States)

Functional genomics (FG) screens, using RNAi or CRISPR technology, have become a standard tool for systematic, genome-wide loss-of-function studies for therapeutic target discovery. As in many large-scale assays, however, off-target effects, variable reagents' potency and experimental noise must be accounted for appropriately control for false positives.
Identification of Variant-Specific Functions of PIK3CA by Rapid Phenotyping of Rare Mutations | Office of Cancer Genomics

Science.gov (United States)

Large-scale sequencing efforts are uncovering the complexity of cancer genomes, which are composed of causal "driver" mutations that promote tumor progression along with many more pathologically neutral "passenger" events. The majority of mutations, both in known cancer drivers and uncharacterized genes, are generally of low occurrence, highlighting the need to functionally annotate the long tail of infrequent mutations present in heterogeneous cancers.
Multi-omics facilitated variable selection in Cox-regression model for cancer prognosis prediction.

Science.gov (United States)

Liu, Cong; Wang, Xujun; Genchev, Georgi Z; Lu, Hui

2017-07-15

New developments in high-throughput genomic technologies have enabled the measurement of diverse types of omics biomarkers in a cost-efficient and clinically-feasible manner. Developing computational methods and tools for analysis and translation of such genomic data into clinically-relevant information is an ongoing and active area of investigation. For example, several studies have utilized an unsupervised learning framework to cluster patients by integrating omics data. Despite such recent advances, predicting cancer prognosis using integrated omics biomarkers remains a challenge. There is also a shortage of computational tools for predicting cancer prognosis by using supervised learning methods. The current standard approach is to fit a Cox regression model by concatenating the different types of omics data in a linear manner, while penalty could be added for feature selection. A more powerful approach, however, would be to incorporate data by considering relationships among omics datatypes. Here we developed two methods: a SKI-Cox method and a wLASSO-Cox method to incorporate the association among different types of omics data. Both methods fit the Cox proportional hazards model and predict a risk score based on mRNA expression profiles. SKI-Cox borrows the information generated by these additional types of omics data to guide variable selection, while wLASSO-Cox incorporates this information as a penalty factor during model fitting. We show that SKI-Cox and wLASSO-Cox models select more true variables than a LASSO-Cox model in simulation studies. We assess the performance of SKI-Cox and wLASSO-Cox using TCGA glioblastoma multiforme and lung adenocarcinoma data. In each case, mRNA expression, methylation, and copy number variation data are integrated to predict the overall survival time of cancer patients. Our methods achieve better performance in predicting patients' survival in glioblastoma and lung adenocarcinoma. Copyright © 2017. Published by Elsevier
Improved annotation through genome-scale metabolic modeling of Aspergillus oryzae

DEFF Research Database (Denmark)

Vongsangnak, Wanwipa; Olsen, Peter; Hansen, Kim

2008-01-01

Background: Since ancient times the filamentous fungus Aspergillus oryzae has been used in the fermentation industry for the production of fermented sauces and the production of industrial enzymes. Recently, the genome sequence of A. oryzae with 12,074 annotated genes was released but the number...... to a genome scale metabolic model of A. oryzae. Results: Our assembled EST sequences we identified 1,046 newly predicted genes in the A. oryzae genome. Furthermore, it was possible to assign putative protein functions to 398 of the newly predicted genes. Noteworthy, our annotation strategy resulted...... model was validated and shown to correctly describe the phenotypic behavior of A. oryzae grown on different carbon sources. Conclusion: A much enhanced annotation of the A. oryzae genome was performed and a genomescale metabolic model of A. oryzae was reconstructed. The model accurately predicted...
Genome-wide profiling of the PIWI-interacting RNA-mRNA regulatory networks in epithelial ovarian cancers.

Science.gov (United States)

Singh, Garima; Roy, Jyoti; Rout, Pratiti; Mallick, Bibekanand

2018-01-01

PIWI-interacting (piRNAs), ~23-36 nucleotide-long small non-coding RNAs (sncRNAs), earlier believed to be germline-specific, have now been identified in somatic cells, including cancer cells. These sncRNAs impact critical biological processes by fine-tuning gene expression at post-transcriptional and epigenetic levels. The expression of piRNAs in ovarian cancer, the most lethal gynecologic cancer is largely uncharted. In this study, we investigated the expression of PIWILs by qRT-PCR and western blotting and then identified piRNA transcriptomes in tissues of normal ovary and two most prevalent epithelial ovarian cancer subtypes, serous and endometrioid by small RNA sequencing. We detected 219, 256 and 234 piRNAs in normal ovary, endometrioid and serous ovarian cancer samples respectively. We observed piRNAs are encoded from various genomic regions, among which introns harbor the majority of them. Surprisingly, piRNAs originated from different genomic contexts showed the varied level of conservations across vertebrates. The functional analysis of predicted targets of differentially expressed piRNAs revealed these could modulate key processes and pathways involved in ovarian oncogenesis. Our study provides the first comprehensive piRNA landscape in these samples and a useful resource for further functional studies to decipher new mechanistic views of piRNA-mediated gene regulatory networks affecting ovarian oncogenesis. The RNA-seq data is submitted to GEO database (GSE83794).
Functional food ingredients against colorectal cancer. An example project integrating functional genomics, nutrition and health

NARCIS (Netherlands)

Stierum, R.; Burgemeister, R.; Helvoort, van A.; Peijnenburg, A.; Schütze, K.; Seidelin, M.; Vang, O.; Ommen, van B.

2001-01-01

Functional Food Ingredients Against Colorectal Cancer is one of the first European Union funded Research Projects at the cross-road of functional genomics [comprising transcriptomics, the measurement of the expression of all messengers RNA (mRNAs) and proteomics, the measurement of expression/state
Azolla--a model organism for plant genomic studies.

Science.gov (United States)

Qiu, Yin-Long; Yu, Jun

2003-02-01

The aquatic ferns of the genus Azolla are nitrogen-fixing plants that have great potentials in agricultural production and environmental conservation. Azolla in many aspects is qualified to serve as a model organism for genomic studies because of its importance in agriculture, its unique position in plant evolution, its symbiotic relationship with the N2-fixing cyanobacterium, Anabaena azollae, and its moderate-sized genome. The goals of this genome project are not only to understand the biology of the Azolla genome to promote its applications in biological research and agriculture practice but also to gain critical insights about evolution of plant genomes. Together with the strategic and technical improvement as well as cost reduction of DNA sequencing, the deciphering of their genetic code is imminent.
Whole-Genome Sequence of the Metastatic PC3 and LNCaP Human Prostate Cancer Cell Lines

Directory of Open Access Journals (Sweden)

Inge Seim

2017-06-01

Full Text Available The bone metastasis-derived PC3 and the lymph node metastasis-derived LNCaP prostate cancer cell lines are widely studied, having been described in thousands of publications over the last four decades. Here, we report short-read whole-genome sequencing (WGS and de novo assembly of PC3 (ATCC CRL-1435 and LNCaP (clone FGC; ATCC CRL-1740 at ∼70 × coverage. A known homozygous mutation in TP53 and homozygous loss of PTEN were robustly identified in the PC3 cell line, whereas the LNCaP cell line exhibited a larger number of putative inactivating somatic point and indel mutations (and in particular a loss of stop codon events. This study also provides preliminary evidence that loss of one or both copies of the tumor suppressor Capicua (CIC contributes to primary tumor relapse and metastatic progression, potentially offering a treatment target for castration-resistant prostate cancer (CRPC. Our work provides a resource for genetic, genomic, and biological studies employing two commonly-used prostate cancer cell lines.
Whole-Genome Sequence of the Metastatic PC3 and LNCaP Human Prostate Cancer Cell Lines.

Science.gov (United States)

Seim, Inge; Jeffery, Penny L; Thomas, Patrick B; Nelson, Colleen C; Chopin, Lisa K

2017-06-07

The bone metastasis-derived PC3 and the lymph node metastasis-derived LNCaP prostate cancer cell lines are widely studied, having been described in thousands of publications over the last four decades. Here, we report short-read whole-genome sequencing (WGS) and de novo assembly of PC3 (ATCC CRL-1435) and LNCaP (clone FGC; ATCC CRL-1740) at ∼70 × coverage. A known homozygous mutation in TP53 and homozygous loss of PTEN were robustly identified in the PC3 cell line, whereas the LNCaP cell line exhibited a larger number of putative inactivating somatic point and indel mutations (and in particular a loss of stop codon events). This study also provides preliminary evidence that loss of one or both copies of the tumor suppressor Capicua ( CIC ) contributes to primary tumor relapse and metastatic progression, potentially offering a treatment target for castration-resistant prostate cancer (CRPC). Our work provides a resource for genetic, genomic, and biological studies employing two commonly-used prostate cancer cell lines. Copyright © 2017 Seim et al.
Leveraging cancer genome information in hematologic malignancies.

Science.gov (United States)

Rampal, Raajit; Levine, Ross L

2013-05-20

The use of candidate gene and genome-wide discovery studies in the last several years has led to an expansion of our knowledge of the spectrum of recurrent, somatic disease alleles, which contribute to the pathogenesis of hematologic malignancies. Notably, these studies have also begun to fundamentally change our ability to develop informative prognostic schema that inform outcome and therapeutic response, yielding substantive insights into mechanisms of hematopoietic transformation in different tissue compartments. Although these studies have already had important biologic and translational impact, significant challenges remain in systematically applying these findings to clinical decision making and in implementing new technologies for genetic analysis into clinical practice to inform real-time decision making. Here, we review recent major genetic advances in myeloid and lymphoid malignancies, the impact of these findings on prognostic models, our understanding of disease initiation and evolution, and the implication of genomic discoveries on clinical decision making. Finally, we discuss general concepts in genetic modeling and the current state-of-the-art technology used in genetic investigation.
Integrative genomic approaches to dissect clinically-significant relationships between the VDR cistrome and gene expression in primary colon cancer.

Science.gov (United States)

Long, Mark D; Campbell, Moray J

2017-10-01

Recently, we undertook a pan-cancer analyses of the nuclear hormone receptor (NR) superfamily in The Cancer Genome Atlas (TCGA), and revealed that the vitamin D receptor (NR1I1/VDR) was commonly and significantly down-regulated specifically in colon adenocarcinoma cohort (COAD). To examine the consequence of down-regulated VDR expression we re-analyzed VDR chromatin immunoprecipitation sequencing (ChIP-Seq) data from LS180 colon cancer cells (GSE31939). This analysis identified 1809 loci that displayed significant (p.adjcolon tumor suppressor, Galactin 4) had significantly shorted disease free survival. These analyses suggest that reduced expression of VDR in colon cancer (but neither loss nor mutation) changes the actions of the VDR by both dampening the expression of tumor suppressors (e.g. LGALS4) whilst either stabilizing or not down-regulating expression of oncogenes (e.g. Carbonic Anhydrase 9 (CA9)). These integrative genomic approaches are relatively generic and applicable to the study of any transcription factor. Copyright © 2016. Published by Elsevier Ltd.
High-order chromatin architecture shapes the landscape of chromosomal alterations in cancer

Science.gov (United States)

Fudenberg, Geoffrey; Getz, Gad; Meyerson, Matthew; Mirny, Leonid

2012-02-01

The rapid growth of cancer genome structural information provides an opportunity for a better understanding of the mutational mechanisms of genomic alterations in cancer and the forces of selection that act upon them. Here we test the evidence for two major forces, spatial chromosome structure and purifying (or negative) selection, that shape the landscape of somatic copy-number alterations (SCNAs) in cancer (Beroukhim et al, 2010). Using a maximum likelihood framework we compare SCNA maps and three-dimensional genome architecture as determined by genome-wide chromosome conformation capture (HiC) and described by the proposed fractal-globule (FG) model (Lieberman-Aiden and Van Berkum et al, 2009). This analysis provides evidence that the distribution of chromosomal alterations in cancer is spatially related to three-dimensional genomic architecture and additionally suggests that purifying selection as well as positive selection shapes the landscape of SCNAs during somatic evolution of cancer cells.
Systematic Identification and Assessment of Therapeutic Targets for Breast Cancer Based on Genome-Wide RNA Interference Transcriptomes

Directory of Open Access Journals (Sweden)

Yang Liu

2017-02-01

Full Text Available With accumulating public omics data, great efforts have been made to characterize the genetic heterogeneity of breast cancer. However, identifying novel targets and selecting the best from the sizeable lists of candidate targets is still a key challenge for targeted therapy, largely owing to the lack of economical, efficient and systematic discovery and assessment to prioritize potential therapeutic targets. Here, we describe an approach that combines the computational evaluation and objective, multifaceted assessment to systematically identify and prioritize targets for biological validation and therapeutic exploration. We first establish the reference gene expression profiles from breast cancer cell line MCF7 upon genome-wide RNA interference (RNAi of a total of 3689 genes, and the breast cancer query signatures using RNA-seq data generated from tissue samples of clinical breast cancer patients in the Cancer Genome Atlas (TCGA. Based on gene set enrichment analysis, we identified a set of 510 genes that when knocked down could significantly reverse the transcriptome of breast cancer state. We then perform multifaceted assessment to analyze the gene set to prioritize potential targets for gene therapy. We also propose drug repurposing opportunities and identify potentially druggable proteins that have been poorly explored with regard to the discovery of small-molecule modulators. Finally, we obtained a small list of candidate therapeutic targets for four major breast cancer subtypes, i.e., luminal A, luminal B, HER2+ and triple negative breast cancer. This RNAi transcriptome-based approach can be a helpful paradigm for relevant researches to identify and prioritize candidate targets for experimental validation.
An object model for genome information at all levels of resolution

Energy Technology Data Exchange (ETDEWEB)

Honda, S.; Parrott, N.W.; Smith, R.; Lawrence, C.

1993-12-31

An object model for genome data at all levels of resolution is described. The model was derived by considering the requirements for representing genome related objects in three application domains: genome maps, large-scale DNA sequencing, and exploring functional information in gene and protein sequences. The methodology used for the object-oriented analysis is also described.
Cancer in Drosophila

DEFF Research Database (Denmark)

Herranz, Héctor; Eichenlaub, Teresa; Cohen, Stephen M

2016-01-01

Cancer genomics has greatly increased our understanding of the complexity of the genetic and epigenetic changes found in human tumors. Understanding the functional relationships among these elements calls for the use of flexible genetic models. We discuss the use of Drosophila models to study...
Integrated Bioinformatics, Environmental Epidemiologic and Genomic Approaches to Identify Environmental and Molecular Links between Endometriosis and Breast Cancer

Directory of Open Access Journals (Sweden)

Deodutta Roy

2015-10-01

Full Text Available We present a combined environmental epidemiologic, genomic, and bioinformatics approach to identify: exposure of environmental chemicals with estrogenic activity; epidemiologic association between endocrine disrupting chemical (EDC and health effects, such as, breast cancer or endometriosis; and gene-EDC interactions and disease associations. Human exposure measurement and modeling confirmed estrogenic activity of three selected class of environmental chemicals, polychlorinated biphenyls (PCBs, bisphenols (BPs, and phthalates. Meta-analysis showed that PCBs exposure, not Bisphenol A (BPA and phthalates, increased the summary odds ratio for breast cancer and endometriosis. Bioinformatics analysis of gene-EDC interactions and disease associations identified several hundred genes that were altered by exposure to PCBs, phthalate or BPA. EDCs-modified genes in breast neoplasms and endometriosis are part of steroid hormone signaling and inflammation pathways. All three EDCs–PCB 153, phthalates, and BPA influenced five common genes—CYP19A1, EGFR, ESR2, FOS, and IGF1—in breast cancer as well as in endometriosis. These genes are environmentally and estrogen responsive, altered in human breast and uterine tumors and endometriosis lesions, and part of Mitogen Activated Protein Kinase (MAPK signaling pathways in cancer. Our findings suggest that breast cancer and endometriosis share some common environmental and molecular risk factors.
Integrative Genomic Analysis of Coincident Cancer Foci Implicates CTNNB1 and PTEN Alterations in Ductal Prostate Cancer.

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Gillard, Marc; Lack, Justin; Pontier, Andrea; Gandla, Divya; Hatcher, David; Sowalsky, Adam G; Rodriguez-Nieves, Jose; Vander Griend, Donald; Paner, Gladell; VanderWeele, David

2017-12-08

Ductal adenocarcinoma of the prostate is an aggressive subtype, with high rates of biochemical recurrence and overall poor prognosis. It is frequently found coincident with conventional acinar adenocarcinoma. The genomic features driving evolution to its ductal histology and the biology associated with its poor prognosis remain unknown. To characterize genomic features distinguishing ductal adenocarcinoma from coincident acinar adenocarcinoma foci from the same patient. Ten patients with coincident acinar and ductal prostate cancer underwent prostatectomy. Laser microdissection was used to separately isolate acinar and ductal foci. DNA and RNA were extracted, and used for integrative genomic and transcriptomic analyses. Single nucleotide mutations, small indels, copy number estimates, and expression profiles were identified. Phylogenetic relationships between coincident foci were determined, and characteristics distinguishing ductal from acinar foci were identified. Exome sequencing, copy number estimates, and fusion genes demonstrated coincident ductal and acinar adenocarcinoma diverged from a common progenitor, yet they harbored distinct alterations unique to each focus. AR expression and activity were similar in both histologies. Nine of 10 cases had mutually exclusive CTNNB1 hotspot mutations or phosphatase and tensin homolog (PTEN) alterations in the ductal component, and these were absent in the acinar foci. These alterations were associated with changes in expression in WNT- and PI3K-pathway genes. Coincident ductal and acinar histologies typically are clonally related and thus arise from the same cell of origin. Ductal foci are enriched for cases with either a CTNNB1 hotspot mutation or a PTEN alteration, and are associated with WNT- or PI3K-pathway activation. These alterations are mutually exclusive and may represent distinct subtypes. The aggressive subtype ductal adenocarcinoma is closely related to conventional acinar prostate cancer. Ductal foci

Establishment and Characterization of a Highly Tumourigenic and Cancer Stem Cell Enriched Pancreatic Cancer Cell Line as a Well Defined Model System

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Fredebohm, Johannes; Boettcher, Michael; Eisen, Christian; Gaida, Matthias M.; Heller, Anette; Keleg, Shereen; Tost, Jörg; Greulich-Bode, Karin M.; Hotz-Wagenblatt, Agnes; Lathrop, Mark; Giese, Nathalia A.; Hoheisel, Jörg D.

2012-01-01

Standard cancer cell lines do not model the intratumoural heterogeneity situation sufficiently. Clonal selection leads to a homogeneous population of cells by genetic drift. Heterogeneity of tumour cells, however, is particularly critical for therapeutically relevant studies, since it is a prerequisite for acquiring drug resistance and reoccurrence of tumours. Here, we report the isolation of a highly tumourigenic primary pancreatic cancer cell line, called JoPaca-1 and its detailed characterization at multiple levels. Implantation of as few as 100 JoPaca-1 cells into immunodeficient mice gave rise to tumours that were histologically very similar to the primary tumour. The high heterogeneity of JoPaca-1 was reflected by diverse cell morphology and a substantial number of chromosomal aberrations. Comparative whole-genome sequencing of JoPaca-1 and BxPC-3 revealed mutations in genes frequently altered in pancreatic cancer. Exceptionally high expression of cancer stem cell markers and a high clonogenic potential in vitro and in vivo was observed. All of these attributes make this cell line an extremely valuable model to study the biology of and pharmaceutical effects on pancreatic cancer. PMID:23152778
*K-means and cluster models for cancer signatures.

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Kakushadze, Zura; Yu, Willie

2017-09-01

We present *K-means clustering algorithm and source code by expanding statistical clustering methods applied in https://ssrn.com/abstract=2802753 to quantitative finance. *K-means is statistically deterministic without specifying initial centers, etc. We apply *K-means to extracting cancer signatures from genome data without using nonnegative matrix factorization (NMF). *K-means' computational cost is a fraction of NMF's. Using 1389 published samples for 14 cancer types, we find that 3 cancers (liver cancer, lung cancer and renal cell carcinoma) stand out and do not have cluster-like structures. Two clusters have especially high within-cluster correlations with 11 other cancers indicating common underlying structures. Our approach opens a novel avenue for studying such structures. *K-means is universal and can be applied in other fields. We discuss some potential applications in quantitative finance.
Real-World Evidence In Support Of Precision Medicine: Clinico-Genomic Cancer Data As A Case Study.

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Agarwala, Vineeta; Khozin, Sean; Singal, Gaurav; O'Connell, Claire; Kuk, Deborah; Li, Gerald; Gossai, Anala; Miller, Vincent; Abernethy, Amy P

2018-05-01

The majority of US adult cancer patients today are diagnosed and treated outside the context of any clinical trial (that is, in the real world). Although these patients are not part of a research study, their clinical data are still recorded. Indeed, data captured in electronic health records form an ever-growing, rich digital repository of longitudinal patient experiences, treatments, and outcomes. Likewise, genomic data from tumor molecular profiling are increasingly guiding oncology care. Linking real-world clinical and genomic data, as well as information from other co-occurring data sets, could create study populations that provide generalizable evidence for precision medicine interventions. However, the infrastructure required to link, ensure quality, and rapidly learn from such composite data is complex. We outline the challenges and describe a novel approach to building a real-world clinico-genomic database of patients with cancer. This work represents a case study in how data collected during routine patient care can inform precision medicine efforts for the population at large. We suggest that health policies can promote innovation by defining appropriate uses of real-world evidence, establishing data standards, and incentivizing data sharing.
MutSpec: a Galaxy toolbox for streamlined analyses of somatic mutation spectra in human and mouse cancer genomes.

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Ardin, Maude; Cahais, Vincent; Castells, Xavier; Bouaoun, Liacine; Byrnes, Graham; Herceg, Zdenko; Zavadil, Jiri; Olivier, Magali

2016-04-18

The nature of somatic mutations observed in human tumors at single gene or genome-wide levels can reveal information on past carcinogenic exposures and mutational processes contributing to tumor development. While large amounts of sequencing data are being generated, the associated analysis and interpretation of mutation patterns that may reveal clues about the natural history of cancer present complex and challenging tasks that require advanced bioinformatics skills. To make such analyses accessible to a wider community of researchers with no programming expertise, we have developed within the web-based user-friendly platform Galaxy a first-of-its-kind package called MutSpec. MutSpec includes a set of tools that perform variant annotation and use advanced statistics for the identification of mutation signatures present in cancer genomes and for comparing the obtained signatures with those published in the COSMIC database and other sources. MutSpec offers an accessible framework for building reproducible analysis pipelines, integrating existing methods and scripts developed in-house with publicly available R packages. MutSpec may be used to analyse data from whole-exome, whole-genome or targeted sequencing experiments performed on human or mouse genomes. Results are provided in various formats including rich graphical outputs. An example is presented to illustrate the package functionalities, the straightforward workflow analysis and the richness of the statistics and publication-grade graphics produced by the tool. MutSpec offers an easy-to-use graphical interface embedded in the popular Galaxy platform that can be used by researchers with limited programming or bioinformatics expertise to analyse mutation signatures present in cancer genomes. MutSpec can thus effectively assist in the discovery of complex mutational processes resulting from exogenous and endogenous carcinogenic insults.
Modeling Lactococcus lactis using a genome-scale flux model

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Nielsen Jens

2005-06-01

Full Text Available Abstract Background Genome-scale flux models are useful tools to represent and analyze microbial metabolism. In this work we reconstructed the metabolic network of the lactic acid bacteria Lactococcus lactis and developed a genome-scale flux model able to simulate and analyze network capabilities and whole-cell function under aerobic and anaerobic continuous cultures. Flux balance analysis (FBA and minimization of metabolic adjustment (MOMA were used as modeling frameworks. Results The metabolic network was reconstructed using the annotated genome sequence from L. lactis ssp. lactis IL1403 together with physiological and biochemical information. The established network comprised a total of 621 reactions and 509 metabolites, representing the overall metabolism of L. lactis. Experimental data reported in the literature was used to fit the model to phenotypic observations. Regulatory constraints had to be included to simulate certain metabolic features, such as the shift from homo to heterolactic fermentation. A minimal medium for in silico growth was identified, indicating the requirement of four amino acids in addition to a sugar. Remarkably, de novo biosynthesis of four other amino acids was observed even when all amino acids were supplied, which is in good agreement with experimental observations. Additionally, enhanced metabolic engineering strategies for improved diacetyl producing strains were designed. Conclusion The L. lactis metabolic network can now be used for a better understanding of lactococcal metabolic capabilities and potential, for the design of enhanced metabolic engineering strategies and for integration with other types of 'omic' data, to assist in finding new information on cellular organization and function.
Identification of genes containing expanded purine repeats in the human genome and their apparent protective role against cancer.

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Singh, Himanshu Narayan; Rajeswari, Moganty R

2016-01-01

Purine repeat sequences present in a gene are unique as they have high propensity to form unusual DNA-triple helix structures. Friedreich's ataxia is the only human disease that is well known to be associated with DNA-triplexes formed by purine repeats. The purpose of this study was to recognize the expanded purine repeats (EPRs) in human genome and find their correlation with cancer pathogenesis. We developed "PuRepeatFinder.pl" algorithm to identify non-overlapping EPRs without pyrimidine interruptions in the human genome and customized for searching repeat lengths, n ≥ 200. A total of 1158 EPRs were identified in the genome which followed Wakeby distribution. Two hundred and ninety-six EPRs were found in geneic regions of 282 genes (EPR-genes). Gene clustering of EPR-genes was done based on their cellular function and a large number of EPR-genes were found to be enzymes/enzyme modulators. Meta-analysis of 282 EPR-genes identified only 63 EPR-genes in association with cancer, mostly in breast, lung, and blood cancers. Protein-protein interaction network analysis of all 282 EPR-genes identified proteins including those in cadherins and VEGF. The two observations, that EPRs can induce mutations under malignant conditions and that identification of some EPR-gene products in vital cell signaling-mediated pathways, together suggest the crucial role of EPRs in carcinogenesis. The new link between EPR-genes and their functionally interacting proteins throws a new dimension in the present understanding of cancer pathogenesis and can help in planning therapeutic strategies. Validation of present results using techniques like NGS is required to establish the role of the EPR genes in cancer pathology.
Building a model: developing genomic resources for common milkweed (Asclepias syriaca) with low coverage genome sequencing.

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Straub, Shannon C K; Fishbein, Mark; Livshultz, Tatyana; Foster, Zachary; Parks, Matthew; Weitemier, Kevin; Cronn, Richard C; Liston, Aaron

2011-05-04

Milkweeds (Asclepias L.) have been extensively investigated in diverse areas of evolutionary biology and ecology; however, there are few genetic resources available to facilitate and compliment these studies. This study explored how low coverage genome sequencing of the common milkweed (Asclepias syriaca L.) could be useful in characterizing the genome of a plant without prior genomic information and for development of genomic resources as a step toward further developing A. syriaca as a model in ecology and evolution. A 0.5× genome of A. syriaca was produced using Illumina sequencing. A virtually complete chloroplast genome of 158,598 bp was assembled, revealing few repeats and loss of three genes: accD, clpP, and ycf1. A nearly complete rDNA cistron (18S-5.8S-26S; 7,541 bp) and 5S rDNA (120 bp) sequence were obtained. Assessment of polymorphism revealed that the rDNA cistron and 5S rDNA had 0.3% and 26.7% polymorphic sites, respectively. A partial mitochondrial genome sequence (130,764 bp), with identical gene content to tobacco, was also assembled. An initial characterization of repeat content indicated that Ty1/copia-like retroelements are the most common repeat type in the milkweed genome. At least one A. syriaca microread hit 88% of Catharanthus roseus (Apocynaceae) unigenes (median coverage of 0.29×) and 66% of single copy orthologs (COSII) in asterids (median coverage of 0.14×). From this partial characterization of the A. syriaca genome, markers for population genetics (microsatellites) and phylogenetics (low-copy nuclear genes) studies were developed. The results highlight the promise of next generation sequencing for development of genomic resources for any organism. Low coverage genome sequencing allows characterization of the high copy fraction of the genome and exploration of the low copy fraction of the genome, which facilitate the development of molecular tools for further study of a target species and its relatives. This study represents a first
Genome-wide identification of key modulators of gene-gene interaction networks in breast cancer.

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Chiu, Yu-Chiao; Wang, Li-Ju; Hsiao, Tzu-Hung; Chuang, Eric Y; Chen, Yidong

2017-10-03

With the advances in high-throughput gene profiling technologies, a large volume of gene interaction maps has been constructed. A higher-level layer of gene-gene interaction, namely modulate gene interaction, is composed of gene pairs of which interaction strengths are modulated by (i.e., dependent on) the expression level of a key modulator gene. Systematic investigations into the modulation by estrogen receptor (ER), the best-known modulator gene, have revealed the functional and prognostic significance in breast cancer. However, a genome-wide identification of key modulator genes that may further unveil the landscape of modulated gene interaction is still lacking. We proposed a systematic workflow to screen for key modulators based on genome-wide gene expression profiles. We designed four modularity parameters to measure the ability of a putative modulator to perturb gene interaction networks. Applying the method to a dataset of 286 breast tumors, we comprehensively characterized the modularity parameters and identified a total of 973 key modulator genes. The modularity of these modulators was verified in three independent breast cancer datasets. ESR1, the encoding gene of ER, appeared in the list, and abundant novel modulators were illuminated. For instance, a prognostic predictor of breast cancer, SFRP1, was found the second modulator. Functional annotation analysis of the 973 modulators revealed involvements in ER-related cellular processes as well as immune- and tumor-associated functions. Here we present, as far as we know, the first comprehensive analysis of key modulator genes on a genome-wide scale. The validity of filtering parameters as well as the conservativity of modulators among cohorts were corroborated. Our data bring new insights into the modulated layer of gene-gene interaction and provide candidates for further biological investigations.
NGS-based approach to determine the presence of HPV and their sites of integration in human cancer genome.

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Chandrani, P; Kulkarni, V; Iyer, P; Upadhyay, P; Chaubal, R; Das, P; Mulherkar, R; Singh, R; Dutt, A

2015-06-09

Human papilloma virus (HPV) accounts for the most common cause of all virus-associated human cancers. Here, we describe the first graphic user interface (GUI)-based automated tool 'HPVDetector', for non-computational biologists, exclusively for detection and annotation of the HPV genome based on next-generation sequencing data sets. We developed a custom-made reference genome that comprises of human chromosomes along with annotated genome of 143 HPV types as pseudochromosomes. The tool runs on a dual mode as defined by the user: a 'quick mode' to identify presence of HPV types and an 'integration mode' to determine genomic location for the site of integration. The input data can be a paired-end whole-exome, whole-genome or whole-transcriptome data set. The HPVDetector is available in public domain for download: http://www.actrec.gov.in/pi-webpages/AmitDutt/HPVdetector/HPVDetector.html. On the basis of our evaluation of 116 whole-exome, 23 whole-transcriptome and 2 whole-genome data, we were able to identify presence of HPV in 20 exomes and 4 transcriptomes of cervical and head and neck cancer tumour samples. Using the inbuilt annotation module of HPVDetector, we found predominant integration of viral gene E7, a known oncogene, at known 17q21, 3q27, 7q35, Xq28 and novel sites of integration in the human genome. Furthermore, co-infection with high-risk HPVs such as 16 and 31 were found to be mutually exclusive compared with low-risk HPV71. HPVDetector is a simple yet precise and robust tool for detecting HPV from tumour samples using variety of next-generation sequencing platforms including whole genome, whole exome and transcriptome. Two different modes (quick detection and integration mode) along with a GUI widen the usability of HPVDetector for biologists and clinicians with minimal computational knowledge.
Genomic predictions across Nordic Holstein and Nordic Red using the genomic best linear unbiased prediction model with different genomic relationship matrices.

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Zhou, L; Lund, M S; Wang, Y; Su, G

2014-08-01

This study investigated genomic predictions across Nordic Holstein and Nordic Red using various genomic relationship matrices. Different sources of information, such as consistencies of linkage disequilibrium (LD) phase and marker effects, were used to construct the genomic relationship matrices (G-matrices) across these two breeds. Single-trait genomic best linear unbiased prediction (GBLUP) model and two-trait GBLUP model were used for single-breed and two-breed genomic predictions. The data included 5215 Nordic Holstein bulls and 4361 Nordic Red bulls, which was composed of three populations: Danish Red, Swedish Red and Finnish Ayrshire. The bulls were genotyped with 50 000 SNP chip. Using the two-breed predictions with a joint Nordic Holstein and Nordic Red reference population, accuracies increased slightly for all traits in Nordic Red, but only for some traits in Nordic Holstein. Among the three subpopulations of Nordic Red, accuracies increased more for Danish Red than for Swedish Red and Finnish Ayrshire. This is because closer genetic relationships exist between Danish Red and Nordic Holstein. Among Danish Red, individuals with higher genomic relationship coefficients with Nordic Holstein showed more increased accuracies in the two-breed predictions. Weighting the two-breed G-matrices by LD phase consistencies, marker effects or both did not further improve accuracies of the two-breed predictions. © 2014 Blackwell Verlag GmbH.
Genome-wide search for gene-gene interactions in colorectal cancer.

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Shuo Jiao

Full Text Available Genome-wide association studies (GWAS have successfully identified a number of single-nucleotide polymorphisms (SNPs associated with colorectal cancer (CRC risk. However, these susceptibility loci known today explain only a small fraction of the genetic risk. Gene-gene interaction (GxG is considered to be one source of the missing heritability. To address this, we performed a genome-wide search for pair-wise GxG associated with CRC risk using 8,380 cases and 10,558 controls in the discovery phase and 2,527 cases and 2,658 controls in the replication phase. We developed a simple, but powerful method for testing interaction, which we term the Average Risk Due to Interaction (ARDI. With this method, we conducted a genome-wide search to identify SNPs showing evidence for GxG with previously identified CRC susceptibility loci from 14 independent regions. We also conducted a genome-wide search for GxG using the marginal association screening and examining interaction among SNPs that pass the screening threshold (p<10(-4. For the known locus rs10795668 (10p14, we found an interacting SNP rs367615 (5q21 with replication p = 0.01 and combined p = 4.19×10(-8. Among the top marginal SNPs after LD pruning (n = 163, we identified an interaction between rs1571218 (20p12.3 and rs10879357 (12q21.1 (nominal combined p = 2.51×10(-6; Bonferroni adjusted p = 0.03. Our study represents the first comprehensive search for GxG in CRC, and our results may provide new insight into the genetic etiology of CRC.
Genome sequence analysis of the model grass Brachypodium distachyon: insights into grass genome evolution

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Schulman, Al

2009-08-09

Three subfamilies of grasses, the Erhardtoideae (rice), the Panicoideae (maize, sorghum, sugar cane and millet), and the Pooideae (wheat, barley and cool season forage grasses) provide the basis of human nutrition and are poised to become major sources of renewable energy. Here we describe the complete genome sequence of the wild grass Brachypodium distachyon (Brachypodium), the first member of the Pooideae subfamily to be completely sequenced. Comparison of the Brachypodium, rice and sorghum genomes reveals a precise sequence- based history of genome evolution across a broad diversity of the grass family and identifies nested insertions of whole chromosomes into centromeric regions as a predominant mechanism driving chromosome evolution in the grasses. The relatively compact genome of Brachypodium is maintained by a balance of retroelement replication and loss. The complete genome sequence of Brachypodium, coupled to its exceptional promise as a model system for grass research, will support the development of new energy and food crops
Stem Cell Differentiation Stage Factors and Their Role in Triggering Symmetry Breaking Processes during Cancer Development: A Quantum Field Theory Model for Reprogramming Cancer Cells to Healthy Phenotypes.

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Biava, Pier Mario; Burigana, Fabio; Germano, Roberto; Kurian, Philip; Verzegnassi, Claudio; Vitiello, Giuseppe

2017-09-20

A long history of research has pursued the use of embryonic factors isolated during cell differentiation processes for the express purpose of transforming cancer cells back to healthy phenotypes. Recent results have clarified that the substances present at different stages of cell differentiation-which we call stem cell differentiation stage factors (SCDSFs)-are proteins with low molecular weight and nucleic acids that regulate genomic expression. The present review summarizes how these substances, taken at different stages of cellular maturation, are able to retard proliferation of many human tumor cell lines and thereby reprogram cancer cells to healthy phenotypes. The model presented here is a quantum field theory (QFT) model in which SCDSFs are able to trigger symmetry breaking processes during cancer development. These symmetry breaking processes, which lie at the root of many phenomena in elementary particle physics and condensed matter physics, govern the phase transitions of totipotent cells to higher degrees of diversity and order, resulting in cell differentiation. In cancers, which share many genomic and metabolic similarities with embryonic stem cells, stimulated re-differentiation often signifies the phenotypic reversion back to health and non-proliferation. In addition to acting on key components of the cellular cycle, SCDSFs are able to reprogram cancer cells by delicately influencing the cancer microenvironment, modulating the electrochemistry and thus the collective electrodynamic behaviors between dipole networks in biomacromolecules and the interstitial water field. Coherent effects in biological water, which are derived from a dissipative QFT framework, may offer new diagnostic and therapeutic targets at a systemic level, before tumor instantiation occurs in specific tissues or organs. Thus, by including the environment as an essential component of our model, we may push the prevailing paradigm of mutation-driven oncogenesis toward a closer
CRISPR/Cas9 Genome Editing of Epidermal Growth Factor Receptor Sufficiently Abolished Oncogenicity in Anaplastic Thyroid Cancer

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Li-Chi Huang

2018-01-01

Full Text Available Anaplastic carcinoma of the thyroid (ATC, also called undifferentiated thyroid cancer, is the least common but most aggressive and deadly thyroid gland malignancy of all thyroid cancers. The aim of this study is to explore essential biomarker and use CRISPR/Cas9 with lentivirus delivery to establish a gene-target therapeutic platform in ATC cells. At the beginning, the gene expression datasets from 1036 cancers from CCLE and 8215 tumors from TCGA were collected and analyzed, showing EGFR is predominantly overexpressed in thyroid cancers than other type of cancers (P=0.017 in CCLE and P=0.001 in TCGA. Using CRISPR/Cas9 genomic edit system, ATC cells with EGFR sgRNA lentivirus transfection obtained great disruptions on gene and protein expression, resulting in cell cycle arrest, cell growth inhibition, and most importantly metastasis turn-off ability. In addition, the FDA-approved TKI of afatinib for EGFR targeting also illustrates great anticancer activity on cancer cell death occurrence, cell growth inhibition, and cell cycle arrest in SW579 cells, an EGFR expressing human ATC cell line. Furthermore, off-target effect of using EGFR sgRNAs was measured and found no genomic editing can be detected in off-target candidate gene. To conclude, this study provides potential ATC therapeutic strategies for current and future clinical needs, which may be possible in increasing the survival rate of ATC patients by translational medicine.
Analysis of Heritability and Shared Heritability Based on Genome-Wide Association Studies for 13 Cancer Types

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Wheeler, William A.; Yeager, Meredith; Panagiotou, Orestis; Wang, Zhaoming; Berndt, Sonja I.; Lan, Qing; Abnet, Christian C.; Amundadottir, Laufey T.; Figueroa, Jonine D.; Landi, Maria Teresa; Mirabello, Lisa; Savage, Sharon A.; Taylor, Philip R.; Vivo, Immaculata De; McGlynn, Katherine A.; Purdue, Mark P.; Rajaraman, Preetha; Adami, Hans-Olov; Ahlbom, Anders; Albanes, Demetrius; Amary, Maria Fernanda; An, She-Juan; Andersson, Ulrika; Andriole, Gerald; Andrulis, Irene L.; Angelucci, Emanuele; Ansell, Stephen M.; Arici, Cecilia; Armstrong, Bruce K.; Arslan, Alan A.; Austin, Melissa A.; Baris, Dalsu; Barkauskas, Donald A.; Bassig, Bryan A.; Becker, Nikolaus; Benavente, Yolanda; Benhamou, Simone; Berg, Christine; Van Den Berg, David; Bernstein, Leslie; Bertrand, Kimberly A.; Birmann, Brenda M.; Black, Amanda; Boeing, Heiner; Boffetta, Paolo; Boutron-Ruault, Marie-Christine; Bracci, Paige M.; Brinton, Louise; Brooks-Wilson, Angela R.; Bueno-de-Mesquita, H. Bas; Burdett, Laurie; Buring, Julie; Butler, Mary Ann; Cai, Qiuyin; Cancel-Tassin, Geraldine; Canzian, Federico; Carrato, Alfredo; Carreon, Tania; Carta, Angela; Chan, John K. C.; Chang, Ellen T.; Chang, Gee-Chen; Chang, I-Shou; Chang, Jiang; Chang-Claude, Jenny; Chen, Chien-Jen; Chen, Chih-Yi; Chen, Chu; Chen, Chung-Hsing; Chen, Constance; Chen, Hongyan; Chen, Kexin; Chen, Kuan-Yu; Chen, Kun-Chieh; Chen, Ying; Chen, Ying-Hsiang; Chen, Yi-Song; Chen, Yuh-Min; Chien, Li-Hsin; Chirlaque, María-Dolores; Choi, Jin Eun; Choi, Yi Young; Chow, Wong-Ho; Chung, Charles C.; Clavel, Jacqueline; Clavel-Chapelon, Françoise; Cocco, Pierluigi; Colt, Joanne S.; Comperat, Eva; Conde, Lucia; Connors, Joseph M.; Conti, David; Cortessis, Victoria K.; Cotterchio, Michelle; Cozen, Wendy; Crouch, Simon; Crous-Bou, Marta; Cussenot, Olivier; Davis, Faith G.; Ding, Ti; Diver, W. Ryan; Dorronsoro, Miren; Dossus, Laure; Duell, Eric J.; Ennas, Maria Grazia; Erickson, Ralph L.; Feychting, Maria; Flanagan, Adrienne M.; Foretova, Lenka; Fraumeni, Joseph F.; Freedman, Neal D.; Beane Freeman, Laura E.; Fuchs, Charles; Gago-Dominguez, Manuela; Gallinger, Steven; Gao, Yu-Tang; Gapstur, Susan M.; Garcia-Closas, Montserrat; García-Closas, Reina; Gascoyne, Randy D.; Gastier-Foster, Julie; Gaudet, Mia M.; Gaziano, J. Michael; Giffen, Carol; Giles, Graham G.; Giovannucci, Edward; Glimelius, Bengt; Goggins, Michael; Gokgoz, Nalan; Goldstein, Alisa M.; Gorlick, Richard; Gross, Myron; Grubb, Robert; Gu, Jian; Guan, Peng; Gunter, Marc; Guo, Huan; Habermann, Thomas M.; Haiman, Christopher A.; Halai, Dina; Hallmans, Goran; Hassan, Manal; Hattinger, Claudia; He, Qincheng; He, Xingzhou; Helzlsouer, Kathy; Henderson, Brian; Henriksson, Roger; Hjalgrim, Henrik; Hoffman-Bolton, Judith; Hohensee, Chancellor; Holford, Theodore R.; Holly, Elizabeth A.; Hong, Yun-Chul; Hoover, Robert N.; Horn-Ross, Pamela L.; Hosain, G. M. Monawar; Hosgood, H. Dean; Hsiao, Chin-Fu; Hu, Nan; Hu, Wei; Hu, Zhibin; Huang, Ming-Shyan; Huerta, Jose-Maria; Hung, Jen-Yu; Hutchinson, Amy; Inskip, Peter D.; Jackson, Rebecca D.; Jacobs, Eric J.; Jenab, Mazda; Jeon, Hyo-Sung; Ji, Bu-Tian; Jin, Guangfu; Jin, Li; Johansen, Christoffer; Johnson, Alison; Jung, Yoo Jin; Kaaks, Rudolph; Kamineni, Aruna; Kane, Eleanor; Kang, Chang Hyun; Karagas, Margaret R.; Kelly, Rachel S.; Khaw, Kay-Tee; Kim, Christopher; Kim, Hee Nam; Kim, Jin Hee; Kim, Jun Suk; Kim, Yeul Hong; Kim, Young Tae; Kim, Young-Chul; Kitahara, Cari M.; Klein, Alison P.; Klein, Robert J.; Kogevinas, Manolis; Kohno, Takashi; Kolonel, Laurence N.; Kooperberg, Charles; Kricker, Anne; Krogh, Vittorio; Kunitoh, Hideo; Kurtz, Robert C.; Kweon, Sun-Seog; LaCroix, Andrea; Lawrence, Charles; Lecanda, Fernando; Lee, Victor Ho Fun; Li, Donghui; Li, Haixin; Li, Jihua; Li, Yao-Jen; Li, Yuqing; Liao, Linda M.; Liebow, Mark; Lightfoot, Tracy; Lim, Wei-Yen; Lin, Chien-Chung; Lin, Dongxin; Lindstrom, Sara; Linet, Martha S.; Link, Brian K.; Liu, Chenwei; Liu, Jianjun; Liu, Li; Ljungberg, Börje; Lloreta, Josep; Lollo, Simonetta Di; Lu, Daru; Lund, Eiluv; Malats, Nuria; Mannisto, Satu; Marchand, Loic Le; Marina, Neyssa; Masala, Giovanna; Mastrangelo, Giuseppe; Matsuo, Keitaro; Maynadie, Marc; McKay, James; McKean-Cowdin, Roberta; Melbye, Mads; Melin, Beatrice S.; Michaud, Dominique S.; Mitsudomi, Tetsuya; Monnereau, Alain; Montalvan, Rebecca; Moore, Lee E.; Mortensen, Lotte Maxild; Nieters, Alexandra; North, Kari E.; Novak, Anne J.; Oberg, Ann L.; Offit, Kenneth; Oh, In-Jae; Olson, Sara H.; Palli, Domenico; Pao, William; Park, In Kyu; Park, Jae Yong; Park, Kyong Hwa; Patiño-Garcia, Ana; Pavanello, Sofia; Peeters, Petra H. M.; Perng, Reury-Perng; Peters, Ulrike; Petersen, Gloria M.; Picci, Piero; Pike, Malcolm C.; Porru, Stefano; Prescott, Jennifer; Prokunina-Olsson, Ludmila; Qian, Biyun; Qiao, You-Lin; Rais, Marco; Riboli, Elio; Riby, Jacques; Risch, Harvey A.; Rizzato, Cosmeri; Rodabough, Rebecca; Roman, Eve; Roupret, Morgan; Ruder, Avima M.; de Sanjose, Silvia; Scelo, Ghislaine; Schned, Alan; Schumacher, Fredrick; Schwartz, Kendra; Schwenn, Molly; Scotlandi, Katia; Seow, Adeline; Serra, Consol; Serra, Massimo; Sesso, Howard D.; Setiawan, Veronica Wendy; Severi, Gianluca; Severson, Richard K.; Shanafelt, Tait D.; Shen, Hongbing; Shen, Wei; Shin, Min-Ho; Shiraishi, Kouya; Shu, Xiao-Ou; Siddiq, Afshan; Sierrasesúmaga, Luis; Sihoe, Alan Dart Loon; Skibola, Christine F.; Smith, Alex; Smith, Martyn T.; Southey, Melissa C.; Spinelli, John J.; Staines, Anthony; Stampfer, Meir; Stern, Marianna C.; Stevens, Victoria L.; Stolzenberg-Solomon, Rachael S.; Su, Jian; Su, Wu-Chou; Sund, Malin; Sung, Jae Sook; Sung, Sook Whan; Tan, Wen; Tang, Wei; Tardón, Adonina; Thomas, David; Thompson, Carrie A.; Tinker, Lesley F.; Tirabosco, Roberto; Tjønneland, Anne; Travis, Ruth C.; Trichopoulos, Dimitrios; Tsai, Fang-Yu; Tsai, Ying-Huang; Tucker, Margaret; Turner, Jenny; Vajdic, Claire M.; Vermeulen, Roel C. H.; Villano, Danylo J.; Vineis, Paolo; Virtamo, Jarmo; Visvanathan, Kala; Wactawski-Wende, Jean; Wang, Chaoyu; Wang, Chih-Liang; Wang, Jiu-Cun; Wang, Junwen; Wei, Fusheng; Weiderpass, Elisabete; Weiner, George J.; Weinstein, Stephanie; Wentzensen, Nicolas; White, Emily; Witzig, Thomas E.; Wolpin, Brian M.; Wong, Maria Pik; Wu, Chen; Wu, Guoping; Wu, Junjie; Wu, Tangchun; Wu, Wei; Wu, Xifeng; Wu, Yi-Long; Wunder, Jay S.; Xiang, Yong-Bing; Xu, Jun; Xu, Ping; Yang, Pan-Chyr; Yang, Tsung-Ying; Ye, Yuanqing; Yin, Zhihua; Yokota, Jun; Yoon, Ho-Il; Yu, Chong-Jen; Yu, Herbert; Yu, Kai; Yuan, Jian-Min; Zelenetz, Andrew; Zeleniuch-Jacquotte, Anne; Zhang, Xu-Chao; Zhang, Yawei; Zhao, Xueying; Zhao, Zhenhong; Zheng, Hong; Zheng, Tongzhang; Zheng, Wei; Zhou, Baosen; Zhu, Meng; Zucca, Mariagrazia; Boca, Simina M.; Cerhan, James R.; Ferri, Giovanni M.; Hartge, Patricia; Hsiung, Chao Agnes; Magnani, Corrado; Miligi, Lucia; Morton, Lindsay M.; Smedby, Karin E.; Teras, Lauren R.; Vijai, Joseph; Wang, Sophia S.; Brennan, Paul; Caporaso, Neil E.; Hunter, David J.; Kraft, Peter; Rothman, Nathaniel; Silverman, Debra T.; Slager, Susan L.; Chanock, Stephen J.; Chatterjee, Nilanjan

2015-01-01

Background: Studies of related individuals have consistently demonstrated notable familial aggregation of cancer. We aim to estimate the heritability and genetic correlation attributable to the additive effects of common single-nucleotide polymorphisms (SNPs) for cancer at 13 anatomical sites. Methods: Between 2007 and 2014, the US National Cancer Institute has generated data from genome-wide association studies (GWAS) for 49 492 cancer case patients and 34 131 control patients. We apply novel mixed model methodology (GCTA) to this GWAS data to estimate the heritability of individual cancers, as well as the proportion of heritability attributable to cigarette smoking in smoking-related cancers, and the genetic correlation between pairs of cancers. Results: GWAS heritability was statistically significant at nearly all sites, with the estimates of array-based heritability, hl 2, on the liability threshold (LT) scale ranging from 0.05 to 0.38. Estimating the combined heritability of multiple smoking characteristics, we calculate that at least 24% (95% confidence interval [CI] = 14% to 37%) and 7% (95% CI = 4% to 11%) of the heritability for lung and bladder cancer, respectively, can be attributed to genetic determinants of smoking. Most pairs of cancers studied did not show evidence of strong genetic correlation. We found only four pairs of cancers with marginally statistically significant correlations, specifically kidney and testes (ρ = 0.73, SE = 0.28), diffuse large B-cell lymphoma (DLBCL) and pediatric osteosarcoma (ρ = 0.53, SE = 0.21), DLBCL and chronic lymphocytic leukemia (CLL) (ρ = 0.51, SE =0.18), and bladder and lung (ρ = 0.35, SE = 0.14). Correlation analysis also indicates that the genetic architecture of lung cancer differs between a smoking population of European ancestry and a nonsmoking Asian population, allowing for the possibility that the genetic etiology for the same disease can vary by population and environmental exposures. Conclusion: Our
Safeguarding genome integrity

DEFF Research Database (Denmark)

Sørensen, Claus Storgaard; Syljuåsen, Randi G

2012-01-01

Mechanisms that preserve genome integrity are highly important during the normal life cycle of human cells. Loss of genome protective mechanisms can lead to the development of diseases such as cancer. Checkpoint kinases function in the cellular surveillance pathways that help cells to cope with D...
Matrix-comparative genomic hybridization from multicenter formalin-fixed paraffin-embedded colorectal cancer tissue blocks

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Köhne Claus-Henning

2007-04-01

Full Text Available Abstract Background The identification of genomic signatures of colorectal cancer for risk stratification requires the study of large series of cancer patients with an extensive clinical follow-up. Multicentric clinical studies represent an ideal source of well documented archived material for this type of analyses. Methods To verify if this material is technically suitable to perform matrix-CGH, we performed a pilot study using macrodissected 29 formalin-fixed, paraffin-embedded tissue samples collected within the framework of the EORTC-GI/PETACC-2 trial for colorectal cancer. The scientific aim was to identify prognostic genomic signatures differentiating locally restricted (UICC stages II-III from systemically advanced (UICC stage IV colorectal tumours. Results The majority of archived tissue samples collected in the different centers was suitable to perform matrix-CGH. 5/7 advanced tumours displayed 13q-gain and 18q-loss. In locally restricted tumours, only 6/12 tumours showed a gain on 13q and 7/12 tumours showed a loss on 18q. Interphase-FISH and high-resolution array-mapping of the gain on 13q confirmed the validity of the array-data and narrowed the chromosomal interval containing potential oncogenes. Conclusion Archival, paraffin-embedded tissue samples collected in multicentric clinical trials are suitable for matrix-CGH analyses and allow the identification of prognostic signatures and aberrations harbouring potential new oncogenes.
Genome-wide meta-analysis identifies five new susceptibility loci for pancreatic cancer.

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Klein, Alison P; Wolpin, Brian M; Risch, Harvey A; Stolzenberg-Solomon, Rachael Z; Mocci, Evelina; Zhang, Mingfeng; Canzian, Federico; Childs, Erica J; Hoskins, Jason W; Jermusyk, Ashley; Zhong, Jun; Chen, Fei; Albanes, Demetrius; Andreotti, Gabriella; Arslan, Alan A; Babic, Ana; Bamlet, William R; Beane-Freeman, Laura; Berndt, Sonja I; Blackford, Amanda; Borges, Michael; Borgida, Ayelet; Bracci, Paige M; Brais, Lauren; Brennan, Paul; Brenner, Hermann; Bueno-de-Mesquita, Bas; Buring, Julie; Campa, Daniele; Capurso, Gabriele; Cavestro, Giulia Martina; Chaffee, Kari G; Chung, Charles C; Cleary, Sean; Cotterchio, Michelle; Dijk, Frederike; Duell, Eric J; Foretova, Lenka; Fuchs, Charles; Funel, Niccola; Gallinger, Steven; M Gaziano, J Michael; Gazouli, Maria; Giles, Graham G; Giovannucci, Edward; Goggins, Michael; Goodman, Gary E; Goodman, Phyllis J; Hackert, Thilo; Haiman, Christopher; Hartge, Patricia; Hasan, Manal; Hegyi, Peter; Helzlsouer, Kathy J; Herman, Joseph; Holcatova, Ivana; Holly, Elizabeth A; Hoover, Robert; Hung, Rayjean J; Jacobs, Eric J; Jamroziak, Krzysztof; Janout, Vladimir; Kaaks, Rudolf; Khaw, Kay-Tee; Klein, Eric A; Kogevinas, Manolis; Kooperberg, Charles; Kulke, Matthew H; Kupcinskas, Juozas; Kurtz, Robert J; Laheru, Daniel; Landi, Stefano; Lawlor, Rita T; Lee, I-Min; LeMarchand, Loic; Lu, Lingeng; Malats, Núria; Mambrini, Andrea; Mannisto, Satu; Milne, Roger L; Mohelníková-Duchoňová, Beatrice; Neale, Rachel E; Neoptolemos, John P; Oberg, Ann L; Olson, Sara H; Orlow, Irene; Pasquali, Claudio; Patel, Alpa V; Peters, Ulrike; Pezzilli, Raffaele; Porta, Miquel; Real, Francisco X; Rothman, Nathaniel; Scelo, Ghislaine; Sesso, Howard D; Severi, Gianluca; Shu, Xiao-Ou; Silverman, Debra; Smith, Jill P; Soucek, Pavel; Sund, Malin; Talar-Wojnarowska, Renata; Tavano, Francesca; Thornquist, Mark D; Tobias, Geoffrey S; Van Den Eeden, Stephen K; Vashist, Yogesh; Visvanathan, Kala; Vodicka, Pavel; Wactawski-Wende, Jean; Wang, Zhaoming; Wentzensen, Nicolas; White, Emily; Yu, Herbert; Yu, Kai; Zeleniuch-Jacquotte, Anne; Zheng, Wei; Kraft, Peter; Li, Donghui; Chanock, Stephen; Obazee, Ofure; Petersen, Gloria M; Amundadottir, Laufey T

2018-02-08

In 2020, 146,063 deaths due to pancreatic cancer are estimated to occur in Europe and the United States combined. To identify common susceptibility alleles, we performed the largest pancreatic cancer GWAS to date, including 9040 patients and 12,496 controls of European ancestry from the Pancreatic Cancer Cohort Consortium (PanScan) and the Pancreatic Cancer Case-Control Consortium (PanC4). Here, we find significant evidence of a novel association at rs78417682 (7p12/TNS3, P = 4.35 × 10 -8 ). Replication of 10 promising signals in up to 2737 patients and 4752 controls from the PANcreatic Disease ReseArch (PANDoRA) consortium yields new genome-wide significant loci: rs13303010 at 1p36.33 (NOC2L, P = 8.36 × 10 -14 ), rs2941471 at 8q21.11 (HNF4G, P = 6.60 × 10 -10 ), rs4795218 at 17q12 (HNF1B, P = 1.32 × 10 -8 ), and rs1517037 at 18q21.32 (GRP, P = 3.28 × 10 -8 ). rs78417682 is not statistically significantly associated with pancreatic cancer in PANDoRA. Expression quantitative trait locus analysis in three independent pancreatic data sets provides molecular support of NOC2L as a pancreatic cancer susceptibility gene.
Comprehensive Analysis of Cancer-Proteogenome to Identify Biomarkers for the Early Diagnosis and Prognosis of Cancer.

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Shukla, Hem D

2017-10-25

During the past century, our understanding of cancer diagnosis and treatment has been based on a monogenic approach, and as a consequence our knowledge of the clinical genetic underpinnings of cancer is incomplete. Since the completion of the human genome in 2003, it has steered us into therapeutic target discovery, enabling us to mine the genome using cutting edge proteogenomics tools. A number of novel and promising cancer targets have emerged from the genome project for diagnostics, therapeutics, and prognostic markers, which are being used to monitor response to cancer treatment. The heterogeneous nature of cancer has hindered progress in understanding the underlying mechanisms that lead to abnormal cellular growth. Since, the start of The Cancer Genome Atlas (TCGA), and the International Genome consortium projects, there has been tremendous progress in genome sequencing and immense numbers of cancer genomes have been completed, and this approach has transformed our understanding of the diagnosis and treatment of different types of cancers. By employing Genomics and proteomics technologies, an immense amount of genomic data is being generated on clinical tumors, which has transformed the cancer landscape and has the potential to transform cancer diagnosis and prognosis. A complete molecular view of the cancer landscape is necessary for understanding the underlying mechanisms of cancer initiation to improve diagnosis and prognosis, which ultimately will lead to personalized treatment. Interestingly, cancer proteome analysis has also allowed us to identify biomarkers to monitor drug and radiation resistance in patients undergoing cancer treatment. Further, TCGA-funded studies have allowed for the genomic and transcriptomic characterization of targeted cancers, this analysis aiding the development of targeted therapies for highly lethal malignancy. High-throughput technologies, such as complete proteome, epigenome, protein-protein interaction, and pharmacogenomics
Visualization of RNA structure models within the Integrative Genomics Viewer.

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Busan, Steven; Weeks, Kevin M

2017-07-01

Analyses of the interrelationships between RNA structure and function are increasingly important components of genomic studies. The SHAPE-MaP strategy enables accurate RNA structure probing and realistic structure modeling of kilobase-length noncoding RNAs and mRNAs. Existing tools for visualizing RNA structure models are not suitable for efficient analysis of long, structurally heterogeneous RNAs. In addition, structure models are often advantageously interpreted in the context of other experimental data and gene annotation information, for which few tools currently exist. We have developed a module within the widely used and well supported open-source Integrative Genomics Viewer (IGV) that allows visualization of SHAPE and other chemical probing data, including raw reactivities, data-driven structural entropies, and data-constrained base-pair secondary structure models, in context with linear genomic data tracks. We illustrate the usefulness of visualizing RNA structure in the IGV by exploring structure models for a large viral RNA genome, comparing bacterial mRNA structure in cells with its structure under cell- and protein-free conditions, and comparing a noncoding RNA structure modeled using SHAPE data with a base-pairing model inferred through sequence covariation analysis. © 2017 Busan and Weeks; Published by Cold Spring Harbor Laboratory Press for the RNA Society.

Genome-Wide Meta-Analyses of Breast, Ovarian, and Prostate Cancer Association Studies Identify Multiple New Susceptibility Loci Shared by at Least Two Cancer Types

DEFF Research Database (Denmark)

Kar, Siddhartha P; Beesley, Jonathan; Amin Al Olama, Ali

2016-01-01

UNLABELLED: Breast, ovarian, and prostate cancers are hormone-related and may have a shared genetic basis, but this has not been investigated systematically by genome-wide association (GWA) studies. Meta-analyses combining the largest GWA meta-analysis data sets for these cancers totaling 112...... (rs200182588/9q31/SMC2; rs8037137/15q26/RCCD1), and two breast and prostate cancer risk loci (rs5013329/1p34/NSUN4; rs9375701/6q23/L3MBTL3). Index variants in five additional regions previously associated with only one cancer also showed clear association with a second cancer type. Cell......-type-specific expression quantitative trait locus and enhancer-gene interaction annotations suggested target genes with potential cross-cancer roles at the new loci. Pathway analysis revealed significant enrichment of death receptor signaling genes near loci with P cancer meta-analysis. SIGNIFICANCE...
Genome-based Modeling and Design of Metabolic Interactions in Microbial Communities.

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Mahadevan, Radhakrishnan; Henson, Michael A

2012-01-01

Biotechnology research is traditionally focused on individual microbial strains that are perceived to have the necessary metabolic functions, or the capability to have these functions introduced, to achieve a particular task. For many important applications, the development of such omnipotent microbes is an extremely challenging if not impossible task. By contrast, nature employs a radically different strategy based on synergistic combinations of different microbial species that collectively achieve the desired task. These natural communities have evolved to exploit the native metabolic capabilities of each species and are highly adaptive to changes in their environments. However, microbial communities have proven difficult to study due to a lack of suitable experimental and computational tools. With the advent of genome sequencing, omics technologies, bioinformatics and genome-scale modeling, researchers now have unprecedented capabilities to analyze and engineer the metabolism of microbial communities. The goal of this review is to summarize recent applications of genome-scale metabolic modeling to microbial communities. A brief introduction to lumped community models is used to motivate the need for genome-level descriptions of individual species and their metabolic interactions. The review of genome-scale models begins with static modeling approaches, which are appropriate for communities where the extracellular environment can be assumed to be time invariant or slowly varying. Dynamic extensions of the static modeling approach are described, and then applications of genome-scale models for design of synthetic microbial communities are reviewed. The review concludes with a summary of metagenomic tools for analyzing community metabolism and an outlook for future research.
Gender-Associated Genomic Differences in Colorectal Cancer: Clinical Insight from Feminization of Male Cancer Cells

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Rola H. Ali

2014-09-01

Full Text Available Gender-related differences in colorectal cancer (CRC are not fully understood. Recent studies have shown that CRC arising in females are significantly associated with CpG island methylator phenotype (CIMP-high. Using array comparative genomic hybridization, we analyzed a cohort of 116 CRCs (57 males, 59 females for chromosomal copy number aberrations (CNA and found that CRC in females had significantly higher numbers of gains involving chromosome arms 1q21.2–q21.3, 4q13.2, 6p21.1 and 16p11.2 and copy number losses of chromosome arm 11q25 compared to males. Interestingly, a subset of male CRCs (46% exhibited a "feminization" phenomenon in the form of gains of X chromosomes (or an arm of X and/or losses of the Y chromosome. Feminization of cancer cells was significantly associated with microsatellite-stable CRCs (p-value 0.003 and wild-type BRAF gene status (p-value 0.009. No significant association with other clinicopathological parameters was identified including disease-free survival. In summary, our data show that some CNAs in CRC may be gender specific and that male cancers characterized by feminization may constitute a specific subset of CRCs that warrants further investigation.
Building a model: developing genomic resources for common milkweed (Asclepias syriaca with low coverage genome sequencing

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Weitemier Kevin

2011-05-01

Full Text Available Abstract Background Milkweeds (Asclepias L. have been extensively investigated in diverse areas of evolutionary biology and ecology; however, there are few genetic resources available to facilitate and compliment these studies. This study explored how low coverage genome sequencing of the common milkweed (Asclepias syriaca L. could be useful in characterizing the genome of a plant without prior genomic information and for development of genomic resources as a step toward further developing A. syriaca as a model in ecology and evolution. Results A 0.5× genome of A. syriaca was produced using Illumina sequencing. A virtually complete chloroplast genome of 158,598 bp was assembled, revealing few repeats and loss of three genes: accD, clpP, and ycf1. A nearly complete rDNA cistron (18S-5.8S-26S; 7,541 bp and 5S rDNA (120 bp sequence were obtained. Assessment of polymorphism revealed that the rDNA cistron and 5S rDNA had 0.3% and 26.7% polymorphic sites, respectively. A partial mitochondrial genome sequence (130,764 bp, with identical gene content to tobacco, was also assembled. An initial characterization of repeat content indicated that Ty1/copia-like retroelements are the most common repeat type in the milkweed genome. At least one A. syriaca microread hit 88% of Catharanthus roseus (Apocynaceae unigenes (median coverage of 0.29× and 66% of single copy orthologs (COSII in asterids (median coverage of 0.14×. From this partial characterization of the A. syriaca genome, markers for population genetics (microsatellites and phylogenetics (low-copy nuclear genes studies were developed. Conclusions The results highlight the promise of next generation sequencing for development of genomic resources for any organism. Low coverage genome sequencing allows characterization of the high copy fraction of the genome and exploration of the low copy fraction of the genome, which facilitate the development of molecular tools for further study of a target species
Deciphering the Code of the Cancer Genome: Mechanisms of Chromosome Rearrangement

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Willis, Nicholas A.; Rass, Emilie; Scully, Ralph

2015-01-01

Chromosome rearrangement plays a causal role in tumorigenesis by contributing to the inactivation of tumor suppressor genes, the dysregulated expression or amplification of oncogenes and the generation of novel gene fusions. Chromosome breaks are important intermediates in this process. How, when and where these breaks arise and the specific mechanisms engaged in their repair strongly influence the resulting patterns of chromosome rearrangement. Here, we review recent progress in understanding how certain distinctive features of the cancer genome, including clustered mutagenesis, tandem segmental duplications, complex breakpoints, chromothripsis, chromoplexy and chromoanasynthesis may arise. PMID:26726318
Exploiting proteomic data for genome annotation and gene model validation in Aspergillus niger

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Grigoriev Igor V

2009-02-01

Full Text Available Abstract Background Proteomic data is a potentially rich, but arguably unexploited, data source for genome annotation. Peptide identifications from tandem mass spectrometry provide prima facie evidence for gene predictions and can discriminate over a set of candidate gene models. Here we apply this to the recently sequenced Aspergillus niger fungal genome from the Joint Genome Institutes (JGI and another predicted protein set from another A.niger sequence. Tandem mass spectra (MS/MS were acquired from 1d gel electrophoresis bands and searched against all available gene models using Average Peptide Scoring (APS and reverse database searching to produce confident identifications at an acceptable false discovery rate (FDR. Results 405 identified peptide sequences were mapped to 214 different A.niger genomic loci to which 4093 predicted gene models clustered, 2872 of which contained the mapped peptides. Interestingly, 13 (6% of these loci either had no preferred predicted gene model or the genome annotators' chosen "best" model for that genomic locus was not found to be the most parsimonious match to the identified peptides. The peptides identified also boosted confidence in predicted gene structures spanning 54 introns from different gene models. Conclusion This work highlights the potential of integrating experimental proteomics data into genomic annotation pipelines much as expressed sequence tag (EST data has been. A comparison of the published genome from another strain of A.niger sequenced by DSM showed that a number of the gene models or proteins with proteomics evidence did not occur in both genomes, further highlighting the utility of the method.
Exploiting proteomic data for genome annotation and gene model validation in Aspergillus niger.

Science.gov (United States)

Wright, James C; Sugden, Deana; Francis-McIntyre, Sue; Riba-Garcia, Isabel; Gaskell, Simon J; Grigoriev, Igor V; Baker, Scott E; Beynon, Robert J; Hubbard, Simon J

2009-02-04

Proteomic data is a potentially rich, but arguably unexploited, data source for genome annotation. Peptide identifications from tandem mass spectrometry provide prima facie evidence for gene predictions and can discriminate over a set of candidate gene models. Here we apply this to the recently sequenced Aspergillus niger fungal genome from the Joint Genome Institutes (JGI) and another predicted protein set from another A.niger sequence. Tandem mass spectra (MS/MS) were acquired from 1d gel electrophoresis bands and searched against all available gene models using Average Peptide Scoring (APS) and reverse database searching to produce confident identifications at an acceptable false discovery rate (FDR). 405 identified peptide sequences were mapped to 214 different A.niger genomic loci to which 4093 predicted gene models clustered, 2872 of which contained the mapped peptides. Interestingly, 13 (6%) of these loci either had no preferred predicted gene model or the genome annotators' chosen "best" model for that genomic locus was not found to be the most parsimonious match to the identified peptides. The peptides identified also boosted confidence in predicted gene structures spanning 54 introns from different gene models. This work highlights the potential of integrating experimental proteomics data into genomic annotation pipelines much as expressed sequence tag (EST) data has been. A comparison of the published genome from another strain of A.niger sequenced by DSM showed that a number of the gene models or proteins with proteomics evidence did not occur in both genomes, further highlighting the utility of the method.
Intratumoural evolutionary landscape of high-risk prostate cancer: The PROGENY study of genomic and immune parameters

DEFF Research Database (Denmark)

Linch, M.; Goh, G.; Hiley, C.

2017-01-01

Background: Intratumoural heterogeneity (ITH) is well recognised in prostate cancer (PC), but its role in high-risk disease is uncertain. A prospective, single-arm, translational study using targeted multiregion prostate biopsies was carried out to study genomic and T-cell ITH in clinically high-...
Human genetics and genomics a decade after the release of the draft sequence of the human genome

Science.gov (United States)

2011-01-01

Substantial progress has been made in human genetics and genomics research over the past ten years since the publication of the draft sequence of the human genome in 2001. Findings emanating directly from the Human Genome Project, together with those from follow-on studies, have had an enormous impact on our understanding of the architecture and function of the human genome. Major developments have been made in cataloguing genetic variation, the International HapMap Project, and with respect to advances in genotyping technologies. These developments are vital for the emergence of genome-wide association studies in the investigation of complex diseases and traits. In parallel, the advent of high-throughput sequencing technologies has ushered in the 'personal genome sequencing' era for both normal and cancer genomes, and made possible large-scale genome sequencing studies such as the 1000 Genomes Project and the International Cancer Genome Consortium. The high-throughput sequencing and sequence-capture technologies are also providing new opportunities to study Mendelian disorders through exome sequencing and whole-genome sequencing. This paper reviews these major developments in human genetics and genomics over the past decade. PMID:22155605
Observing Clonal Dynamics across Spatiotemporal Axes: A Prelude to Quantitative Fitness Models for Cancer.

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McPherson, Andrew W; Chan, Fong Chun; Shah, Sohrab P

2018-02-01

The ability to accurately model evolutionary dynamics in cancer would allow for prediction of progression and response to therapy. As a prelude to quantitative understanding of evolutionary dynamics, researchers must gather observations of in vivo tumor evolution. High-throughput genome sequencing now provides the means to profile the mutational content of evolving tumor clones from patient biopsies. Together with the development of models of tumor evolution, reconstructing evolutionary histories of individual tumors generates hypotheses about the dynamics of evolution that produced the observed clones. In this review, we provide a brief overview of the concepts involved in predicting evolutionary histories, and provide a workflow based on bulk and targeted-genome sequencing. We then describe the application of this workflow to time series data obtained for transformed and progressed follicular lymphomas (FL), and contrast the observed evolutionary dynamics between these two subtypes. We next describe results from a spatial sampling study of high-grade serous (HGS) ovarian cancer, propose mechanisms of disease spread based on the observed clonal mixtures, and provide examples of diversification through subclonal acquisition of driver mutations and convergent evolution. Finally, we state implications of the techniques discussed in this review as a necessary but insufficient step on the path to predictive modelling of disease dynamics. Copyright © 2018 Cold Spring Harbor Laboratory Press; all rights reserved.
Genomic breeding value estimation using nonparametric additive regression models

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Solberg Trygve

2009-01-01

Full Text Available Abstract Genomic selection refers to the use of genomewide dense markers for breeding value estimation and subsequently for selection. The main challenge of genomic breeding value estimation is the estimation of many effects from a limited number of observations. Bayesian methods have been proposed to successfully cope with these challenges. As an alternative class of models, non- and semiparametric models were recently introduced. The present study investigated the ability of nonparametric additive regression models to predict genomic breeding values. The genotypes were modelled for each marker or pair of flanking markers (i.e. the predictors separately. The nonparametric functions for the predictors were estimated simultaneously using additive model theory, applying a binomial kernel. The optimal degree of smoothing was determined by bootstrapping. A mutation-drift-balance simulation was carried out. The breeding values of the last generation (genotyped was predicted using data from the next last generation (genotyped and phenotyped. The results show moderate to high accuracies of the predicted breeding values. A determination of predictor specific degree of smoothing increased the accuracy.
Critical threshold levels of DNA methyltransferase 1 are required to maintain DNA methylation across the genome in human cancer cells.

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Cai, Yi; Tsai, Hsing-Chen; Yen, Ray-Whay Chiu; Zhang, Yang W; Kong, Xiangqian; Wang, Wei; Xia, Limin; Baylin, Stephen B

2017-04-01

Reversing DNA methylation abnormalities and associated gene silencing, through inhibiting DNA methyltransferases (DNMTs) is an important potential cancer therapy paradigm. Maximizing this potential requires defining precisely how these enzymes maintain genome-wide, cancer-specific DNA methylation. To date, there is incomplete understanding of precisely how the three DNMTs, 1, 3A, and 3B, interact for maintaining DNA methylation abnormalities in cancer. By combining genetic and shRNA depletion strategies, we define not only a dominant role for DNA methyltransferase 1 (DNMT1) but also distinct roles of 3A and 3B in genome-wide DNA methylation maintenance. Lowering DNMT1 below a threshold level is required for maximal loss of DNA methylation at all genomic regions, including gene body and enhancer regions, and for maximally reversing abnormal promoter DNA hypermethylation and associated gene silencing to reexpress key genes. It is difficult to reach this threshold with patient-tolerable doses of current DNMT inhibitors (DNMTIs). We show that new approaches, like decreasing the DNMT targeting protein, UHRF1, can augment the DNA demethylation capacities of existing DNA methylation inhibitors for fully realizing their therapeutic potential. © 2017 Cai et al.; Published by Cold Spring Harbor Laboratory Press.
Merging Marine Ecosystem Models and Genomics

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Coles, V.; Hood, R. R.; Stukel, M. R.; Moran, M. A.; Paul, J. H.; Satinsky, B.; Zielinski, B.; Yager, P. L.

2015-12-01

oceanography. One of the grand challenges of oceanography is to develop model techniques to more effectively incorporate genomic information. As one approach, we developed an ecosystem model whose community is determined by randomly assigning functional genes to build each organism's "DNA". Microbes are assigned a size that sets their baseline environmental responses using allometric response cuves. These responses are modified by the costs and benefits conferred by each gene in an organism's genome. The microbes are embedded in a general circulation model where environmental conditions shape the emergent population. This model is used to explore whether organisms constructed from randomized combinations of metabolic capability alone can self-organize to create realistic oceanic biogeochemical gradients. Realistic community size spectra and chlorophyll-a concentrations emerge in the model. The model is run repeatedly with randomly-generated microbial communities and each time realistic gradients in community size spectra, chlorophyll-a, and forms of nitrogen develop. This supports the hypothesis that the metabolic potential of a community rather than the realized species composition is the primary factor setting vertical and horizontal environmental gradients. Vertical distributions of nitrogen and transcripts for genes involved in nitrification are broadly consistent with observations. Modeled gene and transcript abundance for nitrogen cycling and processing of land-derived organic material match observations along the extreme gradients in the Amazon River plume, and they help to explain the factors controlling observed variability.
Genome network medicine: innovation to overcome huge challenges in cancer therapy.

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Roukos, Dimitrios H

2014-01-01

The post-ENCODE era shapes now a new biomedical research direction for understanding transcriptional and signaling networks driving gene expression and core cellular processes such as cell fate, survival, and apoptosis. Over the past half century, the Francis Crick 'central dogma' of single n gene/protein-phenotype (trait/disease) has defined biology, human physiology, disease, diagnostics, and drugs discovery. However, the ENCODE project and several other genomic studies using high-throughput sequencing technologies, computational strategies, and imaging techniques to visualize regulatory networks, provide evidence that transcriptional process and gene expression are regulated by highly complex dynamic molecular and signaling networks. This Focus article describes the linear experimentation-based limitations of diagnostics and therapeutics to cure advanced cancer and the need to move on from reductionist to network-based approaches. With evident a wide genomic heterogeneity, the power and challenges of next-generation sequencing (NGS) technologies to identify a patient's personal mutational landscape for tailoring the best target drugs in the individual patient are discussed. However, the available drugs are not capable of targeting aberrant signaling networks and research on functional transcriptional heterogeneity and functional genome organization is poorly understood. Therefore, the future clinical genome network medicine aiming at overcoming multiple problems in the new fields of regulatory DNA mapping, noncoding RNA, enhancer RNAs, and dynamic complexity of transcriptional circuitry are also discussed expecting in new innovation technology and strong appreciation of clinical data and evidence-based medicine. The problematic and potential solutions in the discovery of next-generation, molecular, and signaling circuitry-based biomarkers and drugs are explored. © 2013 Wiley Periodicals, Inc.
Genomic profiles of lung cancer associated with idiopathic pulmonary fibrosis.

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Hwang, Ji An; Kim, Deokhoon; Chun, Sung-Min; Bae, SooHyun; Song, Joon Seon; Kim, Mi Young; Koo, Hyun Jung; Song, Jin Woo; Kim, Woo Sung; Lee, Jae Cheol; Kim, Hyeong Ryul; Choi, Chang-Min; Jang, Se Jin

2018-01-01

Little is known about the pathogenesis or molecular profiles of idiopathic pulmonary fibrosis-associated lung cancer (IPF-LC). This study was performed to investigate the genomic profiles of IPF-LC and to explore the possibility of defining potential therapeutic targets in IPF-LC. We assessed genomic profiles of IPF-LC by using targeted exome sequencing (OncoPanel version 2) in 35 matched tumour/normal pairs surgically resected between 2004 and 2014. Germline and somatic variant calling was performed with GATK HaplotypeCaller and MuTect with GATK SomaticIndelocator, respectively. Copy number analysis was conducted with CNVkit, with focal events determined by Genomic Identification of Significant Targets in Cancer 2.0, and pathway analysis (KEGG) with DAVID. Germline mutations in TERT (rs2736100, n = 33) and CDKN1A (rs2395655, n = 27) associated with idiopathic pulmonary fibrosis risk were detected in most samples. A total of 410 somatic mutations were identified, with an average of 11.7 per tumour, including 69 synonymous, 177 missense, 17 nonsense, 1 nonstop and 11 splice-site mutations, and 135 small coding indels. Spectra of the somatic mutations revealed predominant C > T transitions despite an extensive smoking history in most patients, suggesting a potential association between APOBEC-related mutagenesis and the development of IPF-LC. TP53 (22/35, 62.9%) and BRAF (6/35, 17.1%) were found to be significantly mutated in IPF-LC. Recurrent focal amplifications in three chromosomal loci (3q26.33, 7q31.2, and 12q14.3) and 9p21.3 deletion were identified, and genes associated with the JAK-STAT signalling pathway were significantly amplified in IPF-LC (P = 0.012). This study demonstrates that IPF-LC is genetically characterized by the presence of somatic mutations reflecting a variety of environmental exposures on the background of specific germline mutations, and is associated with potentially targetable alterations such as BRAF mutations. Copyright © 2017
A system-level model for the microbial regulatory genome.

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Brooks, Aaron N; Reiss, David J; Allard, Antoine; Wu, Wei-Ju; Salvanha, Diego M; Plaisier, Christopher L; Chandrasekaran, Sriram; Pan, Min; Kaur, Amardeep; Baliga, Nitin S

2014-07-15

Microbes can tailor transcriptional responses to diverse environmental challenges despite having streamlined genomes and a limited number of regulators. Here, we present data-driven models that capture the dynamic interplay of the environment and genome-encoded regulatory programs of two types of prokaryotes: Escherichia coli (a bacterium) and Halobacterium salinarum (an archaeon). The models reveal how the genome-wide distributions of cis-acting gene regulatory elements and the conditional influences of transcription factors at each of those elements encode programs for eliciting a wide array of environment-specific responses. We demonstrate how these programs partition transcriptional regulation of genes within regulons and operons to re-organize gene-gene functional associations in each environment. The models capture fitness-relevant co-regulation by different transcriptional control mechanisms acting across the entire genome, to define a generalized, system-level organizing principle for prokaryotic gene regulatory networks that goes well beyond existing paradigms of gene regulation. An online resource (http://egrin2.systemsbiology.net) has been developed to facilitate multiscale exploration of conditional gene regulation in the two prokaryotes. © 2014 The Authors. Published under the terms of the CC BY 4.0 license.
Whole-genome analysis of a patient with early-stage small-cell lung cancer.

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Han, J-Y; Lee, Y-S; Kim, B C; Lee, G K; Lee, S; Kim, E-H; Kim, H-M; Bhak, J

2014-12-01

We performed whole-genome sequencing (WGS) of a case of early-stage small-cell lung cancer (SCLC) to analyze the genomic features. WGS revealed a lot of single-nucleotide variations (SNVs), small insertion/deletions and chromosomal abnormality. Chromosomes 4p, 5q, 13q, 15q, 17p and 22q contained many block deletions. Especially, copy loss was observed in tumor suppressor genes RB1 and TP53, and copy gain in oncogene hTERT. Somatic mutations were found in TP53 and CREBBP. Novel nonsynonymous (ns) SNVs in C6ORF103 and SLC5A4 genes were also found. Sanger sequencing of the SLC5A4 gene in 23 independent SCLC samples showed another nsSNV in the SLC5A4 gene, indicating that nsSNVs in the SLC5A4 gene are recurrent in SCLC. WGS of an early-stage SCLC identified novel recurrent mutations and validated known variations, including copy number variations. These findings provide insight into the genomic landscape contributing to SCLC development.
ATXN1L, CIC, and ETS Transcription Factors Modulate Sensitivity to MAPK Pathway Inhibition | Office of Cancer Genomics

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Intrinsic resistance and RTK-RAS-MAPK pathway reactivation has limited the effectiveness of MEK and RAF inhibitors (MAPKi) in RAS- and RAF-mutant cancers. To identify genes that modulate sensitivity to MAPKi, we performed genome-scale CRISPR-Cas9 loss-of-function screens in two KRAS mutant pancreatic cancer cell lines treated with the MEK1/2 inhibitor trametinib. Loss of CIC, a transcriptional repressor of ETV1, ETV4, and ETV5, promoted survival in the setting of MAPKi in cancer cells derived from several lineages.
GEM System: automatic prototyping of cell-wide metabolic pathway models from genomes

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Nakayama Yoichi

2006-03-01

Full Text Available Abstract Background Successful realization of a "systems biology" approach to analyzing cells is a grand challenge for our understanding of life. However, current modeling approaches to cell simulation are labor-intensive, manual affairs, and therefore constitute a major bottleneck in the evolution of computational cell biology. Results We developed the Genome-based Modeling (GEM System for the purpose of automatically prototyping simulation models of cell-wide metabolic pathways from genome sequences and other public biological information. Models generated by the GEM System include an entire Escherichia coli metabolism model comprising 968 reactions of 1195 metabolites, achieving 100% coverage when compared with the KEGG database, 92.38% with the EcoCyc database, and 95.06% with iJR904 genome-scale model. Conclusion The GEM System prototypes qualitative models to reduce the labor-intensive tasks required for systems biology research. Models of over 90 bacterial genomes are available at our web site.
Structured Matrix Completion with Applications to Genomic Data Integration.

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Cai, Tianxi; Cai, T Tony; Zhang, Anru

2016-01-01

Matrix completion has attracted significant recent attention in many fields including statistics, applied mathematics and electrical engineering. Current literature on matrix completion focuses primarily on independent sampling models under which the individual observed entries are sampled independently. Motivated by applications in genomic data integration, we propose a new framework of structured matrix completion (SMC) to treat structured missingness by design. Specifically, our proposed method aims at efficient matrix recovery when a subset of the rows and columns of an approximately low-rank matrix are observed. We provide theoretical justification for the proposed SMC method and derive lower bound for the estimation errors, which together establish the optimal rate of recovery over certain classes of approximately low-rank matrices. Simulation studies show that the method performs well in finite sample under a variety of configurations. The method is applied to integrate several ovarian cancer genomic studies with different extent of genomic measurements, which enables us to construct more accurate prediction rules for ovarian cancer survival.

Genome-wide association study for ovarian cancer susceptibility using pooled DNA

DEFF Research Database (Denmark)

Lu, Yi; Chen, Xiaoqing; Beesley, Jonathan

2012-01-01

stage 1 GWAS rather than due to problems with the pooling approach. We conclude that there are unlikely to be any moderate or large effects on ovarian cancer risk untagged by less dense arrays. However, our study lacked power to make clear statements on the existence of hitherto untagged small......Recent Genome-Wide Association Studies (GWAS) have identified four low-penetrance ovarian cancer susceptibility loci. We hypothesized that further moderate- or low-penetrance variants exist among the subset of single-nucleotide polymorphisms (SNPs) not well tagged by the genotyping arrays used...... in the previous studies, which would account for some of the remaining risk. We therefore conducted a time- and cost-effective stage 1 GWAS on 342 invasive serous cases and 643 controls genotyped on pooled DNA using the high-density Illumina 1M-Duo array. We followed up 20 of the most significantly associated...
Functional genomics identifies specific vulnerabilities in PTEN-deficient breast cancer.

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Tang, Yew Chung; Ho, Szu-Chi; Tan, Elisabeth; Ng, Alvin Wei Tian; McPherson, John R; Goh, Germaine Yen Lin; Teh, Bin Tean; Bard, Frederic; Rozen, Steven G

2018-03-22

Phosphatase and tensin homolog (PTEN) is one of the most frequently inactivated tumor suppressors in breast cancer. While PTEN itself is not considered a druggable target, PTEN synthetic-sick or synthetic-lethal (PTEN-SSL) genes are potential drug targets in PTEN-deficient breast cancers. Therefore, with the aim of identifying potential targets for precision breast cancer therapy, we sought to discover PTEN-SSL genes present in a broad spectrum of breast cancers. To discover broad-spectrum PTEN-SSL genes in breast cancer, we used a multi-step approach that started with (1) a genome-wide short interfering RNA (siRNA) screen of ~ 21,000 genes in a pair of isogenic human mammary epithelial cell lines, followed by (2) a short hairpin RNA (shRNA) screen of ~ 1200 genes focused on hits from the first screen in a panel of 11 breast cancer cell lines; we then determined reproducibility of hits by (3) identification of overlaps between our results and reanalyzed data from 3 independent gene-essentiality screens, and finally, for selected candidate PTEN-SSL genes we (4) confirmed PTEN-SSL activity using either drug sensitivity experiments in a panel of 19 cell lines or mutual exclusivity analysis of publicly available pan-cancer somatic mutation data. The screens (steps 1 and 2) and the reproducibility analysis (step 3) identified six candidate broad-spectrum PTEN-SSL genes (PIK3CB, ADAMTS20, AP1M2, HMMR, STK11, and NUAK1). PIK3CB was previously identified as PTEN-SSL, while the other five genes represent novel PTEN-SSL candidates. Confirmation studies (step 4) provided additional evidence that NUAK1 and STK11 have PTEN-SSL patterns of activity. Consistent with PTEN-SSL status, inhibition of the NUAK1 protein kinase by the small molecule drug HTH-01-015 selectively impaired viability in multiple PTEN-deficient breast cancer cell lines, while mutations affecting STK11 and PTEN were largely mutually exclusive across large pan-cancer data sets. Six genes showed PTEN
Functional Coverage of the Human Genome by Existing Structures, Structural Genomics Targets, and Homology Models.

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2005-08-01

Full Text Available The bias in protein structure and function space resulting from experimental limitations and targeting of particular functional classes of proteins by structural biologists has long been recognized, but never continuously quantified. Using the Enzyme Commission and the Gene Ontology classifications as a reference frame, and integrating structure data from the Protein Data Bank (PDB, target sequences from the structural genomics projects, structure homology derived from the SUPERFAMILY database, and genome annotations from Ensembl and NCBI, we provide a quantified view, both at the domain and whole-protein levels, of the current and projected coverage of protein structure and function space relative to the human genome. Protein structures currently provide at least one domain that covers 37% of the functional classes identified in the genome; whole structure coverage exists for 25% of the genome. If all the structural genomics targets were solved (twice the current number of structures in the PDB, it is estimated that structures of one domain would cover 69% of the functional classes identified and complete structure coverage would be 44%. Homology models from existing experimental structures extend the 37% coverage to 56% of the genome as single domains and 25% to 31% for complete structures. Coverage from homology models is not evenly distributed by protein family, reflecting differing degrees of sequence and structure divergence within families. While these data provide coverage, conversely, they also systematically highlight functional classes of proteins for which structures should be determined. Current key functional families without structure representation are highlighted here; updated information on the "most wanted list" that should be solved is available on a weekly basis from http://function.rcsb.org:8080/pdb/function_distribution/index.html.
Comprehensive Analysis of Cancer-Proteogenome to Identify Biomarkers for the Early Diagnosis and Prognosis of Cancer

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Hem D. Shukla

2017-10-01

Full Text Available During the past century, our understanding of cancer diagnosis and treatment has been based on a monogenic approach, and as a consequence our knowledge of the clinical genetic underpinnings of cancer is incomplete. Since the completion of the human genome in 2003, it has steered us into therapeutic target discovery, enabling us to mine the genome using cutting edge proteogenomics tools. A number of novel and promising cancer targets have emerged from the genome project for diagnostics, therapeutics, and prognostic markers, which are being used to monitor response to cancer treatment. The heterogeneous nature of cancer has hindered progress in understanding the underlying mechanisms that lead to abnormal cellular growth. Since, the start of The Cancer Genome Atlas (TCGA, and the International Genome consortium projects, there has been tremendous progress in genome sequencing and immense numbers of cancer genomes have been completed, and this approach has transformed our understanding of the diagnosis and treatment of different types of cancers. By employing Genomics and proteomics technologies, an immense amount of genomic data is being generated on clinical tumors, which has transformed the cancer landscape and has the potential to transform cancer diagnosis and prognosis. A complete molecular view of the cancer landscape is necessary for understanding the underlying mechanisms of cancer initiation to improve diagnosis and prognosis, which ultimately will lead to personalized treatment. Interestingly, cancer proteome analysis has also allowed us to identify biomarkers to monitor drug and radiation resistance in patients undergoing cancer treatment. Further, TCGA-funded studies have allowed for the genomic and transcriptomic characterization of targeted cancers, this analysis aiding the development of targeted therapies for highly lethal malignancy. High-throughput technologies, such as complete proteome, epigenome, protein–protein interaction
Model Checking of a Diabetes-Cancer Model

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Gong, Haijun; Zuliani, Paolo; Clarke, Edmund M.

2011-06-01

Accumulating evidence suggests that cancer incidence might be associated with diabetes mellitus, especially Type II diabetes which is characterized by hyperinsulinaemia, hyperglycaemia, obesity, and overexpression of multiple WNT pathway components. These diabetes risk factors can activate a number of signaling pathways that are important in the development of different cancers. To systematically understand the signaling components that link diabetes and cancer risk, we have constructed a single-cell, Boolean network model by integrating the signaling pathways that are influenced by these risk factors to study insulin resistance, cancer cell proliferation and apoptosis. Then, we introduce and apply the Symbolic Model Verifier (SMV), a formal verification tool, to qualitatively study some temporal logic properties of our diabetes-cancer model. The verification results show that the diabetes risk factors might not increase cancer risk in normal cells, but they will promote cell proliferation if the cell is in a precancerous or cancerous stage characterized by losses of the tumor-suppressor proteins ARF and INK4a.
Spherical Cancer Models in Tumor Biology

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Louis-Bastien Weiswald

2015-01-01

Full Text Available Three-dimensional (3D in vitro models have been used in cancer research as an intermediate model between in vitro cancer cell line cultures and in vivo tumor. Spherical cancer models represent major 3D in vitro models that have been described over the past 4 decades. These models have gained popularity in cancer stem cell research using tumorospheres. Thus, it is crucial to define and clarify the different spherical cancer models thus far described. Here, we focus on in vitro multicellular spheres used in cancer research. All these spherelike structures are characterized by their well-rounded shape, the presence of cancer cells, and their capacity to be maintained as free-floating cultures. We propose a rational classification of the four most commonly used spherical cancer models in cancer research based on culture methods for obtaining them and on subsequent differences in sphere biology: the multicellular tumor spheroid model, first described in the early 70s and obtained by culture of cancer cell lines under nonadherent conditions; tumorospheres, a model of cancer stem cell expansion established in a serum-free medium supplemented with growth factors; tissue-derived tumor spheres and organotypic multicellular spheroids, obtained by tumor tissue mechanical dissociation and cutting. In addition, we describe their applications to and interest in cancer research; in particular, we describe their contribution to chemoresistance, radioresistance, tumorigenicity, and invasion and migration studies. Although these models share a common 3D conformation, each displays its own intrinsic properties. Therefore, the most relevant spherical cancer model must be carefully selected, as a function of the study aim and cancer type.
Network analysis of genomic alteration profiles reveals co-altered functional modules and driver genes for glioblastoma.

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Gu, Yunyan; Wang, Hongwei; Qin, Yao; Zhang, Yujing; Zhao, Wenyuan; Qi, Lishuang; Zhang, Yuannv; Wang, Chenguang; Guo, Zheng

2013-03-01

The heterogeneity of genetic alterations in human cancer genomes presents a major challenge to advancing our understanding of cancer mechanisms and identifying cancer driver genes. To tackle this heterogeneity problem, many approaches have been proposed to investigate genetic alterations and predict driver genes at the individual pathway level. However, most of these approaches ignore the correlation of alteration events between pathways and miss many genes with rare alterations collectively contributing to carcinogenesis. Here, we devise a network-based approach to capture the cooperative functional modules hidden in genome-wide somatic mutation and copy number alteration profiles of glioblastoma (GBM) from The Cancer Genome Atlas (TCGA), where a module is a set of altered genes with dense interactions in the protein interaction network. We identify 7 pairs of significantly co-altered modules that involve the main pathways known to be altered in GBM (TP53, RB and RTK signaling pathways) and highlight the striking co-occurring alterations among these GBM pathways. By taking into account the non-random correlation of gene alterations, the property of co-alteration could distinguish oncogenic modules that contain driver genes involved in the progression of GBM. The collaboration among cancer pathways suggests that the redundant models and aggravating models could shed new light on the potential mechanisms during carcinogenesis and provide new indications for the design of cancer therapeutic strategies.
Mathematical modelling as a proof of concept for MPNs as a human inflammation model for cancer development.

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Morten Andersen

Full Text Available The chronic Philadelphia-negative myeloproliferative neoplasms (MPNs are acquired stem cell neoplasms which ultimately may transform to acute myelogenous leukemia. Most recently, chronic inflammation has been described as an important factor for the development and progression of MPNs in the biological continuum from early cancer stage to the advanced myelofibrosis stage, the MPNs being described as "A Human Inflammation Model for Cancer Development". This novel concept has been built upon clinical, experimental, genomic, immunological and not least epidemiological studies. Only a few studies have described the development of MPNs by mathematical models, and none have addressed the role of inflammation for clonal evolution and disease progression. Herein, we aim at using mathematical modelling to substantiate the concept of chronic inflammation as an important trigger and driver of MPNs.The basics of the model describe the proliferation from stem cells to mature cells including mutations of healthy stem cells to become malignant stem cells. We include a simple inflammatory coupling coping with cell death and affecting the basic model beneath. First, we describe the system without feedbacks or regulatory interactions. Next, we introduce inflammatory feedback into the system. Finally, we include other feedbacks and regulatory interactions forming the inflammatory-MPN model. Using mathematical modeling, we add further proof to the concept that chronic inflammation may be both a trigger of clonal evolution and an important driving force for MPN disease progression. Our findings support intervention at the earliest stage of cancer development to target the malignant clone and dampen concomitant inflammation.
Genome Stability Maintenance in Naked Mole-Rat.

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Petruseva, I O; Evdokimov, A N; Lavrik, O I

2017-01-01

The naked mole-rat ( Heterocephalus glaber ) is one of the most promising models used to study genome maintenance systems, including the effective repair of damage to DNA. The naked mole-rat is the longest lived rodent species, which is extraordinarily resistant to cancer and has a number of other unique phenotypic traits. For at least 80% of its lifespan, this animal shows no signs of aging or any increased likelihood of death and retains the ability to reproduce. The naked mole-rat draws the heightened attention of researchers who study the molecular basis of lengthy lifespan and cancer resistance. Despite the fact that the naked mole-rat lives under genotoxic stress conditions (oxidative, etc.), the main characteristics of its genome and proteome are a high stability and effective functioning. Replicative senescence in the somatic cells of naked mole-rats is missing, while an additional p53/pRb-dependent mechanism of early contact inhibition has been revealed in its fibroblasts, which controls cell proliferation and its mechanism of arf- dependent aging. The unique traits of phenotypic and molecular adaptations found in the naked mole-rat speak to a high stability and effective functioning of the molecular machinery that counteract damage accumulation in its genome. This review analyzes existing results in the study of the molecular basis of longevity and high cancer resistance in naked mole-rats.
Endogenous retroviruses of sheep: a model system for understanding physiological adaptation to an evolving ruminant genome.

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Spencer, Thomas E; Palmarini, Massimo

2012-01-01

Endogenous retroviruses (ERVs) are present in the genome of all vertebrates and are remnants of ancient exogenous retroviral infections of the host germline transmitted vertically from generation to generation. Sheep betaretroviruses offer a unique model system to study the complex interaction between retroviruses and their host. The sheep genome contains 27 endogenous betaretroviruses (enJSRVs) related to the exogenous and pathogenic Jaagsiekte sheep retrovirus (JSRV), the causative agent of a transmissible lung cancer in sheep. The enJSRVs can protect their host against JSRV infection by blocking early and late steps of the JSRV replication cycle. In the female reproductive tract, enJSRVs are specifically expressed in the uterine luminal and glandular epithelia as well as in the conceptus (embryo and associated extraembryonic membranes) trophectoderm and in utero loss-of-function experiments found the enJSRVs envelope (env) to be essential for conceptus elongation and trophectoderm growth and development. Collectively, available evidence in sheep and other mammals indicate that ERVs coevolved with their hosts for millions of years and were positively selected for biological roles in genome plasticity and evolution, protection of the host against infection of related pathogenic and exogenous retroviruses, and placental development.
Mouse Models of Gastric Cancer

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Hayakawa, Yoku; Fox, James G.; Gonda, Tamas; Worthley, Daniel L.; Muthupalani, Sureshkumar; Wang, Timothy C.

2013-01-01

Animal models have greatly enriched our understanding of the molecular mechanisms of numerous types of cancers. Gastric cancer is one of the most common cancers worldwide, with a poor prognosis and high incidence of drug-resistance. However, most inbred strains of mice have proven resistant to gastric carcinogenesis. To establish useful models which mimic human gastric cancer phenotypes, investigators have utilized animals infected with Helicobacter species and treated with carcinogens. In addition, by exploiting genetic engineering, a variety of transgenic and knockout mouse models of gastric cancer have emerged, such as INS-GAS mice and TFF1 knockout mice. Investigators have used the combination of carcinogens and gene alteration to accelerate gastric cancer development, but rarely do mouse models show an aggressive and metastatic gastric cancer phenotype that could be relevant to preclinical studies, which may require more specific targeting of gastric progenitor cells. Here, we review current gastric carcinogenesis mouse models and provide our future perspectives on this field. PMID:24216700
Restricted DCJ-indel model: sorting linear genomes with DCJ and indels

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2012-01-01

Background The double-cut-and-join (DCJ) is a model that is able to efficiently sort a genome into another, generalizing the typical mutations (inversions, fusions, fissions, translocations) to which genomes are subject, but allowing the existence of circular chromosomes at the intermediate steps. In the general model many circular chromosomes can coexist in some intermediate step. However, when the compared genomes are linear, it is more plausible to use the so-called restricted DCJ model, in which we proceed the reincorporation of a circular chromosome immediately after its creation. These two consecutive DCJ operations, which create and reincorporate a circular chromosome, mimic a transposition or a block-interchange. When the compared genomes have the same content, it is known that the genomic distance for the restricted DCJ model is the same as the distance for the general model. If the genomes have unequal contents, in addition to DCJ it is necessary to consider indels, which are insertions and deletions of DNA segments. Linear time algorithms were proposed to compute the distance and to find a sorting scenario in a general, unrestricted DCJ-indel model that considers DCJ and indels. Results In the present work we consider the restricted DCJ-indel model for sorting linear genomes with unequal contents. We allow DCJ operations and indels with the following constraint: if a circular chromosome is created by a DCJ, it has to be reincorporated in the next step (no other DCJ or indel can be applied between the creation and the reincorporation of a circular chromosome). We then develop a sorting algorithm and give a tight upper bound for the restricted DCJ-indel distance. Conclusions We have given a tight upper bound for the restricted DCJ-indel distance. The question whether this bound can be reduced so that both the general and the restricted DCJ-indel distances are equal remains open. PMID:23281630
Convergent functional genomics of psychiatric disorders.

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Niculescu, Alexander B

2013-10-01

Genetic and gene expression studies, in humans and animal models of psychiatric and other medical disorders, are becoming increasingly integrated. Particularly for genomics, the convergence and integration of data across species, experimental modalities and technical platforms is providing a fit-to-disease way of extracting reproducible and biologically important signal, in contrast to the fit-to-cohort effect and limited reproducibility of human genetic analyses alone. With the advent of whole-genome sequencing and the realization that a major portion of the non-coding genome may contain regulatory variants, Convergent Functional Genomics (CFG) approaches are going to be essential to identify disease-relevant signal from the tremendous polymorphic variation present in the general population. Such work in psychiatry can provide an example of how to address other genetically complex disorders, and in turn will benefit by incorporating concepts from other areas, such as cancer, cardiovascular diseases, and diabetes. © 2013 Wiley Periodicals, Inc.
Translating genomics: cancer genetics, public health and the making of the (de)molecularised body in Cuba and Brazil.

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Gibbon, Sahra

2016-01-01

This article examines how cancer genetics has emerged as a focus for research and healthcare in Cuba and Brazil. Drawing on ethnographic research undertaken in community genetics clinics and cancer genetics services, the article examines how the knowledge and technologies associated with this novel area of healthcare are translated and put to work by researchers, health professionals, patients and their families in these two contexts. It illuminates the comparative similarities and differences in how cancer genetics is emerging in relation to transnational research priorities, the history and contemporary politics of public health and embodied vulnerability to cancer that reconfigures the scope and meaning of genomics as "personalised" medicine.
Genome-wide association analysis of more than 120,000 individuals identifies 15 new susceptibility loci for breast cancer.

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Michailidou, Kyriaki; Beesley, Jonathan; Lindstrom, Sara; Canisius, Sander; Dennis, Joe; Lush, Michael J; Maranian, Mel J; Bolla, Manjeet K; Wang, Qin; Shah, Mitul; Perkins, Barbara J; Czene, Kamila; Eriksson, Mikael; Darabi, Hatef; Brand, Judith S; Bojesen, Stig E; Nordestgaard, Børge G; Flyger, Henrik; Nielsen, Sune F; Rahman, Nazneen; Turnbull, Clare; Fletcher, Olivia; Peto, Julian; Gibson, Lorna; dos-Santos-Silva, Isabel; Chang-Claude, Jenny; Flesch-Janys, Dieter; Rudolph, Anja; Eilber, Ursula; Behrens, Sabine; Nevanlinna, Heli; Muranen, Taru A; Aittomäki, Kristiina; Blomqvist, Carl; Khan, Sofia; Aaltonen, Kirsimari; Ahsan, Habibul; Kibriya, Muhammad G; Whittemore, Alice S; John, Esther M; Malone, Kathleen E; Gammon, Marilie D; Santella, Regina M; Ursin, Giske; Makalic, Enes; Schmidt, Daniel F; Casey, Graham; Hunter, David J; Gapstur, Susan M; Gaudet, Mia M; Diver, W Ryan; Haiman, Christopher A; Schumacher, Fredrick; Henderson, Brian E; Le Marchand, Loic; Berg, Christine D; Chanock, Stephen J; Figueroa, Jonine; Hoover, Robert N; Lambrechts, Diether; Neven, Patrick; Wildiers, Hans; van Limbergen, Erik; Schmidt, Marjanka K; Broeks, Annegien; Verhoef, Senno; Cornelissen, Sten; Couch, Fergus J; Olson, Janet E; Hallberg, Emily; Vachon, Celine; Waisfisz, Quinten; Meijers-Heijboer, Hanne; Adank, Muriel A; van der Luijt, Rob B; Li, Jingmei; Liu, Jianjun; Humphreys, Keith; Kang, Daehee; Choi, Ji-Yeob; Park, Sue K; Yoo, Keun-Young; Matsuo, Keitaro; Ito, Hidemi; Iwata, Hiroji; Tajima, Kazuo; Guénel, Pascal; Truong, Thérèse; Mulot, Claire; Sanchez, Marie; Burwinkel, Barbara; Marme, Frederik; Surowy, Harald; Sohn, Christof; Wu, Anna H; Tseng, Chiu-chen; Van Den Berg, David; Stram, Daniel O; González-Neira, Anna; Benitez, Javier; Zamora, M Pilar; Perez, Jose Ignacio Arias; Shu, Xiao-Ou; Lu, Wei; Gao, Yu-Tang; Cai, Hui; Cox, Angela; Cross, Simon S; Reed, Malcolm W R; Andrulis, Irene L; Knight, Julia A; Glendon, Gord; Mulligan, Anna Marie; Sawyer, Elinor J; Tomlinson, Ian; Kerin, Michael J; Miller, Nicola; Lindblom, Annika; Margolin, Sara; Teo, Soo Hwang; Yip, Cheng Har; Taib, Nur Aishah Mohd; Tan, Gie-Hooi; Hooning, Maartje J; Hollestelle, Antoinette; Martens, John W M; Collée, J Margriet; Blot, William; Signorello, Lisa B; Cai, Qiuyin; Hopper, John L; Southey, Melissa C; Tsimiklis, Helen; Apicella, Carmel; Shen, Chen-Yang; Hsiung, Chia-Ni; Wu, Pei-Ei; Hou, Ming-Feng; Kristensen, Vessela N; Nord, Silje; Alnaes, Grethe I Grenaker; Giles, Graham G; Milne, Roger L; McLean, Catriona; Canzian, Federico; Trichopoulos, Dimitrios; Peeters, Petra; Lund, Eiliv; Sund, Malin; Khaw, Kay-Tee; Gunter, Marc J; Palli, Domenico; Mortensen, Lotte Maxild; Dossus, Laure; Huerta, Jose-Maria; Meindl, Alfons; Schmutzler, Rita K; Sutter, Christian; Yang, Rongxi; Muir, Kenneth; Lophatananon, Artitaya; Stewart-Brown, Sarah; Siriwanarangsan, Pornthep; Hartman, Mikael; Miao, Hui; Chia, Kee Seng; Chan, Ching Wan; Fasching, Peter A; Hein, Alexander; Beckmann, Matthias W; Haeberle, Lothar; Brenner, Hermann; Dieffenbach, Aida Karina; Arndt, Volker; Stegmaier, Christa; Ashworth, Alan; Orr, Nick; Schoemaker, Minouk J; Swerdlow, Anthony J; Brinton, Louise; Garcia-Closas, Montserrat; Zheng, Wei; Halverson, Sandra L; Shrubsole, Martha; Long, Jirong; Goldberg, Mark S; Labrèche, France; Dumont, Martine; Winqvist, Robert; Pylkäs, Katri; Jukkola-Vuorinen, Arja; Grip, Mervi; Brauch, Hiltrud; Hamann, Ute; Brüning, Thomas; Radice, Paolo; Peterlongo, Paolo; Manoukian, Siranoush; Bernard, Loris; Bogdanova, Natalia V; Dörk, Thilo; Mannermaa, Arto; Kataja, Vesa; Kosma, Veli-Matti; Hartikainen, Jaana M; Devilee, Peter; Tollenaar, Robert A E M; Seynaeve, Caroline; Van Asperen, Christi J; Jakubowska, Anna; Lubinski, Jan; Jaworska, Katarzyna; Huzarski, Tomasz; Sangrajrang, Suleeporn; Gaborieau, Valerie; Brennan, Paul; McKay, James; Slager, Susan; Toland, Amanda E; Ambrosone, Christine B; Yannoukakos, Drakoulis; Kabisch, Maria; Torres, Diana; Neuhausen, Susan L; Anton-Culver, Hoda; Luccarini, Craig; Baynes, Caroline; Ahmed, Shahana; Healey, Catherine S; Tessier, Daniel C; Vincent, Daniel; Bacot, Francois; Pita, Guillermo; Alonso, M Rosario; Álvarez, Nuria; Herrero, Daniel; Simard, Jacques; Pharoah, Paul P D P; Kraft, Peter; Dunning, Alison M; Chenevix-Trench, Georgia; Hall, Per; Easton, Douglas F

2015-04-01

Genome-wide association studies (GWAS) and large-scale replication studies have identified common variants in 79 loci associated with breast cancer, explaining ∼14% of the familial risk of the disease. To identify new susceptibility loci, we performed a meta-analysis of 11 GWAS, comprising 15,748 breast cancer cases and 18,084 controls together with 46,785 cases and 42,892 controls from 41 studies genotyped on a 211,155-marker custom array (iCOGS). Analyses were restricted to women of European ancestry. We generated genotypes for more than 11 million SNPs by imputation using the 1000 Genomes Project reference panel, and we identified 15 new loci associated with breast cancer at P association analysis with ChIP-seq chromatin binding data in mammary cell lines and ChIA-PET chromatin interaction data from ENCODE, we identified likely target genes in two regions: SETBP1 at 18q12.3 and RNF115 and PDZK1 at 1q21.1. One association appears to be driven by an amino acid substitution encoded in EXO1.
Microbial comparative pan-genomics using binomial mixture models

DEFF Research Database (Denmark)

Ussery, David; Snipen, L; Almøy, T

2009-01-01

The size of the core- and pan-genome of bacterial species is a topic of increasing interest due to the growing number of sequenced prokaryote genomes, many from the same species. Attempts to estimate these quantities have been made, using regression methods or mixture models. We extend the latter...... approach by using statistical ideas developed for capture-recapture problems in ecology and epidemiology. RESULTS: We estimate core- and pan-genome sizes for 16 different bacterial species. The results reveal a complex dependency structure for most species, manifested as heterogeneous detection...... probabilities. Estimated pan-genome sizes range from small (around 2600 gene families) in Buchnera aphidicola to large (around 43000 gene families) in Escherichia coli. Results for Echerichia coli show that as more data become available, a larger diversity is estimated, indicating an extensive pool of rarely...
Tumor xenograft modeling identifies an association between TCF4 loss and breast cancer chemoresistance

Directory of Open Access Journals (Sweden)

Gorka Ruiz de Garibay

2018-05-01

Full Text Available Understanding the mechanisms of cancer therapeutic resistance is fundamental to improving cancer care. There is clear benefit from chemotherapy in different breast cancer settings; however, knowledge of the mutations and genes that mediate resistance is incomplete. In this study, by modeling chemoresistance in patient-derived xenografts (PDXs, we show that adaptation to therapy is genetically complex and identify that loss of transcription factor 4 (TCF4; also known as ITF2 is associated with this process. A triple-negative BRCA1-mutated PDX was used to study the genetics of chemoresistance. The PDX was treated in parallel with four chemotherapies for five iterative cycles. Exome sequencing identified few genes with de novo or enriched mutations in common among the different therapies, whereas many common depleted mutations/genes were observed. Analysis of somatic mutations from The Cancer Genome Atlas (TCGA supported the prognostic relevance of the identified genes. A mutation in TCF4 was found de novo in all treatments, and analysis of drug sensitivity profiles across cancer cell lines supported the link to chemoresistance. Loss of TCF4 conferred chemoresistance in breast cancer cell models, possibly by altering cell cycle regulation. Targeted sequencing in chemoresistant tumors identified an intronic variant of TCF4 that may represent an expression quantitative trait locus associated with relapse outcome in TCGA. Immunohistochemical studies suggest a common loss of nuclear TCF4 expression post-chemotherapy. Together, these results from tumor xenograft modeling depict a link between altered TCF4 expression and breast cancer chemoresistance.
A Biochemical Approach to Understanding the Fanconi Anemia Pathway-Regulated Nucleases in Genome Maintenance for Preventing Bone Marrow Failure and Cancer

Science.gov (United States)

2014-04-01

the Fanconi Anemia Pathway- Regulated Nucleases in Genome Maintenance for Preventing Bone Marrow Failure and Cancer PRINCIPAL INVESTIGATOR...GRANT NUMBER 4. TITLE AND SUBTITLE A Biochemical Approach to Understanding the Fanconi Anemia Pathway-Regulated Nucleases in Genome Maintenance for...Unlimited 13. SUPPLEMENTARY NOTES 14. ABSTRACT Fanconi anemia is the most prevalent inherited BMF syndromes, caused by mutations in
The Genome-Wide Analysis of Carcinoembryonic Antigen Signaling by Colorectal Cancer Cells Using RNA Sequencing.

Directory of Open Access Journals (Sweden)

Olga Bajenova

Full Text Available Сarcinoembryonic antigen (CEA, CEACAM5, CD66 is a promoter of metastasis in epithelial cancers that is widely used as a prognostic clinical marker of metastasis. The aim of this study is to identify the network of genes that are associated with CEA-induced colorectal cancer liver metastasis. We compared the genome-wide transcriptomic profiles of CEA positive (MIP101 clone 8 and CEA negative (MIP 101 colorectal cancer cell lines with different metastatic potential in vivo. The CEA-producing cells displayed quantitative changes in the level of expression for 100 genes (over-expressed or down-regulated. They were confirmed by quantitative RT-PCR. The KEGG pathway analysis identified 4 significantly enriched pathways: cytokine-cytokine receptor interaction, MAPK signaling pathway, TGF-beta signaling pathway and pyrimidine metabolism. Our results suggest that CEA production by colorectal cancer cells triggers colorectal cancer progression by inducing the epithelial- mesenchymal transition, increasing tumor cell invasiveness into the surrounding tissues and suppressing stress and apoptotic signaling. The novel gene expression distinctions establish the relationships between the existing cancer markers and implicate new potential biomarkers for colorectal cancer hepatic metastasis.
Genetic profiles of gastroesophageal cancer: combined analysis using expression array and tiling array--comparative genomic hybridization

DEFF Research Database (Denmark)

Isinger-Ekstrand, Anna; Johansson, Jan; Ohlsson, Mattias

2010-01-01

We aimed to characterize the genomic profiles of adenocarcinomas in the gastroesophageal junction in relation to cancers in the esophagus and the stomach. Profiles of gains/losses as well as gene expression profiles were obtained from 27 gastroesophageal adenocarcinomas by means of 32k high......15, 13q34, and 12q13, whereas different profiles with gains at 5p15, 7p22, 2q35, and 13q34 characterized gastric cancers. CDK6 and EGFR were identified as putative target genes in cancers of the esophagus and the gastroesophageal junction, with upregulation in one quarter of the tumors. Gains....../losses and gene expression profiles show strong similarity between cancers in the distal esophagus and the gastroesophageal junction with frequent upregulation of CDK6 and EGFR, whereas gastric cancer displays distinct genetic changes. These data suggest that molecular diagnostics and targeted therapies can...

All the World's a Stage: Facilitating Discovery Science and Improved Cancer Care through the Global Alliance for Genomics and Health.

Science.gov (United States)

Lawler, Mark; Siu, Lillian L; Rehm, Heidi L; Chanock, Stephen J; Alterovitz, Gil; Burn, John; Calvo, Fabien; Lacombe, Denis; Teh, Bin Tean; North, Kathryn N; Sawyers, Charles L

2015-11-01

The recent explosion of genetic and clinical data generated from tumor genome analysis presents an unparalleled opportunity to enhance our understanding of cancer, but this opportunity is compromised by the reluctance of many in the scientific community to share datasets and the lack of interoperability between different data platforms. The Global Alliance for Genomics and Health is addressing these barriers and challenges through a cooperative framework that encourages "team science" and responsible data sharing, complemented by the development of a series of application program interfaces that link different data platforms, thus breaking down traditional silos and liberating the data to enable new discoveries and ultimately benefit patients. ©2015 American Association for Cancer Research.
Genomic Selection in Plant Breeding: Methods, Models, and Perspectives.

Science.gov (United States)

Crossa, José; Pérez-Rodríguez, Paulino; Cuevas, Jaime; Montesinos-López, Osval; Jarquín, Diego; de Los Campos, Gustavo; Burgueño, Juan; González-Camacho, Juan M; Pérez-Elizalde, Sergio; Beyene, Yoseph; Dreisigacker, Susanne; Singh, Ravi; Zhang, Xuecai; Gowda, Manje; Roorkiwal, Manish; Rutkoski, Jessica; Varshney, Rajeev K

2017-11-01

Genomic selection (GS) facilitates the rapid selection of superior genotypes and accelerates the breeding cycle. In this review, we discuss the history, principles, and basis of GS and genomic-enabled prediction (GP) as well as the genetics and statistical complexities of GP models, including genomic genotype×environment (G×E) interactions. We also examine the accuracy of GP models and methods for two cereal crops and two legume crops based on random cross-validation. GS applied to maize breeding has shown tangible genetic gains. Based on GP results, we speculate how GS in germplasm enhancement (i.e., prebreeding) programs could accelerate the flow of genes from gene bank accessions to elite lines. Recent advances in hyperspectral image technology could be combined with GS and pedigree-assisted breeding. Copyright © 2017 Elsevier Ltd. All rights reserved.
Genome-wide methylation analysis identifies a core set of hypermethylated genes in CIMP-H colorectal cancer.

Science.gov (United States)

McInnes, Tyler; Zou, Donghui; Rao, Dasari S; Munro, Francesca M; Phillips, Vicky L; McCall, John L; Black, Michael A; Reeve, Anthony E; Guilford, Parry J

2017-03-28

Aberrant DNA methylation profiles are a characteristic of all known cancer types, epitomized by the CpG island methylator phenotype (CIMP) in colorectal cancer (CRC). Hypermethylation has been observed at CpG islands throughout the genome, but it is unclear which factors determine whether an individual island becomes methylated in cancer. DNA methylation in CRC was analysed using the Illumina HumanMethylation450K array. Differentially methylated loci were identified using Significance Analysis of Microarrays (SAM) and the Wilcoxon Signed Rank (WSR) test. Unsupervised hierarchical clustering was used to identify methylation subtypes in CRC. In this study we characterized the DNA methylation profiles of 94 CRC tissues and their matched normal counterparts. Consistent with previous studies, unsupervized hierarchical clustering of genome-wide methylation data identified three subtypes within the tumour samples, designated CIMP-H, CIMP-L and CIMP-N, that showed high, low and very low methylation levels, respectively. Differential methylation between normal and tumour samples was analysed at the individual CpG level, and at the gene level. The distribution of hypermethylation in CIMP-N tumours showed high inter-tumour variability and appeared to be highly stochastic in nature, whereas CIMP-H tumours exhibited consistent hypermethylation at a subset of genes, in addition to a highly variable background of hypermethylated genes. EYA4, TFPI2 and TLX1 were hypermethylated in more than 90% of all tumours examined. One-hundred thirty-two genes were hypermethylated in 100% of CIMP-H tumours studied and these were highly enriched for functions relating to skeletal system development (Bonferroni adjusted p value =2.88E-15), segment specification (adjusted p value =9.62E-11), embryonic development (adjusted p value =1.52E-04), mesoderm development (adjusted p value =1.14E-20), and ectoderm development (adjusted p value =7.94E-16). Our genome-wide characterization of DNA
A lung cancer risk classifier comprising genome maintenance genes measured in normal bronchial epithelial cells.

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Yeo, Jiyoun; Crawford, Erin L; Zhang, Xiaolu; Khuder, Sadik; Chen, Tian; Levin, Albert; Blomquist, Thomas M; Willey, James C

2017-05-02

Annual low dose CT (LDCT) screening of individuals at high demographic risk reduces lung cancer mortality by more than 20%. However, subjects selected for screening based on demographic criteria typically have less than a 10% lifetime risk for lung cancer. Thus, there is need for a biomarker that better stratifies subjects for LDCT screening. Toward this goal, we previously reported a lung cancer risk test (LCRT) biomarker comprising 14 genome-maintenance (GM) pathway genes measured in normal bronchial epithelial cells (NBEC) that accurately classified cancer (CA) from non-cancer (NC) subjects. The primary goal of the studies reported here was to optimize the LCRT biomarker for high specificity and ease of clinical implementation. Targeted competitive multiplex PCR amplicon libraries were prepared for next generation sequencing (NGS) analysis of transcript abundance at 68 sites among 33 GM target genes in NBEC specimens collected from a retrospective cohort of 120 subjects, including 61 CA cases and 59 NC controls. Genes were selected for analysis based on contribution to the previously reported LCRT biomarker and/or prior evidence for association with lung cancer risk. Linear discriminant analysis was used to identify the most accurate classifier suitable to stratify subjects for screening. After cross-validation, a model comprising expression values from 12 genes (CDKN1A, E2F1, ERCC1, ERCC4, ERCC5, GPX1, GSTP1, KEAP1, RB1, TP53, TP63, and XRCC1) and demographic factors age, gender, and pack-years smoking, had Receiver Operator Characteristic area under the curve (ROC AUC) of 0.975 (95% CI: 0.96-0.99). The overall classification accuracy was 93% (95% CI 88%-98%) with sensitivity 93.1%, specificity 92.9%, positive predictive value 93.1% and negative predictive value 93%. The ROC AUC for this classifier was significantly better (p < 0.0001) than the best model comprising demographic features alone. The LCRT biomarker reported here displayed high accuracy and ease
A genomic copy number signature predicts radiation exposure in post-Chernobyl breast cancer.

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Wilke, Christina M; Braselmann, Herbert; Hess, Julia; Klymenko, Sergiy V; Chumak, Vadim V; Zakhartseva, Liubov M; Bakhanova, Elena V; Walch, Axel K; Selmansberger, Martin; Samaga, Daniel; Weber, Peter; Schneider, Ludmila; Fend, Falko; Bösmüller, Hans C; Zitzelsberger, Horst; Unger, Kristian

2018-04-16

Breast cancer is the second leading cause of cancer death among women worldwide and besides life style, age and genetic risk factors, exposure to ionizing radiation is known to increase the risk for breast cancer. Further, DNA copy number alterations (CNAs), which can result from radiation-induced double-strand breaks, are frequently occurring in breast cancer cells. We set out to identify a signature of CNAs discriminating breast cancers from radiation-exposed and non-exposed female patients. We analyzed resected breast cancer tissues from 68 exposed female Chernobyl clean-up workers and evacuees and 68 matched non-exposed control patients for CNAs by array comparative genomic hybridization analysis (aCGH). Using a stepwise forward-backward selection approach a non-complex CNA signature, that is, less than ten features, was identified in the training data set, which could be subsequently validated in the validation data set (p value < 0.05). The signature consisted of nine copy number regions located on chromosomal bands 7q11.22-11.23, 7q21.3, 16q24.3, 17q21.31, 20p11.23-11.21, 1p21.1, 2q35, 2q35, 6p22.2. The signature was independent of any clinical characteristics of the patients. In all, we identified a CNA signature that has the potential to allow identification of radiation-associated breast cancer at the individual level. © 2018 UICC.
Prospective Genomic Profiling of Prostate Cancer Across Disease States Reveals Germline and Somatic Alterations That May Affect Clinical Decision Making.

Science.gov (United States)

Abida, Wassim; Armenia, Joshua; Gopalan, Anuradha; Brennan, Ryan; Walsh, Michael; Barron, David; Danila, Daniel; Rathkopf, Dana; Morris, Michael; Slovin, Susan; McLaughlin, Brigit; Curtis, Kristen; Hyman, David M; Durack, Jeremy C; Solomon, Stephen B; Arcila, Maria E; Zehir, Ahmet; Syed, Aijazuddin; Gao, Jianjiong; Chakravarty, Debyani; Vargas, Hebert Alberto; Robson, Mark E; Joseph, Vijai; Offit, Kenneth; Donoghue, Mark T A; Abeshouse, Adam A; Kundra, Ritika; Heins, Zachary J; Penson, Alexander V; Harris, Christopher; Taylor, Barry S; Ladanyi, Marc; Mandelker, Diana; Zhang, Liying; Reuter, Victor E; Kantoff, Philip W; Solit, David B; Berger, Michael F; Sawyers, Charles L; Schultz, Nikolaus; Scher, Howard I

2017-07-01

A long natural history and a predominant osseous pattern of metastatic spread are impediments to the adoption of precision medicine in patients with prostate cancer. To establish the feasibility of clinical genomic profiling in the disease, we performed targeted deep sequencing of tumor and normal DNA from patients with locoregional, metastatic non-castrate, and metastatic castration-resistant prostate cancer (CRPC). Patients consented to genomic analysis of their tumor and germline DNA. A hybridization capture-based clinical assay was employed to identify single nucleotide variations, small insertions and deletions, copy number alterations and structural rearrangements in over 300 cancer-related genes in tumors and matched normal blood. We successfully sequenced 504 tumors from 451 patients with prostate cancer. Potentially actionable alterations were identified in DNA damage repair (DDR), PI3K, and MAP kinase pathways. 27% of patients harbored a germline or a somatic alteration in a DDR gene that may predict for response to PARP inhibition. Profiling of matched tumors from individual patients revealed that somatic TP53 and BRCA2 alterations arose early in tumors from patients who eventually developed metastatic disease. In contrast, comparative analysis across disease states revealed that APC alterations were enriched in metastatic tumors, while ATM alterations were specifically enriched in CRPC. Through genomic profiling of prostate tumors representing the disease clinical spectrum, we identified a high frequency of potentially actionable alterations and possible drivers of disease initiation, metastasis and castration-resistance. Our findings support the routine use of tumor and germline DNA profiling for patients with advanced prostate cancer, for the purpose of guiding enrollment in targeted clinical trials and counseling families at increased risk of malignancy.
Error-free versus mutagenic processing of genomic uracil--relevance to cancer.

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Krokan, Hans E; Sætrom, Pål; Aas, Per Arne; Pettersen, Henrik Sahlin; Kavli, Bodil; Slupphaug, Geir

2014-07-01

Genomic uracil is normally processed essentially error-free by base excision repair (BER), with mismatch repair (MMR) as an apparent backup for U:G mismatches. Nuclear uracil-DNA glycosylase UNG2 is the major enzyme initiating BER of uracil of U:A pairs as well as U:G mismatches. Deficiency in UNG2 results in several-fold increases in genomic uracil in mammalian cells. Thus, the alternative uracil-removing glycosylases, SMUG1, TDG and MBD4 cannot efficiently complement UNG2-deficiency. A major function of SMUG1 is probably to remove 5-hydroxymethyluracil from DNA with general back-up for UNG2 as a minor function. TDG and MBD4 remove deamination products U or T mismatched to G in CpG/mCpG contexts, but may have equally or more important functions in development, epigenetics and gene regulation. Genomic uracil was previously thought to arise only from spontaneous cytosine deamination and incorporation of dUMP, generating U:G mismatches and U:A pairs, respectively. However, the identification of activation-induced cytidine deaminase (AID) and other APOBEC family members as DNA-cytosine deaminases has spurred renewed interest in the processing of genomic uracil. Importantly, AID triggers the adaptive immune response involving error-prone processing of U:G mismatches, but also contributes to B-cell lymphomagenesis. Furthermore, mutational signatures in a substantial fraction of other human cancers are consistent with APOBEC-induced mutagenesis, with U:G mismatches as prime suspects. Mutations can be caused by replicative polymerases copying uracil in U:G mismatches, or by translesion polymerases that insert incorrect bases opposite abasic sites after uracil-removal. In addition, kataegis, localized hypermutations in one strand in the vicinity of genomic rearrangements, requires APOBEC protein, UNG2 and translesion polymerase REV1. What mechanisms govern error-free versus error prone processing of uracil in DNA remains unclear. In conclusion, genomic uracil is an
GGRaSP: A R-package for selecting representative genomes using Gaussian mixture models.

Science.gov (United States)

Clarke, Thomas H; Brinkac, Lauren M; Sutton, Granger; Fouts, Derrick E

2018-04-14

The vast number of available sequenced bacterial genomes occasionally exceeds the facilities of comparative genomic methods or is dominated by a single outbreak strain, and thus a diverse and representative subset is required. Generation of the reduced subset currently requires a priori supervised clustering and sequence-only selection of medoid genomic sequences, independent of any additional genome metrics or strain attributes. The GGRaSP R-package described below generates a reduced subset of genomes that prioritizes maintaining genomes of interest to the user as well as minimizing the loss of genetic variation. The package also allows for unsupervised clustering by modeling the genomic relationships using a Gaussian Mixture Model to select an appropriate cluster threshold. We demonstrate the capabilities of GGRaSP by generating a reduced list of 315 genomes from a genomic dataset of 4600 Escherichia coli genomes, prioritizing selection by type strain and by genome completeness. GGRaSP is available at https://github.com/JCVenterInstitute/ggrasp/. tclarke@jcvi.org. Supplementary data are available at the GitHub site.
Filtered selection coupled with support vector machines generate a functionally relevant prediction model for colorectal cancer

Directory of Open Access Journals (Sweden)

Gabere MN

2016-06-01

Full Text Available Musa Nur Gabere,1 Mohamed Aly Hussein,1 Mohammad Azhar Aziz2 1Department of Bioinformatics, King Abdullah International Medical Research Center/King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia; 2Colorectal Cancer Research Program, Department of Medical Genomics, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia Purpose: There has been considerable interest in using whole-genome expression profiles for the classification of colorectal cancer (CRC. The selection of important features is a crucial step before training a classifier.Methods: In this study, we built a model that uses support vector machine (SVM to classify cancer and normal samples using Affymetrix exon microarray data obtained from 90 samples of 48 patients diagnosed with CRC. From the 22,011 genes, we selected the 20, 30, 50, 100, 200, 300, and 500 genes most relevant to CRC using the minimum-redundancy–maximum-relevance (mRMR technique. With these gene sets, an SVM model was designed using four different kernel types (linear, polynomial, radial basis function [RBF], and sigmoid.Results: The best model, which used 30 genes and RBF kernel, outperformed other combinations; it had an accuracy of 84% for both ten fold and leave-one-out cross validations in discriminating the cancer samples from the normal samples. With this 30 genes set from mRMR, six classifiers were trained using random forest (RF, Bayes net (BN, multilayer perceptron (MLP, naïve Bayes (NB, reduced error pruning tree (REPT, and SVM. Two hybrids, mRMR + SVM and mRMR + BN, were the best models when tested on other datasets, and they achieved a prediction accuracy of 95.27% and 91.99%, respectively, compared to other mRMR hybrid models (mRMR + RF, mRMR + NB, mRMR + REPT, and mRMR + MLP. Ingenuity pathway analysis was used to analyze the functions of the 30 genes selected for this model and their potential association with CRC: CDH3, CEACAM7, CLDN1, IL8, IL6R, MMP1
Genome-wide association studies in women of African ancestry identified 3q26.21 as a novel susceptibility locus for oestrogen receptor negative breast cancer.

Science.gov (United States)

Huo, Dezheng; Feng, Ye; Haddad, Stephen; Zheng, Yonglan; Yao, Song; Han, Yoo-Jeong; Ogundiran, Temidayo O; Adebamowo, Clement; Ojengbede, Oladosu; Falusi, Adeyinka G; Zheng, Wei; Blot, William; Cai, Qiuyin; Signorello, Lisa; John, Esther M; Bernstein, Leslie; Hu, Jennifer J; Ziegler, Regina G; Nyante, Sarah; Bandera, Elisa V; Ingles, Sue A; Press, Michael F; Deming, Sandra L; Rodriguez-Gil, Jorge L; Nathanson, Katherine L; Domchek, Susan M; Rebbeck, Timothy R; Ruiz-Narváez, Edward A; Sucheston-Campbell, Lara E; Bensen, Jeannette T; Simon, Michael S; Hennis, Anselm; Nemesure, Barbara; Leske, M Cristina; Ambs, Stefan; Chen, Lin S; Qian, Frank; Gamazon, Eric R; Lunetta, Kathryn L; Cox, Nancy J; Chanock, Stephen J; Kolonel, Laurence N; Olshan, Andrew F; Ambrosone, Christine B; Olopade, Olufunmilayo I; Palmer, Julie R; Haiman, Christopher A

2016-11-01

Multiple breast cancer loci have been identified in previous genome-wide association studies, but they were mainly conducted in populations of European ancestry. Women of African ancestry are more likely to have young-onset and oestrogen receptor (ER) negative breast cancer for reasons that are unknown and understudied. To identify genetic risk factors for breast cancer in women of African descent, we conducted a meta-analysis of two genome-wide association studies of breast cancer; one study consists of 1,657 cases and 2,029 controls genotyped with Illumina’s HumanOmni2.5 BeadChip and the other study included 3,016 cases and 2,745 controls genotyped using Illumina Human1M-Duo BeadChip. The top 18,376 single nucleotide polymorphisms (SNP) from the meta-analysis were replicated in the third study that consists of 1,984 African Americans cases and 2,939 controls. We found that SNP rs13074711, 26.5 Kb upstream of TNFSF10 at 3q26.21, was significantly associated with risk of oestrogen receptor (ER)-negative breast cancer (odds ratio [OR]=1.29, 95% CI: 1.18-1.40; P = 1.8 × 10 − 8). Functional annotations suggest that the TNFSF10 gene may be involved in breast cancer aetiology, but further functional experiments are needed. In addition, we confirmed SNP rs10069690 was the best indicator for ER-negative breast cancer at 5p15.33 (OR = 1.30; P = 2.4 × 10 − 10) and identified rs12998806 as the best indicator for ER-positive breast cancer at 2q35 (OR = 1.34; P = 2.2 × 10 − 8) for women of African ancestry. These findings demonstrated additional susceptibility alleles for breast cancer can be revealed in diverse populations and have important public health implications in building race/ethnicity-specific risk prediction model for breast cancer.
Variable selection models for genomic selection using whole-genome sequence data and singular value decomposition.

Science.gov (United States)

Meuwissen, Theo H E; Indahl, Ulf G; Ødegård, Jørgen

2017-12-27

Non-linear Bayesian genomic prediction models such as BayesA/B/C/R involve iteration and mostly Markov chain Monte Carlo (MCMC) algorithms, which are computationally expensive, especially when whole-genome sequence (WGS) data are analyzed. Singular value decomposition (SVD) of the genotype matrix can facilitate genomic prediction in large datasets, and can be used to estimate marker effects and their prediction error variances (PEV) in a computationally efficient manner. Here, we developed, implemented, and evaluated a direct, non-iterative method for the estimation of marker effects for the BayesC genomic prediction model. The BayesC model assumes a priori that markers have normally distributed effects with probability [Formula: see text] and no effect with probability (1 - [Formula: see text]). Marker effects and their PEV are estimated by using SVD and the posterior probability of the marker having a non-zero effect is calculated. These posterior probabilities are used to obtain marker-specific effect variances, which are subsequently used to approximate BayesC estimates of marker effects in a linear model. A computer simulation study was conducted to compare alternative genomic prediction methods, where a single reference generation was used to estimate marker effects, which were subsequently used for 10 generations of forward prediction, for which accuracies were evaluated. SVD-based posterior probabilities of markers having non-zero effects were generally lower than MCMC-based posterior probabilities, but for some regions the opposite occurred, resulting in clear signals for QTL-rich regions. The accuracies of breeding values estimated using SVD- and MCMC-based BayesC analyses were similar across the 10 generations of forward prediction. For an intermediate number of generations (2 to 5) of forward prediction, accuracies obtained with the BayesC model tended to be slightly higher than accuracies obtained using the best linear unbiased prediction of SNP
Genome-wide association studies identify four ER negative-specific breast cancer risk loci.

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Garcia-Closas, Montserrat; Couch, Fergus J; Lindstrom, Sara; Michailidou, Kyriaki; Schmidt, Marjanka K; Brook, Mark N; Orr, Nick; Rhie, Suhn Kyong; Riboli, Elio; Feigelson, Heather S; Le Marchand, Loic; Buring, Julie E; Eccles, Diana; Miron, Penelope; Fasching, Peter A; Brauch, Hiltrud; Chang-Claude, Jenny; Carpenter, Jane; Godwin, Andrew K; Nevanlinna, Heli; Giles, Graham G; Cox, Angela; Hopper, John L; Bolla, Manjeet K; Wang, Qin; Dennis, Joe; Dicks, Ed; Howat, Will J; Schoof, Nils; Bojesen, Stig E; Lambrechts, Diether; Broeks, Annegien; Andrulis, Irene L; Guénel, Pascal; Burwinkel, Barbara; Sawyer, Elinor J; Hollestelle, Antoinette; Fletcher, Olivia; Winqvist, Robert; Brenner, Hermann; Mannermaa, Arto; Hamann, Ute; Meindl, Alfons; Lindblom, Annika; Zheng, Wei; Devillee, Peter; Goldberg, Mark S; Lubinski, Jan; Kristensen, Vessela; Swerdlow, Anthony; Anton-Culver, Hoda; Dörk, Thilo; Muir, Kenneth; Matsuo, Keitaro; Wu, Anna H; Radice, Paolo; Teo, Soo Hwang; Shu, Xiao-Ou; Blot, William; Kang, Daehee; Hartman, Mikael; Sangrajrang, Suleeporn; Shen, Chen-Yang; Southey, Melissa C; Park, Daniel J; Hammet, Fleur; Stone, Jennifer; Veer, Laura J Van't; Rutgers, Emiel J; Lophatananon, Artitaya; Stewart-Brown, Sarah; Siriwanarangsan, Pornthep; Peto, Julian; Schrauder, Michael G; Ekici, Arif B; Beckmann, Matthias W; Dos Santos Silva, Isabel; Johnson, Nichola; Warren, Helen; Tomlinson, Ian; Kerin, Michael J; Miller, Nicola; Marme, Federick; Schneeweiss, Andreas; Sohn, Christof; Truong, Therese; Laurent-Puig, Pierre; Kerbrat, Pierre; Nordestgaard, Børge G; Nielsen, Sune F; Flyger, Henrik; Milne, Roger L; Perez, Jose Ignacio Arias; Menéndez, Primitiva; Müller, Heiko; Arndt, Volker; Stegmaier, Christa; Lichtner, Peter; Lochmann, Magdalena; Justenhoven, Christina; Ko, Yon-Dschun; Muranen, Taru A; Aittomäki, Kristiina; Blomqvist, Carl; Greco, Dario; Heikkinen, Tuomas; Ito, Hidemi; Iwata, Hiroji; Yatabe, Yasushi; Antonenkova, Natalia N; Margolin, Sara; Kataja, Vesa; Kosma, Veli-Matti; Hartikainen, Jaana M; Balleine, Rosemary; Tseng, Chiu-Chen; Berg, David Van Den; Stram, Daniel O; Neven, Patrick; Dieudonné, Anne-Sophie; Leunen, Karin; Rudolph, Anja; Nickels, Stefan; Flesch-Janys, Dieter; Peterlongo, Paolo; Peissel, Bernard; Bernard, Loris; Olson, Janet E; Wang, Xianshu; Stevens, Kristen; Severi, Gianluca; Baglietto, Laura; McLean, Catriona; Coetzee, Gerhard A; Feng, Ye; Henderson, Brian E; Schumacher, Fredrick; Bogdanova, Natalia V; Labrèche, France; Dumont, Martine; Yip, Cheng Har; Taib, Nur Aishah Mohd; Cheng, Ching-Yu; Shrubsole, Martha; Long, Jirong; Pylkäs, Katri; Jukkola-Vuorinen, Arja; Kauppila, Saila; Knight, Julia A; Glendon, Gord; Mulligan, Anna Marie; Tollenaar, Robertus A E M; Seynaeve, Caroline M; Kriege, Mieke; Hooning, Maartje J; van den Ouweland, Ans M W; van Deurzen, Carolien H M; Lu, Wei; Gao, Yu-Tang; Cai, Hui; Balasubramanian, Sabapathy P; Cross, Simon S; Reed, Malcolm W R; Signorello, Lisa; Cai, Qiuyin; Shah, Mitul; Miao, Hui; Chan, Ching Wan; Chia, Kee Seng; Jakubowska, Anna; Jaworska, Katarzyna; Durda, Katarzyna; Hsiung, Chia-Ni; Wu, Pei-Ei; Yu, Jyh-Cherng; Ashworth, Alan; Jones, Michael; Tessier, Daniel C; González-Neira, Anna; Pita, Guillermo; Alonso, M Rosario; Vincent, Daniel; Bacot, Francois; Ambrosone, Christine B; Bandera, Elisa V; John, Esther M; Chen, Gary K; Hu, Jennifer J; Rodriguez-Gil, Jorge L; Bernstein, Leslie; Press, Michael F; Ziegler, Regina G; Millikan, Robert M; Deming-Halverson, Sandra L; Nyante, Sarah; Ingles, Sue A; Waisfisz, Quinten; Tsimiklis, Helen; Makalic, Enes; Schmidt, Daniel; Bui, Minh; Gibson, Lorna; Müller-Myhsok, Bertram; Schmutzler, Rita K; Hein, Rebecca; Dahmen, Norbert; Beckmann, Lars; Aaltonen, Kirsimari; Czene, Kamila; Irwanto, Astrid; Liu, Jianjun; Turnbull, Clare; Rahman, Nazneen; Meijers-Heijboer, Hanne; Uitterlinden, Andre G; Rivadeneira, Fernando; Olswold, Curtis; Slager, Susan; Pilarski, Robert; Ademuyiwa, Foluso; Konstantopoulou, Irene; Martin, Nicholas G; Montgomery, Grant W; Slamon, Dennis J; Rauh, Claudia; Lux, Michael P; Jud, Sebastian M; Bruning, Thomas; Weaver, Joellen; Sharma, Priyanka; Pathak, Harsh; Tapper, Will; Gerty, Sue; Durcan, Lorraine; Trichopoulos, Dimitrios; Tumino, Rosario; Peeters, Petra H; Kaaks, Rudolf; Campa, Daniele; Canzian, Federico; Weiderpass, Elisabete; Johansson, Mattias; Khaw, Kay-Tee; Travis, Ruth; Clavel-Chapelon, Françoise; Kolonel, Laurence N; Chen, Constance; Beck, Andy; Hankinson, Susan E; Berg, Christine D; Hoover, Robert N; Lissowska, Jolanta; Figueroa, Jonine D; Chasman, Daniel I; Gaudet, Mia M; Diver, W Ryan; Willett, Walter C; Hunter, David J; Simard, Jacques; Benitez, Javier; Dunning, Alison M; Sherman, Mark E; Chenevix-Trench, Georgia; Chanock, Stephen J; Hall, Per; Pharoah, Paul D P; Vachon, Celine; Easton, Douglas F; Haiman, Christopher A; Kraft, Peter

2013-04-01

Estrogen receptor (ER)-negative tumors represent 20-30% of all breast cancers, with a higher proportion occurring in younger women and women of African ancestry. The etiology and clinical behavior of ER-negative tumors are different from those of tumors expressing ER (ER positive), including differences in genetic predisposition. To identify susceptibility loci specific to ER-negative disease, we combined in a meta-analysis 3 genome-wide association studies of 4,193 ER-negative breast cancer cases and 35,194 controls with a series of 40 follow-up studies (6,514 cases and 41,455 controls), genotyped using a custom Illumina array, iCOGS, developed by the Collaborative Oncological Gene-environment Study (COGS). SNPs at four loci, 1q32.1 (MDM4, P = 2.1 × 10(-12) and LGR6, P = 1.4 × 10(-8)), 2p24.1 (P = 4.6 × 10(-8)) and 16q12.2 (FTO, P = 4.0 × 10(-8)), were associated with ER-negative but not ER-positive breast cancer (P > 0.05). These findings provide further evidence for distinct etiological pathways associated with invasive ER-positive and ER-negative breast cancers.
The perennial ryegrass GenomeZipper: targeted use of genome resources for comparative grass genomics.

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Pfeifer, Matthias; Martis, Mihaela; Asp, Torben; Mayer, Klaus F X; Lübberstedt, Thomas; Byrne, Stephen; Frei, Ursula; Studer, Bruno

2013-02-01

Whole-genome sequences established for model and major crop species constitute a key resource for advanced genomic research. For outbreeding forage and turf grass species like ryegrasses (Lolium spp.), such resources have yet to be developed. Here, we present a model of the perennial ryegrass (Lolium perenne) genome on the basis of conserved synteny to barley (Hordeum vulgare) and the model grass genome Brachypodium (Brachypodium distachyon) as well as rice (Oryza sativa) and sorghum (Sorghum bicolor). A transcriptome-based genetic linkage map of perennial ryegrass served as a scaffold to establish the chromosomal arrangement of syntenic genes from model grass species. This scaffold revealed a high degree of synteny and macrocollinearity and was then utilized to anchor a collection of perennial ryegrass genes in silico to their predicted genome positions. This resulted in the unambiguous assignment of 3,315 out of 8,876 previously unmapped genes to the respective chromosomes. In total, the GenomeZipper incorporates 4,035 conserved grass gene loci, which were used for the first genome-wide sequence divergence analysis between perennial ryegrass, barley, Brachypodium, rice, and sorghum. The perennial ryegrass GenomeZipper is an ordered, information-rich genome scaffold, facilitating map-based cloning and genome assembly in perennial ryegrass and closely related Poaceae species. It also represents a milestone in describing synteny between perennial ryegrass and fully sequenced model grass genomes, thereby increasing our understanding of genome organization and evolution in the most important temperate forage and turf grass species.
Genome typing of nonhuman primate models: implications for biomedical research.

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Haus, Tanja; Ferguson, Betsy; Rogers, Jeffrey; Doxiadis, Gaby; Certa, Ulrich; Rose, Nicola J; Teepe, Robert; Weinbauer, Gerhard F; Roos, Christian

2014-11-01

The success of personalized medicine rests on understanding the genetic variation between individuals. Thus, as medical practice evolves and variation among individuals becomes a fundamental aspect of clinical medicine, a thorough consideration of the genetic and genomic information concerning the animals used as models in biomedical research also becomes critical. In particular, nonhuman primates (NHPs) offer great promise as models for many aspects of human health and disease. These are outbred species exhibiting substantial levels of genetic variation; however, understanding of the contribution of this variation to phenotypes is lagging behind in NHP species. Thus, there is a pivotal need to address this gap and define strategies for characterizing both genomic content and variability within primate models of human disease. Here, we discuss the current state of genomics of NHP models and offer guidelines for future work to ensure continued improvement and utility of this line of biomedical research. Copyright © 2014 Elsevier Ltd. All rights reserved.
Topic model-based mass spectrometric data analysis in cancer biomarker discovery studies.

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Wang, Minkun; Tsai, Tsung-Heng; Di Poto, Cristina; Ferrarini, Alessia; Yu, Guoqiang; Ressom, Habtom W

2016-08-18

A fundamental challenge in quantitation of biomolecules for cancer biomarker discovery is owing to the heterogeneous nature of human biospecimens. Although this issue has been a subject of discussion in cancer genomic studies, it has not yet been rigorously investigated in mass spectrometry based proteomic and metabolomic studies. Purification of mass spectometric data is highly desired prior to subsequent analysis, e.g., quantitative comparison of the abundance of biomolecules in biological samples. We investigated topic models to computationally analyze mass spectrometric data considering both integrated peak intensities and scan-level features, i.e., extracted ion chromatograms (EICs). Probabilistic generative models enable flexible representation in data structure and infer sample-specific pure resources. Scan-level modeling helps alleviate information loss during data preprocessing. We evaluated the capability of the proposed models in capturing mixture proportions of contaminants and cancer profiles on LC-MS based serum proteomic and GC-MS based tissue metabolomic datasets acquired from patients with hepatocellular carcinoma (HCC) and liver cirrhosis as well as synthetic data we generated based on the serum proteomic data. The results we obtained by analysis of the synthetic data demonstrated that both intensity-level and scan-level purification models can accurately infer the mixture proportions and the underlying true cancerous sources with small average error ratios (data, we found more proteins and metabolites with significant changes between HCC cases and cirrhotic controls. Candidate biomarkers selected after purification yielded biologically meaningful pathway analysis results and improved disease discrimination power in terms of the area under ROC curve compared to the results found prior to purification. We investigated topic model-based inference methods to computationally address the heterogeneity issue in samples analyzed by LC/GC-MS. We observed
Genomic features of lobular breast carcinoma

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Investigators with The Cancer Genome Atlas (TCGA) Research Network have identified molecular characteristics of a type of breast cancer, invasive lobular carcinoma (ILC), that distinguishes it from invasive ductal carcinoma (IDC), the most common invasive breast cancer subtype.
CRISPR/Cas9 for cancer research and therapy.

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Zhan, Tianzuo; Rindtorff, Niklas; Betge, Johannes; Ebert, Matthias P; Boutros, Michael

2018-04-16

CRISPR/Cas9 has become a powerful method for making changes to the genome of many organisms. First discovered in bacteria as part of an adaptive immune system, CRISPR/Cas9 and modified versions have found a widespread use to engineer genomes and to activate or to repress the expression of genes. As such, CRISPR/Cas9 promises to accelerate cancer research by providing an efficient technology to dissect mechanisms of tumorigenesis, identify targets for drug development, and possibly arm cells for cell-based therapies. Here, we review current applications of the CRISPR/Cas9 technology for cancer research and therapy. We describe novel Cas9 variants and how they are used in functional genomics to discover novel cancer-specific vulnerabilities. Furthermore, we highlight the impact of CRISPR/Cas9 in generating organoid and mouse models of cancer. Finally, we provide an overview of the first clinical trials that apply CRISPR/Cas9 as a therapeutic approach against cancer. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.
Tiling array-CGH for the assessment of genomic similarities among synchronous unilateral and bilateral invasive breast cancer tumor pairs

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Ringnér Markus

2008-07-01

Full Text Available Abstract Background Today, no objective criteria exist to differentiate between individual primary tumors and intra- or intermammary dissemination respectively, in patients diagnosed with two or more synchronous breast cancers. To elucidate whether these tumors most likely arise through clonal expansion, or whether they represent individual primary tumors is of tumor biological interest and may have clinical implications. In this respect, high resolution genomic profiling may provide a more reliable approach than conventional histopathological and tumor biological factors. Methods 32 K tiling microarray-based comparative genomic hybridization (aCGH was used to explore the genomic similarities among synchronous unilateral and bilateral invasive breast cancer tumor pairs, and was compared with histopathological and tumor biological parameters. Results Based on global copy number profiles and unsupervised hierarchical clustering, five of ten (p = 1.9 × 10-5 unilateral tumor pairs displayed similar genomic profiles within the pair, while only one of eight bilateral tumor pairs (p = 0.29 displayed pair-wise genomic similarities. DNA index, histological type and presence of vessel invasion correlated with the genomic analyses. Conclusion Synchronous unilateral tumor pairs are often genomically similar, while synchronous bilateral tumors most often represent individual primary tumors. However, two independent unilateral primary tumors can develop synchronously and contralateral tumor spread can occur. The presence of an intraductal component is not informative when establishing the independence of two tumors, while vessel invasion, the presence of which was found in clustering tumor pairs but not in tumor pairs that did not cluster together, supports the clustering outcome. Our data suggest that genomically similar unilateral tumor pairs may represent a more aggressive disease that requires the addition of more severe treatment modalities, and
Comprehensive Genomic Profiling Facilitates Implementation of the National Comprehensive Cancer Network Guidelines for Lung Cancer Biomarker Testing and Identifies Patients Who May Benefit From Enrollment in Mechanism-Driven Clinical Trials.

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Suh, James H; Johnson, Adrienne; Albacker, Lee; Wang, Kai; Chmielecki, Juliann; Frampton, Garrett; Gay, Laurie; Elvin, Julia A; Vergilio, Jo-Anne; Ali, Siraj; Miller, Vincent A; Stephens, Philip J; Ross, Jeffrey S

2016-06-01

The National Comprehensive Cancer Network (NCCN) guidelines for patients with metastatic non-small cell lung cancer (NSCLC) recommend testing for EGFR, BRAF, ERBB2, and MET mutations; ALK, ROS1, and RET rearrangements; and MET amplification. We investigated the feasibility and utility of comprehensive genomic profiling (CGP), a hybrid capture-based next-generation sequencing (NGS) test, in clinical practice. CGP was performed to a mean coverage depth of 576× on 6,832 consecutive cases of NSCLC (2012-2015). Genomic alterations (GAs) (point mutations, small indels, copy number changes, and rearrangements) involving EGFR, ALK, BRAF, ERBB2, MET, ROS1, RET, and KRAS were recorded. We also evaluated lung adenocarcinoma (AD) cases without GAs, involving these eight genes. The median age of the patients was 64 years (range: 13-88 years) and 53% were female. Among the patients studied, 4,876 (71%) harbored at least one GA involving EGFR (20%), ALK (4.1%), BRAF (5.7%), ERBB2 (6.0%), MET (5.6%), ROS1 (1.5%), RET (2.4%), or KRAS (32%). In the remaining cohort of lung AD without these known drivers, 273 cancer-related genes were altered in at least 0.1% of cases, including STK11 (21%), NF1 (13%), MYC (9.8%), RICTOR (6.4%), PIK3CA (5.4%), CDK4 (4.3%), CCND1 (4.0%), BRCA2 (2.5%), NRAS (2.3%), BRCA1 (1.7%), MAP2K1 (1.2%), HRAS (0.7%), NTRK1 (0.7%), and NTRK3 (0.2%). CGP is practical and facilitates implementation of the NCCN guidelines for NSCLC by enabling simultaneous detection of GAs involving all seven driver oncogenes and KRAS. Furthermore, without additional tissue use or cost, CGP identifies patients with "pan-negative" lung AD who may benefit from enrollment in mechanism-driven clinical trials. National Comprehensive Cancer Network guidelines for patients with metastatic non-small cell lung cancer (NSCLC) recommend testing for several genomic alterations (GAs). The feasibility and utility of comprehensive genomic profiling were studied in NSCLC and in lung adenocarcinoma
Computational correction of copy number effect improves specificity of CRISPR-Cas9 essentiality screens in cancer cells. | Office of Cancer Genomics

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The CRISPR-Cas9 system has revolutionized gene editing both at single genes and in multiplexed loss-of-function screens, thus enabling precise genome-scale identification of genes essential for proliferation and survival of cancer cells. However, previous studies have reported that a gene-independent antiproliferative effect of Cas9-mediated DNA cleavage confounds such measurement of genetic dependency, thereby leading to false-positive results in copy number-amplified regions.

Prostate cancer risk locus at 8q24 as a regulatory hub by physical interactions with multiple genomic loci across the genome.

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Du, Meijun; Yuan, Tiezheng; Schilter, Kala F; Dittmar, Rachel L; Mackinnon, Alexander; Huang, Xiaoyi; Tschannen, Michael; Worthey, Elizabeth; Jacob, Howard; Xia, Shu; Gao, Jianzhong; Tillmans, Lori; Lu, Yan; Liu, Pengyuan; Thibodeau, Stephen N; Wang, Liang

2015-01-01

Chromosome 8q24 locus contains regulatory variants that modulate genetic risk to various cancers including prostate cancer (PC). However, the biological mechanism underlying this regulation is not well understood. Here, we developed a chromosome conformation capture (3C)-based multi-target sequencing technology and systematically examined three PC risk regions at the 8q24 locus and their potential regulatory targets across human genome in six cell lines. We observed frequent physical contacts of this risk locus with multiple genomic regions, in particular, inter-chromosomal interaction with CD96 at 3q13 and intra-chromosomal interaction with MYC at 8q24. We identified at least five interaction hot spots within the predicted functional regulatory elements at the 8q24 risk locus. We also found intra-chromosomal interaction genes PVT1, FAM84B and GSDMC and inter-chromosomal interaction gene CXorf36 in most of the six cell lines. Other gene regions appeared to be cell line-specific, such as RRP12 in LNCaP, USP14 in DU-145 and SMIN3 in lymphoblastoid cell line. We further found that the 8q24 functional domains more likely interacted with genomic regions containing genes enriched in critical pathways such as Wnt signaling and promoter motifs such as E2F1 and TCF3. This result suggests that the risk locus may function as a regulatory hub by physical interactions with multiple genes important for prostate carcinogenesis. Further understanding genetic effect and biological mechanism of these chromatin interactions will shed light on the newly discovered regulatory role of the risk locus in PC etiology and progression. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
Understanding Cancer Prognosis

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Full Text Available ... Unusual Cancers of Childhood Treatment Childhood Cancer Genomics Study Findings Metastatic Cancer Metastatic Cancer Research Common Cancer Types Recurrent Cancer Common Cancer Types ...
A systems approach to predict oncometabolites via context-specific genome-scale metabolic networks.

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Hojung Nam

2014-09-01

Full Text Available Altered metabolism in cancer cells has been viewed as a passive response required for a malignant transformation. However, this view has changed through the recently described metabolic oncogenic factors: mutated isocitrate dehydrogenases (IDH, succinate dehydrogenase (SDH, and fumarate hydratase (FH that produce oncometabolites that competitively inhibit epigenetic regulation. In this study, we demonstrate in silico predictions of oncometabolites that have the potential to dysregulate epigenetic controls in nine types of cancer by incorporating massive scale genetic mutation information (collected from more than 1,700 cancer genomes, expression profiling data, and deploying Recon 2 to reconstruct context-specific genome-scale metabolic models. Our analysis predicted 15 compounds and 24 substructures of potential oncometabolites that could result from the loss-of-function and gain-of-function mutations of metabolic enzymes, respectively. These results suggest a substantial potential for discovering unidentified oncometabolites in various forms of cancers.
Value-based genomics.

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Gong, Jun; Pan, Kathy; Fakih, Marwan; Pal, Sumanta; Salgia, Ravi

2018-03-20

Advancements in next-generation sequencing have greatly enhanced the development of biomarker-driven cancer therapies. The affordability and availability of next-generation sequencers have allowed for the commercialization of next-generation sequencing platforms that have found widespread use for clinical-decision making and research purposes. Despite the greater availability of tumor molecular profiling by next-generation sequencing at our doorsteps, the achievement of value-based care, or improving patient outcomes while reducing overall costs or risks, in the era of precision oncology remains a looming challenge. In this review, we highlight available data through a pre-established and conceptualized framework for evaluating value-based medicine to assess the cost (efficiency), clinical benefit (effectiveness), and toxicity (safety) of genomic profiling in cancer care. We also provide perspectives on future directions of next-generation sequencing from targeted panels to whole-exome or whole-genome sequencing and describe potential strategies needed to attain value-based genomics.
Investigating core genetic-and-epigenetic cell cycle networks for stemness and carcinogenic mechanisms, and cancer drug design using big database mining and genome-wide next-generation sequencing data.

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Li, Cheng-Wei; Chen, Bor-Sen

2016-10-01

Recent studies have demonstrated that cell cycle plays a central role in development and carcinogenesis. Thus, the use of big databases and genome-wide high-throughput data to unravel the genetic and epigenetic mechanisms underlying cell cycle progression in stem cells and cancer cells is a matter of considerable interest. Real genetic-and-epigenetic cell cycle networks (GECNs) of embryonic stem cells (ESCs) and HeLa cancer cells were constructed by applying system modeling, system identification, and big database mining to genome-wide next-generation sequencing data. Real GECNs were then reduced to core GECNs of HeLa cells and ESCs by applying principal genome-wide network projection. In this study, we investigated potential carcinogenic and stemness mechanisms for systems cancer drug design by identifying common core and specific GECNs between HeLa cells and ESCs. Integrating drug database information with the specific GECNs of HeLa cells could lead to identification of multiple drugs for cervical cancer treatment with minimal side-effects on the genes in the common core. We found that dysregulation of miR-29C, miR-34A, miR-98, and miR-215; and methylation of ANKRD1, ARID5B, CDCA2, PIF1, STAMBPL1, TROAP, ZNF165, and HIST1H2AJ in HeLa cells could result in cell proliferation and anti-apoptosis through NFκB, TGF-β, and PI3K pathways. We also identified 3 drugs, methotrexate, quercetin, and mimosine, which repressed the activated cell cycle genes, ARID5B, STK17B, and CCL2, in HeLa cells with minimal side-effects.
Genome-wide DNA methylation sequencing reveals miR-663a is a novel epimutation candidate in CIMP-high endometrial cancer.

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Yanokura, Megumi; Banno, Kouji; Adachi, Masataka; Aoki, Daisuke; Abe, Kuniya

2017-06-01

Aberrant DNA methylation is widely observed in many cancers. Concurrent DNA methylation of multiple genes occurs in endometrial cancer and is referred to as the CpG island methylator phenotype (CIMP). However, the features and causes of CIMP-positive endometrial cancer are not well understood. To investigate DNA methylation features characteristic to CIMP-positive endometrial cancer, we first classified samples from 25 patients with endometrial cancer based on the methylation status of three genes, i.e. MLH1, CDH1 (E-cadherin) and APC: CIMP-high (CIMP-H, 2/25, 8.0%), CIMP-low (CIMP-L, 7/25, 28.0%) and CIMP-negative (CIMP(-), 16/25, 64.0%). We then selected two samples each from CIMP-H and CIMP(-) classes, and analyzed DNA methylation status of both normal (peripheral blood cells: PBCs) and cancer tissues by genome-wide, targeted bisulfite sequencing. Genomes of the CIMP-H cancer tissues were significantly hypermethylated compared to those of the CIMP(-). Surprisingly, in normal tissues of the CIMP-H patients, promoter region of the miR-663a locus is hypermethylated relative to CIMP(-) samples. Consistent with this finding, miR-663a expression was lower in the CIMP-H PBCs than in the CIMP(-) PBCs. The same region of the miR663a locus is found to be highly methylated in cancer tissues of both CIMP-H and CIMP(-) cases. This is the first report showing that aberrant DNA methylation of the miR-663a promoter can occur in normal tissue of the cancer patients, suggesting a possible link between this epigenetic abnormality and endometrial cancer. This raises the possibility that the hypermethylation of the miR-663a promoter represents an epimutation associated with the CIMP-H endometrial cancers. Based on these findings, relationship of the aberrant DNA methylation and CIMP-H phenotype is discussed.
Genomic Deletion at 10q23 in Prostate Cancer: More Than PTEN Loss?

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Raghavendra Tejo Karthik Poluri

2018-06-01

Full Text Available The PTEN gene encodes for the phosphatase and tensin homolog; it is a tumor suppressor gene that is among the most frequently inactivated genes throughout the human cancer spectrum. The most recent sequencing approaches have allowed the identification of PTEN genomic alterations, including deletion, mutation, or rearrangement in about 50% of prostate cancer (PCa cases. It appears that mechanisms leading to PTEN inactivation are cancer-specific, comprising gene mutations, small insertions/deletions, copy number alterations (CNAs, promoter hypermethylation, and RNA interference. The examination of publicly available results from deep-sequencing studies of various cancers showed that PCa appears to be the only cancer in which PTEN is lost mostly through CNA. Instead of inactivating mutations, which are seen in other cancers, deletion of the 10q23 locus is the most common form of PTEN inactivation in PCa. By investigating the minimal deleted region at 10q23, several other genes appear to be lost simultaneously with PTEN. Expression data indicate that, like PTEN, these genes are also downregulated upon loss of 10q23. These analyses raise the possibility that 10q23 is lost upon selective pressure not only to inactivate PTEN but also to impair the expression of surrounding genes. As such, several genes from this deleted region, which represents about 500 kb, may also act as tumor suppressors in PCa, requiring further studies on their respective functions in that context.
Genome-wide Meta-analyses of Breast, Ovarian and Prostate Cancer Association Studies Identify Multiple New Susceptibility Loci Shared by At Least Two Cancer Types

Science.gov (United States)

Kar, Siddhartha P.; Beesley, Jonathan; Al Olama, Ali Amin; Michailidou, Kyriaki; Tyrer, Jonathan; Kote-Jarai, ZSofia; Lawrenson, Kate; Lindstrom, Sara; Ramus, Susan J.; Thompson, Deborah J.; Kibel, Adam S.; Dansonka-Mieszkowska, Agnieszka; Michael, Agnieszka; Dieffenbach, Aida K.; Gentry-Maharaj, Aleksandra; Whittemore, Alice S.; Wolk, Alicja; Monteiro, Alvaro; Peixoto, Ana; Kierzek, Andrzej; Cox, Angela; Rudolph, Anja; Gonzalez-Neira, Anna; Wu, Anna H.; Lindblom, Annika; Swerdlow, Anthony; Ziogas, Argyrios; Ekici, Arif B.; Burwinkel, Barbara; Karlan, Beth Y.; Nordestgaard, Børge G.; Blomqvist, Carl; Phelan, Catherine; McLean, Catriona; Pearce, Celeste Leigh; Vachon, Celine; Cybulski, Cezary; Slavov, Chavdar; Stegmaier, Christa; Maier, Christiane; Ambrosone, Christine B.; Høgdall, Claus K.; Teerlink, Craig C.; Kang, Daehee; Tessier, Daniel C.; Schaid, Daniel J.; Stram, Daniel O.; Cramer, Daniel W.; Neal, David E.; Eccles, Diana; Flesch-Janys, Dieter; Velez Edwards, Digna R.; Wokozorczyk, Dominika; Levine, Douglas A.; Yannoukakos, Drakoulis; Sawyer, Elinor J.; Bandera, Elisa V.; Poole, Elizabeth M.; Goode, Ellen L.; Khusnutdinova, Elza; Høgdall, Estrid; Song, Fengju; Bruinsma, Fiona; Heitz, Florian; Modugno, Francesmary; Hamdy, Freddie C.; Wiklund, Fredrik; Giles, Graham G.; Olsson, Håkan; Wildiers, Hans; Ulmer, Hans-Ulrich; Pandha, Hardev; Risch, Harvey A.; Darabi, Hatef; Salvesen, Helga B.; Nevanlinna, Heli; Gronberg, Henrik; Brenner, Hermann; Brauch, Hiltrud; Anton-Culver, Hoda; Song, Honglin; Lim, Hui-Yi; McNeish, Iain; Campbell, Ian; Vergote, Ignace; Gronwald, Jacek; Lubiński, Jan; Stanford, Janet L.; Benítez, Javier; Doherty, Jennifer A.; Permuth, Jennifer B.; Chang-Claude, Jenny; Donovan, Jenny L.; Dennis, Joe; Schildkraut, Joellen M.; Schleutker, Johanna; Hopper, John L.; Kupryjanczyk, Jolanta; Park, Jong Y.; Figueroa, Jonine; Clements, Judith A.; Knight, Julia A.; Peto, Julian; Cunningham, Julie M.; Pow-Sang, Julio; Batra, Jyotsna; Czene, Kamila; Lu, Karen H.; Herkommer, Kathleen; Khaw, Kay-Tee; Matsuo, Keitaro; Muir, Kenneth; Offitt, Kenneth; Chen, Kexin; Moysich, Kirsten B.; Aittomäki, Kristiina; Odunsi, Kunle; Kiemeney, Lambertus A.; Massuger, Leon F.A.G.; Fitzgerald, Liesel M.; Cook, Linda S.; Cannon-Albright, Lisa; Hooning, Maartje J.; Pike, Malcolm C.; Bolla, Manjeet K.; Luedeke, Manuel; Teixeira, Manuel R.; Goodman, Marc T.; Schmidt, Marjanka K.; Riggan, Marjorie; Aly, Markus; Rossing, Mary Anne; Beckmann, Matthias W.; Moisse, Matthieu; Sanderson, Maureen; Southey, Melissa C.; Jones, Michael; Lush, Michael; Hildebrandt, Michelle A. T.; Hou, Ming-Feng; Schoemaker, Minouk J.; Garcia-Closas, Montserrat; Bogdanova, Natalia; Rahman, Nazneen; Le, Nhu D.; Orr, Nick; Wentzensen, Nicolas; Pashayan, Nora; Peterlongo, Paolo; Guénel, Pascal; Brennan, Paul; Paulo, Paula; Webb, Penelope M.; Broberg, Per; Fasching, Peter A.; Devilee, Peter; Wang, Qin; Cai, Qiuyin; Li, Qiyuan; Kaneva, Radka; Butzow, Ralf; Kopperud, Reidun Kristin; Schmutzler, Rita K.; Stephenson, Robert A.; MacInnis, Robert J.; Hoover, Robert N.; Winqvist, Robert; Ness, Roberta; Milne, Roger L.; Travis, Ruth C.; Benlloch, Sara; Olson, Sara H.; McDonnell, Shannon K.; Tworoger, Shelley S.; Maia, Sofia; Berndt, Sonja; Lee, Soo Chin; Teo, Soo-Hwang; Thibodeau, Stephen N.; Bojesen, Stig E.; Gapstur, Susan M.; Kjær, Susanne Krüger; Pejovic, Tanja; Tammela, Teuvo L.J.; Dörk, Thilo; Brüning, Thomas; Wahlfors, Tiina; Key, Tim J.; Edwards, Todd L.; Menon, Usha; Hamann, Ute; Mitev, Vanio; Kosma, Veli-Matti; Setiawan, Veronica Wendy; Kristensen, Vessela; Arndt, Volker; Vogel, Walther; Zheng, Wei; Sieh, Weiva; Blot, William J.; Kluzniak, Wojciech; Shu, Xiao-Ou; Gao, Yu-Tang; Schumacher, Fredrick; Freedman, Matthew L.; Berchuck, Andrew; Dunning, Alison M.; Simard, Jacques; Haiman, Christopher A.; Spurdle, Amanda; Sellers, Thomas A.; Hunter, David J.; Henderson, Brian E.; Kraft, Peter; Chanock, Stephen J.; Couch, Fergus J.; Hall, Per; Gayther, Simon A.; Easton, Douglas F.; Chenevix-Trench, Georgia; Eeles, Rosalind; Pharoah, Paul D.P.; Lambrechts, Diether

2016-01-01

Breast, ovarian, and prostate cancers are hormone-related and may have a shared genetic basis but this has not been investigated systematically by genome-wide association (GWA) studies. Meta-analyses combining the largest GWA meta-analysis data sets for these cancers totaling 112,349 cases and 116,421 controls of European ancestry, all together and in pairs, identified at P cancer loci: three associated with susceptibility to all three cancers (rs17041869/2q13/BCL2L11; rs7937840/11q12/INCENP; rs1469713/19p13/GATAD2A), two breast and ovarian cancer risk loci (rs200182588/9q31/SMC2; rs8037137/15q26/RCCD1), and two breast and prostate cancer risk loci (rs5013329/1p34/NSUN4; rs9375701/6q23/L3MBTL3). Index variants in five additional regions previously associated with only one cancer also showed clear association with a second cancer type. Cell-type specific expression quantitative trait locus and enhancer-gene interaction annotations suggested target genes with potential cross-cancer roles at the new loci. Pathway analysis revealed significant enrichment of death receptor signaling genes near loci with P cancer meta-analysis. PMID:27432226
Breast tumor copy number aberration phenotypes and genomic instability

International Nuclear Information System (INIS)

Fridlyand, Jane; Jain, Ajay N; McLennan, Jane; Ziegler, John; Chin, Koei; Devries, Sandy; Feiler, Heidi; Gray, Joe W; Waldman, Frederic; Pinkel, Daniel; Albertson, Donna G; Snijders, Antoine M; Ylstra, Bauke; Li, Hua; Olshen, Adam; Segraves, Richard; Dairkee, Shanaz; Tokuyasu, Taku; Ljung, Britt Marie

2006-01-01

Genomic DNA copy number aberrations are frequent in solid tumors, although the underlying causes of chromosomal instability in tumors remain obscure. Genes likely to have genomic instability phenotypes when mutated (e.g. those involved in mitosis, replication, repair, and telomeres) are rarely mutated in chromosomally unstable sporadic tumors, even though such mutations are associated with some heritable cancer prone syndromes. We applied array comparative genomic hybridization (CGH) to the analysis of breast tumors. The variation in the levels of genomic instability amongst tumors prompted us to investigate whether alterations in processes/genes involved in maintenance and/or manipulation of the genome were associated with particular types of genomic instability. We discriminated three breast tumor subtypes based on genomic DNA copy number alterations. The subtypes varied with respect to level of genomic instability. We find that shorter telomeres and altered telomere related gene expression are associated with amplification, implicating telomere attrition as a promoter of this type of aberration in breast cancer. On the other hand, the numbers of chromosomal alterations, particularly low level changes, are associated with altered expression of genes in other functional classes (mitosis, cell cycle, DNA replication and repair). Further, although loss of function instability phenotypes have been demonstrated for many of the genes in model systems, we observed enhanced expression of most genes in tumors, indicating that over expression, rather than deficiency underlies instability. Many of the genes associated with higher frequency of copy number aberrations are direct targets of E2F, supporting the hypothesis that deregulation of the Rb pathway is a major contributor to chromosomal instability in breast tumors. These observations are consistent with failure to find mutations in sporadic tumors in genes that have roles in maintenance or manipulation of the genome
Molecular evolution of colorectal cancer: from multistep carcinogenesis to the big bang.

Science.gov (United States)

Amaro, Adriana; Chiara, Silvana; Pfeffer, Ulrich

2016-03-01

Colorectal cancer is characterized by exquisite genomic instability either in the form of microsatellite instability or chromosomal instability. Microsatellite instability is the result of mutation of mismatch repair genes or their silencing through promoter methylation as a consequence of the CpG island methylator phenotype. The molecular causes of chromosomal instability are less well characterized. Genomic instability and field cancerization lead to a high degree of intratumoral heterogeneity and determine the formation of cancer stem cells and epithelial-mesenchymal transition mediated by the TGF-β and APC pathways. Recent analyses using integrated genomics reveal different phases of colorectal cancer evolution. An initial phase of genomic instability that yields many clones with different mutations (big bang) is followed by an important, previously not detected phase of cancer evolution that consists in the stabilization of several clones and a relatively flat outgrowth. The big bang model can best explain the coexistence of several stable clones and is compatible with the fact that the analysis of the bulk of the primary tumor yields prognostic information.
Genomic research perspectives in Kazakhstan

Directory of Open Access Journals (Sweden)

Ainur Akilzhanova

2014-01-01

Full Text Available Introduction: Technological advancements rapidly propel the field of genome research. Advances in genetics and genomics such as the sequence of the human genome, the human haplotype map, open access databases, cheaper genotyping and chemical genomics, have transformed basic and translational biomedical research. Several projects in the field of genomic and personalized medicine have been conducted at the Center for Life Sciences in Nazarbayev University. The prioritized areas of research include: genomics of multifactorial diseases, cancer genomics, bioinformatics, genetics of infectious diseases and population genomics. At present, DNA-based risk assessment for common complex diseases, application of molecular signatures for cancer diagnosis and prognosis, genome-guided therapy, and dose selection of therapeutic drugs are the important issues in personalized medicine. Results: To further develop genomic and biomedical projects at Center for Life Sciences, the development of bioinformatics research and infrastructure and the establishment of new collaborations in the field are essential. Widespread use of genetic tools will allow the identification of diseases before the onset of clinical symptoms, the individualization of drug treatment, and could induce individual behavioral changes on the basis of calculated disease risk. However, many challenges remain for the successful translation of genomic knowledge and technologies into health advances, such as medicines and diagnostics. It is important to integrate research and education in the fields of genomics, personalized medicine, and bioinformatics, which will be possible with opening of the new Medical Faculty at Nazarbayev University. People in practice and training need to be educated about the key concepts of genomics and engaged so they can effectively apply their knowledge in a matter that will bring the era of genomic medicine to patient care. This requires the development of well
Esophageal Cancer: Insights from Mouse Models

Directory of Open Access Journals (Sweden)

Marie-Pier Tétreault

2015-01-01

Full Text Available Esophageal cancer is the eighth leading cause of cancer and the sixth most common cause of cancer-related death worldwide. Despite recent advances in the development of surgical techniques in combination with the use of radiotherapy and chemotherapy, the prognosis for esophageal cancer remains poor. The cellular and molecular mechanisms that drive the pathogenesis of esophageal cancer are still poorly understood. Hence, understanding these mechanisms is crucial to improving outcomes for patients with esophageal cancer. Mouse models constitute valuable tools for modeling human cancers and for the preclinical testing of therapeutic strategies in a manner not possible in human subjects. Mice are excellent models for studying human cancers because they are similar to humans at the physiological and molecular levels and because they have a shorter gestation time and life cycle. Moreover, a wide range of well-developed technologies for introducing genetic modifications into mice are currently available. In this review, we describe how different mouse models are used to study esophageal cancer.
Genome-wide association analysis of more than 120,000 individuals identifies 15 new susceptibility loci for breast cancer

NARCIS (Netherlands)

K. Michailidou (Kyriaki); J. Beesley (Jonathan); S. Lindstrom (Stephen); S. Canisius (Sander); J. Dennis (Joe); M. Lush (Michael); M. Maranian (Melanie); M.K. Bolla (Manjeet); Q. Wang (Qing); M. Shah (Mitul); B. Perkins (Barbara); K. Czene (Kamila); M. Eriksson (Mikael); H. Darabi (Hatef); J.S. Brand (Judith S.); S.E. Bojesen (Stig); B.G. Nordestgaard (Børge); H. Flyger (Henrik); S.F. Nielsen (Sune); N. Rahman (Nazneen); C. Turnbull (Clare); O. Fletcher (Olivia); J. Peto (Julian); L.J. Gibson (Lorna); I. dos Santos Silva (Isabel); J. Chang-Claude (Jenny); D. Flesch-Janys (Dieter); A. Rudolph (Anja); U. Eilber (Ursula); T.W. Behrens (Timothy); H. Nevanlinna (Heli); T.A. Muranen (Taru); K. Aittomäki (Kristiina); C. Blomqvist (Carl); S. Khan (Sofia); K. Aaltonen (Kirsimari); H. Ahsan (Habibul); M.G. Kibriya (Muhammad); A.S. Whittemore (Alice S.); E.M. John (Esther M.); K.E. Malone (Kathleen E.); M.D. Gammon (Marilie); R.M. Santella (Regina M.); G. Ursin (Giske); E. Makalic (Enes); D.F. Schmidt (Daniel); G. Casey (Graham); D.J. Hunter (David J.); S.M. Gapstur (Susan M.); M.M. Gaudet (Mia); W.R. Diver (Ryan); C.A. Haiman (Christopher A.); F.R. Schumacher (Fredrick); B.E. Henderson (Brian); L. Le Marchand (Loic); C.D. Berg (Christine); S.J. Chanock (Stephen); J.D. Figueroa (Jonine); R.N. Hoover (Robert N.); D. Lambrechts (Diether); P. Neven (Patrick); H. Wildiers (Hans); E. van Limbergen (Erik); M.K. Schmidt (Marjanka); A. Broeks (Annegien); S. Verhoef; S. Cornelissen (Sten); F.J. Couch (Fergus); J.E. Olson (Janet); B. Hallberg (Boubou); C. Vachon (Celine); Q. Waisfisz (Quinten); E.J. Meijers-Heijboer (Hanne); M.A. Adank (Muriel); R.B. van der Luijt (Rob); J. Li (Jingmei); J. Liu (Jianjun); M.K. Humphreys (Manjeet); D. Kang (Daehee); J.-Y. Choi (Ji-Yeob); S.K. Park (Sue K.); K.Y. Yoo; K. Matsuo (Keitaro); H. Ito (Hidemi); H. Iwata (Hiroji); K. Tajima (Kazuo); P. Guénel (Pascal); T. Truong (Thérèse); C. Mulot (Claire); M. Sanchez (Marie); B. Burwinkel (Barbara); F. Marme (Federick); H. Surowy (Harald); C. Sohn (Christof); A.H. Wu (Anna H); C.-C. Tseng (Chiu-chen); D. Van Den Berg (David); D.O. Stram (Daniel O.); A. González-Neira (Anna); J. Benítez (Javier); M.P. Zamora (Pilar); J.I.A. Perez (Jose Ignacio Arias); X.-O. Shu (Xiao-Ou); W. Lu (Wei); Y. Gao; H. Cai (Hui); A. Cox (Angela); S.S. Cross (Simon); M.W.R. Reed (Malcolm); I.L. Andrulis (Irene); J.A. Knight (Julia); G. Glendon (Gord); A.-M. Mulligan (Anna-Marie); E.J. Sawyer (Elinor); I.P. Tomlinson (Ian); M. Kerin (Michael); N. Miller (Nicola); A. Lindblom (Annika); S. Margolin (Sara); S.H. Teo (Soo Hwang); C.H. Yip (Cheng Har); N.A.M. Taib (Nur Aishah Mohd); G.-H. Tan (Gie-Hooi); M.J. Hooning (Maartje); A. Hollestelle (Antoinette); J.W.M. Martens (John); J.M. Collée (Margriet); W.J. Blot (William); L.B. Signorello (Lisa B.); Q. Cai (Qiuyin); J. Hopper (John); M.C. Southey (Melissa); H. Tsimiklis (Helen); C. Apicella (Carmel); C-Y. Shen (Chen-Yang); C.-N. Hsiung (Chia-Ni); P.-E. Wu (Pei-Ei); M.-F. Hou (Ming-Feng); V. Kristensen (Vessela); S. Nord (Silje); G.G. Alnæs (Grethe); G.G. Giles (Graham G.); R.L. Milne (Roger); C.A. McLean (Catriona Ann); F. Canzian (Federico); D. Trichopoulos (Dimitrios); P.H.M. Peeters; E. Lund (Eiliv); R. Sund (Reijo); K.T. Khaw; M.J. Gunter (Marc J.); D. Palli (Domenico); L.M. Mortensen (Lotte Maxild); L. Dossus (Laure); J.-M. Huerta (Jose-Maria); A. Meindl (Alfons); R.K. Schmutzler (Rita); C. Sutter (Christian); R. Yang (Rongxi); K. Muir (Kenneth); A. Lophatananon (Artitaya); S. Stewart-Brown (Sarah); P. Siriwanarangsan (Pornthep); J.M. Hartman (Joost); X. Miao; K.S. Chia (Kee Seng); C.W. Chan (Ching Wan); P.A. Fasching (Peter); R. Hein (Rebecca); M.W. Beckmann (Matthias); L. Haeberle (Lothar); H. Brenner (Hermann); A.K. Dieffenbach (Aida Karina); V. Arndt (Volker); C. Stegmaier (Christa); A. Ashworth (Alan); N. Orr (Nick); M. Schoemaker (Minouk); A.J. Swerdlow (Anthony ); L.A. Brinton (Louise); M. García-Closas (Montserrat); W. Zheng (Wei); S.L. Halverson (Sandra L.); M. Shrubsole (Martha); J. Long (Jirong); M.S. Goldberg (Mark); F. Labrèche (France); M. Dumont (Martine); R. Winqvist (Robert); K. Pykäs (Katri); A. Jukkola-Vuorinen (Arja); M. Grip (Mervi); H. Brauch (Hiltrud); U. Hamann (Ute); T. Brüning (Thomas); P. Radice (Paolo); P. Peterlongo (Paolo); S. Manoukian (Siranoush); L. Bernard (Loris); N.V. Bogdanova (Natalia); T. Dörk (Thilo); A. Mannermaa (Arto); V. Kataja (Vesa); V-M. Kosma (Veli-Matti); J.M. Hartikainen (J.); P. Devilee (Peter); R.A.E.M. Tollenaar (Rob); C.M. Seynaeve (Caroline); C.J. van Asperen (Christi); A. Jakubowska (Anna); J. Lubinski (Jan); K. Jaworska (Katarzyna); T. Huzarski (Tomasz); S. Sangrajrang (Suleeporn); V. Gaborieau (Valerie); P. Brennan (Paul); J.D. McKay (James); S. Slager (Susan); A.E. Toland (Amanda); C.B. Ambrosone (Christine); D. Yannoukakos (Drakoulis); M. Kabisch (Maria); D. Torres (Diana); S.L. Neuhausen (Susan); H. Anton-Culver (Hoda); C. Luccarini (Craig); C. Baynes (Caroline); S. Ahmed (Shahana); S. Healey (Sue); D.C. Tessier (Daniel C.); D. Vincent (Daniel); F. Bacot (Francois); G. Pita (Guillermo); M.R. Alonso (Rosario); N. Álvarez (Nuria); D. Herrero (Daniel); J. Simard (Jacques); P.P.D.P. Pharoah (Paul P.D.P.); P. Kraft (Peter); A.M. Dunning (Alison); G. Chenevix-Trench (Georgia); P. Hall (Per); D.F. Easton (Douglas)

2015-01-01

textabstractGenome-wide association studies (GWAS) and large-scale replication studies have identified common variants in 79 loci associated with breast cancer, explaining ∼14% of the familial risk of the disease. To identify new susceptibility loci, we performed a meta-analysis of 11 GWAS,
DNA Checkpoint and Repair Factors Are Nuclear Sensors for Intracellular Organelle Stresses—Inflammations and Cancers Can Have High Genomic Risks

Directory of Open Access Journals (Sweden)

Huihong Zeng

2018-05-01

Full Text Available Under inflammatory conditions, inflammatory cells release reactive oxygen species (ROS and reactive nitrogen species (RNS which cause DNA damage. If not appropriately repaired, DNA damage leads to gene mutations and genomic instability. DNA damage checkpoint factors (DDCF and DNA damage repair factors (DDRF play a vital role in maintaining genomic integrity. However, how DDCFs and DDRFs are modulated under physiological and pathological conditions are not fully known. We took an experimental database analysis to determine the expression of 26 DNA DDCFs and 42 DNA DDRFs in 21 human and 20 mouse tissues in physiological/pathological conditions. We made the following significant findings: (1 Few DDCFs and DDRFs are ubiquitously expressed in tissues while many are differentially regulated.; (2 the expression of DDCFs and DDRFs are modulated not only in cancers but also in sterile inflammatory disorders and metabolic diseases; (3 tissue methylation status, pro-inflammatory cytokines, hypoxia regulating factors and tissue angiogenic potential can determine the expression of DDCFs and DDRFs; (4 intracellular organelles can transmit the stress signals to the nucleus, which may modulate the cell death by regulating the DDCF and DDRF expression. Our results shows that sterile inflammatory disorders and cancers increase genomic instability, therefore can be classified as pathologies with a high genomic risk. We also propose a new concept that as parts of cellular sensor cross-talking network, DNA checkpoint and repair factors serve as nuclear sensors for intracellular organelle stresses. Further, this work would lead to identification of novel therapeutic targets and new biomarkers for diagnosis and prognosis of metabolic diseases, inflammation, tissue damage and cancers.
Genome-wide association analysis of more than 120,000 individuals identifies 15 new susceptibility loci for breast cancer.

OpenAIRE

Michailidou, Kyriaki; Beesley, Jonathan; Lindstrom, Sara; Canisius, Sander; Dennis, Joe; Lush, Michael J; Maranian, Mel J; Bolla, Manjeet K; Wang, Qin; Shah, Mitulkumar Nandlal; Perkins, Barbara J; Czene, Kamila; Eriksson, Mikael; Darabi, Hatef; Brand, Judith S

2015-01-01

Genome-wide association studies (GWAS) and large-scale replication studies have identified common variants in 79 loci associated with breast cancer, explaining ~14% of the familial risk of the disease. To identify new susceptibility loci, we performed a meta-analysis of 11 GWAS, comprising 15,748 breast cancer cases and 18,084 controls together with 46,785 cases and 42,892 controls from 41 studies genotyped on a 211,155-marker custom array (iCOGS). Analyses were restricted to women of Europea...
Genome-wide association analysis of more than 120,000 individuals identifies 15 new susceptibility loci for breast cancer

OpenAIRE

Michailidou, Kyriaki; Beesley, Jonathan; Lindstrom, Stephen; Canisius, Sander; Dennis, Joe; Lush, Michael; Maranian, Melanie; Bolla, Manjeet; Wang, Qing; Shah, Mitul; Perkins, Barbara; Czene, Kamila; Eriksson, Mikael; Darabi, Hatef; Brand, Judith S.

2015-01-01

textabstractGenome-wide association studies (GWAS) and large-scale replication studies have identified common variants in 79 loci associated with breast cancer, explaining ∼14% of the familial risk of the disease. To identify new susceptibility loci, we performed a meta-analysis of 11 GWAS, comprising 15,748 breast cancer cases and 18,084 controls together with 46,785 cases and 42,892 controls from 41 studies genotyped on a 211,155-marker custom array (iCOGS). Analyses were restricted to wome...
Multiple-integrations of HPV16 genome and altered transcription of viral oncogenes and cellular genes are associated with the development of cervical cancer.

Directory of Open Access Journals (Sweden)

Xulian Lu

Full Text Available The constitutive expression of the high-risk HPV E6 and E7 viral oncogenes is the major cause of cervical cancer. To comprehensively explore the composition of HPV16 early transcripts and their genomic annotation, cervical squamous epithelial tissues from 40 HPV16-infected patients were collected for analysis of papillomavirus oncogene transcripts (APOT. We observed different transcription patterns of HPV16 oncogenes in progression of cervical lesions to cervical cancer and identified one novel transcript. Multiple-integration events in the tissues of cervical carcinoma (CxCa are significantly more often than those of low-grade squamous intraepithelial lesions (LSIL and high-grade squamous intraepithelial lesions (HSIL. Moreover, most cellular genes within or near these integration sites are cancer-associated genes. Taken together, this study suggests that the multiple-integrations of HPV genome during persistent viral infection, which thereby alters the expression patterns of viral oncogenes and integration-related cellular genes, play a crucial role in progression of cervical lesions to cervix cancer.
Sequence modelling and an extensible data model for genomic database

Energy Technology Data Exchange (ETDEWEB)

Li, Peter Wei-Der [California Univ., San Francisco, CA (United States); Univ. of California, Berkeley, CA (United States)

1992-01-01

The Human Genome Project (HGP) plans to sequence the human genome by the beginning of the next century. It will generate DNA sequences of more than 10 billion bases and complex marker sequences (maps) of more than 100 million markers. All of these information will be stored in database management systems (DBMSs). However, existing data models do not have the abstraction mechanism for modelling sequences and existing DBMS`s do not have operations for complex sequences. This work addresses the problem of sequence modelling in the context of the HGP and the more general problem of an extensible object data model that can incorporate the sequence model as well as existing and future data constructs and operators. First, we proposed a general sequence model that is application and implementation independent. This model is used to capture the sequence information found in the HGP at the conceptual level. In addition, abstract and biological sequence operators are defined for manipulating the modelled sequences. Second, we combined many features of semantic and object oriented data models into an extensible framework, which we called the ``Extensible Object Model``, to address the need of a modelling framework for incorporating the sequence data model with other types of data constructs and operators. This framework is based on the conceptual separation between constructors and constraints. We then used this modelling framework to integrate the constructs for the conceptual sequence model. The Extensible Object Model is also defined with a graphical representation, which is useful as a tool for database designers. Finally, we defined a query language to support this model and implement the query processor to demonstrate the feasibility of the extensible framework and the usefulness of the conceptual sequence model.
Sequence modelling and an extensible data model for genomic database

Energy Technology Data Exchange (ETDEWEB)

Li, Peter Wei-Der (California Univ., San Francisco, CA (United States) Lawrence Berkeley Lab., CA (United States))

1992-01-01

The Human Genome Project (HGP) plans to sequence the human genome by the beginning of the next century. It will generate DNA sequences of more than 10 billion bases and complex marker sequences (maps) of more than 100 million markers. All of these information will be stored in database management systems (DBMSs). However, existing data models do not have the abstraction mechanism for modelling sequences and existing DBMS's do not have operations for complex sequences. This work addresses the problem of sequence modelling in the context of the HGP and the more general problem of an extensible object data model that can incorporate the sequence model as well as existing and future data constructs and operators. First, we proposed a general sequence model that is application and implementation independent. This model is used to capture the sequence information found in the HGP at the conceptual level. In addition, abstract and biological sequence operators are defined for manipulating the modelled sequences. Second, we combined many features of semantic and object oriented data models into an extensible framework, which we called the Extensible Object Model'', to address the need of a modelling framework for incorporating the sequence data model with other types of data constructs and operators. This framework is based on the conceptual separation between constructors and constraints. We then used this modelling framework to integrate the constructs for the conceptual sequence model. The Extensible Object Model is also defined with a graphical representation, which is useful as a tool for database designers. Finally, we defined a query language to support this model and implement the query processor to demonstrate the feasibility of the extensible framework and the usefulness of the conceptual sequence model.
Characterization of canine osteosarcoma by array comparative genomic hybridization and RT-qPCR: signatures of genomic imbalance in canine osteosarcoma parallel the human counterpart.

Science.gov (United States)

Angstadt, Andrea Y; Motsinger-Reif, Alison; Thomas, Rachael; Kisseberth, William C; Guillermo Couto, C; Duval, Dawn L; Nielsen, Dahlia M; Modiano, Jaime F; Breen, Matthew

2011-11-01

Osteosarcoma (OS) is the most commonly diagnosed malignant bone tumor in humans and dogs, characterized in both species by extremely complex karyotypes exhibiting high frequencies of genomic imbalance. Evaluation of genomic signatures in human OS using array comparative genomic hybridization (aCGH) has assisted in uncovering genetic mechanisms that result in disease phenotype. Previous low-resolution (10-20 Mb) aCGH analysis of canine OS identified a wide range of recurrent DNA copy number aberrations, indicating extensive genomic instability. In this study, we profiled 123 canine OS tumors by 1 Mb-resolution aCGH to generate a dataset for direct comparison with current data for human OS, concluding that several high frequency aberrations in canine and human OS are orthologous. To ensure complete coverage of gene annotation, we identified the human refseq genes that map to these orthologous aberrant dog regions and found several candidate genes warranting evaluation for OS involvement. Specifically, subsequenct FISH and qRT-PCR analysis of RUNX2, TUSC3, and PTEN indicated that expression levels correlated with genomic copy number status, showcasing RUNX2 as an OS associated gene and TUSC3 as a possible tumor suppressor candidate. Together these data demonstrate the ability of genomic comparative oncology to identify genetic abberations which may be important for OS progression. Large scale screening of genomic imbalance in canine OS further validates the use of the dog as a suitable model for human cancers, supporting the idea that dysregulation discovered in canine cancers will provide an avenue for complementary study in human counterparts. Copyright © 2011 Wiley-Liss, Inc.

Score-based prediction of genomic islands in prokaryotic genomes using hidden Markov models

Directory of Open Access Journals (Sweden)

Surovcik Katharina

2006-03-01

Full Text Available Abstract Background Horizontal gene transfer (HGT is considered a strong evolutionary force shaping the content of microbial genomes in a substantial manner. It is the difference in speed enabling the rapid adaptation to changing environmental demands that distinguishes HGT from gene genesis, duplications or mutations. For a precise characterization, algorithms are needed that identify transfer events with high reliability. Frequently, the transferred pieces of DNA have a considerable length, comprise several genes and are called genomic islands (GIs or more specifically pathogenicity or symbiotic islands. Results We have implemented the program SIGI-HMM that predicts GIs and the putative donor of each individual alien gene. It is based on the analysis of codon usage (CU of each individual gene of a genome under study. CU of each gene is compared against a carefully selected set of CU tables representing microbial donors or highly expressed genes. Multiple tests are used to identify putatively alien genes, to predict putative donors and to mask putatively highly expressed genes. Thus, we determine the states and emission probabilities of an inhomogeneous hidden Markov model working on gene level. For the transition probabilities, we draw upon classical test theory with the intention of integrating a sensitivity controller in a consistent manner. SIGI-HMM was written in JAVA and is publicly available. It accepts as input any file created according to the EMBL-format. It generates output in the common GFF format readable for genome browsers. Benchmark tests showed that the output of SIGI-HMM is in agreement with known findings. Its predictions were both consistent with annotated GIs and with predictions generated by different methods. Conclusion SIGI-HMM is a sensitive tool for the identification of GIs in microbial genomes. It allows to interactively analyze genomes in detail and to generate or to test hypotheses about the origin of acquired
Drosophila as a model to study the role of blood cells in inflammation, innate immunity and cancer

Science.gov (United States)

Wang, Lihui; Kounatidis, Ilias; Ligoxygakis, Petros

2014-01-01

Drosophila has a primitive yet effective blood system with three types of haemocytes which function throughout different developmental stages and environmental stimuli. Haemocytes play essential roles in tissue modeling during embryogenesis and morphogenesis, and also in innate immunity. The open circulatory system of Drosophila makes haemocytes ideal signal mediators to cells and tissues in response to events such as infection and wounding. The application of recently developed and sophisticated genetic tools to the relatively simple genome of Drosophila has made the fly a popular system for modeling human tumorigensis and metastasis. Drosophila is now used for screening and investigation of genes implicated in human leukemia and also in modeling development of solid tumors. This second line of research offers promising opportunities to determine the seemingly conflicting roles of blood cells in tumor progression and invasion. This review provides an overview of the signaling pathways conserved in Drosophila during haematopoiesis, haemostasis, innate immunity, wound healing and inflammation. We also review the most recent progress in the use of Drosophila as a cancer research model with an emphasis on the roles haemocytes can play in various cancer models and in the links between inflammation and cancer. PMID:24409421
Use of Whole Genome Sequencing for Diagnosis and Discovery in the Cancer Genetics Clinic

Directory of Open Access Journals (Sweden)

Samantha B. Foley

2015-01-01

Full Text Available Despite the potential of whole-genome sequencing (WGS to improve patient diagnosis and care, the empirical value of WGS in the cancer genetics clinic is unknown. We performed WGS on members of two cohorts of cancer genetics patients: those with BRCA1/2 mutations (n = 176 and those without (n = 82. Initial analysis of potentially pathogenic variants (PPVs, defined as nonsynonymous variants with allele frequency < 1% in ESP6500 in 163 clinically-relevant genes suggested that WGS will provide useful clinical results. This is despite the fact that a majority of PPVs were novel missense variants likely to be classified as variants of unknown significance (VUS. Furthermore, previously reported pathogenic missense variants did not always associate with their predicted diseases in our patients. This suggests that the clinical use of WGS will require large-scale efforts to consolidate WGS and patient data to improve accuracy of interpretation of rare variants. While loss-of-function (LoF variants represented only a small fraction of PPVs, WGS identified additional cancer risk LoF PPVs in patients with known BRCA1/2 mutations and led to cancer risk diagnoses in 21% of non-BRCA cancer genetics patients after expanding our analysis to 3209 ClinVar genes. These data illustrate how WGS can be used to improve our ability to discover patients' cancer genetic risks.
Precision medicine for advanced prostate cancer.

Science.gov (United States)

Mullane, Stephanie A; Van Allen, Eliezer M

2016-05-01

Precision cancer medicine, the use of genomic profiling of patient tumors at the point-of-care to inform treatment decisions, is rapidly changing treatment strategies across cancer types. Precision medicine for advanced prostate cancer may identify new treatment strategies and change clinical practice. In this review, we discuss the potential and challenges of precision medicine in advanced prostate cancer. Although primary prostate cancers do not harbor highly recurrent targetable genomic alterations, recent reports on the genomics of metastatic castration-resistant prostate cancer has shown multiple targetable alterations in castration-resistant prostate cancer metastatic biopsies. Therapeutic implications include targeting prevalent DNA repair pathway alterations with PARP-1 inhibition in genomically defined subsets of patients, among other genomically stratified targets. In addition, multiple recent efforts have demonstrated the promise of liquid tumor profiling (e.g., profiling circulating tumor cells or cell-free tumor DNA) and highlighted the necessary steps to scale these approaches in prostate cancer. Although still in the initial phase of precision medicine for prostate cancer, there is extraordinary potential for clinical impact. Efforts to overcome current scientific and clinical barriers will enable widespread use of precision medicine approaches for advanced prostate cancer patients.
A meta-analysis of genome-wide association studies of breast cancer identifies two novel susceptibility loci at 6q14 and 20q11

NARCIS (Netherlands)

Siddiq, Afshan; Couch, Fergus J.; Chen, Gary K.; Lindström, Sara; Eccles, Diana; Millikan, Robert C.; Michailidou, Kyriaki; Stram, Daniel O.; Beckmann, Lars; Rhie, Suhn Kyong; Ambrosone, Christine B.; Aittomäki, Kristiina; Amiano, Pilar; Apicella, Carmel; Baglietto, Laura; Bandera, Elisa V.; Beckmann, Matthias W.; Berg, Christine D.; Bernstein, Leslie; Blomqvist, Carl; Brauch, Hiltrud; Brinton, Louise; Bui, Quang M.; Buring, Julie E.; Buys, Saundra S.; Campa, Daniele; Carpenter, Jane E.; Chasman, Daniel I.; Chang-Claude, Jenny; Chen, Constance; Clavel-Chapelon, Françoise; Cox, Angela; Cross, Simon S.; Czene, Kamila; Deming, Sandra L.; Diasio, Robert B.; Diver, W. Ryan; Dunning, Alison M.; Durcan, Lorraine; Ekici, Arif B.; Fasching, Peter A.; Feigelson, Heather Spencer; Fejerman, Laura; Figueroa, Jonine D.; Fletcher, Olivia; Flesch-Janys, Dieter; Gaudet, Mia M.; Gerty, Susan M.; Rodriguez-Gil, Jorge L.; Giles, Graham G.; van Gils, Carla H.; Godwin, Andrew K.; Graham, Nikki; Greco, Dario; Hall, Per; Hankinson, Susan E.; Hartmann, Arndt; Hein, Rebecca; Heinz, Judith; Hoover, Robert N.; Hopper, John L.; Hu, Jennifer J.; Huntsman, Scott; Ingles, Sue A.; Irwanto, Astrid; Isaacs, Claudine; Jacobs, Kevin B.; John, Esther M.; Justenhoven, Christina; Kaaks, Rudolf; Kolonel, Laurence N.; Coetzee, Gerhard A.; Lathrop, Mark; Le Marchand, Loic; Lee, Adam M.; Lee, I.-Min; Lesnick, Timothy; Lichtner, Peter; Liu, Jianjun; Lund, Eiliv; Makalic, Enes; Martin, Nicholas G.; McLean, Catriona A.; Meijers-Heijboer, Hanne; Meindl, Alfons; Miron, Penelope; Monroe, Kristine R.; Montgomery, Grant W.; Müller-Myhsok, Bertram; Nickels, Stefan; Nyante, Sarah J.; Olswold, Curtis; Overvad, Kim; Palli, Domenico; Park, Daniel J.; Palmer, Julie R.; Pathak, Harsh; Peto, Julian; Pharoah, Paul; Rahman, Nazneen; Rivadeneira, Fernando; Schmidt, Daniel F.; Schmutzler, Rita K.; Slager, Susan; Southey, Melissa C.; Stevens, Kristen N.; Sinn, Hans-Peter; Press, Michael F.; Ross, Eric; Riboli, Elio; Ridker, Paul M.; Schumacher, Fredrick R.; Severi, Gianluca; dos Santos Silva, Isabel; Stone, Jennifer; Sund, Malin; Tapper, William J.; Thun, Michael J.; Travis, Ruth C.; Turnbull, Clare; Uitterlinden, Andre G.; Waisfisz, Quinten; Wang, Xianshu; Wang, Zhaoming; Weaver, Joellen; Schulz-Wendtland, Rüdiger; Wilkens, Lynne R.; van den Berg, David; Zheng, Wei; Ziegler, Regina G.; Ziv, Elad; Nevanlinna, Heli; Easton, Douglas F.; Hunter, David J.; Henderson, Brian E.; Chanock, Stephen J.; Garcia-Closas, Montserrat; Kraft, Peter; Haiman, Christopher A.; Vachon, Celine M.

2012-01-01

Genome-wide association studies (GWAS) of breast cancer defined by hormone receptor status have revealed loci contributing to susceptibility of estrogen receptor (ER)-negative subtypes. To identify additional genetic variants for ER-negative breast cancer, we conducted the largest meta-analysis of
Appraising the relevance of DNA copy number loss and gain in prostate cancer using whole genome DNA sequence data.

Directory of Open Access Journals (Sweden)

Niedzica Camacho

2017-09-01

Full Text Available A variety of models have been proposed to explain regions of recurrent somatic copy number alteration (SCNA in human cancer. Our study employs Whole Genome DNA Sequence (WGS data from tumor samples (n = 103 to comprehensively assess the role of the Knudson two hit genetic model in SCNA generation in prostate cancer. 64 recurrent regions of loss and gain were detected, of which 28 were novel, including regions of loss with more than 15% frequency at Chr4p15.2-p15.1 (15.53%, Chr6q27 (16.50% and Chr18q12.3 (17.48%. Comprehensive mutation screens of genes, lincRNA encoding sequences, control regions and conserved domains within SCNAs demonstrated that a two-hit genetic model was supported in only a minor proportion of recurrent SCNA losses examined (15/40. We found that recurrent breakpoints and regions of inversion often occur within Knudson model SCNAs, leading to the identification of ZNF292 as a target gene for the deletion at 6q14.3-q15 and NKX3.1 as a two-hit target at 8p21.3-p21.2. The importance of alterations of lincRNA sequences was illustrated by the identification of a novel mutational hotspot at the KCCAT42, FENDRR, CAT1886 and STCAT2 loci at the 16q23.1-q24.3 loss. Our data confirm that the burden of SCNAs is predictive of biochemical recurrence, define nine individual regions that are associated with relapse, and highlight the possible importance of ion channel and G-protein coupled-receptor (GPCR pathways in cancer development. We concluded that a two-hit genetic model accounts for about one third of SCNA indicating that mechanisms, such haploinsufficiency and epigenetic inactivation, account for the remaining SCNA losses.
Experimental Induction of Genome Chaos.

Science.gov (United States)

Ye, Christine J; Liu, Guo; Heng, Henry H

2018-01-01

Genome chaos, or karyotype chaos, represents a powerful survival strategy for somatic cells under high levels of stress/selection. Since the genome context, not the gene content, encodes the genomic blueprint of the cell, stress-induced rapid and massive reorganization of genome topology functions as a very important mechanism for genome (karyotype) evolution. In recent years, the phenomenon of genome chaos has been confirmed by various sequencing efforts, and many different terms have been coined to describe different subtypes of the chaotic genome including "chromothripsis," "chromoplexy," and "structural mutations." To advance this exciting field, we need an effective experimental system to induce and characterize the karyotype reorganization process. In this chapter, an experimental protocol to induce chaotic genomes is described, following a brief discussion of the mechanism and implication of genome chaos in cancer evolution.
Prognostic Impact of Array-based Genomic Profiles in Esophageal Squamous Cell Cancer

International Nuclear Information System (INIS)

Carneiro, Ana; Isinger, Anna; Karlsson, Anna; Johansson, Jan; Jönsson, Göran; Bendahl, Pär-Ola; Falkenback, Dan; Halvarsson, Britta; Nilbert, Mef

2008-01-01

Esophageal squamous cell carcinoma (ESCC) is a genetically complex tumor type and a major cause of cancer related mortality. Although distinct genetic alterations have been linked to ESCC development and prognosis, the genetic alterations have not gained clinical applicability. We applied array-based comparative genomic hybridization (aCGH) to obtain a whole genome copy number profile relevant for identifying deranged pathways and clinically applicable markers. A 32 k aCGH platform was used for high resolution mapping of copy number changes in 30 stage I-IV ESCC. Potential interdependent alterations and deranged pathways were identified and copy number changes were correlated to stage, differentiation and survival. Copy number alterations affected median 19% of the genome and included recurrent gains of chromosome regions 5p, 7p, 7q, 8q, 10q, 11q, 12p, 14q, 16p, 17p, 19p, 19q, and 20q and losses of 3p, 5q, 8p, 9p and 11q. High-level amplifications were observed in 30 regions and recurrently involved 7p11 (EGFR), 11q13 (MYEOV, CCND1, FGF4, FGF3, PPFIA, FAD, TMEM16A, CTTS and SHANK2) and 11q22 (PDFG). Gain of 7p22.3 predicted nodal metastases and gains of 1p36.32 and 19p13.3 independently predicted poor survival in multivariate analysis. aCGH profiling verified genetic complexity in ESCC and herein identified imbalances of multiple central tumorigenic pathways. Distinct gains correlate with clinicopathological variables and independently predict survival, suggesting clinical applicability of genomic profiling in ESCC
A Somatically Acquired Enhancer of the Androgen Receptor Is a Noncoding Driver in Advanced Prostate Cancer. | Office of Cancer Genomics

Science.gov (United States)

Increased androgen receptor (AR) activity drives therapeutic resistance in advanced prostate cancer. The most common resistance mechanism is amplification of this locus presumably targeting the AR gene. Here, we identify and characterize a somatically acquired AR enhancer located 650 kb centromeric to the AR. Systematic perturbation of this enhancer using genome editing decreased proliferation by suppressing AR levels. Insertion of an additional copy of this region sufficed to increase proliferation under low androgen conditions and to decrease sensitivity to enzalutamide.
Genome-wide association studies identify four ER negative–specific breast cancer risk loci

Science.gov (United States)

Garcia-Closas, Montserrat; Couch, Fergus J; Lindstrom, Sara; Michailidou, Kyriaki; Schmidt, Marjanka K; Brook, Mark N; orr, Nick; Rhie, Suhn Kyong; Riboli, Elio; Feigelson, Heather s; Le Marchand, Loic; Buring, Julie E; Eccles, Diana; Miron, Penelope; Fasching, Peter A; Brauch, Hiltrud; Chang-Claude, Jenny; Carpenter, Jane; Godwin, Andrew K; Nevanlinna, Heli; Giles, Graham G; Cox, Angela; Hopper, John L; Bolla, Manjeet K; Wang, Qin; Dennis, Joe; Dicks, Ed; Howat, Will J; Schoof, Nils; Bojesen, Stig E; Lambrechts, Diether; Broeks, Annegien; Andrulis, Irene L; Guénel, Pascal; Burwinkel, Barbara; Sawyer, Elinor J; Hollestelle, Antoinette; Fletcher, Olivia; Winqvist, Robert; Brenner, Hermann; Mannermaa, Arto; Hamann, Ute; Meindl, Alfons; Lindblom, Annika; Zheng, Wei; Devillee, Peter; Goldberg, Mark S; Lubinski, Jan; Kristensen, Vessela; Swerdlow, Anthony; Anton-Culver, Hoda; Dörk, Thilo; Muir, Kenneth; Matsuo, Keitaro; Wu, Anna H; Radice, Paolo; Teo, Soo Hwang; Shu, Xiao-Ou; Blot, William; Kang, Daehee; Hartman, Mikael; Sangrajrang, Suleeporn; Shen, Chen-Yang; Southey, Melissa C; Park, Daniel J; Hammet, Fleur; Stone, Jennifer; Veer, Laura J Van’t; Rutgers, Emiel J; Lophatananon, Artitaya; Stewart-Brown, Sarah; Siriwanarangsan, Pornthep; Peto, Julian; Schrauder, Michael G; Ekici, Arif B; Beckmann, Matthias W; Silva, Isabel dos Santos; Johnson, Nichola; Warren, Helen; Tomlinson, Ian; Kerin, Michael J; Miller, Nicola; Marme, Federick; Schneeweiss, Andreas; Sohn, Christof; Truong, Therese; Laurent-Puig, Pierre; Kerbrat, Pierre; Nordestgaard, Børge G; Nielsen, Sune F; Flyger, Henrik; Milne, Roger L; Perez, Jose Ignacio Arias; Menéndez, Primitiva; Müller, Heiko; Arndt, Volker; Stegmaier, Christa; Lichtner, Peter; Lochmann, Magdalena; Justenhoven, Christina; Ko, Yon-Dschun; Muranen, Taru A; Aittomäki, Kristiina; Blomqvist, Carl; Greco, Dario; Heikkinen, Tuomas; Ito, Hidemi; Iwata, Hiroji; Yatabe, Yasushi; Antonenkova, Natalia N; Margolin, Sara; Kataja, Vesa; Kosma, Veli-Matti; Hartikainen, Jaana M; Balleine, Rosemary; Tseng, Chiu-Chen; Van Den Berg, David; Stram, Daniel O; Neven, Patrick; Dieudonné, Anne-Sophie; Leunen, Karin; Rudolph, Anja; Nickels, Stefan; Flesch-Janys, Dieter; Peterlongo, Paolo; Peissel, Bernard; Bernard, Loris; Olson, Janet E; Wang, Xianshu; Stevens, Kristen; Severi, Gianluca; Baglietto, Laura; Mclean, Catriona; Coetzee, Gerhard A; Feng, Ye; Henderson, Brian E; Schumacher, Fredrick; Bogdanova, Natalia V; Labrèche, France; Dumont, Martine; Yip, Cheng Har; Taib, Nur Aishah Mohd; Cheng, Ching-Yu; Shrubsole, Martha; Long, Jirong; Pylkäs, Katri; Jukkola-Vuorinen, Arja; Kauppila, Saila; knight, Julia A; Glendon, Gord; Mulligan, Anna Marie; Tollenaar, Robertus A E M; Seynaeve, Caroline M; Kriege, Mieke; Hooning, Maartje J; Van den Ouweland, Ans M W; Van Deurzen, Carolien H M; Lu, Wei; Gao, Yu-Tang; Cai, Hui; Balasubramanian, Sabapathy P; Cross, Simon S; Reed, Malcolm W R; Signorello, Lisa; Cai, Qiuyin; Shah, Mitul; Miao, Hui; Chan, Ching Wan; Chia, Kee Seng; Jakubowska, Anna; Jaworska, Katarzyna; Durda, Katarzyna; Hsiung, Chia-Ni; Wu, Pei-Ei; Yu, Jyh-Cherng; Ashworth, Alan; Jones, Michael; Tessier, Daniel C; González-Neira, Anna; Pita, Guillermo; Alonso, M Rosario; Vincent, Daniel; Bacot, Francois; Ambrosone, Christine B; Bandera, Elisa V; John, Esther M; Chen, Gary K; Hu, Jennifer J; Rodriguez-gil, Jorge L; Bernstein, Leslie; Press, Michael F; Ziegler, Regina G; Millikan, Robert M; Deming-Halverson, Sandra L; Nyante, Sarah; Ingles, Sue A; Waisfisz, Quinten; Tsimiklis, Helen; Makalic, Enes; Schmidt, Daniel; Bui, Minh; Gibson, Lorna; Müller-Myhsok, Bertram; Schmutzler, Rita K; Hein, Rebecca; Dahmen, Norbert; Beckmann, Lars; Aaltonen, Kirsimari; Czene, Kamila; Irwanto, Astrid; Liu, Jianjun; Turnbull, Clare; Rahman, Nazneen; Meijers-Heijboer, Hanne; Uitterlinden, Andre G; Rivadeneira, Fernando; Olswold, Curtis; Slager, Susan; Pilarski, Robert; Ademuyiwa, Foluso; Konstantopoulou, Irene; Martin, Nicholas G; Montgomery, Grant W; Slamon, Dennis J; Rauh, Claudia; Lux, Michael P; Jud, Sebastian M; Bruning, Thomas; Weaver, Joellen; Sharma, Priyanka; Pathak, Harsh; Tapper, Will; Gerty, Sue; Durcan, Lorraine; Trichopoulos, Dimitrios; Tumino, Rosario; Peeters, Petra H; Kaaks, Rudolf; Campa, Daniele; Canzian, Federico; Weiderpass, Elisabete; Johansson, Mattias; Khaw, Kay-Tee; Travis, Ruth; Clavel-Chapelon, Françoise; Kolonel, Laurence N; Chen, Constance; Beck, Andy; Hankinson, Susan E; Berg, Christine D; Hoover, Robert N; Lissowska, Jolanta; Figueroa, Jonine D; Chasman, Daniel I; Gaudet, Mia M; Diver, W Ryan; Willett, Walter C; Hunter, David J; Simard, Jacques; Benitez, Javier; Dunning, Alison M; Sherman, Mark E; Chenevix-Trench, Georgia; Chanock, Stephen J; Hall, Per; Pharoah, Paul D P; Vachon, Celine; Easton, Douglas F; Haiman, Christopher A; Kraft, Peter

2013-01-01

Estrogen receptor (ER)-negative tumors represent 20–30% of all breast cancers, with a higher proportion occurring in younger women and women of African ancestry1. The etiology2 and clinical behavior3 of ER-negative tumors are different from those of tumors expressing ER (ER positive), including differences in genetic predisposition4. To identify susceptibility loci specific to ER-negative disease, we combined in a meta-analysis 3 genome-wide association studies of 4,193 ER-negative breast cancer cases and 35,194 controls with a series of 40 follow-up studies (6,514 cases and 41,455 controls), genotyped using a custom Illumina array, iCOGS, developed by the Collaborative Oncological Gene-environment Study (COGS). SNPs at four loci, 1q32.1 (MDM4, P = 2.1 × 10−12 and LGR6, P = 1.4 × 10−8), 2p24.1 (P = 4.6 × 10−8) and 16q12.2 (FTO, P = 4.0 × 10−8), were associated with ER-negative but not ER-positive breast cancer (P > 0.05). These findings provide further evidence for distinct etiological pathways associated with invasive ER-positive and ER-negative breast cancers. PMID:23535733
Integration of Genomic, Biologic, and Chemical Approaches to Target p53 Loss and Gain-of-Function in Triple Negative Breast Cancer

Science.gov (United States)

2016-09-01

in this progress report: p53 triple-negative breast cancer subtypes gene expression somatic cell genetics CRISPR / Cas 3. ACCOMPLISHMENTS Major...report, we described the creation of an isogenic p53 mutant TNBC cell line panel using CRISPR / Cas -mediated genome editing8 and the resultant...LOF null state. To validate that mutant p53 is directly responsible for this altered transcription, we will use the same CRISPR -mediated genome
Understanding Cancer Prognosis

Medline Plus

Full Text Available ... Breast Cancer Colorectal Cancer Kidney (Renal Cell) Cancer Leukemia Liver Cancer Lung Cancer Lymphoma Pancreatic Cancer Prostate ... Genomics Research Research on Causes of Cancer Cancer Diagnosis Research Cancer Prevention Research Screening & Early Detection Cancer ...
Personalized In Vitro and In Vivo Cancer Models to Guide Precision Medicine

Science.gov (United States)

Pauli, Chantal; Hopkins, Benjamin D.; Prandi, Davide; Shaw, Reid; Fedrizzi, Tarcisio; Sboner, Andrea; Sailer, Verena; Augello, Michael; Puca, Loredana; Rosati, Rachele; McNary, Terra J.; Churakova, Yelena; Cheung, Cynthia; Triscott, Joanna; Pisapia, David; Rao, Rema; Mosquera, Juan Miguel; Robinson, Brian; Faltas, Bishoy M.; Emerling, Brooke E.; Gadi, Vijayakrishna K.; Bernard, Brady; Elemento, Olivier; Beltran, Himisha; Dimichelis, Francesca; Kemp, Christopher J.; Grandori, Carla; Cantley, Lewis C.; Rubin, Mark A.

2017-01-01

Precision Medicine is an approach that takes into account the influence of individuals' genes, environment and lifestyle exposures to tailor interventions. Here, we describe the development of a robust precision cancer care platform, which integrates whole exome sequencing (WES) with a living biobank that enables high throughput drug screens on patient-derived tumor organoids. To date, 56 tumor-derived organoid cultures, and 19 patient-derived xenograft (PDX) models have been established from the 769 patients enrolled in an IRB approved clinical trial. Because genomics alone was insufficient to identify therapeutic options for the majority of patients with advanced disease, we used high throughput drug screening effective strategies. Analysis of tumor derived cells from four cases, two uterine malignancies and two colon cancers, identified effective drugs and drug combinations that were subsequently validated using 3D cultures and PDX models. This platform thereby promotes the discovery of novel therapeutic approaches that can be assessed in clinical trials and provides personalized therapeutic options for individual patients where standard clinical options have been exhausted. PMID:28331002
Genome-wide association analysis of more than 120,000 individuals identifies 15 new susceptibility loci for breast cancer

NARCIS (Netherlands)

Michailidou, Kyriaki; Beesley, Jonathan; Lindstrom, Sara; Canisius, Sander; Dennis, Joe; Lush, Michael J.; Maranian, Mel J.; Bolla, Manjeet K.; Wang, Qin; Shah, Mitul; Perkins, Barbara J.; Czene, Kamila; Eriksson, Mikael; Darabi, Hatef; Brand, Judith S.; Bojesen, Stig E.; Nordestgaard, Borge G.; Flyger, Henrik; Nielsen, Sune F.; Rahman, Nazneen; Turnbull, Clare; Fletcher, Olivia; Peto, Julian; Gibson, Lorna; dos-Santos-Silva, Isabel; Chang-Claude, Jenny; Flesch-Janys, Dieter; Rudolph, Anja; Eilber, Ursula; Behrens, Sabine; Nevanlinna, Heli; Muranen, Taru A.; Aittomaki, Kristiina; Blomqvist, Carl; Khan, Sofia; Aaltonen, Kirsimari; Ahsan, Habibul; Kibriya, Muhammad G.; Whittemore, Alice S.; John, Esther M.; Malone, Kathleen E.; Gammon, Marilie D.; Santella, Regina M.; Ursin, Giske; Makalic, Enes; Schmidt, Daniel F.; Casey, Graham; Hunter, David J.; Gapstur, Susan M.; Gaudet, Mia M.; Diver, W. Ryan; Haiman, Christopher A.; Schumacher, Fredrick; Henderson, Brian E.; Le Marchand, Loic; Berg, Christine D.; Chanock, Stephen J.; Figueroa, Jonine; Hoover, Robert N.; Lambrechts, Diether; Neven, Patrick; Wildiers, Hans; van Limbergen, Erik; Schmidt, Marjanka K.; Broeks, Annegien; Verhoef, Senno; Cornelissen, Sten; Couch, Fergus J.; Olson, Janet E.; Hallberg, Emily; Vachon, Celine; Waisfisz, Quinten; Meijers-Heijboer, Hanne; Adank, Muriel A.; van der Luijt, Rob B.; Li, Jingmei; Liu, Jianjun; Humphreys, Keith; Kang, Daehee; Choi, Ji-Yeob; Park, Sue K.; Yoo, Keun-Young; Matsuo, Keitaro; Ito, Hidemi; Iwata, Hiroji; Tajima, Kazuo; Guenel, Pascal; Truong, Therese; Mulot, Claire; Sanchez, Marie; Burwinkel, Barbara; Marme, Frederik; Surowy, Harald; Sohn, Christof; Wu, Anna H.; Tseng, Chiu-chen; Van den Berg, David; Stram, Daniel O.; Gonzalez-Neira, Anna; Benitez, Javier; Zamora, M. Pilar; Arias Perez, Jose Ignacio; Shu, Xiao-Ou; Lu, Wei; Gao, Yu-Tang; Cai, Hui; Cox, Angela; Cross, Simon S.; Reed, Malcolm W. R.; Andrulis, Irene L.; Knight, Julia A.; Glendon, Gord; Mulligan, Anna Marie; Sawyer, Elinor J.; Tomlinson, Ian; Kerin, Michael J.; Miller, Nicola; Lindblom, Annika; Margolin, Sara; Teo, Soo Hwang; Yip, Cheng Har; Taib, Nur Aishah Mohd; Tan, Gie-Hooi; Hooning, Maartje J.; Hollestelle, Antoinette; Martens, John W. M.; Collee, J. Margriet; Blot, William; Signorello, Lisa B.; Cai, Qiuyin; Hopper, John L.; Southey, Melissa C.; Tsimiklis, Helen; Apicella, Carmel; Shen, Chen-Yang; Hsiung, Chia-Ni; Wu, Pei-Ei; Hou, Ming-Feng; Kristensen, Vessela N.; Nord, Silje; Alnaes, Grethe I. Grenaker; Giles, Graham G.; Milne, Roger L.; McLean, Catriona; Canzian, Federico; Trichopoulos, Dimitrios; Peeters, Petra; Lund, Eiliv; Sund, Malin; Khaw, Kay-Tee; Gunter, Marc J.; Palli, Domenico; Mortensen, Lotte Maxild; Dossus, Laure; Huerta, Jose-Maria; Meindl, Alfons; Schmutzler, Rita K.; Sutter, Christian; Yang, Rongxi; Muir, Kenneth; Lophatananon, Artitaya; Stewart-Brown, Sarah; Siriwanarangsan, Pornthep; Hartman, Mikael; Miao, Hui; Chia, Kee Seng; Chan, Ching Wan; Fasching, Peter A.; Hein, Alexander; Beckmann, Matthias W.; Haeberle, Lothar; Brenner, Hermann; Dieffenbach, Aida Karina; Arndt, Volker; Stegmaier, Christa; Ashworth, Alan; Orr, Nick; Schoemaker, Minouk J.; Swerdlow, Anthony J.; Brinton, Louise; Garcia-Closas, Montserrat; Zheng, Wei; Halverson, Sandra L.; Shrubsole, Martha; Long, Jirong; Goldberg, Mark S.; Labreche, France; Dumont, Martine; Winqvist, Robert; Pylkas, Katri; Jukkola-Vuorinen, Arja; Grip, Mervi; Brauch, Hiltrud; Hamann, Ute; Bruening, Thomas; Radice, Paolo; Peterlongo, Paolo; Manoukian, Siranoush; Bernard, Loris; Bogdanova, Natalia V.; Doerk, Thilo; Mannermaa, Arto; Kataja, Vesa; Kosma, Veli-Matti; Hartikainen, Jaana M.; Devilee, Peter; Tollenaar, Robert A. E. M.; Seynaeve, Caroline; Van Asperen, Christi J.; Jakubowska, Anna; Lubinski, Jan; Jaworska, Katarzyna; Huzarski, Tomasz; Sangrajrang, Suleeporn; Gaborieau, Valerie; Brennan, Paul; Mckay, James; Slager, Susan; Toland, Amanda E.; Ambrosone, Christine B.; Yannoukakos, Drakoulis; Kabisch, Maria; Torres, Diana; Neuhausen, Susan L.; Anton-Culver, Hoda; Luccarini, Craig; Baynes, Caroline; Ahmed, Shahana; Healey, Catherine S.; Tessier, Daniel C.; Vincent, Daniel; Bacot, Francois; Pita, Guillermo; Rosario Alonso, M.; Alvarez, Nuria; Herrero, Daniel; Simard, Jacques; Pharoah, Paul P. D. P.; Kraft, Peter; Dunning, Alison M.; Chenevix-Trench, Georgia; Hall, Per; Easton, Douglas F.

Genome-wide association studies (GWAS) and large-scale replication studies have identified common variants in 79 loci associated with breast cancer, explaining similar to 14% of the familial risk of the disease. To identify new susceptibility loci, we performed a meta-analysis of 11 GWAS, comprising
Genome-wide association analysis of more than 120,000 individuals identifies 15 new susceptibility loci for breast cancer

DEFF Research Database (Denmark)

Michailidou, Kyriaki; Beesley, Jonathan; Lindstrom, Sara

2015-01-01

Genome-wide association studies (GWAS) and large-scale replication studies have identified common variants in 79 loci associated with breast cancer, explaining ∼14% of the familial risk of the disease. To identify new susceptibility loci, we performed a meta-analysis of 11 GWAS, comprising 15,748...
A unifying model of genome evolution under parsimony.

Science.gov (United States)

Paten, Benedict; Zerbino, Daniel R; Hickey, Glenn; Haussler, David

2014-06-19

Parsimony and maximum likelihood methods of phylogenetic tree estimation and parsimony methods for genome rearrangements are central to the study of genome evolution yet to date they have largely been pursued in isolation. We present a data structure called a history graph that offers a practical basis for the analysis of genome evolution. It conceptually simplifies the study of parsimonious evolutionary histories by representing both substitutions and double cut and join (DCJ) rearrangements in the presence of duplications. The problem of constructing parsimonious history graphs thus subsumes related maximum parsimony problems in the fields of phylogenetic reconstruction and genome rearrangement. We show that tractable functions can be used to define upper and lower bounds on the minimum number of substitutions and DCJ rearrangements needed to explain any history graph. These bounds become tight for a special type of unambiguous history graph called an ancestral variation graph (AVG), which constrains in its combinatorial structure the number of operations required. We finally demonstrate that for a given history graph G, a finite set of AVGs describe all parsimonious interpretations of G, and this set can be explored with a few sampling moves. This theoretical study describes a model in which the inference of genome rearrangements and phylogeny can be unified under parsimony.
Understanding Cancer Prognosis

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The IGNITE network: a model for genomic medicine implementation and research.

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Weitzel, Kristin Wiisanen; Alexander, Madeline; Bernhardt, Barbara A; Calman, Neil; Carey, David J; Cavallari, Larisa H; Field, Julie R; Hauser, Diane; Junkins, Heather A; Levin, Phillip A; Levy, Kenneth; Madden, Ebony B; Manolio, Teri A; Odgis, Jacqueline; Orlando, Lori A; Pyeritz, Reed; Wu, R Ryanne; Shuldiner, Alan R; Bottinger, Erwin P; Denny, Joshua C; Dexter, Paul R; Flockhart, David A; Horowitz, Carol R; Johnson, Julie A; Kimmel, Stephen E; Levy, Mia A; Pollin, Toni I; Ginsburg, Geoffrey S

2016-01-05

Patients, clinicians, researchers and payers are seeking to understand the value of using genomic information (as reflected by genotyping, sequencing, family history or other data) to inform clinical decision-making. However, challenges exist to widespread clinical implementation of genomic medicine, a prerequisite for developing evidence of its real-world utility. To address these challenges, the National Institutes of Health-funded IGNITE (Implementing GeNomics In pracTicE; www.ignite-genomics.org ) Network, comprised of six projects and a coordinating center, was established in 2013 to support the development, investigation and dissemination of genomic medicine practice models that seamlessly integrate genomic data into the electronic health record and that deploy tools for point of care decision making. IGNITE site projects are aligned in their purpose of testing these models, but individual projects vary in scope and design, including exploring genetic markers for disease risk prediction and prevention, developing tools for using family history data, incorporating pharmacogenomic data into clinical care, refining disease diagnosis using sequence-based mutation discovery, and creating novel educational approaches. This paper describes the IGNITE Network and member projects, including network structure, collaborative initiatives, clinical decision support strategies, methods for return of genomic test results, and educational initiatives for patients and providers. Clinical and outcomes data from individual sites and network-wide projects are anticipated to begin being published over the next few years. The IGNITE Network is an innovative series of projects and pilot demonstrations aiming to enhance translation of validated actionable genomic information into clinical settings and develop and use measures of outcome in response to genome-based clinical interventions using a pragmatic framework to provide early data and proofs of concept on the utility of these
The PiGeOn project: protocol for a longitudinal study examining psychosocial, behavioural and ethical issues and outcomes in cancer tumour genomic profiling.

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Best, Megan; Newson, Ainsley J; Meiser, Bettina; Juraskova, Ilona; Goldstein, David; Tucker, Kathy; Ballinger, Mandy L; Hess, Dominique; Schlub, Timothy E; Biesecker, Barbara; Vines, Richard; Vines, Kate; Thomas, David; Young, Mary-Anne; Savard, Jacqueline; Jacobs, Chris; Butow, Phyllis

2018-04-05

Genomic sequencing in cancer (both tumour and germline), and development of therapies targeted to tumour genetic status, hold great promise for improvement of patient outcomes. However, the imminent introduction of genomics into clinical practice calls for better understanding of how patients value, experience, and cope with this novel technology and its often complex results. Here we describe a protocol for a novel mixed-methods, prospective study (PiGeOn) that aims to examine patients' psychosocial, cognitive, affective and behavioural responses to tumour genomic profiling and to integrate a parallel critical ethical analysis of returning results. This is a cohort sub-study of a parent tumour genomic profiling programme enrolling patients with advanced cancer. One thousand patients will be recruited for the parent study in Sydney, Australia from 2016 to 2019. They will be asked to complete surveys at baseline, three, and five months. Primary outcomes are: knowledge, preferences, attitudes and values. A purposively sampled subset of patients will be asked to participate in three semi-structured interviews (at each time point) to provide deeper data interpretation. Relevant ethical themes will be critically analysed to iteratively develop or refine normative ethical concepts or frameworks currently used in the return of genetic information. This will be the first Australian study to collect longitudinal data on cancer patients' experience of tumour genomic profiling. Findings will be used to inform ongoing ethical debates on issues such as how to effectively obtain informed consent for genomic profiling return results, distinguish between research and clinical practice and manage patient expectations. The combination of quantitative and qualitative methods will provide comprehensive and critical data on how patients cope with 'actionable' and 'non-actionable' results. This information is needed to ensure that when tumour genomic profiling becomes part of routine
Air pollution and genomic instability: The role of particulate matter in lung carcinogenesis

International Nuclear Information System (INIS)

Santibáñez-Andrade, Miguel; Quezada-Maldonado, Ericka Marel; Osornio-Vargas, Álvaro; Sánchez-Pérez, Yesennia; García-Cuellar, Claudia M.

2017-01-01

In this review, we summarize and discuss the evidence regarding the interaction between air pollution, especially particulate matter (PM), and genomic instability. PM has been widely studied in the context of several diseases, and its role in lung carcinogenesis gained relevance due to an increase in cancer cases for which smoking does not seem to represent the main risk factor. According to epidemiological and toxicological evidence, PM acts as a carcinogenic factor in humans, inducing high rates of genomic alterations. Here, we discuss not only how PM is capable of inducing genomic instability during the carcinogenic process but also how our genetic background influences the response to the sources of damage. - Highlights: • Air pollution represents a worldwide problem with impact on human health. • Particulate matter (PM) has a recognized carcinogenic potential in humans. • Lung cancer susceptibility depends on gene-environment interactions. • Epidemiological and experimental evidence links PM exposure to genomic instability. • PM and genomic instability are co-dependent factors during cancer continuum. - We summarize the association between particulate matter (a component of air pollution) and genomic instability as well as discuss how new strategies to study the impact of air pollution on genomic instability and lung-cancer development could improve our understanding of the lung-cancer genome.

Understanding Cancer Prognosis

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Full Text Available ... Types Recurrent Cancer Common Cancer Types Bladder Cancer Breast Cancer Colorectal Cancer Kidney (Renal Cell) Cancer Leukemia Liver ... Genomics Research Research on Causes of Cancer Cancer Diagnosis Research Cancer Prevention Research Screening & Early Detection Cancer ...
A cell-based model system links chromothripsis with hyperploidy

DEFF Research Database (Denmark)

Mardin, Balca R; Drainas, Alexandros P; Waszak, Sebastian M

2015-01-01

A remarkable observation emerging from recent cancer genome analyses is the identification of chromothripsis as a one-off genomic catastrophe, resulting in massive somatic DNA structural rearrangements (SRs). Largely due to lack of suitable model systems, the mechanistic basis of chromothripsis h...... in hyperploid cells. Analysis of primary medulloblastoma cancer genomes verified the link between hyperploidy and chromothripsis in vivo. CAST provides the foundation for mechanistic dissection of complex DNA rearrangement processes....
Implementing genomics and pharmacogenomics in the clinic: The National Human Genome Research Institute's genomic medicine portfolio.

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Manolio, Teri A

2016-10-01

Increasing knowledge about the influence of genetic variation on human health and growing availability of reliable, cost-effective genetic testing have spurred the implementation of genomic medicine in the clinic. As defined by the National Human Genome Research Institute (NHGRI), genomic medicine uses an individual's genetic information in his or her clinical care, and has begun to be applied effectively in areas such as cancer genomics, pharmacogenomics, and rare and undiagnosed diseases. In 2011 NHGRI published its strategic vision for the future of genomic research, including an ambitious research agenda to facilitate and promote the implementation of genomic medicine. To realize this agenda, NHGRI is consulting and facilitating collaborations with the external research community through a series of "Genomic Medicine Meetings," under the guidance and leadership of the National Advisory Council on Human Genome Research. These meetings have identified and begun to address significant obstacles to implementation, such as lack of evidence of efficacy, limited availability of genomics expertise and testing, lack of standards, and difficulties in integrating genomic results into electronic medical records. The six research and dissemination initiatives comprising NHGRI's genomic research portfolio are designed to speed the evaluation and incorporation, where appropriate, of genomic technologies and findings into routine clinical care. Actual adoption of successful approaches in clinical care will depend upon the willingness, interest, and energy of professional societies, practitioners, patients, and payers to promote their responsible use and share their experiences in doing so. Published by Elsevier Ireland Ltd.
Global copy number profiling of cancer genomes | Office of Cancer Genomics

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In this article, we introduce a robust and efficient strategy for deriving global and allele-specific copy number alternations (CNA) from cancer whole exome sequencing data based on Log R ratios and B-allele frequencies. Applying the approach to the analysis of over 200 skin cancer samples, we demonstrate its utility for discovering distinct CNA events and for deriving ancillary information such as tumor purity. Availability and implementation: https://github.com/xfwang/CLOSE CONTACT: xuefeng.wang@stonybrook.edu or michael.krauthammer@yale.edu. (Publication Abstract)
Genomic selection: genome-wide prediction in plant improvement.

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Desta, Zeratsion Abera; Ortiz, Rodomiro

2014-09-01

Association analysis is used to measure relations between markers and quantitative trait loci (QTL). Their estimation ignores genes with small effects that trigger underpinning quantitative traits. By contrast, genome-wide selection estimates marker effects across the whole genome on the target population based on a prediction model developed in the training population (TP). Whole-genome prediction models estimate all marker effects in all loci and capture small QTL effects. Here, we review several genomic selection (GS) models with respect to both the prediction accuracy and genetic gain from selection. Phenotypic selection or marker-assisted breeding protocols can be replaced by selection, based on whole-genome predictions in which phenotyping updates the model to build up the prediction accuracy. Copyright © 2014 Elsevier Ltd. All rights reserved.
The German cervical cancer screening model: development and validation of a decision-analytic model for cervical cancer screening in Germany.

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Siebert, Uwe; Sroczynski, Gaby; Hillemanns, Peter; Engel, Jutta; Stabenow, Roland; Stegmaier, Christa; Voigt, Kerstin; Gibis, Bernhard; Hölzel, Dieter; Goldie, Sue J

2006-04-01

We sought to develop and validate a decision-analytic model for the natural history of cervical cancer for the German health care context and to apply it to cervical cancer screening. We developed a Markov model for the natural history of cervical cancer and cervical cancer screening in the German health care context. The model reflects current German practice standards for screening, diagnostic follow-up and treatment regarding cervical cancer and its precursors. Data for disease progression and cervical cancer survival were obtained from the literature and German cancer registries. Accuracy of Papanicolaou (Pap) testing was based on meta-analyses. We performed internal and external model validation using observed epidemiological data for unscreened women from different German cancer registries. The model predicts life expectancy, incidence of detected cervical cancer cases, lifetime cervical cancer risks and mortality. The model predicted a lifetime cervical cancer risk of 3.0% and a lifetime cervical cancer mortality of 1.0%, with a peak cancer incidence of 84/100,000 at age 51 years. These results were similar to observed data from German cancer registries, German literature data and results from other international models. Based on our model, annual Pap screening could prevent 98.7% of diagnosed cancer cases and 99.6% of deaths due to cervical cancer in women completely adherent to screening and compliant to treatment. Extending the screening interval from 1 year to 2, 3 or 5 years resulted in reduced screening effectiveness. This model provides a tool for evaluating the long-term effectiveness of different cervical cancer screening tests and strategies.
Quantitation of Murine Stroma and Selective Purification of the Human Tumor Component of Patient-Derived Xenografts for Genomic Analysis.

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Valentina E Schneeberger

Full Text Available Patient-derived xenograft (PDX mouse models are increasingly used for preclinical therapeutic testing of human cancer. A limitation in molecular and genetic characterization of PDX tumors is the presence of integral murine stroma. This is particularly problematic for genomic sequencing of PDX models. Rapid and dependable approaches for quantitating stromal content and purifying the malignant human component of these tumors are needed. We used a recently developed technique exploiting species-specific polymerase chain reaction (PCR amplicon length (ssPAL differences to define the fractional composition of murine and human DNA, which was proportional to the fractional composition of cells in a series of lung cancer PDX lines. We compared four methods of human cancer cell isolation: fluorescence-activated cell sorting (FACS, an immunomagnetic mouse cell depletion (MCD approach, and two distinct EpCAM-based immunomagnetic positive selection methods. We further analyzed DNA extracted from the resulting enriched human cancer cells by targeted sequencing using a clinically validated multi-gene panel. Stromal content varied widely among tumors of similar histology, but appeared stable over multiple serial tumor passages of an individual model. FACS and MCD were superior to either positive selection approach, especially in cases of high stromal content, and consistently allowed high quality human-specific genomic profiling. ssPAL is a dependable approach to quantitation of murine stromal content, and MCD is a simple, efficient, and high yield approach to human cancer cell isolation for genomic analysis of PDX tumors.
Fine mapping of breast cancer genome-wide association studies loci in women of African ancestry identifies novel susceptibility markers.

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Zheng, Yonglan; Ogundiran, Temidayo O; Falusi, Adeyinka G; Nathanson, Katherine L; John, Esther M; Hennis, Anselm J M; Ambs, Stefan; Domchek, Susan M; Rebbeck, Timothy R; Simon, Michael S; Nemesure, Barbara; Wu, Suh-Yuh; Leske, Maria Cristina; Odetunde, Abayomi; Niu, Qun; Zhang, Jing; Afolabi, Chibuzor; Gamazon, Eric R; Cox, Nancy J; Olopade, Christopher O; Olopade, Olufunmilayo I; Huo, Dezheng

2013-07-01

Numerous single nucleotide polymorphisms (SNPs) associated with breast cancer susceptibility have been identified by genome-wide association studies (GWAS). However, these SNPs were primarily discovered and validated in women of European and Asian ancestry. Because linkage disequilibrium is ancestry-dependent and heterogeneous among racial/ethnic populations, we evaluated common genetic variants at 22 GWAS-identified breast cancer susceptibility loci in a pooled sample of 1502 breast cancer cases and 1378 controls of African ancestry. None of the 22 GWAS index SNPs could be validated, challenging the direct generalizability of breast cancer risk variants identified in Caucasians or Asians to other populations. Novel breast cancer risk variants for women of African ancestry were identified in regions including 5p12 (odds ratio [OR] = 1.40, 95% confidence interval [CI] = 1.11-1.76; P = 0.004), 5q11.2 (OR = 1.22, 95% CI = 1.09-1.36; P = 0.00053) and 10p15.1 (OR = 1.22, 95% CI = 1.08-1.38; P = 0.0015). We also found positive association signals in three regions (6q25.1, 10q26.13 and 16q12.1-q12.2) previously confirmed by fine mapping in women of African ancestry. In addition, polygenic model indicated that eight best markers in this study, compared with 22 GWAS-identified SNPs, could better predict breast cancer risk in women of African ancestry (per-allele OR = 1.21, 95% CI = 1.16-1.27; P = 9.7 × 10(-16)). Our results demonstrate that fine mapping is a powerful approach to better characterize the breast cancer risk alleles in diverse populations. Future studies and new GWAS in women of African ancestry hold promise to discover additional variants for breast cancer susceptibility with clinical implications throughout the African diaspora.
Genomic value prediction for quantitative traits under the epistatic model

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Xu Shizhong

2011-01-01

Full Text Available Abstract Background Most quantitative traits are controlled by multiple quantitative trait loci (QTL. The contribution of each locus may be negligible but the collective contribution of all loci is usually significant. Genome selection that uses markers of the entire genome to predict the genomic values of individual plants or animals can be more efficient than selection on phenotypic values and pedigree information alone for genetic improvement. When a quantitative trait is contributed by epistatic effects, using all markers (main effects and marker pairs (epistatic effects to predict the genomic values of plants can achieve the maximum efficiency for genetic improvement. Results In this study, we created 126 recombinant inbred lines of soybean and genotyped 80 makers across the genome. We applied the genome selection technique to predict the genomic value of somatic embryo number (a quantitative trait for each line. Cross validation analysis showed that the squared correlation coefficient between the observed and predicted embryo numbers was 0.33 when only main (additive effects were used for prediction. When the interaction (epistatic effects were also included in the model, the squared correlation coefficient reached 0.78. Conclusions This study provided an excellent example for the application of genome selection to plant breeding.
Understanding Cancer Prognosis

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Genomic dysregulation in gastric tumors.

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Janjigian, Yelena Y; Kelsen, David P

2013-03-01

Gastric cancer is among the most common human malignancies and the second leading cause of cancer-related death. The different epidemiologic and histopathology of subtypes of gastric cancer are associated with different genomic patterns. Data suggests that gene expression patterns of proximal, distal gastric cancers-intestinal type, and diffuse/signet cell are well separated. This review summarizes the genetic and epigenetic changes thought to drive gastric cancer and the emerging paradigm of gastric cancer as three unique disease subtypes. Copyright © 2012 Wiley Periodicals, Inc.
Integrated analysis of epigenomic and genomic changes by DNA methylation dependent mechanisms provides potential novel biomarkers for prostate cancer.

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White-Al Habeeb, Nicole M A; Ho, Linh T; Olkhov-Mitsel, Ekaterina; Kron, Ken; Pethe, Vaijayanti; Lehman, Melanie; Jovanovic, Lidija; Fleshner, Neil; van der Kwast, Theodorus; Nelson, Colleen C; Bapat, Bharati

2014-09-15

Epigenetic silencing mediated by CpG methylation is a common feature of many cancers. Characterizing aberrant DNA methylation changes associated with tumor progression may identify potential prognostic markers for prostate cancer (PCa). We treated two PCa cell lines, 22Rv1 and DU-145 with the demethylating agent 5-Aza 2'-deoxycitidine (DAC) and global methylation status was analyzed by performing methylation-sensitive restriction enzyme based differential methylation hybridization strategy followed by genome-wide CpG methylation array profiling. In addition, we examined gene expression changes using a custom microarray. Gene Set Enrichment Analysis (GSEA) identified the most significantly dysregulated pathways. In addition, we assessed methylation status of candidate genes that showed reduced CpG methylation and increased gene expression after DAC treatment, in Gleason score (GS) 8 vs. GS6 patients using three independent cohorts of patients; the publically available The Cancer Genome Atlas (TCGA) dataset, and two separate patient cohorts. Our analysis, by integrating methylation and gene expression in PCa cell lines, combined with patient tumor data, identified novel potential biomarkers for PCa patients. These markers may help elucidate the pathogenesis of PCa and represent potential prognostic markers for PCa patients.
The Present and Future of Biomarkers in Prostate Cancer: Proteomics, Genomics, and Immunology Advancements

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Pierre-Olivier Gaudreau

2016-01-01

Full Text Available Prostate cancer (PC is the second most common form of cancer in men worldwide. Biomarkers have emerged as essential tools for treatment and assessment since the variability of disease behavior, the cost and diversity of treatments, and the related impairment of quality of life have given rise to a need for a personalized approach. High-throughput technology platforms in proteomics and genomics have accelerated the development of biomarkers. Furthermore, recent successes of several new agents in PC, including immunotherapy, have stimulated the search for predictors of response and resistance and have improved the understanding of the biological mechanisms at work. This review provides an overview of currently established biomarkers in PC, as well as a selection of the most promising biomarkers within these particular fields of development.
Mathematical modeling of cancer metabolism.

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Medina, Miguel Ángel

2018-04-01

Systemic approaches are needed and useful for the study of the very complex issue of cancer. Modeling has a central position in these systemic approaches. Metabolic reprogramming is nowadays acknowledged as an essential hallmark of cancer. Mathematical modeling could contribute to a better understanding of cancer metabolic reprogramming and to identify new potential ways of therapeutic intervention. Herein, I review several alternative approaches to metabolic modeling and their current and future impact in oncology. Copyright © 2018 Elsevier B.V. All rights reserved.
BiGG Models: A platform for integrating, standardizing and sharing genome-scale models

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King, Zachary A.; Lu, Justin; Dräger, Andreas

2016-01-01

Genome-scale metabolic models are mathematically-structured knowledge bases that can be used to predict metabolic pathway usage and growth phenotypes. Furthermore, they can generate and test hypotheses when integrated with experimental data. To maximize the value of these models, centralized repo...
Macronuclear genome sequence of the ciliate Tetrahymena thermophila, a model eukaryote.

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Jonathan A Eisen

2006-09-01

Full Text Available The ciliate Tetrahymena thermophila is a model organism for molecular and cellular biology. Like other ciliates, this species has separate germline and soma functions that are embodied by distinct nuclei within a single cell. The germline-like micronucleus (MIC has its genome held in reserve for sexual reproduction. The soma-like macronucleus (MAC, which possesses a genome processed from that of the MIC, is the center of gene expression and does not directly contribute DNA to sexual progeny. We report here the shotgun sequencing, assembly, and analysis of the MAC genome of T. thermophila, which is approximately 104 Mb in length and composed of approximately 225 chromosomes. Overall, the gene set is robust, with more than 27,000 predicted protein-coding genes, 15,000 of which have strong matches to genes in other organisms. The functional diversity encoded by these genes is substantial and reflects the complexity of processes required for a free-living, predatory, single-celled organism. This is highlighted by the abundance of lineage-specific duplications of genes with predicted roles in sensing and responding to environmental conditions (e.g., kinases, using diverse resources (e.g., proteases and transporters, and generating structural complexity (e.g., kinesins and dyneins. In contrast to the other lineages of alveolates (apicomplexans and dinoflagellates, no compelling evidence could be found for plastid-derived genes in the genome. UGA, the only T. thermophila stop codon, is used in some genes to encode selenocysteine, thus making this organism the first known with the potential to translate all 64 codons in nuclear genes into amino acids. We present genomic evidence supporting the hypothesis that the excision of DNA from the MIC to generate the MAC specifically targets foreign DNA as a form of genome self-defense. The combination of the genome sequence, the functional diversity encoded therein, and the presence of some pathways missing from
Genome-Wide Progesterone Receptor Binding: Cell Type-Specific and Shared Mechanisms in T47D Breast Cancer Cells and Primary Leiomyoma Cells

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Huang, Lei; Owen, Jonas K.; Xie, Anna; Navarro, Antonia; Monsivais, Diana; Coon V, John S.; Kim, J. Julie; Dai, Yang; Bulun, Serdar E.

2012-01-01

Background Progesterone, via its nuclear receptor (PR), exerts an overall tumorigenic effect on both uterine fibroid (leiomyoma) and breast cancer tissues, whereas the antiprogestin RU486 inhibits growth of these tissues through an unknown mechanism. Here, we determined the interaction between common or cell-specific genome-wide binding sites of PR and mRNA expression in RU486-treated uterine leiomyoma and breast cancer cells. Principal Findings ChIP-sequencing revealed 31,457 and 7,034 PR-binding sites in breast cancer and uterine leiomyoma cells, respectively; 1,035 sites overlapped in both cell types. Based on the chromatin-PR interaction in both cell types, we statistically refined the consensus progesterone response element to G•ACA• • •TGT•C. We identified two striking differences between uterine leiomyoma and breast cancer cells. First, the cis-regulatory elements for HSF, TEF-1, and C/EBPα and β were statistically enriched at genomic RU486/PR-targets in uterine leiomyoma, whereas E2F, FOXO1, FOXA1, and FOXF sites were preferentially enriched in breast cancer cells. Second, 51.5% of RU486-regulated genes in breast cancer cells but only 6.6% of RU486-regulated genes in uterine leiomyoma cells contained a PR-binding site within 5 kb from their transcription start sites (TSSs), whereas 75.4% of RU486-regulated genes contained a PR-binding site farther than 50 kb from their TSSs in uterine leiomyoma cells. RU486 regulated only seven mRNAs in both cell types. Among these, adipophilin (PLIN2), a pro-differentiation gene, was induced via RU486 and PR via the same regulatory region in both cell types. Conclusions Our studies have identified molecular components in a RU486/PR-controlled gene network involved in the regulation of cell growth, cell migration, and extracellular matrix function. Tissue-specific and common patterns of genome-wide PR binding and gene regulation may determine the therapeutic effects of antiprogestins in uterine fibroids and
A probabilistic model to predict clinical phenotypic traits from genome sequencing.

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Chen, Yun-Ching; Douville, Christopher; Wang, Cheng; Niknafs, Noushin; Yeo, Grace; Beleva-Guthrie, Violeta; Carter, Hannah; Stenson, Peter D; Cooper, David N; Li, Biao; Mooney, Sean; Karchin, Rachel

2014-09-01

Genetic screening is becoming possible on an unprecedented scale. However, its utility remains controversial. Although most variant genotypes cannot be easily interpreted, many individuals nevertheless attempt to interpret their genetic information. Initiatives such as the Personal Genome Project (PGP) and Illumina's Understand Your Genome are sequencing thousands of adults, collecting phenotypic information and developing computational pipelines to identify the most important variant genotypes harbored by each individual. These pipelines consider database and allele frequency annotations and bioinformatics classifications. We propose that the next step will be to integrate these different sources of information to estimate the probability that a given individual has specific phenotypes of clinical interest. To this end, we have designed a Bayesian probabilistic model to predict the probability of dichotomous phenotypes. When applied to a cohort from PGP, predictions of Gilbert syndrome, Graves' disease, non-Hodgkin lymphoma, and various blood groups were accurate, as individuals manifesting the phenotype in question exhibited the highest, or among the highest, predicted probabilities. Thirty-eight PGP phenotypes (26%) were predicted with area-under-the-ROC curve (AUC)>0.7, and 23 (15.8%) of these were statistically significant, based on permutation tests. Moreover, in a Critical Assessment of Genome Interpretation (CAGI) blinded prediction experiment, the models were used to match 77 PGP genomes to phenotypic profiles, generating the most accurate prediction of 16 submissions, according to an independent assessor. Although the models are currently insufficiently accurate for diagnostic utility, we expect their performance to improve with growth of publicly available genomics data and model refinement by domain experts.
The Art and Science of Thyroid Surgery in the Age of Genomics: 100 years after Theodor Kocher

Directory of Open Access Journals (Sweden)

Seza Gulec

2017-02-01

Full Text Available Cancer is a disorder of the genome. The thyroid cancer genome is being decoded. Recent studies have identified a mutation or a genetic alteration in 95% of thyroid cancers. The National Cancer Institute initiated the Cancer Genome Atlas project in 2006 to catalogue genetic mutations associated with cancer, using genome sequencing and bioinformatics. The project has expanded to carry out genomic characterization and sequence analysis of thyroid cancer. The concept of risk stratification based on traditional parameters will soon vacate their role for clear molecular markers of non-invasive/focal, invasive/metastatic and systemic stages/phases of neoplastic disorder. A refined classification scheme based on genomics and its phenotypic expressions will accurately reflect the biologic differences between the different morphologic definitions we use today. Tumor differentiation/de-differentiation, and clinical behavior of an individual cancer will be defined by molecular markers, in addition to standard morpho-pathology. Empiricism in science of medicine and surgery has acquired a new method for testing the appropriate treatment for individual patients; that is molecular pathology, governed by genomics. The technology is present and rapidly evolving. The surgeons will determine the extent of interventions with molecular evidence and guidance.
Mouse Models for Studying Oral Cancer: Impact in the Era of Cancer Immunotherapy.

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Luo, J J; Young, C D; Zhou, H M; Wang, X J

2018-04-01

Model systems for oral cancer research have progressed from tumor epithelial cell cultures to in vivo systems that mimic oral cancer genetics, pathological characteristics, and tumor-stroma interactions of oral cancer patients. In the era of cancer immunotherapy, it is imperative to use model systems to test oral cancer prevention and therapeutic interventions in the presence of an immune system and to discover mechanisms of stromal contributions to oral cancer carcinogenesis. Here, we review in vivo mouse model systems commonly used for studying oral cancer and discuss the impact these models are having in advancing basic mechanisms, chemoprevention, and therapeutic intervention of oral cancer while highlighting recent discoveries concerning the role of immune cells in oral cancer. Improvements to in vivo model systems that highly recapitulate human oral cancer hold the key to identifying features of oral cancer initiation, progression, and invasion as well as molecular and cellular targets for prevention, therapeutic response, and immunotherapy development.

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