WorldWideScience

Sample records for conquering cancer network

  1. Modeling of Failure Prediction Bayesian Network with Divide-and-Conquer Principle

    Directory of Open Access Journals (Sweden)

    Zhiqiang Cai

    2014-01-01

    Full Text Available For system failure prediction, automatically modeling from historical failure dataset is one of the challenges in practical engineering fields. In this paper, an effective algorithm is proposed to build the failure prediction Bayesian network (FPBN model with data mining technology. First, the conception of FPBN is introduced to describe the state of components and system and the cause-effect relationships among them. The types of network nodes, the directions of network edges, and the conditional probability distributions (CPDs of nodes in FPBN are discussed in detail. According to the characteristics of nodes and edges in FPBN, a divide-and-conquer principle based algorithm (FPBN-DC is introduced to build the best FPBN network structures of different types of nodes separately. Then, the CPDs of nodes in FPBN are calculated by the maximum likelihood estimation method based on the built network. Finally, a simulation study of a helicopter convertor model is carried out to demonstrate the application of FPBN-DC. According to the simulations results, the FPBN-DC algorithm can get better fitness value with the lower number of iterations, which verified its effectiveness and efficiency compared with traditional algorithm.

  2. Conquer Chiari

    Science.gov (United States)

    ... Browse Archives by Subject Symptoms Surgery Pain & Depression Theories Pediatric Topics Other Topics Personal Stories Special Topics ... C&S Patient Education Foundation Mission History & Accomplishments Team Financial Disclosure Forms (990's) Get Involved Contact Us Conquer ...

  3. How Do Emerging Technologies Conquer the World? An Exploration of Patterns of Diffusion and Network Formation

    CERN Document Server

    Leydesdorff, Loet

    2010-01-01

    Grasping the fruits of "emerging technologies" is an objective of many government priority programs in a knowledge-based and globalizing economy. We use the publication records (in the Science Citation Index) of two emerging technologies to study the mechanisms of diffusion in the case of two innovation trajectories: small interference RNA (siRNA) and nano-crystalline solar cells (NCSC). Methods for analyzing and visualizing geographical and cognitive diffusion are specified as indicators of different dynamics. Geographical diffusion is illustrated with overlays to Google Maps; cognitive diffusion is mapped using an overlay to a map based on the ISI Subject Categories. The evolving geographical networks show both preferential attachment and small-world characteristics. The strength of preferential attachment decreases over time, while the network evolves into an oligopolistic control structure with small-world characteristics. The transferability of the research technology in cognitive terms--that is, the tra...

  4. Copper and conquer: copper complexes of di-2-pyridylketone thiosemicarbazones as novel anti-cancer therapeutics.

    Science.gov (United States)

    Park, Kyung Chan; Fouani, Leyla; Jansson, Patric J; Wooi, Danson; Sahni, Sumit; Lane, Darius J R; Palanimuthu, Duraippandi; Lok, Hiu Chuen; Kovačević, Zaklina; Huang, Michael L H; Kalinowski, Danuta S; Richardson, Des R

    2016-09-01

    Copper is an essential trace metal required by organisms to perform a number of important biological processes. Copper readily cycles between its reduced Cu(i) and oxidised Cu(ii) states, which makes it redox active in biological systems. This redox-cycling propensity is vital for copper to act as a catalytic co-factor in enzymes. While copper is essential for normal physiology, enhanced copper levels in tumours leads to cancer progression. In particular, the stimulatory effect of copper on angiogenesis has been established in the last several decades. Additionally, it has been demonstrated that copper affects tumour growth and promotes metastasis. Based on the effects of copper on cancer progression, chelators that bind copper have been developed as anti-cancer agents. In fact, a novel class of thiosemicarbazone compounds, namely the di-2-pyridylketone thiosemicarbazones that bind copper, have shown great promise in terms of their anti-cancer activity. These agents have a unique mechanism of action, in which they form redox-active complexes with copper in the lysosomes of cancer cells. Furthermore, these agents are able to overcome P-glycoprotein (P-gp) mediated multi-drug resistance (MDR) and act as potent anti-oncogenic agents through their ability to up-regulate the metastasis suppressor protein, N-myc downstream regulated gene-1 (NDRG1). This review provides an overview of the metabolism and regulation of copper in normal physiology, followed by a discussion of the dysregulation of copper homeostasis in cancer and the effects of copper on cancer progression. Finally, recent advances in our understanding of the mechanisms of action of anti-cancer agents targeting copper are discussed.

  5. Bridging the US and China together to conquer cancer: report of the 4th annual meeting of the US Chinese Anti-Cancer Association (USCACA)

    Institute of Scientific and Technical Information of China (English)

    Wancai Yang; Lingjie Guan

    2012-01-01

    A global collaborative effort is pivotal to conquer cancer.Themed "Emerging role of China in global clinical development of novel anti-cancer drugs",the US Chinese Anti-Cancer Association (USCACA) held its 4th annual meeting in Chicago on June 2,2012,in conjunction with the American Society of Clinical Oncology (ASCO) annual meeting to further bridge the US and China together to outsmart cancer.Although a young organization,USCACA has made significant contributions to this goal in the 3 years since its inception through extensive collaboration with academic organizations,the pharmaceutical industry,and governmental agencies.USCACA has engaged various stakeholders in developing translational and personalized medical strategies to facilitate new anti-cancer drug development and clinical trials in China.USCACA has initiated and implemented the USCACA-National Foundation for Cancer Research (NFCR) scholarship to encourage overseas returnees to continue cancer research in China.USCACA announced the Hengrui-USCACA scholarship to fund clinical trial staff from China to conduct the observation of early oncologic clinical trials in the US.During the annual meeting,distinguished panelists and the audience discussed the following critical topics:(1) oncologic translational research and early development capabilities in China; (2) novel chemical entity development and partnership with Chinese companies; and (3) Chinese participation in global anti-cancer drug development.USCACA will continue to promote collaborations among cancer researchers and clinicians in the US and China by engaging in more frequent communications and joint efforts across fields,disciplines,and countries,diligently working together toward curing and eliminating cancers.

  6. A divide and conquer approach for construction of large-scale signaling networks from PPI and RNAi data using linear programming.

    Science.gov (United States)

    Ozsoy, Oyku Eren; Can, Tolga

    2013-01-01

    Inference of topology of signaling networks from perturbation experiments is a challenging problem. Recently, the inference problem has been formulated as a reference network editing problem and it has been shown that finding the minimum number of edit operations on a reference network to comply with perturbation experiments is an NP-complete problem. In this paper, we propose an integer linear optimization (ILP) model for reconstruction of signaling networks from RNAi data and a reference network. The ILP model guarantees the optimal solution; however, is practical only for small signaling networks of size 10-15 genes due to computational complexity. To scale for large signaling networks, we propose a divide and conquer-based heuristic, in which a given reference network is divided into smaller subnetworks that are solved separately and the solutions are merged together to form the solution for the large network. We validate our proposed approach on real and synthetic data sets, and comparison with the state of the art shows that our proposed approach is able to scale better for large networks while attaining similar or better biological accuracy.

  7. Bladder Cancer Advocacy Network

    Science.gov (United States)

    ... future bladder cancer research through the Patient Survey Network. Read More... The JPB Foundation 2016 Bladder Cancer ... 2016 Young Investigator Awardees The Bladder Cancer Advocacy Network (BCAN) has announced the recipients of the 2016 ...

  8. Divide and Conquer Approach to Contact Map Overlap Problem Using 2D-Pattern Mining of Protein Contact Networks.

    Science.gov (United States)

    Koneru, Suvarna Vani; Bhavani, Durga S

    2015-01-01

    A novel approach to Contact Map Overlap (CMO) problem is proposed using the two dimensional clusters present in the contact maps. Each protein is represented as a set of the non-trivial clusters of contacts extracted from its contact map. The approach involves finding matching regions between the two contact maps using approximate 2D-pattern matching algorithm and dynamic programming technique. These matched pairs of small contact maps are submitted in parallel to a fast heuristic CMO algorithm. The approach facilitates parallelization at this level since all the pairs of contact maps can be submitted to the algorithm in parallel. Then, a merge algorithm is used in order to obtain the overall alignment. As a proof of concept, MSVNS, a heuristic CMO algorithm is used for global as well as local alignment. The divide and conquer approach is evaluated for two benchmark data sets that of Skolnick and Ding et al. It is interesting to note that along with achieving saving of time, better overlap is also obtained for certain protein folds.

  9. National Comprehensive Cancer Network

    Science.gov (United States)

    ... Nervous System Cancers Cervical Cancer Chronic Lymphocytic Leukemia/Small Lymphocytic Lymphoma Chronic Myeloid Leukemia Colon Cancer Dermatofibrosarcoma Protuberans Esophageal and Esophagogastric Junction Cancers Gastric Cancer Hairy Cell Leukemia Head and Neck Cancer Hepatobiliary Cancers Hodgkin ...

  10. Introduction: Cancer Gene Networks.

    Science.gov (United States)

    Clarke, Robert

    2017-01-01

    Constructing, evaluating, and interpreting gene networks generally sits within the broader field of systems biology, which continues to emerge rapidly, particular with respect to its application to understanding the complexity of signaling in the context of cancer biology. For the purposes of this volume, we take a broad definition of systems biology. Considering an organism or disease within an organism as a system, systems biology is the study of the integrated and coordinated interactions of the network(s) of genes, their variants both natural and mutated (e.g., polymorphisms, rearrangements, alternate splicing, mutations), their proteins and isoforms, and the organic and inorganic molecules with which they interact, to execute the biochemical reactions (e.g., as enzymes, substrates, products) that reflect the function of that system. Central to systems biology, and perhaps the only approach that can effectively manage the complexity of such systems, is the building of quantitative multiscale predictive models. The predictions of the models can vary substantially depending on the nature of the model and its inputoutput relationships. For example, a model may predict the outcome of a specific molecular reaction(s), a cellular phenotype (e.g., alive, dead, growth arrest, proliferation, and motility), a change in the respective prevalence of cell or subpopulations, a patient or patient subgroup outcome(s). Such models necessarily require computers. Computational modeling can be thought of as using machine learning and related tools to integrate the very high dimensional data generated from modern, high throughput omics technologies including genomics (next generation sequencing), transcriptomics (gene expression microarrays; RNAseq), metabolomics and proteomics (ultra high performance liquid chromatography, mass spectrometry), and "subomic" technologies to study the kinome, methylome, and others. Mathematical modeling can be thought of as the use of ordinary

  11. Prostate Cancer Pathology Resource Network

    Science.gov (United States)

    2015-12-01

    disease. The Network combines considerable expertise in multi-disciplinary tissue- based PCa research, excellence in PCa histopathology and molecular ... Memorial Sloan Kettering and University of Washington that successfully collaborated on a PCBN competitive renewal application. 15. SUBJECT TERMS... Memorial Sloan-Kettering Cancer Center (MSKCC: PI Anuradha Gopalan, MD, Co-PI Howard Scher, MD). These 2 Network Sites were chosen deliberately to add

  12. The Prostate Cancer Biorepository Network (PCBN)

    Science.gov (United States)

    2016-10-01

    1 Award Number: W81XWH-14-2-0183 TITLE: The Prostate Cancer Biorepository Network (PCBN) PRINCIPAL INVESTIGATOR: Colm Morrissey CONTRACTING... Cancer Biorepository Network (PCBN) 5a. CONTRACT NUMBER 5b. GRANT NUMBER W81XWH-14-2-0183 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Colm Morrissey PhD...ABSTRACT The Genitourinary Cancer Biorepository at the University of Washington joined the Prostate Cancer Pathology Resource Network (PCBN) September

  13. Network Topologies Decoding Cervical Cancer.

    Directory of Open Access Journals (Sweden)

    Sarika Jalan

    Full Text Available According to the GLOBOCAN statistics, cervical cancer is one of the leading causes of death among women worldwide. It is found to be gradually increasing in the younger population, specifically in the developing countries. We analyzed the protein-protein interaction networks of the uterine cervix cells for the normal and disease states. It was found that the disease network was less random than the normal one, providing an insight into the change in complexity of the underlying network in disease state. The study also portrayed that, the disease state has faster signal processing as the diameter of the underlying network was very close to its corresponding random control. This may be a reason for the normal cells to change into malignant state. Further, the analysis revealed VEGFA and IL-6 proteins as the distinctly high degree nodes in the disease network, which are known to manifest a major contribution in promoting cervical cancer. Our analysis, being time proficient and cost effective, provides a direction for developing novel drugs, therapeutic targets and biomarkers by identifying specific interaction patterns, that have structural importance.

  14. Prostate Cancer Pathology Resource Network

    Science.gov (United States)

    2013-07-01

    May after a long illness. Her responsibilities have been subsumed by Helen Fedor and Medha Darshan, and will be taken over by a Clinical...of the Prostate Cancer Biorepository Network Medha Darshan1*, Qizhi Zheng1*, Helen L. Fedor1*, Nicolas Wyhs2, Srinivasan Yegnasubramanian2...samples using the DNeasy Blood &Tissue kit (Qiagen). DNA quantification and 260:280 ratios were obtained by Nanodrop (Thermo Fisher Scientific Inc

  15. Network systems biology for targeted cancer therapies

    Institute of Scientific and Technical Information of China (English)

    Ting-Ting Zhou

    2012-01-01

    The era of targeted cancer therapies has arrived.However,due to the complexity of biological systems,the current progress is far from enough.From biological network modeling to structural/dynamic network analysis,network systems biology provides unique insight into the potential mechanisms underlying the growth and progression of cancer cells.It has also introduced great changes into the research paradigm of cancer-associated drug discovery and drug resistance.

  16. Advanced AODV Protocol for Identify Victim Nodes Using Divide and Conquer Strategy-in MANET

    Directory of Open Access Journals (Sweden)

    S. Hemalatha

    2014-10-01

    Full Text Available Objective of this study is there are many protocols have been proposed in Ad-hoc network, but none of the protocol is working under the principle of handling and checking on packet delivery. We have developed a routing protocol called the Advanced Ad-hoc on demand Vector protocol. The working principle of this protocol is checking the packet delivery to the destination. If any one of the nodes in the route is not forwarding the packet, that corresponding node will be identified and redirect the packet to the new route. For doing this checking this protocol uses divide and conquer strategy. The number hop between the source to destination is divided into two halves and check whether the up to the middle node the packet are flowing in a proper order or not. Recursively doing the divide and conquer of the route path, can identify the node which is not forward the packet to the next node. The design of this protocol contains several stages from path discovery, packet transmits, apply divide and conquer strategy on route, identify the victim node which is not forward the packet, redirect the new path, alert all the nodes about the victim node. Finally performance graph has been given compared with AODV protocol.

  17. Decoding network dynamics in cancer

    DEFF Research Database (Denmark)

    Linding, Rune

    2014-01-01

    models through computational integration of systematic, large-scale, high-dimensional quantitative data sets. I will review our latest advances in methods for exploring phosphorylation networks. In particular I will discuss how the combination of quantitative mass-spectrometry, systems...... in comparative phospho-proteomics and network evolution [Tan et al. Science Signaling 2009, Tan et al. Science 2009, Tan et al. Science 2011]. Finally, I will discuss our most recent work in analyzing genomic sequencing data from NGS studies and how we have developed new powerful algorithms to predict the impact......Biological systems are composed of highly dynamic and interconnected molecular networks that drive biological decision processes. The goal of network biology is to describe, quantify and predict the information flow and functional behaviour of living systems in a formal language...

  18. Ouroboros: A Tool for Building Generic, Hybrid, Divide& Conquer Algorithms

    Energy Technology Data Exchange (ETDEWEB)

    Johnson, J R; Foster, I

    2003-05-01

    A hybrid divide and conquer algorithm is one that switches from a divide and conquer to an iterative strategy at a specified problem size. Such algorithms can provide significant performance improvements relative to alternatives that use a single strategy. However, the identification of the optimal problem size at which to switch for a particular algorithm and platform can be challenging. We describe an automated approach to this problem that first conducts experiments to explore the performance space on a particular platform and then uses the resulting performance data to construct an optimal hybrid algorithm on that platform. We implement this technique in a tool, ''Ouroboros'', that automatically constructs a high-performance hybrid algorithm from a set of registered algorithms. We present results obtained with this tool for several classical divide and conquer algorithms, including matrix multiply and sorting, and report speedups of up to six times achieved over non-hybrid algorithms.

  19. Engineering the Divide-and-Conquer Closest Pair Algorithm

    Institute of Scientific and Technical Information of China (English)

    Ming-hui Jiang; Joel gillespie

    2007-01-01

    We improve the famous divide-and-conquer algorithm by Bentley and Shamos for the planar closest-pair problem. For n points on the plane, our algorithm keeps the optimal O(n log n) time complexity and, using a circle-packing property, computes at most 7n/2 Euclidean distances, which improves Ge et al.'s bound of (3n log n)/2 Euclidean distances. We present experimental results of our comparative studies on four different versions of the divide-and-conquer closest pair algorithm and propose two effective heuristics.

  20. Conquer Your Computer Hot tips and clever shortcuts

    CERN Document Server

    Megabyte, Ms

    2006-01-01

    Bigger, better and bouncier than ever, 'Conquer Your Computer' is back by popular demand. Featuring over 200 everyday tips for everyday computer users, this fully updated guide is packed with tricks and shortcuts you never knew existed! You'll learn how to start a blog, speed up Google searches, buy up big on Ebay, stop Spam, ward off viruses, create visual charts in Excel, mailmerge and more. Not just a practical guide for the less savvy user, but also a great resource for those oftenforgotten basics, 'Conquer Your Computer' covers Windows, Microsoft Word, Microsoft Excel, Internet Explorer,

  1. Differential network analysis in human cancer research.

    Science.gov (United States)

    Gill, Ryan; Datta, Somnath; Datta, Susmita

    2014-01-01

    A complex disease like cancer is hardly caused by one gene or one protein singly. It is usually caused by the perturbation of the network formed by several genes or proteins. In the last decade several research teams have attempted to construct interaction maps of genes and proteins either experimentally or reverse engineer interaction maps using computational techniques. These networks were usually created under a certain condition such as an environmental condition, a particular disease, or a specific tissue type. Lately, however, there has been greater emphasis on finding the differential structure of the existing network topology under a novel condition or disease status to elucidate the perturbation in a biological system. In this review/tutorial article we briefly mention some of the research done in this area; we mainly illustrate the computational/statistical methods developed by our team in recent years for differential network analysis using publicly available gene expression data collected from a well known cancer study. This data includes a group of patients with acute lymphoblastic leukemia and a group with acute myeloid leukemia. In particular, we describe the statistical tests to detect the change in the network topology based on connectivity scores which measure the association or interaction between pairs of genes. The tests under various scores are applied to this data set to perform a differential network analysis on gene expression for human leukemia. We believe that, in the future, differential network analysis will be a standard way to view the changes in gene expression and protein expression data globally and these types of tests could be useful in analyzing the complex differential signatures.

  2. Navigating cancer network attractors for tumor-specific therapy

    DEFF Research Database (Denmark)

    Creixell, Pau; Schoof, Erwin; Erler, Janine Terra

    2012-01-01

    Cells employ highly dynamic signaling networks to drive biological decision processes. Perturbations to these signaling networks may attract cells to new malignant signaling and phenotypic states, termed cancer network attractors, that result in cancer development. As different cancer cells reach...... these malignant states by accumulating different molecular alterations, uncovering these mechanisms represents a grand challenge in cancer biology. Addressing this challenge will require new systems-based strategies that capture the intrinsic properties of cancer signaling networks and provide deeper...... understanding of the processes by which genetic lesions perturb these networks and lead to disease phenotypes. Network biology will help circumvent fundamental obstacles in cancer treatment, such as drug resistance and metastasis, empowering personalized and tumor-specific cancer therapies....

  3. Random matrix analysis for gene interaction networks in cancer cells

    CERN Document Server

    Kikkawa, Ayumi

    2016-01-01

    Motivation: The investigation of topological modifications of the gene interaction networks in cancer cells is essential for understanding the desease. We study gene interaction networks in various human cancer cells with the random matrix theory. This study is based on the Cancer Network Galaxy (TCNG) database which is the repository of huge gene interactions inferred by Bayesian network algorithms from 256 microarray experimental data downloaded from NCBI GEO. The original GEO data are provided by the high-throughput microarray expression experiments on various human cancer cells. We apply the random matrix theory to the computationally inferred gene interaction networks in TCNG in order to detect the universality in the topology of the gene interaction networks in cancer cells. Results: We found the universal behavior in almost one half of the 256 gene interaction networks in TCNG. The distribution of nearest neighbor level spacing of the gene interaction matrix becomes the Wigner distribution when the net...

  4. Transcriptional Network Architecture of Breast Cancer Molecular Subtypes

    Directory of Open Access Journals (Sweden)

    Guillermo de Anda-Jáuregui

    2016-11-01

    Full Text Available Breast cancer heterogeneity is evident at the clinical, histological and molecular level. High throughput technologies allowed the identification of intrinsic subtypes that capture transcriptional differences among tumors. A remaining question is whether said differences are associated to a particular transcriptional program which involves different connections between the same molecules. In other words, whether particular transcriptional network architectures can be linked to specific phenotypes.In this work we infer, construct and analyze transcriptional networks from whole-genome gene expression microarrays, by using an information theory approach. We use 493 samples of primary breast cancer tissue classified in four molecular subtypes: Luminal A, Luminal B, Basal and HER2-enriched. For comparison, a network for non-tumoral mammary tissue (61 samples is also inferred and analyzed.Transcriptional networks present particular architectures in each breast cancer subtype as well as in the non-tumor breast tissue. We find substantial differences between the non-tumor network and those networks inferred from cancer samples, in both structure and gene composition. More importantly, we find specific network architectural features associated to each breast cancer subtype. Based on breast cancer networks' centrality, we identify genes previously associated to the disease, either, generally (i.e. CNR2 or to a particular subtype (such as LCK. Similarly, we identify LUZP4, a gene barely explored in breast cancer, playing a role in transcriptional networks with subtype-specific relevance.With this approach we observe architectural differences between cancer and non-cancer at network level, as well as differences between cancer subtype networks which might be associated with breast cancer heterogeneity. The centrality measures of these networks allow us to identify genes with potential biomedical implications to breast cancer.

  5. Transcriptional Network Architecture of Breast Cancer Molecular Subtypes.

    Science.gov (United States)

    de Anda-Jáuregui, Guillermo; Velázquez-Caldelas, Tadeo E; Espinal-Enríquez, Jesús; Hernández-Lemus, Enrique

    2016-01-01

    Breast cancer heterogeneity is evident at the clinical, histological and molecular level. High throughput technologies allowed the identification of intrinsic subtypes that capture transcriptional differences among tumors. A remaining question is whether said differences are associated to a particular transcriptional program which involves different connections between the same molecules. In other words, whether particular transcriptional network architectures can be linked to specific phenotypes. In this work we infer, construct and analyze transcriptional networks from whole-genome gene expression microarrays, by using an information theory approach. We use 493 samples of primary breast cancer tissue classified in four molecular subtypes: Luminal A, Luminal B, Basal and HER2-enriched. For comparison, a network for non-tumoral mammary tissue (61 samples) is also inferred and analyzed. Transcriptional networks present particular architectures in each breast cancer subtype as well as in the non-tumor breast tissue. We find substantial differences between the non-tumor network and those networks inferred from cancer samples, in both structure and gene composition. More importantly, we find specific network architectural features associated to each breast cancer subtype. Based on breast cancer networks' centrality, we identify genes previously associated to the disease, either, generally (i.e., CNR2) or to a particular subtype (such as LCK). Similarly, we identify LUZP4, a gene barely explored in breast cancer, playing a role in transcriptional networks with subtype-specific relevance. With this approach we observe architectural differences between cancer and non-cancer at network level, as well as differences between cancer subtype networks which might be associated with breast cancer heterogeneity. The centrality measures of these networks allow us to identify genes with potential biomedical implications to breast cancer.

  6. Transcriptional Network Architecture of Breast Cancer Molecular Subtypes

    Science.gov (United States)

    de Anda-Jáuregui, Guillermo; Velázquez-Caldelas, Tadeo E.; Espinal-Enríquez, Jesús; Hernández-Lemus, Enrique

    2016-01-01

    Breast cancer heterogeneity is evident at the clinical, histological and molecular level. High throughput technologies allowed the identification of intrinsic subtypes that capture transcriptional differences among tumors. A remaining question is whether said differences are associated to a particular transcriptional program which involves different connections between the same molecules. In other words, whether particular transcriptional network architectures can be linked to specific phenotypes. In this work we infer, construct and analyze transcriptional networks from whole-genome gene expression microarrays, by using an information theory approach. We use 493 samples of primary breast cancer tissue classified in four molecular subtypes: Luminal A, Luminal B, Basal and HER2-enriched. For comparison, a network for non-tumoral mammary tissue (61 samples) is also inferred and analyzed. Transcriptional networks present particular architectures in each breast cancer subtype as well as in the non-tumor breast tissue. We find substantial differences between the non-tumor network and those networks inferred from cancer samples, in both structure and gene composition. More importantly, we find specific network architectural features associated to each breast cancer subtype. Based on breast cancer networks' centrality, we identify genes previously associated to the disease, either, generally (i.e., CNR2) or to a particular subtype (such as LCK). Similarly, we identify LUZP4, a gene barely explored in breast cancer, playing a role in transcriptional networks with subtype-specific relevance. With this approach we observe architectural differences between cancer and non-cancer at network level, as well as differences between cancer subtype networks which might be associated with breast cancer heterogeneity. The centrality measures of these networks allow us to identify genes with potential biomedical implications to breast cancer. PMID:27920729

  7. Using Social Network Analysis to Evaluate Community Capacity Building of a Regional Community Cancer Network

    Science.gov (United States)

    Luque, John; Tyson, Dinorah Martinez; Lee, Ji-Hyun; Gwede, Clement; Vadaparampil, Susan; Noel-Thomas, Shalewa; Meade, Cathy

    2010-01-01

    The Tampa Bay Community Cancer Network (TBCCN) is one of 25 Community Network Programs funded by the National Cancer Institute's (NCI's) Center to Reduce Cancer Health Disparities with the objectives to create a collaborative infrastructure of academic and community based organizations and to develop effective and sustainable interventions to…

  8. Multiple-resolution clustering for recursive divide and conquer

    Science.gov (United States)

    Noel, Steven E.; Szu, Harold H.

    1997-04-01

    In recent work, a recursive divide-and-conquer approach was developed for path-minimization problems such as the traveling salesman problem (TSP). The approach is based on multiple-resolution clustering to decompose a problem into minimally-dependent parts. It is particularly effective for large-scale, fractal data sets, which exhibit clustering on all scales, and hence at all resolutions. This leads to the application of wavelets for performing the necessary multiple-resolution clustering. While the general topic of multiple-resolution clustering via wavelets is relatively immature, it has been explored for certain specific applications. However, nothing in the literature addresses the specific type of multiple-resolution clustering needed for the divide-and-conquer approach. That is the primary goal of this paper.

  9. A Divide-and-Conquer Strategy for Parsing

    CERN Document Server

    Shiuan, P L; Shiuan, Peh Li; Ann, Christopher Ting Hian

    1996-01-01

    In this paper, we propose a novel strategy which is designed to enhance the accuracy of the parser by simplifying complex sentences before parsing. This approach involves the separate parsing of the constituent sub-sentences within a complex sentence. To achieve that, the divide-and-conquer strategy first disambiguates the roles of the link words in the sentence and segments the sentence based on these roles. The separate parse trees of the segmented sub-sentences and the noun phrases within them are then synthesized to form the final parse. To evaluate the effects of this strategy on parsing, we compare the original performance of a dependency parser with the performance when it is enhanced with the divide-and-conquer strategy. When tested on 600 sentences of the IPSM'95 data sets, the enhanced parser saw a considerable error reduction of 21.2% in its accuracy.

  10. Conquering Credibility for Monetary Policy Under Sticky Confidence

    Directory of Open Access Journals (Sweden)

    Jaylson Jair da Silveira

    2015-06-01

    Full Text Available We derive a best-reply monetary policy when the confidence by price setters on the monetary authority’s commitment to price level targeting may be both incomplete and sticky. We find that complete confidence (or full credibility is not a necessary condition for the achievement of a price level target even when heterogeneity in firms’ price level expectations is endogenously time-varying and may emerge as a long-run equilibrium outcome. In fact, in the absence of exogenous perturbations to the dynamic of confidence building, it is the achievement of a price level target for long enough that, due to stickiness in the state of confidence, rather ensures the conquering of full credibility. This result has relevant implications for the conduct of monetary policy in pursuit of price stability. One implication is that setting a price level target matters more as a means to provide monetary policy with a sharper focus on price stability than as a device to conquer credibility. As regards the conquering of credibility for monetary policy, it turns out that actions speak louder than words, as the continuing achievement of price stability is what ultimately performs better as a confidence-building device.

  11. Cancer signaling networks and their implications for personalized medicine

    DEFF Research Database (Denmark)

    Creixell, Pau

    of the articles that are part of this PhD thesis (part II). In part III, we illustrate with an article that has been submitted recently, how next-generation sequencing data and mass spectrometry data can be combined to uncover genome-specific signaling networks. In part IV, I describe the two computational......) based on the integration of these cues; this integration and consequently the cellular decisions taken by cancer cells are arguably very distinct from the decisions that would be expected from non-cancer cells. Since cellular signaling networks and its different states are the computational circuits...... that determine cellular outcome, it is clear to many that these networks will be highly dysregulated in cancer cells. Thus, developing and applying methods that will be capable of mapping and predicting how cancer mutations translate into signaling network perturbations, which could explain cancer development...

  12. Gene transcriptional networks integrate microenvironmental signals in human breast cancer.

    Science.gov (United States)

    Xu, Ren; Mao, Jian-Hua

    2011-04-01

    A significant amount of evidence shows that microenvironmental signals generated from extracellular matrix (ECM) molecules, soluble factors, and cell-cell adhesion complexes cooperate at the extra- and intracellular level. This synergetic action of microenvironmental cues is crucial for normal mammary gland development and breast malignancy. To explore how the microenvironmental genes coordinate in human breast cancer at the genome level, we have performed gene co-expression network analysis in three independent microarray datasets and identified two microenvironment networks in human breast cancer tissues. Network I represents crosstalk and cooperation of ECM microenvironment and soluble factors during breast malignancy. The correlated expression of cytokines, chemokines, and cell adhesion proteins in Network II implicates the coordinated action of these molecules in modulating the immune response in breast cancer tissues. These results suggest that microenvironmental cues are integrated with gene transcriptional networks to promote breast cancer development.

  13. A computational model for cancer growth by using complex networks

    Science.gov (United States)

    Galvão, Viviane; Miranda, José G. V.

    2008-09-01

    In this work we propose a computational model to investigate the proliferation of cancerous cell by using complex networks. In our model the network represents the structure of available space in the cancer propagation. The computational scheme considers a cancerous cell randomly included in the complex network. When the system evolves the cells can assume three states: proliferative, non-proliferative, and necrotic. Our results were compared with experimental data obtained from three human lung carcinoma cell lines. The computational simulations show that the cancerous cells have a Gompertzian growth. Also, our model simulates the formation of necrosis, increase of density, and resources diffusion to regions of lower nutrient concentration. We obtain that the cancer growth is very similar in random and small-world networks. On the other hand, the topological structure of the small-world network is more affected. The scale-free network has the largest rates of cancer growth due to hub formation. Finally, our results indicate that for different average degrees the rate of cancer growth is related to the available space in the network.

  14. Sequencing the transcriptional network of androgen receptor in prostate cancer.

    Science.gov (United States)

    Chng, Kern Rei; Cheung, Edwin

    2013-11-01

    The progression of prostate cancer is largely dependent on the activity of the androgen receptor (AR), which in turn, correlates with the net output of the AR transcriptional regulatory network. A detailed and thorough understanding of the AR transcriptional regulatory network is therefore critical in the strategic manipulation of AR activity for the targeted eradication of prostate cancer cells. In this mini-review, we highlight some of the novel and unexpected mechanistic and functional insights of the AR transcriptional network derived from recent targeted sequencing (ChIP-Seq) studies of AR and its coregulatory factors in prostate cancer cells.

  15. A divide-conquer-recombine algorithmic paradigm for large spatiotemporal quantum molecular dynamics simulations

    Science.gov (United States)

    Shimojo, Fuyuki; Hattori, Shinnosuke; Kalia, Rajiv K.; Kunaseth, Manaschai; Mou, Weiwei; Nakano, Aiichiro; Nomura, Ken-ichi; Ohmura, Satoshi; Rajak, Pankaj; Shimamura, Kohei; Vashishta, Priya

    2014-05-01

    We introduce an extension of the divide-and-conquer (DC) algorithmic paradigm called divide-conquer-recombine (DCR) to perform large quantum molecular dynamics (QMD) simulations on massively parallel supercomputers, in which interatomic forces are computed quantum mechanically in the framework of density functional theory (DFT). In DCR, the DC phase constructs globally informed, overlapping local-domain solutions, which in the recombine phase are synthesized into a global solution encompassing large spatiotemporal scales. For the DC phase, we design a lean divide-and-conquer (LDC) DFT algorithm, which significantly reduces the prefactor of the O(N) computational cost for N electrons by applying a density-adaptive boundary condition at the peripheries of the DC domains. Our globally scalable and locally efficient solver is based on a hybrid real-reciprocal space approach that combines: (1) a highly scalable real-space multigrid to represent the global charge density; and (2) a numerically efficient plane-wave basis for local electronic wave functions and charge density within each domain. Hybrid space-band decomposition is used to implement the LDC-DFT algorithm on parallel computers. A benchmark test on an IBM Blue Gene/Q computer exhibits an isogranular parallel efficiency of 0.984 on 786 432 cores for a 50.3 × 106-atom SiC system. As a test of production runs, LDC-DFT-based QMD simulation involving 16 661 atoms is performed on the Blue Gene/Q to study on-demand production of hydrogen gas from water using LiAl alloy particles. As an example of the recombine phase, LDC-DFT electronic structures are used as a basis set to describe global photoexcitation dynamics with nonadiabatic QMD (NAQMD) and kinetic Monte Carlo (KMC) methods. The NAQMD simulations are based on the linear response time-dependent density functional theory to describe electronic excited states and a surface-hopping approach to describe transitions between the excited states. A series of techniques

  16. A divide-conquer-recombine algorithmic paradigm for large spatiotemporal quantum molecular dynamics simulations

    Energy Technology Data Exchange (ETDEWEB)

    Shimojo, Fuyuki; Hattori, Shinnosuke [Collaboratory for Advanced Computing and Simulations, Department of Physics and Astronomy, Department of Computer Science, and Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, California 90089-0242 (United States); Department of Physics, Kumamoto University, Kumamoto 860-8555 (Japan); Kalia, Rajiv K.; Mou, Weiwei; Nakano, Aiichiro; Nomura, Ken-ichi; Rajak, Pankaj; Vashishta, Priya [Collaboratory for Advanced Computing and Simulations, Department of Physics and Astronomy, Department of Computer Science, and Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, California 90089-0242 (United States); Kunaseth, Manaschai [Collaboratory for Advanced Computing and Simulations, Department of Physics and Astronomy, Department of Computer Science, and Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, California 90089-0242 (United States); National Nanotechnology Center, Pathumthani 12120 (Thailand); Ohmura, Satoshi [Collaboratory for Advanced Computing and Simulations, Department of Physics and Astronomy, Department of Computer Science, and Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, California 90089-0242 (United States); Department of Physics, Kumamoto University, Kumamoto 860-8555 (Japan); Department of Physics, Kyoto University, Kyoto 606-8502 (Japan); Shimamura, Kohei [Collaboratory for Advanced Computing and Simulations, Department of Physics and Astronomy, Department of Computer Science, and Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, California 90089-0242 (United States); Department of Physics, Kumamoto University, Kumamoto 860-8555 (Japan); Department of Applied Quantum Physics and Nuclear Engineering, Kyushu University, Fukuoka 819-0395 (Japan)

    2014-05-14

    We introduce an extension of the divide-and-conquer (DC) algorithmic paradigm called divide-conquer-recombine (DCR) to perform large quantum molecular dynamics (QMD) simulations on massively parallel supercomputers, in which interatomic forces are computed quantum mechanically in the framework of density functional theory (DFT). In DCR, the DC phase constructs globally informed, overlapping local-domain solutions, which in the recombine phase are synthesized into a global solution encompassing large spatiotemporal scales. For the DC phase, we design a lean divide-and-conquer (LDC) DFT algorithm, which significantly reduces the prefactor of the O(N) computational cost for N electrons by applying a density-adaptive boundary condition at the peripheries of the DC domains. Our globally scalable and locally efficient solver is based on a hybrid real-reciprocal space approach that combines: (1) a highly scalable real-space multigrid to represent the global charge density; and (2) a numerically efficient plane-wave basis for local electronic wave functions and charge density within each domain. Hybrid space-band decomposition is used to implement the LDC-DFT algorithm on parallel computers. A benchmark test on an IBM Blue Gene/Q computer exhibits an isogranular parallel efficiency of 0.984 on 786 432 cores for a 50.3 × 10{sup 6}-atom SiC system. As a test of production runs, LDC-DFT-based QMD simulation involving 16 661 atoms is performed on the Blue Gene/Q to study on-demand production of hydrogen gas from water using LiAl alloy particles. As an example of the recombine phase, LDC-DFT electronic structures are used as a basis set to describe global photoexcitation dynamics with nonadiabatic QMD (NAQMD) and kinetic Monte Carlo (KMC) methods. The NAQMD simulations are based on the linear response time-dependent density functional theory to describe electronic excited states and a surface-hopping approach to describe transitions between the excited states. A series of

  17. TP53 mutations, expression and interaction networks in human cancers.

    Science.gov (United States)

    Wang, Xiaosheng; Sun, Qingrong

    2017-01-03

    Although the associations of p53 dysfunction, p53 interaction networks and oncogenesis have been widely explored, a systematic analysis of TP53 mutations and its related interaction networks in various types of human cancers is lacking. Our study explored the associations of TP53 mutations, gene expression, clinical outcomes, and TP53 interaction networks across 33 cancer types using data from The Cancer Genome Atlas (TCGA). We show that TP53 is the most frequently mutated gene in a number of cancers, and its mutations appear to be early events in cancer initiation. We identified genes potentially repressed by p53, and genes whose expression correlates significantly with TP53 expression. These gene products may be especially important nodes in p53 interaction networks in human cancers. This study shows that while TP53-truncating mutations often result in decreased TP53 expression, other non-truncating TP53 mutations result in increased TP53 expression in some cancers. Survival analyses in a number of cancers show that patients with TP53 mutations are more likely to have worse prognoses than TP53-wildtype patients, and that elevated TP53 expression often leads to poor clinical outcomes. We identified a set of candidate synthetic lethal (SL) genes for TP53, and validated some of these SL interactions using data from the Cancer Cell Line Project. These predicted SL genes are promising candidates for experimental validation and the development of personalized therapeutics for patients with TP53-mutated cancers.

  18. Discovering cancer genes by integrating network and functional properties

    Directory of Open Access Journals (Sweden)

    Davis David P

    2009-09-01

    Full Text Available Abstract Background Identification of novel cancer-causing genes is one of the main goals in cancer research. The rapid accumulation of genome-wide protein-protein interaction (PPI data in humans has provided a new basis for studying the topological features of cancer genes in cellular networks. It is important to integrate multiple genomic data sources, including PPI networks, protein domains and Gene Ontology (GO annotations, to facilitate the identification of cancer genes. Methods Topological features of the PPI network, as well as protein domain compositions, enrichment of gene ontology categories, sequence and evolutionary conservation features were extracted and compared between cancer genes and other genes. The predictive power of various classifiers for identification of cancer genes was evaluated by cross validation. Experimental validation of a subset of the prediction results was conducted using siRNA knockdown and viability assays in human colon cancer cell line DLD-1. Results Cross validation demonstrated advantageous performance of classifiers based on support vector machines (SVMs with the inclusion of the topological features from the PPI network, protein domain compositions and GO annotations. We then applied the trained SVM classifier to human genes to prioritize putative cancer genes. siRNA knock-down of several SVM predicted cancer genes displayed greatly reduced cell viability in human colon cancer cell line DLD-1. Conclusion Topological features of PPI networks, protein domain compositions and GO annotations are good predictors of cancer genes. The SVM classifier integrates multiple features and as such is useful for prioritizing candidate cancer genes for experimental validations.

  19. Kinematic Identification of Parallel Mechanisms by a Divide and Conquer Strategy

    DEFF Research Database (Denmark)

    Durango, Sebastian; Restrepo, David; Ruiz, Oscar;

    2010-01-01

    This paper presents a Divide and Conquer strategy to estimate the kinematic parameters of parallel symmetrical mechanisms. The Divide and Conquer kinematic identification is designed and performed independently for each leg of the mechanism. The estimation of the kinematic parameters is performed...

  20. Reprogramming of miRNA networks in cancer and leukemia

    Science.gov (United States)

    Volinia, Stefano; Galasso, Marco; Costinean, Stefan; Tagliavini, Luca; Gamberoni, Giacomo; Drusco, Alessandra; Marchesini, Jlenia; Mascellani, Nicoletta; Sana, Maria Elena; Abu Jarour, Ramzey; Desponts, Caroline; Teitell, Michael; Baffa, Raffaele; Aqeilan, Rami; Iorio, Marilena V.; Taccioli, Cristian; Garzon, Ramiro; Di Leva, Gianpiero; Fabbri, Muller; Catozzi, Marco; Previati, Maurizio; Ambs, Stefan; Palumbo, Tiziana; Garofalo, Michela; Veronese, Angelo; Bottoni, Arianna; Gasparini, Pierluigi; Harris, Curtis C.; Visone, Rosa; Pekarsky, Yuri; de la Chapelle, Albert; Bloomston, Mark; Dillhoff, Mary; Rassenti, Laura Z.; Kipps, Thomas J.; Huebner, Kay; Pichiorri, Flavia; Lenze, Dido; Cairo, Stefano; Buendia, Marie-Annick; Pineau, Pascal; Dejean, Anne; Zanesi, Nicola; Rossi, Simona; Calin, George A.; Liu, Chang-Gong; Palatini, Jeff; Negrini, Massimo; Vecchione, Andrea; Rosenberg, Anne; Croce, Carlo M.

    2010-01-01

    We studied miRNA profiles in 4419 human samples (3312 neoplastic, 1107 nonmalignant), corresponding to 50 normal tissues and 51 cancer types. The complexity of our database enabled us to perform a detailed analysis of microRNA (miRNA) activities. We inferred genetic networks from miRNA expression in normal tissues and cancer. We also built, for the first time, specialized miRNA networks for solid tumors and leukemias. Nonmalignant tissues and cancer networks displayed a change in hubs, the most connected miRNAs. hsa-miR-103/106 were downgraded in cancer, whereas hsa-miR-30 became most prominent. Cancer networks appeared as built from disjointed subnetworks, as opposed to normal tissues. A comparison of these nets allowed us to identify key miRNA cliques in cancer. We also investigated miRNA copy number alterations in 744 cancer samples, at a resolution of 150 kb. Members of miRNA families should be similarly deleted or amplified, since they repress the same cellular targets and are thus expected to have similar impacts on oncogenesis. We correctly identified hsa-miR-17/92 family as amplified and the hsa-miR-143/145 cluster as deleted. Other miRNAs, such as hsa-miR-30 and hsa-miR-204, were found to be physically altered at the DNA copy number level as well. By combining differential expression, genetic networks, and DNA copy number alterations, we confirmed, or discovered, miRNAs with comprehensive roles in cancer. Finally, we experimentally validated the miRNA network with acute lymphocytic leukemia originated in Mir155 transgenic mice. Most of miRNAs deregulated in these transgenic mice were located close to hsa-miR-155 in the cancer network. PMID:20439436

  1. Identifying module biomarkers from gastric cancer by differential correlation network

    Directory of Open Access Journals (Sweden)

    Liu X

    2016-09-01

    Full Text Available Xiaoping Liu,1–3,* Xiao Chang1,3,* 1College of Statistics and Applied Mathematics, Anhui University of Finance and Economics, Bengbu, Anhui Province, People’s Republic of China; 2Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, People’s Republic of China; 3Collaborative Research Center for Innovative Mathematical Modeling, Institute of Industrial Science, University of Tokyo, Tokyo, Japan *These authors contributed equally to this work Abstract: Gastric cancer (stomach cancer is a severe disease caused by dysregulation of many functionally correlated genes or pathways instead of the mutation of individual genes. Systematic identification of gastric cancer biomarkers can provide insights into the mechanisms underlying this deadly disease and help in the development of new drugs. In this paper, we present a novel network-based approach to predict module biomarkers of gastric cancer that can effectively distinguish the disease from normal samples. Specifically, by assuming that gastric cancer has mainly resulted from dysfunction of biomolecular networks rather than individual genes in an organism, the genes in the module biomarkers are potentially related to gastric cancer. Finally, we identified a module biomarker with 27 genes, and by comparing the module biomarker with known gastric cancer biomarkers, we found that our module biomarker exhibited a greater ability to diagnose the samples with gastric cancer. Keywords: biomarkers, gastric cancer, stomach cancer, differential network

  2. International network of cancer genome projects

    NARCIS (Netherlands)

    Hudson, Thomas J.; Anderson, Warwick; Aretz, Axel; Barker, Anna D.; Bell, Cindy; Bernabe, Rosa R.; Bhan, M. K.; Calvo, Fabien; Eerola, Iiro; Gerhard, Daniela S.; Guttmacher, Alan; Guyer, Mark; Hemsley, Fiona M.; Jennings, Jennifer L.; Kerr, David; Klatt, Peter; Kolar, Patrik; Kusuda, Jun; Lane, David P.; Laplace, Frank; Lu, Youyong; Nettekoven, Gerd; Ozenberger, Brad; Peterson, Jane; Rao, T. S.; Remacle, Jacques; Schafer, Alan J.; Shibata, Tatsuhiro; Stratton, Michael R.; Vockley, Joseph G.; Watanabe, Koichi; Yang, Huanming; Yuen, Matthew M. F.; Knoppers, M.; Bobrow, Martin; Cambon-Thomsen, Anne; Dressler, Lynn G.; Dyke, Stephanie O. M.; Joly, Yann; Kato, Kazuto; Kennedy, Karen L.; Nicolas, Pilar; Parker, Michael J.; Rial-Sebbag, Emmanuelle; Romeo-Casabona, Carlos M.; Shaw, Kenna M.; Wallace, Susan; Wiesner, Georgia L.; Zeps, Nikolajs; Lichter, Peter; Biankin, Andrew V.; Chabannon, Christian; Chin, Lynda; Clement, Bruno; de Alava, Enrique; Degos, Francoise; Ferguson, Martin L.; Geary, Peter; Hayes, D. Neil; Johns, Amber L.; Nakagawa, Hidewaki; Penny, Robert; Piris, Miguel A.; Sarin, Rajiv; Scarpa, Aldo; Shibata, Tatsuhiro; van de Vijver, Marc; Futreal, P. Andrew; Aburatani, Hiroyuki; Bayes, Monica; Bowtell, David D. L.; Campbell, Peter J.; Estivill, Xavier; Grimmond, Sean M.; Gut, Ivo; Hirst, Martin; Lopez-Otin, Carlos; Majumder, Partha; Marra, Marco; Nakagawa, Hidewaki; Ning, Zemin; Puente, Xose S.; Ruan, Yijun; Shibata, Tatsuhiro; Stratton, Michael R.; Stunnenberg, Hendrik G.; Swerdlow, Harold; Velculescu, Victor E.; Wilson, Richard K.; Xue, Hong H.; Yang, Liu; Spellman, Paul T.; Bader, Gary D.; Boutros, Paul C.; Campbell, Peter J.; Flicek, Paul; Getz, Gad; Guigo, Roderic; Guo, Guangwu; Haussler, David; Heath, Simon; Hubbard, Tim J.; Jiang, Tao; Jones, Steven M.; Li, Qibin; Lopez-Bigas, Nuria; Luo, Ruibang; Pearson, John V.; Puente, Xose S.; Quesada, Victor; Raphael, Benjamin J.; Sander, Chris; Shibata, Tatsuhiro; Speed, Terence P.; Stuart, Joshua M.; Teague, Jon W.; Totoki, Yasushi; Tsunoda, Tatsuhiko; Valencia, Alfonso; Wheeler, David A.; Wu, Honglong; Zhao, Shancen; Zhou, Guangyu; Stein, Lincoln D.; Guigo, Roderic; Hubbard, Tim J.; Joly, Yann; Jones, Steven M.; Lathrop, Mark; Lopez-Bigas, Nuria; Ouellette, B. F. Francis; Spellman, Paul T.; Teague, Jon W.; Thomas, Gilles; Valencia, Alfonso; Yoshida, Teruhiko; Kennedy, Karen L.; Axton, Myles; Dyke, Stephanie O. M.; Futreal, P. Andrew; Gunter, Chris; Guyer, Mark; McPherson, John D.; Miller, Linda J.; Ozenberger, Brad; Kasprzyk, Arek; Zhang, Junjun; Haider, Syed A.; Wang, Jianxin; Yung, Christina K.; Cross, Anthony; Liang, Yong; Gnaneshan, Saravanamuttu; Guberman, Jonathan; Hsu, Jack; Bobrow, Martin; Chalmers, Don R. C.; Hasel, Karl W.; Joly, Yann; Kaan, Terry S. H.; Kennedy, Karen L.; Knoppers, Bartha M.; Lowrance, William W.; Masui, Tohru; Nicolas, Pilar; Rial-Sebbag, Emmanuelle; Rodriguez, Laura Lyman; Vergely, Catherine; Yoshida, Teruhiko; Grimmond, Sean M.; Biankin, Andrew V.; Bowtell, David D. L.; Cloonan, Nicole; Defazio, Anna; Eshleman, James R.; Etemadmoghadam, Dariush; Gardiner, Brooke A.; Kench, James G.; Scarpa, Aldo; Sutherland, Robert L.; Tempero, Margaret A.; Waddell, Nicola J.; Wilson, Peter J.; Gallinger, Steve; Tsao, Ming-Sound; Shaw, Patricia A.; Petersen, Gloria M.; Mukhopadhyay, Debabrata; Chin, Lynda; DePinho, Ronald A.; Thayer, Sarah; Muthuswamy, Lakshmi; Shazand, Kamran; Beck, Timothy; Sam, Michelle; Timms, Lee; Ballin, Vanessa; Lu, Youyong; Ji, Jiafu; Zhang, Xiuqing; Chen, Feng; Hu, Xueda; Zhou, Guangyu; Yang, Qi; Tian, Geng; Zhang, Lianhai; Xing, Xiaofang; Li, Xianghong; Zhu, Zhenggang; Yu, Yingyan; Yu, Jun; Yang, Huanming; Lathrop, Mark; Tost, Joerg; Brennan, Paul; Holcatova, Ivana; Zaridze, David; Brazma, Alvis; Egevad, Lars; Prokhortchouk, Egor; Banks, Rosamonde Elizabeth; Uhlen, Mathias; Cambon-Thomsen, Anne; Viksna, Juris; Ponten, Fredrik; Skryabin, Konstantin; Stratton, Michael R.; Futreal, P. Andrew; Birney, Ewan; Borg, Ake; Borresen-Dale, Anne-Lise; Caldas, Carlos; Foekens, John A.; Martin, Sancha; Reis-Filho, Jorge S.; Richardson, Andrea L.; Sotiriou, Christos; Stunnenberg, Hendrik G.; Thomas, Gilles; van de Vijver, Marc; van't Veer, Laura; Birnbaum, Daniel; Blanche, Helene; Boucher, Pascal; Boyault, Sandrine; Chabannon, Christian; Gut, Ivo; Masson-Jacquemier, Jocelyne D.; Lathrop, Mark; Pauporte, Iris; Pivot, Xavier; Vincent-Salomon, Anne; Tabone, Eric; Theillet, Charles; Thomas, Gilles; Tost, Joerg; Treilleux, Isabelle; Bioulac-Sage, Paulette; Clement, Bruno; Decaens, Thomas; Degos, Francoise; Franco, Dominique; Gut, Ivo; Gut, Marta; Heath, Simon; Lathrop, Mark; Samuel, Didier; Thomas, Gilles; Zucman-Rossi, Jessica; Lichter, Peter; Eils, Roland; Brors, Benedikt; Korbel, Jan O.; Korshunov, Andrey; Landgraf, Pablo; Lehrach, Hans; Pfister, Stefan; Radlwimmer, Bernhard; Reifenberger, Guido; Taylor, Michael D.; von Kalle, Christof; Majumder, Partha P.; Sarin, Rajiv; Scarpa, Aldo; Pederzoli, Paolo; Lawlor, Rita T.; Delledonne, Massimo; Bardelli, Alberto; Biankin, Andrew V.; Grimmond, Sean M.; Gress, Thomas; Klimstra, David; Zamboni, Giuseppe; Shibata, Tatsuhiro; Nakamura, Yusuke; Nakagawa, Hidewaki; Kusuda, Jun; Tsunoda, Tatsuhiko; Miyano, Satoru; Aburatani, Hiroyuki; Kato, Kazuto; Fujimoto, Akihiro; Yoshida, Teruhiko; Campo, Elias; Lopez-Otin, Carlos; Estivill, Xavier; Guigo, Roderic; de Sanjose, Silvia; Piris, Miguel A.; Montserrat, Emili; Gonzalez-Diaz, Marcos; Puente, Xose S.; Jares, Pedro; Valencia, Alfonso; Himmelbaue, Heinz; Quesada, Victor; Bea, Silvia; Stratton, Michael R.; Futreal, P. Andrew; Campbell, Peter J.; Vincent-Salomon, Anne; Richardson, Andrea L.; Reis-Filho, Jorge S.; van de Vijver, Marc; Thomas, Gilles; Masson-Jacquemier, Jocelyne D.; Aparicio, Samuel; Borg, Ake; Borresen-Dale, Anne-Lise; Caldas, Carlos; Foekens, John A.; Stunnenberg, Hendrik G.; van't Veer, Laura; Easton, Douglas F.; Spellman, Paul T.; Martin, Sancha; Chin, Lynda; Collins, Francis S.; Compton, Carolyn C.; Ferguson, Martin L.; Getz, Gad; Gunter, Chris; Guyer, Mark; Hayes, D. Neil; Lander, Eric S.; Ozenberger, Brad; Penny, Robert; Peterson, Jane; Sander, Chris; Speed, Terence P.; Spellman, Paul T.; Wheeler, David A.; Wilson, Richard K.; Chin, Lynda; Knoppers, Bartha M.; Lander, Eric S.; Lichter, Peter; Stratton, Michael R.; Bobrow, Martin; Burke, Wylie; Collins, Francis S.; DePinho, Ronald A.; Easton, Douglas F.; Futreal, P. Andrew; Green, Anthony R.; Guyer, Mark; Hamilton, Stanley R.; Hubbard, Tim J.; Kallioniemi, Olli P.; Kennedy, Karen L.; Ley, Timothy J.; Liu, Edison T.; Lu, Youyong; Majumder, Partha; Marra, Marco; Ozenberger, Brad; Peterson, Jane; Schafer, Alan J.; Spellman, Paul T.; Stunnenberg, Hendrik G.; Wainwright, Brandon J.; Wilson, Richard K.; Yang, Huanming

    2010-01-01

    The International Cancer Genome Consortium (ICGC) was launched to coordinate large-scale cancer genome studies in tumours from 50 different cancer types and/or subtypes that are of clinical and societal importance across the globe. Systematic studies of more than 25,000 cancer genomes at the genomic

  3. Breast cancer prognosis predicted by nuclear receptor-coregulator networks.

    Science.gov (United States)

    Doan, Tram B; Eriksson, Natalie A; Graham, Dinny; Funder, John W; Simpson, Evan R; Kuczek, Elizabeth S; Clyne, Colin; Leedman, Peter J; Tilley, Wayne D; Fuller, Peter J; Muscat, George E O; Clarke, Christine L

    2014-07-01

    Although molecular signatures based on transcript expression in breast cancer samples have provided new insights into breast cancer classification and prognosis, there are acknowledged limitations in current signatures. To provide rational, pathway-based signatures of disrupted physiology in cancer tissues that may be relevant to prognosis, this study has directly quantitated changed gene expression, between normal breast and cancer tissue, as a basis for signature development. The nuclear receptor (NR) family of transcription factors, and their coregulators, are fundamental regulators of every aspect of metazoan life, and were rigorously quantified in normal breast tissues and ERα positive and ERα negative breast cancers. Coregulator expression was highly correlated with that of selected NR in normal breast, particularly from postmenopausal women. These associations were markedly decreased in breast cancer, and the expression of the majority of coregulators was down-regulated in cancer tissues compared with normal. While in cancer the loss of NR-coregulator associations observed in normal breast was common, a small number of NR (Rev-ERBβ, GR, NOR1, LRH-1 and PGR) acquired new associations with coregulators in cancer tissues. Elevated expression of these NR in cancers was associated with poorer outcome in large clinical cohorts, as well as suggesting the activation of ERα -related, but ERα-independent, pathways in ERα negative cancers. In addition, the combined expression of small numbers of NR and coregulators in breast cancer was identified as a signature predicting outcome in ERα negative breast cancer patients, not linked to proliferation and with predictive power superior to existing signatures containing many more genes. These findings highlight the power of predictive signatures derived from the quantitative determination of altered gene expression between normal breast and breast cancers. Taken together, the findings of this study identify networks

  4. Cancer classification based on gene expression using neural networks.

    Science.gov (United States)

    Hu, H P; Niu, Z J; Bai, Y P; Tan, X H

    2015-12-21

    Based on gene expression, we have classified 53 colon cancer patients with UICC II into two groups: relapse and no relapse. Samples were taken from each patient, and gene information was extracted. Of the 53 samples examined, 500 genes were considered proper through analyses by S-Kohonen, BP, and SVM neural networks. Classification accuracy obtained by S-Kohonen neural network reaches 91%, which was more accurate than classification by BP and SVM neural networks. The results show that S-Kohonen neural network is more plausible for classification and has a certain feasibility and validity as compared with BP and SVM neural networks.

  5. A SOCIAL NETWORK ANALYSIS APPROACH TO UNDERSTAND CHANGES IN A CANCER DISPARITIES COMMUNITY PARTNERSHIP NETWORK.

    Science.gov (United States)

    Luque, John S; Tyson, Dinorah Martinez; Bynum, Shalanda A; Noel-Thomas, Shalewa; Wells, Kristen J; Vadaparampil, Susan T; Gwede, Clement K; Meade, Cathy D

    2011-11-01

    The Tampa Bay Community Cancer Network (TBCCN) is one of the Community Network Program sites funded (2005-10) by the National Cancer Institute's Center to Reduce Cancer Health Disparities. TBCCN was tasked to form a sustainable, community-based partnership network focused on the goal of reducing cancer health disparities among racial-ethnic minority and medically underserved populations. This article reports evaluation outcome results from a social network analysis and discusses the varying TBCCN partner roles-in education, training, and research-over a span of three years (2007-09). The network analysis included 20 local community partner organizations covering a tricounty area in Southwest Florida. In addition, multiple externally funded, community-based participatory research pilot projects with community-academic partners have either been completed or are currently in progress, covering research topics including culturally targeted colorectal and prostate cancer screening education, patient navigation focused on preventing cervical cancer in rural Latinas, and community perceptions of biobanking. The social network analysis identified a trend toward increased network decentralization based on betweenness centrality and overall increase in number of linkages, suggesting network sustainability. Degree centrality, trust, and multiplexity exhibited stability over the three-year time period. These results suggest increased interaction and interdependence among partner organizations and less dependence on the cancer center. Social network analysis enabled us to quantitatively evaluate partnership network functioning of TBCCN in terms of network structure and information and resources flows, which are integral to understanding effective coalition practice based on Community Coalition Action Theory ( Butterfoss and Kegler 2009). Sharing the results of the social network analysis with the partnership network is an important component of our coalition building efforts. A

  6. MicroRNA regulation network in colorectal cancer metastasis

    Institute of Scientific and Technical Information of China (English)

    Jiao-Jiao; Zhou; Shu; Zheng; Li-Feng; Sun; Lei; Zheng

    2014-01-01

    Colorectal cancer is the third most common cancer worldwide. Metastasis is a major cause of colorectal cancer-related death. Mechanisms of metastasis remain largely obscure. MicroRNA is one of the most important epigenetic regulators by targeting mRNAs posttranscriptionally. Accumulated evidence has supported its significant role in the metastasis of colorectal cancer, including epithelial-mesenchymal transition and angiogenesis. Dissecting microRNAome potentially identifies specific microRNAs as biomarkers of colorectal cancer metastasis. Better understanding of the complex network of microRNAs in colorectal cancer metastasis provide new insights in the biological process of metastasis and in the potential targets for colorectal cancer therapies and for diagnosis of recurrent and metastatic colorectal cancer.

  7. Radiology Network (ACRIN) - Cancer Imaging Program

    Science.gov (United States)

    ACRIN is funded to improve the quality and utility of imaging in cancer research and cancer care through expert, multi-institutional clinical evaluation of discoveries and technological innovations relevant to imaging science as applied in clinical oncology.

  8. A novel meta-analysis approach of cancer transcriptomes reveals prevailing transcriptional networks in cancer cells.

    Science.gov (United States)

    Niida, Atsushi; Imoto, Seiya; Nagasaki, Masao; Yamaguchi, Rui; Miyano, Satoru

    2010-01-01

    Although microarray technology has revealed transcriptomic diversities underlining various cancer phenotypes, transcriptional programs controlling them have not been well elucidated. To decode transcriptional programs governing cancer transcriptomes, we have recently developed a computational method termed EEM, which searches for expression modules from prescribed gene sets defined by prior biological knowledge like TF binding motifs. In this paper, we extend our EEM approach to predict cancer transcriptional networks. Starting from functional TF binding motifs and expression modules identified by EEM, we predict cancer transcriptional networks containing regulatory TFs, associated GO terms, and interactions between TF binding motifs. To systematically analyze transcriptional programs in broad types of cancer, we applied our EEM-based network prediction method to 122 microarray datasets collected from public databases. The data sets contain about 15000 experiments for tumor samples of various tissue origins including breast, colon, lung etc. This EEM based meta-analysis successfully revealed a prevailing cancer transcriptional network which functions in a large fraction of cancer transcriptomes; they include cell-cycle and immune related sub-networks. This study demonstrates broad applicability of EEM, and opens a way to comprehensive understanding of transcriptional networks in cancer cells.

  9. A Comprehensive Nuclear Receptor Network for Breast Cancer Cells

    Directory of Open Access Journals (Sweden)

    Ralf Kittler

    2013-02-01

    Full Text Available In breast cancer, nuclear receptors (NRs play a prominent role in governing gene expression, have prognostic utility, and are therapeutic targets. We built a regulatory map for 24 NRs, six chromatin state markers, and 14 breast-cancer-associated transcription factors (TFs that are expressed in the breast cancer cell line MCF-7. The resulting network reveals a highly interconnected regulatory matrix where extensive crosstalk occurs among NRs and other breast -cancer-associated TFs. We show that large numbers of factors are coordinately bound to highly occupied target regions throughout the genome, and these regions are associated with active chromatin state and hormone-responsive gene expression. This network also provides a framework for stratifying and predicting patient outcomes, and we use it to show that the peroxisome proliferator-activated receptor delta binds to a set of genes also regulated by the retinoic acid receptors and whose expression is associated with poor prognosis in breast cancer.

  10. Cell cycle-dependent gene networks relevant to cancer

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    The analysis of sophisticated interplays between cell cycle-dependent genes in a disease condition is one of the largely unexplored areas in modern tumor biology research. Many cell cycle-dependent genes are either oncogenes or suppressor genes, or are closely asso- ciated with the transition of a cell cycle. However, it is unclear how the complicated relationships between these cell cycle-dependent genes are, especially in cancers. Here, we sought to identify significant expression relationships between cell cycle-dependent genes by analyzing a HeLa microarray dataset using a local alignment algorithm and constructed a gene transcriptional network specific to the cancer by assembling these newly identified gene-gene relationships. We further characterized this global network by partitioning the whole network into several cell cycle phase-specific sub-networks. All generated networks exhibited the power-law node-degree dis- tribution, and the average clustering coefficients of these networks were remarkably higher than those of pure scale-free networks, indi- cating a property of hierarchical modularity. Based on the known protein-protein interactions and Gene Ontology annotation data, the proteins encoded by cell cycle-dependent interacting genes tended to share the same biological functions or to be involved in the same biological processes, rather than interacting by physical means. Finally, we identified the hub genes related to cancer based on the topo- logical importance that maintain the basic structure of cell cycle-dependent gene networks.

  11. Modeling Cancer Metastasis using Global, Quantitative and Integrative Network Biology

    DEFF Research Database (Denmark)

    Schoof, Erwin; Erler, Janine

    phosphorylation dynamics in a given biological sample. In Chapter III, we move into Integrative Network Biology, where, by combining two fundamental technologies (MS & NGS), we can obtain more in-depth insights into the links between cellular phenotype and genotype. Article 4 describes the proof...... cancer networks using Network Biology. Technologies key to this, such as Mass Spectrometry (MS), Next-Generation Sequencing (NGS) and High-Content Screening (HCS) are briefly described. In Chapter II, we cover how signaling networks and mutational data can be modeled in order to gain a better...

  12. A case against a divide and conquer approach to the nonsymmetric eigenvalue problem

    Energy Technology Data Exchange (ETDEWEB)

    Jessup, E.R.

    1991-12-01

    Divide and conquer techniques based on rank-one updating have proven fast, accurate, and efficient in parallel for the real symmetric tridiagonal and unitary eigenvalue problems and for the bidiagonal singular value problem. Although the divide and conquer mechanism can also be adapted to the real nonsymmetric eigenproblem in a straightforward way, most of the desirable characteristics of the other algorithms are lost. In this paper, we examine the problems of accuracy and efficiency that can stand in the way of a nonsymmetric divide and conquer eigensolver based on low-rank updating. 31 refs., 2 figs.

  13. 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...

  14. Identifying dysregulated pathways in cancers from pathway interaction networks

    Directory of Open Access Journals (Sweden)

    Liu Ke-Qin

    2012-06-01

    Full Text Available Abstract Background Cancers, a group of multifactorial complex diseases, are generally caused by mutation of multiple genes or dysregulation of pathways. Identifying biomarkers that can characterize cancers would help to understand and diagnose cancers. Traditional computational methods that detect genes differentially expressed between cancer and normal samples fail to work due to small sample size and independent assumption among genes. On the other hand, genes work in concert to perform their functions. Therefore, it is expected that dysregulated pathways will serve as better biomarkers compared with single genes. Results In this paper, we propose a novel approach to identify dysregulated pathways in cancer based on a pathway interaction network. Our contribution is three-fold. Firstly, we present a new method to construct pathway interaction network based on gene expression, protein-protein interactions and cellular pathways. Secondly, the identification of dysregulated pathways in cancer is treated as a feature selection problem, which is biologically reasonable and easy to interpret. Thirdly, the dysregulated pathways are identified as subnetworks from the pathway interaction networks, where the subnetworks characterize very well the functional dependency or crosstalk between pathways. The benchmarking results on several distinct cancer datasets demonstrate that our method can obtain more reliable and accurate results compared with existing state of the art methods. Further functional analysis and independent literature evidence also confirm that our identified potential pathogenic pathways are biologically reasonable, indicating the effectiveness of our method. Conclusions Dysregulated pathways can serve as better biomarkers compared with single genes. In this work, by utilizing pathway interaction networks and gene expression data, we propose a novel approach that effectively identifies dysregulated pathways, which can not only be used

  15. The Implementation of Telemedicine within a Community Cancer Network

    OpenAIRE

    London, Jack W; Morton, Daniel E.; Marinucci, Donna; Catalano, Robert; Comis, Robert L.

    1997-01-01

    Telemedicine is being used by physicians at the member hospitals of the Jefferson Cancer Network (JCN) for consultations regarding the diagnosis and management of cancer patients. The technology employed for this telemedicine system was chosen to meet three related specifications: low capital and operating cost, internal maintainability by community hospital data processing staffs, and compatibility with the existing technologic infrastructure. The solution selected is the u...

  16. Network medicine strikes a blow against breast cancer.

    Science.gov (United States)

    Erler, Janine T; Linding, Rune

    2012-05-11

    Drug development for complex diseases is shifting from targeting individual proteins or genes to systems-based attacks targeting dynamic network states. Lee et al. now reveal how the progressive rewiring of a signaling network over time following EGF receptor inhibition leaves triple-negative breast tumors vulnerable to a second, later hit with DNA-damaging drugs, demonstrating that time- and order-dependent drug combinations can be more efficacious in killing cancer cells.

  17. Profiling metabolic networks to study cancer metabolism.

    Science.gov (United States)

    Hiller, Karsten; Metallo, Christian M

    2013-02-01

    Cancer is a disease of unregulated cell growth and survival, and tumors reprogram biochemical pathways to aid these processes. New capabilities in the computational and bioanalytical characterization of metabolism have now emerged, facilitating the identification of unique metabolic dependencies that arise in specific cancers. By understanding the metabolic phenotype of cancers as a function of their oncogenic profiles, metabolic engineering may be applied to design synthetically lethal therapies for some tumors. This process begins with accurate measurement of metabolic fluxes. Here we review advanced methods of quantifying pathway activity and highlight specific examples where these approaches have uncovered potential opportunities for therapeutic intervention.

  18. Transcriptional master regulator analysis in breast cancer genetic networks.

    Science.gov (United States)

    Tovar, Hugo; García-Herrera, Rodrigo; Espinal-Enríquez, Jesús; Hernández-Lemus, Enrique

    2015-12-01

    Gene regulatory networks account for the delicate mechanisms that control gene expression. Under certain circumstances, gene regulatory programs may give rise to amplification cascades. Such transcriptional cascades are events in which activation of key-responsive transcription factors called master regulators trigger a series of gene expression events. The action of transcriptional master regulators is then important for the establishment of certain programs like cell development and differentiation. However, such cascades have also been related with the onset and maintenance of cancer phenotypes. Here we present a systematic implementation of a series of algorithms aimed at the inference of a gene regulatory network and analysis of transcriptional master regulators in the context of primary breast cancer cells. Such studies were performed in a highly curated database of 880 microarray gene expression experiments on biopsy-captured tissue corresponding to primary breast cancer and healthy controls. Biological function and biochemical pathway enrichment analyses were also performed to study the role that the processes controlled - at the transcriptional level - by such master regulators may have in relation to primary breast cancer. We found that transcription factors such as AGTR2, ZNF132, TFDP3 and others are master regulators in this gene regulatory network. Sets of genes controlled by these regulators are involved in processes that are well-known hallmarks of cancer. This kind of analyses may help to understand the most upstream events in the development of phenotypes, in particular, those regarding cancer biology.

  19. Transcriptional networks inferred from molecular signatures of breast cancer.

    Science.gov (United States)

    Tongbai, Ron; Idelman, Gila; Nordgard, Silje H; Cui, Wenwu; Jacobs, Jonathan L; Haggerty, Cynthia M; Chanock, Stephen J; Børresen-Dale, Anne-Lise; Livingston, Gary; Shaunessy, Patrick; Chiang, Chih-Hung; Kristensen, Vessela N; Bilke, Sven; Gardner, Kevin

    2008-02-01

    Global genomic approaches in cancer research have provided new and innovative strategies for the identification of signatures that differentiate various types of human cancers. Computational analysis of the promoter composition of the genes within these signatures may provide a powerful method for deducing the regulatory transcriptional networks that mediate their collective function. In this study we have systematically analyzed the promoter composition of gene classes derived from previously established genetic signatures that recently have been shown to reliably and reproducibly distinguish five molecular subtypes of breast cancer associated with distinct clinical outcomes. Inferences made from the trends of transcription factor binding site enrichment in the promoters of these gene groups led to the identification of regulatory pathways that implicate discrete transcriptional networks associated with specific molecular subtypes of breast cancer. One of these inferred pathways predicted a role for nuclear factor-kappaB in a novel feed-forward, self-amplifying, autoregulatory module regulated by the ERBB family of growth factor receptors. The existence of this pathway was verified in vivo by chromatin immunoprecipitation and shown to be deregulated in breast cancer cells overexpressing ERBB2. This analysis indicates that approaches of this type can provide unique insights into the differential regulatory molecular programs associated with breast cancer and will aid in identifying specific transcriptional networks and pathways as potential targets for tumor subtype-specific therapeutic intervention.

  20. Communication networks of men facing a diagnosis of prostate cancer.

    Science.gov (United States)

    Brown, Dot; Oetzel, John; Henderson, Alison

    2016-11-01

    This study seeks to identify the factors that shape the communication networks of men who face a potential diagnosis of prostate cancer, and how these factors relate to their disclosure about their changing health status. Men facing a potential diagnosis of prostate cancer are in a challenging situation; the support benefits of disclosing their changing health status to others in their communication networks is set against a backdrop of the potential stigma and uncertainty of the diagnosis. All men on a prostate biopsy waiting list were eligible for inclusion in an exploratory and interpretive study. Semi-structured interviews with 40 men explored their network structures and disclosure of health information. Thematic analysis highlighted the factors which contributed to their network structures and their disclosure about their health status. Four network factors shaped men's perspectives about disclosing their health status: (1) tie strength, comprising both strong and weak ties; (2) knowledgeable others, with a focus on medical professionals in the family; (3) homophily, which included other individuals with a similar medical condition; and (4) geographical proximity, with a preference for face-to-face communication. Communication networks influence men's disclosure of their health status and in particular weak ties with medical knowledge have an important role. Men who use the potential for support in their networks may experience improved psychosocial outcomes. Using these four network factors-tie strength, knowledgeable others, homophily or geographical proximity-to forecast men's willingness to disclose helps identify men who lack potential support and so are at risk of poor psychosocial health. Those with few strong ties or knowledgeable others in their networks may be in the at-risk cohort. The support provided in communication networks complements formal medical care from nurses and other health professionals, and encouraging patients to use their

  1. 国内医院设备及医疗仪器工业发展探索II治污到征癌仪器设备的工业创新:大科学旋出力+小细节软实力%Sciences & Technologies of Instrument Industries f or Pollution Treatment and Conquering Cancer II New Markets of China:Mega Science spin off, Micro-Detail Soft Power

    Institute of Scientific and Technical Information of China (English)

    沈经

    2015-01-01

    The HE project in (Science technology and cultural cooperation agreement between PRC and USA) had been operated by economic commission according to planned economy mode. It was over spent. Chairman Deng adopted Li Zhen-Dao’s recommendation to learn the talents of Stanford-SLAC that is the 4 times winner of Nobel prizes. Instead, the scientists take responsible for central committee leadership. The author’s experience from the participation cooperation is that Stanford - SLAC's“spin off of mega science + soft power of small detail” is the immortality of creativity and should be learn by industry for current pollution treatment conquering cancer.%1979《中美科技文化合作协定》中的高能工程,先由经委按计划经济模式干,超支严重。1984邓小平采纳李政道建议,改学“硅谷的核心”——连得4次Nobel奖的Stanford-SLAC方案,由科学家负责,中央书记处领导。笔者从基层参与合作的实践经验是:“得Nobel奖”只是过眼云烟,而Stanford-SLAC的大科学旋出力+小细节软实力才是值得工业界学习的“创新活力”!是当前“治污征癌”产业转型的科技渊源。

  2. Prediction and testing of biological networks underlying intestinal cancer.

    Directory of Open Access Journals (Sweden)

    Vishal N Patel

    Full Text Available Colorectal cancer progresses through an accumulation of somatic mutations, some of which reside in so-called "driver" genes that provide a growth advantage to the tumor. To identify points of intersection between driver gene pathways, we implemented a network analysis framework using protein interactions to predict likely connections--both precedented and novel--between key driver genes in cancer. We applied the framework to find significant connections between two genes, Apc and Cdkn1a (p21, known to be synergistic in tumorigenesis in mouse models. We then assessed the functional coherence of the resulting Apc-Cdkn1a network by engineering in vivo single node perturbations of the network: mouse models mutated individually at Apc (Apc(1638N+/- or Cdkn1a (Cdkn1a(-/-, followed by measurements of protein and gene expression changes in intestinal epithelial tissue. We hypothesized that if the predicted network is biologically coherent (functional, then the predicted nodes should associate more specifically with dysregulated genes and proteins than stochastically selected genes and proteins. The predicted Apc-Cdkn1a network was significantly perturbed at the mRNA-level by both single gene knockouts, and the predictions were also strongly supported based on physical proximity and mRNA coexpression of proteomic targets. These results support the functional coherence of the proposed Apc-Cdkn1a network and also demonstrate how network-based predictions can be statistically tested using high-throughput biological data.

  3. Capillary Network, Cancer and Kleiber Law

    CERN Document Server

    Dattoli, G; Licciardi, S; Guiot, C; Deisboeck, T S

    2014-01-01

    We develop a heuristic model embedding Kleiber and Murray laws to describe mass growth, metastasis and vascularization in cancer. We analyze the relevant dynamics using different evolution equations (Verhulst, Gompertz and others). Their extension to reaction diffusion equation of the Fisher type is then used to describe the relevant metastatic spreading in space. Regarding this last point, we suggest that cancer diffusion may be regulated by Levy flights mechanisms and discuss the possibility that the associated reaction diffusion equations are of the fractional type, with the fractional coefficient being determined by the fractal nature of the capillary evolution.

  4. A divide and conquer approach to multiple alignment.

    Science.gov (United States)

    Dress, A; Füllen, G; Perrey, S

    1995-01-01

    We present a report on work in progress on a divide and conquer approach to multiple alignment. The algorithm makes use of the costs calculated from applying the standard dynamic programming scheme to all pairs of sequences. The resulting cost matrices for pairwise alignment give rise to secondary matrices containing the additional costs imposed by fixing the path through the dynamic programming graph at a particular vertex. Such a constraint corresponds to a division of the problem obtained by slicing both sequences between two particular positions, and aligning the two sequences on the left and the two sequences on the right, charging for gaps introduced at the slicing point. To obtain an estimate for the additional cost imposed by forcing the multiple alignment through a particular vertex in the whole hypercube, we will take a (weighted) sum of secondary costs over all pairwise projections of the division of the problem, as defined by this vertex, that is, by slicing all sequences at the points suggested by the vertex. We then use that partition of every single sequence under consideration into two 'halfs' which imposes a minimal (weighted) sum of pairwise additional costs, making sure that one of the sequences is divided somewhere close to its midpoint. Hence, each iteration can cut the problem size in half. As the enumeration of all possible partitions may restrict this approach to small-size problems, we eliminate futile partitions, and organize their enumeration in a way that starts with the most promising ones.(ABSTRACT TRUNCATED AT 250 WORDS)

  5. Reduced Complexity Divide and Conquer Algorithm for Large Scale TSPs

    Directory of Open Access Journals (Sweden)

    Hoda A. Darwish

    2014-01-01

    Full Text Available The Traveling Salesman Problem (TSP is the problem of finding the shortest path passing through all given cities while only passing by each city once and finishing at the same starting city. This problem has NP-hard complexity making it extremely impractical to get the most optimal path even for problems as small as 20 cities since the number of permutations becomes too high. Many heuristic methods have been devised to reach “good” solutions in reasonable time. In this paper, we present the idea of utilizing a spatial “geographical” Divide and Conquer technique in conjunction with heuristic TSP algorithms specifically the Nearest Neighbor 2-opt algorithm. We have found that the proposed algorithm has lower complexity than algorithms published in the literature. This comes at a lower accuracy expense of around 9%. It is our belief that the presented approach will be welcomed to the community especially for large problems where a reasonable solution could be reached in a fraction of the time.

  6. The National Cancer Institute's Physical Sciences - Oncology Network

    Science.gov (United States)

    Espey, Michael Graham

    In 2009, the NCI launched the Physical Sciences - Oncology Centers (PS-OC) initiative with 12 Centers (U54) funded through 2014. The current phase of the Program includes U54 funded Centers with the added feature of soliciting new Physical Science - Oncology Projects (PS-OP) U01 grant applications through 2017; see NCI PAR-15-021. The PS-OPs, individually and along with other PS-OPs and the Physical Sciences-Oncology Centers (PS-OCs), comprise the Physical Sciences-Oncology Network (PS-ON). The foundation of the Physical Sciences-Oncology initiative is a high-risk, high-reward program that promotes a `physical sciences perspective' of cancer and fosters the convergence of physical science and cancer research by forming transdisciplinary teams of physical scientists (e.g., physicists, mathematicians, chemists, engineers, computer scientists) and cancer researchers (e.g., cancer biologists, oncologists, pathologists) who work closely together to advance our understanding of cancer. The collaborative PS-ON structure catalyzes transformative science through increased exchange of people, ideas, and approaches. PS-ON resources are leveraged to fund Trans-Network pilot projects to enable synergy and cross-testing of experimental and/or theoretical concepts. This session will include a brief PS-ON overview followed by a strategic discussion with the APS community to exchange perspectives on the progression of trans-disciplinary physical sciences in cancer research.

  7. Network for Translational Research - Cancer Imaging Program

    Science.gov (United States)

    Cooperative agreement (U54) awards to establish Specialized Research Resource Centers that will participate as members of a network of inter-disciplinary, inter-institutional research teams for the purpose of supporting translational research in optical imaging and/or spectroscopy in vivo, with an emphasis on multiple modalities.

  8. Network-based reading system for lung cancer screening CT

    Science.gov (United States)

    Fujino, Yuichi; Fujimura, Kaori; Nomura, Shin-ichiro; Kawashima, Harumi; Tsuchikawa, Megumu; Matsumoto, Toru; Nagao, Kei-ichi; Uruma, Takahiro; Yamamoto, Shinji; Takizawa, Hotaka; Kuroda, Chikazumi; Nakayama, Tomio

    2006-03-01

    This research aims to support chest computed tomography (CT) medical checkups to decrease the death rate by lung cancer. We have developed a remote cooperative reading system for lung cancer screening over the Internet, a secure transmission function, and a cooperative reading environment. It is called the Network-based Reading System. A telemedicine system involves many issues, such as network costs and data security if we use it over the Internet, which is an open network. In Japan, broadband access is widespread and its cost is the lowest in the world. We developed our system considering human machine interface and security. It consists of data entry terminals, a database server, a computer aided diagnosis (CAD) system, and some reading terminals. It uses a secure Digital Imaging and Communication in Medicine (DICOM) encrypting method and Public Key Infrastructure (PKI) based secure DICOM image data distribution. We carried out an experimental trial over the Japan Gigabit Network (JGN), which is the testbed for the Japanese next-generation network, and conducted verification experiments of secure screening image distribution, some kinds of data addition, and remote cooperative reading. We found that network bandwidth of about 1.5 Mbps enabled distribution of screening images and cooperative reading and that the encryption and image distribution methods we proposed were applicable to the encryption and distribution of general DICOM images via the Internet.

  9. Transcriptional network of androgen receptor in prostate cancer progression.

    Science.gov (United States)

    Takayama, Ken-ichi; Inoue, Satoshi

    2013-08-01

    The androgen receptor belongs to the nuclear receptor superfamily and functions as a ligand-dependent transcription factor. It binds to the androgen responsive element and recruits coregulatory factors to modulate gene transcription. In addition, the androgen receptor interacts with other transcription factors, such as forkhead box A1, and other oncogenic signaling pathway molecules that bind deoxyribonucleic acid and regulate transcription. Androgen receptor signaling plays an important role in the development of prostate cancer. Prostate cancer cells proliferate in an androgen-dependent manner, and androgen receptor blockade is effective in prostate cancer therapy. However, patients often progress to castration-resistant prostate cancer with elevated androgen receptor expression and hypersensitivity to androgen. Recently, comprehensive analysis tools, such as complementary DNA microarray, chromatin immunoprecipitation-on-chip and chromatin immunoprecipitation-sequence, have described the androgen-mediated diverse transcriptional program and gene networks in prostate cancer. Furthermore, functional and clinical studies have shown that some of the androgen receptor-regulated genes could be prognostic markers and potential therapeutic targets for the treatment of prostate cancer, particularly castration-resistant prostate cancer. Thus, identifying androgen receptor downstream signaling events and investigating the regulation of androgen receptor activity is critical for understanding the mechanism of carcinogenesis and progression to castration-resistant prostate cancer.

  10. Prediction of oncogenic interactions and cancer-related signaling networks based on network topology.

    Science.gov (United States)

    Acencio, Marcio Luis; Bovolenta, Luiz Augusto; Camilo, Esther; Lemke, Ney

    2013-01-01

    Cancer has been increasingly recognized as a systems biology disease since many investigators have demonstrated that this malignant phenotype emerges from abnormal protein-protein, regulatory and metabolic interactions induced by simultaneous structural and regulatory changes in multiple genes and pathways. Therefore, the identification of oncogenic interactions and cancer-related signaling networks is crucial for better understanding cancer. As experimental techniques for determining such interactions and signaling networks are labor-intensive and time-consuming, the development of a computational approach capable to accomplish this task would be of great value. For this purpose, we present here a novel computational approach based on network topology and machine learning capable to predict oncogenic interactions and extract relevant cancer-related signaling subnetworks from an integrated network of human genes interactions (INHGI). This approach, called graph2sig, is twofold: first, it assigns oncogenic scores to all interactions in the INHGI and then these oncogenic scores are used as edge weights to extract oncogenic signaling subnetworks from INHGI. Regarding the prediction of oncogenic interactions, we showed that graph2sig is able to recover 89% of known oncogenic interactions with a precision of 77%. Moreover, the interactions that received high oncogenic scores are enriched in genes for which mutations have been causally implicated in cancer. We also demonstrated that graph2sig is potentially useful in extracting oncogenic signaling subnetworks: more than 80% of constructed subnetworks contain more than 50% of original interactions in their corresponding oncogenic linear pathways present in the KEGG PATHWAY database. In addition, the potential oncogenic signaling subnetworks discovered by graph2sig are supported by experimental evidence. Taken together, these results suggest that graph2sig can be a useful tool for investigators involved in cancer research

  11. Prediction of oncogenic interactions and cancer-related signaling networks based on network topology.

    Directory of Open Access Journals (Sweden)

    Marcio Luis Acencio

    Full Text Available Cancer has been increasingly recognized as a systems biology disease since many investigators have demonstrated that this malignant phenotype emerges from abnormal protein-protein, regulatory and metabolic interactions induced by simultaneous structural and regulatory changes in multiple genes and pathways. Therefore, the identification of oncogenic interactions and cancer-related signaling networks is crucial for better understanding cancer. As experimental techniques for determining such interactions and signaling networks are labor-intensive and time-consuming, the development of a computational approach capable to accomplish this task would be of great value. For this purpose, we present here a novel computational approach based on network topology and machine learning capable to predict oncogenic interactions and extract relevant cancer-related signaling subnetworks from an integrated network of human genes interactions (INHGI. This approach, called graph2sig, is twofold: first, it assigns oncogenic scores to all interactions in the INHGI and then these oncogenic scores are used as edge weights to extract oncogenic signaling subnetworks from INHGI. Regarding the prediction of oncogenic interactions, we showed that graph2sig is able to recover 89% of known oncogenic interactions with a precision of 77%. Moreover, the interactions that received high oncogenic scores are enriched in genes for which mutations have been causally implicated in cancer. We also demonstrated that graph2sig is potentially useful in extracting oncogenic signaling subnetworks: more than 80% of constructed subnetworks contain more than 50% of original interactions in their corresponding oncogenic linear pathways present in the KEGG PATHWAY database. In addition, the potential oncogenic signaling subnetworks discovered by graph2sig are supported by experimental evidence. Taken together, these results suggest that graph2sig can be a useful tool for investigators involved

  12. Evolution and Controllability of Cancer Networks: A Boolean Perspective.

    Science.gov (United States)

    Srihari, Sriganesh; Raman, Venkatesh; Leong, Hon Wai; Ragan, Mark A

    2014-01-01

    Cancer forms a robust system capable of maintaining stable functioning (cell sustenance and proliferation) despite perturbations. Cancer progresses as stages over time typically with increasing aggressiveness and worsening prognosis. Characterizing these stages and identifying the genes driving transitions between them is critical to understand cancer progression and to develop effective anti-cancer therapies. In this work, we propose a novel model for the `cancer system' as a Boolean state space in which a Boolean network, built from protein-interaction and gene-expression data from different stages of cancer, transits between Boolean satisfiability states by "editing" interactions and "flipping" genes. Edits reflect rewiring of the PPI network while flipping of genes reflect activation or silencing of genes between stages. We formulate a minimization problem min flip to identify these genes driving the transitions. The application of our model (called BoolSpace) on three case studies-pancreatic and breast tumours in human and post spinal-cord injury (SCI) in rats-reveals valuable insights into the phenomenon of cancer progression: (i) interactions involved in core cell-cycle and DNA-damage repair pathways are significantly rewired in tumours, indicating significant impact to key genome-stabilizing mechanisms; (ii) several of the genes flipped are serine/threonine kinases which act as biological switches, reflecting cellular switching mechanisms between stages; and (iii) different sets of genes are flipped during the initial and final stages indicating a pattern to tumour progression. Based on these results, we hypothesize that robustness of cancer partly stems from "passing of the baton" between genes at different stages-genes from different biological processes and/or cellular components are involved in different stages of tumour progression thereby allowing tumour cells to evade targeted therapy, and therefore an effective therapy should target a "cover set" of

  13. Pathogenic Network Analysis Predicts Candidate Genes for Cervical Cancer

    Directory of Open Access Journals (Sweden)

    Yun-Xia Zhang

    2016-01-01

    Full Text Available Purpose. The objective of our study was to predicate candidate genes in cervical cancer (CC using a network-based strategy and to understand the pathogenic process of CC. Methods. A pathogenic network of CC was extracted based on known pathogenic genes (seed genes and differentially expressed genes (DEGs between CC and normal controls. Subsequently, cluster analysis was performed to identify the subnetworks in the pathogenic network using ClusterONE. Each gene in the pathogenic network was assigned a weight value, and then candidate genes were obtained based on the weight distribution. Eventually, pathway enrichment analysis for candidate genes was performed. Results. In this work, a total of 330 DEGs were identified between CC and normal controls. From the pathogenic network, 2 intensely connected clusters were extracted, and a total of 52 candidate genes were detected under the weight values greater than 0.10. Among these candidate genes, VIM had the highest weight value. Moreover, candidate genes MMP1, CDC45, and CAT were, respectively, enriched in pathway in cancer, cell cycle, and methane metabolism. Conclusion. Candidate pathogenic genes including MMP1, CDC45, CAT, and VIM might be involved in the pathogenesis of CC. We believe that our results can provide theoretical guidelines for future clinical application.

  14. Spike sorting for polytrodes: a divide and conquer approach

    Directory of Open Access Journals (Sweden)

    Nicholas V. Swindale

    2014-02-01

    Full Text Available In order to determine patterns of neural activity, spike signals recorded by extracellular electrodes have to be clustered (sorted with the aim of ensuring that each cluster represents all the spikes generated by an individual neuron. Many methods for spike sorting have been proposed but few are easily applicable to recordings from polytrodes which may have 16 or more recording sites. As with tetrodes, these are spaced sufficiently closely that signals from single neurons will usually be recorded on several adjacent sites. Although this offers a better chance of distinguishing neurons with similarly shaped spikes, sorting is difficult in such cases because of the high dimensionality of the space in which the signals must be classified. This report details a method for spike sorting based on a divide and conquer approach. Clusters are initially formed by assigning each event to the channel on which it is largest. Each channel-based cluster is then sub-divided into as many distinct clusters as possible. These are then recombined on the basis of pairwise tests into a final set of clusters. Pairwise tests are also performed to establish how distinct each cluster is from the others. A modified gradient ascent clustering (GAC algorithm is used to do the clustering. The method can sort spikes with minimal user input in times comparable to real time for recordings lasting up to 45 minutes. Our results illustrate some of the difficulties inherent in spike sorting, including changes in spike shape over time. We show that some physiologically distinct units may have very similar spike shapes. We show that RMS measures of spike shape similarity are not sensitive enough to discriminate clusters that can otherwise be separated by principal components analysis. Hence spike sorting based on least-squares matching to templates may be unreliable. Our methods should be applicable to tetrodes and scaleable to larger multi-electrode arrays (MEAs.

  15. Knowledge Reduction Based on Divide and Conquer Method in Rough Set Theory

    Directory of Open Access Journals (Sweden)

    Feng Hu

    2012-01-01

    Full Text Available The divide and conquer method is a typical granular computing method using multiple levels of abstraction and granulations. So far, although some achievements based on divided and conquer method in the rough set theory have been acquired, the systematic methods for knowledge reduction based on divide and conquer method are still absent. In this paper, the knowledge reduction approaches based on divide and conquer method, under equivalence relation and under tolerance relation, are presented, respectively. After that, a systematic approach, named as the abstract process for knowledge reduction based on divide and conquer method in rough set theory, is proposed. Based on the presented approach, two algorithms for knowledge reduction, including an algorithm for attribute reduction and an algorithm for attribute value reduction, are presented. Some experimental evaluations are done to test the methods on uci data sets and KDDCUP99 data sets. The experimental results illustrate that the proposed approaches are efficient to process large data sets with good recognition rate, compared with KNN, SVM, C4.5, Naive Bayes, and CART.

  16. The Cancer Cell Map Initiative: Defining the Hallmark Networks of Cancer

    Science.gov (United States)

    Krogan, Nevan J.; Lippman, Scott; Agard, David A.; Ashworth, Alan; Ideker, Trey

    2017-01-01

    Progress in DNA sequencing has revealed the startling complexity of cancer genomes, which typically carry thousands of somatic mutations. However, it remains unclear which are the key driver mutations or dependencies in a given cancer and how these influence pathogenesis and response to therapy. Although tumors of similar types and clinical outcomes can have patterns of mutations that are strikingly different, it is becoming apparent that these mutations recurrently hijack the same hallmark molecular pathways and networks. For this reason, it is likely that successful interpretation of cancer genomes will require comprehensive knowledge of the molecular networks under selective pressure in oncogenesis. Here we announce the creation of a new effort, called The Cancer Cell Map Initiative (CCMI), aimed at systematically detailing these complex interactions among cancer genes and how they differ between diseased and healthy states. We discuss recent progress that enables creation of these Cancer Cell Maps across a range of tumor types and how they can be used to target networks disrupted in individual patients, significantly accelerating the development of precision medicine. PMID:26000852

  17. A Medical Center Network for Optimized Lung Cancer Biospecimen Banking

    Science.gov (United States)

    2015-10-01

    2 .36 2 Yes - Current Smoker 25 NV Asbestos , Jet fuel, Nuclear - powered engines, Second- hand smoke, Toxic waste sites Asbestos , Jet fuel... Nuclear - powered engines, Second- hand smoke, Toxic waste sites W0266 Adenocarcinoma Stage IA Y N 0.2 50 70 0 1 .33 1 No - Quit Smoking 15...statement of the Lung Cancer Biospecimen Resource Network (LCBRN) states that the LCBRN will collect, annotate, store , and distribute human lung

  18. A Network Partition Algorithm for Mining Gene Functional Modules of Colon Cancer from DNA Microarray Data

    Institute of Scientific and Technical Information of China (English)

    Xiao-Gang Ruan; Jin-Lian Wang; Jian-Geng Li

    2006-01-01

    Computational analysis is essential for transforming the masses of microarray data into a mechanistic understanding of cancer. Here we present a method for finding gene functional modules of cancer from microarray data and have applied it to colon cancer. First, a colon cancer gene network and a normal colon tissue gene network were constructed using correlations between the genes. Then the modules that tended to have a homogeneous functional composition were identified by splitting up the network. Analysis of both networks revealed that they are scale-free.Comparison of the gene functional modules for colon cancer and normal tissues showed that the modules' functions changed with their structures.

  19. The Rare Cancer Network: ongoing studies and future strategy

    Directory of Open Access Journals (Sweden)

    Mahmut Ozsahin

    2014-08-01

    Full Text Available The Rare Cancer Network (RCN was formed in the early 1990’s to create a global network that could pool knowledge and resources in the studies of rare malignancies whose infrequency prevented both their study with prospective clinical trials. To date, the RCN has initiated 74 studies resulting in 46 peer reviewed publications. The First International Symposium of the Rare Cancer Network took place in Nice in March of 2014. Status updates and proposals for new studies were heard for fifteen topics. Ongoing studies continue for cardiac sarcomas, thyroid cancers, glomus tumors, and adult medulloblastomas. New proposals were presented at the symposium for primary hepatic lymphoma, solitary fibrous tumors, Rosai-Dorfman disease, tumors of the ampulla of Vater, salivary gland tumors, anorectal melanoma, midline nuclear protein in testes carcinoma, pulmonary lymphoepithelioma-like carcinoma, adenoid cystic carcinoma of the trachea, osteosarcomas of the mandible, and extra-cranial hemangiopericytoma. This manuscript presents the abstracts of those proposals and updates on ongoing studies, as well a brief summary of the vision and future of the RCN.

  20. A comparison of pop and chop to divide and conquer in resident cataract surgery.

    Science.gov (United States)

    Gross, Fredric J; Garcia-Zalisnak, Debra E; Bovee, Courtney E; Strawn, Joy D

    2016-01-01

    In this randomized prospective study, the cumulative dissipated energy and case time of pop and chop and of traditional four-quadrant divide and conquer in the first 60 cases (in total 120 eyes) of cataract surgery performed by two residents at the Veterans Administration Hospital in Hampton, Virginia, were compared. Overall and individually, the residents had significantly shorter case times and used significantly less cumulative dissipated energy for performing pop and chop than that for divide and conquer technique. There was no difference in complication rates or visual outcomes between these two techniques. The results of this study suggest that pop and chop is a more time- and energy-efficient method of nucleofractis than divide and conquer for novice resident surgeons.

  1. Alliance Against Cancer, the network of Italian cancer centers bridging research and care.

    Science.gov (United States)

    De Paoli, Paolo; Ciliberto, Gennaro; Ferrarini, Manlio; Pelicci, PierGiuseppe; Dellabona, Paolo; De Lorenzo, Francesco; Mantovani, Alberto; Musto, Pellegrino; Opocher, Giuseppe; Picci, Piero; Ricciardi, Walter; De Maria, Ruggero

    2015-11-14

    Alliance Against Cancer (ACC) was established in Rome in 2002 as a consortium of six Italian comprehensive cancer centers (Founders). The aims of ACC were to promote a network among Italian oncologic institutions in order to develop specific, advanced projects in clinical and translational research. During the following years, many additional full and associate members joined ACC, that presently includes the National Institute of Health, 17 research-oriented hospitals, scientific and patient organizations. Furthermore, in the last three years ACC underwent a reorganization process that redesigned the structure, governance and major activities. The present goal of ACC is to achieve high standards of care across Italy, to implement and harmonize principles of modern personalized and precision medicine, by developing cost effective processes and to provide tailored information to cancer patients. We herein summarize some of the major initiatives that ACC is currently developing to reach its goal, including tumor genetic screening programs, establishment of clinical trial programs for cancer patients treated in Italian cancer centers, facilitate their access to innovative drugs under development, improve quality through an European accreditation process (European Organization of Cancer Institutes), and develop international partnerships. In conclusion, ACC is a growing organization, trying to respond to the need of networking in Italy and may contribute significantly to improve the way we face cancer in Europe.

  2. On Conquering Psychological Obstacles of Oral English Study in Higher Vocational Colleges

    Institute of Scientific and Technical Information of China (English)

    郭辉

    2015-01-01

    This paper aims to find better ways to conquer psychological obstacles in oral English learning for vocational college English learners.The approaches are based on the important fact that psychological obstacles have impeded the language learning seriously.This paper describes the status quo of oral English study in vocational colleges,presents the problems existing among college learners,and analyzes them to find subjective and objective reasons.Finally,the analysis of the survey results will help to conquer psychological obstacles of spoken English learning.

  3. The redox biology network in cancer pathophysiology and therapeutics

    Directory of Open Access Journals (Sweden)

    Gina Manda

    2015-08-01

    Full Text Available The review pinpoints operational concepts related to the redox biology network applied to the pathophysiology and therapeutics of solid tumors. A sophisticated network of intrinsic and extrinsic cues, integrated in the tumor niche, drives tumorigenesis and tumor progression. Critical mutations and distorted redox signaling pathways orchestrate pathologic events inside cancer cells, resulting in resistance to stress and death signals, aberrant proliferation and efficient repair mechanisms. Additionally, the complex inter-cellular crosstalk within the tumor niche, mediated by cytokines, redox-sensitive danger signals (HMGB1 and exosomes, under the pressure of multiple stresses (oxidative, inflammatory, metabolic, greatly contributes to the malignant phenotype. The tumor-associated inflammatory stress and its suppressive action on the anti-tumor immune response are highlighted. We further emphasize that ROS may act either as supporter or enemy of cancer cells, depending on the context. Oxidative stress-based therapies, such as radiotherapy and photodynamic therapy, take advantage of the cytotoxic face of ROS for killing tumor cells by a non-physiologically sudden, localized and intense oxidative burst. The type of tumor cell death elicited by these therapies is discussed. Therapy outcome depends on the differential sensitivity to oxidative stress of particular tumor cells, such as cancer stem cells, and therefore co-therapies that transiently down-regulate their intrinsic antioxidant system hold great promise. We draw attention on the consequences of the damage signals delivered by oxidative stress-injured cells to neighboring and distant cells, and emphasize the benefits of therapeutically triggered immunologic cell death in metastatic cancer. An integrative approach should be applied when designing therapeutic strategies in cancer, taking into consideration the mutational, metabolic, inflammatory and oxidative status of tumor cells, cellular

  4. Postdiagnosis social networks and breast cancer mortality in the After Breast Cancer Pooling Project.

    Science.gov (United States)

    Kroenke, Candyce H; Michael, Yvonne L; Poole, Elizabeth M; Kwan, Marilyn L; Nechuta, Sarah; Leas, Eric; Caan, Bette J; Pierce, John; Shu, Xiao-Ou; Zheng, Ying; Chen, Wendy Y

    2017-04-01

    Large social networks have been associated with better overall survival, though not consistently with breast cancer (BC)-specific outcomes. This study evaluated associations of postdiagnosis social networks and BC outcomes in a large cohort. Women from the After Breast Cancer Pooling Project (n = 9267) provided data on social networks within approximately 2 years of their diagnosis. A social network index was derived from information about the presence of a spouse/partner, religious ties, community ties, friendship ties, and numbers of living first-degree relatives. Cox models were used to evaluate associations, and a meta-analysis was used to determine whether effect estimates differed by cohort. Stratification by demographic, social, tumor, and treatment factors was performed. There were 1448 recurrences and 1521 deaths (990 due to BC). Associations were similar in 3 of 4 cohorts. After covariate adjustments, socially isolated women (small networks) had higher risks of recurrence (hazard ratio [HR], 1.43; 95% confidence interval [CI], 1.15-1.77), BC-specific mortality (HR, 1.64; 95% CI, 1.33-2.03), and total mortality (HR, 1.69; 95% CI, 1.43-1.99) than socially integrated women; associations were stronger in those with stage I/II cancer. In the fourth cohort, there were no significant associations with BC-specific outcomes. A lack of a spouse/partner (P = .02) and community ties (P = .04) predicted higher BC-specific mortality in older white women but not in other women. However, a lack of relatives (P = .02) and friendship ties (P = .01) predicted higher BC-specific mortality in nonwhite women only. In a large pooled cohort, larger social networks were associated with better BC-specific and overall survival. Clinicians should assess social network information as a marker of prognosis because critical supports may differ with sociodemographic factors. Cancer 2017;123:1228-1237. © 2016 American Cancer Society. © 2016 American Cancer Society.

  5. Comparison of Back propagation neural network and Back propagation neural network Based Particle Swarm intelligence in Diagnostic Breast Cancer

    Directory of Open Access Journals (Sweden)

    Farahnaz SADOUGHI

    2014-03-01

    Full Text Available Breast cancer is the most commonly diagnosed cancer and the most common cause of death in women all over the world. Use of computer technology supporting breast cancer diagnosing is now widespread and pervasive across a broad range of medical areas. Early diagnosis of this disease can greatly enhance the chances of long-term survival of breast cancer victims. Artificial Neural Networks (ANN as mainly method play important role in early diagnoses breast cancer. This paper studies Levenberg Marquardet Backpropagation (LMBP neural network and Levenberg Marquardet Backpropagation based Particle Swarm Optimization(LMBP-PSO for the diagnosis of breast cancer. The obtained results show that LMBP and LMBP based PSO system provides higher classification efficiency. But LMBP based PSO needs minimum training and testing time. It helps in developing Medical Decision System (MDS for breast cancer diagnosing. It can also be used as secondary observer in clinical decision making.

  6. History of the Rare Cancer Network and past research

    Directory of Open Access Journals (Sweden)

    René-Olivier Mirimanoff

    2014-08-01

    Full Text Available Approximately, twenty years ago, the Rare Cancer Network (RCN was formed in Lausanne, Switzerland, to support the study of rare malignancies. The RCN has grown over the years and now includes 130 investigators from twenty-four nations on six continents. The network held its first international symposium in Nice, France, on March 21-22, 2014. The proceedings of that meeting are presented in two companion papers. This manuscript reviews the history of the growth of the RCN and contains the abstracts of fourteen oral presentations made at the meeting of prior RCN studies. From 1993 to 2014, 74 RCN studies have been initiated, of which 54 were completed, 10 are in progress or under analysis, and 9 were stopped due to poor accrual. Forty-four peer reviewed publications have been written on behalf of the RCN.

  7. MicroRNA and transcription factor mediated regulatory network for ovarian cancer: regulatory network of ovarian cancer.

    Science.gov (United States)

    Ying, Huanchun; Lv, Jing; Ying, Tianshu; Li, Jun; Yang, Qing; Ma, Yuan

    2013-10-01

    A better understanding on the regulatory interactions of microRNA (miRNA) target genes and transcription factor (TF) target genes in ovarian cancer may be conducive for developing early diagnosis strategy. Thus, gene expression data and miRNA expression data were downloaded from The Cancer Genome Atlas in this study. Differentially expressed genes and miRNAs were selected out with t test, and Gene Ontology enrichment analysis was performed with DAVID tools. Regulatory interactions were retrieved from miRTarBase, TRED, and TRANSFAC, and then networks for miRNA target genes and TF target genes were constructed to globally present the mechanisms. As a result, a total of 1,939 differentially expressed genes were identified, and they were enriched in 28 functions, among which cell cycle was affected to the most degree. Besides, 213 differentially expressed miRNAs were identified. Two regulatory networks for miRNA target genes and TF target genes were established and then both were combined, in which E2F transcription factor 1, cyclin-dependent kinase inhibitor 1A, cyclin E1, and miR-16 were the hub genes. These genes may be potential biomarkers for ovarian cancer.

  8. Brain network alterations and vulnerability to simulated neurodegeneration in breast cancer.

    Science.gov (United States)

    Kesler, Shelli R; Watson, Christa L; Blayney, Douglas W

    2015-08-01

    Breast cancer and its treatments are associated with mild cognitive impairment and brain changes that could indicate an altered or accelerated brain aging process. We applied diffusion tensor imaging and graph theory to measure white matter organization and connectivity in 34 breast cancer survivors compared with 36 matched healthy female controls. We also investigated how brain networks (connectomes) in each group responded to simulated neurodegeneration based on network attack analysis. Compared with controls, the breast cancer group demonstrated significantly lower fractional anisotropy, altered small-world connectome properties, lower brain network tolerance to systematic region (node), and connection (edge) attacks and significant cognitive impairment. Lower tolerance to network attack was associated with cognitive impairment in the breast cancer group. These findings provide further evidence of diffuse white matter pathology after breast cancer and extend the literature in this area with unique data demonstrating increased vulnerability of the post-breast cancer brain network to future neurodegenerative processes.

  9. Differential Regulatory Analysis Based on Coexpression Network in Cancer Research

    Directory of Open Access Journals (Sweden)

    Junyi Li

    2016-01-01

    Full Text Available With rapid development of high-throughput techniques and accumulation of big transcriptomic data, plenty of computational methods and algorithms such as differential analysis and network analysis have been proposed to explore genome-wide gene expression characteristics. These efforts are aiming to transform underlying genomic information into valuable knowledges in biological and medical research fields. Recently, tremendous integrative research methods are dedicated to interpret the development and progress of neoplastic diseases, whereas differential regulatory analysis (DRA based on gene coexpression network (GCN increasingly plays a robust complement to regular differential expression analysis in revealing regulatory functions of cancer related genes such as evading growth suppressors and resisting cell death. Differential regulatory analysis based on GCN is prospective and shows its essential role in discovering the system properties of carcinogenesis features. Here we briefly review the paradigm of differential regulatory analysis based on GCN. We also focus on the applications of differential regulatory analysis based on GCN in cancer research and point out that DRA is necessary and extraordinary to reveal underlying molecular mechanism in large-scale carcinogenesis studies.

  10. Artificial Neural Network Analysis in Preclinical Breast Cancer

    Directory of Open Access Journals (Sweden)

    Gholamreza Motalleb

    2013-01-01

    Full Text Available Objective: In this study, artificial neural network (ANN analysis of virotherapy in preclinical breast cancer was investigated.Materials and Methods: In this research article, a multilayer feed-forward neural network trained with an error back-propagation algorithm was incorporated in order to develop a predictive model. The input parameters of the model were virus dose, week and tamoxifen citrate, while tumor weight was included in the output parameter. Two different training algorithms, namely quick propagation (QP and Levenberg-Marquardt (LM, were used to train ANN.Results: The results showed that the LM algorithm, with 3-9-1 arrangement is more efficient compared to QP. Using LM algorithm, the coefficient of determination (R2 between the actual and predicted values was determined as 0.897118 for all data.Conclusion: It can be concluded that this ANN model may provide good ability to predict the biometry information of tumor in preclinical breast cancer virotherapy. The results showed that the LM algorithm employed by Neural Power software gave the better performance compared with the QP and virus dose, and it is more important factor compared to tamoxifen and time (week.

  11. The Implementation of Telemedicine within a Community Cancer Network

    Science.gov (United States)

    London, Jack W.; Morton, Daniel E.; Marinucci, Donna; Catalano, Robert; Comis, Robert L.

    1997-01-01

    Telemedicine is being used by physicians at the member hospitals of the Jefferson Cancer Network (JCN) for consultations regarding the diagnosis and management of cancer patients. The technology employed for this telemedicine system was chosen to meet three related specifications: low capital and operating cost, internal maintainability by community hospital data processing staffs, and compatibility with the existing technologic infrastructure. The solution selected is the ubiquitous desktop personal computer and associated software, and Integrated Services Digital Network (ISDN) communications links. The overall performance of this technology has been very satisfactory; ISDN communications has sufficient bandwidth for the transfer of patient data, including text reports, radiographs, and pathology slide images. The presence of the radiologist's interpretation along with the radiographic images allows the presentation of the images on these systems to be acceptable for review purposes. The video frame rates of these systems (12 to 15 frames per second) is adequate, particularly given the “talking heads” nature of the video presentations. Furthermore, the quality of the video image (resolution, size, frame rate) is secondary to the quality of the presentation of the medical information displayed and the capability for mutual annotation of the patient data during the consultation. PMID:8988470

  12. Social Networks Across Common Cancer Types: The Evidence, Gaps, and Areas of Potential Impact.

    Science.gov (United States)

    Rice, L J; Halbert, C H

    2017-01-01

    Although the association between social context and health has been demonstrated previously, much less is known about network interactions by gender, race/ethnicity, and sociodemographic characteristics. Given the variability in cancer outcomes among groups, research on these relationships may have important implications for addressing cancer health disparities. We examined the literature on social networks and cancer across the cancer continuum among adults. Relevant studies (N=16) were identified using two common databases: PubMed and Google Scholar. Most studies used a prospective cohort study design (n=9), included women only (n=11), and were located in the United States (n=14). Seventy-five percent of the studies reviewed used a validated scale or validated items to measure social networks (n=12). Only one study examined social network differences by race, 57.1% (n=8) focused on breast cancer alone, 14.3% (n=2) explored colorectal cancer or multiple cancers simultaneously, and 7.1% (n=1) only prostate cancer. More than half of the studies included multiple ethnicities in the sample, while one study included only low-income subjects. Despite findings of associations between social networks and cancer survival, risk, and screening, none of the studies utilized social networks as a mechanism for reducing health disparities; however, such an approach has been utilized for infectious disease control. Social networks and the support provided within these networks have important implications for health behaviors and ultimately cancer disparities. This review serves as the first step toward dialog on social networks as a missing component in the social determinants of cancer disparities literature that could move the needle upstream to target adverse cancer outcomes among vulnerable populations.

  13. Psychosocial staffing at National Comprehensive Cancer Network member institutions: data from leading cancer centers.

    Science.gov (United States)

    Deshields, Teresa; Kracen, Amanda; Nanna, Shannon; Kimbro, Lisa

    2016-02-01

    The National Comprehensive Cancer Network (NCCN) is comprised of 25 National Cancer Institute-designated cancer centers and arguably could thus set the standard for optimal psychosocial staffing for cancer centers; therefore, information was sought from NCCN Member Institutions about their current staffing for psychosocial services. These findings are put into perspective given the limited existing literature and consensus reports. The NCCN Best Practices Committee surveyed member institutions about their staffing for psychosocial services. The survey was administered electronically in the winter of 2012. The survey was completed by 20 cancer centers. Across institutions, case managers and mental health therapists, typically social workers, were utilized most frequently to provide psychosocial services (67% of full-time-equivalents (FTEs)), with other psychosocial professionals also represented but less consistently. Most psychosocial services are institutionally funded (ranging from 64 to 100%), although additional sources of support include fee for service and grant funding. Training of psychosocial providers is unevenly distributed across responding sites, ranging from 92% of institutions having training programs for psychiatrists to 36% having training programs for mental health therapists. There was variability among the institutions in terms of patient volume, psychosocial services provided, and psychosocial staff employed. As accreditation standards are implemented that provide impetus for psychosocial services in oncology, it is hoped that greater clarity will develop concerning staffing for psychosocial services and uptake of these services by patients with cancer. Copyright © 2015 John Wiley & Sons, Ltd.

  14. Altered small-world properties of gray matter networks in breast cancer

    Directory of Open Access Journals (Sweden)

    Hosseini S M

    2012-05-01

    Full Text Available Abstract Background Breast cancer survivors, particularly those treated with chemotherapy, are at significantly increased risk for long-term cognitive and neurobiologic impairments. These deficits tend to involve skills that are subserved by distributed brain networks. Additionally, neuroimaging studies have shown a diffuse pattern of brain structure changes in chemotherapy-treated breast cancer survivors that might impact large-scale brain networks. Methods We therefore applied graph theoretical analysis to compare the gray matter structural networks of female breast cancer survivors with a history of chemotherapy treatment and healthy age and education matched female controls. Results Results revealed reduced clustering coefficient and small-world index in the brain network of the breast cancer patients across a range of network densities. In addition, the network of the breast cancer group had less highly interactive nodes and reduced degree/centrality in the frontotemporal regions compared to controls, which may help explain the common impairments of memory and executive functioning among these patients. Conclusions These results suggest that breast cancer and chemotherapy may decrease regional connectivity as well as global network organization and integration, reducing efficiency of the network. To our knowledge, this is the first report of altered large-scale brain networks associated with breast cancer and chemotherapy.

  15. The Rare Cancer Network: achievements from 1993 to 2012

    Directory of Open Access Journals (Sweden)

    Ajaykumar Patel

    2012-09-01

    Full Text Available The Rare Cancer Network (RCN, founded in 1993, performs research involving rare tumors that are not common enough to be the focus of prospective study. Over 55 studies have either been completed or are in progress. The aim of the paper is to present an overview of the 30 studies done through the RCN to date, organized by disease site. Five studies focus on breast pathology, including sarcoma, lymphoma, phyllodes tumor, adenoid cystic carcinoma, and ductal carcinoma in situ in young women. Three studies on prostate cancer address prostatic small cell carcinoma and adenocarcinoma of young and elderly patients. Six studies on head and neck cancers include orbital and intraocular lymphoma, mucosal melanoma, pediatric nasopharyngeal carcinoma, olfactory neuroblastoma, and mucosa-associated lymphoid tissue lymphoma of the salivary glands. There were 4 central nervous system studies on patients with cerebellar glioblastoma multiforme, atypical and malignant meningioma, spinal epidural lymphoma and myxopapillary ependymoma. Outside of these disease sites, there is a wide variety of other studies on tumors ranging from uterine leiomyosarcoma to giant cell tumors of the bone. The studies done by the RCN represent a wide range of rare pathologies that were previously only studied in small series or case reports. With further growth of the RCN and collaboration between members our ability to analyze rare tumors will increase and result in better understanding of their behavior and ultimately help direct research that may improve patient outcomes.

  16. Cancer talk on twitter: community structure and information sources in breast and prostate cancer social networks.

    Science.gov (United States)

    Himelboim, Itai; Han, Jeong Yeob

    2014-01-01

    This study suggests taking a social networks theoretical approach to predict and explain patterns of information exchange among Twitter prostate and breast cancer communities. The authors collected profiles and following relationship data about users who posted messages about either cancer over 1 composite week. Using social network analysis, the authors identified the main clusters of interconnected users and their most followed hubs (i.e., information sources sought). Findings suggest that users who populated the persistent-across-time core cancer communities created dense clusters, an indication of taking advantage of the technology to form relationships with one another in ways that traditional one-to-many communication technologies cannot support. The major information sources sought were very specific to the community health interest and were grassroots oriented (e.g., a blog about prostate cancer treatments). Accounts associated with health organizations and news media, despite their focus on health, did not play a role in these core health communities. Methodological and practical implications for researchers and health campaigners are discussed.

  17. Identifying causal networks linking cancer processes and anti-tumor immunity using Bayesian network inference and metagene constructs.

    Science.gov (United States)

    Kaiser, Jacob L; Bland, Cassidy L; Klinke, David J

    2016-03-01

    Cancer arises from a deregulation of both intracellular and intercellular networks that maintain system homeostasis. Identifying the architecture of these networks and how they are changed in cancer is a pre-requisite for designing drugs to restore homeostasis. Since intercellular networks only appear in intact systems, it is difficult to identify how these networks become altered in human cancer using many of the common experimental models. To overcome this, we used the diversity in normal and malignant human tissue samples from the Cancer Genome Atlas (TCGA) database of human breast cancer to identify the topology associated with intercellular networks in vivo. To improve the underlying biological signals, we constructed Bayesian networks using metagene constructs, which represented groups of genes that are concomitantly associated with different immune and cancer states. We also used bootstrap resampling to establish the significance associated with the inferred networks. In short, we found opposing relationships between cell proliferation and epithelial-to-mesenchymal transformation (EMT) with regards to macrophage polarization. These results were consistent across multiple carcinomas in that proliferation was associated with a type 1 cell-mediated anti-tumor immune response and EMT was associated with a pro-tumor anti-inflammatory response. To address the identifiability of these networks from other datasets, we could identify the relationship between EMT and macrophage polarization with fewer samples when the Bayesian network was generated from malignant samples alone. However, the relationship between proliferation and macrophage polarization was identified with fewer samples when the samples were taken from a combination of the normal and malignant samples. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:470-479, 2016.

  18. A Federated Network for Translational Cancer Research Using Clinical Data and Biospecimens.

    Science.gov (United States)

    Jacobson, Rebecca S; Becich, Michael J; Bollag, Roni J; Chavan, Girish; Corrigan, Julia; Dhir, Rajiv; Feldman, Michael D; Gaudioso, Carmelo; Legowski, Elizabeth; Maihle, Nita J; Mitchell, Kevin; Murphy, Monica; Sakthivel, Mayurapriyan; Tseytlin, Eugene; Weaver, JoEllen

    2015-12-15

    Advances in cancer research and personalized medicine will require significant new bridging infrastructures, including more robust biorepositories that link human tissue to clinical phenotypes and outcomes. In order to meet that challenge, four cancer centers formed the Text Information Extraction System (TIES) Cancer Research Network, a federated network that facilitates data and biospecimen sharing among member institutions. Member sites can access pathology data that are de-identified and processed with the TIES natural language processing system, which creates a repository of rich phenotype data linked to clinical biospecimens. TIES incorporates multiple security and privacy best practices that, combined with legal agreements, network policies, and procedures, enable regulatory compliance. The TIES Cancer Research Network now provides integrated access to investigators at all member institutions, where multiple investigator-driven pilot projects are underway. Examples of federated search across the network illustrate the potential impact on translational research, particularly for studies involving rare cancers, rare phenotypes, and specific biologic behaviors. The network satisfies several key desiderata including local control of data and credentialing, inclusion of rich phenotype information, and applicability to diverse research objectives. The TIES Cancer Research Network presents a model for a national data and biospecimen network. ©2015 American Association for Cancer Research.

  19. Comparative indicators for cancer network management in England: Availability, characteristics and presentation

    Directory of Open Access Journals (Sweden)

    Coleman Michel P

    2008-02-01

    Full Text Available Abstract Background In 2000, the national cancer plan for England created 34 cancer networks, new organisational structures to coordinate services across populations varying between a half and three million people. We investigated the availability of data sets reflecting measures of structure, process and outcome that could be used to support network management. Methods We investigated the properties of national data sets relating to four common cancers – breast, colorectal, lung and prostate. We reviewed the availability and completeness of these data sets, identified leading items within each set and put them into tables of the 34 cancer networks. We also investigated methods of presentation. Results The Acute Hospitals Portfolio and the Cancer Standards Peer Review recorded structural characteristics at hospital and cancer service level. Process measures included Hospital Episode Statistics, recording admissions, and Hospital Waiting-List data. Patient outcome measures included the National Survey of Patient Satisfaction for cancer, and cancer survival, drawn from cancer registration. Data were drawn together to provide an exemplar indicator set a single network, and methods of graphical presentation were considered. Conclusion While not as yet used together in practice, comparative indicators are available within the National Health Service in England for use in performance assessment by cancer networks.

  20. Constructing Two-Dimensional Voronoi Diagrams via Divide-and-Conquer of Envelopes in Space

    CERN Document Server

    Setter, Ophir

    2009-05-01

    We present a general framework for computing two-dimensional Voronoi diagrams of different classes of sites under various distance functions. The framework is sufficiently general to support diagrams embedded on a family of two-dimensional parametric surfaces in $R^3$. The computation of the diagrams is carried out through the construction of envelopes of surfaces in 3-space provided by CGAL (the Computational Geometry Algorithm Library). The construction of the envelopes follows a divide-and-conquer approach. A straightforward application of the divide-and-conquer approach for computing Voronoi diagrams yields algorithms that are inefficient in the worst case. We prove that through randomization the expected running time becomes near-optimal in the worst case. We show how to employ our framework to realize various types of Voronoi diagrams with different properties by providing implementations for a vast collection of commonly used Voronoi diagrams. We also show how to apply the new framework and other exist...

  1. Classifications of multispectral colorectal cancer tissues using convolution neural network

    Directory of Open Access Journals (Sweden)

    Hawraa Haj-Hassan

    2017-01-01

    Full Text Available Background: Colorectal cancer (CRC is the third most common cancer among men and women. Its diagnosis in early stages, typically done through the analysis of colon biopsy images, can greatly improve the chances of a successful treatment. This paper proposes to use convolution neural networks (CNNs to predict three tissue types related to the progression of CRC: benign hyperplasia (BH, intraepithelial neoplasia (IN, and carcinoma (Ca. Methods: Multispectral biopsy images of thirty CRC patients were retrospectively analyzed. Images of tissue samples were divided into three groups, based on their type (10 BH, 10 IN, and 10 Ca. An active contour model was used to segment image regions containing pathological tissues. Tissue samples were classified using a CNN containing convolution, max-pooling, and fully-connected layers. Available tissue samples were split into a training set, for learning the CNN parameters, and test set, for evaluating its performance. Results: An accuracy of 99.17% was obtained from segmented image regions, outperforming existing approaches based on traditional feature extraction, and classification techniques. Conclusions: Experimental results demonstrate the effectiveness of CNN for the classification of CRC tissue types, in particular when using presegmented regions of interest.

  2. Conquering the electron the geniuses, visionaries, egomaniacs, and scoundrels who built our electronic age

    CERN Document Server

    Cheung, Derek

    2014-01-01

    Conquering the Electron offers readers a true and engaging history of the world of electronics. Beginning with the discoveries of static electricity and magnetism and ending with the creation of the smartphone and the iPad, this book shows the interconnection of each advance to the next one on the long journey to our modern day technologies. Want to know how AT&T's Bell Labs developed semiconductor technology--and how its leading scientists almost came to blows in the process? Want to understand how radio and television work--and why RCA drove their inventors to financial ruin and an early grave? Conquering the Electron offers these stories and more, presenting each revolutionary technological advance right alongside the blow-by-blow personal battles that all too often took place. By exploring the combination of genius, infighting, and luck that powered the creation of the electronic age we inhabit today, Conquering the Electron shows the interconnection of each advance to the next while also pulling bac...

  3. Convolutional neural networks for prostate cancer recurrence prediction

    Science.gov (United States)

    Kumar, Neeraj; Verma, Ruchika; Arora, Ashish; Kumar, Abhay; Gupta, Sanchit; Sethi, Amit; Gann, Peter H.

    2017-03-01

    Accurate prediction of the treatment outcome is important for cancer treatment planning. We present an approach to predict prostate cancer (PCa) recurrence after radical prostatectomy using tissue images. We used a cohort whose case vs. control (recurrent vs. non-recurrent) status had been determined using post-treatment follow up. Further, to aid the development of novel biomarkers of PCa recurrence, cases and controls were paired based on matching of other predictive clinical variables such as Gleason grade, stage, age, and race. For this cohort, tissue resection microarray with up to four cores per patient was available. The proposed approach is based on deep learning, and its novelty lies in the use of two separate convolutional neural networks (CNNs) - one to detect individual nuclei even in the crowded areas, and the other to classify them. To detect nuclear centers in an image, the first CNN predicts distance transform of the underlying (but unknown) multi-nuclear map from the input HE image. The second CNN classifies the patches centered at nuclear centers into those belonging to cases or controls. Voting across patches extracted from image(s) of a patient yields the probability of recurrence for the patient. The proposed approach gave 0.81 AUC for a sample of 30 recurrent cases and 30 non-recurrent controls, after being trained on an independent set of 80 case-controls pairs. If validated further, such an approach might help in choosing between a combination of treatment options such as active surveillance, radical prostatectomy, radiation, and hormone therapy. It can also generalize to the prediction of treatment outcomes in other cancers.

  4. Controllability in cancer metabolic networks according to drug targets as driver nodes.

    Science.gov (United States)

    Asgari, Yazdan; Salehzadeh-Yazdi, Ali; Schreiber, Falk; Masoudi-Nejad, Ali

    2013-01-01

    Networks are employed to represent many nonlinear complex systems in the real world. The topological aspects and relationships between the structure and function of biological networks have been widely studied in the past few decades. However dynamic and control features of complex networks have not been widely researched, in comparison to topological network features. In this study, we explore the relationship between network controllability, topological parameters, and network medicine (metabolic drug targets). Considering the assumption that targets of approved anticancer metabolic drugs are driver nodes (which control cancer metabolic networks), we have applied topological analysis to genome-scale metabolic models of 15 normal and corresponding cancer cell types. The results show that besides primary network parameters, more complex network metrics such as motifs and clusters may also be appropriate for controlling the systems providing the controllability relationship between topological parameters and drug targets. Consequently, this study reveals the possibilities of following a set of driver nodes in network clusters instead of considering them individually according to their centralities. This outcome suggests considering distributed control systems instead of nodal control for cancer metabolic networks, leading to a new strategy in the field of network medicine.

  5. A cloud-based data network approach for translational cancer research.

    Science.gov (United States)

    Xing, Wei; Tsoumakos, Dimitrios; Ghanem, Moustafa

    2015-01-01

    We develop a new model and associated technology for constructing and managing self-organizing data to support translational cancer research studies. We employ a semantic content network approach to address the challenges of managing cancer research data. Such data is heterogeneous, large, decentralized, growing and continually being updated. Moreover, the data originates from different information sources that may be partially overlapping, creating redundancies as well as contradictions and inconsistencies. Building on the advantages of elasticity of cloud computing, we deploy the cancer data networks on top of the CELAR Cloud platform to enable more effective processing and analysis of Big cancer data.

  6. Immunoregulatory network and cancer-associated genes: molecular links and relevance to aging

    Directory of Open Access Journals (Sweden)

    Robi Tacutu

    2011-09-01

    Full Text Available Although different aspects of cancer immunity are a subject of intensive investigation, an integrative view on the possible molecular links between immunoregulators and cancer-associated genes has not yet been fully considered. In an attempt to get more insights on the problem, we analyzed these links from a network perspective. We showed that the immunoregulators could be organized into a miRNA-regulated PPI network-the immunoregulatory network. This network has numerous links with cancer, including (i cancerassociated immunoregulators, (ii direct and indirect protein-protein interactions (through the common protein partners, and (iii common miRNAs. These links may largely determine the interactions between the host's immunity and cancer, supporting the possibility for co-expression and post-transcriptional co-regulation of immunoregulatory and cancer genes. In addition, the connection between immunoregulation and cancer may lie within the realm of cancer-predisposing conditions, such as chronic inflammation and fibroproliferative repair. A gradual, age-related deterioration of the integrity and functionality of the immunoregulaory network could contribute to impaired immunity and generation of cancer-predisposing conditions.

  7. Bayesian network approach for modeling local failure in lung cancer

    Science.gov (United States)

    Oh, Jung Hun; Craft, Jeffrey; Al-Lozi, Rawan; Vaidya, Manushka; Meng, Yifan; Deasy, Joseph O; Bradley, Jeffrey D; Naqa, Issam El

    2011-01-01

    Locally advanced non-small cell lung cancer (NSCLC) patients suffer from a high local failure rate following radiotherapy. Despite many efforts to develop new dose-volume models for early detection of tumor local failure, there was no reported significant improvement in their application prospectively. Based on recent studies of biomarker proteins’ role in hypoxia and inflammation in predicting tumor response to radiotherapy, we hypothesize that combining physical and biological factors with a suitable framework could improve the overall prediction. To test this hypothesis, we propose a graphical Bayesian network framework for predicting local failure in lung cancer. The proposed approach was tested using two different datasets of locally advanced NSCLC patients treated with radiotherapy. The first dataset was collected retrospectively, which is comprised of clinical and dosimetric variables only. The second dataset was collected prospectively in which in addition to clinical and dosimetric information, blood was drawn from the patients at various time points to extract candidate biomarkers as well. Our preliminary results show that the proposed method can be used as an efficient method to develop predictive models of local failure in these patients and to interpret relationships among the different variables in the models. We also demonstrate the potential use of heterogenous physical and biological variables to improve the model prediction. With the first dataset, we achieved better performance compared with competing Bayesian-based classifiers. With the second dataset, the combined model had a slightly higher performance compared to individual physical and biological models, with the biological variables making the largest contribution. Our preliminary results highlight the potential of the proposed integrated approach for predicting post-radiotherapy local failure in NSCLC patients. PMID:21335651

  8. Breast Cancer Diagnosis using Artificial Neural Networks with Extreme Learning Techniques

    Directory of Open Access Journals (Sweden)

    Chandra Prasetyo Utomo

    2014-07-01

    Full Text Available Breast cancer is the second cause of dead among women. Early detection followed by appropriate cancer treatment can reduce the deadly risk. Medical professionals can make mistakes while identifying a disease. The help of technology such as data mining and machine learning can substantially improve the diagnosis accuracy. Artificial Neural Networks (ANN has been widely used in intelligent breast cancer diagnosis. However, the standard Gradient-Based Back Propagation Artificial Neural Networks (BP ANN has some limitations. There are parameters to be set in the beginning, long time for training process, and possibility to be trapped in local minima. In this research, we implemented ANN with extreme learning techniques for diagnosing breast cancer based on Breast Cancer Wisconsin Dataset. Results showed that Extreme Learning Machine Neural Networks (ELM ANN has better generalization classifier model than BP ANN. The development of this technique is promising as intelligent component in medical decision support systems.

  9. Interactive Naive Bayesian network: A new approach of constructing gene-gene interaction network for cancer classification.

    Science.gov (United States)

    Tian, Xue W; Lim, Joon S

    2015-01-01

    Naive Bayesian (NB) network classifier is a simple and well-known type of classifier, which can be easily induced from a DNA microarray data set. However, a strong conditional independence assumption of NB network sometimes can lead to weak classification performance. In this paper, we propose a new approach of interactive naive Bayesian (INB) network to weaken the conditional independence of NB network and classify cancers using DNA microarray data set. We selected the differently expressed genes (DEGs) to reduce the dimension of the microarray data set. Then, an interactive parent which has the biggest influence among all DEGs is searched for each DEG. And then we calculate a weight to represent the interactive relationship between a DEG and its parent. Finally, the gene-gene interaction network is constructed. We experimentally test the INB network in terms of classification accuracy using leukemia and colon DNA microarray data sets, then we compare it with the NB network. The INB network can get higher classification accuracies than NB network. And INB network can show the gene-gene interactions visually.

  10. Comprehensive assessment and network analysis of the emerging genetic susceptibility landscape of prostate cancer.

    Science.gov (United States)

    Hicks, Chindo; Miele, Lucio; Koganti, Tejaswi; Vijayakumar, Srinivasan

    2013-01-01

    Recent advances in high-throughput genotyping have made possible identification of genetic variants associated with increased risk of developing prostate cancer using genome-wide associations studies (GWAS). However, the broader context in which the identified genetic variants operate is poorly understood. Here we present a comprehensive assessment, network, and pathway analysis of the emerging genetic susceptibility landscape of prostate cancer. We created a comprehensive catalog of genetic variants and associated genes by mining published reports and accompanying websites hosting supplementary data on GWAS. We then performed network and pathway analysis using single nucleotide polymorphism (SNP)-containing genes to identify gene regulatory networks and pathways enriched for genetic variants. We identified multiple gene networks and pathways enriched for genetic variants including IGF-1, androgen biosynthesis and androgen signaling pathways, and the molecular mechanisms of cancer. The results provide putative functional bridges between GWAS findings and gene regulatory networks and biological pathways.

  11. A network of cancer genes with co-occurring and anti-co-occurring mutations.

    Directory of Open Access Journals (Sweden)

    Qinghua Cui

    Full Text Available Certain cancer genes contribute to tumorigenesis in a manner of either co-occurring or mutually exclusive (anti-co-occurring mutations; however, the global picture of when, where and how these functional interactions occur remains unclear. This study presents a systems biology approach for this purpose. After applying this method to cancer gene mutation data generated from large-scale and whole genome sequencing of cancer samples, a network of cancer genes with co-occurring and anti-co-occurring mutations was constructed. Analysis of this network revealed that genes with co-occurring mutations prefer direct signaling transductions and that the interaction relations among cancer genes in the network are related with their functional similarity. It was also revealed that genes with co-occurring mutations tend to have similar mutation frequencies, whereas genes with anti-co-occurring mutations tend to have different mutation frequencies. Moreover, genes with more exons tend to have more co-occurring mutations with other genes, and genes having lower local coherent network structures tend to have higher mutation frequency. The network showed two complementary modules that have distinct functions and have different roles in tumorigenesis. This study presented a framework for the analysis of cancer genome sequencing outputs. The presented data and uncovered patterns are helpful for understanding the contribution of gene mutations to tumorigenesis and valuable in the identification of key biomarkers and drug targets for cancer.

  12. Primary hepatic lymphoma: a retrospective, multicenter Rare Cancer Network study

    Directory of Open Access Journals (Sweden)

    Gamze Ugurluer

    2016-10-01

    Full Text Available Primary hepatic lymphoma (PHL is a rare malignancy. We aimed to assess the clinical profile, outcome and prognostic factors in PHL through the Rare Cancer Network (RCN. A retrospective analysis of 41 patients was performed. Median age was 62 years (range, 23- 86 years with a male-to-female ratio of 1.9:1.0. Abdominal pain or discomfort was the most common presenting symptom. Regarding B-symptoms, 19.5% of patients had fever, 17.1% weight loss, and 9.8% night sweats. The most common radiological presentation was multiple lesions. Liver function tests were elevated in 56.1% of patients. The most common histopathological diagnosis was diffuse large B-cell lymphoma (65.9%. Most of the patients received Chop-like (cyclophosphamide, doxorubicin, vincristine, and prednisone regimens; 4 patients received radiotherapy (dose range, 30.6-40.0 Gy. Median survival was 163 months, and 5- and 10-year overall survival rates were 77 and 59%, respectively. The 5- and 10-year disease-free and lymphoma-specific survival rates were 69, 56, 87 and 70%, respectively. Multivariate analysis revealed that fever, weight loss, and normal hemoglobin level were the independent factors influencing the outcome. In this retrospective multicenter RCN study, patients with PHL had a relatively better prognosis than that reported elsewhere. Multicenter prospective studies are still warranted to establish treatment guidelines, outcome, and prognostic factors.

  13. Primary Hepatic Lymphoma: A Retrospective, Multicenter Rare Cancer Network Study

    Science.gov (United States)

    Ugurluer, Gamze; Miller, Robert C.; Li, Yexiong; Thariat, Juliette; Ghadjar, Pirus; Schick, Ulrike; Ozsahin, Mahmut

    2016-01-01

    Primary hepatic lymphoma (PHL) is a rare malignancy. We aimed to assess the clinical profile, outcome and prognostic factors in PHL through the Rare Cancer Network (RCN). A retrospective analysis of 41 patients was performed. Median age was 62 years (range, 23-86 years) with a male-to-female ratio of 1.9:1.0. Abdominal pain or discomfort was the most common presenting symptom. Regarding B-symptoms, 19.5% of patients had fever, 17.1% weight loss, and 9.8% night sweats. The most common radiological presentation was multiple lesions. Liver function tests were elevated in 56.1% of patients. The most common histopathological diagnosis was diffuse large B-cell lymphoma (65.9%). Most of the patients received Chop-like (cyclophosphamide, doxorubicin, vincristine, and prednisone) regimens; 4 patients received radiotherapy (dose range, 30.6-40.0 Gy). Median survival was 163 months, and 5- and 10-year overall survival rates were 77 and 59%, respectively. The 5- and 10-year disease-free and lymphoma-specific survival rates were 69, 56, 87 and 70%, respectively. Multivariate analysis revealed that fever, weight loss, and normal hemoglobin level were the independent factors influencing the outcome. In this retrospective multicenter RCN study, patients with PHL had a relatively better prognosis than that reported elsewhere. Multicenter prospective studies are still warranted to establish treatment guidelines, outcome, and prognostic factors. PMID:27746888

  14. Classification of breast cancer cytological specimen using convolutional neural network

    Science.gov (United States)

    Żejmo, Michał; Kowal, Marek; Korbicz, Józef; Monczak, Roman

    2017-01-01

    The paper presents a deep learning approach for automatic classification of breast tumors based on fine needle cytology. The main aim of the system is to distinguish benign from malignant cases based on microscopic images. Experiment was carried out on cytological samples derived from 50 patients (25 benign cases + 25 malignant cases) diagnosed in Regional Hospital in Zielona Góra. To classify microscopic images, we used convolutional neural networks (CNN) of two types: GoogLeNet and AlexNet. Due to the very large size of images of cytological specimen (on average 200000 × 100000 pixels), they were divided into smaller patches of size 256 × 256 pixels. Breast cancer classification usually is based on morphometric features of nuclei. Therefore, training and validation patches were selected using Support Vector Machine (SVM) so that suitable amount of cell material was depicted. Neural classifiers were tuned using GPU accelerated implementation of gradient descent algorithm. Training error was defined as a cross-entropy classification loss. Classification accuracy was defined as the percentage ratio of successfully classified validation patches to the total number of validation patches. The best accuracy rate of 83% was obtained by GoogLeNet model. We observed that more misclassified patches belong to malignant cases.

  15. Understanding cancer complexome using networks, spectral graph theory and multilayer framework

    Science.gov (United States)

    Rai, Aparna; Pradhan, Priodyuti; Nagraj, Jyothi; Lohitesh, K.; Chowdhury, Rajdeep; Jalan, Sarika

    2017-02-01

    Cancer complexome comprises a heterogeneous and multifactorial milieu that varies in cytology, physiology, signaling mechanisms and response to therapy. The combined framework of network theory and spectral graph theory along with the multilayer analysis provides a comprehensive approach to analyze the proteomic data of seven different cancers, namely, breast, oral, ovarian, cervical, lung, colon and prostate. Our analysis demonstrates that the protein-protein interaction networks of the normal and the cancerous tissues associated with the seven cancers have overall similar structural and spectral properties. However, few of these properties implicate unsystematic changes from the normal to the disease networks depicting difference in the interactions and highlighting changes in the complexity of different cancers. Importantly, analysis of common proteins of all the cancer networks reveals few proteins namely the sensors, which not only occupy significant position in all the layers but also have direct involvement in causing cancer. The prediction and analysis of miRNAs targeting these sensor proteins hint towards the possible role of these proteins in tumorigenesis. This novel approach helps in understanding cancer at the fundamental level and provides a clue to develop promising and nascent concept of single drug therapy for multiple diseases as well as personalized medicine.

  16. Formal modeling and analysis of ER-α associated Biological Regulatory Network in breast cancer

    NARCIS (Netherlands)

    Khalid, Samra; Hanif, Rumeza; Tareen, Samar H K; Siddiqa, Amnah; Bibi, Zurah; Ahmad, Jamil

    2016-01-01

    BACKGROUND: Breast cancer (BC) is one of the leading cause of death among females worldwide. The increasing incidence of BC is due to various genetic and environmental changes which lead to the disruption of cellular signaling network(s). It is a complex disease in which several interlinking signali

  17. 'Connecting tracks': exploring the roles of an Aboriginal women's cancer support network.

    Science.gov (United States)

    Cuesta-Briand, Beatriz; Bessarab, Dawn; Shahid, Shaouli; Thompson, Sandra C

    2016-11-01

    Aboriginal Australians are at higher risk of developing certain types of cancer and, once diagnosed, they have poorer outcomes than their non-Aboriginal counterparts. Lower access to cancer screening programmes, deficiencies in treatment and cultural barriers contribute to poor outcomes. Additional logistical factors affecting those living in rural areas compound these barriers. Cancer support groups have positive effects on people affected by cancer; however, there is limited evidence on peer-support programmes for Aboriginal cancer patients in Australia. This paper explores the roles played by an Aboriginal women's cancer support network operating in a regional town in Western Australia. Data were collected through semi-structured interviews with 24 participants including Aboriginal and mainstream healthcare service providers, and network members and clients. Interviews were audiotaped and transcribed verbatim. Transcripts were subjected to inductive thematic analysis. Connecting and linking people and services was perceived as the main role of the network. This role had four distinct domains: (i) facilitating access to cancer services; (ii) fostering social interaction; (iii) providing a culturally safe space; and (iv) building relationships with other agencies. Other network roles included providing emotional and practical support, delivering health education and facilitating engagement in cancer screening initiatives. Despite the network's achievements, unresolved tensions around role definition negatively impacted on the working relationship between the network and mainstream service providers, and posed a threat to the network's sustainability. Different perspectives need to be acknowledged and addressed in order to build strong, effective partnerships between service providers and Aboriginal communities. Valuing and honouring the Aboriginal approaches and expertise, and adopting an intercultural approach are suggested as necessary to the way forward.

  18. Transduction motif analysis of gastric cancer based on a human signaling network

    Energy Technology Data Exchange (ETDEWEB)

    Liu, G.; Li, D.Z.; Jiang, C.S.; Wang, W. [Fuzhou General Hospital of Nanjing Command, Department of Gastroenterology, Fuzhou, China, Department of Gastroenterology, Fuzhou General Hospital of Nanjing Command, Fuzhou (China)

    2014-04-04

    To investigate signal regulation models of gastric cancer, databases and literature were used to construct the signaling network in humans. Topological characteristics of the network were analyzed by CytoScape. After marking gastric cancer-related genes extracted from the CancerResource, GeneRIF, and COSMIC databases, the FANMOD software was used for the mining of gastric cancer-related motifs in a network with three vertices. The significant motif difference method was adopted to identify significantly different motifs in the normal and cancer states. Finally, we conducted a series of analyses of the significantly different motifs, including gene ontology, function annotation of genes, and model classification. A human signaling network was constructed, with 1643 nodes and 5089 regulating interactions. The network was configured to have the characteristics of other biological networks. There were 57,942 motifs marked with gastric cancer-related genes out of a total of 69,492 motifs, and 264 motifs were selected as significantly different motifs by calculating the significant motif difference (SMD) scores. Genes in significantly different motifs were mainly enriched in functions associated with cancer genesis, such as regulation of cell death, amino acid phosphorylation of proteins, and intracellular signaling cascades. The top five significantly different motifs were mainly cascade and positive feedback types. Almost all genes in the five motifs were cancer related, including EPOR, MAPK14, BCL2L1, KRT18, PTPN6, CASP3, TGFBR2, AR, and CASP7. The development of cancer might be curbed by inhibiting signal transductions upstream and downstream of the selected motifs.

  19. Transduction motif analysis of gastric cancer based on a human signaling network

    Directory of Open Access Journals (Sweden)

    G. Liu

    2014-05-01

    Full Text Available To investigate signal regulation models of gastric cancer, databases and literature were used to construct the signaling network in humans. Topological characteristics of the network were analyzed by CytoScape. After marking gastric cancer-related genes extracted from the CancerResource, GeneRIF, and COSMIC databases, the FANMOD software was used for the mining of gastric cancer-related motifs in a network with three vertices. The significant motif difference method was adopted to identify significantly different motifs in the normal and cancer states. Finally, we conducted a series of analyses of the significantly different motifs, including gene ontology, function annotation of genes, and model classification. A human signaling network was constructed, with 1643 nodes and 5089 regulating interactions. The network was configured to have the characteristics of other biological networks. There were 57,942 motifs marked with gastric cancer-related genes out of a total of 69,492 motifs, and 264 motifs were selected as significantly different motifs by calculating the significant motif difference (SMD scores. Genes in significantly different motifs were mainly enriched in functions associated with cancer genesis, such as regulation of cell death, amino acid phosphorylation of proteins, and intracellular signaling cascades. The top five significantly different motifs were mainly cascade and positive feedback types. Almost all genes in the five motifs were cancer related, including EPOR, MAPK14, BCL2L1, KRT18, PTPN6, CASP3, TGFBR2, AR, and CASP7. The development of cancer might be curbed by inhibiting signal transductions upstream and downstream of the selected motifs.

  20. Social network characteristics and cervical cancer screening among Quechua women in Andean Peru.

    Science.gov (United States)

    Luque, John S; Opoku, Samuel; Ferris, Daron G; Guevara Condorhuaman, Wendy S

    2016-02-24

    Peru has high cervical cancer incidence and mortality rates compared to other Andean countries. Therefore, partnerships between governmental and international organizations have targeted rural areas of Peru to receive cervical cancer screening via outreach campaigns. Previous studies have found a relationship between a person's social networks and cancer screening behaviors. Screening outreach campaigns conducted by the nonprofit organization CerviCusco created an opportunity for a social network study to examine cervical cancer screening history and social network characteristics in a rural indigenous community that participated in these campaigns in 2012 and 2013. The aim of this study was to explore social network characteristics in this community related to receipt of cervical cancer screening following the campaigns. An egocentric social network questionnaire was used to collect cross-sectional network data on community participants. Each survey participant (ego) was asked to name six other women they knew (alters) and identify the nature of their relationship or tie (family, friend, neighbor, other), residential closeness (within 5 km), length of time known, frequency of communication, topics of conversation, and whether they lent money to the person, provided childcare or helped with transportation. In addition, each participant was asked to report the nature of the relationship between all alters identified (e.g., friend, family, or neighbor). Bivariate and multivariate analyses were used to explore the relationship between Pap test receipt at the CerviCusco outreach screening campaigns and social network characteristics. Bivariate results found significant differences in percentage of alter composition for neighbors and family, and for mean number of years known, mean density, and mean degree centrality between women who had received a Pap test (n = 19) compared to those who had not (n = 50) (p's social support for healthcare related decisions related to

  1. Semiclassical "Divide-and-Conquer" Method for Spectroscopic Calculations of High Dimensional Molecular Systems

    Science.gov (United States)

    Ceotto, Michele; Di Liberto, Giovanni; Conte, Riccardo

    2017-07-01

    A new semiclassical "divide-and-conquer" method is presented with the aim of demonstrating that quantum dynamics simulations of high dimensional molecular systems are doable. The method is first tested by calculating the quantum vibrational power spectra of water, methane, and benzene—three molecules of increasing dimensionality for which benchmark quantum results are available—and then applied to C60 , a system characterized by 174 vibrational degrees of freedom. Results show that the approach can accurately account for quantum anharmonicities, purely quantum features like overtones, and the removal of degeneracy when the molecular symmetry is broken.

  2. Application of Divide and Conquer Extended Genetic Algorithm to Tertiary Protein Structure of Chymotrypsin Inhibitor-2

    Directory of Open Access Journals (Sweden)

    A. Alfaro

    2006-01-01

    Full Text Available Determining the method by which a protein thermodynamically folds and unfolds in three-dimension is one of the most complex and least understood problems in modern biochemistry. Misfolded proteins have been recently linked to diseases including Amyotrophic Lateral Sclerosis and Alzheimer's disease. Because of the large number of parameters involved in defining the tertiary structure of proteins, based on free energy global minimisation, we have developed a new Divide and Conquer (DAC Extended Genetic Algorithm. The approach was applied to explore and verify the energy landscape of protein chymotrypsin inhibitor-2.

  3. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks | Center for Cancer Research

    Science.gov (United States)

    The purpose of this study was to develop a method of classifying cancers to specific diagnostic categories based on their gene expression signatures using artificial neural networks (ANNs). We trained the ANNs using the small, round blue-cell tumors (SRBCTs) as a model. These cancers belong to four distinct diagnostic categories and often present diagnostic dilemmas in clinical practice. The ANNs correctly classified all samples and identified the genes most relevant to the classification.

  4. Atlas of Cancer Signalling Network: a systems biology resource for integrative analysis of cancer data with Google Maps.

    Science.gov (United States)

    Kuperstein, I; Bonnet, E; Nguyen, H-A; Cohen, D; Viara, E; Grieco, L; Fourquet, S; Calzone, L; Russo, C; Kondratova, M; Dutreix, M; Barillot, E; Zinovyev, A

    2015-01-01

    Cancerogenesis is driven by mutations leading to aberrant functioning of a complex network of molecular interactions and simultaneously affecting multiple cellular functions. Therefore, the successful application of bioinformatics and systems biology methods for analysis of high-throughput data in cancer research heavily depends on availability of global and detailed reconstructions of signalling networks amenable for computational analysis. We present here the Atlas of Cancer Signalling Network (ACSN), an interactive and comprehensive map of molecular mechanisms implicated in cancer. The resource includes tools for map navigation, visualization and analysis of molecular data in the context of signalling network maps. Constructing and updating ACSN involves careful manual curation of molecular biology literature and participation of experts in the corresponding fields. The cancer-oriented content of ACSN is completely original and covers major mechanisms involved in cancer progression, including DNA repair, cell survival, apoptosis, cell cycle, EMT and cell motility. Cell signalling mechanisms are depicted in detail, together creating a seamless 'geographic-like' map of molecular interactions frequently deregulated in cancer. The map is browsable using NaviCell web interface using the Google Maps engine and semantic zooming principle. The associated web-blog provides a forum for commenting and curating the ACSN content. ACSN allows uploading heterogeneous omics data from users on top of the maps for visualization and performing functional analyses. We suggest several scenarios for ACSN application in cancer research, particularly for visualizing high-throughput data, starting from small interfering RNA-based screening results or mutation frequencies to innovative ways of exploring transcriptomes and phosphoproteomes. Integration and analysis of these data in the context of ACSN may help interpret their biological significance and formulate mechanistic hypotheses

  5. Boolean Network Model for Cancer Pathways: Predicting Carcinogenesis and Targeted Therapy Outcomes

    Science.gov (United States)

    Fumiã, Herman F.; Martins, Marcelo L.

    2013-01-01

    A Boolean dynamical system integrating the main signaling pathways involved in cancer is constructed based on the currently known protein-protein interaction network. This system exhibits stationary protein activation patterns – attractors – dependent on the cell's microenvironment. These dynamical attractors were determined through simulations and their stabilities against mutations were tested. In a higher hierarchical level, it was possible to group the network attractors into distinct cell phenotypes and determine driver mutations that promote phenotypic transitions. We find that driver nodes are not necessarily central in the network topology, but at least they are direct regulators of central components towards which converge or through which crosstalk distinct cancer signaling pathways. The predicted drivers are in agreement with those pointed out by diverse census of cancer genes recently performed for several human cancers. Furthermore, our results demonstrate that cell phenotypes can evolve towards full malignancy through distinct sequences of accumulated mutations. In particular, the network model supports routes of carcinogenesis known for some tumor types. Finally, the Boolean network model is employed to evaluate the outcome of molecularly targeted cancer therapies. The major find is that monotherapies were additive in their effects and that the association of targeted drugs is necessary for cancer eradication. PMID:23922675

  6. Impact of the Cancer Prevention and Control Research Network: Accelerating the Translation of Research Into Practice.

    Science.gov (United States)

    Ribisl, Kurt M; Fernandez, Maria E; Friedman, Daniela B; Hannon, Peggy A; Leeman, Jennifer; Moore, Alexis; Olson, Lindsay; Ory, Marcia; Risendal, Betsy; Sheble, Laura; Taylor, Vicky M; Williams, Rebecca S; Weiner, Bryan J

    2017-03-01

    The Cancer Prevention and Control Research Network (CPCRN) is a thematic network dedicated to accelerating the adoption of evidence-based cancer prevention and control practices in communities by advancing dissemination and implementation science. Funded by the Centers for Disease Control and Prevention and National Cancer Institute, CPCRN has operated at two levels: Each participating network center conducts research projects with primarily local partners as well as multicenter collaborative research projects with state and national partners. Through multicenter collaboration, thematic networks leverage the expertise, resources, and partnerships of participating centers to conduct research projects collectively that might not be feasible individually. Although multicenter collaboration is often advocated, it is challenging to promote and assess. Using bibliometric network analysis and other graphical methods, this paper describes CPCRN's multicenter publication progression from 2004 to 2014. Searching PubMed, Scopus, and Web of Science in 2014 identified 249 peer-reviewed CPCRN publications involving two or more centers out of 6,534 total. The research and public health impact of these multicenter collaborative projects initiated by CPCRN during that 10-year period were then examined. CPCRN established numerous workgroups around topics such as: 2-1-1, training and technical assistance, colorectal cancer control, federally qualified health centers, cancer survivorship, and human papillomavirus. This paper discusses the challenges that arise in promoting multicenter collaboration and the strategies that CPCRN uses to address those challenges. The lessons learned should broadly interest those seeking to promote multisite collaboration to address public health problems, such as cancer prevention and control.

  7. Artificial intelligence techniques for colorectal cancer drug metabolism: ontology and complex network.

    Science.gov (United States)

    Martínez-Romero, Marcos; Vázquez-Naya, José M; Rabuñal, Juan R; Pita-Fernández, Salvador; Macenlle, Ramiro; Castro-Alvariño, Javier; López-Roses, Leopoldo; Ulla, José L; Martínez-Calvo, Antonio V; Vázquez, Santiago; Pereira, Javier; Porto-Pazos, Ana B; Dorado, Julián; Pazos, Alejandro; Munteanu, Cristian R

    2010-05-01

    Colorectal cancer is one of the most frequent types of cancer in the world and generates important social impact. The understanding of the specific metabolism of this disease and the transformations of the specific drugs will allow finding effective prevention, diagnosis and treatment of the colorectal cancer. All the terms that describe the drug metabolism contribute to the construction of ontology in order to help scientists to link the correlated information and to find the most useful data about this topic. The molecular components involved in this metabolism are included in complex network such as metabolic pathways in order to describe all the molecular interactions in the colorectal cancer. The graphical method of processing biological information such as graphs and complex networks leads to the numerical characterization of the colorectal cancer drug metabolic network by using invariant values named topological indices. Thus, this method can help scientists to study the most important elements in the metabolic pathways and the dynamics of the networks during mutations, denaturation or evolution for any type of disease. This review presents the last studies regarding ontology and complex networks of the colorectal cancer drug metabolism and a basic topology characterization of the drug metabolic process sub-ontology from the Gene Ontology.

  8. Characterization of the Usage of the Serine Metabolic Network in Human Cancer

    Directory of Open Access Journals (Sweden)

    Mahya Mehrmohamadi

    2014-11-01

    Full Text Available The serine, glycine, one-carbon (SGOC metabolic network is implicated in cancer pathogenesis, but its general functions are unknown. We carried out a computational reconstruction of the SGOC network and then characterized its expression across thousands of cancer tissues. Pathways including methylation and redox metabolism exhibited heterogeneous expression indicating a strong context dependency of their usage in tumors. From an analysis of coexpression, simultaneous up- or downregulation of nucleotide synthesis, NADPH, and glutathione synthesis was found to be a common occurrence in all cancers. Finally, we developed a method to trace the metabolic fate of serine using stable isotopes, high-resolution mass spectrometry, and a mathematical model. Although the expression of single genes didn’t appear indicative of flux, the collective expression of several genes in a given pathway allowed for successful flux prediction. Altogether, these findings identify expansive and heterogeneous functions for the SGOC metabolic network in human cancer.

  9. Kinome-wide Decoding of Network-Attacking Mutations Rewiring Cancer Signaling

    DEFF Research Database (Denmark)

    Creixell, Pau; Schoof, Erwin M; Simpson, Craig D.

    2015-01-01

    Cancer cells acquire pathological phenotypes through accumulation of mutations that perturb signaling networks. However, global analysis of these events is currently limited. Here, we identify six types of network-attacking mutations (NAMs), including changes in kinase and SH2 modulation, network...... and experimentally validated several NAMs, including PKCγ M501I and PKD1 D665N, which encode specificity switches analogous to the appearance of kinases de novo within the kinome. We discover mutant molecular logic gates, a drift toward phospho-threonine signaling, weakening of phosphorylation motifs, and kinase......-inactivating hotspots in cancer. Our method pinpoints functional NAMs, scales with the complexity of cancer genomes and cell signaling, and may enhance our capability to therapeutically target tumor-specific networks....

  10. Involvement of the CREB5 regulatory network in colorectal cancer metastasis.

    Science.gov (United States)

    Qi, Lu; Ding, Yanqing

    2014-07-01

    The signal regulatory network involved in colorectal cancer metastasis is complicated and thus the search for key control steps in the network is of great significance for unraveling colorectal cancer metastasis mechanism and finding drug-target site. Previous studies suggested that CREB5 (cAMP responsive element binding protein 5) might play key role in the metastatic signal network of colorectal cancer. Through colorectal cancer expression profile and enriching analysis of the effect of CREB5 gene expression levels on colorectal cancer molecular events, we found that these molecular events are correlated with tumor metastasis. Based on the feature that CREB5 could combine with c-Jun to form heterodimer, together with enriched binding sites for transcription factor AP-1, we identified 16 genes which were up-regulated in the CREB5 high-expression group, contained AP-1 binding sites, and participated in cancer pathway. The molecular network involving these 16 genes, in particular, CSF1R, MMP9, PDGFRB, FIGF and IL6, regulates cell migration. Therefore, CREB5 might accelerate the metastasis of colorectal cancer by regulating these five key genes.

  11. Mucosal Kaposi sarcoma, a Rare Cancer Network study

    Directory of Open Access Journals (Sweden)

    Robert C. Miller

    2012-10-01

    Full Text Available Kaposi’s sarcoma (KS most often affect the skin but occasionally affect the mucosa of different anatomic sites. The management of mucosal KS is seldom described in the literature. Data from 15 eligible patients with mucosal KS treated between 1994 and 2008 in five institutions within three countries of the Rare Cancer Network group were collected. The inclusion criteria were as follows: age >16 years, confirmed pathological diagnosis, mucosal stages I and II, and a minimum of 6 months’ follow-up after treatment. Head and neck sites were the most common (66%. Eleven cases were HIV-positive. CD4 counts correlated with disease stage. Twelve patients had biopsy only while three patients underwent local resection. Radiotherapy (RT was delivered whatever their CD4 status was. Median total radiation dose was 16.2 Gy (0-45 delivered in median 17 days (0-40 with four patients receiving no RT. Six patients underwent chemotherapy and received from 1 to 11 cycles of various regimens namely vinblastin, caelyx, bleomycine, or interferon, whatever their CD4 counts was. Five-year disease free survival were 81.6% and 75.0% in patients undergoing RT or not, respectively. Median survival was 66.9 months. Radiation-induced toxicity was at worse grade 1-2 and was manageable whatever patients’ HIV status. This small series of mucosal KSs revealed that relatively low-dose RT is overall safe and efficient in HIV-positive and negative patients. Since there are distant relapses either in multicentric cutaneous or visceral forms in head and neck cases, the role of systemic treatments may be worth investigations in addition to RT of localized disease. Surgery may be used for symptomatic lesions, with caution given the risk of bleeding.

  12. Network Pharmacology of Ayurveda Formulation Triphala with Special Reference to Anti-Cancer Property.

    Science.gov (United States)

    Chandran, Uma; Mehendale, Neelay; Tillu, Girish; Patwardhan, Bhushan

    2015-01-01

    Network pharmacology is an emerging technique, which integrates systems biology and computational biology to study multi-component and multi-targeted formulations. Ayurveda, the traditional system of Indian medicine, uses intelligent formulations; however, their scientific rationale and mechanisms remain largely unexplored. This paper presents the potential of network pharmacology to understand the rationale of a commonly used Ayurveda formulation known as Triphala. We have developed pharmacology networks of Triphala based on the information gathered from different databases and using the software Cytoscape. The networks depict the interaction of bioactives with molecular targets and their relation with diseases, especially cancer. The network pharmacology analysis of Triphala has offered new relationships among bioactives, targets and putative applications of cancer etiology. This pioneering effort might open new possibilities to know pharmacodynamics of Ayurvedic drugs like Triphala and also help in the discovery of new leads and targets for various diseases.

  13. [Pathologists and the French network of expertise on rare cancers ENT: The REFCORpath].

    Science.gov (United States)

    Badoual, Cécile; Baglin, Anne-Catherine; Wassef, Michel; Thariat, Juliette; Reyt, Emile; Janot, François; Baujat, Bertrand

    2014-02-01

    Aerodigestive tract tumors are very diverse, either in terms of location, or histologically. Also, this heterogeneity poses particular problems for the histological diagnosis but also for the establishment of the most appropriate treatment. Thus, the network REFCOR (réseau d'expertise français sur les cancers ORL rares/French expert network on rare ENT cancers) was created to better understand these issues, by proposing an epidemiological and diagnostic approach with research collaborations. This network is dedicated to all primary malignant tumors of the salivary glands, ear, nasal cavity and sinuses and all head and neck malignancies other than conventional squamous cell carcinoma. The REFCORpath network consists of expert pathologists and offers, through a network of scanned images, a second opinion or even a third.

  14. Transcription factor-microRNA-target gene networks associated with ovarian cancer survival and recurrence.

    Science.gov (United States)

    Delfino, Kristin R; Rodriguez-Zas, Sandra L

    2013-01-01

    The identification of reliable transcriptome biomarkers requires the simultaneous consideration of regulatory and target elements including microRNAs (miRNAs), transcription factors (TFs), and target genes. A novel approach that integrates multivariate survival analysis, feature selection, and regulatory network visualization was used to identify reliable biomarkers of ovarian cancer survival and recurrence. Expression profiles of 799 miRNAs, 17,814 TFs and target genes and cohort clinical records on 272 patients diagnosed with ovarian cancer were simultaneously considered and results were validated on an independent group of 146 patients. Three miRNAs (hsa-miR-16, hsa-miR-22*, and ebv-miR-BHRF1-2*) were associated with both ovarian cancer survival and recurrence and 27 miRNAs were associated with either one hazard. Two miRNAs (hsa-miR-521 and hsa-miR-497) were cohort-dependent, while 28 were cohort-independent. This study confirmed 19 miRNAs previously associated with ovarian cancer and identified two miRNAs that have previously been associated with other cancer types. In total, the expression of 838 and 734 target genes and 12 and eight TFs were associated (FDR-adjusted P-value cancer survival and recurrence, respectively. Functional analysis highlighted the association between cellular and nucleotide metabolic processes and ovarian cancer. The more direct connections and higher centrality of the miRNAs, TFs and target genes in the survival network studied suggest that network-based approaches to prognosticate or predict ovarian cancer survival may be more effective than those for ovarian cancer recurrence. This study demonstrated the feasibility to infer reliable miRNA-TF-target gene networks associated with survival and recurrence of ovarian cancer based on the simultaneous analysis of co-expression profiles and consideration of the clinical characteristics of the patients.

  15. Transcription factor-microRNA-target gene networks associated with ovarian cancer survival and recurrence.

    Directory of Open Access Journals (Sweden)

    Kristin R Delfino

    Full Text Available The identification of reliable transcriptome biomarkers requires the simultaneous consideration of regulatory and target elements including microRNAs (miRNAs, transcription factors (TFs, and target genes. A novel approach that integrates multivariate survival analysis, feature selection, and regulatory network visualization was used to identify reliable biomarkers of ovarian cancer survival and recurrence. Expression profiles of 799 miRNAs, 17,814 TFs and target genes and cohort clinical records on 272 patients diagnosed with ovarian cancer were simultaneously considered and results were validated on an independent group of 146 patients. Three miRNAs (hsa-miR-16, hsa-miR-22*, and ebv-miR-BHRF1-2* were associated with both ovarian cancer survival and recurrence and 27 miRNAs were associated with either one hazard. Two miRNAs (hsa-miR-521 and hsa-miR-497 were cohort-dependent, while 28 were cohort-independent. This study confirmed 19 miRNAs previously associated with ovarian cancer and identified two miRNAs that have previously been associated with other cancer types. In total, the expression of 838 and 734 target genes and 12 and eight TFs were associated (FDR-adjusted P-value <0.05 with ovarian cancer survival and recurrence, respectively. Functional analysis highlighted the association between cellular and nucleotide metabolic processes and ovarian cancer. The more direct connections and higher centrality of the miRNAs, TFs and target genes in the survival network studied suggest that network-based approaches to prognosticate or predict ovarian cancer survival may be more effective than those for ovarian cancer recurrence. This study demonstrated the feasibility to infer reliable miRNA-TF-target gene networks associated with survival and recurrence of ovarian cancer based on the simultaneous analysis of co-expression profiles and consideration of the clinical characteristics of the patients.

  16. Identifying Cancer Subtypes from miRNA-TF-mRNA Regulatory Networks and Expression Data.

    Directory of Open Access Journals (Sweden)

    Taosheng Xu

    Full Text Available Identifying cancer subtypes is an important component of the personalised medicine framework. An increasing number of computational methods have been developed to identify cancer subtypes. However, existing methods rarely use information from gene regulatory networks to facilitate the subtype identification. It is widely accepted that gene regulatory networks play crucial roles in understanding the mechanisms of diseases. Different cancer subtypes are likely caused by different regulatory mechanisms. Therefore, there are great opportunities for developing methods that can utilise network information in identifying cancer subtypes.In this paper, we propose a method, weighted similarity network fusion (WSNF, to utilise the information in the complex miRNA-TF-mRNA regulatory network in identifying cancer subtypes. We firstly build the regulatory network where the nodes represent the features, i.e. the microRNAs (miRNAs, transcription factors (TFs and messenger RNAs (mRNAs and the edges indicate the interactions between the features. The interactions are retrieved from various interatomic databases. We then use the network information and the expression data of the miRNAs, TFs and mRNAs to calculate the weight of the features, representing the level of importance of the features. The feature weight is then integrated into a network fusion approach to cluster the samples (patients and thus to identify cancer subtypes. We applied our method to the TCGA breast invasive carcinoma (BRCA and glioblastoma multiforme (GBM datasets. The experimental results show that WSNF performs better than the other commonly used computational methods, and the information from miRNA-TF-mRNA regulatory network contributes to the performance improvement. The WSNF method successfully identified five breast cancer subtypes and three GBM subtypes which show significantly different survival patterns. We observed that the expression patterns of the features in some mi

  17. Application of artificial neural network model combined with four biomarkers in auxiliary diagnosis of lung cancer.

    Science.gov (United States)

    Duan, Xiaoran; Yang, Yongli; Tan, Shanjuan; Wang, Sihua; Feng, Xiaolei; Cui, Liuxin; Feng, Feifei; Yu, Songcheng; Wang, Wei; Wu, Yongjun

    2017-08-01

    The purpose of the study was to explore the application of artificial neural network model in the auxiliary diagnosis of lung cancer and compare the effects of back-propagation (BP) neural network with Fisher discrimination model for lung cancer screening by the combined detections of four biomarkers of p16, RASSF1A and FHIT gene promoter methylation levels and the relative telomere length. Real-time quantitative methylation-specific PCR was used to detect the levels of three-gene promoter methylation, and real-time PCR method was applied to determine the relative telomere length. BP neural network and Fisher discrimination analysis were used to establish the discrimination diagnosis model. The levels of three-gene promoter methylation in patients with lung cancer were significantly higher than those of the normal controls. The values of Z(P) in two groups were 2.641 (0.008), 2.075 (0.038) and 3.044 (0.002), respectively. The relative telomere lengths of patients with lung cancer (0.93 ± 0.32) were significantly lower than those of the normal controls (1.16 ± 0.57), t = 4.072, P neural network were 0.670 (0.569-0.761) and 0.760 (0.664-0.840). The AUC of BP neural network was higher than that of Fisher discrimination analysis, and Z(P) was 0.76. Four biomarkers are associated with lung cancer. BP neural network model for the prediction of lung cancer is better than Fisher discrimination analysis, and it can provide an excellent and intelligent diagnosis tool for lung cancer.

  18. Classification of Cancer Gene Selection Using Random Forest and Neural Network Based Ensemble Classifier

    Directory of Open Access Journals (Sweden)

    Jogendra Kushwah

    2013-06-01

    Full Text Available The free radical gene classification of cancer diseases is challenging job in biomedical data engineering. The improving of classification of gene selection of cancer diseases various classifier are used, but the classification of classifier are not validate. So ensemble classifier is used for cancer gene classification using neural network classifier with random forest tree. The random forest tree is ensembling technique of classifier in this technique the number of classifier ensemble of their leaf node of class of classifier. In this paper we combined neural network with random forest ensemble classifier for classification of cancer gene selection for diagnose analysis of cancer diseases. The proposed method is different from most of the methods of ensemble classifier, which follow an input output paradigm of neural network, where the members of the ensemble are selected from a set of neural network classifier. the number of classifiers is determined during the rising procedure of the forest. Furthermore, the proposed method produces an ensemble not only correct, but also assorted, ensuring the two important properties that should characterize an ensemble classifier. For empirical evaluation of our proposed method we used UCI cancer diseases data set for classification. Our experimental result shows that better result in compression of random forest tree classification.

  19. Incorporating gene co-expression network in identification of cancer prognosis markers

    Directory of Open Access Journals (Sweden)

    Li Yang

    2010-05-01

    Full Text Available Abstract Background Extensive biomedical studies have shown that clinical and environmental risk factors may not have sufficient predictive power for cancer prognosis. The development of high-throughput profiling technologies makes it possible to survey the whole genome and search for genomic markers with predictive power. Many existing studies assume the interchangeability of gene effects and ignore the coordination among them. Results We adopt the weighted co-expression network to describe the interplay among genes. Although there are several different ways of defining gene networks, the weighted co-expression network may be preferred because of its computational simplicity, satisfactory empirical performance, and because it does not demand additional biological experiments. For cancer prognosis studies with gene expression measurements, we propose a new marker selection method that can properly incorporate the network connectivity of genes. We analyze six prognosis studies on breast cancer and lymphoma. We find that the proposed approach can identify genes that are significantly different from those using alternatives. We search published literature and find that genes identified using the proposed approach are biologically meaningful. In addition, they have better prediction performance and reproducibility than genes identified using alternatives. Conclusions The network contains important information on the functionality of genes. Incorporating the network structure can improve cancer marker identification.

  20. Evaluation of a community-academic partnership: lessons from Latinos in a network for cancer control.

    Science.gov (United States)

    Corbin, J Hope; Fernandez, Maria E; Mullen, Patricia D

    2015-05-01

    Established in 2002, Latinos in a Network for Cancer Control is a community-academic network supported by the Centers for Disease Control and Prevention and the National Cancer Institute. The network includes >130 individuals from 65 community and academic organizations committed to reducing cancer-related health disparities. Using an empirically derived systems model--the Bergen Model of Collaborative Functioning--as the analytic frame, we interviewed 19 partners to identify challenges and successful processes. Findings indicated that sustained partner interaction created "meaningful relationships" that were routinely called on for collaboration. The leadership was regarded positively on vision, charisma, and capacity. Limitations included overreliance on a single leader. Suggestions supported more delegation of decision making, consistent communication, and more equitable resource distribution. The study highlighted new insights into dynamics of collaboration: Greater inclusiveness of inputs (partners, finances, mission) and loosely defined roles and structure produced strong connections but less network-wide productivity (output). Still, this profile enabled the creation of more tightly defined and highly productive subgroups, with clear goals and roles but less inclusive of inputs than the larger network. Important network outputs included practice-based research publications, cancer control intervention materials, and training to enhance the use of evidence-based interventions, as well as continued and diversified funding.

  1. Comparison of gene regulatory networks of benign and malignant breast cancer samples with normal samples.

    Science.gov (United States)

    Chen, D B; Yang, H J

    2014-11-11

    The aim of this study was to explain the pathogenesis and deterioration process of breast cancer. Breast cancer expression profile data GSE27567 was downloaded from the Gene Expression Omnibus (GEO) database, and breast cancer-related genes were extracted from databases, including Cancer-Resource and Online Mendelian Inheritance In Man (OMIM). Next, h17 transcription factor data were obtained from the University of California, Santa Cruz. Database for Annotation, Visualization, and Integrated Discovery (DAVID)-enrichment analysis was applied and gene-regulatory networks were constructed by double-two-way t-tests in 3 states, including normal, benign, and malignant. Furthermore, network topological properties were compared between 2 states, and breast cancer-related bub genes were ranked according to their different degrees between each of the two states. A total of 2380 breast cancer-related genes and 215 transcription factors were screened by exploring databases; the genes were mainly enriched in their functions, such as cell apoptosis and proliferation, and pathways, such as p53 signaling and apoptosis, which were related with carcinogenesis. In addition, gene-regulatory networks in the 3 conditions were constructed. By comparing their network topological properties, we found that there is a larger transition of differences between malignant and benign breast cancer. Moreover, 8 hub genes (YBX1, ZFP36, YY1, XRCC5, XRCC4, ZFHX3, ZMAT3, and XPC) were identified in the top 10 genes ranked by different degrees. Through comparative analysis of gene-regulation networks, we identified the link between related genes and the pathogenesis of breast cancer. However, further experiments are needed to confirm our results.

  2. [A Study on the Knowledge Structure of Cancer Survivors based on Social Network Analysis].

    Science.gov (United States)

    Kwon, Sun Young; Bae, Ka Ryeong

    2016-02-01

    The purpose of this study was to identify the knowledge structure of cancer survivors. For data, 1099 articles were collected, with 365 keywords as a Noun phrase extracted from the articles and standardized for analyzing. Co-occurrence matrix were generated via a cosine similarity measure, and then the network analysis and visualization using PFNet and NodeXL were applied to visualize intellectual interchanges among keywords. According to the result of the content analysis and the cluster analysis of author keywords from cancer survivors articles, keywords such as 'quality of life', 'breast neoplasms', 'cancer survivors', 'neoplasms', 'exercise' had a high degree centrality. The 9 most important research topics concerning cancer survivors were 'cancer-related symptoms and nursing', 'cancer treatment-related issues', 'late effects', 'psychosocial issues', 'healthy living managements', 'social supports', 'palliative cares', 'research methodology', and 'research participants'. Through this study, the knowledge structure of cancer survivors was identified. The 9 topics identified in this study can provide useful research direction for the development of nursing in cancer survivor research areas. The Network analysis used in this study will be useful for identifying the knowledge structure and identifying general views and current cancer survivor research trends.

  3. Artificial neural networks as classification and diagnostic tools for lymph node-negative breast cancers

    Energy Technology Data Exchange (ETDEWEB)

    Eswari J, Satya; Chandrakar, Neha [National Institute of Technology Raipur, Raipur (India)

    2016-04-15

    Artificial neural networks (ANNs) can be used to develop a technique to classify lymph node negative breast cancer that is prone to distant metastases based on gene expression signatures. The neural network used is a multilayered feed forward network that employs back propagation algorithm. Once trained with DNA microarraybased gene expression profiles of genes that were predictive of distant metastasis recurrence of lymph node negative breast cancer, the ANNs became capable of correctly classifying all samples and recognizing the genes most appropriate to the classification. To test the ability of the trained ANN models in recognizing lymph node negative breast cancer, we analyzed additional idle samples that were not used beforehand for the training procedure and obtained the correctly classified result in the validation set. For more substantial result, bootstrapping of training and testing dataset was performed as external validation. This study illustrates the potential application of ANN for breast tumor diagnosis and the identification of candidate targets in patients for therapy.

  4. Haploinsufficiency networks identify targetable patterns of allelic deficiency in low mutation ovarian cancer

    Science.gov (United States)

    Delaney, Joe Ryan; Patel, Chandni B.; Willis, Katelyn McCabe; Haghighiabyaneh, Mina; Axelrod, Joshua; Tancioni, Isabelle; Lu, Dan; Bapat, Jaidev; Young, Shanique; Cadassou, Octavia; Bartakova, Alena; Sheth, Parthiv; Haft, Carley; Hui, Sandra; Saenz, Cheryl; Schlaepfer, David D.; Harismendy, Olivier; Stupack, Dwayne G.

    2017-01-01

    Identification of specific oncogenic gene changes has enabled the modern generation of targeted cancer therapeutics. In high-grade serous ovarian cancer (OV), the bulk of genetic changes is not somatic point mutations, but rather somatic copy-number alterations (SCNAs). The impact of SCNAs on tumour biology remains poorly understood. Here we build haploinsufficiency network analyses to identify which SCNA patterns are most disruptive in OV. Of all KEGG pathways (N=187), autophagy is the most significantly disrupted by coincident gene deletions. Compared with 20 other cancer types, OV is most severely disrupted in autophagy and in compensatory proteostasis pathways. Network analysis prioritizes MAP1LC3B (LC3) and BECN1 as most impactful. Knockdown of LC3 and BECN1 expression confers sensitivity to cells undergoing autophagic stress independent of platinum resistance status. The results support the use of pathway network tools to evaluate how the copy-number landscape of a tumour may guide therapy. PMID:28198375

  5. LAMBERSART "LES CONQUERANTS" (DEULE VALLEY, NORTH OF FRANCE) : A WEICHSELIAN EARLY-PLENIGLACIAL SLOPE-BOTTOM VALLEY TRANSITION

    NARCIS (Netherlands)

    Deschodt, Laurent; Munaut, Andre-Valentin; Limondin-Lozouet, Nicole; Boulen, Muriel

    2008-01-01

    The Lambersart "les Conquerants" trench sequence is made of a Shelly loam topped by coarse alluviums. The whole is covered by several meters thick pleniglacial loess. The palynological and malacological data shows that this Shelly loam deposit occured during Early Glacial, in cold and moist conditio

  6. Network Medicine Strikes a Blow against Breast Cancer

    DEFF Research Database (Denmark)

    Erler, Janine Terra; Linding, Rune

    2012-01-01

    Drug development for complex diseases is shifting from targeting individual proteins or genes to systems-based attacks targeting dynamic network states. Lee et al. now reveal how the progressive rewiring of a signaling network over time following EGF receptor inhibition leaves triple-negative...

  7. Artificial neural network in studying factors of hepatic cancer recurrence after hepatectomy

    Institute of Scientific and Technical Information of China (English)

    HE Jia; HE Xian-min; ZHANG Zhi-jian

    2002-01-01

    Objective: To explore the affecting factors of liver cancer recurrence after hepatectomy. Methods:The BP artificial neural network - Cox regression was introduced to analyze the factors of recurrence in1 457 patients. Results: The affecting factors statistically significant to liver cancer prognosis was selected.There were 18 factors to be selected by uni-factor analysis, and 9 factors to be selected by multi-factor analysis. Conclusion: The 9 factors selected can be used as important indexes to evaluate the recurrence of liver cancer after hepatectomy. The artificial neural network is a better method to analyze the clinical data, which provides scientific and objective data for evaluating prognosis of liver cancer.

  8. A network medicine approach to build a comprehensive atlas for the prognosis of human cancer.

    Science.gov (United States)

    Zhang, Fan; Ren, Chunyan; Lau, Kwun Kit; Zheng, Zihan; Lu, Geming; Yi, Zhengzi; Zhao, Yongzhong; Su, Fei; Zhang, Shaojun; Zhang, Bin; Sobie, Eric A; Zhang, Weijia; Walsh, Martin J

    2016-11-01

    The Cancer Genome Atlas project has generated multi-dimensional and highly integrated genomic data from a large number of patient samples with detailed clinical records across many cancer types, but it remains unclear how to best integrate the massive amount of genomic data into clinical practice. We report here our methodology to build a multi-dimensional subnetwork atlas for cancer prognosis to better investigate the potential impact of multiple genetic and epigenetic (gene expression, copy number variation, microRNA expression and DNA methylation) changes on the molecular states of networks that in turn affects complex cancer survivorship. We uncover an average of 38 novel subnetworks in the protein-protein interaction network that correlate with prognosis across four prominent cancer types. The clinical utility of these subnetwork biomarkers was further evaluated by prognostic impact evaluation, functional enrichment analysis, drug target annotation, tumor stratification and independent validation. Some pathways including the dynactin, cohesion and pyruvate dehydrogenase-related subnetworks are identified as promising new targets for therapy in specific cancer types. In conclusion, this integrative analysis of existing protein interactome and cancer genomics data allows us to systematically dissect the molecular mechanisms that underlie unexpected outcomes for cancer, which could be used to better understand and predict clinical outcomes, optimize treatment and to provide new opportunities for developing therapeutics related to the subnetworks identified.

  9. Understanding cancer networks better to implement them more effectively: a mixed methods multi-case study.

    Science.gov (United States)

    Tremblay, Dominique; Touati, Nassera; Roberge, Danièle; Breton, Mylaine; Roch, Geneviève; Denis, Jean-Louis; Candas, Bernard; Francoeur, Danièle

    2016-03-21

    Managed cancer networks are widely promoted in national cancer control programs as an organizational form that enables integrated care as well as enhanced patient outcomes. While national programs are set by policy-makers, the detailed implementation of networks is delegated at the service delivery and institutional levels. It is likely that the capacity to ensure more integrated cancer services requires multi-level governance processes responsive to the strengths and limitations of the contexts and capable of supporting network-based working. Based on an empirical case, this study aims to analyze the implementation of a mandated cancer network, focusing on governance and health services integration as core concepts in the study. This nested multi-case study uses mixed methods to explore the implementation of a mandated cancer network in Quebec, a province of Canada. The case is the National Cancer Network (NCN) subdivided into three micro-cases, each defined by the geographic territory of a health and social services region. For each region, two local health services centers (LHSCs) are selected based on their differences with respect to determining characteristics. Qualitative data will be collected from various sources using three strategies: review of documents, focus groups, and semi-directed interviews with stakeholders. The qualitative data will be supplemented with a survey that will measure the degree of integration as a proxy for implementation of the NCN. A score will be constructed, and then triangulated with the qualitative data, which will have been subjected to content analysis. Qualitative, quantitative, and mixed methods data will be interpreted within and across cases in order to identify governance patterns similarities and differences and degree of integration in contexts. This study is designed to inform decision-making to develop more effective network implementation strategies by thoroughly describing multi-level governance processes of a

  10. System-scale network modeling of cancer using EPoC.

    Science.gov (United States)

    Abenius, Tobias; Jörnsten, Rebecka; Kling, Teresia; Schmidt, Linnéa; Sánchez, José; Nelander, Sven

    2012-01-01

    One of the central problems of cancer systems biology is to understand the complex molecular changes of cancerous cells and tissues, and use this understanding to support the development of new targeted therapies. EPoC (Endogenous Perturbation analysis of Cancer) is a network modeling technique for tumor molecular profiles. EPoC models are constructed from combined copy number aberration (CNA) and mRNA data and aim to (1) identify genes whose copy number aberrations significantly affect target mRNA expression and (2) generate markers for long- and short-term survival of cancer patients. Models are constructed by a combination of regression and bootstrapping methods. Prognostic scores are obtained from a singular value decomposition of the networks. We have previously analyzed the performance of EPoC using glioblastoma data from The Cancer Genome Atlas (TCGA) consortium, and have shown that resulting network models contain both known and candidate disease-relevant genes as network hubs, as well as uncover predictors of patient survival. Here, we give a practical guide how to perform EPoC modeling in practice using R, and present a set of alternative modeling frameworks.

  11. Kinome-wide Decoding of Network-Attacking Mutations Rewiring Cancer Signaling

    Science.gov (United States)

    Creixell, Pau; Schoof, Erwin M.; Simpson, Craig D.; Longden, James; Miller, Chad J.; Lou, Hua Jane; Perryman, Lara; Cox, Thomas R.; Zivanovic, Nevena; Palmeri, Antonio; Wesolowska-Andersen, Agata; Helmer-Citterich, Manuela; Ferkinghoff-Borg, Jesper; Itamochi, Hiroaki; Bodenmiller, Bernd; Erler, Janine T.; Turk, Benjamin E.; Linding, Rune

    2015-01-01

    Summary Cancer cells acquire pathological phenotypes through accumulation of mutations that perturb signaling networks. However, global analysis of these events is currently limited. Here, we identify six types of network-attacking mutations (NAMs), including changes in kinase and SH2 modulation, network rewiring, and the genesis and extinction of phosphorylation sites. We developed a computational platform (ReKINect) to identify NAMs and systematically interpreted the exomes and quantitative (phospho-)proteomes of five ovarian cancer cell lines and the global cancer genome repository. We identified and experimentally validated several NAMs, including PKCγ M501I and PKD1 D665N, which encode specificity switches analogous to the appearance of kinases de novo within the kinome. We discover mutant molecular logic gates, a drift toward phospho-threonine signaling, weakening of phosphorylation motifs, and kinase-inactivating hotspots in cancer. Our method pinpoints functional NAMs, scales with the complexity of cancer genomes and cell signaling, and may enhance our capability to therapeutically target tumor-specific networks. PMID:26388441

  12. Assessing needs and assets for building a regional network infrastructure to reduce cancer related health disparities.

    Science.gov (United States)

    Wells, Kristen J; Lima, Diana S; Meade, Cathy D; Muñoz-Antonia, Teresita; Scarinci, Isabel; McGuire, Allison; Gwede, Clement K; Pledger, W Jack; Partridge, Edward; Lipscomb, Joseph; Matthews, Roland; Matta, Jaime; Flores, Idhaliz; Weiner, Roy; Turner, Timothy; Miele, Lucio; Wiese, Thomas E; Fouad, Mona; Moreno, Carlos S; Lacey, Michelle; Christie, Debra W; Price-Haywood, Eboni G; Quinn, Gwendolyn P; Coppola, Domenico; Sodeke, Stephen O; Green, B Lee; Lichtveld, Maureen Y

    2014-06-01

    Significant cancer health disparities exist in the United States and Puerto Rico. While numerous initiatives have been implemented to reduce cancer disparities, regional coordination of these efforts between institutions is often limited. To address cancer health disparities nation-wide, a series of regional transdisciplinary networks through the Geographic Management Program (GMaP) and the Minority Biospecimen/Biobanking Geographic Management Program (BMaP) were established in six regions across the country. This paper describes the development of the Region 3 GMaP/BMaP network composed of over 100 investigators from nine institutions in five Southeastern states and Puerto Rico to develop a state-of-the-art network for cancer health disparities research and training. We describe a series of partnership activities that led to the formation of the infrastructure for this network, recount the participatory processes utilized to develop and implement a needs and assets assessment and implementation plan, and describe our approach to data collection. Completion, by all nine institutions, of the needs and assets assessment resulted in several beneficial outcomes for Region 3 GMaP/BMaP. This network entails ongoing commitment from the institutions and institutional leaders, continuous participatory and engagement activities, and effective coordination and communication centered on team science goals.

  13. Application of Artificial Neural Network in Predicting the Survival Rate of Gastric Cancer Patients

    OpenAIRE

    Biglarian, A; E. Hajizadeh; Kazemnejad, A; Zali, MR

    2011-01-01

    "nBackground: The aim of this study was to predict the survival rate of Iranian gastric cancer patients using the Cox proportional hazard and artificial neural network models as well as comparing the ability of these approaches in predicting the survival of these patients."nMethods: In this historical cohort study, the data gathered from 436 registered gastric cancer patients who have had surgery between 2002 and 2007 at the Taleghani Hospital (a referral center for gastrointestinal...

  14. Transcription factor FOXA2-centered transcriptional regulation network in non-small cell lung cancer

    Energy Technology Data Exchange (ETDEWEB)

    Jang, Sang-Min; An, Joo-Hee; Kim, Chul-Hong; Kim, Jung-Woong, E-mail: jungkim@cau.ac.kr; Choi, Kyung-Hee, E-mail: khchoi@cau.ac.kr

    2015-08-07

    Lung cancer is the leading cause of cancer-mediated death. Although various therapeutic approaches are used for lung cancer treatment, these mainly target the tumor suppressor p53 transcription factor, which is involved in apoptosis and cell cycle arrest. However, p53-targeted therapies have limited application in lung cancer, since p53 is found to be mutated in more than half of lung cancers. In this study, we propose tumor suppressor FOXA2 as an alternative target protein for therapies against lung cancer and reveal a possible FOXA2-centered transcriptional regulation network by identifying new target genes and binding partners of FOXA2 by using various screening techniques. The genes encoding Glu/Asp-rich carboxy-terminal domain 2 (CITED2), nuclear receptor subfamily 0, group B, member 2 (NR0B2), cell adhesion molecule 1 (CADM1) and BCL2-associated X protein (BAX) were identified as putative target genes of FOXA2. Additionally, the proteins including highly similar to heat shock protein HSP 90-beta (HSP90A), heat shock 70 kDa protein 1A variant (HSPA1A), histone deacetylase 1 (HDAC1) and HDAC3 were identified as novel interacting partners of FOXA2. Moreover, we showed that FOXA2-dependent promoter activation of BAX and p21 genes is significantly reduced via physical interactions between the identified binding partners and FOXA2. These results provide opportunities to understand the FOXA2-centered transcriptional regulation network and novel therapeutic targets to modulate this network in p53-deficient lung cancer. - Highlights: • Identification of new target genes of FOXA2. • Identifications of novel interaction proteins of FOXA2. • Construction of FOXA2-centered transcriptional regulatory network in non-small cell lung cancer.

  15. The greater Denver Latino Cancer Prevention/Control Network. Prevention and research through a community-based approach.

    Science.gov (United States)

    Flores, Estevan; Espinoza, Paula; Jacobellis, Jillian; Bakemeier, Richard; Press, Norma

    2006-10-15

    The Latino/a Research & Policy Center (LRPC), at the University of Colorado (UC) at Denver and Health Sciences Center built the Greater Denver Latino Cancer Prevention Network, a successful cancer prevention network, in 6 Denver metro area counties. The Network consisted of 23 Latino community-based organizations, health clinics, social service agencies, faith-based groups, and employee-based organizations; 2 migrant health clinics; and 14 scientific partners including the UC Comprehensive Cancer Center, the Colorado Department of Public Health and Environment, and the American Cancer Society. The Network focused on 5 significant cancers: breast, cervical, lung, colorectal, and prostate cancer. The Steering Committee initiated a review process for junior researchers that resulted in 5 NCI-funded pilot projects. Pilot projects were conducted with various Latino populations. The Network developed community education and health promotion projects including the bilingual outreach play The Cancer Monologues. The Network's partnership also started and held 2 annual health fairs, Dia de la Mujer Latina/Day of the Latina Woman, and annual health prevention summits. The Special Population Network (SPN) adapted and revised a clinical trials education outreach module that reached Network community partners. SPN partners recruited Latino/a students to cancer research through a6-week NCI training program held yearly at the UCHSC campus. The Network methodology of bringing together the Latino community with the scientific community increased the level of awareness of cancer in the Latino community and increased cancer research and the level of engagement of the scientific partners with the Latino community. Cancer 2006. (c) 2006 American Cancer Society.

  16. Network modelling reveals the mechanism underlying colitis-associated colon cancer and identifies novel combinatorial anti-cancer targets.

    Science.gov (United States)

    Lu, Junyan; Zeng, Hanlin; Liang, Zhongjie; Chen, Limin; Zhang, Liyi; Zhang, Hao; Liu, Hong; Jiang, Hualiang; Shen, Bairong; Huang, Ming; Geng, Meiyu; Spiegel, Sarah; Luo, Cheng

    2015-10-08

    The connection between inflammation and tumourigenesis has been well established. However, the detailed molecular mechanism underlying inflammation-associated tumourigenesis remains unknown because this process involves a complex interplay between immune microenvironments and epithelial cells. To obtain a more systematic understanding of inflammation-associated tumourigenesis as well as to identify novel therapeutic approaches, we constructed a knowledge-based network describing the development of colitis-associated colon cancer (CAC) by integrating the extracellular microenvironment and intracellular signalling pathways. Dynamic simulations of the CAC network revealed a core network module, including P53, MDM2, and AKT, that may govern the malignant transformation of colon epithelial cells in a pro-tumor inflammatory microenvironment. Furthermore, in silico mutation studies and experimental validations led to a novel finding that concurrently targeting ceramide and PI3K/AKT pathway by chemical probes or marketed drugs achieves synergistic anti-cancer effects. Overall, our network model can guide further mechanistic studies on CAC and provide new insights into the design of combinatorial cancer therapies in a rational manner.

  17. Neural networks improve brain cancer detection with Raman spectroscopy in the presence of light artifacts

    Science.gov (United States)

    Jermyn, Michael; Desroches, Joannie; Mercier, Jeanne; St-Arnaud, Karl; Guiot, Marie-Christine; Petrecca, Kevin; Leblond, Frederic

    2016-03-01

    It is often difficult to identify cancer tissue during brain cancer (glioma) surgery. Gliomas invade into areas of normal brain, and this cancer invasion is frequently not detected using standard preoperative magnetic resonance imaging (MRI). This results in enduring invasive cancer following surgery and leads to recurrence. A hand-held Raman spectroscopy is able to rapidly detect cancer invasion in patients with grade 2-4 gliomas. However, ambient light sources can produce spectral artifacts which inhibit the ability to distinguish between cancer and normal tissue using the spectral information available. To address this issue, we have demonstrated that artificial neural networks (ANN) can accurately classify invasive cancer versus normal brain tissue, even when including measurements with significant spectral artifacts from external light sources. The non-parametric and adaptive model used by ANN makes it suitable for detecting complex non-linear spectral characteristics associated with different tissues and the confounding presence of light artifacts. The use of ANN for brain cancer detection with Raman spectroscopy, in the presence of light artifacts, improves the robustness and clinical translation potential for intraoperative use. Integration with the neurosurgical workflow is facilitated by accounting for the effect of light artifacts which may occur, due to operating room lights, neuronavigation systems, windows, or other light sources. The ability to rapidly detect invasive brain cancer under these conditions may reduce residual cancer remaining after surgery, and thereby improve patient survival.

  18. Detection of gene communities in multi-networks reveals cancer drivers

    Science.gov (United States)

    Cantini, Laura; Medico, Enzo; Fortunato, Santo; Caselle, Michele

    2015-12-01

    We propose a new multi-network-based strategy to integrate different layers of genomic information and use them in a coordinate way to identify driving cancer genes. The multi-networks that we consider combine transcription factor co-targeting, microRNA co-targeting, protein-protein interaction and gene co-expression networks. The rationale behind this choice is that gene co-expression and protein-protein interactions require a tight coregulation of the partners and that such a fine tuned regulation can be obtained only combining both the transcriptional and post-transcriptional layers of regulation. To extract the relevant biological information from the multi-network we studied its partition into communities. To this end we applied a consensus clustering algorithm based on state of art community detection methods. Even if our procedure is valid in principle for any pathology in this work we concentrate on gastric, lung, pancreas and colorectal cancer and identified from the enrichment analysis of the multi-network communities a set of candidate driver cancer genes. Some of them were already known oncogenes while a few are new. The combination of the different layers of information allowed us to extract from the multi-network indications on the regulatory pattern and functional role of both the already known and the new candidate driver genes.

  19. Functional analysis of prognostic gene expression network genes in metastatic breast cancer models.

    Directory of Open Access Journals (Sweden)

    Thomas R Geiger

    Full Text Available Identification of conserved co-expression networks is a useful tool for clustering groups of genes enriched for common molecular or cellular functions [1]. The relative importance of genes within networks can frequently be inferred by the degree of connectivity, with those displaying high connectivity being significantly more likely to be associated with specific molecular functions [2]. Previously we utilized cross-species network analysis to identify two network modules that were significantly associated with distant metastasis free survival in breast cancer. Here, we validate one of the highly connected genes as a metastasis associated gene. Tpx2, the most highly connected gene within a proliferation network specifically prognostic for estrogen receptor positive (ER+ breast cancers, enhances metastatic disease, but in a tumor autonomous, proliferation-independent manner. Histologic analysis suggests instead that variation of TPX2 levels within disseminated tumor cells may influence the transition between dormant to actively proliferating cells in the secondary site. These results support the co-expression network approach for identification of new metastasis-associated genes to provide new information regarding the etiology of breast cancer progression and metastatic disease.

  20. The mystery of the seven spheres how homo sapiens will conquer space

    CERN Document Server

    Bignami, Giovanni F

    2015-01-01

    In this book, Giovanni Bignami, the outstanding Italian scientist and astronomer, takes the reader on a journey through the “seven spheres”, from our own planet to neighboring stars. The author offers a gripping account of the evolution of Homo Sapiens to the stage where our species is developing capabilities, in the form of new energy propulsion systems, that will enable us to conquer space. The reader will learn how we first expanded our activities to reach beyond our planet, to the Moon, and how nuclear energy, nuclear fusion, and matter–antimatter annihilation will enable us to extend our exploration. After Mars and Jupiter we shall finally reach the nearest stars, which we now know are surrounded by numerous planets, some of which are bound to be habitable. The book includes enticing descriptions of such newly discovered planets and also brings alive key historical characters in our story, such as Jules Verne and Werner von Braun.

  1. Toward a High Performance Tile Divide and Conquer Algorithm for the Dense Symmetric Eigenvalue Problem

    KAUST Repository

    Haidar, Azzam

    2012-01-01

    Classical solvers for the dense symmetric eigenvalue problem suffer from the first step, which involves a reduction to tridiagonal form that is dominated by the cost of accessing memory during the panel factorization. The solution is to reduce the matrix to a banded form, which then requires the eigenvalues of the banded matrix to be computed. The standard divide and conquer algorithm can be modified for this purpose. The paper combines this insight with tile algorithms that can be scheduled via a dynamic runtime system to multicore architectures. A detailed analysis of performance and accuracy is included. Performance improvements of 14-fold and 4-fold speedups are reported relative to LAPACK and Intel\\'s Math Kernel Library.

  2. A fast divide-and-conquer algorithm for indexing human genome sequences

    CERN Document Server

    Loh, Woong-Kee; Lee, Wookey

    2010-01-01

    Since the release of human genome sequences, one of the most important research issues is about indexing the genome sequences, and the suffix tree is most widely adopted for that purpose. The traditional suffix tree construction algorithms have severe performance degradation due to the memory bottleneck problem. The recent disk-based algorithms also have limited performance improvement due to random disk accesses. Moreover, they do not fully utilize the recent CPUs with multiple cores. In this paper, we propose a fast algorithm based on 'divide-and-conquer' strategy for indexing the human genome sequences. Our algorithm almost eliminates random disk accesses by accessing the disk in the unit of contiguous chunks. In addition, our algorithm fully utilizes the multi-core CPUs by dividing the genome sequences into multiple partitions and then assigning each partition to a different core for parallel processing. Experimental results show that our algorithm outperforms the previous fastest DIGEST algorithm by up t...

  3. Divide and conquer the Hilbert space of translation-symmetric spin systems.

    Science.gov (United States)

    Weisse, Alexander

    2013-04-01

    Iterative methods that operate with the full Hamiltonian matrix in the untrimmed Hilbert space of a finite system continue to be important tools for the study of one- and two-dimensional quantum spin models, in particular in the presence of frustration. To reach sensible system sizes such numerical calculations heavily depend on the use of symmetries. We describe a divide-and-conquer strategy for implementing translation symmetries of finite spin clusters, which efficiently uses and extends the "sublattice coding" of H. Q. Lin [Phys. Rev. B 42, 6561 (1990)]. With our method, the Hamiltonian matrix can be generated on-the-fly in each matrix vector multiplication, and problem dimensions beyond 10^{11} become accessible.

  4. Block-adaptive quantum mechanics: an adaptive divide-and-conquer approach to interactive quantum chemistry.

    Science.gov (United States)

    Bosson, Maël; Grudinin, Sergei; Redon, Stephane

    2013-03-05

    We present a novel Block-Adaptive Quantum Mechanics (BAQM) approach to interactive quantum chemistry. Although quantum chemistry models are known to be computationally demanding, we achieve interactive rates by focusing computational resources on the most active parts of the system. BAQM is based on a divide-and-conquer technique and constrains some nucleus positions and some electronic degrees of freedom on the fly to simplify the simulation. As a result, each time step may be performed significantly faster, which in turn may accelerate attraction to the neighboring local minima. By applying our approach to the nonself-consistent Atom Superposition and Electron Delocalization Molecular Orbital theory, we demonstrate interactive rates and efficient virtual prototyping for systems containing more than a thousand of atoms on a standard desktop computer.

  5. P53 tumor suppression network in cancer epigenetics.

    Science.gov (United States)

    Mishra, Alok; Brat, Daniel J; Verma, Mukesh

    2015-01-01

    The tumor suppressor p53 is one of the most complex and widely studied genes in cancer biology. In spite of the vast on literature the transcriptional regulation of p53, aspects of its especially epigenetic regulation are not completely understood. This chapter presents a concise overview of p53-related epigenetic events involved in oncogenesis and tumor suppression. We limit the scope to epigenetic modifications of the p53 promoter per se as well as its well-established downstream targets. The indirect role of p53 affecting the epigenetic machinery of cancer cells via specific proteins and transcription factors is discussed. Current concepts of p53-related cancer epigenetics offer myriad avenues for cancer therapies. Challenges in the field are also discussed.

  6. A new focus for the International Cancer Screening Network

    Science.gov (United States)

    The ICSN is thinking about how to take advantage of the nearly three decades of work in cancer screening program research and implementation and reach out more actively to low- and middle-income countries considering screening. For that purpose, ICSN is migrating from its historical place under NCI Division of Cancer Control and Population Sciences to assume its new role within the Center for Global Health.

  7. The intellectual property management for data sharing in a German liver cancer research network.

    Science.gov (United States)

    He, Shan; Ganzinger, Matthias; Knaup, Petra

    2012-01-01

    Sharing data in biomedical research networks has great potential benefits including efficient use of resources, avoiding duplicate experiments and promoting collaboration. However, concerns from data producers about difficulties of getting proper acknowledgement for their contributions are becoming obstacles for efficient and network wide data sharing in reality. Effective and convenient ways of intellectual property management and acknowledging contributions to the data producers are required. This paper analyzed the system requirements for intellectual property management in a German liver cancer research network and proposed solutions for facilitating acknowledgement of data contributors using informatics tools instead of pure policy level strategies.

  8. Inferring regulatory element landscapes and transcription factor networks from cancer methylomes.

    Science.gov (United States)

    Yao, Lijing; Shen, Hui; Laird, Peter W; Farnham, Peggy J; Berman, Benjamin P

    2015-05-21

    Recent studies indicate that DNA methylation can be used to identify transcriptional enhancers, but no systematic approach has been developed for genome-wide identification and analysis of enhancers based on DNA methylation. We describe ELMER (Enhancer Linking by Methylation/Expression Relationships), an R-based tool that uses DNA methylation to identify enhancers and correlates enhancer state with expression of nearby genes to identify transcriptional targets. Transcription factor motif analysis of enhancers is coupled with expression analysis of transcription factors to infer upstream regulators. Using ELMER, we investigated more than 2,000 tumor samples from The Cancer Genome Atlas. We identified networks regulated by known cancer drivers such as GATA3 and FOXA1 (breast cancer), SOX17 and FOXA2 (endometrial cancer), and NFE2L2, SOX2, and TP63 (squamous cell lung cancer). We also identified novel networks with prognostic associations, including RUNX1 in kidney cancer. We propose ELMER as a powerful new paradigm for understanding the cis-regulatory interface between cancer-associated transcription factors and their functional target genes.

  9. Integrative gene network construction to analyze cancer recurrence using semi-supervised learning.

    Directory of Open Access Journals (Sweden)

    Chihyun Park

    Full Text Available BACKGROUND: The prognosis of cancer recurrence is an important research area in bioinformatics and is challenging due to the small sample sizes compared to the vast number of genes. There have been several attempts to predict cancer recurrence. Most studies employed a supervised approach, which uses only a few labeled samples. Semi-supervised learning can be a great alternative to solve this problem. There have been few attempts based on manifold assumptions to reveal the detailed roles of identified cancer genes in recurrence. RESULTS: In order to predict cancer recurrence, we proposed a novel semi-supervised learning algorithm based on a graph regularization approach. We transformed the gene expression data into a graph structure for semi-supervised learning and integrated protein interaction data with the gene expression data to select functionally-related gene pairs. Then, we predicted the recurrence of cancer by applying a regularization approach to the constructed graph containing both labeled and unlabeled nodes. CONCLUSIONS: The average improvement rate of accuracy for three different cancer datasets was 24.9% compared to existing supervised and semi-supervised methods. We performed functional enrichment on the gene networks used for learning. We identified that those gene networks are significantly associated with cancer-recurrence-related biological functions. Our algorithm was developed with standard C++ and is available in Linux and MS Windows formats in the STL library. The executable program is freely available at: http://embio.yonsei.ac.kr/~Park/ssl.php.

  10. Wisconsin’s Environmental Public Health Tracking Network: Information Systems Design for Childhood Cancer Surveillance

    Science.gov (United States)

    Hanrahan, Lawrence P.; Anderson, Henry A.; Busby, Brian; Bekkedal, Marni; Sieger, Thomas; Stephenson, Laura; Knobeloch, Lynda; Werner, Mark; Imm, Pamela; Olson, Joseph

    2004-01-01

    In this article we describe the development of an information system for environmental childhood cancer surveillance. The Wisconsin Cancer Registry annually receives more than 25,000 incident case reports. Approximately 269 cases per year involve children. Over time, there has been considerable community interest in understanding the role the environment plays as a cause of these cancer cases. Wisconsin’s Public Health Information Network (WI-PHIN) is a robust web portal integrating both Health Alert Network and National Electronic Disease Surveillance System components. WI-PHIN is the information technology platform for all public health surveillance programs. Functions include the secure, automated exchange of cancer case data between public health–based and hospital-based cancer registrars; web-based supplemental data entry for environmental exposure confirmation and hypothesis testing; automated data analysis, visualization, and exposure–outcome record linkage; directories of public health and clinical personnel for role-based access control of sensitive surveillance information; public health information dissemination and alerting; and information technology security and critical infrastructure protection. For hypothesis generation, cancer case data are sent electronically to WI-PHIN and populate the integrated data repository. Environmental data are linked and the exposure–disease relationships are explored using statistical tools for ecologic exposure risk assessment. For hypothesis testing, case–control interviews collect exposure histories, including parental employment and residential histories. This information technology approach can thus serve as the basis for building a comprehensive system to assess environmental cancer etiology. PMID:15471739

  11. AHNS Series - Do you know your guidelines? Principles of treatment for nasopharyngeal cancer: A review of the National Comprehensive Cancer Network guidelines.

    Science.gov (United States)

    Gooi, Zhen; Richmon, Jeremy; Agrawal, Nishant; Blair, Elizabeth; Portugal, Louis; Vokes, Everett; Seiwert, Tanguy; de Souza, Jonas; Saloura, Vassiliki; Haraf, Daniel; Goldenberg, David; Chan, Jason

    2017-02-01

    This article is a continuation of the "Do You Know Your Guidelines" series, an initiative of the American Head and Neck Society's Education Committee to increase awareness of current best practices pertaining to head and neck cancer. The National Comprehensive Cancer Network guidelines for the management of nasopharyngeal cancer are reviewed here in a systematic fashion. These guidelines outline the workup, treatment and surveillance of patients with nasopharyngeal cancer. © 2016 Wiley Periodicals, Inc. Head Neck 39: 201-205, 2017.

  12. DriverNet: uncovering the impact of somatic driver mutations on transcriptional networks in cancer.

    Science.gov (United States)

    Bashashati, Ali; Haffari, Gholamreza; Ding, Jiarui; Ha, Gavin; Lui, Kenneth; Rosner, Jamie; Huntsman, David G; Caldas, Carlos; Aparicio, Samuel A; Shah, Sohrab P

    2012-12-22

    Simultaneous interrogation of tumor genomes and transcriptomes is underway in unprecedented global efforts. Yet, despite the essential need to separate driver mutations modulating gene expression networks from transcriptionally inert passenger mutations, robust computational methods to ascertain the impact of individual mutations on transcriptional networks are underdeveloped. We introduce a novel computational framework, DriverNet, to identify likely driver mutations by virtue of their effect on mRNA expression networks. Application to four cancer datasets reveals the prevalence of rare candidate driver mutations associated with disrupted transcriptional networks and a simultaneous modulation of oncogenic and metabolic networks, induced by copy number co-modification of adjacent oncogenic and metabolic drivers. DriverNet is available on Bioconductor or at http://compbio.bccrc.ca/software/drivernet/.

  13. Molecular Mechanism by Which Retinoids Prevent Breast Cancer Development

    Science.gov (United States)

    2007-06-01

    clinicians to conquer this disease is to prevent the incidence, detect early and treat breast cancer with effective therapy resulting in long overall... biological functions such as embryogenesis, growth, differentiation, vision and reproduction (3-6). Retinoids also contain anti- proliferative...and are currently available to treat psoriasis , acne, photoaging, actinic keratosis or cancers such as acute promelocytic leukemia, cutaneous T-cell

  14. Inference of Cancer-specific Gene Regulatory Networks Using Soft Computing Rules

    Directory of Open Access Journals (Sweden)

    Xiaosheng Wang

    2010-03-01

    Full Text Available Perturbations of gene regulatory networks are essentially responsible for oncogenesis. Therefore, inferring the gene regulatory networks is a key step to overcoming cancer. In this work, we propose a method for inferring directed gene regulatory networks based on soft computing rules, which can identify important cause-effect regulatory relations of gene expression. First, we identify important genes associated with a specific cancer (colon cancer using a supervised learning approach. Next, we reconstruct the gene regulatory networks by inferring the regulatory relations among the identified genes, and their regulated relations by other genes within the genome. We obtain two meaningful findings. One is that upregulated genes are regulated by more genes than downregulated ones, while downregulated genes regulate more genes than upregulated ones. The other one is that tumor suppressors suppress tumor activators and activate other tumor suppressors strongly, while tumor activators activate other tumor activators and suppress tumor suppressors weakly, indicating the robustness of biological systems. These findings provide valuable insights into the pathogenesis of cancer.

  15. The Cervix Cancer Research Network (CCRN: Increasing access to cancer clinical trials in low- and middle-income countries

    Directory of Open Access Journals (Sweden)

    Gita eSuneja

    2015-02-01

    Full Text Available Introduction: The burden of cervical cancer is large and growing in developing countries, due in large part to limited access to screening services and lack of human papillomavirus (HPV vaccination. In spite of modern advances in diagnostic and therapeutic modalities, outcomes from cervical cancer have not markedly improved in recent years. Novel clinical trials are urgently needed to improve outcomes from cervical cancer worldwide. Methods: The Cervix Cancer Research Network (CCRN, a subsidiary of the Gynecologic Cancer InterGroup (GCIG, is a multi-national, multi-institutional consortium of physicians and scientists focused on improving cervical cancer outcomes worldwide by making cancer clinical trials available in low-, middle-, and high-income countries. Standard operating procedures for participation in CCRN include a pre-qualifying questionnaire to evaluate clinical activities and research infrastructure, followed by a site visit. Once a site is approved, they may choose to participate in one of four currently accruing clinical trials.Results: To date, 13 different CCRN site visits have been performed. Of these 13 sites visited, 10 have been approved as CCRN sites including Tata Memorial Hospital, India; Bangalore, India; Trivandrum, India; Ramathibodi, Thailand; Siriaj, Thailand; Pramongkutklao, Thailand; Ho Chi Minh, Vietnam; Blokhin Russian Cancer Research Center; the Hertzen Moscow Cancer Research Institute; and the Russian Scientific Center of Roentgenoradiology. The four currently accruing clinical trials are TACO, OUTBACK, INTERLACE, and SHAPE.Discussion: The CCRN has successfully enrolled 10 sites in developing countries to participate in four randomized clinical trials. The primary objectives are to provide novel therapeutics to regions with the greatest need and to improve the validity and generalizability of clinical trial results by enrolling a diverse sample of patients.

  16. Modeling microRNA-transcription factor networks in cancer.

    Science.gov (United States)

    Aguda, Baltazar D

    2013-01-01

    An increasing number of transcription factors (TFs) and microRNAs (miRNAs) is known to form feedback loops (FBLs) of interactions where a TF positively or negatively regulates the expression of a miRNA, and the miRNA suppresses the translation of the TF messenger RNA. FBLs are potential sources of instability in a gene regulatory network. Positive FBLs can give rise to switching behaviors while negative FBLs can generate periodic oscillations. This chapter presents documented examples of FBLs and their relevance to stem cell renewal and differentiation in gliomas. Feed-forward loops (FFLs) are only discussed briefly because they do not affect network stability unless they are members of cycles. A primer on qualitative network stability analysis is given and then used to demonstrate the network destabilizing role of FBLs. Steps in model formulation and computer simulations are illustrated using the miR-17-92/Myc/E2F network as an example. This example possesses both negative and positive FBLs.

  17. A research about breast cancer detection using different neural networks and K-MICA algorithm.

    Science.gov (United States)

    Kalteh, A A; Zarbakhsh, Payam; Jirabadi, Meysam; Addeh, Jalil

    2013-01-01

    Breast cancer is the second leading cause of death for women all over the world. The correct diagnosis of breast cancer is one of the major problems in the medical field. From the literature it has been found that different pattern recognition techniques can help them to improve in this domain. This paper presents a novel hybrid intelligent method for detection of breast cancer. The proposed method includes two main modules: Clustering module and the classifier module. In the clustering module, first the input data will be clustered by a new technique. This technique is a suitable combination of the modified imperialist competitive algorithm (MICA) and K-means algorithm. Then the Euclidean distance of each pattern is computed from the determined clusters. The classifier module determines the membership of the patterns using the computed distance. In this module, several neural networks, such as the multilayer perceptron, probabilistic neural networks and the radial basis function neural networks are investigated. Using the experimental study, we choose the best classifier in order to recognize the breast cancer. The proposed system is tested on Wisconsin Breast Cancer (WBC) database and the simulation results show that the recommended system has high accuracy.

  18. Regulators of genetic risk of breast cancer identified by integrative network analysis.

    Science.gov (United States)

    Castro, Mauro A A; de Santiago, Ines; Campbell, Thomas M; Vaughn, Courtney; Hickey, Theresa E; Ross, Edith; Tilley, Wayne D; Markowetz, Florian; Ponder, Bruce A J; Meyer, Kerstin B

    2016-01-01

    Genetic risk for breast cancer is conferred by a combination of multiple variants of small effect. To better understand how risk loci might combine, we examined whether risk-associated genes share regulatory mechanisms. We created a breast cancer gene regulatory network comprising transcription factors and groups of putative target genes (regulons) and asked whether specific regulons are enriched for genes associated with risk loci via expression quantitative trait loci (eQTLs). We identified 36 overlapping regulons that were enriched for risk loci and formed a distinct cluster within the network, suggesting shared biology. The risk transcription factors driving these regulons are frequently mutated in cancer and lie in two opposing subgroups, which relate to estrogen receptor (ER)(+) luminal A or luminal B and ER(-) basal-like cancers and to different luminal epithelial cell populations in the adult mammary gland. Our network approach provides a foundation for determining the regulatory circuits governing breast cancer, to identify targets for intervention, and is transferable to other disease settings.

  19. A research about breast cancer detection using different neural networks and K-MICA algorithm

    Directory of Open Access Journals (Sweden)

    A A Kalteh

    2013-01-01

    Full Text Available Breast cancer is the second leading cause of death for women all over the world. The correct diagnosis of breast cancer is one of the major problems in the medical field. From the literature it has been found that different pattern recognition techniques can help them to improve in this domain. This paper presents a novel hybrid intelligent method for detection of breast cancer. The proposed method includes two main modules: Clustering module and the classifier module. In the clustering module, first the input data will be clustered by a new technique. This technique is a suitable combination of the modified imperialist competitive algorithm (MICA and K-means algorithm. Then the Euclidean distance of each pattern is computed from the determined clusters. The classifier module determines the membership of the patterns using the computed distance. In this module, several neural networks, such as the multilayer perceptron, probabilistic neural networks and the radial basis function neural networks are investigated. Using the experimental study, we choose the best classifier in order to recognize the breast cancer. The proposed system is tested on Wisconsin Breast Cancer (WBC database and the simulation results show that the recommended system has high accuracy.

  20. [Meta analysis of the use of Bayesian networks in breast cancer diagnosis].

    Science.gov (United States)

    Simões, Priscyla Waleska; Silva, Geraldo Doneda da; Moretti, Gustavo Pasquali; Simon, Carla Sasso; Winnikow, Erik Paul; Nassar, Silvia Modesto; Medeiros, Lidia Rosi; Rosa, Maria Inês

    2015-01-01

    The aim of this study was to determine the accuracy of Bayesian networks in supporting breast cancer diagnoses. Systematic review and meta-analysis were carried out, including articles and papers published between January 1990 and March 2013. We included prospective and retrospective cross-sectional studies of the accuracy of diagnoses of breast lesions (target conditions) made using Bayesian networks (index test). Four primary studies that included 1,223 breast lesions were analyzed, 89.52% (444/496) of the breast cancer cases and 6.33% (46/727) of the benign lesions were positive based on the Bayesian network analysis. The area under the curve (AUC) for the summary receiver operating characteristic curve (SROC) was 0.97, with a Q* value of 0.92. Using Bayesian networks to diagnose malignant lesions increased the pretest probability of a true positive from 40.03% to 90.05% and decreased the probability of a false negative to 6.44%. Therefore, our results demonstrated that Bayesian networks provide an accurate and non-invasive method to support breast cancer diagnosis.

  1. Understanding and Targeting Cell Growth Networks in Breast Cancer

    Science.gov (United States)

    2013-04-01

    pathology  187(1):112-­‐126.   2.   Sherr  CJ  &  Weber  JD  (2000)  The  ARF/p53  pathway.  Current...J,  Solimini  NL,  &  Elledge  SJ  (2009)  Principles  of  cancer  therapy:  oncogene  and  non-­‐oncogene   addiction ...Cancer Res 2010; 70: 4749–4758. 32 Diederichs S, Haber DA. Dual role for argonautes in microRNA processing and posttranscriptional regulation of

  2. Social networks, social support mechanisms, and quality of life after breast cancer diagnosis.

    Science.gov (United States)

    Kroenke, Candyce H; Kwan, Marilyn L; Neugut, Alfred I; Ergas, Isaac J; Wright, Jaime D; Caan, Bette J; Hershman, Dawn; Kushi, Lawrence H

    2013-06-01

    We examined mechanisms through which social relationships influence quality of life (QOL) in breast cancer survivors. This study included 3,139 women from the Pathways Study who were diagnosed with breast cancer from 2006 to 2011 and provided data on social networks (the presence of a spouse or intimate partner, religious/social ties, volunteering, and numbers of close friends and relatives), social support (tangible support, emotional/informational support, affection, positive social interaction), and QOL, measured by the FACT-B, approximately 2 months post diagnosis. We used logistic models to evaluate associations between social network size, social support, and lower versus higher than median QOL scores. We further stratified by stage at diagnosis and treatment. In multivariate-adjusted analyses, women who were characterized as socially isolated had significantly lower FACT-B (OR = 2.18, 95 % CI: 1.72-2.77), physical well-being (WB) (OR = 1.61, 95 % CI: 1.27-2.03), functional WB (OR = 2.08, 95 % CI: 1.65-2.63), social WB (OR = 3.46, 95 % CI: 2.73-4.39), and emotional WB (OR = 1.67, 95 % CI: 1.33-2.11) scores and higher breast cancer symptoms (OR = 1.48, 95 % CI: 1.18-1.87) compared with socially integrated women. Each social network member independently predicted higher QOL. Simultaneous adjustment for social networks and social support partially attenuated associations between social networks and QOL. The strongest mediator and type of social support that was most predictive of QOL outcomes was "positive social interaction." However, each type of support was important depending on outcome, stage, and treatment status. Larger social networks and greater social support were related to higher QOL after a diagnosis of breast cancer. Effective social support interventions need to evolve beyond social-emotional interventions and need to account for disease severity and treatment status.

  3. Establishment of the Fox Chase Network Breast Cancer Risk Registry.

    Science.gov (United States)

    1998-10-01

    booklets, color slides and flip chart prints which describe the normal anatomy and physiology of the breast, ovary, colon and prostate glands, cancer risk...Manual Appendix F Oncology Nursing Society Abstract Appendix G Flip Chart Appendix H Procedures for Implementation 1996 Appendix A Recruitment Procedures

  4. Early Detection of Lung Cancer Using Neural Network Techniques

    Directory of Open Access Journals (Sweden)

    Prashant Naresh

    2014-08-01

    Full Text Available Effective identification of lung cancer at an initial stage is an important and crucial aspect of image processing. Several data mining methods have been used to detect lung cancer at early stage. In this paper, an approach has been presented which will diagnose lung cancer at an initial stage using CT scan images which are in Dicom (DCM format. One of the key challenges is to remove white Gaussian noise from the CT scan image, which is done using non local mean filter and to segment the lung Otsu’s thresholding is used. The textural and structural features are extracted from the processed image to form feature vector. In this paper, three classifiers namely SVM, ANN, and k-NN are applied for the detection of lung cancer to find the severity of disease (stage I or stage II and comparison is made with ANN, and k-NN classifier with respect to different quality attributes such as accuracy, sensitivity(recall, precision and specificity. It has been found from results that SVM achieves higher accuracy of 95.12% while ANN achieves 92.68% accuracy on the given data set and k-NN shows least accuracy of 85.37%. SVM algorithm which achieves 95.12% accuracy helps patients to take remedial action on time and reduces mortality rate from this deadly disease.

  5. SNP-SNP interaction network in angiogenesis genes associated with prostate cancer aggressiveness.

    Directory of Open Access Journals (Sweden)

    Hui-Yi Lin

    Full Text Available Angiogenesis has been shown to be associated with prostate cancer development. The majority of prostate cancer studies focused on individual single nucleotide polymorphisms (SNPs while SNP-SNP interactions are suggested having a great impact on unveiling the underlying mechanism of complex disease. Using 1,151 prostate cancer patients in the Cancer Genetic Markers of Susceptibility (CGEMS dataset, 2,651 SNPs in the angiogenesis genes associated with prostate cancer aggressiveness were evaluated. SNP-SNP interactions were primarily assessed using the two-stage Random Forests plus Multivariate Adaptive Regression Splines (TRM approach in the CGEMS group, and were then re-evaluated in the Moffitt group with 1,040 patients. For the identified gene pairs, cross-evaluation was applied to evaluate SNP interactions in both study groups. Five SNP-SNP interactions in three gene pairs (MMP16+ ROBO1, MMP16+ CSF1, and MMP16+ EGFR were identified to be associated with aggressive prostate cancer in both groups. Three pairs of SNPs (rs1477908+ rs1387665, rs1467251+ rs7625555, and rs1824717+ rs7625555 were in MMP16 and ROBO1, one pair (rs2176771+ rs333970 in MMP16 and CSF1, and one pair (rs1401862+ rs6964705 in MMP16 and EGFR. The results suggest that MMP16 may play an important role in prostate cancer aggressiveness. By integrating our novel findings and available biomedical literature, a hypothetical gene interaction network was proposed. This network demonstrates that our identified SNP-SNP interactions are biologically relevant and shows that EGFR may be the hub for the interactions. The findings provide valuable information to identify genotype combinations at risk of developing aggressive prostate cancer and improve understanding on the genetic etiology of angiogenesis associated with prostate cancer aggressiveness.

  6. Dynamic modularity in protein interaction networks predicts breast cancer outcome

    DEFF Research Database (Denmark)

    Taylor, Ian W; Linding, Rune; Warde-Farley, David

    2009-01-01

    Changes in the biochemical wiring of oncogenic cells drives phenotypic transformations that directly affect disease outcome. Here we examine the dynamic structure of the human protein interaction network (interactome) to determine whether changes in the organization of the interactome can be used...

  7. Cooperation among cancer cells as public goods games on Voronoi networks.

    Science.gov (United States)

    Archetti, Marco

    2016-05-07

    Cancer cells produce growth factors that diffuse and sustain tumour proliferation, a form of cooperation that can be studied using mathematical models of public goods in the framework of evolutionary game theory. Cell populations, however, form heterogeneous networks that cannot be described by regular lattices or scale-free networks, the types of graphs generally used in the study of cooperation. To describe the dynamics of growth factor production in populations of cancer cells, I study public goods games on Voronoi networks, using a range of non-linear benefits that account for the known properties of growth factors, and different types of diffusion gradients. The results are surprisingly similar to those obtained on regular graphs and different from results on scale-free networks, revealing that network heterogeneity per se does not promote cooperation when public goods diffuse beyond one-step neighbours. The exact shape of the diffusion gradient is not crucial, however, whereas the type of non-linear benefit is an essential determinant of the dynamics. Public goods games on Voronoi networks can shed light on intra-tumour heterogeneity, the evolution of resistance to therapies that target growth factors, and new types of cell therapy.

  8. Social networks as predictors of colorectal cancer screening in African Americans.

    Science.gov (United States)

    Alema-Mensah, Ernest; Smith, Selina A; Claridy, Mechelle; Ede, Victor; Ansa, Benjamin; Blumenthal, Daniel S

    2017-01-01

    Early detection can reduce colorectal cancer (CRC) mortality by 15%-33%, and screening is widely recommended for average-risk adults beginning at age 50 years. Colorectal cancer mortality rates are higher in African Americans than in whites, while screening rates are somewhat lower. Individual social networks can reduce emotional and/or logistical barriers to health-promoting but distasteful procedures such as CRC screening. The aim of this study was to examine social network interactions, and their impact on CRC screening among African Americans. We hypothesized a positive association between social network index (SNI) scores and CRC screening. In a community intervention trial with four arms, we previously demonstrated the efficacy of a small group educational intervention to promote CRC screening among African Americans. This intervention outperformed a one-on-one educational intervention, a reduced out-of-pocket expense intervention, and a control condition. In the present analysis, we compared the SNI scores for participants in the small group intervention cohort with a comparison group comprised of the other three cohorts. Social networks were assessed using the Social Network Index developed by Cohen. Small group participants had a significantly higher network diversity score (Mean difference 0.71; 95% CI, 0.12-1.31; p=0.0017) than the comparison group. In the second component of the SNI score - the number of people talked to over a two week period - the small group intervention cohort also scored significantly higher than the comparison group. (Mean difference, 9.29; 95% CI, 3.963-14.6266; p=0.0004). The findings suggest that social interaction and support was at least partially responsible for the relatively high post-intervention screening rate in the small group intervention participants. Education in small groups could foster strong social networks. Strong and positive network diversity and a large number of people in social networks may enhance CRC

  9. History of cholelithiasis and cancer risk in a network of case-control studies.

    Science.gov (United States)

    Tavani, A; Rosato, V; Di Palma, F; Bosetti, C; Talamini, R; Dal Maso, L; Zucchetto, A; Levi, F; Montella, M; Negri, E; Franceschi, S; La Vecchia, C

    2012-08-01

    We analyzed the relationship between cholelithiasis and cancer risk in a network of case-control studies conducted in Italy and Switzerland in 1982-2009. The analyses included 1997 oropharyngeal, 917 esophageal, 999 gastric, 23 small intestinal, 3726 colorectal, 684 liver, 688 pancreatic, 1240 laryngeal, 6447 breast, 1458 endometrial, 2002 ovarian, 1582 prostate, 1125 renal cell, 741 bladder cancers, and 21 284 controls. The odds ratios (ORs) were estimated by multiple logistic regression models. The ORs for subjects with history of cholelithiasis compared with those without were significantly elevated for small intestinal (OR=3.96), prostate (OR=1.36), and kidney cancers (OR=1.57). These positive associations were observed ≥10 years after diagnosis of cholelithiasis and were consistent across strata of age, sex, and body mass index. No relation was found with the other selected cancers. A meta-analysis including this and three other studies on the relation of cholelithiasis with small intestinal cancer gave a pooled relative risk of 2.35 [95% confidence interval (CI) 1.82-3.03]. In subjects with cholelithiasis, we showed an appreciably increased risk of small intestinal cancer and suggested a moderate increased risk of prostate and kidney cancers. We found no material association with the other cancers considered.

  10. Similarity in gene-regulatory networks suggests that cancer cells share characteristics of embryonic neural cells.

    Science.gov (United States)

    Zhang, Zan; Lei, Anhua; Xu, Liyang; Chen, Lu; Chen, Yonglong; Zhang, Xuena; Gao, Yan; Yang, Xiaoli; Zhang, Min; Cao, Ying

    2017-08-04

    Cancer cells are immature cells resulting from cellular reprogramming by gene misregulation, and redifferentiation is expected to reduce malignancy. It is unclear, however, whether cancer cells can undergo terminal differentiation. Here, we show that inhibition of the epigenetic modification enzyme enhancer of zeste homolog 2 (EZH2), histone deacetylases 1 and 3 (HDAC1 and -3), lysine demethylase 1A (LSD1), or DNA methyltransferase 1 (DNMT1), which all promote cancer development and progression, leads to postmitotic neuron-like differentiation with loss of malignant features in distinct solid cancer cell lines. The regulatory effect of these enzymes in neuronal differentiation resided in their intrinsic activity in embryonic neural precursor/progenitor cells. We further found that a major part of pan-cancer-promoting genes and the signal transducers of the pan-cancer-promoting signaling pathways, including the epithelial-to-mesenchymal transition (EMT) mesenchymal marker genes, display neural specific expression during embryonic neurulation. In contrast, many tumor suppressor genes, including the EMT epithelial marker gene that encodes cadherin 1 (CDH1), exhibited non-neural or no expression. This correlation indicated that cancer cells and embryonic neural cells share a regulatory network, mediating both tumorigenesis and neural development. This observed similarity in regulatory mechanisms suggests that cancer cells might share characteristics of embryonic neural cells. © 2017 by The American Society for Biochemistry and Molecular Biology, Inc.

  11. Social Networking Site Usage Among Childhood Cancer Survivors - A Potential Tool for Research Recruitment?

    Science.gov (United States)

    Seltzer, Erica D.; Stolley, Melinda R.; Mensah, Edward K.; Sharp, Lisa K.

    2014-01-01

    Purpose The recent and rapid growth of social networking site (SNS) use presents a unique public health opportunity to develop effective strategies for the recruitment of hard-to-reach participants for cancer research studies. This survey investigated childhood cancer survivors’ reported use of SNS such as facebook or MySpace and their perceptions of using SNS, for recruitment into survivorship research. Methods Sixty White, Black and Hispanic, adult childhood cancer survivors (range 18 – 48 years of age) that were randomly selected from a larger childhood cancer study, the Chicago Healthy Living Study (CHLS), participated in this pilot survey. Telephone surveys were conducted to understand current SNS activity and attitudes towards using SNS as a cancer research recruitment tool. Results Seventy percent of participants reported SNS usage of which 80% were at least weekly users and 79 % reported positive attitudes towards the use of SNS as a recruitment tool for survivorship research. Conclusions and implications for cancer survivors The results of this pilot study revealed that SNS use was high and regular among the childhood cancer survivors sampled. Most had positive attitudes towards using SNS for recruitment of research. The results of this pilot survey suggest that SNS may offer an alternative approach for recruitment of childhood cancer survivors into research. PMID:24532046

  12. Prediction of near-term breast cancer risk using a Bayesian belief network

    Science.gov (United States)

    Zheng, Bin; Ramalingam, Pandiyarajan; Hariharan, Harishwaran; Leader, Joseph K.; Gur, David

    2013-03-01

    Accurately predicting near-term breast cancer risk is an important prerequisite for establishing an optimal personalized breast cancer screening paradigm. In previous studies, we investigated and tested the feasibility of developing a unique near-term breast cancer risk prediction model based on a new risk factor associated with bilateral mammographic density asymmetry between the left and right breasts of a woman using a single feature. In this study we developed a multi-feature based Bayesian belief network (BBN) that combines bilateral mammographic density asymmetry with three other popular risk factors, namely (1) age, (2) family history, and (3) average breast density, to further increase the discriminatory power of our cancer risk model. A dataset involving "prior" negative mammography examinations of 348 women was used in the study. Among these women, 174 had breast cancer detected and verified in the next sequential screening examinations, and 174 remained negative (cancer-free). A BBN was applied to predict the risk of each woman having cancer detected six to 18 months later following the negative screening mammography. The prediction results were compared with those using single features. The prediction accuracy was significantly increased when using the BBN. The area under the ROC curve increased from an AUC=0.70 to 0.84 (pbreast cancer risk than with a single feature.

  13. Transcription factor FOXA2-centered transcriptional regulation network in non-small cell lung cancer.

    Science.gov (United States)

    Jang, Sang-Min; An, Joo-Hee; Kim, Chul-Hong; Kim, Jung-Woong; Choi, Kyung-Hee

    2015-08-01

    Lung cancer is the leading cause of cancer-mediated death. Although various therapeutic approaches are used for lung cancer treatment, these mainly target the tumor suppressor p53 transcription factor, which is involved in apoptosis and cell cycle arrest. However, p53-targeted therapies have limited application in lung cancer, since p53 is found to be mutated in more than half of lung cancers. In this study, we propose tumor suppressor FOXA2 as an alternative target protein for therapies against lung cancer and reveal a possible FOXA2-centered transcriptional regulation network by identifying new target genes and binding partners of FOXA2 by using various screening techniques. The genes encoding Glu/Asp-rich carboxy-terminal domain 2 (CITED2), nuclear receptor subfamily 0, group B, member 2 (NR0B2), cell adhesion molecule 1 (CADM1) and BCL2-associated X protein (BAX) were identified as putative target genes of FOXA2. Additionally, the proteins including highly similar to heat shock protein HSP 90-beta (HSP90A), heat shock 70 kDa protein 1A variant (HSPA1A), histone deacetylase 1 (HDAC1) and HDAC3 were identified as novel interacting partners of FOXA2. Moreover, we showed that FOXA2-dependent promoter activation of BAX and p21 genes is significantly reduced via physical interactions between the identified binding partners and FOXA2. These results provide opportunities to understand the FOXA2-centered transcriptional regulation network and novel therapeutic targets to modulate this network in p53-deficient lung cancer.

  14. Offline Social Relationships and Online Cancer Communication: Effects of Social and Family Support on Online Social Network Building.

    Science.gov (United States)

    Namkoong, Kang; Shah, Dhavan V; Gustafson, David H

    2016-11-08

    This study investigates how social support and family relationship perceptions influence breast cancer patients' online communication networks in a computer-mediated social support (CMSS) group. To examine social interactions in the CMSS group, we identified two types of online social networks: open and targeted communication networks. The open communication network reflects group communication behaviors (i.e., one-to-many or "broadcast" communication) in which the intended audience is not specified; in contrast, the targeted communication network reflects interpersonal discourses (i.e., one-to-one or directed communication) in which the audience for the message is specified. The communication networks were constructed by tracking CMSS group usage data of 237 breast cancer patients who participated in one of two National Cancer Institute-funded randomized clinical trials. Eligible subjects were within 2 months of a diagnosis of primary breast cancer or recurrence at the time of recruitment. Findings reveal that breast cancer patients who perceived less availability of offline social support had a larger social network size in the open communication network. In contrast, those who perceived less family cohesion had a larger targeted communication network in the CMSS group, meaning they were inclined to use the CMSS group for developing interpersonal relationships.

  15. Global Analysis of miRNA-mRNA Interaction Network in Breast Cancer with Brain Metastasis.

    Science.gov (United States)

    Li, Zhixin; Peng, Zhiqiang; Gu, Siyu; Zheng, Junfang; Feng, Duiping; Qin, Qiong; He, Junqi

    2017-08-01

    MicroRNAs (miRNAs) have been linked to a number of cancer types including breast cancer. The rate of brain metastases is 10-30% in patients with advanced breast cancer which is associated with poor prognosis. The potential application of miRNAs in the diagnostics and therapeutics of breast cancer with brain metastasis is an area of intense interest. In an initial effort to systematically address the differential expression of miRNAs and mRNAs in primary breast cancer which may provide clues for early detection of brain metastasis, we analyzed the consequent changes in global patterns of gene expression in Gene Expression Omnibus (GEO) data set obtained by microarray from patients with in situ carcinoma and patients with brain metastasis. The miRNA-pathway regulatory network and miRNA-mRNA regulatory network were investigated in breast cancer specimens from patients with brain metastasis to screen for significantly dysregulated miRNAs followed by prediction of their target genes and pathways by Gene Ontology (GO) analysis. Functional coordination of the changes of gene expression can be modulated by individual miRNAs. Two miRNAs, hsa-miR-17-5p and hsa-miR-16-5p, were identified as having the highest associations with targeted mRNAs [such as B-cell lymphoma 2 (BCL2), small body size/mothers against decapentaplegic 3 (SMAD3) and suppressor of cytokine signaling 1 (SOCS1)] and pathways associated with epithelial-mesenchymal transitions and other processes linked with cancer metastasis (including cell cycle, adherence junctions and extracellular matrix-receptor interaction). mRNAs for two genes [HECT, UBA and WWE domain containing 1 (HUWE1) and BCL2] were found to have the highest associations with miRNAs, which were down-regulated in brain metastasis specimens of breast cancer. The change of 11 selected miRNAs was verified in The Cancer Genome Atlas (TCGA) breast cancer dataset. Up-regulation of hsa-miR-17-5p was detected in triple-negative breast cancer tissues in

  16. Leadership in complex networks: the importance of network position and strategic action in a translational cancer research network.

    Science.gov (United States)

    Long, Janet C; Cunningham, Frances C; Wiley, Janice; Carswell, Peter; Braithwaite, Jeffrey

    2013-10-11

    Leadership behaviour in complex networks is under-researched, and little has been written concerning leadership of translational research networks (TRNs) that take discoveries made 'at the bench' and translate them into practices used 'at the bedside.' Understanding leaders' opportunities and behaviours within TRNs working to solve this key problem in implementing evidence into clinical practice is therefore important. This study explored the network position of governing body members and perceptions of their role in a new TRN in Sydney, Australia. The paper asks three questions: Firstly, do the formal, mandated leaders of this TRN hold key positions of centrality or brokerage in the informal social network of collaborative ties? Secondly, if so, do they recognise the leadership opportunities that their network positions afford them? Thirdly, what activities associated with these key roles do they believe will maximise the TRN's success? Semi-structured interviews of all 14 governing body members conducted in early 2012 explored perceptions of their roles and sought comments on a list of activities drawn from review of successful transdisciplinary collaboratives combined with central and brokerage roles. An on-line, whole network survey of all 68 TRN members sought to understand and map existing collaborative connections. Leaders' positions in the network were assessed using UCInet, and graphs were generated in NetDraw. Social network analysis identified that governing body members had high centrality and high brokerage potential in the informal network of work-related ties. Interviews showed perceived challenges including 'silos' and the mismatch between academic and clinical goals of research. Governing body members recognised their central positions, which would facilitate the leadership roles of leading, making decisions, and providing expert advice necessary for the co-ordination of effort and relevant input across domains. Brokerage potential was recognised

  17. CTD² Dashboard: a searchable web interface to connect validated results from the Cancer Target Discovery and Development Network | Office of Cancer Genomics

    Science.gov (United States)

    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.

  18. Role of the lncRNA-p53 regulatory network in cancer.

    Science.gov (United States)

    Zhang, Ali; Xu, Min; Mo, Yin-Yuan

    2014-06-01

    Advances in functional genomics have led to discovery of a large group of previous uncharacterized long non-coding RNAs (lncRNAs). Emerging evidence indicates that lncRNAs may serve as master gene regulators through various mechanisms. Dysregulation of lncRNAs is often associated with a variety of human diseases including cancer. Of significant interest, recent studies suggest that lncRNAs participate in the p53 tumor suppressor regulatory network. In this review, we discuss how lncRNAs serve as p53 regulators or p53 effectors. Further characterization of these p53-associated lncRNAs in cancer will provide a better understanding of lncRNA-mediated gene regulation in the p53 pathway. As a result, lncRNAs may prove to be valuable biomarkers for cancer diagnosis or potential targets for cancer therapy.

  19. A divide-and-conquer linear scaling three dimensional fragment method for large scale electronic structure calculations

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Lin-Wang; Zhao, Zhengji; Meza, Juan; Wang, Lin-Wang

    2008-07-11

    We present a new linear scaling ab initio total energy electronic structure calculation method based on the divide-and-conquer strategy. This method is simple to implement, easily to parallelize, and produces very accurate results when compared with the direct ab initio method. The method has been tested using up to 8,000 processors, and has been used to calculate nanosystems up to 15,000 atoms.

  20. An Optimal Control of Bone Marrow in Cancer Chemotherapy by Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    H. Hosseinipour

    2015-09-01

    Full Text Available Although neural network models for cancer chemotherapy have been analyzed since the early seventies, less research has been done in actually formulating them as optimal control problems. In this paper an artificial neural networks-based method for optimal control of bone marrow in cell-cycle-specific chemotherapy is proposed. In this method, we use artificial neural networks for approximating the optimal control problem which maximizes both bone marrow mass and drug`s dose at the same time. The corresponding model be transfer to Hamiltonian function and using Pontryagin principle we create the boundary conditions. After defining boundary conditions, we use the approximating property of artificial networks and put the boundary conditions in error functions to satisfy the limitations..

  1. Causal Network Models for Predicting Compound Targets and Driving Pathways in Cancer.

    Science.gov (United States)

    Jaeger, Savina; Min, Junxia; Nigsch, Florian; Camargo, Miguel; Hutz, Janna; Cornett, Allen; Cleaver, Stephen; Buckler, Alan; Jenkins, Jeremy L

    2014-06-01

    Gene-expression data are often used to infer pathways regulating transcriptional responses. For example, differentially expressed genes (DEGs) induced by compound treatment can help characterize hits from phenotypic screens, either by correlation with known drug signatures or by pathway enrichment. Pathway enrichment is, however, typically computed with DEGs rather than "upstream" nodes that are potentially causal of "downstream" changes. Here, we present graph-based models to predict causal targets from compound-microarray data. We test several approaches to traversing network topology, and show that a consensus minimum-rank score (SigNet) beat individual methods and could highly rank compound targets among all network nodes. In addition, larger, less canonical networks outperformed linear canonical interactions. Importantly, pathway enrichment using causal nodes rather than DEGs recovers relevant pathways more often. To further validate our approach, we used integrated data sets from the Cancer Genome Atlas to identify driving pathways in triple-negative breast cancer. Critical pathways were uncovered, including the epidermal growth factor receptor 2-phosphatidylinositide 3-kinase-AKT-MAPK growth pathway andATR-p53-BRCA DNA damage pathway, in addition to unexpected pathways, such as TGF-WNT cytoskeleton remodeling, IL12-induced interferon gamma production, and TNFR-IAP (inhibitor of apoptosis) apoptosis; the latter was validated by pooled small hairpin RNA profiling in cancer cells. Overall, our approach can bridge transcriptional profiles to compound targets and driving pathways in cancer.

  2. Extending pathways and processes using molecular interaction networks to analyse cancer genome data

    Directory of Open Access Journals (Sweden)

    Krasnogor Natalio

    2010-12-01

    Full Text Available Abstract Background Cellular processes and pathways, whose deregulation may contribute to the development of cancers, are often represented as cascades of proteins transmitting a signal from the cell surface to the nucleus. However, recent functional genomic experiments have identified thousands of interactions for the signalling canonical proteins, challenging the traditional view of pathways as independent functional entities. Combining information from pathway databases and interaction networks obtained from functional genomic experiments is therefore a promising strategy to obtain more robust pathway and process representations, facilitating the study of cancer-related pathways. Results We present a methodology for extending pre-defined protein sets representing cellular pathways and processes by mapping them onto a protein-protein interaction network, and extending them to include densely interconnected interaction partners. The added proteins display distinctive network topological features and molecular function annotations, and can be proposed as putative new components, and/or as regulators of the communication between the different cellular processes. Finally, these extended pathways and processes are used to analyse their enrichment in pancreatic mutated genes. Significant associations between mutated genes and certain processes are identified, enabling an analysis of the influence of previously non-annotated cancer mutated genes. Conclusions The proposed method for extending cellular pathways helps to explain the functions of cancer mutated genes by exploiting the synergies of canonical knowledge and large-scale interaction data.

  3. Network-based approaches for drug response prediction and targeted therapy development in cancer.

    Science.gov (United States)

    Dorel, Mathurin; Barillot, Emmanuel; Zinovyev, Andrei; Kuperstein, Inna

    2015-08-21

    Signaling pathways implicated in cancer create a complex network with numerous regulatory loops and redundant pathways. This complexity explains frequent failure of one-drug-one-target paradigm of treatment, resulting in drug resistance in patients. To overcome the robustness of cell signaling network, cancer treatment should be extended to a combination therapy approach. Integrating and analyzing patient high-throughput data together with the information about biological signaling machinery may help deciphering molecular patterns specific to each patient and finding the best combinations of candidates for therapeutic targeting. We review state of the art in the field of targeted cancer medicine from the computational systems biology perspective. We summarize major signaling network resources and describe their characteristics with respect to applicability for drug response prediction and intervention targets suggestion. Thus discuss methods for prediction of drug sensitivity and intervention combinations using signaling networks together with high-throughput data. Gradual integration of these approaches into clinical routine will improve prediction of response to standard treatments and adjustment of intervention schemes.

  4. Control of cancer-related signal transduction networks

    Science.gov (United States)

    Albert, Reka

    2013-03-01

    Intra-cellular signaling networks are crucial to the maintenance of cellular homeostasis and for cell behavior (growth, survival, apoptosis, movement). Mutations or alterations in the expression of elements of cellular signaling networks can lead to incorrect behavioral decisions that could result in tumor development and/or the promotion of cell migration and metastasis. Thus, mitigation of the cascading effects of such dysregulations is an important control objective. My group at Penn State is collaborating with wet-bench biologists to develop and validate predictive models of various biological systems. Over the years we found that discrete dynamic modeling is very useful in molding qualitative interaction information into a predictive model. We recently demonstrated the effectiveness of network-based targeted manipulations on mitigating the disease T cell large granular lymphocyte (T-LGL) leukemia. The root of this disease is the abnormal survival of T cells which, after successfully fighting an infection, should undergo programmed cell death. We synthesized the relevant network of within-T-cell interactions from the literature, integrated it with qualitative knowledge of the dysregulated (abnormal) states of several network components, and formulated a Boolean dynamic model. The model indicated that the system possesses a steady state corresponding to the normal cell death state and a T-LGL steady state corresponding to the abnormal survival state. For each node, we evaluated the restorative manipulation consisting of maintaining the node in the state that is the opposite of its T-LGL state, e.g. knocking it out if it is overexpressed in the T-LGL state. We found that such control of any of 15 nodes led to the disappearance of the T-LGL steady state, leaving cell death as the only potential outcome from any initial condition. In four additional cases the probability of reaching the T-LGL state decreased dramatically, thus these nodes are also possible control

  5. Randomized Trial of a Social Networking Intervention for Cancer-Related Distress.

    Science.gov (United States)

    Owen, Jason E; O'Carroll Bantum, Erin; Pagano, Ian S; Stanton, Annette

    2017-02-27

    Web and mobile technologies appear to hold promise for delivering evidence-informed and evidence-based intervention to cancer survivors and others living with trauma and other psychological concerns. Health-space.net was developed as a comprehensive online social networking and coping skills training program for cancer survivors living with distress. The purpose of this study was to evaluate the effects of a 12-week social networking intervention on distress, depression, anxiety, vigor, and fatigue in cancer survivors reporting high levels of cancer-related distress. We recruited 347 participants from a local cancer registry and internet, and all were randomized to either a 12-week waiting list control group or to immediate access to the intervention. Intervention participants received secure access to the study website, which provided extensive social networking capabilities and coping skills training exercises facilitated by a professional facilitator. Across time, the prevalence of clinically significant depression symptoms declined from 67 to 34 % in both conditions. The health-space.net intervention had greater declines in fatigue than the waitlist control group, but the intervention did not improve outcomes for depression, trauma-related anxiety symptoms, or overall mood disturbance. For those with more severe levels of anxiety at baseline, greater engagement with the intervention was associated with higher levels of symptom reduction over time. The intervention resulted in small but significant effects on fatigue but not other primary or secondary outcomes. Results suggest that this social networking intervention may be most effective for those who have distress that is not associated with high levels of anxiety symptoms or very poor overall psychological functioning. The trial was registered with the ClinicalTrials.gov database ( ClinicalTrials.gov #NCT01976949).

  6. Prediction of key genes in ovarian cancer treated with decitabine based on network strategy.

    Science.gov (United States)

    Wang, Yu-Zhen; Qiu, Sheng-Chun

    2016-06-01

    The objective of the present study was to predict key genes in ovarian cancer before and after treatment with decitabine utilizing a network approach and to reveal the molecular mechanism. Pathogenic networks of ovarian cancer before and after treatment were identified based on known pathogenic genes (seed genes) and differentially expressed genes (DEGs) detected by Significance Analysis of Microarrays (SAM) method. A weight was assigned to each gene in the pathogenic network and then candidate genes were evaluated. Topological properties (degree, betweenness, closeness and stress) of candidate genes were analyzed to investigate more confident pathogenic genes. Pathway enrichment analysis for candidate and seed genes were conducted. Validation of candidate gene expression in ovarian cancer was performed by reverse transcriptase-polymerase chain reaction (RT-PCR) assays. There were 73 nodes and 147 interactions in the pathogenic network before treatment, while 47 nodes and 66 interactions after treatment. A total of 32 candidate genes were identified in the before treatment group of ovarian cancer, of which 16 were rightly candidate genes after treatment and the others were silenced. We obtained 5 key genes (PIK3R2, CCNB1, IL2, IL1B and CDC6) for decitabine treatment that were validated by RT-PCR. In conclusion, we successfully identified 5 key genes (PIK3R2, CCNB1, IL2, IL1B and CDC6) and validated them, which provides insight into the molecular mechanisms of decitabine treatment and may be potential pathogenic biomarkers for the therapy of ovarian cancer.

  7. Ab-Initio-Based Approach to Study Complete Metalloproteins: Divide and Conquer Geometry Optimization of Nitric-Oxide Reductase

    Science.gov (United States)

    Yue, Yutao; Chachiyo, Teepanis; Rodriguez, Jorge H.

    2007-03-01

    The direct application of ab-initio methods (Hartree-Fock or density functional theory) to study complete biomolecules has been impossible due to the huge computational cost of fully quantum mechanical calculations. As an initial step towards overcoming this problem, we implemented an ab-initio-based method to predict geometric structures of large metalloproteins using the principle of ``divide and conquer.'' The method has been applied to small test systems showing satisfactory agreement with all-atom ab initio calculations. We have successfully applied the divide and conquer approach to partially optimize the geometry of a ligand-enzyme system, namely NO binding to nitric-oxide reductases (NOR, P450nor). NOR is a metalloenzyme that catalyzes the reduction of NO to N2O. To compare our results with all atom calculations we studied a biochemically relevant subsystem (375 atoms) of the ligand-enzyme complex. The deviation between the divide and conquer geometry and the all atom partial geometry optimization is minor, on order of 10-1 å for bond lengths. The computational cost of the method is moderately expensive making its application to large (bio) molecules plausible. Supported by NSF CAREER Award CHE-0349189 (JHR).

  8. Social networking site usage among childhood cancer survivors--a potential tool for research recruitment?

    Science.gov (United States)

    Seltzer, Erica D; Stolley, Melinda R; Mensah, Edward K; Sharp, Lisa K

    2014-09-01

    The recent and rapid growth of social networking site (SNS) use presents a unique public health opportunity to develop effective strategies for the recruitment of hard-to-reach participants for cancer research studies. This survey investigated childhood cancer survivors' reported use of SNS such as Facebook or MySpace and their perceptions of using SNS, for recruitment into survivorship research. Sixty White, Black, and Hispanic adult childhood cancer survivors (range 18-48 years of age) that were randomly selected from a larger childhood cancer study, the Chicago Healthy Living Study, participated in this pilot survey. Telephone surveys were conducted to understand current SNS activity and attitudes towards using SNS as a cancer research recruitment tool. Seventy percent of participants reported SNS usage of which 80 % were at least weekly users and 79 % reported positive attitudes towards the use of SNS as a recruitment tool for survivorship research. The results of this pilot study revealed that SNS use was high and regular among the childhood cancer survivors sampled. Most had positive attitudes towards using SNS for recruitment of research. The results of this pilot survey suggest that SNS may offer an alternative approach for recruitment of childhood cancer survivors into research.

  9. Usage of Probabilistic and General Regression Neural Network for Early Detection and Prevention of Oral Cancer

    Directory of Open Access Journals (Sweden)

    Neha Sharma

    2015-01-01

    Full Text Available In India, the oral cancers are usually presented in advanced stage of malignancy. It is critical to ascertain the diagnosis in order to initiate most advantageous treatment of the suspicious lesions. The main hurdle in appropriate treatment and control of oral cancer is identification and risk assessment of early disease in the community in a cost-effective fashion. The objective of this research is to design a data mining model using probabilistic neural network and general regression neural network (PNN/GRNN for early detection and prevention of oral malignancy. The model is built using the oral cancer database which has 35 attributes and 1025 records. All the attributes pertaining to clinical symptoms and history are considered to classify malignant and non-malignant cases. Subsequently, the model attempts to predict particular type of cancer, its stage and extent with the help of attributes pertaining to symptoms, gross examination and investigations. Also, the model envisages anticipating the survivability of a patient on the basis of treatment and follow-up details. Finally, the performance of the PNN/GRNN model is compared with that of other classification models. The classification accuracy of PNN/GRNN model is 80% and hence is better for early detection and prevention of the oral cancer.

  10. Usage of Probabilistic and General Regression Neural Network for Early Detection and Prevention of Oral Cancer.

    Science.gov (United States)

    Sharma, Neha; Om, Hari

    2015-01-01

    In India, the oral cancers are usually presented in advanced stage of malignancy. It is critical to ascertain the diagnosis in order to initiate most advantageous treatment of the suspicious lesions. The main hurdle in appropriate treatment and control of oral cancer is identification and risk assessment of early disease in the community in a cost-effective fashion. The objective of this research is to design a data mining model using probabilistic neural network and general regression neural network (PNN/GRNN) for early detection and prevention of oral malignancy. The model is built using the oral cancer database which has 35 attributes and 1025 records. All the attributes pertaining to clinical symptoms and history are considered to classify malignant and non-malignant cases. Subsequently, the model attempts to predict particular type of cancer, its stage and extent with the help of attributes pertaining to symptoms, gross examination and investigations. Also, the model envisages anticipating the survivability of a patient on the basis of treatment and follow-up details. Finally, the performance of the PNN/GRNN model is compared with that of other classification models. The classification accuracy of PNN/GRNN model is 80% and hence is better for early detection and prevention of the oral cancer.

  11. The meaning and validation of social support networks for close family of persons with advanced cancer

    Directory of Open Access Journals (Sweden)

    Sjolander Catarina

    2012-09-01

    Full Text Available Abstract Background To strengthen the mental well-being of close family of persons newly diagnosed as having cancer, it is necessary to acquire a greater understanding of their experiences of social support networks, so as to better assess what resources are available to them from such networks and what professional measures are required. The main aim of the present study was to explore the meaning of these networks for close family of adult persons in the early stage of treatment for advanced lung or gastrointestinal cancer. An additional aim was to validate the study’s empirical findings by means of the Finfgeld-Connett conceptual model for social support. The intention was to investigate whether these findings were in accordance with previous research in nursing. Methods Seventeen family members with a relative who 8–14 weeks earlier had been diagnosed as having lung or gastrointestinal cancer were interviewed. The data were subjected to qualitative latent content analysis and validated by means of identifying antecedents and critical attributes. Results The meaning or main attribute of the social support network was expressed by the theme Confirmation through togetherness, based on six subthemes covering emotional and, to a lesser extent, instrumental support. Confirmation through togetherness derived principally from information, understanding, encouragement, involvement and spiritual community. Three subthemes were identified as the antecedents to social support: Need of support, Desire for a deeper relationship with relatives, Network to turn to. Social support involves reciprocal exchange of verbal and non-verbal information provided mainly by lay persons. Conclusions The study provides knowledge of the antecedents and attributes of social support networks, particularly from the perspective of close family of adult persons with advanced lung or gastrointestinal cancer. There is a need for measurement instruments that could

  12. Systems-level cancer gene identification from protein interaction network topology applied to melanogenesis-related functional genomics data.

    Science.gov (United States)

    Milenkovic, Tijana; Memisevic, Vesna; Ganesan, Anand K; Przulj, Natasa

    2010-03-06

    Many real-world phenomena have been described in terms of large networks. Networks have been invaluable models for the understanding of biological systems. Since proteins carry out most biological processes, we focus on analysing protein-protein interaction (PPI) networks. Proteins interact to perform a function. Thus, PPI networks reflect the interconnected nature of biological processes and analysing their structural properties could provide insights into biological function and disease. We have already demonstrated, by using a sensitive graph theoretic method for comparing topologies of node neighbourhoods called 'graphlet degree signatures', that proteins with similar surroundings in PPI networks tend to perform the same functions. Here, we explore whether the involvement of genes in cancer suggests the similarity of their topological 'signatures' as well. By applying a series of clustering methods to proteins' topological signature similarities, we demonstrate that the obtained clusters are significantly enriched with cancer genes. We apply this methodology to identify novel cancer gene candidates, validating 80 per cent of our predictions in the literature. We also validate predictions biologically by identifying cancer-related negative regulators of melanogenesis identified in our siRNA screen. This is encouraging, since we have done this solely from PPI network topology. We provide clear evidence that PPI network structure around cancer genes is different from the structure around non-cancer genes. Understanding the underlying principles of this phenomenon is an open question, with a potential for increasing our understanding of complex diseases.

  13. Barrett's Esophagus Translational Research Network (BETRNet) | Division of Cancer Prevention

    Science.gov (United States)

    The goal of BETRNet is to reduce the incidence, morbidity, and mortality of esophageal adenocarcinoma by answering key questions related to the progression of the disease, especially in the premalignant stage. In partnership with NCI’s Division of Cancer Biology, multidisciplinary translational research centers collaborate to better understand the biology of Barrett's esophagus and esophageal adenocarcinoma to improve risk stratification and develop prevention strategies.  | Multi-disciplinary, multi-institutional collaboration to enhance understanding of Barrett's esophagus and to prevent esophageal adenocarcinoma.

  14. Cancer

    Science.gov (United States)

    ... cancer Non-Hodgkin lymphoma Ovarian cancer Pancreatic cancer Testicular cancer Thyroid cancer Uterine cancer Symptoms Symptoms of cancer ... tumor Obesity Pancreatic cancer Prostate cancer Stomach cancer Testicular cancer Throat or larynx cancer Thyroid cancer Patient Instructions ...

  15. National Cancer Information Service in Italy: an information points network as a new model for providing information for cancer patients.

    Science.gov (United States)

    Truccolo, Ivana; Bufalino, Rosaria; Annunziata, Maria Antonietta; Caruso, Anita; Costantini, Anna; Cognetti, Gaetana; Florita, Antonio; Pero, Dina; Pugliese, Patrizia; Tancredi, Roberta; De Lorenzo, Francesco

    2011-01-01

    The international literature data report that good information and communication are fundamental components of a therapeutic process. They contribute to improve the patient-health care professional relationship, to facilitate doctor-patient relationships, therapeutic compliance and adherence, and to the informed consent in innovative clinical trials. We report the results of a multicentric national initiative that developed a 17-information-structure network: 16 Information Points located in the major state-funded certified cancer centers and general hospitals across Italy and a national Help-line at the nonprofit organization AIMaC (the Italian oncologic patients, families and friends association), and updated the already existing services with the aim to create the National Cancer Information Service (SION). The project is the result of a series of pilot and research projects funded by the Italian Ministry of Health. The Information Service model proposed is based on some fundamental elements: 1) human interaction with experienced operators, adequately trained in communication and information, complemented with 2) virtual interaction (Help line, Internet, blog, forum and social network); 3) informative material adequate for both scientific accuracy and communicative style; 4) adequate locations for appropriate positioning and privacy (adequate visibility); 5) appropriate advertising. First results coming from these initiatives contributed to introduce issues related to "Communication and Information to patients" as a "Public Health Instrument" to the National Cancer Plan approved by the Ministry of Health for the years 2010-2012.

  16. Median Filter Noise Reduction of Image and Backpropagation Neural Network Model for Cervical Cancer Classification

    Science.gov (United States)

    Wutsqa, D. U.; Marwah, M.

    2017-06-01

    In this paper, we consider spatial operation median filter to reduce the noise in the cervical images yielded by colposcopy tool. The backpropagation neural network (BPNN) model is applied to the colposcopy images to classify cervical cancer. The classification process requires an image extraction by using a gray level co-occurrence matrix (GLCM) method to obtain image features that are used as inputs of BPNN model. The advantage of noise reduction is evaluated by comparing the performances of BPNN models with and without spatial operation median filter. The experimental result shows that the spatial operation median filter can improve the accuracy of the BPNN model for cervical cancer classification.

  17. An Efficient Similarity Digests Database Lookup - A Logarithmic Divide & Conquer Approach

    Directory of Open Access Journals (Sweden)

    Frank Breitinger

    2014-09-01

    Full Text Available Investigating seized devices within digital forensics represents a challenging task due to the increasing amount of data. Common procedures utilize automated file identification, which reduces the amount of data an investigator has to examine manually. In the past years the research field of approximate matching arises to detect similar data. However, if n denotes the number of similarity digests in a database, then the lookup for a single similarity digest is of complexity of O(n. This paper presents a concept to extend existing approximate matching algorithms, which reduces the lookup complexity from O(n to O(log(n. Our proposed approach is based on the well-known divide and conquer paradigm and builds a Bloom filter-based tree data structure in order to enable an efficient lookup of similarity digests. Further, it is demonstrated that the presented technique is highly scalable operating a trade-off between storage requirements and computational efficiency. We perform a theoretical assessment based on recently published results and reasonable magnitudes of input data, and show that the complexity reduction achieved by the proposed technique yields a 220-fold acceleration of look-up costs.

  18. Interactive quantum chemistry: a divide-and-conquer ASED-MO method.

    Science.gov (United States)

    Bosson, Mäel; Richard, Caroline; Plet, Antoine; Grudinin, Sergei; Redon, Stephane

    2012-03-15

    We present interactive quantum chemistry simulation at the atom superposition and electron delocalization molecular orbital (ASED-MO) level of theory. Our method is based on the divide-and-conquer (D&C) approach, which we show is accurate and efficient for this non-self-consistent semiempirical theory. The method has a linear complexity in the number of atoms, scales well with the number of cores, and has a small prefactor. The time cost is completely controllable, as all steps are performed with direct algorithms, i.e., no iterative schemes are used. We discuss the errors induced by the D&C approach, first empirically on a few examples, and then via a theoretical study of two toy models that can be analytically solved for any number of atoms. Thanks to the precision and speed of the D&C approach, we are able to demonstrate interactive quantum chemistry simulations for systems up to a few hundred atoms on a current multicore desktop computer. When drawing and editing molecular systems, interactive simulations provide immediate, intuitive feedback on chemical structures. As the number of cores on personal computers increases, and larger and larger systems can be dealt with, we believe such interactive simulations-even at lower levels of theory-should thus prove most useful to effectively understand, design and prototype molecules, devices and materials.

  19. The potential, limitations, and challenges of divide and conquer quantum electronic structure calculations on energetic materials.

    Energy Technology Data Exchange (ETDEWEB)

    Tucker, Jon R.; Magyar, Rudolph J.

    2012-02-01

    High explosives are an important class of energetic materials used in many weapons applications. Even with modern computers, the simulation of the dynamic chemical reactions and energy release is exceedingly challenging. While the scale of the detonation process may be macroscopic, the dynamic bond breaking responsible for the explosive release of energy is fundamentally quantum mechanical. Thus, any method that does not adequately describe bonding is destined to lack predictive capability on some level. Performing quantum mechanics calculations on systems with more than dozens of atoms is a gargantuan task, and severe approximation schemes must be employed in practical calculations. We have developed and tested a divide and conquer (DnC) scheme to obtain total energies, forces, and harmonic frequencies within semi-empirical quantum mechanics. The method is intended as an approximate but faster solution to the full problem and is possible due to the sparsity of the density matrix in many applications. The resulting total energy calculation scales linearly as the number of subsystems, and the method provides a path-forward to quantum mechanical simulations of millions of atoms.

  20. A divide and conquer real space finite-element Hartree-Fock method

    Science.gov (United States)

    Alizadegan, R.; Hsia, K. J.; Martinez, T. J.

    2010-01-01

    Since the seminal contribution of Roothaan, quantum chemistry methods are traditionally expressed using finite basis sets comprised of smooth and continuous functions (atom-centered Gaussians) to describe the electronic degrees of freedom. Although this approach proved quite powerful, it is not well suited for large basis sets because of linear dependence problems and ill conditioning of the required matrices. The finite element method (FEM), on the other hand, is a powerful numerical method whose convergence is also guaranteed by variational principles and can be achieved systematically by increasing the number of degrees of freedom and/or the polynomial order of the shape functions. Here we apply the real-space FEM to Hartree-Fock calculations in three dimensions. The method produces sparse, banded Hermitian matrices while allowing for variable spatial resolution. This local-basis approach to electronic structure theory allows for systematic convergence and promises to provide an accurate and efficient way toward the full ab initio analysis of materials at larger scales. We introduce a new acceleration technique for evaluating the exchange contribution within FEM and explore the accuracy and robustness of the method for some selected test atoms and molecules. Furthermore, we applied a divide-and-conquer (DC) method to the finite-element Hartree-Fock ab initio electronic-structure calculations in three dimensions. This DC approach leads to facile parallelization and should enable reduced scaling for large systems.

  1. Parallel divide and conquer bio-sequence comparison based on Smith-Waterman algorithm

    Institute of Scientific and Technical Information of China (English)

    ZHANG Fa; QIAO Xiangzhen; LIU Zhiyong

    2004-01-01

    Tools for pair-wise bio-sequence alignment have for long played a central role in computation biology. Several algorithms for bio-sequence alignment have been developed. The Smith-Waterman algorithm, based on dynamic programming, is considered the most fundamental alignment algorithm in bioinformatics. However the existing parallel Smith-Waterman algorithm needs large memory space, and this disadvantage limits the size of a sequence to be handled. As the data of biological sequences expand rapidly, the memory requirement of the existing parallel SmithWaterman algorithm has become a critical problem. For solving this problem, we develop a new parallel bio-sequence alignment algorithm, using the strategy of divide and conquer, named PSW-DC algorithm. In our algorithm, first, we partition the query sequence into several subsequences and distribute them to every processor respectively,then compare each subsequence with the whole subject sequence in parallel, using the Smith-Waterman algorithm, and get an interim result, finally obtain the optimal alignment between the query sequence and subject sequence, through the special combination and extension method. Memory space required in our algorithm is reduced significantly in comparison with existing ones. We also develop a key technique of combination and extension, named the C&E method, to manipulate the interim results and obtain the final sequences alignment. We implement the new parallel bio-sequences alignment algorithm,the PSW-DC, in a cluster parallel system.

  2. Intraprotein electrostatics derived from first principles: divide-and-conquer approaches for QM/MM calculations.

    Science.gov (United States)

    Molina, Pablo A; Li, Hui; Jensen, Jan H

    2003-12-01

    Two divide-and-conquer (DAQ) approaches for building multipole-based molecular electrostatic potentials of proteins are presented and evaluated for use in QM/MM calculations. One approach is a further development of the neutralization method of Bellido and Rullmann (J Comput Chem 1989, 10, 479-487) while the other is based on removing part of the electron density before performing the multipole expansion. Both methods create systems with integer charges without using charge renormalization. To determine their performance in terms of location of cuts and distance to QM region, the new DAQ approaches are tested in calculations of the proton affinity of N(zeta) of Lys55 in the inhibitor turkey ovomucoid third domain. Finally, the two methods are used to build a variety of MM regions, applied to calculations of the pK(a) of Lys55, and compared to other computational methodologies in which force field charges are employed. Copyright 2003 Wiley Periodicals, Inc. J Comput Chem 24: 1971-1979, 2003

  3. Living with intestinal failure caused by Crohn disease: not letting the disease conquer life.

    Science.gov (United States)

    Carlsson, Eva; Persson, Eva

    2015-01-01

    This article reports the findings of what it means to live with intestinal failure caused by Crohn disease and how it influences daily life. Ten patients, 7 with an ostomy and 7 on home parenteral nutrition followed up at an outpatient clinic for patients with intestinal failure, were interviewed using a qualitative, phenomenological-hermeneutic method. The analysis of the transcribed data is described thematically and resulted in 3 main themes; (a) struggling to not be controlled by the disease, (b) walking on a thin thread, and (c) being seen as a person, not just as a patient. These themes led to the comprehensive understanding that living with intestinal failure was interpreted as the criticality of maintaining control over one's life and body while maintaining autonomy and not letting the disease conquer life. Life entails a constant struggle with much planning to live as normally as possible and get the most out of life. It was of great importance to be seen as a person and not just as a disease, affirm that life as it is has meaning, there is a state of suffering related to the disease, there are existential issues, and suffering is related to care.

  4. Neural Network Analysis of Breast Cancer from Mammographic Evaluation

    Directory of Open Access Journals (Sweden)

    P. Abdolmaleki

    2006-06-01

    Full Text Available Background/Objective: Mammographic differentiation of benign lesions from malignancies is a difficult task. We developed an artificial neural network (ANN as a diagnostic aid in mammography using radiographic features as input. Materials & Methods: A three-layered ANN was used to differentiate malignant from benign findings in a group of patients with proven breast lesions on the basis of morphological data extracted from conventional mammograms. Our database included 122 patient records on 14qualitative variables. The database was randomly divided into training and validation samples including 82 and 40 patient records, respectively, to construct the ANN and validate its performance. Sensitivity, specificity, accuracy and receiver operating characteristic curve (ROC analysis for this method and the radiologist were compared. Results: Our results showed that the neural network model was able to correctly classify 30 out of 40 cases presented in the validation sample. Comparing the output with that of the radiologist, showed a reasonable diagnostic accuracy (75%, a moderate specificity (64% and a relatively high sensitivity (89%. Conclusion: A diagnostic aid was developed that accurately differentiates malignant from benign pattern using radiological features extracted from mammograms.

  5. Selective detection of histologically aggressive prostate cancer: an Early Detection Research Network Prediction model to reduce unnecessary prostate biopsies with validation in the Prostate Cancer Prevention Trial.

    Science.gov (United States)

    Williams, Stephen B; Salami, Simpa; Regan, Meredith M; Ankerst, Donna P; Wei, John T; Rubin, Mark A; Thompson, Ian M; Sanda, Martin G

    2012-05-15

    Limited survival benefit and excess treatment because of prostate-specific antigen (PSA) screening in randomized trials suggests a need for more restricted selection of prostate biopsy candidates by discerning risk of histologically aggressive versus indolent cancer before biopsy. Subjects undergoing first prostate biopsy enrolled in a multicenter, prospective cohort of the National Cancer Institute Early Detection Research Network (N = 635) were analyzed to develop a model for predicting histologically aggressive prostate cancers. The control arm of the Prostate Cancer Prevention Trial (N = 3833) was used to validate the generalization of the predictive model. The Early Detection Research Network cohort was comprised of men among whom 57% had no cancer, 14% had indolent cancer, and 29% had aggressive cancer. Age, body mass index, family history of prostate cancer, abnormal digital rectal examination (DRE), and PSA density (PSAD) were associated with aggressive cancer (all P cancer (area under the curve [AUC] = 0.81 vs 0.71, P Prostate Cancer Prevention Trial cohort accurately identified men at low (cancer for whom biopsy could be averted (AUC = 0.78; 95% confidence interval, 0.75-0.80). Under criteria from the Early Detection Research Network model, prostate biopsy can be restricted to men with PSAD >0.1 ng/mL/cc or abnormal DRE. When PSAD is obesity can identify biopsy candidates. A predictive model incorporating age, family history, obesity, PSAD, and DRE elucidates criteria whereby ¼ of prostate biopsies can be averted while retaining high sensitivity in detecting aggressive prostate cancer. Copyright © 2011 American Cancer Society.

  6. Reconstruction and signal propagation analysis of the Syk signaling network in breast cancer cells

    Science.gov (United States)

    Urbach, Serge; Montcourrier, Philippe; Roy, Christian; Solassol, Jérôme; Freiss, Gilles; Radulescu, Ovidiu

    2017-01-01

    The ability to build in-depth cell signaling networks from vast experimental data is a key objective of computational biology. The spleen tyrosine kinase (Syk) protein, a well-characterized key player in immune cell signaling, was surprisingly first shown by our group to exhibit an onco-suppressive function in mammary epithelial cells and corroborated by many other studies, but the molecular mechanisms of this function remain largely unsolved. Based on existing proteomic data, we report here the generation of an interaction-based network of signaling pathways controlled by Syk in breast cancer cells. Pathway enrichment of the Syk targets previously identified by quantitative phospho-proteomics indicated that Syk is engaged in cell adhesion, motility, growth and death. Using the components and interactions of these pathways, we bootstrapped the reconstruction of a comprehensive network covering Syk signaling in breast cancer cells. To generate in silico hypotheses on Syk signaling propagation, we developed a method allowing to rank paths between Syk and its targets. We first annotated the network according to experimental datasets. We then combined shortest path computation with random walk processes to estimate the importance of individual interactions and selected biologically relevant pathways in the network. Molecular and cell biology experiments allowed to distinguish candidate mechanisms that underlie the impact of Syk on the regulation of cortactin and ezrin, both involved in actin-mediated cell adhesion and motility. The Syk network was further completed with the results of our biological validation experiments. The resulting Syk signaling sub-networks can be explored via an online visualization platform. PMID:28306714

  7. Artificial neural networks and prostate cancer--tools for diagnosis and management.

    Science.gov (United States)

    Hu, Xinhai; Cammann, Henning; Meyer, Hellmuth-A; Miller, Kurt; Jung, Klaus; Stephan, Carsten

    2013-03-01

    Artificial neural networks (ANNs) are mathematical models that are based on biological neural networks and are composed of interconnected groups of artificial neurons. ANNs are used to map and predict outcomes in complex relationships between given 'inputs' and sought-after 'outputs' and can also be used find patterns in datasets. In medicine, ANN applications have been used in cancer diagnosis, staging and recurrence prediction since the mid-1990s, when an enormous effort was initiated, especially in prostate cancer detection. Modern ANNs can incorporate new biomarkers and imaging data to improve their predictive power and can offer a number of advantages as clinical decision making tools, such as easy handling of distribution-free input parameters. Most importantly, ANNs consider nonlinear relationships among input data that cannot always be recognized by conventional analyses. In the future, complex medical diagnostic and treatment decisions will be increasingly based on ANNs and other multivariate models.

  8. Fine Needle Aspiration Cytology Evaluation for Classifying Breast Cancer Using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Nor A.M.   Isa

    2007-01-01

    Full Text Available Thirteen cytology of fine needle aspiration image (i.e. cellularity, background information, cohesiveness, significant stromal component, clump thickness, nuclear membrane, bare nuclei, normal nuclei, mitosis, nucleus stain, uniformity of cell, fragility and number of cells in cluster are evaluated their possibility to be used as input data for artificial neural network in order to classify the breast pre-cancerous cases into four stages, namely malignant, fibroadenoma, fibrocystic disease, and other benign diseases. A total of 1300 reported breast pre-cancerous cases which was collected from Penang General Hospital and Hospital Universiti Sains Malaysia, Kelantan, Malaysia was used to train and test the artificial neural networks. The diagnosis system which was developed using the Hybrid Multilayered Perceptron and trained using Modified Recursive Prediction Error produced excellent diagnosis performance with 100% accuracy, 100% sensitivity and 100% specificity.

  9. Noncoding RNAs in Cancer Immunology.

    Science.gov (United States)

    Li, Qian; Liu, Qiang

    2016-01-01

    Cancer immunology is the study of interaction between cancer cells and immune system by the application of immunology principle and theory. With the recent approval of several new drugs targeting immune checkpoints in cancer, cancer immunology has become a very attractive field of research and is thought to be the new hope to conquer cancer. This chapter introduces the aberrant expression and function of noncoding RNAs, mainly microRNAs and long noncoding RNAs, in tumor-infiltrating immune cells, and their significance in tumor immunity. It also illustrates how noncoding RNAs are shuttled between tumor cells and immune cells in tumor microenvironments via exosomes or other microvesicles to modulate tumor immunity.

  10. The structural network of Interleukin-10 and its implications in inflammation and cancer

    OpenAIRE

    Acuner-Özbabacan, Ece Saliha; Engin, Billur Hatice; Güven-Maiorov, Emine; Kuzu, Güray; Muratçıoğlu, Serena; Başpınar, Alper; Gürsoy, Attila; Chen, Zhong; Van Waes, Carter; Nussinov, Ruth

    2014-01-01

    RESEARCH Open Access The structural network of Interleukin-10 and its implications in inflammation and cancer Ece Saliha Acuner-Ozbabacan1, Billur Hatice Engin1, Emine Guven-Maiorov1, Guray Kuzu1, Serena Muratcioglu1, Alper Baspinar1, Zhong Chen3, Carter Van Waes3, Attila Gursoy1, Ozlem Keskin1, Ruth Nussinov2,4* From SNP-SIG 2013: Identification and annotation of genetic variants in the context of structure, function, and disease Berlin, Germany. 19 July 2013 Abstract...

  11. Assessment of the psychological distress difficulties in patients with cancer using the national comprehensive cancer network rapid screening measure

    Institute of Scientific and Technical Information of China (English)

    Hamid Saeedi Saedi; Mona Koochak Pour; Emad Sabahi; Soodabeh Shahidsales

    2012-01-01

    Objective: Clinical guidelines like National Comprehensive Cancer Network Disease recommend routine psychological distress screening as a common problem among patients with cancer. The purpose of this study was to assess the prevalence of clinically significant emotional distress related to demographic and clinical association by standard distress thermometer (DT) within the patients lived in different regions of Gilan state, Iran. Methods: Participants (n = 256) completed the DT, rapid screening measure for distress and identified the presence or absence of 34 problems using the standardized checklist. Results: More than 59 percent of participants had more than 4 cut-off score for distress. The scores varied significantly in case of reported emotional source of distress, physical, physiological and total number of concerns (P < 0.001).DT scores more than four were more likely to report 22 of 32 problems on the problem list. In case of the practical and family problems, the main problems were related to child care and dealing with children, respectively. Moreover worrisome and nervousness were considered the prominent emotional problems in the list. Conclusion: Our result promise that distress thermometer measurement tool compare favorably with longer measures used to screening of distress in cancerous patients. Accompaniment of a psychologist expert in lethal or chronic disease consultation with the therapeutic team and training the rest of members of the team might be able to decrease the emotional distress problems of the cancerous patients.

  12. Network-Based Integration of Disparate Omic Data To Identify "Silent Players" in Cancer.

    Directory of Open Access Journals (Sweden)

    Matthew Ruffalo

    2015-12-01

    Full Text Available Development of high-throughput monitoring technologies enables interrogation of cancer samples at various levels of cellular activity. Capitalizing on these developments, various public efforts such as The Cancer Genome Atlas (TCGA generate disparate omic data for large patient cohorts. As demonstrated by recent studies, these heterogeneous data sources provide the opportunity to gain insights into the molecular changes that drive cancer pathogenesis and progression. However, these insights are limited by the vast search space and as a result low statistical power to make new discoveries. In this paper, we propose methods for integrating disparate omic data using molecular interaction networks, with a view to gaining mechanistic insights into the relationship between molecular changes at different levels of cellular activity. Namely, we hypothesize that genes that play a role in cancer development and progression may be implicated by neither frequent mutation nor differential expression, and that network-based integration of mutation and differential expression data can reveal these "silent players". For this purpose, we utilize network-propagation algorithms to simulate the information flow in the cell at a sample-specific resolution. We then use the propagated mutation and expression signals to identify genes that are not necessarily mutated or differentially expressed genes, but have an essential role in tumor development and patient outcome. We test the proposed method on breast cancer and glioblastoma multiforme data obtained from TCGA. Our results show that the proposed method can identify important proteins that are not readily revealed by molecular data, providing insights beyond what can be gleaned by analyzing different types of molecular data in isolation.

  13. MicroRNA functional network in pancreatic cancer: From biology to biomarkers of disease

    Indian Academy of Sciences (India)

    Jin Wang; Subrata Sen

    2011-08-01

    MicroRNAs (miRs), the 17- to 25-nucleotide-long non-coding RNAs, regulate expression of approximately 30% of the protein-coding genes at the post-transcriptional level and have emerged as critical components of the complex functional pathway networks controlling important cellular processes, such as proliferation, development, differentiation, stress response' and apoptosis. Abnormal expression levels of miRs, regulating critical cancerassociated pathways, have been implicated to play important roles in the oncogenic processes, functioning both as oncogenes and as tumour suppressor genes. Elucidation of the genetic networks regulated by the abnormally expressing miRs in cancer cells is proving to be extremely significant in understanding the role of these miRs in the induction of malignant-transformation-associated phenotypic changes. As a result, the miRs involved in the oncogenic transformation process are being investigated as novel biomarkers of disease detection and prognosis as well as potential therapeutic targets for human cancers. In this \\article, we review the existing literature in the field documenting the significance of aberrantly expressed miRs in human pancreatic cancer and discuss how the oncogenic miRs may be involved in the genetic networks regulating functional pathways deregulated in this malignancy.

  14. Classification of Cancer Gene Selection Using Random Forest and Neural Network Based Ensemble Classifier

    Directory of Open Access Journals (Sweden)

    Jogendra Kushwah

    2013-06-01

    Full Text Available The free radical gene classification of cancerdiseasesis challenging job in biomedical dataengineering. The improving of classification of geneselection of cancer diseases various classifier areused, but the classification of classifier are notvalidate. So ensemble classifier is used for cancergene classification using neural network classifierwith random forest tree. The random forest tree isensembling technique of classifier in this techniquethe number of classifier ensemble of their leaf nodeof class of classifier. In this paper we combinedneuralnetwork with random forest ensembleclassifier for classification of cancer gene selectionfor diagnose analysis of cancer diseases.Theproposed method is different from most of themethods of ensemble classifier, which follow aninput output paradigm ofneural network, where themembers of the ensemble are selected from a set ofneural network classifier. the number of classifiersis determined during the rising procedure of theforest. Furthermore, the proposed method producesan ensemble not only correct, but also assorted,ensuring the two important properties that shouldcharacterize an ensemble classifier. For empiricalevaluation of our proposed method we used UCIcancer diseases data set for classification. Ourexperimental result shows that betterresult incompression of random forest tree classification

  15. The use of gene interaction networks to improve the identification of cancer driver genes

    Directory of Open Access Journals (Sweden)

    Emilie Ramsahai

    2017-01-01

    Full Text Available Bioinformaticians have implemented different strategies to distinguish cancer driver genes from passenger genes. One of the more recent advances uses a pathway-oriented approach. Methods that employ this strategy are highly dependent on the quality and size of the pathway interaction network employed, and require a powerful statistical environment for analyses. A number of genomic libraries are available in R. DriverNet and DawnRank employ pathway-based methods that use gene interaction graphs in matrix form. We investigated the benefit of combining data from 3 different sources on the prediction outcome of cancer driver genes by DriverNet and DawnRank. An enriched dataset was derived comprising 13,862 genes with 372,250 interactions, which increased its accuracy by 17% and 28%, respectively, compared to their original networks. The study identified 33 new candidate driver genes. Our study highlights the potential of combining networks and weighting edges to provide greater accuracy in the identification of cancer driver genes.

  16. The network of pluripotency, epithelial–mesenchymal transition, and prognosis of breast cancer

    Directory of Open Access Journals (Sweden)

    Voutsadakis IA

    2015-09-01

    Full Text Available Ioannis A Voutsadakis1,2 1Division of Medical Oncology, Department of Internal Medicine, Sault Area Hospital, Sault Ste Marie, ON, Canada; 2Division of Clinical Sciences, Northern Ontario School of Medicine, Sudbury, ON, Canada Abstract: Breast cancer is the leading female cancer in terms of prevalence. Progress in molecular biology has brought forward a better understanding of its pathogenesis that has led to better prognostication and treatment. Subtypes of breast cancer have been identified at the genomic level and guide therapeutic decisions based on their biology and the expected benefit from various interventions. Despite this progress, a significant percentage of patients die from their disease and further improvements are needed. The cancer stem cell theory and the epithelial–mesenchymal transition are two comparatively novel concepts that have been introduced in the area of cancer research and are actively investigated. Both processes have their physiologic roots in normal development and common mediators have begun to surface. This review discusses the associations of these networks as a prognostic framework in breast cancer. Keywords: stem cells, epithelial-to-mesenchymal transition, mesenchymal-to-epithelial transition

  17. Gene expression patterns combined with network analysis identify hub genes associated with bladder cancer.

    Science.gov (United States)

    Bi, Dongbin; Ning, Hao; Liu, Shuai; Que, Xinxiang; Ding, Kejia

    2015-06-01

    To explore molecular mechanisms of bladder cancer (BC), network strategy was used to find biomarkers for early detection and diagnosis. The differentially expressed genes (DEGs) between bladder carcinoma patients and normal subjects were screened using empirical Bayes method of the linear models for microarray data package. Co-expression networks were constructed by differentially co-expressed genes and links. Regulatory impact factors (RIF) metric was used to identify critical transcription factors (TFs). The protein-protein interaction (PPI) networks were constructed by the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and clusters were obtained through molecular complex detection (MCODE) algorithm. Centralities analyses for complex networks were performed based on degree, stress and betweenness. Enrichment analyses were performed based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Co-expression networks and TFs (based on expression data of global DEGs and DEGs in different stages and grades) were identified. Hub genes of complex networks, such as UBE2C, ACTA2, FABP4, CKS2, FN1 and TOP2A, were also obtained according to analysis of degree. In gene enrichment analyses of global DEGs, cell adhesion, proteinaceous extracellular matrix and extracellular matrix structural constituent were top three GO terms. ECM-receptor interaction, focal adhesion, and cell cycle were significant pathways. Our results provide some potential underlying biomarkers of BC. However, further validation is required and deep studies are needed to elucidate the pathogenesis of BC. Copyright © 2015 Elsevier Ltd. All rights reserved.

  18. A critical evaluation of network and pathway based classifiers for outcome prediction in breast cancer

    CERN Document Server

    Staiger, C; Kooter, R; Dittrich, M; Mueller, T; Klau, G W; Wessels, L F A

    2011-01-01

    Recently, several classifiers that combine primary tumor data, like gene expression data, and secondary data sources, such as protein-protein interaction networks, have been proposed for predicting outcome in breast cancer. In these approaches, new composite features are typically constructed by aggregating the expression levels of several genes. The secondary data sources are employed to guide this aggregation. Although many studies claim that these approaches improve classification performance over single gene classifiers, the gain in performance is difficult to assess. This stems mainly from the fact that different breast cancer data sets and validation procedures are employed to assess the performance. Here we address these issues by employing a large cohort of six breast cancer data sets as benchmark set and by performing an unbiased evaluation of the classification accuracies of the different approaches. Contrary to previous claims, we find that composite feature classifiers do not outperform simple sin...

  19. Evaluation of the Diagnostic Power of Thermography in Breast Cancer Using Bayesian Network Classifiers

    Science.gov (United States)

    Nicandro, Cruz-Ramírez; Efrén, Mezura-Montes; María Yaneli, Ameca-Alducin; Enrique, Martín-Del-Campo-Mena; Héctor Gabriel, Acosta-Mesa; Nancy, Pérez-Castro; Alejandro, Guerra-Hernández; Guillermo de Jesús, Hoyos-Rivera; Rocío Erandi, Barrientos-Martínez

    2013-01-01

    Breast cancer is one of the leading causes of death among women worldwide. There are a number of techniques used for diagnosing this disease: mammography, ultrasound, and biopsy, among others. Each of these has well-known advantages and disadvantages. A relatively new method, based on the temperature a tumor may produce, has recently been explored: thermography. In this paper, we will evaluate the diagnostic power of thermography in breast cancer using Bayesian network classifiers. We will show how the information provided by the thermal image can be used in order to characterize patients suspected of having cancer. Our main contribution is the proposal of a score, based on the aforementioned information, that could help distinguish sick patients from healthy ones. Our main results suggest the potential of this technique in such a goal but also show its main limitations that have to be overcome to consider it as an effective diagnosis complementary tool. PMID:23762182

  20. Network analysis of microRNAs and their regulation in human ovarian cancer

    KAUST Repository

    Schmeier, Sebastian

    2011-11-03

    Background: MicroRNAs (miRNAs) are small non-coding RNA molecules that repress the translation of messenger RNAs (mRNAs) or degrade mRNAs. These functions of miRNAs allow them to control key cellular processes such as development, differentiation and apoptosis, and they have also been implicated in several cancers such as leukaemia, lung, pancreatic and ovarian cancer (OC). Unfortunately, the specific machinery of miRNA regulation, involving transcription factors (TFs) and transcription co-factors (TcoFs), is not well understood. In the present study we focus on computationally deciphering the underlying network of miRNAs, their targets, and their control mechanisms that have an influence on OC development.Results: We analysed experimentally verified data from multiple sources that describe miRNA influence on diseases, miRNA targeting of mRNAs, and on protein-protein interactions, and combined this data with ab initio transcription factor binding site predictions within miRNA promoter regions. From these analyses, we derived a network that describes the influence of miRNAs and their regulation in human OC. We developed a methodology to analyse the network in order to find the nodes that have the largest potential of influencing the network\\'s behaviour (network hubs). We further show the potentially most influential miRNAs, TFs and TcoFs, showing subnetworks illustrating the involved mechanisms as well as regulatory miRNA network motifs in OC. We find an enrichment of miRNA targeted OC genes in the highly relevant pathways cell cycle regulation and apoptosis.Conclusions: We combined several sources of interaction and association data to analyse and place miRNAs within regulatory pathways that influence human OC. These results represent the first comprehensive miRNA regulatory network analysis for human OC. This suggests that miRNAs and their regulation may play a major role in OC and that further directed research in this area is of utmost importance to enhance

  1. Molecular network analysis of human microRNA targetome: from cancers to Alzheimer’s disease

    Directory of Open Access Journals (Sweden)

    Satoh Jun-ichi

    2012-10-01

    Full Text Available Abstract MicroRNAs (miRNAs, a class of endogenous small noncoding RNAs, mediate posttranscriptional regulation of protein-coding genes by binding chiefly to the 3’ untranslated region of target mRNAs, leading to translational inhibition, mRNA destabilization or degradation. A single miRNA concurrently downregulates hundreds of target mRNAs designated “targetome”, and thereby fine-tunes gene expression involved in diverse cellular functions, such as development, differentiation, proliferation, apoptosis and metabolism. Recently, we characterized the molecular network of the whole human miRNA targetome by using bioinformatics tools for analyzing molecular interactions on the comprehensive knowledgebase. We found that the miRNA targetome regulated by an individual miRNA generally constitutes the biological network of functionally-associated molecules in human cells, closely linked to pathological events involved in cancers and neurodegenerative diseases. We also identified a collaborative regulation of gene expression by transcription factors and miRNAs in cancer-associated miRNA targetome networks. This review focuses on the workflow of molecular network analysis of miRNA targetome in silico. We applied the workflow to two representative datasets, composed of miRNA expression profiling of adult T cell leukemia (ATL and Alzheimer’s disease (AD, retrieved from Gene Expression Omnibus (GEO repository. The results supported the view that miRNAs act as a central regulator of both oncogenesis and neurodegeneration.

  2. Identifying New Candidate Genes and Chemicals Related to Prostate Cancer Using a Hybrid Network and Shortest Path Approach

    Science.gov (United States)

    Yuan, Fei; Zhou, You; Wang, Meng; Yang, Jing; Wu, Kai; Lu, Changhong; Kong, Xiangyin; Cai, Yu-Dong

    2015-01-01

    Prostate cancer is a type of cancer that occurs in the male prostate, a gland in the male reproductive system. Because prostate cancer cells may spread to other parts of the body and can influence human reproduction, understanding the mechanisms underlying this disease is critical for designing effective treatments. The identification of as many genes and chemicals related to prostate cancer as possible will enhance our understanding of this disease. In this study, we proposed a computational method to identify new candidate genes and chemicals based on currently known genes and chemicals related to prostate cancer by applying a shortest path approach in a hybrid network. The hybrid network was constructed according to information concerning chemical-chemical interactions, chemical-protein interactions, and protein-protein interactions. Many of the obtained genes and chemicals are associated with prostate cancer. PMID:26504486

  3. Construction of pancreatic cancer double-factor regulatory network based on chip data on the transcriptional level.

    Science.gov (United States)

    Zhao, Li-Li; Zhang, Tong; Liu, Bing-Rong; Liu, Tie-Fu; Tao, Na; Zhuang, Li-Wei

    2014-05-01

    Transcription factor (TF) and microRNA (miRNA) have been discovered playing crucial roles in cancer development. However, the effect of TFs and miRNAs in pancreatic cancer pathogenesis remains vague. We attempted to reveal the possible mechanism of pancreatic cancer based on transcription level. Using GSE16515 datasets downloaded from gene expression omnibus database, we first identified the differentially expressed genes (DEGs) in pancreatic cancer by the limma package in R. Then the DEGs were mapped into DAVID to conduct the kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis. TFs and miRNAs that DEGs significantly enriched were identified by Fisher's test, and then the pancreatic cancer double-factor regulatory network was constructed. In our study, total 1117 DEGs were identified and they significantly enriched in 4 KEGG pathways. A double-factor regulatory network was established, including 29 DEGs, 24 TFs, 25 miRNAs. In the network, LAMC2, BRIP1 and miR155 were identified which may be involved in pancreatic cancer development. In conclusion, the double-factor regulatory network was found to play an important role in pancreatic cancer progression and our results shed new light on the molecular mechanism of pancreatic cancer.

  4. Changes in Female Support Network Systems and Adaptation after Breast Cancer Diagnosis: Differences between Older and Younger Patients

    Science.gov (United States)

    Ashida, Sato; Palmquist, Aunchalee E. L.; Basen-Engquist, Karen; Singletary, S. Eva; Koehly, Laura M.

    2009-01-01

    Purpose: This study evaluates the changes in social networks of older and younger breast cancer patients over a 6-month period following their first diagnosis and how such modifications are associated with changes in the patients' mood state. Design and Methods: Newly diagnosed breast cancer patients were interviewed shortly after their diagnosis…

  5. Recording of hormone therapy and breast density in breast screening programs: summary and recommendations of the International Cancer Screening Network.

    NARCIS (Netherlands)

    Cox, B.; Ballard-Barbash, R.; Broeders, M.J.M.; Dowling, E.; Malila, N.; Shumak, R.; Taplin, S.; Buist, D.; Miglioretti, D.

    2010-01-01

    Breast density and the use of hormone therapy (HT) for menopausal symptoms alter the risk of breast cancer and both factors influence screening mammography performance. The International Cancer Screening Network (ICSN) surveyed its 29 member countries and found that few programs record breast densit

  6. Recording of hormone therapy and breast density in breast screening programs: summary and recommendations of the International Cancer Screening Network.

    NARCIS (Netherlands)

    Cox, B.; Ballard-Barbash, R.; Broeders, M.J.M.; Dowling, E.; Malila, N.; Shumak, R.; Taplin, S.; Buist, D.; Miglioretti, D.

    2010-01-01

    Breast density and the use of hormone therapy (HT) for menopausal symptoms alter the risk of breast cancer and both factors influence screening mammography performance. The International Cancer Screening Network (ICSN) surveyed its 29 member countries and found that few programs record breast densit

  7. Variation in detection of ductal carcinoma in situ during screening mammography: a survey within the International Cancer Screening Network

    NARCIS (Netherlands)

    Lynge, E.; Ponti, A.; James, T.; Majek, O.; Euler-Chelpin, M. von; Anttila, A.; Fitzpatrick, P.; Frigerio, A.; Kawai, M.; Scharpantgen, A.; Broeders, M.J.; Hofvind, S.; Vidal, C.; Ederra, M.; Salas, D.; Bulliard, J.L.; Tomatis, M.; Kerlikowske, K.; Taplin, S.

    2014-01-01

    BACKGROUND: There is concern about detection of ductal carcinoma in situ (DCIS) in screening mammography. DCIS accounts for a substantial proportion of screen-detected lesions but its effect on breast cancer mortality is debated. The International Cancer Screening Network conducted a comparative ana

  8. [Clinical research activity of the French cancer cooperative network: Overview and perspectives].

    Science.gov (United States)

    Dubois, Claire; Morin, Franck; Moro-Sibilot, Denis; Langlais, Alexandra; Seitz, Jean-François; Girault, Cécile; Salles, Gilles; Haioun, Corinne; Deschaseaux, Pascal; Casassus, Philippe; Mathiot, Claire; Pujade-Lauraine, Éric; Votan, Bénédicte; Louvet, Christophe; Delpeut, Christine; Bardet, Étienne; Vintonenko, Nadejda; Hoang Xuan, Khê; Vo, Maryline; Michon, Jean; Milleron, Bernard

    The French Cancer Plan 2014-2019 stresses the importance of strengthening collaboration between all stakeholders involved in the fight against cancer, including cancer cooperative groups and intergroups. This survey aimed to describe the basics characteristics and clinical research activity among the Cancer Cooperative Groups (Groupes coopérateurs en oncologie). The second objective was to identify facilitators and barriers to their research activity. A questionnaire was sent to all the clinicians involved in 2014 as investigators in a clinical trial sponsored by one of the ten members of the Cancer Cooperative Groups network. The questions were related to their profile, research activity and the infrastructure existing within their healthcare center to support clinical research and related compliance activities. In total, 366 investigators responded to our survey. The academic clinical trials sponsored by the Cancer Cooperative Groups represented an important part of the research activity of the investigators in France in 2014. These academic groups contributed to the opening of many research sites throughout all regions in France. Factors associated with a higher participation of investigators (more than 10 patients enrolled in a trial over a year) include the existing support of healthcare professionals (more than 2 clinical research associate (CRA) OR=11.16 [3.82-32.6] compared to none) and the practice of their research activity in a University Hospital Center (CHU) rather than a Hospital Center (CH) (OR=2.15 [1.20-3.83]). This study highlighted factors that can strengthen investigator clinical research activities and subsequently improve patient access to evidence-based new cancer therapies in France. Copyright © 2017 Société Française du Cancer. Published by Elsevier Masson SAS. All rights reserved.

  9. Target inhibition networks: predicting selective combinations of druggable targets to block cancer survival pathways.

    Directory of Open Access Journals (Sweden)

    Jing Tang

    Full Text Available A recent trend in drug development is to identify drug combinations or multi-target agents that effectively modify multiple nodes of disease-associated networks. Such polypharmacological effects may reduce the risk of emerging drug resistance by means of attacking the disease networks through synergistic and synthetic lethal interactions. However, due to the exponentially increasing number of potential drug and target combinations, systematic approaches are needed for prioritizing the most potent multi-target alternatives on a global network level. We took a functional systems pharmacology approach toward the identification of selective target combinations for specific cancer cells by combining large-scale screening data on drug treatment efficacies and drug-target binding affinities. Our model-based prediction approach, named TIMMA, takes advantage of the polypharmacological effects of drugs and infers combinatorial drug efficacies through system-level target inhibition networks. Case studies in MCF-7 and MDA-MB-231 breast cancer and BxPC-3 pancreatic cancer cells demonstrated how the target inhibition modeling allows systematic exploration of functional interactions between drugs and their targets to maximally inhibit multiple survival pathways in a given cancer type. The TIMMA prediction results were experimentally validated by means of systematic siRNA-mediated silencing of the selected targets and their pairwise combinations, showing increased ability to identify not only such druggable kinase targets that are essential for cancer survival either individually or in combination, but also synergistic interactions indicative of non-additive drug efficacies. These system-level analyses were enabled by a novel model construction method utilizing maximization and minimization rules, as well as a model selection algorithm based on sequential forward floating search. Compared with an existing computational solution, TIMMA showed both enhanced

  10. The structural network of Interleukin-10 and its implications in inflammation and cancer

    Science.gov (United States)

    2014-01-01

    Background Inflammation has significant roles in all phases of tumor development, including initiation, progression and metastasis. Interleukin-10 (IL-10) is a well-known immuno-modulatory cytokine with an anti-inflammatory activity. Lack of IL-10 allows induction of pro-inflammatory cytokines and hinders anti-tumor immunity, thereby favoring tumor growth. The IL-10 network is among the most important paths linking cancer and inflammation. The simple node-and-edge network representation is useful, but limited, hampering the understanding of the mechanistic details of signaling pathways. Structural networks complete the missing parts, and provide details. The IL-10 structural network may shed light on the mechanisms through which disease-related mutations work and the pathogenesis of malignancies. Results Using PRISM (a PRotein Interactions by Structural Matching tool), we constructed the structural network of IL-10, which includes its first and second degree protein neighbor interactions. We predicted the structures of complexes involved in these interactions, thereby enriching the available structural data. In order to reveal the significance of the interactions, we exploited mutations identified in cancer patients, mapping them onto key proteins of this network. We analyzed the effect of these mutations on the interactions, and demonstrated a relation between these and inflammation and cancer. Our results suggest that mutations that disrupt the interactions of IL-10 with its receptors (IL-10RA and IL-10RB) and α2-macroglobulin (A2M) may enhance inflammation and modulate anti-tumor immunity. Likewise, mutations that weaken the A2M-APP (amyloid precursor protein) association may increase the proliferative effect of APP through preventing β-amyloid degradation by the A2M receptor, and mutations that abolish the A2M-Kallikrein-13 (KLK13) interaction may lead to cell proliferation and metastasis through the destructive effect of KLK13 on the extracellular matrix

  11. Identification of Lung-Cancer-Related Genes with the Shortest Path Approach in a Protein-Protein Interaction Network

    Directory of Open Access Journals (Sweden)

    Bi-Qing Li

    2013-01-01

    Full Text Available Lung cancer is one of the leading causes of cancer mortality worldwide. The main types of lung cancer are small cell lung cancer (SCLC and nonsmall cell lung cancer (NSCLC. In this work, a computational method was proposed for identifying lung-cancer-related genes with a shortest path approach in a protein-protein interaction (PPI network. Based on the PPI data from STRING, a weighted PPI network was constructed. 54 NSCLC- and 84 SCLC-related genes were retrieved from associated KEGG pathways. Then the shortest paths between each pair of these 54 NSCLC genes and 84 SCLC genes were obtained with Dijkstra’s algorithm. Finally, all the genes on the shortest paths were extracted, and 25 and 38 shortest genes with a permutation P value less than 0.05 for NSCLC and SCLC were selected for further analysis. Some of the shortest path genes have been reported to be related to lung cancer. Intriguingly, the candidate genes we identified from the PPI network contained more cancer genes than those identified from the gene expression profiles. Furthermore, these genes possessed more functional similarity with the known cancer genes than those identified from the gene expression profiles. This study proved the efficiency of the proposed method and showed promising results.

  12. Fast divide-and-conquer algorithm for evaluating polarization in classical force fields

    Science.gov (United States)

    Nocito, Dominique; Beran, Gregory J. O.

    2017-03-01

    Evaluation of the self-consistent polarization energy forms a major computational bottleneck in polarizable force fields. In large systems, the linear polarization equations are typically solved iteratively with techniques based on Jacobi iterations (JI) or preconditioned conjugate gradients (PCG). Two new variants of JI are proposed here that exploit domain decomposition to accelerate the convergence of the induced dipoles. The first, divide-and-conquer JI (DC-JI), is a block Jacobi algorithm which solves the polarization equations within non-overlapping sub-clusters of atoms directly via Cholesky decomposition, and iterates to capture interactions between sub-clusters. The second, fuzzy DC-JI, achieves further acceleration by employing overlapping blocks. Fuzzy DC-JI is analogous to an additive Schwarz method, but with distance-based weighting when averaging the fuzzy dipoles from different blocks. Key to the success of these algorithms is the use of K-means clustering to identify natural atomic sub-clusters automatically for both algorithms and to determine the appropriate weights in fuzzy DC-JI. The algorithm employs knowledge of the 3-D spatial interactions to group important elements in the 2-D polarization matrix. When coupled with direct inversion in the iterative subspace (DIIS) extrapolation, fuzzy DC-JI/DIIS in particular converges in a comparable number of iterations as PCG, but with lower computational cost per iteration. In the end, the new algorithms demonstrated here accelerate the evaluation of the polarization energy by 2-3 fold compared to existing implementations of PCG or JI/DIIS.

  13. Divide and Conquer (DC BLAST: fast and easy BLAST execution within HPC environments

    Directory of Open Access Journals (Sweden)

    Won Cheol Yim

    2017-06-01

    Full Text Available Bioinformatics is currently faced with very large-scale data sets that lead to computational jobs, especially sequence similarity searches, that can take absurdly long times to run. For example, the National Center for Biotechnology Information (NCBI Basic Local Alignment Search Tool (BLAST and BLAST+ suite, which is by far the most widely used tool for rapid similarity searching among nucleic acid or amino acid sequences, is highly central processing unit (CPU intensive. While the BLAST suite of programs perform searches very rapidly, they have the potential to be accelerated. In recent years, distributed computing environments have become more widely accessible and used due to the increasing availability of high-performance computing (HPC systems. Therefore, simple solutions for data parallelization are needed to expedite BLAST and other sequence analysis tools. However, existing software for parallel sequence similarity searches often requires extensive computational experience and skill on the part of the user. In order to accelerate BLAST and other sequence analysis tools, Divide and Conquer BLAST (DCBLAST was developed to perform NCBI BLAST searches within a cluster, grid, or HPC environment by using a query sequence distribution approach. Scaling from one (1 to 256 CPU cores resulted in significant improvements in processing speed. Thus, DCBLAST dramatically accelerates the execution of BLAST searches using a simple, accessible, robust, and parallel approach. DCBLAST works across multiple nodes automatically and it overcomes the speed limitation of single-node BLAST programs. DCBLAST can be used on any HPC system, can take advantage of hundreds of nodes, and has no output limitations. This freely available tool simplifies distributed computation pipelines to facilitate the rapid discovery of sequence similarities between very large data sets.

  14. Integrative modelling of the influence of MAPK network on cancer cell fate decision.

    Directory of Open Access Journals (Sweden)

    Luca Grieco

    2013-10-01

    Full Text Available The Mitogen-Activated Protein Kinase (MAPK network consists of tightly interconnected signalling pathways involved in diverse cellular processes, such as cell cycle, survival, apoptosis and differentiation. Although several studies reported the involvement of these signalling cascades in cancer deregulations, the precise mechanisms underlying their influence on the balance between cell proliferation and cell death (cell fate decision in pathological circumstances remain elusive. Based on an extensive analysis of published data, we have built a comprehensive and generic reaction map for the MAPK signalling network, using CellDesigner software. In order to explore the MAPK responses to different stimuli and better understand their contributions to cell fate decision, we have considered the most crucial components and interactions and encoded them into a logical model, using the software GINsim. Our logical model analysis particularly focuses on urinary bladder cancer, where MAPK network deregulations have often been associated with specific phenotypes. To cope with the combinatorial explosion of the number of states, we have applied novel algorithms for model reduction and for the compression of state transition graphs, both implemented into the software GINsim. The results of systematic simulations for different signal combinations and network perturbations were found globally coherent with published data. In silico experiments further enabled us to delineate the roles of specific components, cross-talks and regulatory feedbacks in cell fate decision. Finally, tentative proliferative or anti-proliferative mechanisms can be connected with established bladder cancer deregulations, namely Epidermal Growth Factor Receptor (EGFR over-expression and Fibroblast Growth Factor Receptor 3 (FGFR3 activating mutations.

  15. Cancer association study of aminoacyl-tRNA synthetase signaling network in glioblastoma.

    Directory of Open Access Journals (Sweden)

    Yong-Wan Kim

    Full Text Available Aminoacyl-tRNA synthetases (ARSs and ARS-interacting multifunctional proteins (AIMPs exhibit remarkable functional versatility beyond their catalytic activities in protein synthesis. Their non-canonical functions have been pathologically linked to cancers. Here we described our integrative genome-wide analysis of ARSs to show cancer-associated activities in glioblastoma multiforme (GBM, the most aggressive malignant primary brain tumor. We first selected 23 ARS/AIMPs (together referred to as ARSN, 124 cancer-associated druggable target genes (DTGs and 404 protein-protein interactors (PPIs of ARSs using NCI's cancer gene index. 254 GBM affymetrix microarray data in The Cancer Genome Atlas (TCGA were used to identify the probe sets whose expression were most strongly correlated with survival (Kaplan-Meier plots versus survival times, log-rank t-test <0.05. The analysis identified 122 probe sets as survival signatures, including 5 of ARSN (VARS, QARS, CARS, NARS, FARS, and 115 of DTGs and PPIs (PARD3, RXRB, ATP5C1, HSP90AA1, CD44, THRA, TRAF2, KRT10, MED12, etc. Of note, 61 survival-related probes were differentially expressed in three different prognosis subgroups in GBM patients and showed correlation with established prognosis markers such as age and phenotypic molecular signatures. CARS and FARS also showed significantly higher association with different molecular networks in GBM patients. Taken together, our findings demonstrate evidence for an ARSN biology-dominant contribution in the biology of GBM.

  16. MicroRNAs as Regulator of Signaling Networks in Metastatic Colon Cancer

    Science.gov (United States)

    Wang, Jian; Du, Yong; Liu, Xiaoming; Cho, William C.; Yang, Yinxue

    2015-01-01

    MicroRNAs (miRNAs) are a class of small, noncoding RNA molecules capable of regulating gene expression translationally and/or transcriptionally. A large number of evidence have demonstrated that miRNAs have a functional role in both physiological and pathological processes by regulating the expression of their target genes. Recently, the functionalities of miRNAs in the initiation, progression, angiogenesis, metastasis, and chemoresistance of tumors have gained increasing attentions. Particularly, the alteration of miRNA profiles has been correlated with the transformation and metastasis of various cancers, including colon cancer. This paper reports the latest findings on miRNAs involved in different signaling networks leading to colon cancer metastasis, mainly focusing on miRNA profiling and their roles in PTEN/PI3K, EGFR, TGFβ, and p53 signaling pathways of metastatic colon cancer. The potential of miRNAs used as biomarkers in the diagnosis, prognosis, and therapeutic targets in colon cancer is also discussed. PMID:26064956

  17. A Distributed Network for Intensive Longitudinal Monitoring in Metastatic Triple-Negative Breast Cancer

    Science.gov (United States)

    Blau, C. Anthony; Ramirez, Arturo B.; Blau, Sibel; Pritchard, Colin C.; Dorschner, Michael O.; Schmechel, Stephen C.; Martins, Timothy J.; Mahen, Elisabeth M.; Burton, Kimberly A.; Komashko, Vitalina M.; Radenbaugh, Amie J.; Dougherty, Katy; Thomas, Anju; Miller, Christopher P.; Annis, James; Fromm, Jonathan R.; Song, Chaozhong; Chang, Elizabeth; Howard, Kellie; Austin, Sharon; Schmidt, Rodney A.; Linenberger, Michael L.; Becker, Pamela S.; Senecal, Francis M.; Mecham, Brigham H.; Lee, Su-In; Madan, Anup; Ronen, Roy; Dutkowski, Janusz; Heimfeld, Shelly; Wood, Brent L.; Stilwell, Jackie L.; Kaldjian, Eric P.; Haussler, David; Zhu, Jingchun

    2016-01-01

    Accelerating cancer research is expected to require new types of clinical trials. This report describes the Intensive Trial of OMics in Cancer (ITOMIC) and a participant with triple-negative breast cancer metastatic to bone, who had markedly elevated circulating tumor cells (CTCs) that were monitored 48 times over 9 months. A total of 32 researchers from 14 institutions were engaged in the patient’s evaluation; 20 researchers had no prior involvement in patient care and 18 were recruited specifically for this patient. Whole-exome sequencing of 3 bone marrow samples demonstrated a novel ROS1 variant that was estimated to be present in most or all tumor cells. After an initial response to cisplatin, a hypothesis of crizotinib sensitivity was disproven. Leukapheresis followed by partial CTC enrichment allowed for the development of a differential high-throughput drug screen and demonstrated sensitivity to investigational BH3-mimetic inhibitors of BCL-2 that could not be tested in the patient because requests to the pharmaceutical sponsors were denied. The number and size of CTC clusters correlated with clinical status and eventually death. Focusing the expertise of a distributed network of investigators on an intensively monitored patient with cancer can generate high-resolution views of the natural history of cancer and suggest new opportunities for therapy. Optimization requires access to investigational drugs. PMID:26733551

  18. Follicular cell thyroid neoplasia: insights from genomics and The Cancer Genome Atlas research network.

    Science.gov (United States)

    Giordano, Thomas J

    2016-01-01

    The present review is focused on the recently published study on the genomics of papillary thyroid carcinoma performed by The Cancer Genome Atlas Research Network and its implications for the follicular variant of papillary carcinoma. The Cancer Genome Atlas study of papillary thyroid carcinoma comprehensively examined the cancer genome of nearly 500 primary tumors. Using a highly integrated bioinformatic analysis, papillary carcinoma was shown at the genomic level to consist of two highly distinct classes that reflected both tumor histology and underlying genotype. Tumors with true papillary architecture were dominated by BRAF(V600E) mutations and RET kinase fusions and were designated as BRAF(V600E)-like. Tumors with follicular architecture were conversely dominated by RAS mutations and were designated as RAS-like. Given the strong genotype:phenotype correlation known to be present in thyroid cancer, the separation of BRAF(V600E)-like and RAS-like tumors has profound implications for its classification, especially the follicular variant of papillary carcinoma. The recent genomic characterization of papillary thyroid carcinoma is challenging the established pathological classification of thyroid cancer with significance for the care of patients.

  19. A Distributed Network for Intensive Longitudinal Monitoring in Metastatic Triple-Negative Breast Cancer.

    Science.gov (United States)

    Blau, C Anthony; Ramirez, Arturo B; Blau, Sibel; Pritchard, Colin C; Dorschner, Michael O; Schmechel, Stephen C; Martins, Timothy J; Mahen, Elisabeth M; Burton, Kimberly A; Komashko, Vitalina M; Radenbaugh, Amie J; Dougherty, Katy; Thomas, Anju; Miller, Christopher P; Annis, James; Fromm, Jonathan R; Song, Chaozhong; Chang, Elizabeth; Howard, Kellie; Austin, Sharon; Schmidt, Rodney A; Linenberger, Michael L; Becker, Pamela S; Senecal, Francis M; Mecham, Brigham H; Lee, Su-In; Madan, Anup; Ronen, Roy; Dutkowski, Janusz; Heimfeld, Shelly; Wood, Brent L; Stilwell, Jackie L; Kaldjian, Eric P; Haussler, David; Zhu, Jingchun

    2016-01-01

    Accelerating cancer research is expected to require new types of clinical trials. This report describes the Intensive Trial of OMics in Cancer (ITOMIC) and a participant with triple-negative breast cancer metastatic to bone, who had markedly elevated circulating tumor cells (CTCs) that were monitored 48 times over 9 months. A total of 32 researchers from 14 institutions were engaged in the patient's evaluation; 20 researchers had no prior involvement in patient care and 18 were recruited specifically for this patient. Whole-exome sequencing of 3 bone marrow samples demonstrated a novel ROS1 variant that was estimated to be present in most or all tumor cells. After an initial response to cisplatin, a hypothesis of crizotinib sensitivity was disproven. Leukapheresis followed by partial CTC enrichment allowed for the development of a differential high-throughput drug screen and demonstrated sensitivity to investigational BH3-mimetic inhibitors of BCL-2 that could not be tested in the patient because requests to the pharmaceutical sponsors were denied. The number and size of CTC clusters correlated with clinical status and eventually death. Focusing the expertise of a distributed network of investigators on an intensively monitored patient with cancer can generate high-resolution views of the natural history of cancer and suggest new opportunities for therapy. Optimization requires access to investigational drugs.

  20. miR-137 Modulates a Tumor Suppressor Network-Inducing Senescence in Pancreatic Cancer Cells

    Directory of Open Access Journals (Sweden)

    Mathieu Neault

    2016-03-01

    Full Text Available Activating K-Ras mutations occurs frequently in pancreatic cancers and is implicated in their development. Cancer-initiating events, such as oncogenic Ras activation, lead to the induction of cellular senescence, a tumor suppressor response. During senescence, the decreased levels of KDM4A lysine demethylase contribute to p53 activation, however, the mechanism by which KDM4A is downregulated is unknown. We show that miR-137 targets KDM4A mRNA during Ras-induced senescence and activates both p53 and retinoblastoma (pRb tumor suppressor pathways. Restoring the KDM4A expression contributed to bypass of miR-137-induced senescence and inhibition of endogenous miR-137 with an miRNA sponge-compromised Ras-induced senescence. miR-137 levels are significantly reduced in human pancreatic tumors, consistent with previous studies revealing a defective senescence response in this cancer type. Restoration of miR-137 expression inhibited proliferation and promoted senescence of pancreatic cancer cells. These results suggest that modulating levels of miR-137 may be important for triggering tumor suppressor networks in pancreatic cancer.

  1. Lumen-based detection of prostate cancer via convolutional neural networks

    Science.gov (United States)

    Kwak, Jin Tae; Hewitt, Stephen M.

    2017-03-01

    We present a deep learning approach for detecting prostate cancers. The approach consists of two steps. In the first step, we perform tissue segmentation that identifies lumens within digitized prostate tissue specimen images. Intensity- and texture-based image features are computed at five different scales, and a multiview boosting method is adopted to cooperatively combine the image features from differing scales and to identify lumens. In the second step, we utilize convolutional neural networks (CNN) to automatically extract high-level image features of lumens and to predict cancers. The segmented lumens are rescaled to reduce computational complexity and data augmentation by scaling, rotating, and flipping the rescaled image is applied to avoid overfitting. We evaluate the proposed method using two tissue microarrays (TMA) - TMA1 includes 162 tissue specimens (73 Benign and 89 Cancer) and TMA2 comprises 185 tissue specimens (70 Benign and 115 Cancer). In cross-validation on TMA1, the proposed method achieved an AUC of 0.95 (CI: 0.93-0.98). Trained on TMA1 and tested on TMA2, CNN obtained an AUC of 0.95 (CI: 0.92-0.98). This demonstrates that the proposed method can potentially improve prostate cancer pathology.

  2. Metabolic and protein interaction sub-networks controlling the proliferation rate of cancer cells and their impact on patient survival.

    Science.gov (United States)

    Feizi, Amir; Bordel, Sergio

    2013-10-24

    Cancer cells can have a broad scope of proliferation rates. Here we aim to identify the molecular mechanisms that allow some cancer cell lines to grow up to 4 times faster than other cell lines. The correlation of gene expression profiles with the growth rate in 60 different cell lines has been analyzed using several genome-scale biological networks and new algorithms. New possible regulatory feedback loops have been suggested and the known roles of several cell cycle related transcription factors have been confirmed. Over 100 growth-correlated metabolic sub-networks have been identified, suggesting a key role of simultaneous lipid synthesis and degradation in the energy supply of the cancer cells growth. Many metabolic sub-networks involved in cell line proliferation appeared also to correlate negatively with the survival expectancy of colon cancer patients.

  3. Identification of Gene Biomarkers for Distinguishing Small-Cell Lung Cancer from Non-Small-Cell Lung Cancer Using a Network-Based Approach

    Directory of Open Access Journals (Sweden)

    Fei Long

    2015-01-01

    Full Text Available Lung cancer consists of two main subtypes: small-cell lung cancer (SCLC and non-small-cell lung cancer (NSCLC that are classified according to their physiological phenotypes. In this study, we have developed a network-based approach to identify molecular biomarkers that can distinguish SCLC from NSCLC. By identifying positive and negative coexpression gene pairs in normal lung tissues, SCLC, or NSCLC samples and using functional association information from the STRING network, we first construct a lung cancer-specific gene association network. From the network, we obtain gene modules in which genes are highly functionally associated with each other and are either positively or negatively coexpressed in the three conditions. Then, we identify gene modules that not only are differentially expressed between cancer and normal samples, but also show distinctive expression patterns between SCLC and NSCLC. Finally, we select genes inside those modules with discriminating coexpression patterns between the two lung cancer subtypes and predict them as candidate biomarkers that are of diagnostic use.

  4. Network pharmacology-based virtual screening of natural products from Clerodendrum species for identification of novel anti-cancer therapeutics.

    Science.gov (United States)

    Gogoi, Barbi; Gogoi, Dhrubajyoti; Silla, Yumnam; Kakoti, Bibhuti Bhushan; Bhau, Brijmohan Singh

    2017-01-31

    Plant-derived natural products (NPs) play a vital role in the discovery of new drug molecules and these are used for development of novel therapeutic drugs for a specific disease target. Literature review suggests that natural products possess strong inhibitory efficacy against various types of cancer cells. Clerodendrum indicum and Clerodendrum serratum are reported to have anticancer activity; therefore a study was carried out to identify selective anticancer agents from these plants species. In this report, we employed a docking weighted network pharmacological approach to understand the multi-therapeutics potentiality of C. indicum and C. serratum against various types of cancer. A library of 53 natural products derived from these plants was compiled from the literature and three dimensional space analyses were performed in order to establish the drug-likeness of the NPs library. Further, an NPs-cancer network was built based on docking. We predicted five compounds, namely apigenin 7-glucoside, hispidulin, scutellarein-7-O-beta-d-glucuronate, acteoside and verbascoside, to be potential binding therapeutics for cancer target proteins. Apigenin 7-glucoside and hispidulin were found to have maximum binding interactions (relationship) with 17 cancer drug targets in terms of docking weighted network pharmacological analysis. Hence, we used an integrative approach obtained from network pharmacology for identifying combinatorial drug actions against the cancer targets. We believe that our present study may provide important clues for finding novel drug inhibitors for cancer.

  5. Inferring gene dependency network specific to phenotypic alteration based on gene expression data and clinical information of breast cancer.

    Directory of Open Access Journals (Sweden)

    Xionghui Zhou

    Full Text Available Although many methods have been proposed to reconstruct gene regulatory network, most of them, when applied in the sample-based data, can not reveal the gene regulatory relations underlying the phenotypic change (e.g. normal versus cancer. In this paper, we adopt phenotype as a variable when constructing the gene regulatory network, while former researches either neglected it or only used it to select the differentially expressed genes as the inputs to construct the gene regulatory network. To be specific, we integrate phenotype information with gene expression data to identify the gene dependency pairs by using the method of conditional mutual information. A gene dependency pair (A,B means that the influence of gene A on the phenotype depends on gene B. All identified gene dependency pairs constitute a directed network underlying the phenotype, namely gene dependency network. By this way, we have constructed gene dependency network of breast cancer from gene expression data along with two different phenotype states (metastasis and non-metastasis. Moreover, we have found the network scale free, indicating that its hub genes with high out-degrees may play critical roles in the network. After functional investigation, these hub genes are found to be biologically significant and specially related to breast cancer, which suggests that our gene dependency network is meaningful. The validity has also been justified by literature investigation. From the network, we have selected 43 discriminative hubs as signature to build the classification model for distinguishing the distant metastasis risks of breast cancer patients, and the result outperforms those classification models with published signatures. In conclusion, we have proposed a promising way to construct the gene regulatory network by using sample-based data, which has been shown to be effective and accurate in uncovering the hidden mechanism of the biological process and identifying the gene

  6. Formal modeling and analysis of ER-α associated Biological Regulatory Network in breast cancer

    Directory of Open Access Journals (Sweden)

    Samra Khalid

    2016-10-01

    Full Text Available Background Breast cancer (BC is one of the leading cause of death among females worldwide. The increasing incidence of BC is due to various genetic and environmental changes which lead to the disruption of cellular signaling network(s. It is a complex disease in which several interlinking signaling cascades play a crucial role in establishing a complex regulatory network. The logical modeling approach of René Thomas has been applied to analyze the behavior of estrogen receptor-alpha (ER-α associated Biological Regulatory Network (BRN for a small part of complex events that leads to BC metastasis. Methods A discrete model was constructed using the kinetic logic formalism and its set of logical parameters were obtained using the model checking technique implemented in the SMBioNet software which is consistent with biological observations. The discrete model was further enriched with continuous dynamics by converting it into an equivalent Petri Net (PN to analyze the logical parameters of the involved entities. Results In-silico based discrete and continuous modeling of ER-α associated signaling network involved in BC provides information about behaviors and gene-gene interaction in detail. The dynamics of discrete model revealed, imperative behaviors represented as cyclic paths and trajectories leading to pathogenic states such as metastasis. Results suggest that the increased expressions of receptors ER-α, IGF-1R and EGFR slow down the activity of tumor suppressor genes (TSGs such as BRCA1, p53 and Mdm2 which can lead to metastasis. Therefore, IGF-1R and EGFR are considered as important inhibitory targets to control the metastasis in BC. Conclusion The in-silico approaches allow us to increase our understanding of the functional properties of living organisms. It opens new avenues of investigations of multiple inhibitory targets (ER-α, IGF-1R and EGFR for wet lab experiments as well as provided valuable insights in the treatment of cancers

  7. Formal modeling and analysis of ER-α associated Biological Regulatory Network in breast cancer

    Science.gov (United States)

    Tareen, Samar H.K.; Siddiqa, Amnah; Bibi, Zurah; Ahmad, Jamil

    2016-01-01

    Background Breast cancer (BC) is one of the leading cause of death among females worldwide. The increasing incidence of BC is due to various genetic and environmental changes which lead to the disruption of cellular signaling network(s). It is a complex disease in which several interlinking signaling cascades play a crucial role in establishing a complex regulatory network. The logical modeling approach of René Thomas has been applied to analyze the behavior of estrogen receptor-alpha (ER-α) associated Biological Regulatory Network (BRN) for a small part of complex events that leads to BC metastasis. Methods A discrete model was constructed using the kinetic logic formalism and its set of logical parameters were obtained using the model checking technique implemented in the SMBioNet software which is consistent with biological observations. The discrete model was further enriched with continuous dynamics by converting it into an equivalent Petri Net (PN) to analyze the logical parameters of the involved entities. Results In-silico based discrete and continuous modeling of ER-α associated signaling network involved in BC provides information about behaviors and gene-gene interaction in detail. The dynamics of discrete model revealed, imperative behaviors represented as cyclic paths and trajectories leading to pathogenic states such as metastasis. Results suggest that the increased expressions of receptors ER-α, IGF-1R and EGFR slow down the activity of tumor suppressor genes (TSGs) such as BRCA1, p53 and Mdm2 which can lead to metastasis. Therefore, IGF-1R and EGFR are considered as important inhibitory targets to control the metastasis in BC. Conclusion The in-silico approaches allow us to increase our understanding of the functional properties of living organisms. It opens new avenues of investigations of multiple inhibitory targets (ER-α, IGF-1R and EGFR) for wet lab experiments as well as provided valuable insights in the treatment of cancers such as BC.

  8. Network-based survival analysis reveals subnetwork signatures for predicting outcomes of ovarian cancer treatment.

    Directory of Open Access Journals (Sweden)

    Wei Zhang

    Full Text Available Cox regression is commonly used to predict the outcome by the time to an event of interest and in addition, identify relevant features for survival analysis in cancer genomics. Due to the high-dimensionality of high-throughput genomic data, existing Cox models trained on any particular dataset usually generalize poorly to other independent datasets. In this paper, we propose a network-based Cox regression model called Net-Cox and applied Net-Cox for a large-scale survival analysis across multiple ovarian cancer datasets. Net-Cox integrates gene network information into the Cox's proportional hazard model to explore the co-expression or functional relation among high-dimensional gene expression features in the gene network. Net-Cox was applied to analyze three independent gene expression datasets including the TCGA ovarian cancer dataset and two other public ovarian cancer datasets. Net-Cox with the network information from gene co-expression or functional relations identified highly consistent signature genes across the three datasets, and because of the better generalization across the datasets, Net-Cox also consistently improved the accuracy of survival prediction over the Cox models regularized by L(2 or L(1. This study focused on analyzing the death and recurrence outcomes in the treatment of ovarian carcinoma to identify signature genes that can more reliably predict the events. The signature genes comprise dense protein-protein interaction subnetworks, enriched by extracellular matrix receptors and modulators or by nuclear signaling components downstream of extracellular signal-regulated kinases. In the laboratory validation of the signature genes, a tumor array experiment by protein staining on an independent patient cohort from Mayo Clinic showed that the protein expression of the signature gene FBN1 is a biomarker significantly associated with the early recurrence after 12 months of the treatment in the ovarian cancer patients who are

  9. Network-based survival analysis reveals subnetwork signatures for predicting outcomes of ovarian cancer treatment.

    Directory of Open Access Journals (Sweden)

    Wei Zhang

    Full Text Available Cox regression is commonly used to predict the outcome by the time to an event of interest and in addition, identify relevant features for survival analysis in cancer genomics. Due to the high-dimensionality of high-throughput genomic data, existing Cox models trained on any particular dataset usually generalize poorly to other independent datasets. In this paper, we propose a network-based Cox regression model called Net-Cox and applied Net-Cox for a large-scale survival analysis across multiple ovarian cancer datasets. Net-Cox integrates gene network information into the Cox's proportional hazard model to explore the co-expression or functional relation among high-dimensional gene expression features in the gene network. Net-Cox was applied to analyze three independent gene expression datasets including the TCGA ovarian cancer dataset and two other public ovarian cancer datasets. Net-Cox with the network information from gene co-expression or functional relations identified highly consistent signature genes across the three datasets, and because of the better generalization across the datasets, Net-Cox also consistently improved the accuracy of survival prediction over the Cox models regularized by L(2 or L(1. This study focused on analyzing the death and recurrence outcomes in the treatment of ovarian carcinoma to identify signature genes that can more reliably predict the events. The signature genes comprise dense protein-protein interaction subnetworks, enriched by extracellular matrix receptors and modulators or by nuclear signaling components downstream of extracellular signal-regulated kinases. In the laboratory validation of the signature genes, a tumor array experiment by protein staining on an independent patient cohort from Mayo Clinic showed that the protein expression of the signature gene FBN1 is a biomarker significantly associated with the early recurrence after 12 months of the treatment in the ovarian cancer patients who are

  10. Complex regulation of autophagy in cancer - integrated approaches to discover the networks that hold a double-edged sword.

    Science.gov (United States)

    Kubisch, János; Türei, Dénes; Földvári-Nagy, László; Dunai, Zsuzsanna A; Zsákai, Lilian; Varga, Máté; Vellai, Tibor; Csermely, Péter; Korcsmáros, Tamás

    2013-08-01

    Autophagy, a highly regulated self-degradation process of eukaryotic cells, is a context-dependent tumor-suppressing mechanism that can also promote tumor cell survival upon stress and treatment resistance. Because of this ambiguity, autophagy is considered as a double-edged sword in oncology, making anti-cancer therapeutic approaches highly challenging. In this review, we present how systems-level knowledge on autophagy regulation can help to develop new strategies and efficiently select novel anti-cancer drug targets. We focus on the protein interactors and transcriptional/post-transcriptional regulators of autophagy as the protein and regulatory networks significantly influence the activity of core autophagy proteins during tumor progression. We list several network resources to identify interactors and regulators of autophagy proteins. As in silico analysis of such networks often necessitates experimental validation, we briefly summarize tractable model organisms to examine the role of autophagy in cancer. We also discuss fluorescence techniques for high-throughput monitoring of autophagy in humans. Finally, the challenges of pharmacological modulation of autophagy are reviewed. We suggest network-based concepts to overcome these difficulties. We point out that a context-dependent modulation of autophagy would be favored in anti-cancer therapy, where autophagy is stimulated in normal cells, while inhibited only in stressed cancer cells. To achieve this goal, we introduce the concept of regulo-network drugs targeting specific transcription factors or miRNA families identified with network analysis. The effect of regulo-network drugs propagates indirectly through transcriptional or post-transcriptional regulation of autophagy proteins, and, as a multi-directional intervention tool, they can both activate and inhibit specific proteins in the same time. The future identification and validation of such regulo-network drug targets may serve as novel intervention

  11. How can a place conquer a position in the mind of potential business investors? : A case study on Dubai

    OpenAIRE

    Kindblom, Henrik; Karlsson, David

    2006-01-01

    Abstract In the age of globalization it has become more and more common that places – cities, regions and nations – work actively to attract business investors; all with the aim to support economic development. Nevertheless, the competition is tough and it is hard to get through the information clutter and conquer a position in the mind of the business investors. Dubai, one of the seven Emirates that form the nation United Arab Emirates, was for many years a general unknown place for business...

  12. Social network ties and inflammation in U.S. adults with cancer.

    Science.gov (United States)

    Yang, Yang Claire; Li, Ting; Frenk, Steven M

    2014-01-01

    The growing evidence linking social connectedness and chronic diseases such as cancer calls for a better understanding of the underlying biophysiological mechanisms. This study assessed the associations between social network ties and multiple measures of inflammation in a nationally representative sample of adults with a history of cancer (N = 1,075) from the National Health and Nutrition Examination Survey III (1988-94). Individuals with lower social network index (SNI) scores showed significantly greater inflammation marked by C-reactive protein and fibrinogen, adjusting for age and sex. Compared to fully socially integrated individuals (SNI = 4), those who were more socially isolated or had a SNI score of 3 or less exhibited increasingly elevated inflammation burdens. Specifically, the age- and sex-adjusted odds ratios (95%CI) for SNIs of 3, 2, and 0-1 were 1.49 (1.08, 2.06), 1.69 (1.21, 2.36), and 2.35 (1.62, 3.40), respectively (p < .001). Adjusting for other covariates attenuated these associations. The SNI gradients in the risks of inflammation were particularly salient for the lower socioeconomic status groups and remained significant after adjusting for other social, health behavioral, and illness factors. This study provided initial insights into the immunological pathways by which social connections are related to morbidity and mortality outcomes of cancer in particular and aging-related diseases in general.

  13. microRNAs and ceRNAs: RNA networks in pathogenesis of cancer

    Institute of Scientific and Technical Information of China (English)

    Xiangqian Su; Jiadi Xing; Zaozao Wang; Lei Chen; Ming Cui; Beihai Jiang

    2013-01-01

    microRNAs (miRNAs) are a class of endogenous,single-stranded non-coding RNAs of 20-23 nucleotides in length,functioning as negative regulators of gene expression at the post-transcriptional level.The dysregulation of miRNAs has been demonstrated to play critical roles in tumorigenesis,either through inhibiting tumor suppressor genes or activating oncogenes inappropriately.Besides their promising clinical applications in cancer diagnosis and treatment,recent studies have uncovered that miRNAs could act as a regulatory language,through which messenger RNAs,transcribed pseudogenes,and long noncoding RNAs crosstalk with each other and form a novel regulatory network.RNA transcripts involved in this network have been termed as competing endogenous RNAs (ceRNAs),since they influence each other's level by competing for the same pool of miRNAs through miRNA response elements (MREs) on their target transcripts.The discovery of miRNA-ceRNA network not only provides the possibility of an additional level of post-transcriptional regulation,but also dictates a reassessment of the existing regulatory pathways involved in cancer initiation and progression.

  14. Innovative and community-driven communication practices of the South Carolina cancer prevention and control research network.

    Science.gov (United States)

    Friedman, Daniela B; Brandt, Heather M; Freedman, Darcy A; Adams, Swann Arp; Young, Vicki M; Ureda, John R; McCracken, James Lyndon; Hébert, James R

    2014-07-24

    The South Carolina Cancer Prevention and Control Research Network (SC-CPCRN) is 1 of 10 networks funded by the Centers for Disease Control and Prevention and the National Cancer Institute (NCI) that works to reduce cancer-related health disparities. In partnership with federally qualified health centers and community stakeholders, the SC-CPCRN uses evidence-based approaches (eg, NCI Research-tested Intervention Programs) to disseminate and implement cancer prevention and control messages, programs, and interventions. We describe the innovative stakeholder- and community-driven communication efforts conducted by the SC-CPCRN to improve overall health and reduce cancer-related health disparities among high-risk and disparate populations in South Carolina. We describe how our communication efforts are aligned with 5 core values recommended for dissemination and implementation science: 1) rigor and relevance, 2) efficiency and speed, 3) collaboration, 4) improved capacity, and 5) cumulative knowledge.

  15. Risk of Marrow Neoplasms After Adjuvant Breast Cancer Therapy: The National Comprehensive Cancer Network Experience

    Science.gov (United States)

    Wolff, Antonio C.; Blackford, Amanda L.; Visvanathan, Kala; Rugo, Hope S.; Moy, Beverly; Goldstein, Lori J.; Stockerl-Goldstein, Keith; Neumayer, Leigh; Langbaum, Terry S.; Theriault, Richard L.; Hughes, Melissa E.; Weeks, Jane C.; Karp, Judith E.

    2015-01-01

    Purpose Outcomes for early-stage breast cancer have improved. First-generation adjuvant chemotherapy trials reported a 0.27% 8-year cumulative incidence of myelodysplastic syndrome/acute myelogenous leukemia. Incomplete ascertainment and follow-up may have underestimated subsequent risk of treatment-associated marrow neoplasm (MN). Patients and Methods We examined the MN frequency in 20,063 patients with stage I to III breast cancer treated at US academic centers between 1998 and 2007. Time-to-event analyses were censored at first date of new cancer event, last contact date, or death and considered competing risks. Cumulative incidence, hazard ratios (HRs), and comparisons with Surveillance, Epidemiology, and End Results estimates were obtained. Marrow cytogenetics data were reviewed. Results Fifty patients developed MN (myeloid, n = 42; lymphoid, n = 8) after breast cancer (median follow-up, 5.1 years). Patients who developed MN had similar breast cancer stage distribution, race, and chemotherapy exposure but were older compared with patients who did not develop MN (median age, 59.1 v 53.9 years, respectively; P = .03). Two thirds of patients had complex MN cytogenetics. Risk of MN was significantly increased after surgery plus chemotherapy (HR, 6.8; 95% CI, 1.3 to 36.1) or after all modalities (surgery, chemotherapy, and radiation; HR, 7.6; 95% CI, 1.6 to 35.8), compared with no treatment with chemotherapy. MN rates per 1,000 person-years were 0.16 (surgery), 0.43 (plus radiation), 0.46 (plus chemotherapy), and 0.54 (all three modalities). Cumulative incidence of MN doubled between years 5 and 10 (0.24% to 0.48%); 9% of patients were alive at 10 years. Conclusion In this large early-stage breast cancer cohort, MN risk after radiation and/or adjuvant chemotherapy was low but higher than previously described. Risk continued to increase beyond 5 years. Individual risk of MN must be balanced against the absolute survival benefit of adjuvant chemotherapy. PMID

  16. P30 Cancer Center Support Grant Administrative Supplements to NCI-designated Cancer Centers not affiliated with the Experimental Therapeutics Clinical Trials Network (ETCTN) to support participation in the ETCTN

    Science.gov (United States)

    P30 Cancer Center Support Grant Administrative Supplements to NCI-designated Cancer Centers not affiliated with the Experimental Therapeutics Clinical Trials Network (ETCTN) to support participation in the ETCTN

  17. Network-Based Integration of GWAS and Gene Expression Identifies a HOX-Centric Network Associated with Serous Ovarian Cancer Risk

    DEFF Research Database (Denmark)

    Kar, Siddhartha P; Tyrer, Jonathan P; Li, Qiyuan

    2015-01-01

    BACKGROUND: Genome-wide association studies (GWAS) have so far reported 12 loci associated with serous epithelial ovarian cancer (EOC) risk. We hypothesized that some of these loci function through nearby transcription factor (TF) genes and that putative target genes of these TFs as identified...... identified six networks centered on TF genes (HOXB2, HOXB5, HOXB6, HOXB7 at 17q21.32 and HOXD1, HOXD3 at 2q31) that were significantly enriched for genes from the risk-associated end of the ranked list (P ... (7,035 cases/21,693 controls). Genes underlying enrichment in the six networks were pooled into a combined network. CONCLUSION: We identified a HOX-centric network associated with serous EOC risk containing several genes with known or emerging roles in serous EOC development. IMPACT: Network analysis...

  18. Identifying significant genetic regulatory networks in the prostate cancer from microarray data based on transcription factor analysis and conditional independency

    Directory of Open Access Journals (Sweden)

    Yeh Cheng-Yu

    2009-12-01

    Full Text Available Abstract Background Prostate cancer is a world wide leading cancer and it is characterized by its aggressive metastasis. According to the clinical heterogeneity, prostate cancer displays different stages and grades related to the aggressive metastasis disease. Although numerous studies used microarray analysis and traditional clustering method to identify the individual genes during the disease processes, the important gene regulations remain unclear. We present a computational method for inferring genetic regulatory networks from micorarray data automatically with transcription factor analysis and conditional independence testing to explore the potential significant gene regulatory networks that are correlated with cancer, tumor grade and stage in the prostate cancer. Results To deal with missing values in microarray data, we used a K-nearest-neighbors (KNN algorithm to determine the precise expression values. We applied web services technology to wrap the bioinformatics toolkits and databases to automatically extract the promoter regions of DNA sequences and predicted the transcription factors that regulate the gene expressions. We adopt the microarray datasets consists of 62 primary tumors, 41 normal prostate tissues from Stanford Microarray Database (SMD as a target dataset to evaluate our method. The predicted results showed that the possible biomarker genes related to cancer and denoted the androgen functions and processes may be in the development of the prostate cancer and promote the cell death in cell cycle. Our predicted results showed that sub-networks of genes SREBF1, STAT6 and PBX1 are strongly related to a high extent while ETS transcription factors ELK1, JUN and EGR2 are related to a low extent. Gene SLC22A3 may explain clinically the differentiation associated with the high grade cancer compared with low grade cancer. Enhancer of Zeste Homolg 2 (EZH2 regulated by RUNX1 and STAT3 is correlated to the pathological stage

  19. Module network inference from a cancer gene expression data set identifies microRNA regulated modules.

    Directory of Open Access Journals (Sweden)

    Eric Bonnet

    Full Text Available BACKGROUND: MicroRNAs (miRNAs are small RNAs that recognize and regulate mRNA target genes. Multiple lines of evidence indicate that they are key regulators of numerous critical functions in development and disease, including cancer. However, defining the place and function of miRNAs in complex regulatory networks is not straightforward. Systems approaches, like the inference of a module network from expression data, can help to achieve this goal. METHODOLOGY/PRINCIPAL FINDINGS: During the last decade, much progress has been made in the development of robust and powerful module network inference algorithms. In this study, we analyze and assess experimentally a module network inferred from both miRNA and mRNA expression data, using our recently developed module network inference algorithm based on probabilistic optimization techniques. We show that several miRNAs are predicted as statistically significant regulators for various modules of tightly co-expressed genes. A detailed analysis of three of those modules demonstrates that the specific assignment of miRNAs is functionally coherent and supported by literature. We further designed a set of experiments to test the assignment of miR-200a as the top regulator of a small module of nine genes. The results strongly suggest that miR-200a is regulating the module genes via the transcription factor ZEB1. Interestingly, this module is most likely involved in epithelial homeostasis and its dysregulation might contribute to the malignant process in cancer cells. CONCLUSIONS/SIGNIFICANCE: Our results show that a robust module network analysis of expression data can provide novel insights of miRNA function in important cellular processes. Such a computational approach, starting from expression data alone, can be helpful in the process of identifying the function of miRNAs by suggesting modules of co-expressed genes in which they play a regulatory role. As shown in this study, those modules can then be

  20. Using graphical adaptive lasso approach to construct transcription factor and microRNA's combinatorial regulatory network in breast cancer.

    Science.gov (United States)

    Su, Naifang; Dai, Ding; Deng, Chao; Qian, Minping; Deng, Minghua

    2014-06-01

    Discovering the regulation of cancer-related gene is of great importance in cancer biology. Transcription factors and microRNAs are two kinds of crucial regulators in gene expression, and they compose a combinatorial regulatory network with their target genes. Revealing the structure of this network could improve the authors' understanding of gene regulation, and further explore the molecular pathway in cancer. In this article, the authors propose a novel approach graphical adaptive lasso (GALASSO) to construct the regulatory network in breast cancer. GALASSO use a Gaussian graphical model with adaptive lasso penalties to integrate the sequence information as well as gene expression profiles. The simulation study and the experimental profiles verify the accuracy of the authors' approach. The authors further reveal the structure of the regulatory network, and explore the role of feedforward loops in gene regulation. In addition, the authors discuss the combinatorial regulatory effect between transcription factors and microRNAs, and select miR-155 for detailed analysis of microRNA's role in cancer. The proposed GALASSO approach is an efficient method to construct the combinatorial regulatory network. It also provides a new way to integrate different data sources and could find more applications in meta-analysis problem.

  1. CyNetSVM: A Cytoscape App for Cancer Biomarker Identification Using Network Constrained Support Vector Machines.

    Science.gov (United States)

    Shi, Xu; Banerjee, Sharmi; Chen, Li; Hilakivi-Clarke, Leena; Clarke, Robert; Xuan, Jianhua

    2017-01-01

    One of the important tasks in cancer research is to identify biomarkers and build classification models for clinical outcome prediction. In this paper, we develop a CyNetSVM software package, implemented in Java and integrated with Cytoscape as an app, to identify network biomarkers using network-constrained support vector machines (NetSVM). The Cytoscape app of NetSVM is specifically designed to improve the usability of NetSVM with the following enhancements: (1) user-friendly graphical user interface (GUI), (2) computationally efficient core program and (3) convenient network visualization capability. The CyNetSVM app has been used to analyze breast cancer data to identify network genes associated with breast cancer recurrence. The biological function of these network genes is enriched in signaling pathways associated with breast cancer progression, showing the effectiveness of CyNetSVM for cancer biomarker identification. The CyNetSVM package is available at Cytoscape App Store and http://sourceforge.net/projects/netsvmjava; a sample data set is also provided at sourceforge.net.

  2. Hypoxia induces a phase transition within a kinase signaling network in cancer cells.

    Science.gov (United States)

    Wei, Wei; Shi, Qihui; Remacle, Francoise; Qin, Lidong; Shackelford, David B; Shin, Young Shik; Mischel, Paul S; Levine, R D; Heath, James R

    2013-04-09

    Hypoxia is a near-universal feature of cancer, promoting glycolysis, cellular proliferation, and angiogenesis. The molecular mechanisms of hypoxic signaling have been intensively studied, but the impact of changes in oxygen partial pressure (pO2) on the state of signaling networks is less clear. In a glioblastoma multiforme (GBM) cancer cell model, we examined the response of signaling networks to targeted pathway inhibition between 21% and 1% pO2. We used a microchip technology that facilitates quantification of a panel of functional proteins from statistical numbers of single cells. We find that near 1.5% pO2, the signaling network associated with mammalian target of rapamycin (mTOR) complex 1 (mTORC1)--a critical component of hypoxic signaling and a compelling cancer drug target--is deregulated in a manner such that it will be unresponsive to mTOR kinase inhibitors near 1.5% pO2, but will respond at higher or lower pO2 values. These predictions were validated through experiments on bulk GBM cell line cultures and on neurosphere cultures of a human-origin GBM xenograft tumor. We attempt to understand this behavior through the use of a quantitative version of Le Chatelier's principle, as well as through a steady-state kinetic model of protein interactions, both of which indicate that hypoxia can influence mTORC1 signaling as a switch. The Le Chatelier approach also indicates that this switch may be thought of as a type of phase transition. Our analysis indicates that certain biologically complex cell behaviors may be understood using fundamental, thermodynamics-motivated principles.

  3. A social network analysis of communication about hereditary nonpolyposis colorectal cancer genetic testing and family functioning.

    Science.gov (United States)

    Koehly, Laura M; Peterson, Susan K; Watts, Beatty G; Kempf, Kari K G; Vernon, Sally W; Gritz, Ellen R

    2003-04-01

    Hereditary cancers are relational diseases. A primary focus of research in the past has been the biological relations that exist within the families and how genes are passed along family lines. However, hereditary cancers are relational in a psychosocial sense, as well. They can impact communication relationships within a family, as well as support relationships among family members. Furthermore, the familial culture can affect an individual's participation in genetic counseling and testing endeavors. Our aims are (a) to describe the composition of familial networks, (b) to characterize the patterns of family functioning within families, (c) to analyze how these patterns relate to communications about genetic counseling and testing among family members, and (d) to identify influential family members. Specifically, we asked how the relationship between mutation status, kinship ties, and family functioning constructs, e.g., communication, cohesion, affective involvement, leadership, and conflict, was associated with discussions about genetic counseling and testing. We used social network analysis and random graph techniques to examine 783 dyadic relationships in 36 members of 5 hereditary nonpolyposis colorectal cancer (HNPCC) families interviewed from 1999-2000. Results suggest that in these five HNPCC families, two family members are more likely to discuss genetic counseling and testing if either one carries the mutation, if either one is a spouse or a first-degree relative of the other, or if the relationship is defined by positive cohesion, leadership, or lack of conflict. Furthermore, the family functioning patterns suggest that mothers tend to be the most influential persons in the family network. Results of this study suggest encouraging family members who act in the mother role to take a "team approach" with the family proband when discussing HNPCC risks and management with family members.

  4. Building capacity for clinical research in developing countries: the INDOX Cancer Research Network experience.

    Science.gov (United States)

    Ali, Raghib; Finlayson, Alexander; Indox Cancer Research Network

    2012-01-01

    Transnational Organisations increasingly prioritise the need to support local research capacity in low and middle income countries in order that local priorities are addressed with due consideration of contextual issues. There remains limited evidence on the best way in which this should be done or the ways in which external agencies can support this process.We present an analysis of the learning from the INDOX Research Network, established in 2005 as a partnership between the Institute of Cancer Medicine at the University of Oxford and India's top nine comprehensive cancer centres. INDOX aims to enable Indian centres to conduct clinical research to the highest international standards; to ensure that trials are developed to address the specific needs of Indian patients by involving Indian investigators from the outset; and to provide the training to enable them to design and conduct their own studies. We report on the implementation, outputs and challenges of simultaneously trying to build capacity and deliver meaningful research output.

  5. Network model explains why cancer cells use inefficient pathway to produce energy

    Science.gov (United States)

    Lee, Joo Sang; Marko, John; Motter, Adilson

    2012-02-01

    The Warburg effect---the use of the (energetically inefficient) fermentative pathway as opposed to (energetically efficient) respiration even in the presence of oxygen---is a common property of cancer metabolism. Here, we propose that the Warburg effect is in fact a consequence of a trade-off between the benefit of rapid growth and the cost for protein synthesis. Using genome-scale metabolic networks, we have modeled the cellular resources for protein synthesis as a growth defect that increases with enzyme concentration. Based on our model, we demonstrate that the cost of protein production during rapid growth drives the cell to rely on fermentation to produce ATP. We also identify an intimate link between extensive fermentation and rapid biosynthesis. Our findings emphasize the importance of protein synthesis as a limiting factor on cell proliferation and provide a novel mathematical framework to analyze cancer metabolism.

  6. Discrimination of liver cancer in cellular level based on backscatter micro-spectrum with PCA algorithm and BP neural network

    Science.gov (United States)

    Yang, Jing; Wang, Cheng; Cai, Gan; Dong, Xiaona

    2016-10-01

    The incidence and mortality rate of the primary liver cancer are very high and its postoperative metastasis and recurrence have become important factors to the prognosis of patients. Circulating tumor cells (CTC), as a new tumor marker, play important roles in the early diagnosis and individualized treatment. This paper presents an effective method to distinguish liver cancer based on the cellular scattering spectrum, which is a non-fluorescence technique based on the fiber confocal microscopic spectrometer. Combining the principal component analysis (PCA) with back propagation (BP) neural network were utilized to establish an automatic recognition model for backscatter spectrum of the liver cancer cells from blood cell. PCA was applied to reduce the dimension of the scattering spectral data which obtained by the fiber confocal microscopic spectrometer. After dimensionality reduction by PCA, a neural network pattern recognition model with 2 input layer nodes, 11 hidden layer nodes, 3 output nodes was established. We trained the network with 66 samples and also tested it. Results showed that the recognition rate of the three types of cells is more than 90%, the relative standard deviation is only 2.36%. The experimental results showed that the fiber confocal microscopic spectrometer combining with the algorithm of PCA and BP neural network can automatically identify the liver cancer cell from the blood cells. This will provide a better tool for investigating the metastasis of liver cancers in vivo, the biology metabolic characteristics of liver cancers and drug transportation. Additionally, it is obviously referential in practical application.

  7. Construction of a cancer-perturbed protein-protein interaction network for discovery of apoptosis drug targets

    Directory of Open Access Journals (Sweden)

    Chen Bor-Sen

    2008-06-01

    Full Text Available Abstract Background Cancer is caused by genetic abnormalities, such as mutations of oncogenes or tumor suppressor genes, which alter downstream signal transduction pathways and protein-protein interactions. Comparisons of the interactions of proteins in cancerous and normal cells can shed light on the mechanisms of carcinogenesis. Results We constructed initial networks of protein-protein interactions involved in the apoptosis of cancerous and normal cells by use of two human yeast two-hybrid data sets and four online databases. Next, we applied a nonlinear stochastic model, maximum likelihood parameter estimation, and Akaike Information Criteria (AIC to eliminate false-positive protein-protein interactions in our initial protein interaction networks by use of microarray data. Comparisons of the networks of apoptosis in HeLa (human cervical carcinoma cells and in normal primary lung fibroblasts provided insight into the mechanism of apoptosis and allowed identification of potential drug targets. The potential targets include BCL2, caspase-3 and TP53. Our comparison of cancerous and normal cells also allowed derivation of several party hubs and date hubs in the human protein-protein interaction networks involved in caspase activation. Conclusion Our method allows identification of cancer-perturbed protein-protein interactions involved in apoptosis and identification of potential molecular targets for development of anti-cancer drugs.

  8. Deriving margins in prostate cancer radiotherapy treatment: comparison of neural network and fuzzy logic models.

    Science.gov (United States)

    Mzenda, Bongile; Gegov, Alexander; Brown, David J; Petrov, Nedyalko

    2012-01-01

    This study investigates the feasibility of using Artificial Neural Network (ANN) and fuzzy logic based techniques to select treatment margins for dynamically moving targets in the radiotherapy treatment of prostate cancer. The use of data from 15 patients relating error effects to the Tumour Control Probability (TCP) and Normal Tissue Complication Probability (NTCP) radiobiological indices was contrasted against the use of data based on the prostate volume receiving 99% of the prescribed dose (V99%) and the rectum volume receiving more than 60Gy (V60). For the same input data, the results of the ANN were compared to results obtained using a fuzzy system, a fuzzy network and current clinically used statistical techniques. Compared to fuzzy and statistical methods, the ANN derived margins were found to be up to 2 mm larger at small and high input errors and up to 3.5 mm larger at medium input error magnitudes.

  9. Cost effective Internet access and video conferencing for a community cancer network.

    Science.gov (United States)

    London, J W; Morton, D E; Marinucci, D; Catalano, R; Comis, R L

    1995-01-01

    Utilizing the ubiquitous personal computer as a platform, and Integrated Services Digital Network (ISDN) communications, cost effective medical information access and consultation can be provided for physicians at geographically remote sites. Two modes of access are provided: information retrieval via the Internet, and medical consultation video conferencing. Internet access provides general medical information such as current treatment options, literature citations, and active clinical trials. During video consultations, radiographic and pathology images, and medical text reports (e.g., history and physical, pathology, radiology, clinical laboratory reports), may be viewed and simultaneously annotated by either video conference participant. Both information access modes have been employed by physicians at community hospitals which are members of the Jefferson Cancer Network, and oncologists at Thomas Jefferson University Hospital. This project has demonstrated the potential cost effectiveness and benefits of this technology.

  10. Cancer risk at low doses of ionizing radiation: artificial neural networks inference from atomic bomb survivors.

    Science.gov (United States)

    Sasaki, Masao S; Tachibana, Akira; Takeda, Shunichi

    2014-05-01

    Cancer risk at low doses of ionizing radiation remains poorly defined because of ambiguity in the quantitative link to doses below 0.2 Sv in atomic bomb survivors in Hiroshima and Nagasaki arising from limitations in the statistical power and information available on overall radiation dose. To deal with these difficulties, a novel nonparametric statistics based on the 'integrate-and-fire' algorithm of artificial neural networks was developed and tested in cancer databases established by the Radiation Effects Research Foundation. The analysis revealed unique features at low doses that could not be accounted for by nominal exposure dose, including (i) the presence of a threshold that varied with organ, gender and age at exposure, and (ii) a small but significant bumping increase in cancer risk at low doses in Nagasaki that probably reflects internal exposure to (239)Pu. The threshold was distinct from the canonical definition of zero effect in that it was manifested as negative excess relative risk, or suppression of background cancer rates. Such a unique tissue response at low doses of radiation exposure has been implicated in the context of the molecular basis of radiation-environment interplay in favor of recently emerging experimental evidence on DNA double-strand break repair pathway choice and its epigenetic memory by histone marking.

  11. Identification of calgranulin B interacting proteins and network analysis in gastrointestinal cancer cells

    Science.gov (United States)

    Yoo, Byong Chul

    2017-01-01

    Calgranulin B is known to be involved in tumor development, but the underlying molecular mechanism is not clear. To gain insight into possible roles of calgranulin B, we screened for calgranulin B-interacting molecules in the SNU-484 gastric cancer and the SNU-81 colon cancer cells. Calgranulin B-interacting partners were identified by yeast two-hybrid and functional information was obtained by computational analysis. Most of the calgranulin B-interacting partners were involved in metabolic and cellular processes, and found to have molecular function of binding and catalytic activities. Interestingly, 46 molecules in the network of the calgranulin B-interacting proteins are known to be associated with cancer and FKBP2 was found to interact with calgranulin B in both SNU-484 and SNU-81 cells. Polyubiquitin-C encoded by UBC, which exhibited an interaction with calgranulin B, has been associated with various molecules of the extracellular space and plasma membrane identified in our screening, including Na-K-Cl cotransporter 1 and dystonin in SNU-484 cells, and ATPase subunit beta-1 in SNU-81 cells. Our data provide novel insight into the roles of calgranulin B of gastrointestinal cancer cells, and offer new clues suggesting calgranulin B acts as an effector molecule through which the cell can communicate with the tumor microenvironment via polyubiquitin-C. PMID:28152021

  12. Biomechanical cell regulatory networks as complex adaptive systems in relation to cancer.

    Science.gov (United States)

    Feller, Liviu; Khammissa, Razia Abdool Gafaar; Lemmer, Johan

    2017-01-01

    Physiological structure and function of cells are maintained by ongoing complex dynamic adaptive processes in the intracellular molecular pathways controlling the overall profile of gene expression, and by genes in cellular gene regulatory circuits. Cytogenetic mutations and non-genetic factors such as chronic inflammation or repetitive trauma, intrinsic mechanical stresses within extracellular matrix may induce redirection of gene regulatory circuits with abnormal reactivation of embryonic developmental programmes which can now drive cell transformation and cancer initiation, and later cancer progression and metastasis. Some of the non-genetic factors that may also favour cancerization are dysregulation in epithelial-mesenchymal interactions, in cell-to-cell communication, in extracellular matrix turnover, in extracellular matrix-to-cell interactions and in mechanotransduction pathways. Persistent increase in extracellular matrix stiffness, for whatever reason, has been shown to play an important role in cell transformation, and later in cancer cell invasion. In this article we review certain cell regulatory networks driving carcinogenesis, focussing on the role of mechanical stresses modulating structure and function of cells and their extracellular matrices.

  13. Genome network medicine: innovation to overcome huge challenges in cancer therapy.

    Science.gov (United States)

    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.

  14. Correlating transcriptional networks with pathological complete response following neoadjuvant chemotherapy for breast cancer.

    Science.gov (United States)

    Liu, Rong; Lv, Qiao-Li; Yu, Jing; Hu, Lei; Zhang, Li-Hua; Cheng, Yu; Zhou, Hong-Hao

    2015-06-01

    We aimed to investigate the association between gene co-expression modules and responses to neoadjuvant chemotherapy in breast cancer by using a systematic biological approach. The gene expression profiles and clinico-pathological data of 508 (discovery set) and 740 (validation set) patients with breast cancer who received neoadjuvant chemotherapy were analyzed. Weighted gene co-expression network analysis was performed and identified seven co-regulated gene modules. Each module and gene signature were evaluated with logistic regression models for pathological complete response (pCR). The association between modules and pCR in each intrinsic molecular subtype was also investigated. Two transcriptional modules were correlated with tumor grade, estrogen receptor status, progesterone receptor status, and chemotherapy response in breast cancer. One module that constitutes upregulated cell proliferation genes was associated with a high probability for pCR in the whole (odds ratio (OR) = 5.20 and 3.45 in the discovery and validation datasets, respectively), luminal B, and basal-like subtypes. The prognostic potentials of novel genes, such as MELK, and pCR-related genes, such as ESR1 and TOP2A, were identified. The upregulation of another gene co-expression module was associated with weak chemotherapy responses (OR = 0.19 and 0.33 in the discovery and validation datasets, respectively). The novel gene CA12 was identified as a potential prognostic indicator in this module. A systems biology network-based approach may facilitate the discovery of biomarkers for predicting chemotherapy responses in breast cancer and contribute in developing personalized medicines.

  15. Chinese hamster ovary cell performance enhanced by a rational divide-and-conquer strategy for chemically defined medium development.

    Science.gov (United States)

    Liu, Yaya; Zhang, Weiyan; Deng, Xiancun; Poon, Hong Fai; Liu, Xuping; Tan, Wen-Song; Zhou, Yan; Fan, Li

    2015-12-01

    Basal medium design is considered one of the most important steps in process development. To optimize chemically defined (CD) media efficiently and effectively for the biopharmaceutical industry, a two-step rational strategy was applied to optimize four antibody producing Chinese hamster ovary (CHO) cell lines. In the first step, 48 of 52 components of our in-house medium were divided into three groups according to their characteristics. In the next step, these groups were optimized by spent medium analysis, response surface methodology and mixture design. Because these steps in our strategy involved dividing medium components into groups and subsequently adjusting the concentration of the components, we termed this medium development strategy "divide and conquer". By applying the strategy, we were able to improve the titers of CHO-S, CHO-DG44 and two CHO-K1 cell lines 1.92, 1.86, 2.92 and 1.62-fold, respectively, in 8 weeks with fewer than 60 tests. This divide-and-conquer strategy was efficient, effective, scalable and universal in our current study and offered a new approach to CD media development.

  16. Assessment of FBA Based Gene Essentiality Analysis in Cancer with a Fast Context-Specific Network Reconstruction Method.

    Directory of Open Access Journals (Sweden)

    Luis Tobalina

    Full Text Available Gene Essentiality Analysis based on Flux Balance Analysis (FBA-based GEA is a promising tool for the identification of novel metabolic therapeutic targets in cancer. The reconstruction of cancer-specific metabolic networks, typically based on gene expression data, constitutes a sensible step in this approach. However, to our knowledge, no extensive assessment on the influence of the reconstruction process on the obtained results has been carried out to date.In this article, we aim to study context-specific networks and their FBA-based GEA results for the identification of cancer-specific metabolic essential genes. To that end, we used gene expression datasets from the Cancer Cell Line Encyclopedia (CCLE, evaluating the results obtained in 174 cancer cell lines. In order to more clearly observe the effect of cancer-specific expression data, we did the same analysis using randomly generated expression patterns. Our computational analysis showed some essential genes that are fairly common in the reconstructions derived from both gene expression and randomly generated data. However, though of limited size, we also found a subset of essential genes that are very rare in the randomly generated networks, while recurrent in the sample derived networks, and, thus, would presumably constitute relevant drug targets for further analysis. In addition, we compare the in-silico results to high-throughput gene silencing experiments from Project Achilles with conflicting results, which leads us to raise several questions, particularly the strong influence of the selected biomass reaction on the obtained results. Notwithstanding, using previous literature in cancer research, we evaluated the most relevant of our targets in three different cancer cell lines, two derived from Gliobastoma Multiforme and one from Non-Small Cell Lung Cancer, finding that some of the predictions are in the right track.

  17. The attention network changes in breast cancer patients receiving neoadjuvant chemotherapy: Evidence from an arterial spin labeling perfusion study

    Science.gov (United States)

    Chen, Xingui; He, Xiaoxuan; Tao, Longxiang; Cheng, Huaidong; Li, Jingjing; Zhang, Jingjie; Qiu, Bensheng; Yu, Yongqiang; Wang, Kai

    2017-01-01

    To investigate the neural mechanisms underlying attention deficits that are related to neoadjuvant chemotherapy in combination with cerebral perfusion. Thirty one patients with breast cancer who were scheduled to receive neoadjuvant chemotherapy and 34 healthy control subjects were included. The patients completed two assessments of the attention network tasks (ANT), neuropsychological background tests, and the arterial spin labeling scan, which were performed before neoadjuvant chemotherapy and after completing chemotherapy. After neoadjuvant chemotherapy, the patients exhibited reduced performance in the alerting and executive control attention networks but not the orienting network (p breast cancer. The results demonstrated that neoadjuvant chemotherapy influences hemodynamic activity in different brain areas through increasing cerebral perfusion, which reduces the attention abilities in breast cancer patients. PMID:28209975

  18. Key concerns about the current state of bladder cancer: a position paper from the Bladder Cancer Think Tank, the Bladder Cancer Advocacy Network, and the Society of Urologic Oncology.

    Science.gov (United States)

    Lotan, Yair; Kamat, Ashish M; Porter, Michael P; Robinson, Victoria L; Shore, Neal; Jewett, Michael; Schelhammer, Paul F; deVere White, Ralph; Quale, Diane; Lee, Cheryl T

    2009-09-15

    Bladder cancer is the fifth most common cancer in the United States and, on a per capita basis, is the most expensive cancer from diagnosis to death. Unfortunately, National Cancer Institute funding for bladder cancer is quite low when compared with other common malignancies. Limited funding has stifled research opportunities for new and established investigators, ultimately encouraging them to redirect research efforts to other organ sites. Waning interest of scientists has further fueled the cycle of modest funding for bladder cancer. One important consequence of this has been a lack of scientific advancement in the field. Patient advocates have decidedly advanced research efforts in many cancer sites. Breast, prostate, pancreatic, and ovarian cancer advocates have organized highly successful campaigns to lobby the federal government and the medical community to devote increased attention and funding to understudied malignancies and to conduct relevant studies to better understand the therapy, diagnosis, and prevention of these diseases. Bladder cancer survivors have lacked a coordinated advocacy voice until recently. A concerted effort to align bladder cancer advocates, clinicians, and urologic organizations is essential to define the greatest needs in bladder cancer and to develop related solutions. This position paper represents a collaborative discussion to define the most concerning trends and greatest needs in the field of bladder cancer as outlined by the Bladder Cancer Think Tank, the Bladder Cancer Advocacy Network, and the Society of Urologic Oncology.

  19. Development and validation of a staging system for HPV-related oropharyngeal cancer by the International Collaboration on Oropharyngeal cancer Network for Staging (ICON-S)

    DEFF Research Database (Denmark)

    O'Sullivan, Brian; Huang, Shao Hui; Su, Jie

    2016-01-01

    . The International Collaboration on Oropharyngeal cancer Network for Staging (ICON-S) aimed to develop a TNM classification specific to HPV+ oropharyngeal cancer. METHODS: The ICON-S study included patients with non-metastatic oropharyngeal cancer from seven cancer centres located across Europe and North America......; one centre comprised the training cohort and six formed the validation cohorts. We ascertained patients' HPV status with p16 staining or in-situ hybridisation. We compared overall survival at 5 years between training and validation cohorts according to 7th edition TNM classifications and HPV status...... training cohort to assess relevance within the ICON-S classification. FINDINGS: Of 1907 patients with HPV+ oropharyngeal cancer, 661 (35%) were recruited at the training centre and 1246 (65%) were enrolled at the validation centres. 5-year overall survival was similar for 7th edition TNM stage I, II, III...

  20. Topological Fidelity in Sensor Networks

    CERN Document Server

    Chintakunta, Harish

    2011-01-01

    Sensor Networks are inherently complex networks, and many of their associated problems require analysis of some of their global characteristics. These are primarily affected by the topology of the network. We present in this paper, a general framework for a topological analysis of a network, and develop distributed algorithms in a generalized combinatorial setting in order to solve two seemingly unrelated problems, 1) Coverage hole detection and Localization and 2) Worm hole attack detection and Localization. We also note these solutions remain coordinate free as no priori localization information of the nodes is assumed. For the coverage hole problem, we follow a "divide and conquer approach", by strategically dissecting the network so that the overall topology is preserved, while efficiently pursuing the detection and localization of failures. The detection of holes, is enabled by first attributing a combinatorial object called a "Rips Complex" to each network segment, and by subsequently checking the exist...

  1. Network-based approach to identify prognostic biomarkers for estrogen receptor-positive breast cancer treatment with tamoxifen.

    Science.gov (United States)

    Liu, Rong; Guo, Cheng-Xian; Zhou, Hong-Hao

    2015-01-01

    This study aims to identify effective gene networks and prognostic biomarkers associated with estrogen receptor positive (ER+) breast cancer using human mRNA studies. Weighted gene coexpression network analysis was performed with a complex ER+ breast cancer transcriptome to investigate the function of networks and key genes in the prognosis of breast cancer. We found a significant correlation of an expression module with distant metastasis-free survival (HR = 2.25; 95% CI .21.03-4.88 in discovery set; HR = 1.78; 95% CI = 1.07-2.93 in validation set). This module contained genes enriched in the biological process of the M phase. From this module, we further identified and validated 5 hub genes (CDK1, DLGAP5, MELK, NUSAP1, and RRM2), the expression levels of which were strongly associated with poor survival. Highly expressed MELK indicated poor survival in luminal A and luminal B breast cancer molecular subtypes. This gene was also found to be associated with tamoxifen resistance. Results indicated that a network-based approach may facilitate the discovery of biomarkers for the prognosis of ER+ breast cancer and may also be used as a basis for establishing personalized therapies. Nevertheless, before the application of this approach in clinical settings, in vivo and in vitro experiments and multi-center randomized controlled clinical trials are still needed.

  2. Network pharmacology of cancer: From understanding of complex interactomes to the design of multi-target specific therapeutics from nature.

    Science.gov (United States)

    Poornima, Paramasivan; Kumar, Jothi Dinesh; Zhao, Qiaoli; Blunder, Martina; Efferth, Thomas

    2016-09-01

    Despite massive investments in drug research and development, the significant decline in the number of new drugs approved or translated to clinical use raises the question, whether single targeted drug discovery is the right approach. To combat complex systemic diseases that harbour robust biological networks such as cancer, single target intervention is proved to be ineffective. In such cases, network pharmacology approaches are highly useful, because they differ from conventional drug discovery by addressing the ability of drugs to target numerous proteins or networks involved in a disease. Pleiotropic natural products are one of the promising strategies due to their multi-targeting and due to lower side effects. In this review, we discuss the application of network pharmacology for cancer drug discovery. We provide an overview of the current state of knowledge on network pharmacology, focus on different technical approaches and implications for cancer therapy (e.g. polypharmacology and synthetic lethality), and illustrate the therapeutic potential with selected examples green tea polyphenolics, Eleutherococcus senticosus, Rhodiola rosea, and Schisandra chinensis). Finally, we present future perspectives on their plausible applications for diagnosis and therapy of cancer.

  3. Conditional robustness analysis for fragility discovery and target identification in biochemical networks and in cancer systems biology

    OpenAIRE

    Bianconi, Fortunato; Baldelli, Elisa; Luovini, Vienna; Petricoin, Emanuel F.; Crinò, Lucio; Valigi, Paolo

    2015-01-01

    Background The study of cancer therapy is a key issue in the field of oncology research and the development of target therapies is one of the main problems currently under investigation. This is particularly relevant in different types of tumor where traditional chemotherapy approaches often fail, such as lung cancer. Results We started from the general definition of robustness introduced by Kitano and applied it to the analysis of dynamical biochemical networks, proposing a new algorithm bas...

  4. Identification of key genes associated with colorectal cancer based on the transcriptional network.

    Science.gov (United States)

    Chen, Guoting; Li, Hengping; Niu, Xianping; Li, Guofeng; Han, Ning; Li, Xin; Li, Guang; Liu, Yangzhou; Sun, Guixin; Wang, Yong; Li, Zengchun; Li, Qinchuan

    2015-07-01

    Colorectal cancer (CRC) is among the most lethal human cancers, but the mechanism of the cancer is still unclear enough. We aimed to explore the key genes in CRC progression. The gene expression profile (GSE4183) of CRC was obtained from Gene Expression Omnibus database which included 8 normal samples, 15 adenoma samples, 15 CRC samples and 15 inflammatory bowel disease (IBD) samples. Thereinto, 8 normal, 15 adenoma, and 15 CRC samples were chosen for our research. The differentially expressed genes (DEGs) in normal vs. adenoma, normal vs. CRC, and adenoma vs. CRC, were identified using the Wilcoxon test method in R respectively. The interactive network of DEGs was constructed to select the significant modules using the Pearson's correlation. Meanwhile, transcriptional network of DEGs was also constructed using the g: Profiler. Totally, 2,741 DEGs in normal vs. adenoma, 1,484 DEGs in normal vs. CRC, and 396 DEGs in adenoma vs. CRC were identified. Moreover, function analysis of DEGs in each group showed FcR-mediated phagocytosis pathway in module 1, cardiac muscle contraction pathway in module 6, and Jak-STAT signaling pathway in module 19 were also enriched. Furthermore, MZF1 and AP2 were the transcription factor in module 6, with the target SP1, while SP1 was also a transcription in module 20. DEGs like NCF1, AKT, SP1, AP2, MZF1, and TPM might be used as specific biomarkers in CRC development. Therapy targeting on the functions of these key genes might provide novel perspective for CRC treatment.

  5. Diagnosis of Breast Cancer using a Combination of Genetic Algorithm and Artificial Neural Network in Medical Infrared Thermal Imaging

    Directory of Open Access Journals (Sweden)

    Hossein Ghayoumi zadeh

    2013-03-01

    Full Text Available Introduction This study is an effort to diagnose breast cancer by processing the quantitative and qualitative information obtained from medical infrared imaging. The medical infrared imaging is free from any harmful radiation and it is one of the best advantages of the proposed method. By analyzing this information, the best diagnostic parameters among the available parameters are selected and its sensitivity and precision in cancer diagnosis is improved by utilizing genetic algorithm and artificial neural network. Materials and Methods In this research, the necessary information is obtained from thermal imaging of 200 people, and 8 diagnostic parameters are extracted from these images by the research team. Then these 8 parameters are used as input of our proposed combinatorial model which is formed using artificial neural network and genetic algorithm. Results Our results have revealed that comparison of the breast areas; thermal pattern and kurtosis are the most important parameters in breast cancer diagnosis from proposed medical infrared imaging. The proposed combinatorial model with a 50% sensitivity, 75% specificity and, 70% accuracy shows good precision in cancer diagnosis. Conclusion The main goal of this article is to describe the capability of infrared imaging in preliminary diagnosis of breast cancer. This method is beneficial to patients with and without symptoms. The results indicate that the proposed combinatorial model produces optimum and efficacious parameters in comparison to other parameters and can improve the capability and power of globalizing the artificial neural network. This will help physicians in more accurate diagnosis of this type of cancer.

  6. Garlic extract in bladder cancer prevention: Evidence from T24 bladder cancer cell xenograft model, tissue microarray, and gene network analysis.

    Science.gov (United States)

    Kim, Won Tae; Seo, Sung-Pil; Byun, Young Joon; Kang, Ho-Won; Kim, Yong-June; Lee, Sang-Cheol; Jeong, Pildu; Seo, Yoonhee; Choe, Soo Young; Kim, Dong-Joon; Kim, Seon-Kyu; Moon, Sung-Kwon; Choi, Yung-Hyun; Lee, Geun Taek; Kim, Isaac Yi; Yun, Seok Joong; Kim, Wun-Jae

    2017-07-01

    There is a growing interest in the use of naturally occurring agents in cancer prevention. This study investigated the garlic extract affects in bladder cancer (BC) prevention. The effect of garlic extract in cancer prevention was evaluated using the T24 BC BALB/C-nude mouse xenograft model. Microarray analysis of tissues was performed to identify differences in gene expression between garlic extract intake and control diet, and gene network analysis was performed to assess candidate mechanisms of action. Furthermore, we investigated the expression value of selected genes in the data of 165 BC patients. Compared to the control group, significant differences in tumor volume and tumor weight were observed in the groups fed 20 mg/kg (p2 and ptissue microarray analysis. A gene network analysis of 279 of these genes (p<0.01) was performed using Cytoscape/ClueGo software: 36 genes and 37 gene ontologies were mapped to gene networks. Protein kinase A (PKA) signaling pathway including AKAP12, RDX, and RAB13 genes were identified as potential mechanisms for the activity of garlic extract in cancer prevention. In BC patients, AKAP12 and RDX were decreased but, RAB13 was increased. Oral garlic extract has strong cancer prevention activity in vivo and an acceptable safety profile. PKA signaling process, especially increasing AKAP12 and RDX and decreasing RAB13, are candidate pathways that may mediate this prevention effect.

  7. Integrating text mining, data mining, and network analysis for identifying genetic breast cancer trends.

    Science.gov (United States)

    Jurca, Gabriela; Addam, Omar; Aksac, Alper; Gao, Shang; Özyer, Tansel; Demetrick, Douglas; Alhajj, Reda

    2016-04-26

    Breast cancer is a serious disease which affects many women and may lead to death. It has received considerable attention from the research community. Thus, biomedical researchers aim to find genetic biomarkers indicative of the disease. Novel biomarkers can be elucidated from the existing literature. However, the vast amount of scientific publications on breast cancer make this a daunting task. This paper presents a framework which investigates existing literature data for informative discoveries. It integrates text mining and social network analysis in order to identify new potential biomarkers for breast cancer. We utilized PubMed for the testing. We investigated gene-gene interactions, as well as novel interactions such as gene-year, gene-country, and abstract-country to find out how the discoveries varied over time and how overlapping/diverse are the discoveries and the interest of various research groups in different countries. Interesting trends have been identified and discussed, e.g., different genes are highlighted in relationship to different countries though the various genes were found to share functionality. Some text analysis based results have been validated against results from other tools that predict gene-gene relations and gene functions.

  8. Incorporating higher-order representative features improves prediction in network-based cancer prognosis analysis

    Directory of Open Access Journals (Sweden)

    Huang Jian

    2011-01-01

    Full Text Available Abstract Background In cancer prognosis studies with gene expression measurements, an important goal is to construct gene signatures with predictive power. In this study, we describe the coordination among genes using the weighted coexpression network, where nodes represent genes and nodes are connected if the corresponding genes have similar expression patterns across samples. There are subsets of nodes, called modules, that are tightly connected to each other. In several published studies, it has been suggested that the first principal components of individual modules, also referred to as "eigengenes", may sufficiently represent the corresponding modules. Results In this article, we refer to principal components and their functions as representative features". We investigate higher-order representative features, which include the principal components other than the first ones and second order terms (quadratics and interactions. Two gradient thresholding methods are adopted for regularized estimation and feature selection. Analysis of six prognosis studies on lymphoma and breast cancer shows that incorporating higher-order representative features improves prediction performance over using eigengenes only. Simulation study further shows that prediction performance can be less satisfactory if the representative feature set is not properly chosen. Conclusions This study introduces multiple ways of defining the representative features and effective thresholding regularized estimation approaches. It provides convincing evidence that the higher-order representative features may have important implications for the prediction of cancer prognosis.

  9. Incorporating higher-order representative features improves prediction in network-based cancer prognosis analysis.

    Science.gov (United States)

    Ma, Shuangge; Kosorok, Michael R; Huang, Jian; Dai, Ying

    2011-01-12

    In cancer prognosis studies with gene expression measurements, an important goal is to construct gene signatures with predictive power. In this study, we describe the coordination among genes using the weighted coexpression network, where nodes represent genes and nodes are connected if the corresponding genes have similar expression patterns across samples. There are subsets of nodes, called modules, that are tightly connected to each other. In several published studies, it has been suggested that the first principal components of individual modules, also referred to as "eigengenes", may sufficiently represent the corresponding modules. In this article, we refer to principal components and their functions as representative features". We investigate higher-order representative features, which include the principal components other than the first ones and second order terms (quadratics and interactions). Two gradient thresholding methods are adopted for regularized estimation and feature selection. Analysis of six prognosis studies on lymphoma and breast cancer shows that incorporating higher-order representative features improves prediction performance over using eigengenes only. Simulation study further shows that prediction performance can be less satisfactory if the representative feature set is not properly chosen. This study introduces multiple ways of defining the representative features and effective thresholding regularized estimation approaches. It provides convincing evidence that the higher-order representative features may have important implications for the prediction of cancer prognosis.

  10. Prioritizing cancer-related genes with aberrant methylation based on a weighted protein-protein interaction network

    Directory of Open Access Journals (Sweden)

    Lv Jie

    2011-10-01

    Full Text Available Abstract Background As an important epigenetic modification, DNA methylation plays a crucial role in the development of mammals and in the occurrence of complex diseases. Genes that interact directly or indirectly may have the same or similar functions in the biological processes in which they are involved and together contribute to the related disease phenotypes. The complicated relations between genes can be clearly represented using network theory. A protein-protein interaction (PPI network offers a platform from which to systematically identify disease-related genes from the relations between genes with similar functions. Results We constructed a weighted human PPI network (WHPN using DNA methylation correlations based on human protein-protein interactions. WHPN represents the relationships of DNA methylation levels in gene pairs for four cancer types. A cancer-associated subnetwork (CASN was obtained from WHPN by selecting genes associated with seed genes which were known to be methylated in the four cancers. We found that CASN had a more densely connected network community than WHPN, indicating that the genes in CASN were much closer to seed genes. We prioritized 154 potential cancer-related genes with aberrant methylation in CASN by neighborhood-weighting decision rule. A function enrichment analysis for GO and KEGG indicated that the optimized genes were mainly involved in the biological processes of regulating cell apoptosis and programmed cell death. An analysis of expression profiling data revealed that many of the optimized genes were expressed differentially in the four cancers. By examining the PubMed co-citations, we found 43 optimized genes were related with cancers and aberrant methylation, and 10 genes were validated to be methylated aberrantly in cancers. Of 154 optimized genes, 27 were as diagnostic markers and 20 as prognostic markers previously identified in literature for cancers and other complex diseases by searching Pub

  11. A Divide-and-Conquer Strategy for Embedding a Distance-Net Point Set Into E~n and Its Application

    Institute of Scientific and Technical Information of China (English)

    周加农; 刘立

    1994-01-01

    A divide-and-conquer strategy is given for embedding a distance-net point set into Euclidean space En, and the problem of embedding a bounded distance-net point set into E3 and its application to the macromolecular conformation with Nuclear Magnetic Resonance data are discussed.

  12. Analyzing collaboration networks and developmental patterns of nano-enabled drug delivery (NEDD) for brain cancer.

    Science.gov (United States)

    Huang, Ying; Ma, Jing; Porter, Alan L; Kwon, Seokbeom; Zhu, Donghua

    2015-01-01

    The rapid development of new and emerging science & technologies (NESTs) brings unprecedented challenges, but also opportunities. In this paper, we use bibliometric and social network analyses, at country, institution, and individual levels, to explore the patterns of scientific networking for a key nano area - nano-enabled drug delivery (NEDD). NEDD has successfully been used clinically to modulate drug release and to target particular diseased tissues. The data for this research come from a global compilation of research publication information on NEDD directed at brain cancer. We derive a family of indicators that address multiple facets of research collaboration and knowledge transfer patterns. Results show that: (1) international cooperation is increasing, but networking characteristics change over time; (2) highly productive institutions also lead in influence, as measured by citation to their work, with American institutes leading; (3) research collaboration is dominated by local relationships, with interesting information available from authorship patterns that go well beyond journal impact factors. Results offer useful technical intelligence to help researchers identify potential collaborators and to help inform R&D management and science & innovation policy for such nanotechnologies.

  13. Analyzing collaboration networks and developmental patterns of nano-enabled drug delivery (NEDD for brain cancer

    Directory of Open Access Journals (Sweden)

    Ying Huang

    2015-07-01

    Full Text Available The rapid development of new and emerging science & technologies (NESTs brings unprecedented challenges, but also opportunities. In this paper, we use bibliometric and social network analyses, at country, institution, and individual levels, to explore the patterns of scientific networking for a key nano area – nano-enabled drug delivery (NEDD. NEDD has successfully been used clinically to modulate drug release and to target particular diseased tissues. The data for this research come from a global compilation of research publication information on NEDD directed at brain cancer. We derive a family of indicators that address multiple facets of research collaboration and knowledge transfer patterns. Results show that: (1 international cooperation is increasing, but networking characteristics change over time; (2 highly productive institutions also lead in influence, as measured by citation to their work, with American institutes leading; (3 research collaboration is dominated by local relationships, with interesting information available from authorship patterns that go well beyond journal impact factors. Results offer useful technical intelligence to help researchers identify potential collaborators and to help inform R&D management and science & innovation policy for such nanotechnologies.

  14. Diagnostic Classification of Normal Persons and Cancer Patients by Using Neural Network Based on Trace Metal Contents in Serum Samples

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    Artificial neural network with the back-propagation(BP-ANN) approach was applied to the classification of normal persons and various cancer patients based on the elemental contents in serum samples. This method was verified by the cross-validation method. The effects of the net work parameters were investigated and the related problems were discussed. The samples of 72, 42, and 52 for lung, liver, and stomach cancer patients and normal persons, respectively, were used for the classification study. About 95% of the samples can be classified correctly. There fore, the method can be used as an auxiliary means of the diagnosis of cancer.

  15. Cancer stem cells display extremely large evolvability: alternating plastic and rigid networks as a potential Mechanism: network models, novel therapeutic target strategies, and the contributions of hypoxia, inflammation and cellular senescence.

    Science.gov (United States)

    Csermely, Peter; Hódsági, János; Korcsmáros, Tamás; Módos, Dezső; Perez-Lopez, Áron R; Szalay, Kristóf; Veres, Dániel V; Lenti, Katalin; Wu, Ling-Yun; Zhang, Xiang-Sun

    2015-02-01

    Cancer is increasingly perceived as a systems-level, network phenomenon. The major trend of malignant transformation can be described as a two-phase process, where an initial increase of network plasticity is followed by a decrease of plasticity at late stages of tumor development. The fluctuating intensity of stress factors, like hypoxia, inflammation and the either cooperative or hostile interactions of tumor inter-cellular networks, all increase the adaptation potential of cancer cells. This may lead to the bypass of cellular senescence, and to the development of cancer stem cells. We propose that the central tenet of cancer stem cell definition lies exactly in the indefinability of cancer stem cells. Actual properties of cancer stem cells depend on the individual "stress-history" of the given tumor. Cancer stem cells are characterized by an extremely large evolvability (i.e. a capacity to generate heritable phenotypic variation), which corresponds well with the defining hallmarks of cancer stem cells: the possession of the capacity to self-renew and to repeatedly re-build the heterogeneous lineages of cancer cells that comprise a tumor in new environments. Cancer stem cells represent a cell population, which is adapted to adapt. We argue that the high evolvability of cancer stem cells is helped by their repeated transitions between plastic (proliferative, symmetrically dividing) and rigid (quiescent, asymmetrically dividing, often more invasive) phenotypes having plastic and rigid networks. Thus, cancer stem cells reverse and replay cancer development multiple times. We describe network models potentially explaining cancer stem cell-like behavior. Finally, we propose novel strategies including combination therapies and multi-target drugs to overcome the Nietzschean dilemma of cancer stem cell targeting: "what does not kill me makes me stronger".

  16. Correlating transcriptional networks to breast cancer survival: a large-scale coexpression analysis.

    Science.gov (United States)

    Clarke, Colin; Madden, Stephen F; Doolan, Padraig; Aherne, Sinead T; Joyce, Helena; O'Driscoll, Lorraine; Gallagher, William M; Hennessy, Bryan T; Moriarty, Michael; Crown, John; Kennedy, Susan; Clynes, Martin

    2013-10-01

    Weighted gene coexpression network analysis (WGCNA) is a powerful 'guilt-by-association'-based method to extract coexpressed groups of genes from large heterogeneous messenger RNA expression data sets. We have utilized WGCNA to identify 11 coregulated gene clusters across 2342 breast cancer samples from 13 microarray-based gene expression studies. A number of these transcriptional modules were found to be correlated to clinicopathological variables (e.g. tumor grade), survival endpoints for breast cancer as a whole (disease-free survival, distant disease-free survival and overall survival) and also its molecular subtypes (luminal A, luminal B, HER2+ and basal-like). Examples of findings arising from this work include the identification of a cluster of proliferation-related genes that when upregulated correlated to increased tumor grade and were associated with poor survival in general. The prognostic potential of novel genes, for example, ubiquitin-conjugating enzyme E2S (UBE2S) within this group was confirmed in an independent data set. In addition, gene clusters were also associated with survival for breast cancer molecular subtypes including a cluster of genes that was found to correlate with prognosis exclusively for basal-like breast cancer. The upregulation of several single genes within this coexpression cluster, for example, the potassium channel, subfamily K, member 5 (KCNK5) was associated with poor outcome for the basal-like molecular subtype. We have developed an online database to allow user-friendly access to the coexpression patterns and the survival analysis outputs uncovered in this study (available at http://glados.ucd.ie/Coexpression/).

  17. Deep sequencing and in silico analyses identify MYB-regulated gene networks and signaling pathways in pancreatic cancer.

    Science.gov (United States)

    Azim, Shafquat; Zubair, Haseeb; Srivastava, Sanjeev K; Bhardwaj, Arun; Zubair, Asif; Ahmad, Aamir; Singh, Seema; Khushman, Moh'd; Singh, Ajay P

    2016-06-29

    We have recently demonstrated that the transcription factor MYB can modulate several cancer-associated phenotypes in pancreatic cancer. In order to understand the molecular basis of these MYB-associated changes, we conducted deep-sequencing of transcriptome of MYB-overexpressing and -silenced pancreatic cancer cells, followed by in silico pathway analysis. We identified significant modulation of 774 genes upon MYB-silencing (p networks by in silico analysis. Further analyses placed genes in our RNA sequencing-generated dataset to several canonical signalling pathways, such as cell-cycle control, DNA-damage and -repair responses, p53 and HIF1α. Importantly, we observed downregulation of the pancreatic adenocarcinoma signaling pathway in MYB-silenced pancreatic cancer cells exhibiting suppression of EGFR and NF-κB. Decreased expression of EGFR and RELA was validated by both qPCR and immunoblotting and they were both shown to be under direct transcriptional control of MYB. These observations were further confirmed in a converse approach wherein MYB was overexpressed ectopically in a MYB-null pancreatic cancer cell line. Our findings thus suggest that MYB potentially regulates growth and genomic stability of pancreatic cancer cells via targeting complex gene networks and signaling pathways. Further in-depth functional studies are warranted to fully understand MYB signaling in pancreatic cancer.

  18. Neural networks improve brain cancer detection with Raman spectroscopy in the presence of operating room light artifacts

    Science.gov (United States)

    Jermyn, Michael; Desroches, Joannie; Mercier, Jeanne; Tremblay, Marie-Andrée; St-Arnaud, Karl; Guiot, Marie-Christine; Petrecca, Kevin; Leblond, Frederic

    2016-09-01

    Invasive brain cancer cells cannot be visualized during surgery and so they are often not removed. These residual cancer cells give rise to recurrences. In vivo Raman spectroscopy can detect these invasive cancer cells in patients with grade 2 to 4 gliomas. The robustness of this Raman signal can be dampened by spectral artifacts generated by lights in the operating room. We found that artificial neural networks (ANNs) can overcome these spectral artifacts using nonparametric and adaptive models to detect complex nonlinear spectral characteristics. Coupling ANN with Raman spectroscopy simplifies the intraoperative use of Raman spectroscopy by limiting changes required to the standard neurosurgical workflow. The ability to detect invasive brain cancer under these conditions may reduce residual cancer remaining after surgery and improve patient survival.

  19. German second-opinion network for testicular cancer: sealing the leaky pipe between evidence and clinical practice.

    Science.gov (United States)

    Zengerling, Friedemann; Hartmann, Michael; Heidenreich, Axel; Krege, Susanne; Albers, Peter; Karl, Alexander; Weissbach, Lothar; Wagner, Walter; Bedke, Jens; Retz, Margitta; Schmelz, Hans U; Kliesch, Sabine; Kuczyk, Markus; Winter, Eva; Pottek, Tobias; Dieckmann, Klaus-Peter; Schrader, Andres Jan; Schrader, Mark

    2014-06-01

    In 2006, the German Testicular Cancer Study Group initiated an extensive evidence-based national second-opinion network to improve the care of testicular cancer patients. The primary aims were to reflect the current state of testicular cancer treatment in Germany and to analyze the project's effect on the quality of care delivered to testicular cancer patients. A freely available internet-based platform was developed for the exchange of data between the urologists seeking advice and the 31 second-opinion givers. After providing all data relevant to the primary treatment decision, urologists received a second opinion on their therapy plan within testicular cancer patient in Germany were submitted to second-opinion centers. Second-opinion centers can help to improve the implementation of evidence into clinical practice.

  20. Transcription Factor Networks derived from Breast Cancer Stem Cells control the immune response in the Basal subtype

    DEFF Research Database (Denmark)

    da Silveira, W A; Palma, P V B; Sicchieri, R D

    2017-01-01

    from putative bCSC and reverse engineering of transcription control networks, we identified two networks associated with this phenotype. One controlled by SNAI2, TWIST1, BNC2, PRRX1 and TBX5 drives a mesenchymal or CSC-like phenotype. The second network is controlled by the SCML4, ZNF831, SP140...... and IKZF3 transcription factors which correspond to immune response modulators. Immune response network expression is correlated with pathological response to chemotherapy, and in the Basal subtype is related to better recurrence-free survival. In patient-derived xenografts, the expression...... of these networks in patient tumours is predictive of engraftment success. Our findings point out a potential molecular mechanism underlying the balance between immune surveillance and EMT activation in breast cancer. This molecular mechanism may be useful to the development of new target therapies....

  1. Social networks, social support, and burden in relationships, and mortality after breast cancer diagnosis in the Life After Breast Cancer Epidemiology (LACE) study.

    Science.gov (United States)

    Kroenke, Candyce H; Quesenberry, Charles; Kwan, Marilyn L; Sweeney, Carol; Castillo, Adrienne; Caan, Bette J

    2013-01-01

    Larger social networks have been associated with lower breast cancer mortality. The authors evaluated how levels of social support and burden influenced this association. We included 2,264 women from the Life After Cancer Epidemiology study who were diagnosed with early-stage, invasive breast cancer between 1997 and 2000, and provided data on social networks (spouse or intimate partner, religious/social ties, volunteering, time socializing with friends, and number of first-degree female relatives), social support, and caregiving. 401 died during a median follow-up of 10.8 years follow-up with 215 from breast cancer. We used delayed entry Cox proportional hazards regression to evaluate associations. In multivariate-adjusted analyses, social isolation was unrelated to recurrence or breast cancer-specific mortality. However, socially isolated women had higher all-cause mortality (HR = 1.34, 95 % CI: 1.03-1.73) and mortality from other causes (HR = 1.79, 95 % CI: 1.19-2.68). Levels of social support and burden modified associations. Among those with low, but not high, levels of social support from friends and family, lack of religious/social participation (HR = 1.58, 95 % CI: 1.07-2.36, p = 0.02, p interaction = 0.01) and lack of volunteering (HR = 1.78, 95 % CI: 1.15-2.77, p = 0.01, p interaction = 0.01) predicted higher all-cause mortality. In cross-classification analyses, only women with both small networks and low levels of support (HR = 1.61, 95 % CI: 1.10-2.38) had a significantly higher risk of mortality than women with large networks and high levels of support; women with small networks and high levels of support had no higher risk of mortality (HR = 1.13, 95 % CI: 0.74-1.72). Social networks were also more important for caregivers versus noncaregivers. Larger social networks predicted better prognosis after breast cancer, but associations depended on the quality and burden of family relationships.

  2. Optimal constrained multi-degree reduction of Bézier curves with explicit expressions based on divide and conquer

    Institute of Scientific and Technical Information of China (English)

    Lian ZHOU; Guo-jin WANG

    2009-01-01

    We decompose the problem of the optimal multi-degree reduction of Bezier curves with comers constraint into two simpler subproblems, namely making high order interpolations at the two endpoints without degree reduction, and doing optimal degree reduction without making high order interpolations at the two endpoints. Further, we convert the second subproblem into multi-degree reduction of Jacobi polynomials. Then, we can easily derive the optimal solution using orthonormality of Jaeobi polynomials and the least square method of unequally accurate measurement. This method of 'divide and conquer' has several advantages including maintaining high continuity at the two endpoints of the curve, doing multi-degree reduction only once, using explicit approximation expressions, estimating error in advance, low time cost, and high precision. More importantly, it is not only deduced simply and directly, but also can be easily extended to the degree reduction of surfaces. Finally, we present two examples to demonstrate the effectiveness of our algorithm.

  3. Large-scale atomistic simulations of nanostructured materials based on divide-and-conquer density functional theory

    Directory of Open Access Journals (Sweden)

    Vashishta P.

    2011-05-01

    Full Text Available A linear-scaling algorithm based on a divide-and-conquer (DC scheme is designed to perform large-scale molecular-dynamics simulations, in which interatomic forces are computed quantum mechanically in the framework of the density functional theory (DFT. This scheme is applied to the thermite reaction at an Al/Fe2O3 interface. It is found that mass diffusion and reaction rate at the interface are enhanced by a concerted metal-oxygen flip mechanism. Preliminary simulations are carried out for an aluminum particle in water based on the conventional DFT, as a target system for large-scale DC-DFT simulations. A pair of Lewis acid and base sites on the aluminum surface preferentially catalyzes hydrogen production in a low activation-barrier mechanism found in the simulations

  4. An efficient method for calculating spatially extended electronic states of large systems with a divide-and-conquer approach

    CERN Document Server

    Yamada, Shunsuke; Akashi, Ryosuke; Tsuneyuki, Shinji

    2016-01-01

    We present an efficient post-processing method for calculating the electronic structure of nanosystems based on the divide-and-conquer approach to density functional theory (DC-DFT), in which a system is divided into subsystems whose electronic structure is solved separately. In this post process, the Kohn-Sham Hamiltonian of the total system is easily derived from the orbitals and orbital energies of subsystems obtained by DC-DFT without time-consuming and redundant computation. The resultant orbitals spatially extended over the total system are described as linear combinations of the orbitals of the subsystems. The size of the Hamiltonian matrix can be much reduced from that for conventional calculation, so that our method is fast and applicable to general huge systems for investigating the nature of electronic states.

  5. An effective energy gradient expression for divide-and-conquer second-order Møller-Plesset perturbation theorya)

    Science.gov (United States)

    Kobayashi, Masato; Nakai, Hiromi

    2013-01-01

    We recently proposed a linear-scaling evaluation scheme for the second-order Møller-Plesset perturbation (MP2) energy based on the divide-and-conquer (DC) method [M. Kobayashi, Y. Imamura, and H. Nakai, J. Chem. Phys. 127, 074103 (2007), 10.1063/1.2761878]. In this paper, we propose an approximate but effective expression for the first derivative of the DC-MP2 energy. The present scheme evaluates the one- and two-body density matrices, which appear in the MP2 gradient formula, in the DC manner; that is, the entire matrix is obtained as the sum of subsystem matrices masked by the partition matrix. Therefore, the method requires solving only the local Z-vector equations. Illustrative applications to three types of systems, peptides, Si surface model, and delocalized polyenes, reveal the effectiveness of the present method.

  6. Efficient method for calculating spatially extended electronic states of large systems with a divide-and-conquer approach

    Science.gov (United States)

    Yamada, Shunsuke; Shimojo, Fuyuki; Akashi, Ryosuke; Tsuneyuki, Shinji

    2017-01-01

    We present an efficient postprocessing method for calculating the electronic structure of nanosystems based on the divide-and-conquer approach to density functional theory (DC-DFT), in which a system is divided into subsystems whose electronic structure is solved separately. In this postprocess, the Kohn-Sham Hamiltonian of the total system is easily derived from the orbitals and orbital energies of subsystems obtained by DC-DFT without time-consuming and redundant computation. The resultant orbitals spatially extended over the total system are described as linear combinations of the orbitals of the subsystems. The size of the Hamiltonian matrix can be much reduced from that for the conventional calculation, so our method is fast and applicable to general huge systems for investigating the nature of electronic states.

  7. Gene expression correlations in human cancer cell lines define molecular interaction networks for epithelial phenotype.

    Directory of Open Access Journals (Sweden)

    Kurt W Kohn

    Full Text Available Using gene expression data to enhance our knowledge of control networks relevant to cancer biology and therapy is a challenging but urgent task. Based on the premise that genes that are expressed together in a variety of cell types are likely to functions together, we derived mutually correlated genes that function together in various processes in epithelial-like tumor cells. Expression-correlated genes were derived from data for the NCI-60 human tumor cell lines, as well as data from the Broad Institute's CCLE cell lines. NCI-60 cell lines that selectively expressed a mutually correlated subset of tight junction genes served as a signature for epithelial-like cancer cells. Those signature cell lines served as a seed to derive other correlated genes, many of which had various other epithelial-related functions. Literature survey yielded molecular interaction and function information about those genes, from which molecular interaction maps were assembled. Many of the genes had epithelial functions unrelated to tight junctions, demonstrating that new function categories were elicited. The most highly correlated genes were implicated in the following epithelial functions: interactions at tight junctions (CLDN7, CLDN4, CLDN3, MARVELD3, MARVELD2, TJP3, CGN, CRB3, LLGL2, EPCAM, LNX1; interactions at adherens junctions (CDH1, ADAP1, CAMSAP3; interactions at desmosomes (PPL, PKP3, JUP; transcription regulation of cell-cell junction complexes (GRHL1 and 2; epithelial RNA splicing regulators (ESRP1 and 2; epithelial vesicle traffic (RAB25, EPN3, GRHL2, EHF, ADAP1, MYO5B; epithelial Ca(+2 signaling (ATP2C2, S100A14, BSPRY; terminal differentiation of epithelial cells (OVOL1 and 2, ST14, PRSS8, SPINT1 and 2; maintenance of apico-basal polarity (RAB25, LLGL2, EPN3. The findings provide a foundation for future studies to elucidate the functions of regulatory networks specific to epithelial-like cancer cells and to probe for anti-cancer drug targets.

  8. Small Cell Carcinoma of the Urinary Bladder: A Retrospective, Multicenter Rare Cancer Network Study of 107 Patients

    NARCIS (Netherlands)

    Pasquier, D.; Barney, B.; Sundar, S.; Poortmans, P.M.P.; Villa, S.; Nasrallah, H.; Boujelbene, N.; Ghadjar, P.; Lassen-Ramshad, Y.; Senkus, E.; Oar, A.; Roelandts, M.; Amichetti, M.; Vees, H.; Zilli, T.; Ozsahin, M.

    2015-01-01

    PURPOSE: Small cell carcinomas of the bladder (SCCB) account for fewer than 1% of all urinary bladder tumors. There is no consensus regarding the optimal treatment for SCCB. METHODS AND MATERIALS: Fifteen academic Rare Cancer Network medical centers contributed SCCB cases. The eligibility criteria w

  9. Changes in Brain Structural Networks and Cognitive Functions in Testicular Cancer Patients Receiving Cisplatin-Based Chemotherapy

    NARCIS (Netherlands)

    Amidi, Ali; Hosseini, S. M.Hadi; Leemans, Alexander|info:eu-repo/dai/nl/340300108; Kesler, Shelli R.; Agerbæk, Mads; Wu, Lisa M.; Zachariae, Robert

    2017-01-01

    Background: Cisplatin-based chemotherapy may have neurotoxic effects within the central nervous system. The aims of this study were 1) to longitudinally investigate the impact of cisplatin-based chemotherapy on whole-brain networks in testicular cancer patients undergoing treatment and 2) to explore

  10. Network structure and the role of key players in a translational cancer research network: a study protocol

    OpenAIRE

    Long, Janet C; Cunningham, Frances C.; Braithwaite, Jeffrey

    2012-01-01

    Introduction Translational research networks are a deliberate strategy to bridge the gulf between biomedical research and clinical practice through interdisciplinary collaboration, supportive funding and infrastructure. The social network approach examines how the structure of the network and players who hold important positions within it constrain or enable function. This information can be used to guide network management and optimise its operations. The aim of this study was to describe th...

  11. A divide-and-conquer approach to determine the Pareto frontier for optimization of protein engineering experiments.

    Science.gov (United States)

    He, Lu; Friedman, Alan M; Bailey-Kellogg, Chris

    2012-03-01

    In developing improved protein variants by site-directed mutagenesis or recombination, there are often competing objectives that must be considered in designing an experiment (selecting mutations or breakpoints): stability versus novelty, affinity versus specificity, activity versus immunogenicity, and so forth. Pareto optimal experimental designs make the best trade-offs between competing objectives. Such designs are not "dominated"; that is, no other design is better than a Pareto optimal design for one objective without being worse for another objective. Our goal is to produce all the Pareto optimal designs (the Pareto frontier), to characterize the trade-offs and suggest designs most worth considering, but to avoid explicitly considering the large number of dominated designs. To do so, we develop a divide-and-conquer algorithm, Protein Engineering Pareto FRontier (PEPFR), that hierarchically subdivides the objective space, using appropriate dynamic programming or integer programming methods to optimize designs in different regions. This divide-and-conquer approach is efficient in that the number of divisions (and thus calls to the optimizer) is directly proportional to the number of Pareto optimal designs. We demonstrate PEPFR with three protein engineering case studies: site-directed recombination for stability and diversity via dynamic programming, site-directed mutagenesis of interacting proteins for affinity and specificity via integer programming, and site-directed mutagenesis of a therapeutic protein for activity and immunogenicity via integer programming. We show that PEPFR is able to effectively produce all the Pareto optimal designs, discovering many more designs than previous methods. The characterization of the Pareto frontier provides additional insights into the local stability of design choices as well as global trends leading to trade-offs between competing criteria. Copyright © 2011 Wiley Periodicals, Inc.

  12. A modulated empirical Bayes model for identifying topological and temporal estrogen receptor α regulatory networks in breast cancer

    Directory of Open Access Journals (Sweden)

    Zhao Yuming

    2011-05-01

    Full Text Available Abstract Background Estrogens regulate diverse physiological processes in various tissues through genomic and non-genomic mechanisms that result in activation or repression of gene expression. Transcription regulation upon estrogen stimulation is a critical biological process underlying the onset and progress of the majority of breast cancer. Dynamic gene expression changes have been shown to characterize the breast cancer cell response to estrogens, the every molecular mechanism of which is still not well understood. Results We developed a modulated empirical Bayes model, and constructed a novel topological and temporal transcription factor (TF regulatory network in MCF7 breast cancer cell line upon stimulation by 17β-estradiol stimulation. In the network, significant TF genomic hubs were identified including ER-alpha and AP-1; significant non-genomic hubs include ZFP161, TFDP1, NRF1, TFAP2A, EGR1, E2F1, and PITX2. Although the early and late networks were distinct ( Conclusions We identified a number of estrogen regulated target genes and established estrogen-regulated network that distinguishes the genomic and non-genomic actions of estrogen receptor. Many gene targets of this network were not active anymore in anti-estrogen resistant cell lines, possibly because their DNA methylation and histone acetylation patterns have changed.

  13. RGD-Functionalization of Poly(2-oxazoline-Based Networks for Enhanced Adhesion to Cancer Cells

    Directory of Open Access Journals (Sweden)

    Verena Schenk

    2014-01-01

    Full Text Available Poly(2-oxazoline networks with varying swelling degrees and varying hydrophilicity can be synthesized from 2-ethyl-2-oxazoline, 2-nonyl-2-oxazoline, 2-9’-decenyl-2-oxazoline and 2,2’-tetramethylene-bis-2-oxazoline in one-pot/one-step strategies. These gels can be loaded with organic molecules, such as fluorescein isothiocyanate, either during the polymerization (covalent attachment of the dye or according to post-synthetic swelling/deswelling strategies (physical inclusion of the dye. Surface functionalization of ground gels by thiol-ene reactions with cysteine-bearing peptides exhibiting the arginine-glycine-aspartic acid (RGD motif yields microparticles with enhanced recognition of human cancer cells compared to healthy endothelial cells.

  14. Identifying the common interaction networks of amoeboid motility and cancer cell metastasis

    Directory of Open Access Journals (Sweden)

    Ahmed H. Zeitoun

    2012-06-01

    Full Text Available The recently analyzed genome of Naegleria gruberi, a free-living amoeboflagellate of the Heterolobosea clade, revealed a remarkably complex ancestral eukaryote with a rich repertoire of cytoskeletal-, motility- and signaling-genes. This protist, which diverged from other eukaryotic lineages over a billion years ago, possesses the ability for both amoeboid and flagellar motility. In a phylogenomic comparison of two free living eukaryotes with large proteomic datasets of three metastatic tumour entities (malignant melanoma, breast- and prostate-carcinoma, we find common proteins with potential importance for cell motility and cancer cell metastasis. To identify the underlying signaling modules, we constructed for each tumour type a protein-protein interaction network including these common proteins. The connectivity within this interactome revealed specific interactions and pathways which constitute prospective points of intervention for novel anti-metastatic tumour therapies.

  15. Longitudinal analysis of discussion topics in an online breast cancer community using convolutional neural networks.

    Science.gov (United States)

    Zhang, Shaodian; Grave, Edouard; Sklar, Elizabeth; Elhadad, Noémie

    2017-05-01

    Identifying topics of discussions in online health communities (OHC) is critical to various information extraction applications, but can be difficult because topics of OHC content are usually heterogeneous and domain-dependent. In this paper, we provide a multi-class schema, an annotated dataset, and supervised classifiers based on convolutional neural network (CNN) and other models for the task of classifying discussion topics. We apply the CNN classifier to the most popular breast cancer online community, and carry out cross-sectional and longitudinal analyses to show topic distributions and topic dynamics throughout members' participation. Our experimental results suggest that CNN outperforms other classifiers in the task of topic classification and identify several patterns and trajectories. For example, although members discuss mainly disease-related topics, their interest may change through time and vary with their disease severities. Copyright © 2017. Published by Elsevier Inc.

  16. Genetic network and gene set enrichment analysis to identify biomarkers related to cigarette smoking and lung cancer.

    Science.gov (United States)

    Fang, Xiaocong; Netzer, Michael; Baumgartner, Christian; Bai, Chunxue; Wang, Xiangdong

    2013-02-01

    Cigarette smoking is the most demonstrated risk factor for the development of lung cancer, while the related genetic mechanisms are still unclear. The preprocessed microarray expression dataset was downloaded from Gene Expression Omnibus database. Samples were classified according to the disease state, stage and smoking state. A new computational strategy was applied for the identification and biological interpretation of new candidate genes in lung cancer and smoking by coupling a network-based approach with gene set enrichment analysis. Network analysis was performed by pair-wise comparison according to the disease states (tumor or normal), smoking states (current smokers or nonsmokers or former smokers), or the disease stage (stages I-IV). The most activated metabolic pathways were identified by gene set enrichment analysis. Panels of top ranked gene candidates in smoking or cancer development were identified, including genes involved in cell proliferation and drug metabolism like cytochrome P450 and WW domain containing transcription regulator 1. Semaphorin 5A and protein phosphatase 1F are the common genes represented as major hubs in both the smoking and cancer related network. Six pathways, e.g. cell cycle, DNA replication, RNA transport, protein processing in endoplasmic reticulum, vascular smooth muscle contraction and endocytosis were commonly involved in smoking and lung cancer when comparing the top ten selected pathways. New approach of bioinformatics for biomarker identification and validation can probe into deep genetic relationships between cigarette smoking and lung cancer. Our studies indicate that disease-specific network biomarkers, interaction between genes/proteins, or cross-talking of pathways provide more specific values for the development of precision therapies for lung. Copyright © 2012 Elsevier Ltd. All rights reserved.

  17. A validated gene expression profile for detecting clinical outcome in breast cancer using artificial neural networks.

    Science.gov (United States)

    Lancashire, L J; Powe, D G; Reis-Filho, J S; Rakha, E; Lemetre, C; Weigelt, B; Abdel-Fatah, T M; Green, A R; Mukta, R; Blamey, R; Paish, E C; Rees, R C; Ellis, I O; Ball, G R

    2010-02-01

    Gene expression microarrays allow for the high throughput analysis of huge numbers of gene transcripts and this technology has been widely applied to the molecular and biological classification of cancer patients and in predicting clinical outcome. A potential handicap of such data intensive molecular technologies is the translation to clinical application in routine practice. In using an artificial neural network bioinformatic approach, we have reduced a 70 gene signature to just 9 genes capable of accurately predicting distant metastases in the original dataset. Upon validation in a follow-up cohort, this signature was an independent predictor of metastases free and overall survival in the presence of the 70 gene signature and other factors. Interestingly, the ANN signature and CA9 expression also split the groups defined by the 70 gene signature into prognostically distinct groups. Subsequently, the presence of protein for the principal prognosticator gene was categorically assessed in breast cancer tissue of an experimental and independent validation patient cohort, using immunohistochemistry. Importantly our principal prognosticator, CA9, showed that it is capable of selecting an aggressive subgroup of patients who are known to have poor prognosis.

  18. Combining artificial neural networks and transrectal ultrasound in the diagnosis of prostate cancer.

    Science.gov (United States)

    Porter, Christopher R; Crawford, E David

    2003-10-01

    Arguably the most important step in the prognosis of prostate cancer is early diagnosis. More than 1 million transrectal ultrasound (TRUS)-guided prostate needle biopsies are performed annually in the United States, resulting in the detection of 200,000 new cases per year. Unfortunately, the urologist's ability to diagnose prostate cancer has not kept pace with therapeutic advances; currently, many men are facing the need for prostate biopsy with the likelihood that the result will be inconclusive. This paper will focus on the tools available to assist the clinician in predicting the outcome of the prostate needle biopsy. We will examine the use of "machine learning" models (artificial intelligence), in the form of artificial neural networks (ANNs), to predict prostate biopsy outcomes using prebiopsy variables. Currently, six validated predictive models are available. Of these, five are machine learning models, and one is based on logistic regression. The role of ANNs in providing valuable predictive models to be used in conjunction with TRUS appears promising. In the few studies that have compared machine learning to traditional statistical methods, ANN and logistic regression appear to function equivalently when predicting biopsy outcome. With the introduction of more complex prebiopsy variables, ANNs are in a commanding position for use in predictive models. Easy and immediate physician access to these models will be imperative if their full potential is to be realized.

  19. Multifaceted Leptin network: the molecular connection between obesity and breast cancer

    Science.gov (United States)

    Saxena, Neeraj K.; Sharma, Dipali

    2016-01-01

    High plasma levels of leptin, a major adipocytokine produced by adipocytes, are correlated with increased fat mass in obese state. Leptin is emerging as a key candidate molecule linking obesity with breast cancer. Acting via endocrine, paracrine, and autocrine manner, leptin impacts various stages of breast tumorigenesis from initiation and primary tumor growth to metastatic progression. Leptin also modulates the tumor microenvironment mainly through supporting migration of endothelial cells, neo-angiogenesis and sustaining recruitment of macrophage and monocytes. Various studies have shown that hyperactive leptin-signaling network leads to concurrent activation of multiple oncogenic pathways resulting in enhanced proliferation, decreased apoptosis, acquisition of mesenchymal phenotype, potentiated migration and enhanced invasion potential of tumor cells. Furthermore, the capability of leptin to interact with other molecular effectors of obese state including, estrogen, IGF-1, insulin, VEGF and inflammatory cytokines further increases its impact on breast tumor progression in obese state. This article presents an overview of the studies investigating the involvement of leptin in breast cancer. PMID:24214584

  20. Immunodynamics: a cancer immunotherapy trials network review of immune monitoring in immuno-oncology clinical trials.

    Science.gov (United States)

    Kohrt, Holbrook E; Tumeh, Paul C; Benson, Don; Bhardwaj, Nina; Brody, Joshua; Formenti, Silvia; Fox, Bernard A; Galon, Jerome; June, Carl H; Kalos, Michael; Kirsch, Ilan; Kleen, Thomas; Kroemer, Guido; Lanier, Lewis; Levy, Ron; Lyerly, H Kim; Maecker, Holden; Marabelle, Aurelien; Melenhorst, Jos; Miller, Jeffrey; Melero, Ignacio; Odunsi, Kunle; Palucka, Karolina; Peoples, George; Ribas, Antoni; Robins, Harlan; Robinson, William; Serafini, Tito; Sondel, Paul; Vivier, Eric; Weber, Jeff; Wolchok, Jedd; Zitvogel, Laurence; Disis, Mary L; Cheever, Martin A

    2016-01-01

    The efficacy of PD-1/PD-L1 targeted therapies in addition to anti-CTLA-4 solidifies immunotherapy as a modality to add to the anticancer arsenal. Despite raising the bar of clinical efficacy, immunologically targeted agents raise new challenges to conventional drug development paradigms by highlighting the limited relevance of assessing standard pharmacokinetics (PK) and pharmacodynamics (PD). Specifically, systemic and intratumoral immune effects have not consistently correlated with standard relationships between systemic dose, toxicity, and efficacy for cytotoxic therapies. Hence, PK and PD paradigms remain inadequate to guide the selection of doses and schedules, both starting and recommended Phase 2 for immunotherapies. The promise of harnessing the immune response against cancer must also be considered in light of unique and potentially serious toxicities. Refining immune endpoints to better inform clinical trial design represents a high priority challenge. The Cancer Immunotherapy Trials Network investigators review the immunodynamic effects of specific classes of immunotherapeutic agents to focus immune assessment modalities and sites, both systemic and importantly intratumoral, which are critical to the success of the rapidly growing field of immuno-oncology.

  1. Network-constrained group lasso for high-dimensional multinomial classification with application to cancer subtype prediction.

    Science.gov (United States)

    Tian, Xinyu; Wang, Xuefeng; Chen, Jun

    2014-01-01

    Classic multinomial logit model, commonly used in multiclass regression problem, is restricted to few predictors and does not take into account the relationship among variables. It has limited use for genomic data, where the number of genomic features far exceeds the sample size. Genomic features such as gene expressions are usually related by an underlying biological network. Efficient use of the network information is important to improve classification performance as well as the biological interpretability. We proposed a multinomial logit model that is capable of addressing both the high dimensionality of predictors and the underlying network information. Group lasso was used to induce model sparsity, and a network-constraint was imposed to induce the smoothness of the coefficients with respect to the underlying network structure. To deal with the non-smoothness of the objective function in optimization, we developed a proximal gradient algorithm for efficient computation. The proposed model was compared to models with no prior structure information in both simulations and a problem of cancer subtype prediction with real TCGA (the cancer genome atlas) gene expression data. The network-constrained mode outperformed the traditional ones in both cases.

  2. Management of Adenoid Cystic Carcinoma of the Breast: A Rare Cancer Network Study

    Energy Technology Data Exchange (ETDEWEB)

    Khanfir, Kaouthar, E-mail: kaouthar.khanfir@rsv-gnw.ch [Hopital de Sion, CHCVs, Sion (Switzerland); Kallel, Adel [Institut Gustave Roussy, Villejuif (France); Villette, Sylviane [Centre Rene Huguenin, Paris (France); Belkacemi, Yazid [CHU Henri Mondor, Centre Oscar Lambret, Lille (France); Vautravers, Claire [Centre George Francois Leclerc, Dijon (France); Nguyen, TanDat [Institut Jean Gaudinot, Reims (France); Miller, Robert [Mayo Clinic, Rochester, Minnesota (United States); Li Yexiong [Peking Union Medical College, Beijing (China); Taghian, Alphonse G. [Massachusetts General Hospital, Boston, Massachusetts (United States); Boersma, Liesbeth [Maastricht University Medical Center (MAASTRO clinic), Maastricht (Netherlands); Poortmans, Philip [Dr. Bernard Verbeeten Institute, Tilburg (Netherlands); Goldberg, Hadassah [Western Galilee Hospital-Nahariya, Nahariya (Israel); Vees, Hansjorg [Hopitaux Universitaires de Geneve, Geneva (Switzerland); Senkus, Elzbieta [Medical University of Gdansk, Gdansk (Poland); Igdem, Sefik; Ozsahin, Mahmut [Istanbul Bilim University, Istanbul (Turkey); Jeanneret Sozzi, Wendy [Centre Hospitalier Universitaire Vaudois, Lausanne (Switzerland)

    2012-04-01

    Background: Mammary adenoid cystic carcinoma (ACC) is a rare breast cancer. The aim of this retrospective study was to assess prognostic factors and patterns of failure, as well as the role of radiation therapy (RT), in ACC. Methods: Between January 1980 and December 2007, 61 women with breast ACC were treated at participating centers of the Rare Cancer Network. Surgery consisted of lumpectomy in 41 patients and mastectomy in 20 patients. There were 51(84%) stage pN0 and 10 stage cN0 (16%) patients. Postoperative RT was administered to 40 patients (35 after lumpectomy, 5 after mastectomy). Results: With a median follow-up of 79 months (range, 6-285), 5-year overall and disease-free survival rates were 94% (95% confidence interval [CI], 88%-100%) and 82% (95% CI, 71%-93%), respectively. The 5-year locoregional control (LRC) rate was 95% (95% CI, 89%-100%). Axillary lymph node dissection or sentinel node biopsy was performed in 84% of cases. All patients had stage pN0 disease. In univariate analysis, survival was not influenced by the type of surgery or the use of postoperative RT. The 5-year LRC rate was 100% in the mastectomy group versus 93% (95% CI, 83%-100%) in the breast-conserving surgery group, respectively (p = 0.16). For the breast-conserving surgery group, the use of RT significantly correlated with LRC (p = 0.03); the 5-year LRC rates were 95% (95% CI, 86%-100%) for the RT group versus 83% (95% CI, 54%-100%) for the group receiving no RT. No local failures occurred in patients with positive margins, all of whom received postoperative RT. Conclusion: Breast-conserving surgery is the treatment of choice for patients with ACC breast cancer. Axillary lymph node dissection or sentinel node biopsy might not be recommended. Postoperative RT should be proposed in the case of breast-conserving surgery.

  3. Dynamic transcription factor networks in epithelial-mesenchymal transition in breast cancer models.

    Science.gov (United States)

    Siletz, Anaar; Schnabel, Michael; Kniazeva, Ekaterina; Schumacher, Andrew J; Shin, Seungjin; Jeruss, Jacqueline S; Shea, Lonnie D

    2013-01-01

    The epithelial-mesenchymal transition (EMT) is a complex change in cell differentiation that allows breast carcinoma cells to acquire invasive properties. EMT involves a cascade of regulatory changes that destabilize the epithelial phenotype and allow mesenchymal features to manifest. As transcription factors (TFs) are upstream effectors of the genome-wide expression changes that result in phenotypic change, understanding the sequential changes in TF activity during EMT provides rich information on the mechanism of this process. Because molecular interactions will vary as cells progress from an epithelial to a mesenchymal differentiation program, dynamic networks are needed to capture the changing context of molecular processes. In this study we applied an emerging high-throughput, dynamic TF activity array to define TF activity network changes in three cell-based models of EMT in breast cancer based on HMLE Twist ER and MCF-7 mammary epithelial cells. The TF array distinguished conserved from model-specific TF activity changes in the three models. Time-dependent data was used to identify pairs of TF activities with significant positive or negative correlation, indicative of interdependent TF activity throughout the six-day study period. Dynamic TF activity patterns were clustered into groups of TFs that change along a time course of gene expression changes and acquisition of invasive capacity. Time-dependent TF activity data was combined with prior knowledge of TF interactions to construct dynamic models of TF activity networks as epithelial cells acquire invasive characteristics. These analyses show EMT from a unique and targetable vantage and may ultimately contribute to diagnosis and therapy.

  4. Dynamic transcription factor networks in epithelial-mesenchymal transition in breast cancer models.

    Directory of Open Access Journals (Sweden)

    Anaar Siletz

    Full Text Available The epithelial-mesenchymal transition (EMT is a complex change in cell differentiation that allows breast carcinoma cells to acquire invasive properties. EMT involves a cascade of regulatory changes that destabilize the epithelial phenotype and allow mesenchymal features to manifest. As transcription factors (TFs are upstream effectors of the genome-wide expression changes that result in phenotypic change, understanding the sequential changes in TF activity during EMT provides rich information on the mechanism of this process. Because molecular interactions will vary as cells progress from an epithelial to a mesenchymal differentiation program, dynamic networks are needed to capture the changing context of molecular processes. In this study we applied an emerging high-throughput, dynamic TF activity array to define TF activity network changes in three cell-based models of EMT in breast cancer based on HMLE Twist ER and MCF-7 mammary epithelial cells. The TF array distinguished conserved from model-specific TF activity changes in the three models. Time-dependent data was used to identify pairs of TF activities with significant positive or negative correlation, indicative of interdependent TF activity throughout the six-day study period. Dynamic TF activity patterns were clustered into groups of TFs that change along a time course of gene expression changes and acquisition of invasive capacity. Time-dependent TF activity data was combined with prior knowledge of TF interactions to construct dynamic models of TF activity networks as epithelial cells acquire invasive characteristics. These analyses show EMT from a unique and targetable vantage and may ultimately contribute to diagnosis and therapy.

  5. Analysis of the attention network to the cervical cancer from the trajectory of users in the Federal District-BR

    Directory of Open Access Journals (Sweden)

    Jeíza Andrade Santana

    2012-04-01

    Full Text Available This study deals with the attention network to the cervical cancer (CC in the Federal District (DF from the trajectory of users of these services. Objectives: to delineate the pathway of care for women from the onset of symptoms until the completion of treatment; identify the potential and limits of the attention network to the CC in the DF. Methodology: ten qualitative and quantitative case studies were held from individual narratives. Data was collected through interviews and analysis of medical records, in 2010. Results: women have easy access to the basic health network, for consultation and colpo-cytological, but they live with the diversity of medical conducts, delayed test results, barriers in the access to the specialty consultations and examinations that support a diagnosis, leading them to have recourse to the private network. Conclusions: there is disconnection between the attention points, jeopardizing the quality and continuity of care for women with CC.

  6. Colorectal cancer prevention: Perspectives of key players from social networks in a low-income rural US region

    Directory of Open Access Journals (Sweden)

    Nancy E. Schoenberg

    2016-02-01

    Full Text Available Social networks influence health behavior and health status. Within social networks, “key players” often influence those around them, particularly in traditionally underserved areas like the Appalachian region in the USA. From a total sample of 787 Appalachian residents, we identified and interviewed 10 key players in complex networks, asking them what comprises a key player, their role in their network and community, and ideas to overcome and increase colorectal cancer (CRC screening. Key players emphasized their communication skills, resourcefulness, and special occupational and educational status in the community. Barriers to CRC screening included negative perceptions of the colonoscopy screening procedure, discomfort with the medical system, and misinformed perspectives on screening. Ideas to improve screening focused on increasing awareness of women's susceptibility to CRC, providing information on different screening tests, improving access, and the key role of health-care providers and key players themselves. We provide recommendations to leverage these vital community resources.

  7. Cancer systems biology in the genome sequencing era: part 1, dissecting and modeling of tumor clones and their networks.

    Science.gov (United States)

    Wang, Edwin; Zou, Jinfeng; Zaman, Naif; Beitel, Lenore K; Trifiro, Mark; Paliouras, Miltiadis

    2013-08-01

    Recent tumor genome sequencing confirmed that one tumor often consists of multiple cell subpopulations (clones) which bear different, but related, genetic profiles such as mutation and copy number variation profiles. Thus far, one tumor has been viewed as a whole entity in cancer functional studies. With the advances of genome sequencing and computational analysis, we are able to quantify and computationally dissect clones from tumors, and then conduct clone-based analysis. Emerging technologies such as single-cell genome sequencing and RNA-Seq could profile tumor clones. Thus, we should reconsider how to conduct cancer systems biology studies in the genome sequencing era. We will outline new directions for conducting cancer systems biology by considering that genome sequencing technology can be used for dissecting, quantifying and genetically characterizing clones from tumors. Topics discussed in Part 1 of this review include computationally quantifying of tumor subpopulations; clone-based network modeling, cancer hallmark-based networks and their high-order rewiring principles and the principles of cell survival networks of fast-growing clones. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.

  8. Dynamic network of transcription and pathway crosstalk to reveal molecular mechanism of MGd-treated human lung cancer cells.

    Directory of Open Access Journals (Sweden)

    Liyan Shao

    Full Text Available Recent research has revealed various molecular markers in lung cancer. However, the organizational principles underlying their genetic regulatory networks still await investigation. Here we performed Network Component Analysis (NCA and Pathway Crosstalk Analysis (PCA to construct a regulatory network in human lung cancer (A549 cells which were treated with 50 uM motexafin gadolinium (MGd, a metal cation-containing chemotherapeutic drug for 4, 12, and 24 hours. We identified a set of key TFs, known target genes for these TFs, and signaling pathways involved in regulatory networks. Our work showed that putative interactions between these TFs (such as ESR1/Sp1, E2F1/Sp1, c-MYC-ESR, Smad3/c-Myc, and NFKB1/RELA, between TFs and their target genes (such as BMP41/Est1, TSC2/Myc, APE1/Sp1/p53, RARA/HOXA1, and SP1/USF2, and between signaling pathways (such as PPAR signaling pathway and Adipocytokines signaling pathway. These results will provide insights into the regulatory mechanism of MGd-treated human lung cancer cells.

  9. [German national second-opinion network for testicular cancer and penile carcinoma : Two sources for evidence-based information].

    Science.gov (United States)

    Schrader, M; Zengerling, F; Hakenberg, O W; Protzel, C

    2016-09-01

    The second-opinion network for testicular cancer is an internet-based platform addressing physicians treating testicular cancer patients. They are offered a second-opinion before determining further therapy after orchiectomy and completion of staging. The high rate of discrepancies between the first and second opinion in more than 30 % supports the assumption of a deficit in the implementation of treatment guidelines. In 2015, approximately 22 % of the newly diagnosed cases with testicular cancer in Germany were covered by this system. According to the present interim analysis, the second-opinion platform helps to avoid overtreatment of testicular cancer patients. The high acceptance of the project and the encouraging results of this interim analysis gave rise to considerations to apply the second-opinion model to penile carcinoma. Data from the UK and the Netherlands show that the second-opinion network for penile cancer could help to improve treatment standards and results in Germany. Current data and the intended further development of the system are discussed.

  10. Unravelling the complexity of signalling networks in cancer: A review of the increasing role for computational modelling.

    Science.gov (United States)

    Garland, John

    2017-09-01

    Cancer induction is a highly complex process involving hundreds of different inducers but whose eventual outcome is the same. Clearly, it is essential to understand how signalling pathways and networks generated by these inducers interact to regulate cell behaviour and create the cancer phenotype. While enormous strides have been made in identifying key networking profiles, the amount of data generated far exceeds our ability to understand how it all "fits together". The number of potential interactions is astronomically large and requires novel approaches and extreme computation methods to dissect them out. However, such methodologies have high intrinsic mathematical and conceptual content which is difficult to follow. This review explains how computation modelling is progressively finding solutions and also revealing unexpected and unpredictable nano-scale molecular behaviours extremely relevant to how signalling and networking are coherently integrated. It is divided into linked sections illustrated by numerous figures from the literature describing different approaches and offering visual portrayals of networking and major conceptual advances in the field. First, the problem of signalling complexity and data collection is illustrated for only a small selection of known oncogenes. Next, new concepts from biophysics, molecular behaviours, kinetics, organisation at the nano level and predictive models are presented. These areas include: visual representations of networking, Energy Landscapes and energy transfer/dissemination (entropy); diffusion, percolation; molecular crowding; protein allostery; quinary structure and fractal distributions; energy management, metabolism and re-examination of the Warburg effect. The importance of unravelling complex network interactions is then illustrated for some widely-used drugs in cancer therapy whose interactions are very extensive. Finally, use of computational modelling to develop micro- and nano- functional models

  11. Post-diagnosis social networks, and lifestyle and treatment factors in the After Breast Cancer Pooling Project.

    Science.gov (United States)

    Kroenke, Candyce H; Michael, Yvonne L; Shu, Xiao-Ou; Poole, Elizabeth M; Kwan, Marilyn L; Nechuta, Sarah; Caan, Bette J; Pierce, John P; Chen, Wendy Y

    2017-04-01

    Larger social networks have been associated with better breast cancer survival. To investigate potential mediators, we evaluated associations of social network size and diversity with lifestyle and treatment factors associated with prognosis. We included 9331 women from the After Breast Cancer Pooling Project who provided data on social networks within approximately two years following diagnosis. A social network index was derived from information about the presence of a spouse or intimate partner, religious ties, community participation, friendship ties, and numbers of living relatives. Diversity was assessed as variety of ties, independent of size. We used logistic regression to evaluate associations with outcomes and evaluated whether effect estimates differed using meta-analytic techniques. Associations were similar across cohorts though analyses of smoking and alcohol included US cohorts only because of low prevalence of these behaviors in the Shanghai cohort. Socially isolated women were more likely to be obese (OR = 1.21, 95% CI:1.03-1.42), have low physical activity (socially integrated women. Among node positive cases from three cohorts, socially isolated women were more likely not to receive chemotherapy (OR = 2.10, 95% CI:1.30-3.39); associations differed in a fourth cohort. Other associations (nonsignificant) were consistent with less intensive treatment in socially isolated women. Low social network diversity was independently associated with more adverse lifestyle, but not clinical, factors. Small, less diverse social networks measured post-diagnosis were associated with more adverse lifestyle factors and less intensive cancer treatment. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

  12. Urothelial cancer gene regulatory networks inferred from large-scale RNAseq, Bead and Oligo gene expression data.

    Science.gov (United States)

    de Matos Simoes, Ricardo; Dalleau, Sabine; Williamson, Kate E; Emmert-Streib, Frank

    2015-05-14

    Urothelial pathogenesis is a complex process driven by an underlying network of interconnected genes. The identification of novel genomic target regions and gene targets that drive urothelial carcinogenesis is crucial in order to improve our current limited understanding of urothelial cancer (UC) on the molecular level. The inference of genome-wide gene regulatory networks (GRN) from large-scale gene expression data provides a promising approach for a detailed investigation of the underlying network structure associated to urothelial carcinogenesis. In our study we inferred and compared three GRNs by the application of the BC3Net inference algorithm to large-scale transitional cell carcinoma gene expression data sets from Illumina RNAseq (179 samples), Illumina Bead arrays (165 samples) and Affymetrix Oligo microarrays (188 samples). We investigated the structural and functional properties of GRNs for the identification of molecular targets associated to urothelial cancer. We found that the urothelial cancer (UC) GRNs show a significant enrichment of subnetworks that are associated with known cancer hallmarks including cell cycle, immune response, signaling, differentiation and translation. Interestingly, the most prominent subnetworks of co-located genes were found on chromosome regions 5q31.3 (RNAseq), 8q24.3 (Oligo) and 1q23.3 (Bead), which all represent known genomic regions frequently deregulated or aberated in urothelial cancer and other cancer types. Furthermore, the identified hub genes of the individual GRNs, e.g., HID1/DMC1 (tumor development), RNF17/TDRD4 (cancer antigen) and CYP4A11 (angiogenesis/ metastasis) are known cancer associated markers. The GRNs were highly dataset specific on the interaction level between individual genes, but showed large similarities on the biological function level represented by subnetworks. Remarkably, the RNAseq UC GRN showed twice the proportion of significant functional subnetworks. Based on our analysis of inferential

  13. A new mode of organizing in health care? Governmentality and managed networks in cancer services in England.

    Science.gov (United States)

    Ferlie, Ewan; McGivern, Gerry; Fitzgerald, Louise

    2012-02-01

    We explore the argument that a new mode of health care organizing is emerging which moves beyond the established professional dominance versus New Public Management (NPM) debate. We review Foucault's work on 'governmentality', as applied to health care organizations. We specify two specific Foucauldian themes (the power/knowledge nexus in Evidence Based Medicine (EBM); and the technologies of the clinical managerial self) to analyse organizing in the English cancer services field. We introduce two qualitative case studies of Managed Cancer Networks. We suggest their governance can be fruitfully seen through a 'governmentality' lens. We consider implications for developing Foucauldian analysis of health care organizations.

  14. “庄踽王滇”新议%A New Discussion on “Zhuang Qiao Conquered Dian”

    Institute of Scientific and Technical Information of China (English)

    程鸿彬

    2011-01-01

    The legend of “Zhuang Qiao Conquered Dian ”first appeared in Records of the Grand Historian: The Account of the Southwestern Barbarians. For a very long time it had been an important documental basis for the researches of the ethnic groups in the southwest borderland of China. However there were so many unexplained and selfcontradictory statements in this legend that the later historians had to continually correct it while they followed its basic frame. In addition, some historians doubted the truth of it as early as Tang Dynasty. Today “Zhuang Qiao Conquered Dian”is still a controversial academic question. Based on the recent results of archae- ology and philology, some scholars drew the negative conclusions about the historical value of it. If we transfer our visual field of studying from historicity of texts to textuality of histories, we shall discover some new knowledge growth points in the highly-flawed legend. “Zhuang Qiao Conquered Dian” is probably doubtful as a truthful historical event, but it truthfully reflects the complicated relation between different ethnic groups in a given historical period and Chinese historical imagination about ethnic groups in Chinese borderlands under the influence of Cathay-centered consciousness.%“庄蹯王滇”传说最早见于《史记·西南夷列传》,是考察西南边疆族群起源的必征史料之一。然而作为史料,“庄蹄王滇”存在着众多难以诠解的疑点和自相矛盾之处,历代史书在承袭其基本框架的同时也在不断对之进行改写,自唐代始即有史家对其可信度提出质疑。洎乎当代,“庄蹯王滇”依然是一桩聚讼不休的史学悬案,甚至有学者根据文献研究和考古发掘的新近成果对其史学价值作出了基本否定的结论。如果我们把观照视野从历史文本的实在性转移到其话语性,未始不能在这则漏洞百出的史料中发现某些新的知识生长点。作为一个历

  15. CyNetSVM: A Cytoscape App for Cancer Biomarker Identification Using Network Constrained Support Vector Machines

    OpenAIRE

    Shi, Xu; Banerjee, Sharmi; Chen, Li; Hilakivi-Clarke, Leena; Clarke, Robert; Xuan, Jianhua

    2017-01-01

    One of the important tasks in cancer research is to identify biomarkers and build classification models for clinical outcome prediction. In this paper, we develop a CyNetSVM software package, implemented in Java and integrated with Cytoscape as an app, to identify network biomarkers using network-constrained support vector machines (NetSVM). The Cytoscape app of NetSVM is specifically designed to improve the usability of NetSVM with the following enhancements: (1) user-friendly graphical user...

  16. Conquering the Mesoscale of Africa's Landscapes: deciphering the Genomic Record of Individuating Landforms with Geoecodynamics

    Science.gov (United States)

    Cotterill, Fenton P. D.

    2016-04-01

    through to continental scales). Our ability to reconstruct narratives of landscape dynamics of encompassing - mega-geomorphic - patterns can only be as good as the details of individual events we can discern in Earth history. Obviously, recognizing the centrality of "Conquering the Mesoscale" as the intrinsic prerequisite to test competing hypotheses of landscape dynamics, in the earth system context, calls for innovative research approaches. This is where Africa holds vast potential. The continent is the most remarkable natural laboratory to explore and tackle these challenges where we seek to build the composite mega-geomorphic chronicle informed in the detail of mesoscale process and form. But how does geomorphology, embedded in an earth system framework, advance beyond the established approaches in process and mega-geomorphology? The latter's limitations to reconstruct the tempo and mode of African landforms and palaeoenviroments reveal the stark limits for researchers. This is where a geobiological approach brings interesting opportunities, especially for Africa. Consider, for one, the interlinking patterns of high endemism and geographical heterogeneity of extant biodiversity across the continent, and moreover the interplay in biotic turnovers since the Mesozoic that shaped these regional and more local patterns. These individuated biotic assemblages making up the continent's biomes and ecoregions reveal strident congruence with physiographic controls: especially relief, drainage and edaphic variables. Calibrated by molecular clocks, resolved with DNA evidence, timetrees of this phylogenetic diversity reveal a richness of evolutionary signals; the spectrum of these spectacular biotic radiations of African biodiversity range from the Late Mesozoic to Recent. The temporal spread of this phylogenetic diversity is exemplified, for example, in the extant mammal fauna: witness the Afrotheria compared to the Bovidae (Kingdon J et al. 2013. Mammals of Africa. Bloomsbury

  17. Transcriptional coexpression network reveals the involvement of varying stem cell features with different dysregulations in different gastric cancer subtypes.

    Science.gov (United States)

    Kalamohan, Kalaivani; Periasamy, Jayaprakash; Bhaskar Rao, Divya; Barnabas, Georgina D; Ponnaiyan, Srigayatri; Ganesan, Kumaresan

    2014-10-01

    Despite the advancements in the cancer therapeutics, gastric cancer ranks as the second most common cancers with high global mortality rate. Integrative functional genomic investigation is a powerful approach to understand the major dysregulations and to identify the potential targets toward the development of targeted therapeutics for various cancers. Intestinal and diffuse type gastric tumors remain the major subtypes and the molecular determinants and drivers of these distinct subtypes remain unidentified. In this investigation, by exploring the network of gene coexpression association in gastric tumors, mRNA expressions of 20,318 genes across 200 gastric tumors were categorized into 21 modules. The genes and the hub genes of the modules show gastric cancer subtype specific expression. The expression patterns of the modules were correlated with intestinal and diffuse subtypes as well as with the differentiation status of gastric tumors. Among these, G1 module has been identified as a major driving force of diffuse type gastric tumors with the features of (i) enriched mesenchymal, mesenchymal stem cell like, and mesenchymal derived multiple lineages, (ii) elevated OCT1 mediated transcription, (iii) involvement of Notch activation, and (iv) reduced polycomb mediated epigenetic repression. G13 module has been identified as key factor in intestinal type gastric tumors and found to have the characteristic features of (i) involvement of embryonic stem cell like properties, (ii) Wnt, MYC and E2F mediated transcription programs, and (iii) involvement of polycomb mediated repression. Thus the differential transcription programs, differential epigenetic regulation and varying stem cell features involved in two major subtypes of gastric cancer were delineated by exploring the gene coexpression network. The identified subtype specific dysregulations could be optimally employed in developing subtype specific therapeutic targeting strategies for gastric cancer.

  18. Application of genetic algorithms and constructive neural networks for the analysis of microarray cancer data.

    Science.gov (United States)

    Luque-Baena, Rafael Marcos; Urda, Daniel; Subirats, Jose Luis; Franco, Leonardo; Jerez, Jose M

    2014-05-07

    Extracting relevant information from microarray data is a very complex task due to the characteristics of the data sets, as they comprise a large number of features while few samples are generally available. In this sense, feature selection is a very important aspect of the analysis helping in the tasks of identifying relevant genes and also for maximizing predictive information. Due to its simplicity and speed, Stepwise Forward Selection (SFS) is a widely used feature selection technique. In this work, we carry a comparative study of SFS and Genetic Algorithms (GA) as general frameworks for the analysis of microarray data with the aim of identifying group of genes with high predictive capability and biological relevance. Six standard and machine learning-based techniques (Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), Naive Bayes (NB), C-MANTEC Constructive Neural Network, K-Nearest Neighbors (kNN) and Multilayer perceptron (MLP)) are used within both frameworks using six free-public datasets for the task of predicting cancer outcome. Better cancer outcome prediction results were obtained using the GA framework noting that this approach, in comparison to the SFS one, leads to a larger selection set, uses a large number of comparison between genetic profiles and thus it is computationally more intensive. Also the GA framework permitted to obtain a set of genes that can be considered to be more biologically relevant. Regarding the different classifiers used standard feedforward neural networks (MLP), LDA and SVM lead to similar and best results, while C-MANTEC and k-NN followed closely but with a lower accuracy. Further, C-MANTEC, MLP and LDA permitted to obtain a more limited set of genes in comparison to SVM, NB and kNN, and in particular C-MANTEC resulted in the most robust classifier in terms of changes in the parameter settings. This study shows that if prediction accuracy is the objective, the GA-based approach lead to better results

  19. Texture Classification using Artificial Neural Network for Diagnosis of Skin Cancer

    Directory of Open Access Journals (Sweden)

    Dalia N. Abdul-Wadood

    2014-07-01

    Full Text Available This paper attempts to improve the efficiency of the system that proposed in [1] to determine whether a given skin lesion microscopic image is malignant or benign; in case of malignancy, the system can specify its type; whether it is squamous cell carcinoma or basal cell carcinoma (the two leading skin cancer types. The testing of this system was conducted using 80 microscopic images of skin tissues of the types normal, benign and the two types of skin cancer (squamous and basal; the images have been collected from different hospital pathology departments as part of the research work. Some of the collected samples have been used as training and others as testing materials. The proposed system consists of 3 main steps. First, extraction of a set of textural descriptors to localize the abnormal visual attributes which may appear in the tested skin tissue images. Second, selection of the best discriminating texture features. Third, identify the type of skin tissue images using artificial neural network (ANN. In the training phase, the system was trained using 50 skin tissue images, the textural features extracted from training samples were analyzed and their discrimination powers were evaluated in order to get a list of the most suitable features for auto recognition task. When ANN is trained on co-occurrence features the attained allocation accuracy rates was (%97.71 and the diagnosis accuracy rate was (%98.75. While when using ANN with combinations of different types of textural features; the allocation accuracy rate reached to (%97.90 while the diagnosis accuracy rate became (%98.75

  20. A Bayesian network approach for modeling local failure in lung cancer

    Energy Technology Data Exchange (ETDEWEB)

    Oh, Jung Hun; Craft, Jeffrey; Al Lozi, Rawan; Vaidya, Manushka; Meng, Yifan; Deasy, Joseph O; Bradley, Jeffrey D; El Naqa, Issam, E-mail: elnaqa@wustl.edu [Department of Radiation Oncology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, MO 63110 (United States)

    2011-03-21

    Locally advanced non-small cell lung cancer (NSCLC) patients suffer from a high local failure rate following radiotherapy. Despite many efforts to develop new dose-volume models for early detection of tumor local failure, there was no reported significant improvement in their application prospectively. Based on recent studies of biomarker proteins' role in hypoxia and inflammation in predicting tumor response to radiotherapy, we hypothesize that combining physical and biological factors with a suitable framework could improve the overall prediction. To test this hypothesis, we propose a graphical Bayesian network framework for predicting local failure in lung cancer. The proposed approach was tested using two different datasets of locally advanced NSCLC patients treated with radiotherapy. The first dataset was collected retrospectively, which comprises clinical and dosimetric variables only. The second dataset was collected prospectively in which in addition to clinical and dosimetric information, blood was drawn from the patients at various time points to extract candidate biomarkers as well. Our preliminary results show that the proposed method can be used as an efficient method to develop predictive models of local failure in these patients and to interpret relationships among the different variables in the models. We also demonstrate the potential use of heterogeneous physical and biological variables to improve the model prediction. With the first dataset, we achieved better performance compared with competing Bayesian-based classifiers. With the second dataset, the combined model had a slightly higher performance compared to individual physical and biological models, with the biological variables making the largest contribution. Our preliminary results highlight the potential of the proposed integrated approach for predicting post-radiotherapy local failure in NSCLC patients.

  1. Singapore Cancer Network (SCAN) Guidelines for the Initial Evaluation, Diagnosis, and Management of Extremity Soft Tissue Sarcoma and Osteosarcoma.

    Science.gov (United States)

    2015-10-01

    The SCAN sarcoma workgroup aimed to develop Singapore Cancer Network (SCAN) clinical practice guidelines for the initial evaluation, diagnosis, and management of extremity soft tissue sarcoma and osteosarcoma. The workgroup utilised a consensus approach to create high quality evidence-based clinical practice guidelines suited for our local setting. Various international guidelines from the fields of radiology, pathology, orthopaedic surgery, medical, radiation and paediatric oncology were reviewed, including those developed by von Mehren Metal (J Natl Compr Canc Netw 2014), the National Collaborating Centre for Cancer (2006), the European Sarcoma Network Working Group (2012) and Grimer RJ et al (Sarcoma 2008). Our clinical practice guidelines contextualised to the local patient will streamline care and improve clinical outcomes for patients with extremity soft tissue and osteosarcoma. These guidelines form the SCAN Guidelines 2015 for the initial evaluation, diagnosis, and management of extremity soft tissue sarcoma and osteosarcoma.

  2. From cell signaling to cancer therapy

    Institute of Scientific and Technical Information of China (English)

    Jin DING; Yun FENG; Hong-yang WANG

    2007-01-01

    Cancer has been seriously threatening the health and life of humans for a long period. Despite the intensive effort put into revealing the underlying mechanisms of cancer, the detailled machinery of carcinogenesis is still far from fully understood.Numerous studies have illustrated that cell signaling is extensively involved in tumor initiation, promotion and progression. Therefore, targeting the key mol-ecules in the oncogenic signaling pathway might be one of the most promising ways to conquer cancer. Some targeted drugs, such as imatinib mesylate (Gleevec),herceptin, gefitinib (Iressa), sorafenib (Nexavar) and sunitinib (Sutent), which evolve from monotarget drug into multitarget ones, have been developed with encouraging effects.

  3. Reconstruction of pathway modification induced by nicotinamide using multi-omic network analyses in triple negative breast cancer

    OpenAIRE

    Kim, Ji Young; Lee, Hyebin; Woo, Jongmin; Yue, Wang; Kim, Kwangsoo; Choi, Seongmin; Jang, Ja-June; Kim, Youngsoo; Park, In Ae; Han, Dohyun; Ryu, Han Suk

    2017-01-01

    Triple negative breast cancer (TNBC) is characterized by an aggressive biological behavior in the absence of a specific target agent. Nicotinamide has recently been proven to be a novel therapeutic agent for skin tumors in an ONTRAC trial. We performed combinatory transcriptomic and in-depth proteomic analyses to characterize the network of molecular interactions in TNBC cells treated with nicotinamide. The multi-omic profiles revealed that nicotinamide drives significant functional alteratio...

  4. Integrating Structure to Protein-Protein Interaction Networks That Drive Metastasis to Brain and Lung in Breast Cancer

    OpenAIRE

    H Billur Engin; Emre Guney; Ozlem Keskin; Baldo Oliva; Attila Gursoy

    2013-01-01

    Integrating Structure to Protein-Protein Interaction Networks That Drive Metastasis to Brain and Lung in Breast Cancer H. Billur Engin1, Emre Guney2, Ozlem Keskin1, Baldo Oliva2, Attila Gursoy1* 1 Center for Computational Biology and Bioinformatics and College of Engineering, Koc University, Istanbul, Turkey, 2 Structural Bioinformatics Group (GRIB), Universitat Pompeu Fabra Abstract Blocking specific protein interactions can lead to human diseases. Accordingly, protein i...

  5. Comparing the effectiveness of competing tests for reducing colorectal cancer mortality: a network meta-analysis.

    Science.gov (United States)

    Elmunzer, B Joseph; Singal, Amit G; Sussman, Jeremy B; Deshpande, Amar R; Sussman, Daniel A; Conte, Marisa L; Dwamena, Ben A; Rogers, Mary A M; Schoenfeld, Philip S; Inadomi, John M; Saini, Sameer D; Waljee, Akbar K

    2015-03-01

    Comparative effectiveness data pertaining to competing colorectal cancer (CRC) screening tests do not exist but are necessary to guide clinical decision making and policy. To perform a comparative synthesis of clinical outcomes studies evaluating the effects of competing tests on CRC-related mortality. Traditional and network meta-analyses. Two reviewers identified studies evaluating the effect of guaiac-based fecal occult blood testing (gFOBT), flexible sigmoidoscopy (FS), or colonoscopy on CRC-related mortality. gFOBT, FS, colonoscopy. Traditional meta-analysis was performed to produce pooled estimates of the effect of each modality on CRC mortality. Bayesian network meta-analysis (NMA) was performed to indirectly compare the effectiveness of screening modalities. Multiple sensitivity analyses were performed. Traditional meta-analysis revealed that, compared with no intervention, colonoscopy reduced CRC-related mortality by 57% (relative risk [RR] 0.43; 95% confidence interval [CI], 0.33-0.58), whereas FS reduced CRC-related mortality by 40% (RR 0.60; 95% CI, 0.45-0.78), and gFOBT reduced CRC-related mortality by 18% (RR 0.82; 95% CI, 0.76-0.88). NMA demonstrated nonsignificant trends favoring colonoscopy over FS (RR 0.71; 95% CI, 0.45-1.11) and FS over gFOBT (RR 0.74; 95% CI, 0.51-1.09) for reducing CRC-related deaths. NMA-based simulations, however, revealed that colonoscopy has a 94% probability of being the most effective test for reducing CRC mortality and a 99% probability of being most effective when the analysis is restricted to screening studies. Randomized trials and observational studies were combined within the same analysis. Clinical outcomes studies demonstrate that gFOBT, FS, and colonoscopy are all effective in reducing CRC-related mortality. Network meta-analysis suggests that colonoscopy is the most effective test. Copyright © 2015 American Society for Gastrointestinal Endoscopy. Published by Elsevier Inc. All rights reserved.

  6. Delineating transcriptional networks of prognostic gene signatures refines treatment recommendations for lymph node-negative breast cancer patients.

    Science.gov (United States)

    Lanigan, Fiona; Brien, Gerard L; Fan, Yue; Madden, Stephen F; Jerman, Emilia; Maratha, Ashwini; Aloraifi, Fatima; Hokamp, Karsten; Dunne, Eiseart J; Lohan, Amanda J; Flanagan, Louise; Garbe, James C; Stampfer, Martha R; Fridberg, Marie; Jirstrom, Karin; Quinn, Cecily M; Loftus, Brendan; Gallagher, William M; Geraghty, James; Bracken, Adrian P

    2015-09-01

    The majority of women diagnosed with lymph node-negative breast cancer are unnecessarily treated with damaging chemotherapeutics after surgical resection. This highlights the importance of understanding and more accurately predicting patient prognosis. In the present study, we define the transcriptional networks regulating well-established prognostic gene expression signatures. We find that the same set of transcriptional regulators consistently lie upstream of both 'prognosis' and 'proliferation' gene signatures, suggesting that a central transcriptional network underpins a shared phenotype within these signatures. Strikingly, the master transcriptional regulators within this network predict recurrence risk for lymph node-negative breast cancer better than currently used multigene prognostic assays, particularly in estrogen receptor-positive patients. Simultaneous examination of p16(INK4A) expression, which predicts tumours that have bypassed cellular senescence, revealed that intermediate levels of p16(INK4A) correlate with an intact pRB pathway and improved survival. A combination of these master transcriptional regulators and p16(INK4A), termed the OncoMasTR score, stratifies tumours based on their proliferative and senescence capacity, facilitating a clearer delineation of lymph node-negative breast cancer patients at high risk of recurrence, and thus requiring chemotherapy. Furthermore, OncoMasTR accurately classifies over 60% of patients as 'low risk', an improvement on existing prognostic assays, which has the potential to reduce overtreatment in early-stage patients. Taken together, the present study provides new insights into the transcriptional regulation of cellular proliferation in breast cancer and provides an opportunity to enhance and streamline methods of predicting breast cancer prognosis.

  7. Combining PubMed knowledge and EHR data to develop a weighted bayesian network for pancreatic cancer prediction.

    Science.gov (United States)

    Zhao, Di; Weng, Chunhua

    2011-10-01

    In this paper, we propose a novel method that combines PubMed knowledge and Electronic Health Records to develop a weighted Bayesian Network Inference (BNI) model for pancreatic cancer prediction. We selected 20 common risk factors associated with pancreatic cancer and used PubMed knowledge to weigh the risk factors. A keyword-based algorithm was developed to extract and classify PubMed abstracts into three categories that represented positive, negative, or neutral associations between each risk factor and pancreatic cancer. Then we designed a weighted BNI model by adding the normalized weights into a conventional BNI model. We used this model to extract the EHR values for patients with or without pancreatic cancer, which then enabled us to calculate the prior probabilities for the 20 risk factors in the BNI. The software iDiagnosis was designed to use this weighted BNI model for predicting pancreatic cancer. In an evaluation using a case-control dataset, the weighted BNI model significantly outperformed the conventional BNI and two other classifiers (k-Nearest Neighbor and Support Vector Machine). We conclude that the weighted BNI using PubMed knowledge and EHR data shows remarkable accuracy improvement over existing representative methods for pancreatic cancer prediction.

  8. Active Barrett's Esophagus Translational Research Network Grants | Division of Cancer Prevention

    Science.gov (United States)

    The Division of Cancer Prevention (DCP) conducts and supports research to determine a person's risk of cancer and to find ways to reduce the risk. This knowledge is critical to making progress against cancer because risk varies over the lifespan as genetic and epigenetic changes can transform healthy tissue into invasive cancer.

  9. Research from the Early Detection Research Network on New Methods to Detect Prostate Cancer | Division of Cancer Prevention

    Science.gov (United States)

    Prostate cancer is the most frequently diagnosed non-skin cancer in men in the United States. In 2010 there were 218,000 men diagnosed with prostate cancer. The prevalence of the diagnosis makes the disease a major health burden. While the majority of the diagnosed men will survive the disease, about 15% will die from it, a rate that is affected by over-diagnosis and the consequent over-treatment. |

  10. Impact of dimensionality and network disruption on microrheology of cancer cells in 3D environments.

    Directory of Open Access Journals (Sweden)

    Michael Mak

    2014-11-01

    Full Text Available Dimensionality is a fundamental component that can have profound implications on the characteristics of physical systems. In cell biology, however, the majority of studies on cell physical properties, from rheology to force generation to migration, have been performed on 2D substrates, and it is not clear how a more realistic 3D environment influences cell properties. Here, we develop an integrated approach and demonstrate the combination of mitochondria-tracking microrheology, microfluidics, and Brownian dynamics simulations to explore the impact of dimensionality on intracellular mechanics and on the effects of intracellular disruption. Additionally, we consider both passive thermal and active motor-driven processes within the cell and demonstrate through modeling how active internal fluctuations are modulated via dimensionality. Our results demonstrate that metastatic breast cancer cells (MDA-MB-231 exhibit more solid-like internal motions in 3D compared to 2D, and actin network disruption via Cytochalasin D has a more pronounced effect on internal cell fluctuations in 2D. Our computational results and modeling show that motor-induced active stress fluctuations are enhanced in 2D, leading to increased local intracellular particle fluctuations and apparent fluid-like behavior.

  11. Investigation of osteosarcoma genomics and its impact on targeted therapy:an international collaboration to conquer human osteosarcoma

    Institute of Scientific and Technical Information of China (English)

    Ji-Long Yang

    2014-01-01

    Osteosarcoma is a genetical y unstable malignancy that most frequently occurs in children and young adults. The lack of progress in managing this devastating disease in the clinic has prompted international researchers to collaborate to profile key genomic alterations that define osteosarcoma. A team of researchers and clinicians from China, Finland, and the United States investigated human osteosarcoma by integrating transcriptome sequencing (RNA-seq), high-density genome-wide array comparative genomic hybridization (aCGH), fluorescence in situ hybridization (FISH), reverse transcription-polymerase chain reaction (RT-PCR), Sanger sequencing, cell culture, and molecular biological approaches. Systematic analysis of genetic/genomic alterations and further functional studies have led to several important findings, including novel rearrangement hotspots, osteosarcoma-specific LRP1-SNRNP25 and KCNMB4-CCND3 fusion genes, VEGF and Wnt signaling pathway alterations, deletion of the WWOX gene, and amplification of the APEX1 and RUNX2 genes. Importantly, these genetic events associate significantly with pathogenesis, prognosis, progression, and therapeutic activity in osteosarcoma, suggesting their potential impact on improved managements of human osteosarcoma. This international initiative provides opportunities for developing new treatment modalities to conquer osteosarcoma.

  12. Investigation of osteosarcoma genomics and its impact on targeted therapy: an international collaboration to conquer human osteosarcoma.

    Science.gov (United States)

    Yang, Ji-Long

    2014-12-01

    Osteosarcoma is a genetically unstable malignancy that most frequently occurs in children and young adults. The lack of progress in managing this devastating disease in the clinic has prompted international researchers to collaborate to profile key genomic alterations that define osteosarcoma. A team of researchers and clinicians from China, Finland, and the United States investigated human osteosarcoma by integrating transcriptome sequencing (RNA-seq), high-density genome-wide array comparative genomic hybridization (aCGH), fluorescence in situ hybridization (FISH), reverse transcription-polymerase chain reaction (RT-PCR), Sanger sequencing, cell culture, and molecular biological approaches. Systematic analysis of genetic/genomic alterations and further functional studies have led to several important findings, including novel rearrangement hotspots, osteosarcoma-specific LRP1-SNRNP25 and KCNMB4-CCND3 fusion genes, VEGF and Wnt signaling pathway alterations, deletion of the WWOX gene, and amplification of the APEX1 and RUNX2 genes. Importantly, these genetic events associate significantly with pathogenesis, prognosis, progression, and therapeutic activity in osteosarcoma, suggesting their potential impact on improved managements of human osteosarcoma. This international initiative provides opportunities for developing new treatment modalities to conquer osteosarcoma.

  13. Divide-and-Conquer Method for L1 Norm Matrix Factorization in the Presence of Outliers and Missing Data

    CERN Document Server

    Meng, Deyu

    2012-01-01

    The low-rank matrix factorization as a L1 norm minimization problem has recently attracted much attention due to its intrinsic robustness to the presence of outliers and missing data. In this paper, we propose a new method, called the divide-and-conquer method, for solving this problem. The main idea is to break the original problem into a series of smallest possible sub-problems, each involving only unique scalar parameter. Each of these subproblems is proved to be convex and has closed-form solution. By recursively optimizing these small problems in an analytical way, efficient algorithm, entirely avoiding the time-consuming numerical optimization as an inner loop, for solving the original problem can naturally be constructed. The computational complexity of the proposed algorithm is approximately linear in both data size and dimensionality, making it possible to handle large-scale L1 norm matrix factorization problems. The algorithm is also theoretically proved to be convergent. Based on a series of experi...

  14. A Divide-and-Conquer Approach for Solving Fuzzy Max-Archimedean t-Norm Relational Equations

    Directory of Open Access Journals (Sweden)

    Jun-Lin Lin

    2014-01-01

    Full Text Available A system of fuzzy relational equations with the max-Archimedean t-norm composition was considered. The relevant literature indicated that this problem can be reduced to the problem of finding all the irredundant coverings of a binary matrix. A divide-and-conquer approach is proposed to solve this problem and, subsequently, to solve the original problem. This approach was used to analyze the binary matrix and then decompose the matrix into several submatrices such that the irredundant coverings of the original matrix could be constructed using the irredundant coverings of each of these submatrices. This step was performed recursively for each of these submatrices to obtain the irredundant coverings. Finally, once all the irredundant coverings of the original matrix were found, they were easily converted into the minimal solutions of the fuzzy relational equations. Experiments on binary matrices, with the number of irredundant coverings ranging from 24 to 9680, were also performed. The results indicated that, for test matrices that could initially be partitioned into more than one submatrix, this approach reduced the execution time by more than three orders of magnitude. For the other test matrices, this approach was still useful because certain submatrices could be partitioned into more than one submatrix.

  15. Singularity-conquering ZG controllers of z2g1 type for tracking control of the IPC system

    Science.gov (United States)

    Zhang, Yunong; Yu, Xiaotian; Yin, Yonghua; Peng, Chen; Fan, Zhengping

    2014-09-01

    With wider investigations and applications of autonomous robotics and intelligent vehicles, the inverted pendulum on a cart (IPC) system has become more attractive for numerous researchers due to its concise and representative structure. In this article, the tracking-control problem of the IPC system is considered and investigated. Based on Zhang dynamics (ZD) and gradient dynamics (GD), a novel kind of ZG controllers are developed and investigated for achieving the tracking-control purpose, which contains controllers of z2g0 and z2g1 types according to the number of times of using the ZD and GD methods. Besides, theoretical analyses are presented to guarantee the global and exponential convergence performance of both z2g0 and z2g1 controllers. Computer simulations are further performed to substantiate the feasibility and effectiveness of ZG controllers. More importantly, comparative simulation results demonstrate that controllers of z2g1 type can conquer the singularity problem (i.e. the division-by-zero problem).

  16. Analysis of tumor heterogeneity and cancer gene networks using deep sequencing of MMTV-induced mouse mammary tumors.

    Directory of Open Access Journals (Sweden)

    Christiaan Klijn

    Full Text Available Cancer develops through a multistep process in which normal cells progress to malignant tumors via the evolution of their genomes as a result of the acquisition of mutations in cancer driver genes. The number, identity and mode of action of cancer driver genes, and how they contribute to tumor evolution is largely unknown. This study deployed the Mouse Mammary Tumor Virus (MMTV as an insertional mutagen to find both the driver genes and the networks in which they function. Using deep insertion site sequencing we identified around 31000 retroviral integration sites in 604 MMTV-induced mammary tumors from mice with mammary gland-specific deletion of Trp53, Pten heterozygous knockout mice, or wildtype strains. We identified 18 known common integration sites (CISs and 12 previously unknown CISs marking new candidate cancer genes. Members of the Wnt, Fgf, Fgfr, Rspo and Pdgfr gene families were commonly mutated in a mutually exclusive fashion. The sequence data we generated yielded also information on the clonality of insertions in individual tumors, allowing us to develop a data-driven model of MMTV-induced tumor development. Insertional mutations near Wnt and Fgf genes mark the earliest "initiating" events in MMTV induced tumorigenesis, whereas Fgfr genes are targeted later during tumor progression. Our data shows that insertional mutagenesis can be used to discover the mutational networks, the timing of mutations, and the genes that initiate and drive tumor evolution.

  17. Use of an artificial neural network to predict risk factors of nosocomial infection in lung cancer patients.

    Science.gov (United States)

    Chen, Jie; Pan, Qin-Shi; Hong, Wan-Dong; Pan, Jingye; Zhang, Wen-Hui; Xu, Gang; Wang, Yu-Min

    2014-01-01

    Statistical methods to analyze and predict the related risk factors of nosocomial infection in lung cancer patients are various, but the results are inconsistent. A total of 609 patients with lung cancer were enrolled to allow factor comparison using Student's t-test or the Mann-Whitney test or the Chi-square test. Variables that were significantly related to the presence of nosocomial infection were selected as candidates for input into the final ANN model. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the performance of the artificial neural network (ANN) model and logistic regression (LR) model. The prevalence of nosocomial infection from lung cancer in this entire study population was 20.1% (165/609), nosocomial infections occurring in sputum specimens (85.5%), followed by blood (6.73%), urine (6.0%) and pleural effusions (1.82%). It was shown that long term hospitalization (≥ 22 days, P= 0.000), poor clinical stage (IIIb and IV stage, P=0.002), older age (≥ 61 year old, P=0.023), and use the hormones were linked to nosocomial infection and the ANN model consisted of these four factors .The artificial neural network model with variables consisting of age, clinical stage, time of hospitalization, and use of hormones should be useful for predicting nosocomial infection in lung cancer cases.

  18. Detection of breast cancer using advanced techniques of data mining with neural networks; Deteccion de cancer de mama usando tecnicas avanzadas de mineria de datos con redes neuronales

    Energy Technology Data Exchange (ETDEWEB)

    Ortiz M, J. A.; Celaya P, J. M.; Martinez B, M. R.; Solis S, L. O.; Castaneda M, R.; Garza V, I.; Martinez F, M.; Lopez H, Y.; Ortiz R, J. M. [Universidad Autonoma de Zacatecas, Av. Ramon Lopez Velarde 801, Col. Centro, 98000 Zacatecas, Zac. (Mexico)

    2016-10-15

    The breast cancer is one of the biggest health problems worldwide, is the most diagnosed cancer in women and prevention seems impossible since its cause is unknown, due to this; the early detection has a key role in the patient prognosis. In developing countries such as Mexico, where access to specialized health services is minimal, the regular clinical review is infrequent and there are not enough radiologists; the most common form of detection of breast cancer is through self-exploration, but this is only detected in later stages, when is already palpable. For these reasons, the objective of the present work is the creation of a system of computer assisted diagnosis (CAD x) using information analysis techniques such as data mining and advanced techniques of artificial intelligence, seeking to offer a previous medical diagnosis or a second opinion, as if it was a second radiologist in order to reduce the rate of mortality from breast cancer. In this paper, advances in the design of computational algorithms using computer vision techniques for the extraction of features derived from mammograms are presented. Using data mining techniques of data mining is possible to identify patients with a high risk of breast cancer. With the information obtained from the mammography analysis, the objective in the next stage will be to establish a methodology for the generation of imaging bio-markers to establish a breast cancer risk index for Mexican patients. In this first stage we present results of the classification of patients with high and low risk of suffering from breast cancer using neural networks. (Author)

  19. Global microRNA level regulation of EGFR-driven cell-cycle protein network in breast cancer

    Science.gov (United States)

    Uhlmann, Stefan; Mannsperger, Heiko; Zhang, Jitao David; Horvat, Emöke-Ágnes; Schmidt, Christian; Küblbeck, Moritz; Henjes, Frauke; Ward, Aoife; Tschulena, Ulrich; Zweig, Katharina; Korf, Ulrike; Wiemann, Stefan; Sahin, Özgür

    2012-01-01

    The EGFR-driven cell-cycle pathway has been extensively studied due to its pivotal role in breast cancer proliferation and pathogenesis. Although several studies reported regulation of individual pathway components by microRNAs (miRNAs), little is known about how miRNAs coordinate the EGFR protein network on a global miRNA (miRNome) level. Here, we combined a large-scale miRNA screening approach with a high-throughput proteomic readout and network-based data analysis to identify which miRNAs are involved, and to uncover potential regulatory patterns. Our results indicated that the regulation of proteins by miRNAs is dominated by the nucleotide matching mechanism between seed sequences of the miRNAs and 3′-UTR of target genes. Furthermore, the novel network-analysis methodology we developed implied the existence of consistent intrinsic regulatory patterns where miRNAs simultaneously co-regulate several proteins acting in the same functional module. Finally, our approach led us to identify and validate three miRNAs (miR-124, miR-147 and miR-193a-3p) as novel tumor suppressors that co-target EGFR-driven cell-cycle network proteins and inhibit cell-cycle progression and proliferation in breast cancer. PMID:22333974

  20. Automated diagnosis of prostate cancer in multi-parametric MRI based on multimodal convolutional neural networks

    Science.gov (United States)

    Le, Minh Hung; Chen, Jingyu; Wang, Liang; Wang, Zhiwei; Liu, Wenyu; (Tim Cheng, Kwang-Ting; Yang, Xin

    2017-08-01

    Automated methods for prostate cancer (PCa) diagnosis in multi-parametric magnetic resonance imaging (MP-MRIs) are critical for alleviating requirements for interpretation of radiographs while helping to improve diagnostic accuracy (Artan et al 2010 IEEE Trans. Image Process. 19 2444-55, Litjens et al 2014 IEEE Trans. Med. Imaging 33 1083-92, Liu et al 2013 SPIE Medical Imaging (International Society for Optics and Photonics) p 86701G, Moradi et al 2012 J. Magn. Reson. Imaging 35 1403-13, Niaf et al 2014 IEEE Trans. Image Process. 23 979-91, Niaf et al 2012 Phys. Med. Biol. 57 3833, Peng et al 2013a SPIE Medical Imaging (International Society for Optics and Photonics) p 86701H, Peng et al 2013b Radiology 267 787-96, Wang et al 2014 BioMed. Res. Int. 2014). This paper presents an automated method based on multimodal convolutional neural networks (CNNs) for two PCa diagnostic tasks: (1) distinguishing between cancerous and noncancerous tissues and (2) distinguishing between clinically significant (CS) and indolent PCa. Specifically, our multimodal CNNs effectively fuse apparent diffusion coefficients (ADCs) and T2-weighted MP-MRI images (T2WIs). To effectively fuse ADCs and T2WIs we design a new similarity loss function to enforce consistent features being extracted from both ADCs and T2WIs. The similarity loss is combined with the conventional classification loss functions and integrated into the back-propagation procedure of CNN training. The similarity loss enables better fusion results than existing methods as the feature learning processes of both modalities are mutually guided, jointly facilitating CNN to ‘see’ the true visual patterns of PCa. The classification results of multimodal CNNs are further combined with the results based on handcrafted features using a support vector machine classifier. To achieve a satisfactory accuracy for clinical use, we comprehensively investigate three critical factors which could greatly affect the performance of our

  1. Untangling the role of one-carbon metabolism in colorectal cancer risk: a comprehensive Bayesian network analysis

    Science.gov (United States)

    Myte, Robin; Gylling, Björn; Häggström, Jenny; Schneede, Jörn; Magne Ueland, Per; Hallmans, Göran; Johansson, Ingegerd; Palmqvist, Richard; Van Guelpen, Bethany

    2017-01-01

    The role of one-carbon metabolism (1CM), particularly folate, in colorectal cancer (CRC) development has been extensively studied, but with inconclusive results. Given the complexity of 1CM, the conventional approach, investigating components individually, may be insufficient. We used a machine learning-based Bayesian network approach to study, simultaneously, 14 circulating one-carbon metabolites, 17 related single nucleotide polymorphisms (SNPs), and several environmental factors in relation to CRC risk in 613 cases and 1190 controls from the prospective Northern Sweden Health and Disease Study. The estimated networks corresponded largely to known biochemical relationships. Plasma concentrations of folate (direct), vitamin B6 (pyridoxal 5-phosphate) (inverse), and vitamin B2 (riboflavin) (inverse) had the strongest independent associations with CRC risk. Our study demonstrates the importance of incorporating B-vitamins in future studies of 1CM and CRC development, and the usefulness of Bayesian network learning for investigating complex biological systems in relation to disease. PMID:28233834

  2. Assessing the effect of quantitative and qualitative predictors on gastric cancer individuals survival using hierarchical artificial neural network models.

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    Amiri, Zohreh; Mohammad, Kazem; Mahmoudi, Mahmood; Parsaeian, Mahbubeh; Zeraati, Hojjat

    2013-01-01

    There are numerous unanswered questions in the application of artificial neural network models for analysis of survival data. In most studies, independent variables have been studied as qualitative dichotomous variables, and results of using discrete and continuous quantitative, ordinal, or multinomial categorical predictive variables in these models are not well understood in comparison to conventional models. This study was designed and conducted to examine the application of these models in order to determine the survival of gastric cancer patients, in comparison to the Cox proportional hazards model. We studied the postoperative survival of 330 gastric cancer patients who suffered surgery at a surgical unit of the Iran Cancer Institute over a five-year period. Covariates of age, gender, history of substance abuse, cancer site, type of pathology, presence of metastasis, stage, and number of complementary treatments were entered in the models, and survival probabilities were calculated at 6, 12, 18, 24, 36, 48, and 60 months using the Cox proportional hazards and neural network models. We estimated coefficients of the Cox model and the weights in the neural network (with 3, 5, and 7 nodes in the hidden layer) in the training group, and used them to derive predictions in the study group. Predictions with these two methods were compared with those of the Kaplan-Meier product limit estimator as the gold standard. Comparisons were performed with the Friedman and Kruskal-Wallis tests. Survival probabilities at different times were determined using the Cox proportional hazards and a neural network with three nodes in the hidden layer; the ratios of standard errors with these two methods to the Kaplan-Meier method were 1.1593 and 1.0071, respectively, revealed a significant difference between Cox and Kaplan-Meier (P neural network, and the neural network and the standard (Kaplan-Meier), as well as better accuracy for the neural network (with 3 nodes in the hidden layer

  3. Comparison of molecular signatures in large scale protein interaction networks in normal and cancer conditions of brain, cervix, lung, ovary and prostate

    Directory of Open Access Journals (Sweden)

    Rajat Suvra Banik

    2016-04-01

    Full Text Available Background Cancer, the disease of intricateness, has remained beyond our complete perception so far. Network systems biology (termed NSB is one of the most recent approaches to understand the unsolved problems of cancer development. From this perspective, differential protein networks (PINs have been developed based on the expression and interaction data of brain, cervix, lung, ovary and prostate for normal and cancer conditions. Methods Differential expression database GeneHub-GEPIS and interaction database STRING were applied for primary data retrieval. Cytoscape platform and related plugins named network analyzer; MCODE and ModuLand were used for visualization of complex networks and subsequent analysis. Results Significant differences were observedamong different common network parameters between normal and cancer states. Moreover, molecular complex numbers and overlapping modularization found to be varying significantly between normal and cancerous tissues. The number of the ranked molecular complex and the nodes involved in the overlapping modules were meaningfully higher in cancer condition.We identified79 commonly up regulated and 6 down regulated proteins in all five tissues. Number of nodes, edges; multi edge node pair, and average number of neighbor are found with significant fluctuations in case of cervix and ovarian tissues.Cluster analysis showed that the association of Myc and Cdk4 proteins is very close with other proteins within the network.Cervix and ovarian tissue showed higher increment of the molecular complex number and overlapping module network during cancer in comparison to normal state. Conclusions The differential molecular signatures identified from the work can be studied further to understand the cancer signaling process, and potential therapeutic and detection approach. [Biomed Res Ther 2016; 3(4.000: 605-615

  4. Graph-theoretical model of global human interactome reveals enhanced long-range communicability in cancer networks.

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    Gladilin, Evgeny

    2017-01-01

    Malignant transformation is known to involve substantial rearrangement of the molecular genetic landscape of the cell. A common approach to analysis of these alterations is a reductionist one and consists of finding a compact set of differentially expressed genes or associated signaling pathways. However, due to intrinsic tumor heterogeneity and tissue specificity, biomarkers defined by a small number of genes/pathways exhibit substantial variability. As an alternative to compact differential signatures, global features of genetic cell machinery are conceivable. Global network descriptors suggested in previous works are, however, known to potentially be biased by overrepresentation of interactions between frequently studied genes-proteins. Here, we construct a cellular network of 74538 directional and differential gene expression weighted protein-protein and gene regulatory interactions, and perform graph-theoretical analysis of global human interactome using a novel, degree-independent feature-the normalized total communicability (NTC). We apply this framework to assess differences in total information flow between different cancer (BRCA/COAD/GBM) and non-cancer interactomes. Our experimental results reveal that different cancer interactomes are characterized by significant enhancement of long-range NTC, which arises from circulation of information flow within robustly organized gene subnetworks. Although enhancement of NTC emerges in different cancer types from different genomic profiles, we identified a subset of 90 common genes that are related to elevated NTC in all studied tumors. Our ontological analysis shows that these genes are associated with enhanced cell division, DNA replication, stress response, and other cellular functions and processes typically upregulated in cancer. We conclude that enhancement of long-range NTC manifested in the correlated activity of genes whose tight coordination is required for survival and proliferation of all tumor cells

  5. Boolean ErbB network reconstructions and perturbation simulations reveal individual drug response in different breast cancer cell lines

    Science.gov (United States)

    2014-01-01

    Background Despite promising progress in targeted breast cancer therapy, drug resistance remains challenging. The monoclonal antibody drugs trastuzumab and pertuzumab as well as the small molecule inhibitor erlotinib were designed to prevent ErbB-2 and ErbB-1 receptor induced deregulated protein signalling, contributing to tumour progression. The oncogenic potential of ErbB receptors unfolds in case of overexpression or mutations. Dimerisation with other receptors allows to bypass pathway blockades. Our intention is to reconstruct the ErbB network to reveal resistance mechanisms. We used longitudinal proteomic data of ErbB receptors and downstream targets in the ErbB-2 amplified breast cancer cell lines BT474, SKBR3 and HCC1954 treated with erlotinib, trastuzumab or pertuzumab, alone or combined, up to 60 minutes and 30 hours, respectively. In a Boolean modelling approach, signalling networks were reconstructed based on these data in a cell line and time course specific manner, including prior literature knowledge. Finally, we simulated network response to inhibitor combinations to detect signalling nodes reflecting growth inhibition. Results The networks pointed to cell line specific activation patterns of the MAPK and PI3K pathway. In BT474, the PI3K signal route was favoured, while in SKBR3, novel edges highlighted MAPK signalling. In HCC1954, the inferred edges stimulated both pathways. For example, we uncovered feedback loops amplifying PI3K signalling, in line with the known trastuzumab resistance of this cell line. In the perturbation simulations on the short-term networks, we analysed ERK1/2, AKT and p70S6K. The results indicated a pathway specific drug response, driven by the type of growth factor stimulus. HCC1954 revealed an edgetic type of PIK3CA-mutation, contributing to trastuzumab inefficacy. Drug impact on the AKT and ERK1/2 signalling axes is mirrored by effects on RB and RPS6, relating to phenotypic events like cell growth or proliferation

  6. Use of an Artificial Neural Network to Construct a Model of Predicting Deep Fungal Infection in Lung Cancer Patients.

    Science.gov (United States)

    Chen, Jian; Chen, Jie; Ding, Hong-Yan; Pan, Qin-Shi; Hong, Wan-Dong; Xu, Gang; Yu, Fang-You; Wang, Yu-Min

    2015-01-01

    The statistical methods to analyze and predict the related dangerous factors of deep fungal infection in lung cancer patients were several, such as logic regression analysis, meta-analysis, multivariate Cox proportional hazards model analysis, retrospective analysis, and so on, but the results are inconsistent. A total of 696 patients with lung cancer were enrolled. The factors were compared employing Student's t-test or the Mann-Whitney test or the Chi-square test and variables that were significantly related to the presence of deep fungal infection selected as candidates for input into the final artificial neural network analysis (ANN) model. The receiver operating characteristic (ROC) and area under curve (AUC) were used to evaluate the performance of the artificial neural network (ANN) model and logistic regression (LR) model. The prevalence of deep fungal infection from lung cancer in this entire study population was 32.04%(223/696), deep fungal infections occur in sputum specimens 44.05% (200/454). The ratio of candida albicans was 86.99% (194/223) in the total fungi. It was demonstrated that older (≥65 years), use of antibiotics, low serum albumin concentrations (≤37.18 g /L), radiotherapy, surgery, low hemoglobin hyperlipidemia (≤93.67 g /L), long time of hospitalization (≥14 days) were apt to deep fungal infection and the ANN model consisted of the seven factors. The AUC of ANN model (0.829±0.019) was higher than that of LR model (0.756±0.021). The artificial neural network model with variables consisting of age, use of antibiotics, serum albumin concentrations, received radiotherapy, received surgery, hemoglobin, time of hospitalization should be useful for predicting the deep fungal infection in lung cancer.

  7. Deep Proteomics of Breast Cancer Cells Reveals that Metformin Rewires Signaling Networks Away from a Pro-growth State.

    Science.gov (United States)

    Sacco, Francesca; Silvestri, Alessandra; Posca, Daniela; Pirrò, Stefano; Gherardini, Pier Federico; Castagnoli, Luisa; Mann, Matthias; Cesareni, Gianni

    2016-03-23

    Metformin is the most frequently prescribed drug for type 2 diabetes. In addition to its hypoglycemic effects, metformin also lowers cancer incidence. This anti-cancer activity is incompletely understood. Here, we profiled the metformin-dependent changes in the proteome and phosphoproteome of breast cancer cells using high-resolution mass spectrometry. In total, we quantified changes of 7,875 proteins and 15,813 phosphosites after metformin changes. To interpret these datasets, we developed a generally applicable strategy that overlays metformin-dependent changes in the proteome and phosphoproteome onto a literature-derived network. This approach suggested that metformin treatment makes cancer cells more sensitive to apoptotic stimuli and less sensitive to pro-growth stimuli. These hypotheses were tested in vivo; as a proof-of-principle, we demonstrated that metformin inhibits the p70S6K-rpS6 axis in a PP2A-phosphatase dependent manner. In conclusion, analysis of deep proteomics reveals both detailed and global mechanisms that contribute to the anti-cancer activity of metformin.

  8. Gene co-expression analyses differentiate networks associated with diverse cancers harbouring TP53 missense or null mutations

    Directory of Open Access Journals (Sweden)

    Kathleen Oros Klein

    2016-08-01

    Full Text Available In a variety of solid cancers, missense mutations in the well-established TP53 tumour suppressor gene may lead to presence of a partially-functioning protein molecule, whereas mutations affecting the protein encoding reading frame, often referred to as null mutations, result in the absence of p53 protein. Both types of mutations have been observed in the same cancer type. As the resulting tumour biology may be quite different between these two groups, we used RNA-sequencing data from The Cancer Genome Atlas (TCGA from four different cancers with poor prognosis, namely ovarian, breast, lung and skin cancers, to compare the patterns of co-expression of genes in tumours grouped according to their TP53 missense or null mutation status. We used Weighted Gene Coexpression Network analysis (WGCNA and a new test statistic built on differences between groups in the measures of gene connectivity. For each cancer, our analysis identified a set of genes showing differential coexpression patterns between the TP53 missense- and null mutation-carrying groups that was robust to the choice of the tuning parameter in WGCNA. After comparing these sets of genes across the four cancers, one gene (KIR3DL2 consistently showed differential coexpression patterns between the null and missense groups. KIR3DL2 is known to play an important role in regulating the immune response, which is consistent with our observation that this gene’s strongly-correlated partners implicated many immune-related pathways. Examining mutation-type-related changes in correlations between sets of genes may provide new insight into tumour biology.

  9. SNRFCB: sub-network based random forest classifier for predicting chemotherapy benefit on survival for cancer treatment.

    Science.gov (United States)

    Shi, Mingguang; He, Jianmin

    2016-04-01

    Adjuvant chemotherapy (CTX) should be individualized to provide potential survival benefit and avoid potential harm to cancer patients. Our goal was to establish a computational approach for making personalized estimates of the survival benefit from adjuvant CTX. We developed Sub-Network based Random Forest classifier for predicting Chemotherapy Benefit (SNRFCB) based gene expression datasets of lung cancer. The SNRFCB approach was then validated in independent test cohorts for identifying chemotherapy responder cohorts and chemotherapy non-responder cohorts. SNRFCB involved the pre-selection of gene sub-network signatures based on the mutations and on protein-protein interaction data as well as the application of the random forest algorithm to gene expression datasets. Adjuvant CTX was significantly associated with the prolonged overall survival of lung cancer patients in the chemotherapy responder group (P = 0.008), but it was not beneficial to patients in the chemotherapy non-responder group (P = 0.657). Adjuvant CTX was significantly associated with the prolonged overall survival of lung cancer squamous cell carcinoma (SQCC) subtype patients in the chemotherapy responder cohorts (P = 0.024), but it was not beneficial to patients in the chemotherapy non-responder cohorts (P = 0.383). SNRFCB improved prediction performance as compared to the machine learning method, support vector machine (SVM). To test the general applicability of the predictive model, we further applied the SNRFCB approach to human breast cancer datasets and also observed superior performance. SNRFCB could provide recurrent probability for individual patients and identify which patients may benefit from adjuvant CTX in clinical trials.

  10. Challenges in initiating and conducting personalized cancer therapy trials: perspectives from WINTHER, a Worldwide Innovative Network (WIN) Consortium trial.

    Science.gov (United States)

    Rodon, J; Soria, J C; Berger, R; Batist, G; Tsimberidou, A; Bresson, C; Lee, J J; Rubin, E; Onn, A; Schilsky, R L; Miller, W H; Eggermont, A M; Mendelsohn, J; Lazar, V; Kurzrock, R

    2015-08-01

    Advances in 'omics' technology and targeted therapeutic molecules are together driving the incorporation of molecular-based diagnostics into the care of patients with cancer. There is an urgent need to assess the efficacy of therapy determined by molecular matching of patients with particular targeted therapies. WINTHER is a clinical trial that uses cutting edge genomic and transcriptomic assays to guide treatment decisions. Through the lens of this ambitious multinational trial (five countries, six sites) coordinated by the Worldwide Innovative Networking Consortium for personalized cancer therapy, we discovered key challenges in initiation and conduct of a prospective, omically driven study. To date, the time from study concept to activation has varied between 19 months at Gustave Roussy Cancer Campus in France to 30 months at the Segal Cancer Center, McGill University (Canada). It took 3+ years to be able to activate US sites due to national regulatory hurdles. Access to medications proposed by the molecular analysis remains a major challenge, since their availability through active clinical trials is highly variable over time within sites and across the network. Rules regarding the off-label use of drugs, or drugs not yet approved at all in some countries, pose a further challenge, and many biopharmaceutical companies lack a simple internal mechanism to supply the drugs even if they wish to do so. These various obstacles should be addressed to test and then implement precision medicine in cancer. © 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.

  11. Early-Stage Primary Bone Lymphoma: A Retrospective, Multicenter Rare Cancer Network (RCN) Study

    Energy Technology Data Exchange (ETDEWEB)

    Cai Ling [Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, VD (Switzerland); Sun Yat-sen University Cancer Center, Guangzhou, Guangdong (China); Stauder, Michael C. [Mayo Clinic, Rochester, MN (United States); Zhang Yujing [Sun Yat-sen University Cancer Center, Guangzhou, Guangdong (China); Poortmans, Philip [Verbeeten Institute, Tilburg (Netherlands); Li Yexiong [Cancer Hospital, Chinese Academy of Medical Sciences, Beijing (China); Constantinou, Nicolaos [Theagenio Cancer Hospital, Thessaloniki, Macedonia (Greece); Thariat, Juliette [Centre Anti-Cancereux Antoine-Lacassagne, Nice, Cote d' Azur (France); Kadish, Sidney P. [University of Massachusetts Medical School, Worcester, MA (United States); Nguyen, Tan Dat [Institut Jean-Godinot, Reims, Champagne-Ardenne (France); Kirova, Youlia M. [Institut Curie, Paris (France); Ghadjar, Pirus [Inselspital, Bern University Hospital, and University of Bern (Switzerland); Weber, Damien C. [Hopitaux Universitaires de Geneve (Switzerland); Bertran, Victoria Tuset [Hospital Universitari Germans Trias i Pujol, Barcelona (Spain); Ozsahin, Mahmut [Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, VD (Switzerland); Mirimanoff, Rene-Olivier, E-mail: Rene-Olivier.Mirimanoff@chuv.ch [Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, VD (Switzerland)

    2012-05-01

    Purpose: Primary bone lymphoma (PBL) represents less than 1% of all malignant lymphomas. In this study, we assessed the disease profile, outcome, and prognostic factors in patients with Stages I and II PBL. Patients and Methods: Thirteen Rare Cancer Network (RCN) institutions enrolled 116 consecutive patients with PBL treated between 1987 and 2008 in this study. Eighty-seven patients underwent chemoradiotherapy (CXRT) without (78) or with (9) surgery, 15 radiotherapy (RT) without (13) or with (2) surgery, and 14 chemotherapy (CXT) without (9) or with (5) surgery. Median RT dose was 40 Gy (range, 4-60). The median number of CXT cycles was six (range, 2-8). Median follow-up was 41 months (range, 6-242). Results: The overall response rate at the end of treatment was 91% (complete response [CR] 74%, partial response [PR] 17%). Local recurrence or progression was observed in 12 (10%) patients and systemic recurrence in 17 (15%). The 5-year overall survival (OS), lymphoma-specific survival (LSS), and local control (LC) were 76%, 78%, and 92%, respectively. In univariate analyses (log-rank test), favorable prognostic factors for OS and LSS were International Prognostic Index (IPI) score {<=}1 (p = 0.009), high-grade histology (p = 0.04), CXRT (p = 0.05), CXT (p = 0.0004), CR (p < 0.0001), and RT dose >40 Gy (p = 0.005). For LC, only CR and Stage I were favorable factors. In multivariate analysis, IPI score, RT dose, CR, and CXT were independently influencing the outcome (OS and LSS). CR was the only predicting factor for LC. Conclusion: This large multicenter retrospective study confirms the good prognosis of early-stage PBL treated with combined CXRT. An adequate dose of RT and complete CXT regime were associated with better outcome.

  12. Outcome and Prognostic Factors in Endometrial Stromal Tumors: A Rare Cancer Network Study

    Energy Technology Data Exchange (ETDEWEB)

    Schick, Ulrike, E-mail: Ulrike.schick@icr.ac.uk [Department of Radiation Oncology, University Hospital, Geneva (Switzerland); Bolukbasi, Yasmin [Department of Radiation Oncology, Ege University Hospital, Izmir (Turkey); Thariat, Juliette [Department of Radiation Oncology, Antoine Lacassagne Center, Nice (France); Abdah-Bortnyak, Roxolyana; Kuten, Abraham [Department of Radiation Oncology, Rambam Medical Center, Haifa (Israel); Igdem, Sefik [Department of Radiation Oncology, Metropolitan Hospital, Istanbul (Turkey); Caglar, Hale [Department of Radiation Oncology, Marmara University Hospital, Istanbul (Turkey); Ozsaran, Zeynep [Department of Radiation Oncology, Ege University Hospital, Izmir (Turkey); Loessl, Kristina [Department of Radiation Oncology, University Hospital, Bern (Switzerland); Schleicher, Ursula [Department of Radiation Oncology, Dueren Hospital, Dueren (Germany); Zwahlen, Daniel [Department of Radiation Oncology, William Buckland Radiotherapy Centre, Melbourne (Australia); Villette, Sylviane [Department of Radiation Oncology, Rene Huguenin Center, Saint-Cloud (France); Vees, Hansjoerg [Department of Radiation Oncology, University Hospital, Geneva (Switzerland); Department of Radiation Oncology, Sion Hospital, Sion (Switzerland)

    2012-04-01

    Purpose: To provide further understanding regarding outcome and prognostic factors of endometrial stromal tumors (EST). Methods and Materials: A retrospective analysis was performed on the records of 59 women diagnosed with EST and treated with curative intent between 1983 and 2007 in the framework of the Rare Cancer Network. Results: Endometrial stromal sarcomas (ESS) were found in 44% and undifferentiated ESS (UES) in 49% of the cases. In 7% the grading was unclear. Of the total number of patients, 33 had Stage I, 4 Stage II, 20 Stage III, and 1 presented with Stage IVB disease. Adjuvant chemotherapy was administered to 12 patients, all with UES. External-beam radiotherapy (RT) was administered postoperatively to 48 women. The median follow-up was 41.4 months. The 5-year overall survival (OS) rate was 96.2% and 64.8% for ESS and UES, respectively, with a corresponding 5-year disease-free survival (DFS) rate of 49.4% and 43.4%, respectively. On multivariate analysis, adjuvant RT was an independent prognostic factor for OS (p = 0.007) and DFS (p = 0.013). Locoregional control, DFS, and OS were significantly associated with age ({<=}60 vs. >60 years), grade (ESS vs. UES), and International Federation of Gynecology and Obstetrics stage (I-II vs. III-IV). Positive lymph node staging had an impact on OS (p < 0.001). Conclusion: The prognosis of ESS differed from that of UES. Endometrial stromal sarcomas had an excellent 5-year OS, whereas the OS in UES was rather low. However, half of ESS patients had a relapse. For this reason, adjuvant treatment such as RT should be considered even in low-grade tumors. Multicenter randomized studies are still warranted to establish clear guidelines.

  13. Integrated miRNA-risk gene-pathway pair network analysis provides prognostic biomarkers for gastric cancer

    Directory of Open Access Journals (Sweden)

    Cai H

    2016-05-01

    Full Text Available Hui Cai,1 Jiping Xu,2 Yifang Han,3 Zhengmao Lu,1 Ting Han,1 Yibo Ding,4 Liye Ma1 1Department of General Surgery, Changhai Hospital, Second Military Medical University, Shanghai, 2Department of Medical Administration, Changhai Hospital, Second Military Medical University, Shanghai, 3Department of Epidemiology, Research Institute for Medicine of Nanjing Command, Nanjing, 4Department of Epidemiology, Changhai Hospital, Second Military Medical University, Shanghai, People’s Republic of China Purpose: This study aimed to identify molecular prognostic biomarkers for gastric cancer. Methods: mRNA and miRNA expression profiles of eligible gastric cancer and control samples were downloaded from Gene Expression Omnibus to screen the differentially expressed genes (DEGs and differentially expressed miRNAs (DEmiRs, using MetaDE and limma packages, respectively. Target genes of the DEmiRs were also collected from both predictive and experimentally validated target databases of miRNAs. The overlapping genes between selected targets and DEGs were identified as risk genes, followed by functional enrichment analysis. Human pathways and their corresponding genes were downloaded from the Kyoto Encyclopedia of Genes and Genomes (KEGG database for the expression analysis of each pathway in gastric cancer samples. Next, co-pathway pairs were selected according to the Pearson correlation coefficients. Finally, the co-pathway pairs, miRNA–target pairs, and risk gene–pathway pairs were merged into a complex interaction network, the most important nodes (miRNAs/target genes/co-pathway pairs of which were selected by calculating their degrees.Results: Totally, 1,260 DEGs and 144 DEmiRs were identified. There were 336 risk genes found in the 9,572 miRNA–target pairs. Judging from the pathway expression files, 45 co-pathway pairs were screened out. There were 1,389 interactive pairs and 480 nodes in the integrated network. Among all nodes in the network, focal

  14. Inference of hierarchical regulatory network of estrogen-dependent breast cancer through ChIP-based data

    Directory of Open Access Journals (Sweden)

    Parvin Jeffrey

    2010-12-01

    Full Text Available Abstract Background Global profiling of in vivo protein-DNA interactions using ChIP-based technologies has evolved rapidly in recent years. Although many genome-wide studies have identified thousands of ERα binding sites and have revealed the associated transcription factor (TF partners, such as AP1, FOXA1 and CEBP, little is known about ERα associated hierarchical transcriptional regulatory networks. Results In this study, we applied computational approaches to analyze three public available ChIP-based datasets: ChIP-seq, ChIP-PET and ChIP-chip, and to investigate the hierarchical regulatory network for ERα and ERα partner TFs regulation in estrogen-dependent breast cancer MCF7 cells. 16 common TFs and two common new TF partners (RORA and PITX2 were found among ChIP-seq, ChIP-chip and ChIP-PET datasets. The regulatory networks were constructed by scanning the ChIP-peak region with TF specific position weight matrix (PWM. A permutation test was performed to test the reliability of each connection of the network. We then used DREM software to perform gene ontology function analysis on the common genes. We found that FOS, PITX2, RORA and FOXA1 were involved in the up-regulated genes. We also conducted the ERα and Pol-II ChIP-seq experiments in tamoxifen resistance MCF7 cells (denoted as MCF7-T in this study and compared the difference between MCF7 and MCF7-T cells. The result showed very little overlap between these two cells in terms of targeted genes (21.2% of common genes and targeted TFs (25% of common TFs. The significant dissimilarity may indicate totally different transcriptional regulatory mechanisms between these two cancer cells. Conclusions Our study uncovers new estrogen-mediated regulatory networks by mining three ChIP-based data in MCF7 cells and ChIP-seq data in MCF7-T cells. We compared the different ChIP-based technologies as well as different breast cancer cells. Our computational analytical approach may guide biologists to

  15. Inference of hierarchical regulatory network of estrogen-dependent breast cancer through ChIP-based data

    Science.gov (United States)

    2010-01-01

    Background Global profiling of in vivo protein-DNA interactions using ChIP-based technologies has evolved rapidly in recent years. Although many genome-wide studies have identified thousands of ERα binding sites and have revealed the associated transcription factor (TF) partners, such as AP1, FOXA1 and CEBP, little is known about ERα associated hierarchical transcriptional regulatory networks. Results In this study, we applied computational approaches to analyze three public available ChIP-based datasets: ChIP-seq, ChIP-PET and ChIP-chip, and to investigate the hierarchical regulatory network for ERα and ERα partner TFs regulation in estrogen-dependent breast cancer MCF7 cells. 16 common TFs and two common new TF partners (RORA and PITX2) were found among ChIP-seq, ChIP-chip and ChIP-PET datasets. The regulatory networks were constructed by scanning the ChIP-peak region with TF specific position weight matrix (PWM). A permutation test was performed to test the reliability of each connection of the network. We then used DREM software to perform gene ontology function analysis on the common genes. We found that FOS, PITX2, RORA and FOXA1 were involved in the up-regulated genes. We also conducted the ERα and Pol-II ChIP-seq experiments in tamoxifen resistance MCF7 cells (denoted as MCF7-T in this study) and compared the difference between MCF7 and MCF7-T cells. The result showed very little overlap between these two cells in terms of targeted genes (21.2% of common genes) and targeted TFs (25% of common TFs). The significant dissimilarity may indicate totally different transcriptional regulatory mechanisms between these two cancer cells. Conclusions Our study uncovers new estrogen-mediated regulatory networks by mining three ChIP-based data in MCF7 cells and ChIP-seq data in MCF7-T cells. We compared the different ChIP-based technologies as well as different breast cancer cells. Our computational analytical approach may guide biologists to further study the

  16. Mitosis detection in breast cancer histology images with deep neural networks.

    Science.gov (United States)

    Cireşan, Dan C; Giusti, Alessandro; Gambardella, Luca M; Schmidhuber, Jürgen

    2013-01-01

    We use deep max-pooling convolutional neural networks to detect mitosis in breast histology images. The networks are trained to classify each pixel in the images, using as context a patch centered on the pixel. Simple postprocessing is then applied to the network output. Our approach won the ICPR 2012 mitosis detection competition, outperforming other contestants by a significant margin.

  17. Assessment of Breast Cancer Risk in an Iranian Female Population Using Bayesian Networks with Varying Node Number

    Science.gov (United States)

    Rezaianzadeh, Abbas; Sepandi, Mojtaba; Rahimikazerooni, Salar

    2016-11-01

    Objective: As a source of information, medical data can feature hidden relationships. However, the high volume of datasets and complexity of decision-making in medicine introduce difficulties for analysis and interpretation and processing steps may be needed before the data can be used by clinicians in their work. This study focused on the use of Bayesian models with different numbers of nodes to aid clinicians in breast cancer risk estimation. Methods: Bayesian networks (BNs) with a retrospectively collected dataset including mammographic details, risk factor exposure, and clinical findings was assessed for prediction of the probability of breast cancer in individual patients. Area under the receiver-operating characteristic curve (AUC), accuracy, sensitivity, specificity, and positive and negative predictive values were used to evaluate discriminative performance. Result: A network incorporating selected features performed better (AUC = 0.94) than that incorporating all the features (AUC = 0.93). The results revealed no significant difference among 3 models regarding performance indices at the 5% significance level. Conclusion: BNs could effectively discriminate malignant from benign abnormalities and accurately predict the risk of breast cancer in individuals. Moreover, the overall performance of the 9-node BN was better, and due to the lower number of nodes it might be more readily be applied in clinical settings.

  18. Protein-protein interaction network construction for cancer using a new L1/2-penalized Net-SVM model.

    Science.gov (United States)

    Chai, H; Huang, H H; Jiang, H K; Liang, Y; Xia, L Y

    2016-07-25

    Identifying biomarker genes and characterizing interaction pathways with high-dimensional and low-sample size microarray data is a major challenge in computational biology. In this field, the construction of protein-protein interaction (PPI) networks using disease-related selected genes has garnered much attention. Support vector machines (SVMs) are commonly used to classify patients, and a number of useful tools such as lasso, elastic net, SCAD, or other regularization methods can be combined with SVM models to select genes that are related to a disease. In the current study, we propose a new Net-SVM model that is different from other SVM models as it is combined with L1/2-norm regularization, which has good performance with high-dimensional and low-sample size microarray data for cancer classification, gene selection, and PPI network construction. Both simulation studies and real data experiments demonstrated that our proposed method outperformed other regularization methods such as lasso, SCAD, and elastic net. In conclusion, our model may help to select fewer but more relevant genes, and can be used to construct simple and informative PPI networks that are highly relevant to cancer.

  19. Conquering the intolerable burden of malaria: what's new, what's needed: a summary.

    Science.gov (United States)

    Breman, Joel G; Alilio, Martin S; Mills, Anne

    2004-08-01

    vaccine becomes deployed, consideration must be given to disease burden, cost-effectiveness, financing, delivery systems, and approval by regulatory agencies. Key to evaluation of vaccine effectiveness will be collection and prompt analysis of epidemiologic information. Training of persons in every aspect of malaria research and control is essential for programs to succeed. The Multilateral Initiative on Malaria (MIM) is actively promoting research capacity strengthening and has established networks of institutions and scientists throughout the African continent, most of whom are now linked by modern information-sharing networks. Evidence over the past century is that successful control malaria programs have been linked to strong research activities. To ensure effective coordination and cooperation between the growing number of research and control coalitions forming in support of malaria activities, an umbrella group is needed. With continued support for scientists and control workers globally, particularly in low-income malarious countries, the long-deferred dream of malaria elimination can become a reality. Copyright 2004 The American Society of Tropical Medicine and Hygiene

  20. Involvement of Different networks in mammary gland involution after the pregnancy/lactation cycle: Implications in breast cancer.

    Science.gov (United States)

    Zaragozá, Rosa; García-Trevijano, Elena R; Lluch, Ana; Ribas, Gloria; Viña, Juan R

    2015-04-01

    Early pregnancy is associated with a reduction in a woman's lifetime risk for breast cancer. However, different studies have demonstrated an increase in breast cancer risk in the years immediately following pregnancy. Early and long-term risk is even higher if the mother age is above 35 years at the time of first parity. The proinflammatory microenvironment within the mammary gland after pregnancy renders an "ideal niche" for oncogenic events. Signaling pathways involved in programmed cell death and tissue remodeling during involution are also activated in breast cancer. Herein, the major signaling pathways involved in mammary gland involution, signal transducer and activator of transcription (STAT3), nuclear factor-kappa B (NF-κB), transforming growth factor beta (TGFβ), and retinoid acid receptors (RARs)/retinoid X receptors (RXRs), are reviewed as part of the complex network of signaling pathways that crosstalk in a contextual-dependent manner. These factors, also involved in breast cancer development, are important regulatory nodes for signaling amplification after weaning. Indeed, during involution, p65/p300 target genes such as MMP9, Capn1, and Capn2 are upregulated. Elevated expression and activities of these proteases in breast cancer have been extensively documented. The role of these proteases during mammary gland involution is further discussed. MMPs, calpains, and cathepsins exert their effect by modification of the extracellular matrix and intracellular proteins. Calpains, activated in the mammary gland during involution, cleave several proteins located in cell membrane, lysosomes, mitochondria, and nuclei favoring cell death. Besides, during this period, Capn1 is most probably involved in the modulation of preadipocyte differentiation through chromatin remodeling. Calpains can be implicated in cell anchoring loss, providing a proper microenvironment for tumor growth. A better understanding of the role of any of these proteases in tumorigenesis may

  1. 人工神经网络模型在肺癌与胃癌或肠癌中的鉴别分析%The distinguishment of lung cancer with gastric cancer or colon cancer by artificial neural network

    Institute of Scientific and Technical Information of China (English)

    周晓蕾; 冯斐斐; 张昭; 秦利娟; 吴拥军; 聂广金; 倪然; 吴逸明; 王静

    2011-01-01

    Objective To distinguish lung cancer from gastric cancer or colon cancer by artificial neural network (ANN) combined with six serum tumor markers. Methods The levels of serum carcino-embryonic antigen (CEA), gastrin, neurone specific enolase (NSE) , sialic acid(SA), Cu/Zn, Ca in 67 lung cancer patients, 47 gastric cancer patients and 50 colon cancer were detected by radioimmunology, spectrophotometry, or atomic absorption spectrophotometry, respectively.and artificial neural network were established with six serum tumor markers to distinguish lung cancer from the other cancers. Results The sensitivity, specificity and accuracy of distinguishing lung cancer by lung cancer-gastric cancer ANN model were 100% , 83.3% and 93.5% , respectively. And by lung cancer-colon cancer were 76.9% , 100% and 87.0%. Conclusions There is a clinical significant effect to distinguish lung cancer from gastric cancer and colon cancer by ANN model combined with optimal serum markers, which is very helpful for diagnosis of lung cancer.%目的:应用人工神经网络技术,联合检测6种肿瘤标志时肺癌与胃癌或肠癌进行区分判别,建立肿瘤标志联合检测肺癌的辅助诊断模型.方法:采用放射免疫学、分光光度法、原子吸收分光光度法等方法,测定67例肺癌患者、47例胃癌患者和50例大肠癌患者血清中癌胚抗原(CEA)、胃泌素(gastrin)、神经元特异性烯醇化酶(NSE)、唾液酸(SA)、铜锌比值(Cu/Zn)、钙(Ca)等6项指标.建立基于人工神经网络的肺癌肿瘤标志智能诊断模型.结果:肺癌-胃癌的人工神经网络模型判别肺癌的灵敏度,特异度和准确度分别为100%、83.3%和93.5%:肺癌-肠癌模型判别肺癌的灵敏度、特异度和准确度分别为76.9%、100%和87.O%.结论:本研究成功建立基于人工神经网络技术的肿瘤标志物联合检测的人工智能诊断模型,对肺癌-胃癌、肺癌-肠癌中肺癌的鉴别诊断有助于提高肺癌的诊断率.

  2. An integrative analysis of cellular contexts, miRNAs and mRNAs reveals network clusters associated with antiestrogen-resistant breast cancer cells

    Directory of Open Access Journals (Sweden)

    Nam Seungyoon

    2012-12-01

    Full Text Available Abstract Background A major goal of the field of systems biology is to translate genome-wide profiling data (e.g., mRNAs, miRNAs into interpretable functional networks. However, employing a systems biology approach to better understand the complexities underlying drug resistance phenotypes in cancer continues to represent a significant challenge to the field. Previously, we derived two drug-resistant breast cancer sublines (tamoxifen- and fulvestrant-resistant cell lines from the MCF7 breast cancer cell line and performed genome-wide mRNA and microRNA profiling to identify differential molecular pathways underlying acquired resistance to these important antiestrogens. In the current study, to further define molecular characteristics of acquired antiestrogen resistance we constructed an “integrative network”. We combined joint miRNA-mRNA expression profiles, cancer contexts, miRNA-target mRNA relationships, and miRNA upstream regulators. In particular, to reduce the probability of false positive connections in the network, experimentally validated, rather than prediction-oriented, databases were utilized to obtain connectivity. Also, to improve biological interpretation, cancer contexts were incorporated into the network connectivity. Results Based on the integrative network, we extracted “substructures” (network clusters representing the drug resistant states (tamoxifen- or fulvestrant-resistance cells compared to drug sensitive state (parental MCF7 cells. We identified un-described network clusters that contribute to antiestrogen resistance consisting of miR-146a, -27a, -145, -21, -155, -15a, -125b, and let-7s, in addition to the previously described miR-221/222. Conclusions By integrating miRNA-related network, gene/miRNA expression and text-mining, the current study provides a computational-based systems biology approach for further investigating the molecular mechanism underlying antiestrogen resistance in breast cancer cells. In

  3. Genome-wide mRNA and miRNA expression profiling reveal multiple regulatory networks in colorectal cancer

    DEFF Research Database (Denmark)

    Vishnubalaji, R; Hamam, R; Abdulla, M-H;

    2015-01-01

    upregulated and 1902 downregulated genes in CRC. Pathway analysis revealed significant enrichment in cell cycle, integrated cancer, Wnt (wingless-type MMTV integration site family member), matrix metalloproteinase, and TGF-β pathways in CRC. Pharmacological inhibition of Wnt (using XAV939 or IWP-2) or TGF......-β (using SB-431542) pathways led to dose- and time-dependent inhibition of CRC cell growth. Similarly, our data revealed up- (42) and downregulated (61) microRNAs in the same matched samples. Using target prediction and bioinformatics, ~77% of the upregulated genes were predicted to be targeted by micro...... in cell proliferation, and migration in vitro. Concordantly, small interfering RNA-mediated knockdown of EZH2 led to similar effects on CRC cell growth in vitro. Therefore, our data have revealed several hundred potential miRNA-mRNA regulatory networks in CRC and suggest targeting relevant networks...

  4. Multiplication free neural network for cancer stem cell detection in H-and-E stained liver images

    Science.gov (United States)

    Badawi, Diaa; Akhan, Ece; Mallah, Ma'en; Üner, Ayşegül; ćetin-Atalay, Rengül; ćetin, A. Enis

    2017-05-01

    Markers such as CD13 and CD133 have been used to identify Cancer Stem Cells (CSC) in various tissue images. It is highly likely that CSC nuclei appear as brown in CD13 stained liver tissue images. We observe that there is a high correlation between the ratio of brown to blue colored nuclei in CD13 images and the ratio between the dark blue to blue colored nuclei in H&E stained liver images. Therefore, we recommend that a pathologist observing many dark blue nuclei in an H&E stained tissue image may also order CD13 staining to estimate the CSC ratio. In this paper, we describe a computer vision method based on a neural network estimating the ratio of dark blue to blue colored nuclei in an H&E stained liver tissue image. The neural network structure is based on a multiplication free operator using only additions and sign operations. Experimental results are presented.

  5. Large-scale analysis of genome and transcriptome alterations in multiple tumors unveils novel cancer-relevant splicing networks

    Science.gov (United States)

    Sebestyén, Endre; Singh, Babita; Miñana, Belén; Pagès, Amadís; Mateo, Francesca; Pujana, Miguel Angel; Valcárcel, Juan; Eyras, Eduardo

    2016-01-01

    Alternative splicing is regulated by multiple RNA-binding proteins and influences the expression of most eukaryotic genes. However, the role of this process in human disease, and particularly in cancer, is only starting to be unveiled. We systematically analyzed mutation, copy number, and gene expression patterns of 1348 RNA-binding protein (RBP) genes in 11 solid tumor types, together with alternative splicing changes in these tumors and the enrichment of binding motifs in the alternatively spliced sequences. Our comprehensive study reveals widespread alterations in the expression of RBP genes, as well as novel mutations and copy number variations in association with multiple alternative splicing changes in cancer drivers and oncogenic pathways. Remarkably, the altered splicing patterns in several tumor types recapitulate those of undifferentiated cells. These patterns are predicted to be mainly controlled by MBNL1 and involve multiple cancer drivers, including the mitotic gene NUMA1. We show that NUMA1 alternative splicing induces enhanced cell proliferation and centrosome amplification in nontumorigenic mammary epithelial cells. Our study uncovers novel splicing networks that potentially contribute to cancer development and progression. PMID:27197215

  6. Recommendations From the International Colorectal Cancer Screening Network on the Evaluation of the Cost of Screening Programs.

    Science.gov (United States)

    Subramanian, Sujha; Tangka, Florence K L; Hoover, Sonja; Nadel, Marion; Smith, Robert; Atkin, Wendy; Patnick, Julietta

    2016-01-01

    Worldwide, colorectal cancer is the fourth leading cause of death from cancer and the incidence is projected to increase. Many countries are exploring the introduction of organized screening programs, but there is limited information on the resources required and guidance for cost-effective implementation. To facilitate the generating of the economics evidence base for program implementation, we collected and analyzed detailed program cost data from 5 European members of the International Colorectal Cancer Screening Network. The cost per person screened estimates, often used to compare across programs as an overall measure, varied significantly across the programs. In addition, there were substantial differences in the programmatic and clinical cost incurred, even when the same type of screening test was used. Based on these findings, several recommendations are provided to enhance the underlying methodology and validity of the comparative economic assessments. The recommendations include the need for detailed activity-based cost information, the use of a comprehensive set of effectiveness measures to adequately capture differences between programs, and the incorporation of data from multiple programs in cost-effectiveness models to increase generalizability. Economic evaluation of real-world colorectal cancer-screening programs is essential to derive valuable insights to improve program operations and ensure optimal use of available resources.

  7. An analysis of Social Work Oncology Network Listserv Postings on the Commission of Cancer's distress screening guidelines.

    Science.gov (United States)

    Burg, Mary Ann; Adorno, Gail; Hidalgo, Jorge

    2012-01-01

    This is a qualitative study of listserv postings by members of the Social Work Oncology Network (SWON) in response to the Commission on Cancer's 2011 guidelines for distress screening of cancer patients. Archived listserv postings for the period of December 2010 to November 2011 were deidentified and a sample was derived by a list of keywords for the analysis. Aims of the study included describing the general categories and themes of the postings devoted to the new distress screening standard and examining the process of facilitation of mutual support and information exchange by oncology social workers in response to the new screening standards. During the 12-month timeframe there were 242 unique listserv postings sampled for the analysis. Oncology social worker (OSW) discussion of the distress screening guidelines remained a constant topic over the 12 months, and major themes that emerged from the data included processes of implementation of distress screening in cancer centers, screening policies and protocols, screening tool choice, and oncology social worker professional identity. The SWON listserv members used the listserv as a mechanism to post their requests for information on screening, to share their experiences in the beginning stages of implementing the guidelines, and to build support for legitimizing oncology social workers as the lead profession in the implementation of the guidelines in member cancer centers.

  8. Alternative linear-scaling methodology for the second-order Møller-Plesset perturbation calculation based on the divide-and-conquer method.

    Science.gov (United States)

    Kobayashi, Masato; Imamura, Yutaka; Nakai, Hiromi

    2007-08-21

    A new scheme for obtaining the approximate correlation energy in the divide-and-conquer (DC) method of Yang [Phys. Rev. Lett. 66, 1438 (1991)] is presented. In this method, the correlation energy of the total system is evaluated by summing up subsystem contributions, which are calculated from subsystem orbitals based on a scheme for partitioning the correlation energy. We applied this method to the second-order Moller-Plesset perturbation theory (MP2), which we call DC-MP2. Numerical assessment revealed that this scheme provides a reliable correlation energy with significantly less computational cost than the conventional MP2 calculation.

  9. Development of an excited-state calculation method for large systems using dynamical polarizability: A divide-and-conquer approach at the time-dependent density functional level

    Science.gov (United States)

    Nakai, Hiromi; Yoshikawa, Takeshi

    2017-03-01

    In this study, we developed an excited-state calculation method for large systems using dynamical polarizabilities at the time-dependent density functional theory level. Three equivalent theories, namely, coupled-perturbed self-consistent field (CPSCF), random phase approximation (RPA), and Green function (GF), were extended to linear-scaling methods using the divide-and-conquer (DC) technique. The implementations of the standard and DC-based CPSCF, RPA, and GF methods are described. Numerical applications of these methods to polyene chains, single-wall carbon nanotubes, and water clusters confirmed the accuracy and efficiency of the DC-based methods, especially DC-GF.

  10. Quantitative network measures as biomarkers for classifying prostate cancer disease states: a systems approach to diagnostic biomarkers.

    Directory of Open Access Journals (Sweden)

    Matthias Dehmer

    Full Text Available Identifying diagnostic biomarkers based on genomic features for an accurate disease classification is a problem of great importance for both, basic medical research and clinical practice. In this paper, we introduce quantitative network measures as structural biomarkers and investigate their ability for classifying disease states inferred from gene expression data from prostate cancer. We demonstrate the utility of our approach by using eigenvalue and entropy-based graph invariants and compare the results with a conventional biomarker analysis of the underlying gene expression data.

  11. An introduction to artificial neural networks in bioinformatics--application to complex microarray and mass spectrometry datasets in cancer studies.

    Science.gov (United States)

    Lancashire, Lee J; Lemetre, Christophe; Ball, Graham R

    2009-05-01

    Applications of genomic and proteomic technologies have seen a major increase, resulting in an explosion in the amount of highly dimensional and complex data being generated. Subsequently this has increased the effort by the bioinformatics community to develop novel computational approaches that allow for meaningful information to be extracted. This information must be of biological relevance and thus correlate to disease phenotypes of interest. Artificial neural networks are a form of machine learning from the field of artificial intelligence with proven pattern recognition capabilities and have been utilized in many areas of bioinformatics. This is due to their ability to cope with highly dimensional complex datasets such as those developed by protein mass spectrometry and DNA microarray experiments. As such, neural networks have been applied to problems such as disease classification and identification of biomarkers. This review introduces and describes the concepts related to neural networks, the advantages and caveats to their use, examples of their applications in mass spectrometry and microarray research (with a particular focus on cancer studies), and illustrations from recent literature showing where neural networks have performed well in comparison to other machine learning methods. This should form the necessary background knowledge and information enabling researchers with an interest in these methodologies, but not necessarily from a machine learning background, to apply the concepts to their own datasets, thus maximizing the information gain from these complex biological systems.

  12. Robustness and backbone motif of a cancer network regulated by miR-17-92 cluster during the G1/S transition.

    Directory of Open Access Journals (Sweden)

    Lijian Yang

    Full Text Available Based on interactions among transcription factors, oncogenes, tumor suppressors and microRNAs, a Boolean model of cancer network regulated by miR-17-92 cluster is constructed, and the network is associated with the control of G1/S transition in the mammalian cell cycle. The robustness properties of this regulatory network are investigated by virtue of the Boolean network theory. It is found that, during G1/S transition in the cell cycle process, the regulatory networks are robustly constructed, and the robustness property is largely preserved with respect to small perturbations to the network. By using the unique process-based approach, the structure of this network is analyzed. It is shown that the network can be decomposed into a backbone motif which provides the main biological functions, and a remaining motif which makes the regulatory system more stable. The critical role of miR-17-92 in suppressing the G1/S cell cycle checkpoint and increasing the uncontrolled proliferation of the cancer cells by targeting a genetic network of interacting proteins is displayed with our model.

  13. Applied Proteogenomics OrganizationaL Learning and Outcomes (APOLLO) Network - Office of Cancer Clinical Proteomics Research

    Science.gov (United States)

    In the spirit of collaboration inspired by the Vice President’s Cancer Moonshot, the Department of Veterans Affairs (VA), the Department of Defense (DoD), and the National Cancer Institute (NCI) are proud to announce a new tri-agency coalition (APOLLO Network — Applied Proteogenomics OrganizationaL Learning and Outcomes) that will help cancer patients by enabling their oncologists to more rapidly and accurately identify effective drugs to treat cancer based on a patient’s unique proteogenomic profile.

  14. Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features.

    Science.gov (United States)

    Wang, Haibo; Cruz-Roa, Angel; Basavanhally, Ajay; Gilmore, Hannah; Shih, Natalie; Feldman, Mike; Tomaszewski, John; Gonzalez, Fabio; Madabhushi, Anant

    2014-10-01

    Breast cancer (BCa) grading plays an important role in predicting disease aggressiveness and patient outcome. A key component of BCa grade is the mitotic count, which involves quantifying the number of cells in the process of dividing (i.e., undergoing mitosis) at a specific point in time. Currently, mitosis counting is done manually by a pathologist looking at multiple high power fields (HPFs) on a glass slide under a microscope, an extremely laborious and time consuming process. The development of computerized systems for automated detection of mitotic nuclei, while highly desirable, is confounded by the highly variable shape and appearance of mitoses. Existing methods use either handcrafted features that capture certain morphological, statistical, or textural attributes of mitoses or features learned with convolutional neural networks (CNN). Although handcrafted features are inspired by the domain and the particular application, the data-driven CNN models tend to be domain agnostic and attempt to learn additional feature bases that cannot be represented through any of the handcrafted features. On the other hand, CNN is computationally more complex and needs a large number of labeled training instances. Since handcrafted features attempt to model domain pertinent attributes and CNN approaches are largely supervised feature generation methods, there is an appeal in attempting to combine these two distinct classes of feature generation strategies to create an integrated set of attributes that can potentially outperform either class of feature extraction strategies individually. We present a cascaded approach for mitosis detection that intelligently combines a CNN model and handcrafted features (morphology, color, and texture features). By employing a light CNN model, the proposed approach is far less demanding computationally, and the cascaded strategy of combining handcrafted features and CNN-derived features enables the possibility of maximizing the performance

  15. Cross-cancer profiling of molecular alterations within the human autophagy interaction network.

    Science.gov (United States)

    Lebovitz, Chandra B; Robertson, A Gordon; Goya, Rodrigo; Jones, Steven J; Morin, Ryan D; Marra, Marco A; Gorski, Sharon M

    2015-01-01

    Aberrant activation or disruption of autophagy promotes tumorigenesis in various preclinical models of cancer, but whether the autophagy pathway is a target for recurrent molecular alteration in human cancer patient samples is unknown. To address this outstanding question, we surveyed 211 human autophagy-associated genes for tumor-related alterations to DNA sequence and RNA expression levels and examined their association with patient survival outcomes in multiple cancer types with sequence data from The Cancer Genome Atlas consortium. We found 3 (RB1CC1/FIP200, ULK4, WDR45/WIPI4) and one (ATG7) core autophagy genes to be under positive selection for somatic mutations in endometrial carcinoma and clear cell renal carcinoma, respectively, while 29 autophagy regulators and pathway interactors, including previously identified KEAP1, NFE2L2, and MTOR, were significantly mutated in 6 of the 11 cancer types examined. Gene expression analyses revealed that GABARAPL1 and MAP1LC3C/LC3C transcripts were less abundant in breast cancer and non-small cell lung cancers than in matched normal tissue controls; ATG4D transcripts were increased in lung squamous cell carcinoma, as were ATG16L2 transcripts in kidney cancer. Unsupervised clustering of autophagy-associated mRNA levels in tumors stratified patient overall survival in 3 of 9 cancer types (acute myeloid leukemia, clear cell renal carcinoma, and head and neck cancer). These analyses provide the first comprehensive resource of recurrently altered autophagy-associated genes in human tumors, and highlight cancer types and subtypes where perturbed autophagy may be relevant to patient overall survival.

  16. [Scientific production and cancer-related collaboration networks in Peru 2000-2011: a bibliometric study in Scopus and Science Citation Index].

    Science.gov (United States)

    Mayta-Tristán, Percy; Huamaní, Charles; Montenegro-Idrogo, Juan José; Samanez-Figari, César; González-Alcaide, Gregorio

    2013-03-01

    A bibliometric study was carried out to describe the scientific production on cancer written by Peruvians and published in international health journals, as well as to assess the scientific collaboration networks. It included articles on cancer written in Peru between the years 2000 and 2011 and published in health journals indexed in SCOPUS or Science Citation Index Expanded. In the 358 articles identified, an increase in the production was seen, from 4 articles in 2000 to 57 in 2011.The most studied types were cervical cancer (77 publications); breast cancer (53), and gastric cancer (37). The National Institute of Neoplastic Diseases (INEN) was the most productive institution (121 articles) and had the highest number of collaborations (180 different institutions). 52 clinical trials were identified, 29 of which had at least one author from INEN. We can conclude that, cancer research is increasing in Peru, the INEN being the most productive institution, with an important participation in clinical trials.

  17. Artificial neural network for predicting pathological stage of clinically localized prostate cancer in a Taiwanese population

    Directory of Open Access Journals (Sweden)

    Chih-Wei Tsao

    2014-10-01

    Conclusion: ANN was superior to LR at predicting OCD in prostate cancer. Compared with the validation of current Partin Tables for the Taiwanese population, the ANN model resulted in larger AUCs and more accurate prediction of the pathologic stage of prostate cancer.

  18. Multi-OMICs and Genome Editing Perspectives on Liver Cancer Signaling Networks

    Directory of Open Access Journals (Sweden)

    Shengda Lin

    2016-01-01

    Full Text Available The advent of the human genome sequence and the resulting ~20,000 genes provide a crucial framework for a transition from traditional biology to an integrative “OMICs” arena (Lander et al., 2001; Venter et al., 2001; Kitano, 2002. This brings in a revolution for cancer research, which now enters a big data era. In the past decade, with the facilitation by next-generation sequencing, there have been a huge number of large-scale sequencing efforts, such as The Cancer Genome Atlas (TCGA, the HapMap, and the 1000 genomes project. As a result, a deluge of genomic information becomes available from patients stricken by a variety of cancer types. The list of cancer-associated genes is ever expanding. New discoveries are made on how frequent and highly penetrant mutations, such as those in the telomerase reverse transcriptase (TERT and TP53, function in cancer initiation, progression, and metastasis. Most genes with relatively frequent but weakly penetrant cancer mutations still remain to be characterized. In addition, genes that harbor rare but highly penetrant cancer-associated mutations continue to emerge. Here, we review recent advances related to cancer genomics, proteomics, and systems biology and suggest new perspectives in targeted therapy and precision medicine.

  19. Multi-OMICs and Genome Editing Perspectives on Liver Cancer Signaling Networks

    Science.gov (United States)

    Lin, Shengda; Yin, Yi A.; Jiang, Xiaoqian; Sahni, Nidhi; Yi, Song

    2016-01-01

    The advent of the human genome sequence and the resulting ~20,000 genes provide a crucial framework for a transition from traditional biology to an integrative “OMICs” arena (Lander et al., 2001; Venter et al., 2001; Kitano, 2002). This brings in a revolution for cancer research, which now enters a big data era. In the past decade, with the facilitation by next-generation sequencing, there have been a huge number of large-scale sequencing efforts, such as The Cancer Genome Atlas (TCGA), the HapMap, and the 1000 genomes project. As a result, a deluge of genomic information becomes available from patients stricken by a variety of cancer types. The list of cancer-associated genes is ever expanding. New discoveries are made on how frequent and highly penetrant mutations, such as those in the telomerase reverse transcriptase (TERT) and TP53, function in cancer initiation, progression, and metastasis. Most genes with relatively frequent but weakly penetrant cancer mutations still remain to be characterized. In addition, genes that harbor rare but highly penetrant cancer-associated mutations continue to emerge. Here, we review recent advances related to cancer genomics, proteomics, and systems biology and suggest new perspectives in targeted therapy and precision medicine. PMID:27403431

  20. Integrated analysis of the miRNA, gene and pathway regulatory network in gastric cancer.

    Science.gov (United States)

    Zhang, Haiyang; Qu, Yanjun; Duan, Jingjing; Deng, Ting; Liu, Rui; Zhang, Le; Bai, Ming; Li, Jialu; Zhou, Likun; Ning, Tao; Li, Hongli; Ge, Shaohua; Li, Hua; Ying, Guoguang; Huang, Dingzhi; Ba, Yi

    2016-02-01

    Gastric cancer is one of the most common malignant tumors worldwide; however, the efficacy of clinical treatment is limited. MicroRNAs (miRNAs) are a class of small non-coding RNAs that have been reported to play a key role in the development of cancer. They also provide novel candidates for targeted therapy. To date, in-depth studies on the molecular mechanisms of gastric cancer involving miRNAs are still absent. We previously reported that 5 miRNAs were identified as being significantly increased in gastric cancer, and the role of these miRNAs was investigated in the present study. By using bioinformatics tools, we found that more than 4,000 unique genes are potential downstream targets of gastric cancer miRNAs, and these targets belong to the protein class of nucleic acid binding, transcription factor, enzyme modulator, transferase and receptor. Pathway mapping showed that the targets of gastric cancer miRNAs are involved in the MAPK signaling pathway, pathways in cancer, the PI3K-Akt signaling pathway, the HTLV-1 signaling pathway and Ras signaling pathway, thus regulating cell growth, differentiation, apoptosis and metastasis. Analysis of the pathways related to miRNAs may provides potential drug targets for future therapy of gastric cancer.

  1. Diabetes mellitus and cancer risk in a network of case-control studies.

    Science.gov (United States)

    Bosetti, Cristina; Rosato, Valentina; Polesel, Jerry; Levi, Fabio; Talamini, Renato; Montella, Maurizio; Negri, Eva; Tavani, Alessandra; Zucchetto, Antonella; Franceschi, Silvia; Corrao, Giovanni; La Vecchia, Carlo

    2012-01-01

    Diabetes has been associated to the risk of a few cancer sites, though quantification of this association in various populations remains open to discussion. We analyzed the relation between diabetes and the risk of various cancers in an integrated series of case-control studies conducted in Italy and Switzerland between 1991 and 2009. The studies included 1,468 oral and pharyngeal, 505 esophageal, 230 gastric, 2,390 colorectal, 185 liver, 326 pancreatic, 852 laryngeal, 3,034 breast, 607 endometrial, 1,031 ovarian, 1,294 prostate, and 767 renal cell cancer cases and 12,060 hospital controls. The multivariate odds ratios (OR) for subjects with diabetes as compared to those without-adjusted for major identified confounding factors for the cancers considered through logistic regression models-were significantly elevated for cancers of the oral cavity/pharynx (OR = 1.58), esophagus (OR = 2.52), colorectum (OR = 1.23), liver (OR = 3.52), pancreas (OR = 3.32), postmenopausal breast (OR = 1.76), and endometrium (OR = 1.70). For cancers of the oral cavity, esophagus, colorectum, liver, and postmenopausal breast, the excess risk persisted over 10 yr since diagnosis of diabetes. Our data confirm and further quantify the association of diabetes with colorectal, liver, pancreatic, postmenopausal breast, and endometrial cancer and suggest forthe first time that diabetes may also increase the risk of oral/pharyngeal and esophageal cancer.

  2. Transcriptional networks controlling breast cancer metastasis : molecular mechanisms shaping the SOX4 response

    NARCIS (Netherlands)

    Vervoort, S.J.

    2015-01-01

    Breast cancer is the most commonly diagnosed cancer in women. Despite great improvements in diagnosis and treatment of this disease, mortality remains high due to the development of metastatic disease resulting in clinical relapse. The majority of current treatment options primarily target the prima

  3. Global characterization of signalling networks associated with tamoxifen resistance in breast cancer

    DEFF Research Database (Denmark)

    Browne, Brigid C.; Hochgräfe, Falko; Wu, Jianmin;

    2013-01-01

    Acquired resistance to the anti‐estrogen tamoxifen remains a significant challenge in breast cancer management. In this study, we used an integrative approach to characterize global protein expression and tyrosine phosphorylation events in tamoxifen‐resistant MCF7 breast cancer cells (TamR) compa...

  4. German second-opinion network for testicular cancer: Sealing the leaky pipe between evidence and clinical practice

    Science.gov (United States)

    ZENGERLING, FRIEDEMANN; HARTMANN, MICHAEL; HEIDENREICH, AXEL; KREGE, SUSANNE; ALBERS, PETER; KARL, ALEXANDER; WEISSBACH, LOTHAR; WAGNER, WALTER; BEDKE, JENS; RETZ, MARGITTA; SCHMELZ, HANS U.; KLIESCH, SABINE; KUCZYK, MARKUS; WINTER, EVA; POTTEK, TOBIAS; DIECKMANN, KLAUS-PETER; SCHRADER, ANDRES JAN; SCHRADER, MARK

    2014-01-01

    In 2006, the German Testicular Cancer Study Group initiated an extensive evidence-based national second-opinion network to improve the care of testicular cancer patients. The primary aims were to reflect the current state of testicular cancer treatment in Germany and to analyze the project’s effect on the quality of care delivered to testicular cancer patients. A freely available internet-based platform was developed for the exchange of data between the urologists seeking advice and the 31 second-opinion givers. After providing all data relevant to the primary treatment decision, urologists received a second opinion on their therapy plan within <48 h. Endpoints were congruence between the first and second opinion, conformity of applied therapy with the corresponding recommendation and progression-free survival rate of the introduced patients. Significance was determined by two-sided Pearson’s χ2 test. A total of 1,284 second-opinion requests were submitted from November 2006 to October 2011, and 926 of these cases were eligible for further analysis. A discrepancy was found between first and second opinion in 39.5% of the cases. Discrepant second opinions led to less extensive treatment in 28.1% and to more extensive treatment in 15.6%. Patients treated within the framework of the second-opinion project had an overall 2-year progression-free survival rate of 90.4%. Approximately every 6th second opinion led to a relevant change in therapy. Despite the lack of financial incentives, data from every 8th testicular cancer patient in Germany were submitted to second-opinion centers. Second-opinion centers can help to improve the implementation of evidence into clinical practice. PMID:24788853

  5. Cdc42EP3/BORG2 and Septin Network Enables Mechano-transduction and the Emergence of Cancer-Associated Fibroblasts

    Directory of Open Access Journals (Sweden)

    Fernando Calvo

    2015-12-01

    Full Text Available Cancer-associated fibroblasts (CAFs are non-cancerous cells found in solid tumors that remodel the tumor matrix and promote cancer invasion and angiogenesis. Here, we demonstrate that Cdc42EP3/BORG2 is required for the matrix remodeling, invasion, angiogenesis, and tumor-growth-promoting abilities of CAFs. Cdc42EP3 functions by coordinating the actin and septin networks. Furthermore, depletion of SEPT2 has similar effects to those of loss of Cdc42EP3, indicating a role for the septin network in the tumor stroma. Cdc42EP3 is upregulated early in fibroblast activation and precedes the emergence of the highly contractile phenotype characteristic of CAFs. Depletion of Cdc42EP3 in normal fibroblasts prevents their activation by cancer cells. We propose that Cdc42EP3 sensitizes fibroblasts to further cues—in particular, those activating actomyosin contractility—and thereby enables the generation of the pathological activated fibroblast state.

  6. An evaluation of the Head and Neck Cancer Patient Concerns Inventory across the Merseyside and Cheshire Network.

    Science.gov (United States)

    Rogers, Simon N; Lowe, Derek

    2014-09-01

    The Patient Concerns Inventory (PCI-H&N) is a carefully designed 57-item checklist specifically for use in routine follow-up clinics for patients with head and neck cancer. Although developmental work at one hospital has been very positive, its use had not been evaluated across a wider network. The aim of this project was to evaluate use of the inventory across the Merseyside and Cheshire cancer network. Patients from 5 hospitals were included and 66 patients, 8 doctors, and 6 nurse specialists took part. Almost all patients found the inventory easy or very easy to complete and it caused no notable problems with the running of appointments. Two-thirds felt that all or most of the items mentioned were talked about in the consultations and no patient felt that the consultation had been made worse. Two-thirds felt that it had helped them communicate with the doctor, while some felt that communication was already excellent and beyond improvement. Only a small minority (12%) thought that it could or definitely would lead to disappointment because needs might not be met. Most patients definitely wanted to continue using the inventory and only 5% definitely did not. Most of the doctors and specialist nurses saw its potential benefit in clinical practice. However, some practical, administrative, and educational aspects need to be addressed before it can be used more widely. It is likely that the inventory will be incorporated into practice at each clinic and locality in different ways.

  7. Integrated analyses identify a master microRNA regulatory network for the mesenchymal subtype in serous ovarian cancer.

    Science.gov (United States)

    Yang, Da; Sun, Yan; Hu, Limei; Zheng, Hong; Ji, Ping; Pecot, Chad V; Zhao, Yanrui; Reynolds, Sheila; Cheng, Hanyin; Rupaimoole, Rajesha; Cogdell, David; Nykter, Matti; Broaddus, Russell; Rodriguez-Aguayo, Cristian; Lopez-Berestein, Gabriel; Liu, Jinsong; Shmulevich, Ilya; Sood, Anil K; Chen, Kexin; Zhang, Wei

    2013-02-11

    Integrated genomic analyses revealed a miRNA-regulatory network that further defined a robust integrated mesenchymal subtype associated with poor overall survival in 459 cases of serous ovarian cancer (OvCa) from The Cancer Genome Atlas and 560 cases from independent cohorts. Eight key miRNAs, including miR-506, miR-141, and miR-200a, were predicted to regulate 89% of the targets in this network. Follow-up functional experiments illustrate that miR-506 augmented E-cadherin expression, inhibited cell migration and invasion, and prevented TGFβ-induced epithelial-mesenchymal transition by targeting SNAI2, a transcriptional repressor of E-cadherin. In human OvCa, miR-506 expression was correlated with decreased SNAI2 and VIM, elevated E-cadherin, and beneficial prognosis. Nanoparticle delivery of miR-506 in orthotopic OvCa mouse models led to E-cadherin induction and reduced tumor growth.

  8. The use and abuse of religious beliefs in dividing and conquering between socially marginalized groups: the same-sex marriage debate.

    Science.gov (United States)

    Greene, Beverly

    2009-11-01

    This article discusses the use and abuse of religious beliefs and their role in divide-and-conquer strategies. Divide-and-conquer strategies are engaged to disrupt potential coalitions between and among marginalized group members, specifically sexual minority groups and people of color. Tensions between these groups have been exacerbated by the debate on same-sex marriage and comparisons between the discriminatory treatment of each group. A component of this discussion includes a brief exploration of one of the historical abuses of religious doctrine used to legitimize the marginalization of people of color and sexual minorities in the United States. For African Americans, one form of marginalization was reflected in criminalizing interracial marriage, and for members of sexual minority groups, a form of marginalization is denying group members the right to marry. The author also explores culturally competent and respectful disciplinary and clinical responses to religiously derived prejudice against sexual minority group members and people of color and discusses the implications for multicultural discourse. Copyright 2009 by the American Psychological Association

  9. ErbB2-Driven Breast Cancer Cell Invasion Depends on a Complex Signaling Network Activating Myeloid Zinc Finger-1-Dependent Cathepsin B Expression

    DEFF Research Database (Denmark)

    Rafn, Bo; Nielsen, Christian Thomas Friberg; Andersen, Sofie Hagel;

    2012-01-01

    signaling network activates the transcription of cathepsin B gene (CTSB) via myeloid zinc finger-1 transcription factor that binds to an ErbB2-responsive enhancer element in the first intron of CTSB. This work provides a model system for ErbB2-induced breast cancer cell invasiveness, reveals a signaling...... network that is crucial for invasion in vitro, and defines a specific role and targets for the identified serine-threonine kinases....

  10. Characteristics of cancer patients presenting to an integrative medicine practice-based research network.

    Science.gov (United States)

    Edman, Joel S; Roberts, Rhonda S; Dusek, Jeffery A; Dolor, Rowena; Wolever, Ruth Q; Abrams, Donald I

    2014-09-01

    To assess psychosocial characteristics, symptoms and reasons for seeking integrative medicine (IM) care in cancer patients presenting to IM clinical practices. A survey of 3940 patients was conducted at 8 IM sites. Patient reported outcome measures were collected and clinicians provided health status data. This analysis compares 353 participants self-identified as cancer patients with the larger noncancer cohort. Mean age of the cancer cohort was 55.0 years. Participants were predominantly white (85.9%), female (76.4%), and well educated (80.5% completed college). For 15.2% of cancer patients, depression scores were consistent with depressive symptoms, and average scores for perceived stress were higher than normal, but neither were significantly different from noncancer patients. The most prevalent comorbid symptoms were chronic pain (39.8%), fatigue (33.5%), and insomnia (23.3%). In the cancer cohort, perceived stress was significantly associated with depression, fatigue, insomnia, pain, and QOL. Cancer patients who chose an IM clinical practice "seeking healthcare settings that address spirituality as an aspect of care" had significantly higher levels of perceived stress, depression, and pain than those not selecting this reason. Demographic characteristics, depression scores, perceived stress scores, and reasons for seeking integrative cancer care were not significantly different between cancer patients and noncancer patients. Perceived stress may be an important indicator of QOL. The association of perceived stress, depression and pain with seeking spirituality suggests that providing IM interventions, such as effective stress management techniques and pastoral or spiritual counseling, may be helpful to patients living with cancer. © The Author(s) 2014.

  11. Aurora-A controls cancer cell radio- and chemoresistance via ATM/Chk2-mediated DNA repair networks.

    Science.gov (United States)

    Sun, Huizhen; Wang, Yan; Wang, Ziliang; Meng, Jiao; Qi, Zihao; Yang, Gong

    2014-05-01

    High expression of Aurora kinase A (Aurora-A) has been found to confer cancer cell radio- and chemoresistance, however, the underlying mechanism is unclear. In this study, by using Aurora-A cDNA/shRNA or the specific inhibitor VX680, we show that Aurora-A upregulates cell proliferation, cell cycle progression, and anchorage-independent growth to enhance cell resistance to cisplatin and X-ray irradiation through dysregulation of DNA damage repair networks. Mechanistic studies showed that Aurora-A promoted the expression of ATM/Chk2, but suppressed the expression of BRCA1/2, ATR/Chk1, p53, pp53 (Ser15), H2AX, γH2AX (Ser319), and RAD51. Aurora-A inhibited the focus formation of γH2AX in response to ionizing irradiation. Treatment of cells overexpressing Aurora-A and ATM/Chk2 with the ATM specific inhibitor KU-55933 increased the cell sensitivity to cisplatin and irradiation through increasing the phosphorylation of p53 at Ser15 and inhibiting the expression of Chk2, γH2AX (Ser319), and RAD51. Further study revealed that BRCA1/2 counteracted the function of Aurora-A to suppress the expression of ATM/Chk2, but to activate the expression of ATR/Chk1, pp53, γH2AX, and RAD51, leading to the enhanced cell sensitivity to irradiation and cisplatin, which was also supported by the results from animal assays. Thus, our data provide strong evidences that Aurora-A and BRCA1/2 inversely control the sensitivity of cancer cells to radio- and chemotherapy through the ATM/Chk2-mediated DNA repair networks, indicating that the DNA repair molecules including ATM/Chk2 may be considered for the targeted therapy against cancers with overexpression of Aurora-A.

  12. Integrative identification of deregulated miRNA/TF-mediated gene regulatory loops and networks in prostate cancer.

    Science.gov (United States)

    Afshar, Ali Sobhi; Xu, Joseph; Goutsias, John

    2014-01-01

    MicroRNAs (miRNAs) have attracted a great deal of attention in biology and medicine. It has been hypothesized that miRNAs interact with transcription factors (TFs) in a coordinated fashion to play key roles in regulating signaling and transcriptional pathways and in achieving robust gene regulation. Here, we propose a novel integrative computational method to infer certain types of deregulated miRNA-mediated regulatory circuits at the transcriptional, post-transcriptional and signaling levels. To reliably predict miRNA-target interactions from mRNA/miRNA expression data, our method collectively utilizes sequence-based miRNA-target predictions obtained from several algorithms, known information about mRNA and miRNA targets of TFs available in existing databases, certain molecular structures identified to be statistically over-represented in gene regulatory networks, available molecular subtyping information, and state-of-the-art statistical techniques to appropriately constrain the underlying analysis. In this way, the method exploits almost every aspect of extractable information in the expression data. We apply our procedure on mRNA/miRNA expression data from prostate tumor and normal samples and detect numerous known and novel miRNA-mediated deregulated loops and networks in prostate cancer. We also demonstrate instances of the results in a number of distinct biological settings, which are known to play crucial roles in prostate and other types of cancer. Our findings show that the proposed computational method can be used to effectively achieve notable insights into the poorly understood molecular mechanisms of miRNA-mediated interactions and dissect their functional roles in cancer in an effort to pave the way for miRNA-based therapeutics in clinical settings.

  13. Convolutional neural network based deep-learning architecture for prostate cancer detection on multiparametric magnetic resonance images

    Science.gov (United States)

    Tsehay, Yohannes K.; Lay, Nathan S.; Roth, Holger R.; Wang, Xiaosong; Kwak, Jin Tae; Turkbey, Baris I.; Pinto, Peter A.; Wood, Brad J.; Summers, Ronald M.

    2017-03-01

    Prostate cancer (PCa) is the second most common cause of cancer related deaths in men. Multiparametric MRI (mpMRI) is the most accurate imaging method for PCa detection; however, it requires the expertise of experienced radiologists leading to inconsistency across readers of varying experience. To increase inter-reader agreement and sensitivity, we developed a computer-aided detection (CAD) system that can automatically detect lesions on mpMRI that readers can use as a reference. We investigated a convolutional neural network based deep-learing (DCNN) architecture to find an improved solution for PCa detection on mpMRI. We adopted a network architecture from a state-of-the-art edge detector that takes an image as an input and produces an image probability map. Two-fold cross validation along with a receiver operating characteristic (ROC) analysis and free-response ROC (FROC) were used to determine our deep-learning based prostate-CAD's (CADDL) performance. The efficacy was compared to an existing prostate CAD system that is based on hand-crafted features, which was evaluated on the same test-set. CADDL had an 86% detection rate at 20% false-positive rate while the top-down learning CAD had 80% detection rate at the same false-positive rate, which translated to 94% and 85% detection rate at 10 false-positives per patient on the FROC. A CNN based CAD is able to detect cancerous lesions on mpMRI of the prostate with results comparable to an existing prostate-CAD showing potential for further development.

  14. [Awareness about medical expenses and certifications of eligibility for limited health insurance payments for chemotherapy among clinicians at the Ehime Cancer Care Network Priority Hospitals (Ehime Cancer Kyoten Hospitals)].

    Science.gov (United States)

    Yakushijin, Yoshihiro; Morita, Junko; Yano, Takuya; Matsuhisa, Tetsuaki; Kawazoe, Hitoshi; Kojima, You; Okada, Kenzou; Kamei, Haruhito; Hara, Masamichi; Fujii, Motohiro; Matsuno, Takeshi; Tanimizu, Masahito; Shinkai, Tetsu

    2014-05-01

    The "Cancer Chemotherapy and its Management" subcommittee at the Ehime Cancer Care Network Priority Hospitals (Ehime Cancer Kyoten Hospitals)with a focus on medical expenses associated with chemotherapy, surveyed awareness among 98 clinicians regarding certifications of eligibility for Limited Health Insurance Payments during cancer treatment. This committee also lists social and clinical problems encountered at the Ehime Cancer Care Network Priority Hospitals. In our survey, 78% of clinicians were consulted about medical expenses associated with chemotherapy and were actively involved in resolving medical expense problems and resulting correspondences. However, only 38% of clinicians could explain the details of the Japanese guideline on the catastrophic cap and the certifications of eligibility for Limited Health Insurance Payments. This knowledge deficit was more pronounced in younger residents. From our analyses of the awareness about medical expenses among clinicians, we recommend the establishment of the following systems for the management of cancer patients. First, establish a reporting system and early consultation on the catastrophic cap and the certifications of eligibility before initiating cancer treatment. Second, education regarding medical expenses should be mandatory for clinicians, especially for young residents. Third, patients with cancer suffering in the interval of the medical expense and the social system should be relieved with new systems.

  15. Efficacy and Safety of HER2-Targeted Agents for Breast Cancer with HER2-Overexpression: A Network Meta-Analysis.

    Directory of Open Access Journals (Sweden)

    Qiuyan Yu

    Full Text Available Clinical trials of human epidermal growth factor receptor 2 (HER2-targeted agents added to standard treatment have been efficacious for HER2-positive (HER2+ advanced breast cancer. To our knowledge, no meta-analysis has evaluated HER2-targeted therapy including trastuzumab emtansine (T-DM1 and pertuzumab for HER2-positive breast caner and ranked the targeted treatments. We performed a network meta-analysis of both direct and indirect comparisons to evaluate the effect of adding HER2-targeted agents to standard treatment and examined side effects.We performed a Bayesian-framework network meta-analysis of randomized controlled trials to compare 6 HER2-targeted treatment regimens and 1 naïve standard treatment (NST, without any-targeted drugs in targeted treatment of HER2+ breast cancer in adults. These treatment regimens were T-DM1, LC (lapatinib, HC (trastuzumab, PEC (pertuzumab, LHC (lapatinib and trastuzumab, and PEHC (pertuzumab and trastuzumab. The main outcomes were overall survival and response rates. We also examined side effects of rash, LVEF (left ventricular ejection fraction, fatigue, and gastrointestinal disorders, and performed subgroup analysis for the different treatment regimens in metastatic or advanced breast cancer.We identified 25 articles of 21 trials, with data for 11,276 participants. T-DM1 and PEHC were more efficient drug regimens with regard to overall survival as compared with LHC, LC, HC and PEC. The incidence of treatment-related rash occurs more frequently in the patients who received LC treatment regimen than PEHC and T-DM1 and HC. In subgroup analysis, T-DM1 was associated with increased overall survival as compared with LC and HC. PEHC was associated with increased overall response as compared with LC, HC, and NST.Overall, the regimen of T-DM1 as well as pertuzumab in combination with trastuzumab and docetaxel is efficacious with fewer side effects as compared with other regimens, especially for advanced HER2

  16. Antithetical NFATc1-Sox2 and p53-miR200 signaling networks govern pancreatic cancer cell plasticity.

    Science.gov (United States)

    Singh, Shiv K; Chen, Nai-Ming; Hessmann, Elisabeth; Siveke, Jens; Lahmann, Marlen; Singh, Garima; Voelker, Nadine; Vogt, Sophia; Esposito, Irene; Schmidt, Ansgar; Brendel, Cornelia; Stiewe, Thorsten; Gaedcke, Jochen; Mernberger, Marco; Crawford, Howard C; Bamlet, William R; Zhang, Jin-San; Li, Xiao-Kun; Smyrk, Thomas C; Billadeau, Daniel D; Hebrok, Matthias; Neesse, Albrecht; Koenig, Alexander; Ellenrieder, Volker

    2015-02-12

    In adaptation to oncogenic signals, pancreatic ductal adenocarcinoma (PDAC) cells undergo epithelial-mesenchymal transition (EMT), a process combining tumor cell dedifferentiation with acquisition of stemness features. However, the mechanisms linking oncogene-induced signaling pathways with EMT and stemness remain largely elusive. Here, we uncover the inflammation-induced transcription factor NFATc1 as a central regulator of pancreatic cancer cell plasticity. In particular, we show that NFATc1 drives EMT reprogramming and maintains pancreatic cancer cells in a stem cell-like state through Sox2-dependent transcription of EMT and stemness factors. Intriguingly, NFATc1-Sox2 complex-mediated PDAC dedifferentiation and progression is opposed by antithetical p53-miR200c signaling, and inactivation of the tumor suppressor pathway is essential for tumor dedifferentiation and dissemination both in genetically engineered mouse models (GEMM) and human PDAC. Based on these findings, we propose the existence of a hierarchical signaling network regulating PDAC cell plasticity and suggest that the molecular decision between epithelial cell preservation and conversion into a dedifferentiated cancer stem cell-like phenotype depends on opposing levels of p53 and NFATc1 signaling activities.

  17. Application of gene expression programming and neural networks to predict adverse events of radical hysterectomy in cervical cancer patients.

    Science.gov (United States)

    Kusy, Maciej; Obrzut, Bogdan; Kluska, Jacek

    2013-12-01

    The aim of this article was to compare gene expression programming (GEP) method with three types of neural networks in the prediction of adverse events of radical hysterectomy in cervical cancer patients. One-hundred and seven patients treated by radical hysterectomy were analyzed. Each record representing a single patient consisted of 10 parameters. The occurrence and lack of perioperative complications imposed a two-class classification problem. In the simulations, GEP algorithm was compared to a multilayer perceptron (MLP), a radial basis function network neural, and a probabilistic neural network. The generalization ability of the models was assessed on the basis of their accuracy, the sensitivity, the specificity, and the area under the receiver operating characteristic curve (AUROC). The GEP classifier provided best results in the prediction of the adverse events with the accuracy of 71.96 %. Comparable but slightly worse outcomes were obtained using MLP, i.e., 71.87 %. For each of measured indices: accuracy, sensitivity, specificity, and the AUROC, the standard deviation was the smallest for the models generated by GEP classifier.

  18. Privacy Practices of Health Social Networking Sites: Implications for Privacy and Data Security in Online Cancer Communities.

    Science.gov (United States)

    Charbonneau, Deborah H

    2016-08-01

    While online communities for social support continue to grow, little is known about the state of privacy practices of health social networking sites. This article reports on a structured content analysis of privacy policies and disclosure practices for 25 online ovarian cancer communities. All of the health social networking sites in the study sample provided privacy statements to users, yet privacy practices varied considerably across the sites. The majority of sites informed users that personal information was collected about participants and shared with third parties (96%, n = 24). Furthermore, more than half of the sites (56%, n = 14) stated that cookies technology was used to track user behaviors. Despite these disclosures, only 36% (n = 9) offered opt-out choices for sharing data with third parties. In addition, very few of the sites (28%, n = 7) allowed individuals to delete their personal information. Discussions about specific security measures used to protect personal information were largely missing. Implications for privacy, confidentiality, consumer choice, and data safety in online environments are discussed. Overall, nurses and other health professionals can utilize these findings to encourage individuals seeking online support and participating in social networking sites to build awareness of privacy risks to better protect their personal health information in the digital age.

  19. Divide and Conquer.

    Science.gov (United States)

    Ollerton, Richard; And Others

    1996-01-01

    Presents activities related to some obscure tests for divisibility, which teachers may wish to develop as illustrative examples in the classroom, or as extension activities for groups of students. Begins with an exploration of divisibility by three, then discusses application of the technique to other numbers, and for numbers written in other…

  20. Dividing and Conquering Biostatistics

    Science.gov (United States)

    Walker, James K.; Weiner, Myron

    1977-01-01

    An approach to the teaching of biostatistics is discussed that is presented in a multistage fashion beginning with a core of knowledge in a separate course but rapidly integrated with other basic science and clinical courses in the pharmacy curriculum. Biostatistics is thus seen as more interesting and relevant to the student. (LBH)

  1. Dividing and Conquering Biostatistics

    Science.gov (United States)

    Walker, James K.; Weiner, Myron

    1977-01-01

    An approach to the teaching of biostatistics is discussed that is presented in a multistage fashion beginning with a core of knowledge in a separate course but rapidly integrated with other basic science and clinical courses in the pharmacy curriculum. Biostatistics is thus seen as more interesting and relevant to the student. (LBH)

  2. [Improving the management of rare brain cancers with the POLA network].

    Science.gov (United States)

    Terziev, Robert; Ravin, Mylène; Carpentier, Catherine; Dehais, Caroline

    2014-04-01

    The national POLA network is dedicated to the management of certain rare brain tumours, mainly anaplastic oligodendrogliomas, anaplastic oligoastrocytomas and glioblastomas with oligodendroglioma component. The nursing team and the patient are at the heart of the organisation.

  3. VEGF, HIF-1α expression and MVD as an angiogenic network in familial breast cancer.

    Science.gov (United States)

    Saponaro, Concetta; Malfettone, Andrea; Ranieri, Girolamo; Danza, Katia; Simone, Giovanni; Paradiso, Angelo; Mangia, Anita

    2013-01-01

    Angiogenesis, which plays an important role in tumor growth and progression of breast cancer, is regulated by a balance between pro- and anti-angiogenic factors. Expression of vascular endothelial growth factor (VEGF) is up-regulated during hypoxia by hypoxia-inducible factor-1α (HIF-1α). It is known that there is an interaction between HIF-1α and BRCA1 carrier cancers, but little has been reported about angiogenesis in BRCA1-2 carrier and BRCAX breast cancers. In this study, we investigated the expression of VEGF and HIF-1α and microvessel density (MVD) in 26 BRCA1-2 carriers and 58 BRCAX compared to 77 sporadic breast cancers, by immunohistochemistry. VEGF expression in BRCA1-2 carriers was higher than in BRCAX cancer tissues (p = 0.0001). Furthermore, VEGF expression was higher in both BRCA1-2 carriers and BRCAX than the sporadic group (p<0.0001). VEGF immunoreactivity was correlated with poor tumor grade (p = 0.0074), hormone receptors negativity (p = 0.0206, p = 0.0002 respectively), and MIB-1-labeling index (p = 0.0044) in familial cancers (BRCA1-2 and BRCAX). The percentage of nuclear HIF-1α expression was higher in the BRCA1-2 carriers than in BRCAX cancers (p<0.05), and in all familial than in sporadic tumor tissues (p = 0.0045). A higher MVD was observed in BRCA1-2 carrier than in BRCAX and sporadic cancer tissues (p = 0.002, p = 0.0001 respectively), and in all familial tumors than in sporadic tumors (p = 0.01). MVD was positively related to HIF-1α expression in BRCA1-2 carriers (r = 0.521, p = 0.006), and, in particular, we observed a highly significant correlation in the familial group (r = 0.421, p<0.0001). Our findings suggest that angiogenesis plays a crucial role in BRCA1-2 carrier breast cancers. Prospective studies in larger BRCA1-2 carrier series are needed to improve the best therapeutic strategies for this subgroup of breast cancer patients.

  4. Immune modulation by a cellular network of mesenchymal stem cells and breast cancer cell subsets: Implication for cancer therapy.

    Science.gov (United States)

    Eltoukhy, Hussam S; Sinha, Garima; Moore, Caitlyn A; Sandiford, Oleta A; Rameshwar, Pranela

    2017-08-01

    The immune modulatory properties of mesenchymal stem cells (MSCs) are mostly controlled by the particular microenvironment. Cancer stem cells (CSCs), which can initiate a clinical tumor, have been the subject of intense research. This review article discusses investigative studies of the roles of MSCs on cancer biology including on CSCs, and the potential as drug delivery to tumors. An understanding of how MSCs behave in the tumor microenvironment to facilitate the survival of tumor cells would be crucial to identify drug targets. More importantly, since CSCs survive for decades in dormancy for later resurgence, studies are presented to show how MSCs could be involved in maintaining dormancy. Although the mechanism by which CSCs survive is complex, this article focus on the cellular involvement of MSCs with regard to immune responses. We discuss the immunomodulatory mechanisms of MSC-CSC interaction in the context of therapeutic outcomes in oncology. We also discuss immunotherapy as a potential to circumventing this immune modulation. Copyright © 2017 Elsevier Inc. All rights reserved.

  5. Analysis of the molecular networks in androgen dependent and independent prostate cancer revealed fragile and robust subsystems.

    Directory of Open Access Journals (Sweden)

    Ryan Tasseff

    Full Text Available Androgen ablation therapy is currently the primary treatment for metastatic prostate cancer. Unfortunately, in nearly all cases, androgen ablation fails to permanently arrest cancer progression. As androgens like testosterone are withdrawn, prostate cancer cells lose their androgen sensitivity and begin to proliferate without hormone growth factors. In this study, we constructed and analyzed a mathematical model of the integration between hormone growth factor signaling, androgen receptor activation, and the expression of cyclin D and Prostate-Specific Antigen in human LNCaP prostate adenocarcinoma cells. The objective of the study was to investigate which signaling systems were important in the loss of androgen dependence. The model was formulated as a set of ordinary differential equations which described 212 species and 384 interactions, including both the mRNA and protein levels for key species. An ensemble approach was chosen to constrain model parameters and to estimate the impact of parametric uncertainty on model predictions. Model parameters were identified using 14 steady-state and dynamic LNCaP data sets taken from literature sources. Alterations in the rate of Prostatic Acid Phosphatase expression was sufficient to capture varying levels of androgen dependence. Analysis of the model provided insight into the importance of network components as a function of androgen dependence. The importance of androgen receptor availability and the MAPK/Akt signaling axes was independent of androgen status. Interestingly, androgen receptor availability was important even in androgen-independent LNCaP cells. Translation became progressively more important in androgen-independent LNCaP cells. Further analysis suggested a positive synergy between the MAPK and Akt signaling axes and the translation of key proliferative markers like cyclin D in androgen-independent cells. Taken together, the results support the targeting of both the Akt and MAPK

  6. Construction and analysis of regulatory genetic networks in cervical cancer based on involved microRNAs, target genes, transcription factors and host genes.

    Science.gov (United States)

    Wang, Ning; Xu, Zhiwen; Wang, Kunhao; Zhu, Minghui; Li, Yang

    2014-04-01

    Over recent years, genes and microRNA (miRNA/miR) have been considered as key biological factors in human carcinogenesis. During cancer development, genes may act as multiple identities, including target genes of miRNA, transcription factors and host genes. The present study concentrated on the regulatory networks consisting of the biological factors involved in cervical cancer in order to investigate their features and affect on this specific pathology. Numerous raw data was collected and organized into purposeful structures, and adaptive procedures were defined for application to the prepared data. The networks were therefore built with the factors as basic components according to their interacting associations. The networks were constructed at three levels of interdependency, including a differentially-expressed network, a related network and a global network. Comparisons and analyses were made at a systematic level rather than from an isolated gene or miRNA. Critical hubs were extracted in the core networks and notable features were discussed, including self-adaption feedback regulation. The present study expounds the pathogenesis from a novel point of view and is proposed to provide inspiration for further investigation and therapy.

  7. Discovering biomarkers from gene expression data for predicting cancer subgroups using neural networks and relational fuzzy clustering

    Directory of Open Access Journals (Sweden)

    Sharma Animesh

    2007-01-01

    Full Text Available Abstract Background The four heterogeneous childhood cancers, neuroblastoma, non-Hodgkin lymphoma, rhabdomyosarcoma, and Ewing sarcoma present a similar histology of small round blue cell tumor (SRBCT and thus often leads to misdiagnosis. Identification of biomarkers for distinguishing these cancers is a well studied problem. Existing methods typically evaluate each gene separately and do not take into account the nonlinear interaction between genes and the tools that are used to design the diagnostic prediction system. Consequently, more genes are usually identified as necessary for prediction. We propose a general scheme for finding a small set of biomarkers to design a diagnostic system for accurate classification of the cancer subgroups. We use multilayer networks with online gene selection ability and relational fuzzy clustering to identify a small set of biomarkers for accurate classification of the training and blind test cases of a well studied data set. Results Our method discerned just seven biomarkers that precisely categorized the four subgroups of cancer both in training and blind samples. For the same problem, others suggested 19–94 genes. These seven biomarkers include three novel genes (NAB2, LSP1 and EHD1 – not identified by others with distinct class-specific signatures and important role in cancer biology, including cellular proliferation, transendothelial migration and trafficking of MHC class antigens. Interestingly, NAB2 is downregulated in other tumors including Non-Hodgkin lymphoma and Neuroblastoma but we observed moderate to high upregulation in a few cases of Ewing sarcoma and Rabhdomyosarcoma, suggesting that NAB2 might be mutated in these tumors. These genes can discover the subgroups correctly with unsupervised learning, can differentiate non-SRBCT samples and they perform equally well with other machine learning tools including support vector machines. These biomarkers lead to four simple human interpretable

  8. Reducing disparities in breast cancer survival: a Columbia University and Avon Breast Cancer Research and Care Network Symposium.

    Science.gov (United States)

    Antman, Karen; Abraido-Lanza, Ana F; Blum, Diane; Brownfield, Erica; Cicatelli, Barbara; Debor, Mary Dale; Emmons, Karen; Fitzgibbon, Marian; Gapstur, Susan M; Gradishar, William; Hiatt, Robert A; Hubbell, F Allan; Joe, Andrew K; Klassen, Ann C; Lee, Nancy C; Linden, Hannah M; McMullin, Juliet; Mishra, Shiraz I; Neuhaus, Charlotte; Olopade, Funmi I; Walas, Kathleen

    2002-10-01

    On November 8th, 2001, faculty from Universities, government and non-profit community organizations met to determine how, separately and together, they could address disparities in survival of women with breast cancer in the diverse patient populations served by their institutions. Studies and initiatives directed at increasing access had to date met modest success. The day was divided into three sections, defining the issues, model programs, government initiatives and finally potential collaborations. By publishing these proceedings, interested readers will be aware of the ongoing programs and studies and can contact the investigators for more information. The Avon Foundation funded this symposium to bring together interested investigators to share programmatic experiences, data and innovative approaches to the problem.

  9. Targeting the insulin-like growth factor network in cancer therapy.

    Science.gov (United States)

    Heidegger, Isabel; Pircher, Andreas; Klocker, Helmut; Massoner, Petra

    2011-04-15

    During the last decades, changes in the insulin-like growth factor (IGF) signaling have been related to the pathogenesis of cancer. Therefore, IGFs became highly attractive therapeutic cancer targets. Several drugs including monoclonal antibodies (mAB), small molecule tyrosine kinase inhibitors (RTKIs), anti-sense oligonucleotids (ASOs) and IGF-binding proteins (IGFBPs) targeting the IGF axis were developed. With over 60 ongoing clinical trials, the IGF1 receptor (IGF1R) is currently one of the most studied molecular targets in the field of oncology. In this review, we provide an overview on the IGF axis, its signaling pathways and its significance in neoplasia. We critically review the preclinical and clinical studies investigating the role of IGF1R as a cancer target and discuss preliminary results and possible limitations.

  10. Diagnostic Classification of Normal Persons and Cancer Patients by Using Neural Network Based on Trace Metal Contents in Serum Samples

    Institute of Scientific and Technical Information of China (English)

    ZHANG; Zhuo-yong

    2001-01-01

    [1]Miatto, O. , Casaril, M. , Gabriell, G. B. , et al. , Cancer, 55, 774(1985)[2]Margalioth, E. J., Udassin, R., Maor, J. , et al. , Cancer, 56, 856(1986)[3]Xu, B., Chinese Journal of Tumor, 12, 512(1990)[4]Jayadeep, A. , Raveendran, P. K. , Kannan, S. , et al. , J. Exp. Clin. Cancer Res. , 16, 295 (1997)[5]Sattar, N. , Scott, H. R. , McMillan, D. C. , et al. , Nutr. Cancer, 28, 308(1997)[6]Koksoy, C. , Kavas, G. O. , Akcil, E. , et al. , Breast Cancer Res. Treat. , 45, 1(1997)[7]Leung,P. L. , Huang, H. M. , Biol. Trace Elem. Res. , 57, 19(1997)[8]Antila, E. , Mussalo-Rauhamaa, H. , Kantola, M. , et al. , Sci. Total Environ. , 186, 251(1996)[9]Tariq, M. A. , Qama-un-Nisa, Fatima, A. , Sci. Total Environ. , 175, 43(1995)[10]Martin-Lagos, F. , Navarro-Alarcon, M. , Terres-Martos, C. , et al. , Sci. Total Environ. , 204, 27(1997)[11]Poo, J. L. , Romero, R. R. , Robles, J. A. , et al. , Arch. Med. Res. , 28, 259(1997)[12]Magalova, T., Bella, V. , Brtkova, A. , et al. , Neoplasma, 46, 100(1999)[13]Ferrigno, D. , Buccheri, G. , Camilla, T. , et al. , Archives for Chest Disease, 54, 204(1999)[14]Huang, Y. L. , Sheu, J. Y. , Lin, T. H. , Clinical Biochem. , 32, 131(1999)[15]Songchitsomboon, S. , Komindr, S. , Komindr, A. , et al. , J. Med. Assoc. Thai, 82, 701(1999)[16]Mason, R. P. , Cancer, 85, 2 093(1999)[17]Wargovith, M. J. , Ed. Moon T. E. , Micozz M. S. , Calcium, Vitamin D and the Prevention of Gastrointestinal Cancer, in Nutrition and Cancer Prevention, Marcel Dekker Inc. , New York, 1989:291[18]Leung, P. L. , Li, X. L. , Li, Z. X. , et al. , Biol. Trace Elem. Res. , 42, 1(1994)[19]Jing, X. ,Han, C., Cancer Research on Prevention and Treatment, 25, 186(1998)[20]Huang, Y. , Li, J. , Carcinogenesis, Teratogenesis and Mutagenesis, 10, 123(1998)[21]Wang, X. , Zhu, E. ,Yan, X. , et al. , Acta Chimica Sinica, 51, 1 094(1993)[22]Wan, T. , Qin, S. , Zhuang, S. , et al. , Rock and Mineral

  11. MicroRNA-regulated gene networks during mammary cell differentiation are associated with breast cancer.

    Science.gov (United States)

    Aydoğdu, Eylem; Katchy, Anne; Tsouko, Efrosini; Lin, Chin-Yo; Haldosén, Lars-Arne; Helguero, Luisa; Williams, Cecilia

    2012-08-01

    MicroRNAs (miRNAs) play pivotal roles in stem cell biology, differentiation and oncogenesis and are of high interest as potential breast cancer therapeutics. However, their expression and function during normal mammary differentiation and in breast cancer remain to be elucidated. In order to identify which miRNAs are involved in mammary differentiation, we thoroughly investigated miRNA expression during functional differentiation of undifferentiated, stem cell-like, murine mammary cells using two different large-scale approaches followed by qPCR. Significant changes in expression of 21 miRNAs were observed in repeated rounds of mammary cell differentiation. The majority, including the miR-200 family and known tumor suppressor miRNAs, was upregulated during differentiation. Only four miRNAs, including oncomiR miR-17, were downregulated. Pathway analysis indicated complex interactions between regulated miRNA clusters and major pathways involved in differentiation, proliferation and stem cell maintenance. Comparisons with human breast cancer tumors showed the gene profile from the undifferentiated, stem-like stage clustered with that of poor-prognosis breast cancer. A common nominator in these groups was the E2F pathway, which was overrepresented among genes targeted by the differentiation-induced miRNAs. A subset of miRNAs could further discriminate between human non-cancer and breast cancer cell lines, and miR-200a/miR-200b, miR-146b and miR-148a were specifically downregulated in triple-negative breast cancer cells. We show that miR-200a/miR-200b can inhibit epithelial-mesenchymal transition (EMT)-characteristic morphological changes in undifferentiated, non-tumorigenic mammary cells. Our studies propose EphA2 as a novel and important target gene for miR-200a. In conclusion, we present evidentiary data on how miRNAs are involved in mammary cell differentiation and indicate their related roles in breast cancer.

  12. Comparison between artificial neural network and Cox regression model in predicting the survival rate of gastric cancer patients.

    Science.gov (United States)

    Zhu, Lucheng; Luo, Wenhua; Su, Meng; Wei, Hangping; Wei, Juan; Zhang, Xuebang; Zou, Changlin

    2013-09-01

    The aim of this study was to determine the prognostic factors and their significance in gastric cancer (GC) patients, using the artificial neural network (ANN) and Cox regression hazard (CPH) models. A retrospective analysis was undertaken, including 289 patients with GC who had undergone gastrectomy between 2006 and 2007. According to the CPH analysis, disease stage, peritoneal dissemination, radical surgery and body mass index (BMI) were selected as the significant variables. According to the ANN model, disease stage, radical surgery, serum CA19-9 levels, peritoneal dissemination and BMI were selected as the significant variables. The true prediction of the ANN was 85.3% and of the CPH model 81.9%. In conclusion, the present study demonstrated that the ANN model is a more powerful tool in determining the significant prognostic variables for GC patients, compared to the CPH model. Therefore, this model is recommended for determining the risk factors of such patients.

  13. SU-E-T-206: Improving Radiotherapy Toxicity Based On Artificial Neural Network (ANN) for Head and Neck Cancer Patients

    Energy Technology Data Exchange (ETDEWEB)

    Cho, Daniel D; Wernicke, A Gabriella; Nori, Dattatreyudu; Chao, KSC; Parashar, Bhupesh; Chang, Jenghwa [Weill Cornell Medical College, NY, NY (United States)

    2014-06-01

    Purpose/Objective(s): The aim of this study is to build the estimator of toxicity using artificial neural network (ANN) for head and neck cancer patients Materials/Methods: An ANN can combine variables into a predictive model during training and considered all possible correlations of variables. We constructed an ANN based on the data from 73 patients with advanced H and N cancer treated with external beam radiotherapy and/or chemotherapy at our institution. For the toxicity estimator we defined input data including age, sex, site, stage, pathology, status of chemo, technique of external beam radiation therapy (EBRT), length of treatment, dose of EBRT, status of post operation, length of follow-up, the status of local recurrences and distant metastasis. These data were digitized based on the significance and fed to the ANN as input nodes. We used 20 hidden nodes (for the 13 input nodes) to take care of the correlations of input nodes. For training ANN, we divided data into three subsets such as training set, validation set and test set. Finally, we built the estimator for the toxicity from ANN output. Results: We used 13 input variables including the status of local recurrences and distant metastasis and 20 hidden nodes for correlations. 59 patients for training set, 7 patients for validation set and 7 patients for test set and fed the inputs to Matlab neural network fitting tool. We trained the data within 15% of errors of outcome. In the end we have the toxicity estimation with 74% of accuracy. Conclusion: We proved in principle that ANN can be a very useful tool for predicting the RT outcomes for high risk H and N patients. Currently we are improving the results using cross validation.

  14. AUTOMATED DETECTION OF MITOTIC FIGURES IN BREAST CANCER HISTOPATHOLOGY IMAGES USING GABOR FEATURES AND DEEP NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    Maqlin Paramanandam

    2016-11-01

    Full Text Available The count of mitotic figures in Breast cancer histopathology slides is the most significant independent prognostic factor enabling determination of the proliferative activity of the tumor. In spite of the strict protocols followed, the mitotic counting activity suffers from subjectivity and considerable amount of observer variability despite being a laborious task. Interest in automated detection of mitotic figures has been rekindled with the advent of Whole Slide Scanners. Subsequently mitotic detection grand challenge contests have been held in recent years and several research methodologies developed by their participants. This paper proposes an efficient mitotic detection methodology for Hematoxylin and Eosin stained Breast cancer Histopathology Images using Gabor features and a Deep Belief Network- Deep Neural Network architecture (DBN-DNN. The proposed method has been evaluated on breast histopathology images from the publicly available dataset from MITOS contest held at the ICPR 2012 conference. It contains 226 mitoses annotated on 35 HPFs by several pathologists and 15 testing HPFs, yielding an F-measure of 0.74. In addition the said methodology was also tested on 3 slides from the MITOSIS- ATYPIA grand challenge held at the ICPR 2014 conference, an extension of MITOS containing 749 mitoses annotated on 1200 HPFs, by pathologists worldwide. This study has employed 3 slides (294 HPFs from the MITOS-ATYPIA training dataset in its evaluation and the results showed F-measures 0.65, 0.72and 0.74 for each slide. The proposed method is fast and computationally simple yet its accuracy and specificity is comparable to the best winning methods of the aforementioned grand challenges

  15. Network medicine

    DEFF Research Database (Denmark)

    Pawson, Tony; Linding, Rune

    2008-01-01

    for new therapeutic intervention. We argue that by targeting the architecture of aberrant signaling networks associated with cancer and other diseases new therapeutic strategies can be implemented. Transforming medicine into a network driven endeavour will require quantitative measurements of cell...... signaling processes; we will describe how this may be performed and combined with new algorithms to predict the trajectories taken by a cellular system either in time or through disease states. We term this approach, network medicine....

  16. Genome-wide analysis of the homeobox C6 transcriptional network in prostate cancer.

    Science.gov (United States)

    McCabe, Colleen D; Spyropoulos, Demetri D; Martin, David; Moreno, Carlos S

    2008-03-15

    Homeobox transcription factors are developmentally regulated genes that play crucial roles in tissue patterning. Homeobox C6 (HOXC6) is overexpressed in prostate cancers and correlated with cancer progression, but the downstream targets of HOXC6 are largely unknown. We have performed genome-wide localization analysis to identify promoters bound by HOXC6 in prostate cancer cells. This analysis identified 468 reproducibly bound promoters whose associated genes are involved in functions such as cell proliferation and apoptosis. We have complemented these data with expression profiling of prostates from mice with homozygous disruption of the Hoxc6 gene to identify 31 direct regulatory target genes of HOXC6. We show that HOXC6 directly regulates expression of bone morphogenic protein 7, fibroblast growth factor receptor 2, insulin-like growth factor binding protein 3, and platelet-derived growth factor receptor alpha (PDGFRA) in prostate cells and indirectly influences the Notch and Wnt signaling pathways in vivo. We further show that inhibition of PDGFRA reduces proliferation of prostate cancer cells, and that overexpression of HOXC6 can overcome the effects of PDGFRA inhibition. HOXC6 regulates genes with both oncogenic and tumor suppressor activities as well as several genes such as CD44 that are important for prostate branching morphogenesis and metastasis to the bone microenvironment.

  17. Overexpression of E2F mRNAs associated with gastric cancer progression identified by the transcription factor and miRNA co-regulatory network analysis.

    Science.gov (United States)

    Zhang, XiaoTian; Ni, ZhaoHui; Duan, ZiPeng; Xin, ZhuoYuan; Wang, HuaiDong; Tan, JiaYi; Wang, GuoQing; Li, Fan

    2015-01-01

    Gene expression is regulated at the transcription and translation levels; thus, both transcription factors (TFs) and microRNAs (miRNA) play roles in regulation of gene expression. This study profiled differentially expressed mRNAs and miRNAs in gastric cancer tissues to construct a TF and miRNA co-regulatory network in order to identify altered genes in gastric cancer progression. A total of 70 cases gastric cancer and paired adjacent normal tissues were subjected to cDNA and miRNA microarray analyses. We obtained 887 up-regulated and 93 down-regulated genes and 41 down-regulated and 4 up-regulated miRNAs in gastric cancer tissues. Using the Transcriptional Regulatory Element Database, we obtained 105 genes that are regulated by the E2F family of genes and using Targetscan, miRanda, miRDB and miRWalk tools, we predicted potential targeting genes of these 45 miRNAs. We then built up the E2F-related TF and miRNA co-regulatory gene network and identified 9 hub-genes. Furthermore, we found that levels of E2F1, 2, 3, 4, 5, and 7 mRNAs associated with gastric cancer cell invasion capacity, and has associated with tumor differentiation. These data showed Overexpression of E2F mRNAs associated with gastric cancer progression.

  18. GalNAc-T4 putatively modulates the estrogen regulatory network through FOXA1 glycosylation in human breast cancer cells.

    Science.gov (United States)

    Niang, Bachir; Jin, Liyuan; Chen, Xixi; Guo, Xiaohan; Zhang, Hongshuo; Wu, Qiong; Padhiar, Arshad Ahmed; Xiao, Min; Fang, Deyu; Zhang, Jianing

    2016-01-01

    GALNT4 belongs to a family of N-acetylgalactosaminyltransferases, which catalyze the transfer of GalNAc to Serine or Threonine residues in the initial step of mucin-type O-linked protein glycosylation. This glycosylation type is the most complex post-translational modification of proteins, playing important roles during cellular differentiation and in pathological disorders. Most of the breast cancer subtypes are estrogen receptor positive, and hence, the estrogen pathway represents a key regulatory network. We investigated the expression of GalNAc-T4 in a panel of mammary epithelial cell lines and found its expression is associated with the estrogen status of the cells. FOXA1, a key transcription factor, functions to promote estrogen responsive gene expression by acting as a cofactor to estrogen receptor alpha (ERα), but all the aspects of this regulatory mechanism are not fully explored. This study found that knockdown of GALNT4 expression in human breast cancer cells attenuated the protein expression of ERα, FOXA1, and Cyclin D1. Further, our immunoprecipitation assays depicted the possibility of FOXA1 to undergo O-GalNAc modifications with a decrease of GalNAc residues in the GALN