Novelty detection for breast cancer image classification

Science.gov (United States)

Cichosz, Pawel; Jagodziński, Dariusz; Matysiewicz, Mateusz; Neumann, Łukasz; Nowak, Robert M.; Okuniewski, Rafał; Oleszkiewicz, Witold

2016-09-01

Using classification learning algorithms for medical applications may require not only refined model creation techniques and careful unbiased model evaluation, but also detecting the risk of misclassification at the time of model application. This is addressed by novelty detection, which identifies instances for which the training set is not sufficiently representative and for which it may be safer to restrain from classification and request a human expert diagnosis. The paper investigates two techniques for isolated instance identification, based on clustering and one-class support vector machines, which represent two different approaches to multidimensional outlier detection. The prediction quality for isolated instances in breast cancer image data is evaluated using the random forest algorithm and found to be substantially inferior to the prediction quality for non-isolated instances. Each of the two techniques is then used to create a novelty detection model which can be combined with a classification model and used at the time of prediction to detect instances for which the latter cannot be reliably applied. Novelty detection is demonstrated to improve random forest prediction quality and argued to deserve further investigation in medical applications.

Influence of nuclei segmentation on breast cancer malignancy classification

Science.gov (United States)

Jelen, Lukasz; Fevens, Thomas; Krzyzak, Adam

2009-02-01

Breast Cancer is one of the most deadly cancers affecting middle-aged women. Accurate diagnosis and prognosis are crucial to reduce the high death rate. Nowadays there are numerous diagnostic tools for breast cancer diagnosis. In this paper we discuss a role of nuclear segmentation from fine needle aspiration biopsy (FNA) slides and its influence on malignancy classification. Classification of malignancy plays a very important role during the diagnosis process of breast cancer. Out of all cancer diagnostic tools, FNA slides provide the most valuable information about the cancer malignancy grade which helps to choose an appropriate treatment. This process involves assessing numerous nuclear features and therefore precise segmentation of nuclei is very important. In this work we compare three powerful segmentation approaches and test their impact on the classification of breast cancer malignancy. The studied approaches involve level set segmentation, fuzzy c-means segmentation and textural segmentation based on co-occurrence matrix. Segmented nuclei were used to extract nuclear features for malignancy classification. For classification purposes four different classifiers were trained and tested with previously extracted features. The compared classifiers are Multilayer Perceptron (MLP), Self-Organizing Maps (SOM), Principal Component-based Neural Network (PCA) and Support Vector Machines (SVM). The presented results show that level set segmentation yields the best results over the three compared approaches and leads to a good feature extraction with a lowest average error rate of 6.51% over four different classifiers. The best performance was recorded for multilayer perceptron with an error rate of 3.07% using fuzzy c-means segmentation.

A collection of annotated and harmonized human breast cancer transcriptome datasets, including immunologic classification [version 2; referees: 2 approved

Directory of Open Access Journals (Sweden)

Jessica Roelands

2018-02-01

Full Text Available The increased application of high-throughput approaches in translational research has expanded the number of publicly available data repositories. Gathering additional valuable information contained in the datasets represents a crucial opportunity in the biomedical field. To facilitate and stimulate utilization of these datasets, we have recently developed an interactive data browsing and visualization web application, the Gene Expression Browser (GXB. In this note, we describe a curated compendium of 13 public datasets on human breast cancer, representing a total of 2142 transcriptome profiles. We classified the samples according to different immune based classification systems and integrated this information into the datasets. Annotated and harmonized datasets were uploaded to GXB. Study samples were categorized in different groups based on their immunologic tumor response profiles, intrinsic molecular subtypes and multiple clinical parameters. Ranked gene lists were generated based on relevant group comparisons. In this data note, we demonstrate the utility of GXB to evaluate the expression of a gene of interest, find differential gene expression between groups and investigate potential associations between variables with a specific focus on immunologic classification in breast cancer. This interactive resource is publicly available online at: http://breastcancer.gxbsidra.org/dm3/geneBrowser/list.

IDM-PhyChm-Ens: intelligent decision-making ensemble methodology for classification of human breast cancer using physicochemical properties of amino acids.

Science.gov (United States)

Ali, Safdar; Majid, Abdul; Khan, Asifullah

2014-04-01

Development of an accurate and reliable intelligent decision-making method for the construction of cancer diagnosis system is one of the fast growing research areas of health sciences. Such decision-making system can provide adequate information for cancer diagnosis and drug discovery. Descriptors derived from physicochemical properties of protein sequences are very useful for classifying cancerous proteins. Recently, several interesting research studies have been reported on breast cancer classification. To this end, we propose the exploitation of the physicochemical properties of amino acids in protein primary sequences such as hydrophobicity (Hd) and hydrophilicity (Hb) for breast cancer classification. Hd and Hb properties of amino acids, in recent literature, are reported to be quite effective in characterizing the constituent amino acids and are used to study protein foldings, interactions, structures, and sequence-order effects. Especially, using these physicochemical properties, we observed that proline, serine, tyrosine, cysteine, arginine, and asparagine amino acids offer high discrimination between cancerous and healthy proteins. In addition, unlike traditional ensemble classification approaches, the proposed 'IDM-PhyChm-Ens' method was developed by combining the decision spaces of a specific classifier trained on different feature spaces. The different feature spaces used were amino acid composition, split amino acid composition, and pseudo amino acid composition. Consequently, we have exploited different feature spaces using Hd and Hb properties of amino acids to develop an accurate method for classification of cancerous protein sequences. We developed ensemble classifiers using diverse learning algorithms such as random forest (RF), support vector machines (SVM), and K-nearest neighbor (KNN) trained on different feature spaces. We observed that ensemble-RF, in case of cancer classification, performed better than ensemble-SVM and ensemble-KNN. Our

Clinical classification of cancer cachexia: phenotypic correlates in human skeletal muscle.

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Neil Johns

Full Text Available BACKGROUND: Cachexia affects the majority of patients with advanced cancer and is associated with a reduction in treatment tolerance, response to therapy, and duration of survival. One impediment towards the effective treatment of cachexia is a validated classification system. METHODS: 41 patients with resectable upper gastrointestinal (GI or pancreatic cancer underwent characterisation for cachexia based on weight-loss (WL and/or low muscularity (LM. Four diagnostic criteria were used >5%WL, >10%WL, LM, and LM+>2%WL. All patients underwent biopsy of the rectus muscle. Analysis included immunohistochemistry for fibre size and type, protein and nucleic acid concentration, Western blots for markers of autophagy, SMAD signalling, and inflammation. FINDINGS: Compared with non-cachectic cancer patients, patients with LM or LM+>2%WL, mean muscle fibre diameter was reduced by about 25% (p = 0.02 and p = 0.001 respectively. No significant difference in fibre diameter was observed if patients had WL alone. Regardless of classification, there was no difference in fibre number or proportion of fibre type across all myosin heavy chain isoforms. Mean muscle protein content was reduced and the ratio of RNA/DNA decreased in patients with either >5%WL or LM+>2%WL. Compared with non-cachectic patients, SMAD3 protein levels were increased in patients with >5%WL (p = 0.022 and with >10%WL, beclin (p = 0.05 and ATG5 (p = 0.01 protein levels were increased. There were no differences in phospho-NFkB or phospho-STAT3 levels across any of the groups. CONCLUSION: Muscle fibre size, biochemical composition and pathway phenotype can vary according to whether the diagnostic criteria for cachexia are based on weight loss alone, a measure of low muscularity alone or a combination of the two. For intervention trials where the primary end-point is a change in muscle mass or function, use of combined diagnostic criteria may allow identification of a more

Magnetic resonance imaging texture analysis classification of primary breast cancer

International Nuclear Information System (INIS)

Waugh, S.A.; Lerski, R.A.; Purdie, C.A.; Jordan, L.B.; Vinnicombe, S.; Martin, P.; Thompson, A.M.

2016-01-01

Patient-tailored treatments for breast cancer are based on histological and immunohistochemical (IHC) subtypes. Magnetic Resonance Imaging (MRI) texture analysis (TA) may be useful in non-invasive lesion subtype classification. Women with newly diagnosed primary breast cancer underwent pre-treatment dynamic contrast-enhanced breast MRI. TA was performed using co-occurrence matrix (COM) features, by creating a model on retrospective training data, then prospectively applying to a test set. Analyses were blinded to breast pathology. Subtype classifications were performed using a cross-validated k-nearest-neighbour (k = 3) technique, with accuracy relative to pathology assessed and receiver operator curve (AUROC) calculated. Mann-Whitney U and Kruskal-Wallis tests were used to assess raw entropy feature values. Histological subtype classifications were similar across training (n = 148 cancers) and test sets (n = 73 lesions) using all COM features (training: 75 %, AUROC = 0.816; test: 72.5 %, AUROC = 0.823). Entropy features were significantly different between lobular and ductal cancers (p < 0.001; Mann-Whitney U). IHC classifications using COM features were also similar for training and test data (training: 57.2 %, AUROC = 0.754; test: 57.0 %, AUROC = 0.750). Hormone receptor positive and negative cancers demonstrated significantly different entropy features. Entropy features alone were unable to create a robust classification model. Textural differences on contrast-enhanced MR images may reflect underlying lesion subtypes, which merits testing against treatment response. (orig.)

Magnetic resonance imaging texture analysis classification of primary breast cancer

Energy Technology Data Exchange (ETDEWEB)

Waugh, S.A.; Lerski, R.A. [Ninewells Hospital and Medical School, Department of Medical Physics, Dundee (United Kingdom); Purdie, C.A.; Jordan, L.B. [Ninewells Hospital and Medical School, Department of Pathology, Dundee (United Kingdom); Vinnicombe, S. [University of Dundee, Division of Imaging and Technology, Ninewells Hospital and Medical School, Dundee (United Kingdom); Martin, P. [Ninewells Hospital and Medical School, Department of Clinical Radiology, Dundee (United Kingdom); Thompson, A.M. [University of Texas MD Anderson Cancer Center, Department of Surgical Oncology, Houston, TX (United States)

2016-02-15

Patient-tailored treatments for breast cancer are based on histological and immunohistochemical (IHC) subtypes. Magnetic Resonance Imaging (MRI) texture analysis (TA) may be useful in non-invasive lesion subtype classification. Women with newly diagnosed primary breast cancer underwent pre-treatment dynamic contrast-enhanced breast MRI. TA was performed using co-occurrence matrix (COM) features, by creating a model on retrospective training data, then prospectively applying to a test set. Analyses were blinded to breast pathology. Subtype classifications were performed using a cross-validated k-nearest-neighbour (k = 3) technique, with accuracy relative to pathology assessed and receiver operator curve (AUROC) calculated. Mann-Whitney U and Kruskal-Wallis tests were used to assess raw entropy feature values. Histological subtype classifications were similar across training (n = 148 cancers) and test sets (n = 73 lesions) using all COM features (training: 75 %, AUROC = 0.816; test: 72.5 %, AUROC = 0.823). Entropy features were significantly different between lobular and ductal cancers (p < 0.001; Mann-Whitney U). IHC classifications using COM features were also similar for training and test data (training: 57.2 %, AUROC = 0.754; test: 57.0 %, AUROC = 0.750). Hormone receptor positive and negative cancers demonstrated significantly different entropy features. Entropy features alone were unable to create a robust classification model. Textural differences on contrast-enhanced MR images may reflect underlying lesion subtypes, which merits testing against treatment response. (orig.)

Pathological Bases for a Robust Application of Cancer Molecular Classification

Directory of Open Access Journals (Sweden)

Salvador J. Diaz-Cano

2015-04-01

Full Text Available Any robust classification system depends on its purpose and must refer to accepted standards, its strength relying on predictive values and a careful consideration of known factors that can affect its reliability. In this context, a molecular classification of human cancer must refer to the current gold standard (histological classification and try to improve it with key prognosticators for metastatic potential, staging and grading. Although organ-specific examples have been published based on proteomics, transcriptomics and genomics evaluations, the most popular approach uses gene expression analysis as a direct correlate of cellular differentiation, which represents the key feature of the histological classification. RNA is a labile molecule that varies significantly according with the preservation protocol, its transcription reflect the adaptation of the tumor cells to the microenvironment, it can be passed through mechanisms of intercellular transference of genetic information (exosomes, and it is exposed to epigenetic modifications. More robust classifications should be based on stable molecules, at the genetic level represented by DNA to improve reliability, and its analysis must deal with the concept of intratumoral heterogeneity, which is at the origin of tumor progression and is the byproduct of the selection process during the clonal expansion and progression of neoplasms. The simultaneous analysis of multiple DNA targets and next generation sequencing offer the best practical approach for an analytical genomic classification of tumors.

Involvement of Machine Learning for Breast Cancer Image Classification: A Survey.

Science.gov (United States)

Nahid, Abdullah-Al; Kong, Yinan

2017-01-01

Breast cancer is one of the largest causes of women's death in the world today. Advance engineering of natural image classification techniques and Artificial Intelligence methods has largely been used for the breast-image classification task. The involvement of digital image classification allows the doctor and the physicians a second opinion, and it saves the doctors' and physicians' time. Despite the various publications on breast image classification, very few review papers are available which provide a detailed description of breast cancer image classification techniques, feature extraction and selection procedures, classification measuring parameterizations, and image classification findings. We have put a special emphasis on the Convolutional Neural Network (CNN) method for breast image classification. Along with the CNN method we have also described the involvement of the conventional Neural Network (NN), Logic Based classifiers such as the Random Forest (RF) algorithm, Support Vector Machines (SVM), Bayesian methods, and a few of the semisupervised and unsupervised methods which have been used for breast image classification.

Involvement of Machine Learning for Breast Cancer Image Classification: A Survey

Directory of Open Access Journals (Sweden)

Abdullah-Al Nahid

2017-01-01

Full Text Available Breast cancer is one of the largest causes of women’s death in the world today. Advance engineering of natural image classification techniques and Artificial Intelligence methods has largely been used for the breast-image classification task. The involvement of digital image classification allows the doctor and the physicians a second opinion, and it saves the doctors’ and physicians’ time. Despite the various publications on breast image classification, very few review papers are available which provide a detailed description of breast cancer image classification techniques, feature extraction and selection procedures, classification measuring parameterizations, and image classification findings. We have put a special emphasis on the Convolutional Neural Network (CNN method for breast image classification. Along with the CNN method we have also described the involvement of the conventional Neural Network (NN, Logic Based classifiers such as the Random Forest (RF algorithm, Support Vector Machines (SVM, Bayesian methods, and a few of the semisupervised and unsupervised methods which have been used for breast image classification.

Classification system for reporting events involving human malfunctions

DEFF Research Database (Denmark)

Rasmussen, Jens; Pedersen, O.M.; Mancini, G.

1981-01-01

The report describes a set of categories for reporting indus-trial incidents and events involving human malfunction. The classification system aims at ensuring information adequate for improvement of human work situations and man-machine interface systems and for attempts to quantify "human error......" rates. The classification system has a multifacetted non-hierarchical struc-ture and its compatibility with Isprals ERDS classification is described. The collection of the information in general and for quantification purposes are discussed. 24 categories, 12 of which being human factors oriented......, are listed with their respective subcategories, and comments are given. Underlying models of human data processes and their typical malfunc-tions and of a human decision sequence are described....

Classification system for reporting events involving human malfunctions

International Nuclear Information System (INIS)

Rasmussen, J.; Pedersen, O.M.; Mancini, G.; Carnino, A.; Griffon, M.; Gagnolet, P.

1981-03-01

The report describes a set of categories for reporting industrial incidents and events involving human malfunction. The classification system aims at ensuring information adequate for improvement of human work situations and man-machine interface systems and for attempts to quantify ''human error'' rates. The classification system has a multifacetted non-hierarchial structure and its compatibility with Ispra's ERDS classification is described. The collection of the information in general and for quantification purposes are discussed. 24 categories, 12 of which being human factors oriented, are listed with their respective subcategories, and comments are given. Underlying models of human data processes and their typical malfunctions and of a human decision sequence are described. (author)

Association between gastric cancer and the Kyoto classification of gastritis.

Science.gov (United States)

Shichijo, Satoki; Hirata, Yoshihiro; Niikura, Ryota; Hayakawa, Yoku; Yamada, Atsuo; Koike, Kazuhiko

2017-09-01

Histological gastritis is associated with gastric cancer, but its diagnosis requires biopsy. Many classifications of endoscopic gastritis are available, but not all are useful for risk stratification of gastric cancer. The Kyoto Classification of Gastritis was proposed at the 85th Congress of the Japan Gastroenterological Endoscopy Society. This cross-sectional study evaluated the usefulness of the Kyoto Classification of Gastritis for risk stratification of gastric cancer. From August 2013 to September 2014, esophagogastroduodenoscopy was performed and the gastric findings evaluated according to the Kyoto Classification of Gastritis in a total of 4062 patients. The following five endoscopic findings were selected based on previous reports: atrophy, intestinal metaplasia, enlarged folds, nodularity, and diffuse redness. A total of 3392 patients (1746 [51%] men and 1646 [49%] women) were analyzed. Among them, 107 gastric cancers were diagnosed. Atrophy was found in 2585 (78%) and intestinal metaplasia in 924 (27%). Enlarged folds, nodularity, and diffuse redness were found in 197 (5.8%), 22 (0.6%), and 573 (17%), respectively. In univariate analyses, the severity of atrophy, intestinal metaplasia, diffuse redness, age, and male sex were associated with gastric cancer. In a multivariate analysis, atrophy and male sex were found to be independent risk factors. Younger age and severe atrophy were determined to be associated with diffuse-type gastric cancer. Endoscopic detection of atrophy was associated with the risk of gastric cancer. Thus, patients with severe atrophy should be examined carefully and may require intensive follow-up. © 2017 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.

Classification system for reporting events involving human malfunctions

International Nuclear Information System (INIS)

Rasmussen, J.; Pedersen, O.M.; Mancini, G.

1981-01-01

The report describes a set of categories for reporting industrial incidents and events involving human malfunction. The classification system aims at ensuring information adequate for improvement of human work situations and man-machine interface systems and for attempts to quantify ''human error'' rates. The classification system has a multifacetted non-hierarchical structure and its compatibility with Ispra's ERDS classification is described. The collection of the information in general and for quantification purposes are discussed. 24 categories, 12 of which being human factors-oriented, are listed with their respective subcategories, and comments are given. Underlying models of human data process and their typical malfuntions and of a human decision sequence are described. The work reported is a joint contribution to the CSNI Group of Experts on Human Error Data and Assessment

Human papilloma virus in oral cancer.

Science.gov (United States)

Kim, Soung Min

2016-12-01

Cervical cancer is the second most prevalent cancer among women, and it arises from cells that originate in the cervix uteri. Among several causes of cervical malignancies, infection with some types of human papilloma virus (HPV) is well known to be the greatest cervical cancer risk factor. Over 150 subtypes of HPV have been identified; more than 40 types of HPVs are typically transmitted through sexual contact and infect the anogenital region and oral cavity. The recently introduced vaccine for HPV infection is effective against certain subtypes of HPV that are associated with cervical cancer, genital warts, and some less common cancers, including oropharyngeal cancer. Two HPV vaccines, quadrivalent and bivalent types that use virus-like particles (VLPs), are currently used in the medical commercial market. While the value of HPV vaccination for oral cancer prevention is still controversial, some evidence supports the possibility that HPV vaccination may be effective in reducing the incidence of oral cancer. This paper reviews HPV-related pathogenesis in cancer, covering HPV structure and classification, trends in worldwide applications of HPV vaccines, effectiveness and complications of HPV vaccination, and the relationship of HPV with oral cancer prevalence.

An enhanced topologically significant directed random walk in cancer classification using gene expression datasets

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Choon Sen Seah

2017-12-01

Full Text Available Microarray technology has become one of the elementary tools for researchers to study the genome of organisms. As the complexity and heterogeneity of cancer is being increasingly appreciated through genomic analysis, cancerous classification is an emerging important trend. Significant directed random walk is proposed as one of the cancerous classification approach which have higher sensitivity of risk gene prediction and higher accuracy of cancer classification. In this paper, the methodology and material used for the experiment are presented. Tuning parameter selection method and weight as parameter are applied in proposed approach. Gene expression dataset is used as the input datasets while pathway dataset is used to build a directed graph, as reference datasets, to complete the bias process in random walk approach. In addition, we demonstrate that our approach can improve sensitive predictions with higher accuracy and biological meaningful classification result. Comparison result takes place between significant directed random walk and directed random walk to show the improvement in term of sensitivity of prediction and accuracy of cancer classification.

Three-class classification in computer-aided diagnosis of breast cancer by support vector machine

Science.gov (United States)

Sun, Xuejun; Qian, Wei; Song, Dansheng

2004-05-01

Design of classifier in computer-aided diagnosis (CAD) scheme of breast cancer plays important role to its overall performance in sensitivity and specificity. Classification of a detected object as malignant lesion, benign lesion, or normal tissue on mammogram is a typical three-class pattern recognition problem. This paper presents a three-class classification approach by using two-stage classifier combined with support vector machine (SVM) learning algorithm for classification of breast cancer on mammograms. The first classification stage is used to detect abnormal areas and normal breast tissues, and the second stage is for classification of malignant or benign in detected abnormal objects. A series of spatial, morphology and texture features have been extracted on detected objects areas. By using genetic algorithm (GA), different feature groups for different stage classification have been investigated. Computerized free-response receiver operating characteristic (FROC) and receiver operating characteristic (ROC) analyses have been employed in different classification stages. Results have shown that obvious performance improvement in both sensitivity and specificity was observed through proposed classification approach compared with conventional two-class classification approaches, indicating its effectiveness in classification of breast cancer on mammograms.

Evolving cancer classification in the era of personalized medicine: A primer for radiologists

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O' Neill, Alibhe C.; Jagannathan, Jyothi P.; Ramaiya, Nikhil H. [Dept. of of Imaging, Dana Farber Cancer Institute, Boston (United States)

2017-01-15

Traditionally tumors were classified based on anatomic location but now specific genetic mutations in cancers are leading to treatment of tumors with molecular targeted therapies. This has led to a paradigm shift in the classification and treatment of cancer. Tumors treated with molecular targeted therapies often show morphological changes rather than change in size and are associated with class specific and drug specific toxicities, different from those encountered with conventional chemotherapeutic agents. It is important for the radiologists to be familiar with the new cancer classification and the various treatment strategies employed, in order to effectively communicate and participate in the multi-disciplinary care. In this paper we will focus on lung cancer as a prototype of the new molecular classification.

Mimicking human texture classification

NARCIS (Netherlands)

Rogowitz, B.E.; van Rikxoort, Eva M.; van den Broek, Egon; Pappas, T.N.; Schouten, Theo E.; Daly, S.J.

2005-01-01

In an attempt to mimic human (colorful) texture classification by a clustering algorithm three lines of research have been encountered, in which as test set 180 texture images (both their color and gray-scale equivalent) were drawn from the OuTex and VisTex databases. First, a k-means algorithm was

A Classification of Human-to-Human Communication during the Use of Immersive Teleoperation Interfaces

DEFF Research Database (Denmark)

Kraus, Martin; Kibsgaard, Martin

2015-01-01

We propose a new classification of the human-to-human communication during the use of immersive teleoperation interfaces based on real-life examples. While a large body of research is concerned with communication in collaborative virtual environments (CVEs), less research focuses on cases where...... of the proposed classification to real environments can offer useful reference cases. Using this extended classification not only allows us to discuss and understand differences and similarities of various forms of communication in a more systematic way, but it also provides guidelines and reference cases...... for the design of immersive teleoperation interfaces that support human-to-human communication....

Classification of cancerous cells based on the one-class problem approach

Science.gov (United States)

Murshed, Nabeel A.; Bortolozzi, Flavio; Sabourin, Robert

1996-03-01

One of the most important factors in reducing the effect of cancerous diseases is the early diagnosis, which requires a good and a robust method. With the advancement of computer technologies and digital image processing, the development of a computer-based system has become feasible. In this paper, we introduce a new approach for the detection of cancerous cells. This approach is based on the one-class problem approach, through which the classification system need only be trained with patterns of cancerous cells. This reduces the burden of the training task by about 50%. Based on this approach, a computer-based classification system is developed, based on the Fuzzy ARTMAP neural networks. Experimental results were performed using a set of 542 patterns taken from a sample of breast cancer. Results of the experiment show 98% correct identification of cancerous cells and 95% correct identification of non-cancerous cells.

Prognostic classifications of lymph node involvement in lung cancer and current International Association for the Study of Lung Cancer descriptive classification in zones.

Science.gov (United States)

Riquet, Marc; Arame, Alex; Foucault, Christophe; Le Pimpec Barthes, Françoise

2010-09-01

The lymphatic drainage of solid organ tumors crosses through the lymph nodes (LNs) whose tumoral involvement may still be considered as local disease. Concerning lung cancer, LN involvement may be intrapulmonary (N1), and mediastinal and/or extra-thoracic. More than 30 years ago, mediastinal involved LNs were all considered as N2, and outside the scope of surgery. In 1978, Naruke presented an original article entitled 'Lymph node mapping and curability at various levels of metastasis in resected lung cancer', demonstrating that N2 was not a contraindication to surgery in all patients. The map permitted to localize the favorable N2 on the lung cancer ipsilateral side of the mediastinum. Several maps ensued aiming to discriminate between right and left involvement (1983), and to distinguish N2 (ipsilateral) and N3 (contralateral) mediastinal LN involvement (1983, 1986). The last map (1997 regional LN classification) was recently replaced by a descriptive classification in anatomical zones. This new LN map of the TNM classification for lung cancer is a step toward using anatomical view points which might be the best way to better understand lung cancer lymphatic spread. Nowadays, the LNs are easily identified by current radiological imaging, and their resectability may be anticipated. Each LN chain may be removed by en-bloc lymphadenectomy performed during radical lung resection, a safe procedure which seems to be more oncological based than sampling, and which avoids the source of discrepancies pointed out during the labeling of LN stations by surgeons.

Involvement of Machine Learning for Breast Cancer Image Classification: A Survey

OpenAIRE

Nahid, Abdullah-Al; Kong, Yinan

2017-01-01

Breast cancer is one of the largest causes of women’s death in the world today. Advance engineering of natural image classification techniques and Artificial Intelligence methods has largely been used for the breast-image classification task. The involvement of digital image classification allows the doctor and the physicians a second opinion, and it saves the doctors’ and physicians’ time. Despite the various publications on breast image classification, very few review papers are available w...

Cancer Classification Based on Support Vector Machine Optimized by Particle Swarm Optimization and Artificial Bee Colony.

Science.gov (United States)

Gao, Lingyun; Ye, Mingquan; Wu, Changrong

2017-11-29

Intelligent optimization algorithms have advantages in dealing with complex nonlinear problems accompanied by good flexibility and adaptability. In this paper, the FCBF (Fast Correlation-Based Feature selection) method is used to filter irrelevant and redundant features in order to improve the quality of cancer classification. Then, we perform classification based on SVM (Support Vector Machine) optimized by PSO (Particle Swarm Optimization) combined with ABC (Artificial Bee Colony) approaches, which is represented as PA-SVM. The proposed PA-SVM method is applied to nine cancer datasets, including five datasets of outcome prediction and a protein dataset of ovarian cancer. By comparison with other classification methods, the results demonstrate the effectiveness and the robustness of the proposed PA-SVM method in handling various types of data for cancer classification.

KITENIN is associated with tumor progression in human gastric cancer.

Science.gov (United States)

Ryu, Ho-Seong; Park, Young-Lan; Park, Su-Jin; Lee, Ji-Hee; Cho, Sung-Bum; Lee, Wan-Sik; Chung, Ik-Joo; Kim, Kyung-Keun; Lee, Kyung-Hwa; Kweon, Sun-Seog; Joo, Young-Eun

2010-09-01

KAI1 COOH-terminal interacting tetraspanin (KITENIN) promotes tumor cell migration, invasion and metastasis in colon, bladder, head and neck cancer. The aims of current study were to evaluate whether KITENIN affects tumor cell behavior in human gastric cancer cell line and to document the expression of KITENIN in a well-defined series of gastric tumors, including complete long-term follow-up, with special reference to patient prognosis. To evaluate the impact of KITENIN knockdown on behavior of a human gastric cancer cell line, AGS, migration, invasion and proliferation assays using small-interfering RNA were performed. The expression of activator protein-1 (AP-1) target genes and AP-1 transcriptional activity were evaluated by reverse transcription-polymerase chain reaction (RT-PCR) and luciferase reporter assay. The expression of KITENIN and AP-1 target genes by RT-PCR and Western blotting or immunohistochemistry was also investigated in human gastric cancer tissues. The knockdown of KITENIN suppressed tumor cell migration, invasion and proliferation in AGS cells. The mRNA expression of matrix metalloproteinase-1 (MMP-1), MMP-3, cyclooxygenase-2 (COX-2), and CD44 was reduced by knockdown of KITENIN in AGS. AP-1 transcriptional activity was significantly decreased by knockdown of KITENIN in AGS cells. KITENIN expression was significantly increased in human cancer tissues at RNA and protein levels. Expression of MMP-1, MMP-3, COX-2 and CD44 were significantly increased in human gastric cancer tissues. Immunostaining of KITENIN was predominantly identified in the cytoplasm of cancer cells. Expression of KITENIN was significantly associated with tumor size, Lauren classification, depth of invasion, lymph node metastasis, tumor stage and poor survival. These results indicate that KITENIN plays an important role in human gastric cancer progression by AP-1 activation.

Efficacy of the Kyoto Classification of Gastritis in Identifying Patients at High Risk for Gastric Cancer.

Science.gov (United States)

Sugimoto, Mitsushige; Ban, Hiromitsu; Ichikawa, Hitomi; Sahara, Shu; Otsuka, Taketo; Inatomi, Osamu; Bamba, Shigeki; Furuta, Takahisa; Andoh, Akira

2017-01-01

Objective The Kyoto gastritis classification categorizes the endoscopic characteristics of Helicobacter pylori (H. pylori) infection-associated gastritis and identifies patterns associated with a high risk of gastric cancer. We investigated its efficacy, comparing scores in patients with H. pylori-associated gastritis and with gastric cancer. Methods A total of 1,200 patients with H. pylori-positive gastritis alone (n=932), early-stage H. pylori-positive gastric cancer (n=189), and successfully treated H. pylori-negative cancer (n=79) were endoscopically graded according to the Kyoto gastritis classification for atrophy, intestinal metaplasia, fold hypertrophy, nodularity, and diffuse redness. Results The prevalence of O-II/O-III-type atrophy according to the Kimura-Takemoto classification in early-stage H. pylori-positive gastric cancer and successfully treated H. pylori-negative cancer groups was 45.1%, which was significantly higher than in subjects with gastritis alone (12.7%, pgastritis scores of atrophy and intestinal metaplasia in the H. pylori-positive cancer group were significantly higher than in subjects with gastritis alone (all pgastritis classification may thus be useful for detecting these patients.

Using fuzzy association rule mining in cancer classification

International Nuclear Information System (INIS)

Mahmoodian, Hamid; Marhaban, M.H.; Abdulrahim, Raha; Rosli, Rozita; Saripan, Iqbal

2011-01-01

Full text: The classification of the cancer tumors based on gene expression profiles has been extensively studied in numbers of studies. A wide variety of cancer datasets have been implemented by the various methods of gene selec tion and classification to identify the behavior of the genes in tumors and find the relationships between them and outcome of diseases. Interpretability of the model, which is developed by fuzzy rules and linguistic variables in this study, has been rarely considered. In addition, creating a fuzzy classifier with high performance in classification that uses a subset of significant genes which have been selected by different types of gene selection methods is another goal of this study. A new algorithm has been developed to identify the fuzzy rules and significant genes based on fuzzy association rule mining. At first, different subset of genes which have been selected by different methods, were used to generate primary fuzzy classifiers separately and then proposed algorithm was implemented to mix the genes which have been associated in the primary classifiers and generate a new classifier. The results show that fuzzy classifier can classify the tumors with high performance while presenting the relationships between the genes by linguistic variables

Zone-specific logistic regression models improve classification of prostate cancer on multi-parametric MRI

Energy Technology Data Exchange (ETDEWEB)

Dikaios, Nikolaos; Halligan, Steve; Taylor, Stuart; Atkinson, David; Punwani, Shonit [University College London, Centre for Medical Imaging, London (United Kingdom); University College London Hospital, Departments of Radiology, London (United Kingdom); Alkalbani, Jokha; Sidhu, Harbir Singh [University College London, Centre for Medical Imaging, London (United Kingdom); Abd-Alazeez, Mohamed; Ahmed, Hashim U.; Emberton, Mark [University College London, Research Department of Urology, Division of Surgery and Interventional Science, London (United Kingdom); Kirkham, Alex [University College London Hospital, Departments of Radiology, London (United Kingdom); Freeman, Alex [University College London Hospital, Department of Histopathology, London (United Kingdom)

2015-09-15

To assess the interchangeability of zone-specific (peripheral-zone (PZ) and transition-zone (TZ)) multiparametric-MRI (mp-MRI) logistic-regression (LR) models for classification of prostate cancer. Two hundred and thirty-one patients (70 TZ training-cohort; 76 PZ training-cohort; 85 TZ temporal validation-cohort) underwent mp-MRI and transperineal-template-prostate-mapping biopsy. PZ and TZ uni/multi-variate mp-MRI LR-models for classification of significant cancer (any cancer-core-length (CCL) with Gleason > 3 + 3 or any grade with CCL ≥ 4 mm) were derived from the respective cohorts and validated within the same zone by leave-one-out analysis. Inter-zonal performance was tested by applying TZ models to the PZ training-cohort and vice-versa. Classification performance of TZ models for TZ cancer was further assessed in the TZ validation-cohort. ROC area-under-curve (ROC-AUC) analysis was used to compare models. The univariate parameters with the best classification performance were the normalised T2 signal (T2nSI) within the TZ (ROC-AUC = 0.77) and normalized early contrast-enhanced T1 signal (DCE-nSI) within the PZ (ROC-AUC = 0.79). Performance was not significantly improved by bi-variate/tri-variate modelling. PZ models that contained DCE-nSI performed poorly in classification of TZ cancer. The TZ model based solely on maximum-enhancement poorly classified PZ cancer. LR-models dependent on DCE-MRI parameters alone are not interchangeable between prostatic zones; however, models based exclusively on T2 and/or ADC are more robust for inter-zonal application. (orig.)

A protein and mRNA expression-based classification of gastric cancer.

Science.gov (United States)

Setia, Namrata; Agoston, Agoston T; Han, Hye S; Mullen, John T; Duda, Dan G; Clark, Jeffrey W; Deshpande, Vikram; Mino-Kenudson, Mari; Srivastava, Amitabh; Lennerz, Jochen K; Hong, Theodore S; Kwak, Eunice L; Lauwers, Gregory Y

2016-07-01

The overall survival of gastric carcinoma patients remains poor despite improved control over known risk factors and surveillance. This highlights the need for new classifications, driven towards identification of potential therapeutic targets. Using sophisticated molecular technologies and analysis, three groups recently provided genetic and epigenetic molecular classifications of gastric cancer (The Cancer Genome Atlas, 'Singapore-Duke' study, and Asian Cancer Research Group). Suggested by these classifications, here, we examined the expression of 14 biomarkers in a cohort of 146 gastric adenocarcinomas and performed unsupervised hierarchical clustering analysis using less expensive and widely available immunohistochemistry and in situ hybridization. Ultimately, we identified five groups of gastric cancers based on Epstein-Barr virus (EBV) positivity, microsatellite instability, aberrant E-cadherin, and p53 expression; the remaining cases constituted a group characterized by normal p53 expression. In addition, the five categories correspond to the reported molecular subgroups by virtue of clinicopathologic features. Furthermore, evaluation between these clusters and survival using the Cox proportional hazards model showed a trend for superior survival in the EBV and microsatellite-instable related adenocarcinomas. In conclusion, we offer as a proposal a simplified algorithm that is able to reproduce the recently proposed molecular subgroups of gastric adenocarcinoma, using immunohistochemical and in situ hybridization techniques.

Convolutional Neural Network Achieves Human-level Accuracy in Music Genre Classification

OpenAIRE

Dong, Mingwen

2018-01-01

Music genre classification is one example of content-based analysis of music signals. Traditionally, human-engineered features were used to automatize this task and 61% accuracy has been achieved in the 10-genre classification. However, it's still below the 70% accuracy that humans could achieve in the same task. Here, we propose a new method that combines knowledge of human perception study in music genre classification and the neurophysiology of the auditory system. The method works by trai...

Classification of mitocans, anti-cancer drugs acting on mitochondria

Czech Academy of Sciences Publication Activity Database

Neužil, Jiří; Dong, L. F.; Rohlena, Jakub; Truksa, Jaroslav; Ralph, S. J.

2013-01-01

Roč. 13, č. 3 (2013), s. 199-208 ISSN 1567-7249 Institutional research plan: CEZ:AV0Z50520701 Keywords : Mitocans * Anti-cancer therapeutics * Classification Subject RIV: EB - Genetics ; Molecular Biology Impact factor: 3.524, year: 2013

Cancer Feature Selection and Classification Using a Binary Quantum-Behaved Particle Swarm Optimization and Support Vector Machine

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Maolong Xi

2016-01-01

Full Text Available This paper focuses on the feature gene selection for cancer classification, which employs an optimization algorithm to select a subset of the genes. We propose a binary quantum-behaved particle swarm optimization (BQPSO for cancer feature gene selection, coupling support vector machine (SVM for cancer classification. First, the proposed BQPSO algorithm is described, which is a discretized version of original QPSO for binary 0-1 optimization problems. Then, we present the principle and procedure for cancer feature gene selection and cancer classification based on BQPSO and SVM with leave-one-out cross validation (LOOCV. Finally, the BQPSO coupling SVM (BQPSO/SVM, binary PSO coupling SVM (BPSO/SVM, and genetic algorithm coupling SVM (GA/SVM are tested for feature gene selection and cancer classification on five microarray data sets, namely, Leukemia, Prostate, Colon, Lung, and Lymphoma. The experimental results show that BQPSO/SVM has significant advantages in accuracy, robustness, and the number of feature genes selected compared with the other two algorithms.

Cancer Feature Selection and Classification Using a Binary Quantum-Behaved Particle Swarm Optimization and Support Vector Machine

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Sun, Jun; Liu, Li; Fan, Fangyun; Wu, Xiaojun

2016-01-01

This paper focuses on the feature gene selection for cancer classification, which employs an optimization algorithm to select a subset of the genes. We propose a binary quantum-behaved particle swarm optimization (BQPSO) for cancer feature gene selection, coupling support vector machine (SVM) for cancer classification. First, the proposed BQPSO algorithm is described, which is a discretized version of original QPSO for binary 0-1 optimization problems. Then, we present the principle and procedure for cancer feature gene selection and cancer classification based on BQPSO and SVM with leave-one-out cross validation (LOOCV). Finally, the BQPSO coupling SVM (BQPSO/SVM), binary PSO coupling SVM (BPSO/SVM), and genetic algorithm coupling SVM (GA/SVM) are tested for feature gene selection and cancer classification on five microarray data sets, namely, Leukemia, Prostate, Colon, Lung, and Lymphoma. The experimental results show that BQPSO/SVM has significant advantages in accuracy, robustness, and the number of feature genes selected compared with the other two algorithms. PMID:27642363

Ultrasonographic characteristics and BI-RADS-US classification of BRCA1 mutation-associated breast cancer in Guangxi, China.

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Li, Cheng; Liu, Junjie; Wang, Sida; Chen, Yuanyuan; Yuan, Zhigang; Zeng, Jian; Li, Zhixian

2015-01-01

To retrospectively analyze and compare the ultrasonographic characteristics and BI-RADS-US classification between patients with BRCA1 mutation-associated breast cancer and those without BRCA1 gene mutation in Guangxi, China. The study was performed in 36 lesions from 34 BRCA1 mutation-associated breast cancer patients. A total of 422 lesions from 422 breast cancer patients without BRCA1 mutations served as control group. The comparison of the ultrasonographic features and BI-RADS-US classification between two the groups were reviewed. More complex inner echo was disclosed in BRCA1 mutation-associated breast cancer patients (x(2) = 4.741, P = 0.029). The BI-RADS classification of BRCA1 mutation-associated breast cancer was lower (U = 6094.0, P = 0.022). BRCA1 mutation-associated breast cancer frequently displays as microlobulated margin and complex echo. It also shows more benign characteristics in morphology, and the BI-RADS classification is prone to be underestimated.

Two-Stage Classification Approach for Human Detection in Camera Video in Bulk Ports

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Mi Chao

2015-09-01

Full Text Available With the development of automation in ports, the video surveillance systems with automated human detection begun to be applied in open-air handling operation areas for safety and security. The accuracy of traditional human detection based on the video camera is not high enough to meet the requirements of operation surveillance. One of the key reasons is that Histograms of Oriented Gradients (HOG features of the human body will show great different between front & back standing (F&B and side standing (Side human body. Therefore, the final training for classifier will only gain a few useful specific features which have contribution to classification and are insufficient to support effective classification, while using the HOG features directly extracted by the samples from different human postures. This paper proposes a two-stage classification method to improve the accuracy of human detection. In the first stage, during preprocessing classification, images is mainly divided into possible F&B human body and not F&B human body, and then they were put into the second-stage classification among side human and non-human recognition. The experimental results in Tianjin port show that the two-stage classifier can improve the classification accuracy of human detection obviously.

Classification of mitocans, anti-cancer drugs acting on mitochondria

Czech Academy of Sciences Publication Activity Database

Neužil, Jiří; Dong, L. F.; Rohlena, Jakub; Truksa, Jaroslav; Ralph, S. J.

2013-01-01

Roč. 13, č. 3 (2013), s. 199-208 ISSN 1567-7249 Institutional research plan: CEZ:AV0Z50520701 Keywords : Mitocans * Anti-cancer therapeutics * Classification Subject RIV: EB - Gene tics ; Molecular Biology Impact factor: 3.524, year: 2013

EMT is the dominant program in human colon cancer

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Tollenaar Rob AEM

2011-01-01

Full Text Available Abstract Background Colon cancer has been classically described by clinicopathologic features that permit the prediction of outcome only after surgical resection and staging. Methods We performed an unsupervised analysis of microarray data from 326 colon cancers to identify the first principal component (PC1 of the most variable set of genes. PC1 deciphered two primary, intrinsic molecular subtypes of colon cancer that predicted disease progression and recurrence. Results Here we report that the most dominant pattern of intrinsic gene expression in colon cancer (PC1 was tightly correlated (Pearson R = 0.92, P -135 with the EMT signature-- both in gene identity and directionality. In a global micro-RNA screen, we further identified the most anti-correlated microRNA with PC1 as MiR200, known to regulate EMT. Conclusions These data demonstrate that the biology underpinning the native, molecular classification of human colon cancer--previously thought to be highly heterogeneous-- was clarified through the lens of comprehensive transcriptome analysis.

Significance and Application of Digital Breast Tomosynthesis for the BI-RADS Classification of Breast Cancer.

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Cai, Si-Qing; Yan, Jian-Xiang; Chen, Qing-Shi; Huang, Mei-Ling; Cai, Dong-Lu

2015-01-01

Full-field digital mammography (FFDM) with dense breasts has a high rate of missed diagnosis, and digital breast tomosynthesis (DBT) could reduce organization overlapping and provide more reliable images for BI-RADS classification. This study aims to explore application of COMBO (FFDM+DBT) for effect and significance of BI-RADS classification of breast cancer. In this study, we selected 832 patients who had been treated from May 2013 to November 2013. Classify FFDM and COMBO examination according to BI-RADS separately and compare the differences for glands in the image of the same patient in judgment, mass characteristics display and indirect signs. Employ Paired Wilcoxon rank sum test was used in 79 breast cancer patients to find differences between two examine methods. The results indicated that COMBO pattern is able to observe more details in distribution of glands when estimating content. Paired Wilcoxon rank sum test showed that overall classification level of COMBO is higher significantly compared to FFDM to BI-RADS diagnosis and classification of breast (PBI-RADS classification in breast cancer in clinical.

[New molecular classification of colorectal cancer, pancreatic cancer and stomach cancer: Towards "à la carte" treatment?].

Science.gov (United States)

Dreyer, Chantal; Afchain, Pauline; Trouilloud, Isabelle; André, Thierry

2016-01-01

This review reports 3 of recently published molecular classifications of the 3 main gastro-intestinal cancers: gastric, pancreatic and colorectal adenocarcinoma. In colorectal adenocarcinoma, 6 independent classifications were combined to finally hold 4 molecular sub-groups, Consensus Molecular Subtypes (CMS 1-4), linked to various clinical, molecular and survival data. CMS1 (14% MSI with immune activation); CMS2 (37%: canonical with epithelial differentiation and activation of the WNT/MYC pathway); CMS3 (13% metabolic with epithelial differentiation and RAS mutation); CMS4 (23%: mesenchymal with activation of TGFβ pathway and angiogenesis with stromal invasion). In gastric adenocarcinoma, 4 groups were established: subtype "EBV" (9%, high frequency of PIK3CA mutations, hypermetylation and amplification of JAK2, PD-L1 and PD-L2), subtype "MSI" (22%, high rate of mutation), subtype "genomically stable tumor" (20%, diffuse histology type and mutations of RAS and genes encoding integrins and adhesion proteins including CDH1) and subtype "tumors with chromosomal instability" (50%, intestinal type, aneuploidy and receptor tyrosine kinase amplification). In pancreatic adenocarcinomas, a classification in four sub-groups has been proposed, stable subtype (20%, aneuploidy), locally rearranged subtype (30%, focal event on one or two chromosoms), scattered subtype (36%,200 structural variation events, defects in DNA maintenance). Although currently away from the care of patients, these classifications open the way to "à la carte" treatment depending on molecular biology. Copyright © 2016 Société Française du Cancer. Published by Elsevier Masson SAS. All rights reserved.

A New Feature Ensemble with a Multistage Classification Scheme for Breast Cancer Diagnosis

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Idil Isikli Esener

2017-01-01

Full Text Available A new and effective feature ensemble with a multistage classification is proposed to be implemented in a computer-aided diagnosis (CAD system for breast cancer diagnosis. A publicly available mammogram image dataset collected during the Image Retrieval in Medical Applications (IRMA project is utilized to verify the suggested feature ensemble and multistage classification. In achieving the CAD system, feature extraction is performed on the mammogram region of interest (ROI images which are preprocessed by applying a histogram equalization followed by a nonlocal means filtering. The proposed feature ensemble is formed by concatenating the local configuration pattern-based, statistical, and frequency domain features. The classification process of these features is implemented in three cases: a one-stage study, a two-stage study, and a three-stage study. Eight well-known classifiers are used in all cases of this multistage classification scheme. Additionally, the results of the classifiers that provide the top three performances are combined via a majority voting technique to improve the recognition accuracy on both two- and three-stage studies. A maximum of 85.47%, 88.79%, and 93.52% classification accuracies are attained by the one-, two-, and three-stage studies, respectively. The proposed multistage classification scheme is more effective than the single-stage classification for breast cancer diagnosis.

Comparative Study of Classification Techniques on Breast Cancer FNA Biopsy Data

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George Rumbe

2010-12-01

Full Text Available Accurate diagnostic detection of the cancerous cells in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Bayesian classifier and other Artificial neural network classifiers (Backpropagation, linear programming, Learning vector quantization, and K nearest neighborhood on the Wisconsin breast cancer classification problem.

Application of machine learning on brain cancer multiclass classification

Science.gov (United States)

Panca, V.; Rustam, Z.

2017-07-01

Classification of brain cancer is a problem of multiclass classification. One approach to solve this problem is by first transforming it into several binary problems. The microarray gene expression dataset has the two main characteristics of medical data: extremely many features (genes) and only a few number of samples. The application of machine learning on microarray gene expression dataset mainly consists of two steps: feature selection and classification. In this paper, the features are selected using a method based on support vector machine recursive feature elimination (SVM-RFE) principle which is improved to solve multiclass classification, called multiple multiclass SVM-RFE. Instead of using only the selected features on a single classifier, this method combines the result of multiple classifiers. The features are divided into subsets and SVM-RFE is used on each subset. Then, the selected features on each subset are put on separate classifiers. This method enhances the feature selection ability of each single SVM-RFE. Twin support vector machine (TWSVM) is used as the method of the classifier to reduce computational complexity. While ordinary SVM finds single optimum hyperplane, the main objective Twin SVM is to find two non-parallel optimum hyperplanes. The experiment on the brain cancer microarray gene expression dataset shows this method could classify 71,4% of the overall test data correctly, using 100 and 1000 genes selected from multiple multiclass SVM-RFE feature selection method. Furthermore, the per class results show that this method could classify data of normal and MD class with 100% accuracy.

Bladder cancer: Analysis of the 2004 WHO classification in ...

African Journals Online (AJOL)

Objectives: Bladder cancer (BCA) is aworldwide disease and shows a wide range of geographical variation. The aim of this study is to analyze the prevalence of schistosomal and non-schistosomal associated BCA as well as compare our findings with the 2004 WHO consensus classification of urothelial neoplasms and ...

The classification of secondary colorectal liver cancer in human biopsy samples using angular dispersive x-ray diffraction and multivariate analysis

International Nuclear Information System (INIS)

Theodorakou, Chrysoula; Farquharson, Michael J

2009-01-01

The motivation behind this study is to assess whether angular dispersive x-ray diffraction (ADXRD) data, processed using multivariate analysis techniques, can be used for classifying secondary colorectal liver cancer tissue and normal surrounding liver tissue in human liver biopsy samples. The ADXRD profiles from a total of 60 samples of normal liver tissue and colorectal liver metastases were measured using a synchrotron radiation source. The data were analysed for 56 samples using nonlinear peak-fitting software. Four peaks were fitted to all of the ADXRD profiles, and the amplitude, area, amplitude and area ratios for three of the four peaks were calculated and used for the statistical and multivariate analysis. The statistical analysis showed that there are significant differences between all the peak-fitting parameters and ratios between the normal and the diseased tissue groups. The technique of soft independent modelling of class analogy (SIMCA) was used to classify normal liver tissue and colorectal liver metastases resulting in 67% of the normal tissue samples and 60% of the secondary colorectal liver tissue samples being classified correctly. This study has shown that the ADXRD data of normal and secondary colorectal liver cancer are statistically different and x-ray diffraction data analysed using multivariate analysis have the potential to be used as a method of tissue classification.

IARC use of oxidative stress as key mode of action characteristic for facilitating cancer classification: Glyphosate case example illustrating a lack of robustness in interpretative implementation.

Science.gov (United States)

Bus, James S

2017-06-01

The International Agency for Research on Cancer (IARC) has formulated 10 key characteristics of human carcinogens to incorporate mechanistic data into cancer hazard classifications. The analysis used glyphosate as a case example to examine the robustness of IARC's determination of oxidative stress as "strong" evidence supporting a plausible cancer mechanism in humans. The IARC analysis primarily relied on 14 human/mammalian studies; 19 non-mammalian studies were uninformative of human cancer given the broad spectrum of test species and extensive use of formulations and aquatic testing. The mammalian studies had substantial experimental limitations for informing cancer mechanism including use of: single doses and time points; cytotoxic/toxic test doses; tissues not identified as potential cancer targets; glyphosate formulations or mixtures; technically limited oxidative stress biomarkers. The doses were many orders of magnitude higher than human exposures determined in human biomonitoring studies. The glyphosate case example reveals that the IARC evaluation fell substantially short of "strong" supporting evidence of oxidative stress as a plausible human cancer mechanism, and suggests that other IARC monographs relying on the 10 key characteristics approach should be similarly examined for a lack of robust data integration fundamental to reasonable mode of action evaluations. Copyright © 2017 Elsevier Inc. All rights reserved.

[The physiological classification of human thermal states under high environmental temperatures].

Science.gov (United States)

Bobrov, A F; Kuznets, E I

1995-01-01

The paper deals with the physiological classification of human thermal states in a hot environment. A review of the basic systems of classifications of thermal states is given, their main drawbacks are discussed. On the basis of human functional state research in a broad range of environmental temperatures the system of evaluation and classification of human thermal states is proposed. New integral one-dimensional multi-parametric criteria for evaluation are used. For the development of these criteria methods of factor, cluster and canonical correlation analyses are applied. Stochastic nomograms capable of identification of human thermal state for different intensity of influence are given. In this case evaluation of intensity is estimated according to one-dimensional criteria taking into account environmental temperature, physical load and time of man's staying in overheating conditions.

MO-DE-207B-03: Improved Cancer Classification Using Patient-Specific Biological Pathway Information Via Gene Expression Data

Energy Technology Data Exchange (ETDEWEB)

Young, M; Craft, D [Massachusetts General Hospital and Harvard Medical School, Boston, MA (United States)

2016-06-15

Purpose: To develop an efficient, pathway-based classification system using network biology statistics to assist in patient-specific response predictions to radiation and drug therapies across multiple cancer types. Methods: We developed PICS (Pathway Informed Classification System), a novel two-step cancer classification algorithm. In PICS, a matrix m of mRNA expression values for a patient cohort is collapsed into a matrix p of biological pathways. The entries of p, which we term pathway scores, are obtained from either principal component analysis (PCA), normal tissue centroid (NTC), or gene expression deviation (GED). The pathway score matrix is clustered using both k-means and hierarchical clustering, and a clustering is judged by how well it groups patients into distinct survival classes. The most effective pathway scoring/clustering combination, per clustering p-value, thus generates various ‘signatures’ for conventional and functional cancer classification. Results: PICS successfully regularized large dimension gene data, separated normal and cancerous tissues, and clustered a large patient cohort spanning six cancer types. Furthermore, PICS clustered patient cohorts into distinct, statistically-significant survival groups. For a suboptimally-debulked ovarian cancer set, the pathway-classified Kaplan-Meier survival curve (p = .00127) showed significant improvement over that of a prior gene expression-classified study (p = .0179). For a pancreatic cancer set, the pathway-classified Kaplan-Meier survival curve (p = .00141) showed significant improvement over that of a prior gene expression-classified study (p = .04). Pathway-based classification confirmed biomarkers for the pyrimidine, WNT-signaling, glycerophosphoglycerol, beta-alanine, and panthothenic acid pathways for ovarian cancer. Despite its robust nature, PICS requires significantly less run time than current pathway scoring methods. Conclusion: This work validates the PICS method to improve

Apparent diffusion coefficient value of gastric cancer by diffusion-weighted imaging: Correlations with the histological differentiation and Lauren classification

International Nuclear Information System (INIS)

Liu, Song; Guan, Wenxian; Wang, Hao; Pan, Liang; Zhou, Zhuping; Yu, Haiping; Liu, Tian; Yang, Xiaofeng; He, Jian; Zhou, Zhengyang

2014-01-01

Highlights: • Gastric cancers’ ADC values were significantly lower than normal gastric wall. • Gastric adenocarcinomas with different differentiation had different ADC values. • Gastric adenocarcinomas’ ADC values correlated with histologic differentiations. • Gastric cancers’ ADC values correlated with Lauren classifications. • Mean ADC value was better than min ADC value in characterizing gastric cancers. - Abstract: Objective: The purpose of this study was to evaluate the correlations between histological differentiation and Lauren classification of gastric cancer and the apparent diffusion coefficient (ADC) value of diffusion weighted imaging (DWI). Materials and methods: Sixty-nine patients with gastric cancer lesions underwent preoperative magnetic resonance imaging (MRI) (3.0T) and surgical resection. DWI was obtained with a single-shot, echo-planar imaging sequence in the axial plane (b values: 0 and 1000 s/mm 2 ). Mean and minimum ADC values were obtained for each gastric cancer and normal gastric walls by two radiologists, who were blinded to the histological findings. Histological type, degree of differentiation and Lauren classification of each resected specimen were determined by one pathologist. Mean and minimum ADC values of gastric cancers with different histological types, degrees of differentiation and Lauren classifications were compared. Correlations between ADC values and histological differentiation and Lauren classification were analyzed. Results: The mean and minimum ADC values of gastric cancers, as a whole and separately, were significantly lower than those of normal gastric walls (all p values <0.001). There were significant differences in the mean and minimum ADC values among gastric cancers with different histological types, degrees of differentiation and Lauren classifications (p < 0.05). Mean and minimum ADC values correlated significantly (all p < 0.001) with histological differentiation (r = 0.564, 0.578) and Lauren

Classification of breast cancer patients using somatic mutation profiles and machine learning approaches.

Science.gov (United States)

Vural, Suleyman; Wang, Xiaosheng; Guda, Chittibabu

2016-08-26

The high degree of heterogeneity observed in breast cancers makes it very difficult to classify the cancer patients into distinct clinical subgroups and consequently limits the ability to devise effective therapeutic strategies. Several classification strategies based on ER/PR/HER2 expression or the expression profiles of a panel of genes have helped, but such methods often produce misleading results due to their dynamic nature. In contrast, somatic DNA mutations are relatively stable and lead to initiation and progression of many sporadic cancers. Hence in this study, we explore the use of gene mutation profiles to classify, characterize and predict the subgroups of breast cancers. We analyzed the whole exome sequencing data from 358 ethnically similar breast cancer patients in The Cancer Genome Atlas (TCGA) project. Somatic and non-synonymous single nucleotide variants identified from each patient were assigned a quantitative score (C-score) that represents the extent of negative impact on the gene function. Using these scores with non-negative matrix factorization method, we clustered the patients into three subgroups. By comparing the clinical stage of patients, we identified an early-stage-enriched and a late-stage-enriched subgroup. Comparison of the mutation scores of early and late-stage-enriched subgroups identified 358 genes that carry significantly higher mutations rates in the late stage subgroup. Functional characterization of these genes revealed important functional gene families that carry a heavy mutational load in the late state rich subgroup of patients. Finally, using the identified subgroups, we also developed a supervised classification model to predict the stage of the patients. This study demonstrates that gene mutation profiles can be effectively used with unsupervised machine-learning methods to identify clinically distinguishable breast cancer subgroups. The classification model developed in this method could provide a reasonable

Cross-platform analysis of cancer microarray data improves gene expression based classification of phenotypes

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Eils Roland

2005-11-01

Full Text Available Abstract Background The extensive use of DNA microarray technology in the characterization of the cell transcriptome is leading to an ever increasing amount of microarray data from cancer studies. Although similar questions for the same type of cancer are addressed in these different studies, a comparative analysis of their results is hampered by the use of heterogeneous microarray platforms and analysis methods. Results In contrast to a meta-analysis approach where results of different studies are combined on an interpretative level, we investigate here how to directly integrate raw microarray data from different studies for the purpose of supervised classification analysis. We use median rank scores and quantile discretization to derive numerically comparable measures of gene expression from different platforms. These transformed data are then used for training of classifiers based on support vector machines. We apply this approach to six publicly available cancer microarray gene expression data sets, which consist of three pairs of studies, each examining the same type of cancer, i.e. breast cancer, prostate cancer or acute myeloid leukemia. For each pair, one study was performed by means of cDNA microarrays and the other by means of oligonucleotide microarrays. In each pair, high classification accuracies (> 85% were achieved with training and testing on data instances randomly chosen from both data sets in a cross-validation analysis. To exemplify the potential of this cross-platform classification analysis, we use two leukemia microarray data sets to show that important genes with regard to the biology of leukemia are selected in an integrated analysis, which are missed in either single-set analysis. Conclusion Cross-platform classification of multiple cancer microarray data sets yields discriminative gene expression signatures that are found and validated on a large number of microarray samples, generated by different laboratories and

Actionable gene-based classification toward precision medicine in gastric cancer

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Hiroshi Ichikawa

2017-10-01

Full Text Available Abstract Background Intertumoral heterogeneity represents a significant hurdle to identifying optimized targeted therapies in gastric cancer (GC. To realize precision medicine for GC patients, an actionable gene alteration-based molecular classification that directly associates GCs with targeted therapies is needed. Methods A total of 207 Japanese patients with GC were included in this study. Formalin-fixed, paraffin-embedded (FFPE tumor tissues were obtained from surgical or biopsy specimens and were subjected to DNA extraction. We generated comprehensive genomic profiling data using a 435-gene panel including 69 actionable genes paired with US Food and Drug Administration-approved targeted therapies, and the evaluation of Epstein-Barr virus (EBV infection and microsatellite instability (MSI status. Results Comprehensive genomic sequencing detected at least one alteration of 435 cancer-related genes in 194 GCs (93.7% and of 69 actionable genes in 141 GCs (68.1%. We classified the 207 GCs into four The Cancer Genome Atlas (TCGA subtypes using the genomic profiling data; EBV (N = 9, MSI (N = 17, chromosomal instability (N = 119, and genomically stable subtype (N = 62. Actionable gene alterations were not specific and were widely observed throughout all TCGA subtypes. To discover a novel classification which more precisely selects candidates for targeted therapies, 207 GCs were classified using hypermutated phenotype and the mutation profile of 69 actionable genes. We identified a hypermutated group (N = 32, while the others (N = 175 were sub-divided into six clusters including five with actionable gene alterations: ERBB2 (N = 25, CDKN2A, and CDKN2B (N = 10, KRAS (N = 10, BRCA2 (N = 9, and ATM cluster (N = 12. The clinical utility of this classification was demonstrated by a case of unresectable GC with a remarkable response to anti-HER2 therapy in the ERBB2 cluster. Conclusions This actionable gene

Recursive Partitioning Analysis for New Classification of Patients With Esophageal Cancer Treated by Chemoradiotherapy

International Nuclear Information System (INIS)

Nomura, Motoo; Shitara, Kohei; Kodaira, Takeshi; Kondoh, Chihiro; Takahari, Daisuke; Ura, Takashi; Kojima, Hiroyuki; Kamata, Minoru; Muro, Kei; Sawada, Satoshi

2012-01-01

Background: The 7th edition of the American Joint Committee on Cancer staging system does not include lymph node size in the guidelines for staging patients with esophageal cancer. The objectives of this study were to determine the prognostic impact of the maximum metastatic lymph node diameter (ND) on survival and to develop and validate a new staging system for patients with esophageal squamous cell cancer who were treated with definitive chemoradiotherapy (CRT). Methods: Information on 402 patients with esophageal cancer undergoing CRT at two institutions was reviewed. Univariate and multivariate analyses of data from one institution were used to assess the impact of clinical factors on survival, and recursive partitioning analysis was performed to develop the new staging classification. To assess its clinical utility, the new classification was validated using data from the second institution. Results: By multivariate analysis, gender, T, N, and ND stages were independently and significantly associated with survival (p < 0.05). The resulting new staging classification was based on the T and ND. The four new stages led to good separation of survival curves in both the developmental and validation datasets (p < 0.05). Conclusions: Our results showed that lymph node size is a strong independent prognostic factor and that the new staging system, which incorporated lymph node size, provided good prognostic power, and discriminated effectively for patients with esophageal cancer undergoing CRT.

CRISPR/Cas9-mediated noncoding RNA editing in human cancers.

Science.gov (United States)

Yang, Jie; Meng, Xiaodan; Pan, Jinchang; Jiang, Nan; Zhou, Chengwei; Wu, Zhenhua; Gong, Zhaohui

2018-01-02

Cancer is characterized by multiple genetic and epigenetic alterations, including a higher prevalence of mutations of oncogenes and/or tumor suppressors. Mounting evidences have shown that noncoding RNAs (ncRNAs) are involved in the epigenetic regulation of cancer genes and their associated pathways. The clustered regularly interspaced short palindromic repeats (CRISPR)-associated nuclease 9 (CRISPR/Cas9) system, a revolutionary genome-editing technology, has shed light on ncRNA-based cancer therapy. Here, we briefly introduce the classifications and mechanisms of CRISPR/Cas9 system. Importantly, we mainly focused on the applications of CRISPR/Cas9 system as a molecular tool for ncRNA (microRNA, long noncoding RNA and circular RNA, etc.) editing in human cancers, and the novel techniques that are based on CRISPR/Cas9 system. Additionally, the off-target effects and the corresponding solutions as well as the challenges toward CRISPR/Cas9 were also evaluated and discussed. Long- and short-ncRNAs have been employed as targets in precision oncology, and CRISPR/Cas9-mediated ncRNA editing may provide an excellent way to cure cancer.

Integrating human and machine intelligence in galaxy morphology classification tasks

Science.gov (United States)

Beck, Melanie R.; Scarlata, Claudia; Fortson, Lucy F.; Lintott, Chris J.; Simmons, B. D.; Galloway, Melanie A.; Willett, Kyle W.; Dickinson, Hugh; Masters, Karen L.; Marshall, Philip J.; Wright, Darryl

2018-06-01

Quantifying galaxy morphology is a challenging yet scientifically rewarding task. As the scale of data continues to increase with upcoming surveys, traditional classification methods will struggle to handle the load. We present a solution through an integration of visual and automated classifications, preserving the best features of both human and machine. We demonstrate the effectiveness of such a system through a re-analysis of visual galaxy morphology classifications collected during the Galaxy Zoo 2 (GZ2) project. We reprocess the top-level question of the GZ2 decision tree with a Bayesian classification aggregation algorithm dubbed SWAP, originally developed for the Space Warps gravitational lens project. Through a simple binary classification scheme, we increase the classification rate nearly 5-fold classifying 226 124 galaxies in 92 d of GZ2 project time while reproducing labels derived from GZ2 classification data with 95.7 per cent accuracy. We next combine this with a Random Forest machine learning algorithm that learns on a suite of non-parametric morphology indicators widely used for automated morphologies. We develop a decision engine that delegates tasks between human and machine and demonstrate that the combined system provides at least a factor of 8 increase in the classification rate, classifying 210 803 galaxies in just 32 d of GZ2 project time with 93.1 per cent accuracy. As the Random Forest algorithm requires a minimal amount of computational cost, this result has important implications for galaxy morphology identification tasks in the era of Euclid and other large-scale surveys.

CrossLink: a novel method for cross-condition classification of cancer subtypes.

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Ma, Chifeng; Sastry, Konduru S; Flore, Mario; Gehani, Salah; Al-Bozom, Issam; Feng, Yusheng; Serpedin, Erchin; Chouchane, Lotfi; Chen, Yidong; Huang, Yufei

2016-08-22

We considered the prediction of cancer classes (e.g. subtypes) using patient gene expression profiles that contain both systematic and condition-specific biases when compared with the training reference dataset. The conventional normalization-based approaches cannot guarantee that the gene signatures in the reference and prediction datasets always have the same distribution for all different conditions as the class-specific gene signatures change with the condition. Therefore, the trained classifier would work well under one condition but not under another. To address the problem of current normalization approaches, we propose a novel algorithm called CrossLink (CL). CL recognizes that there is no universal, condition-independent normalization mapping of signatures. In contrast, it exploits the fact that the signature is unique to its associated class under any condition and thus employs an unsupervised clustering algorithm to discover this unique signature. We assessed the performance of CL for cross-condition predictions of PAM50 subtypes of breast cancer by using a simulated dataset modeled after TCGA BRCA tumor samples with a cross-validation scheme, and datasets with known and unknown PAM50 classification. CL achieved prediction accuracy >73 %, highest among other methods we evaluated. We also applied the algorithm to a set of breast cancer tumors derived from Arabic population to assign a PAM50 classification to each tumor based on their gene expression profiles. A novel algorithm CrossLink for cross-condition prediction of cancer classes was proposed. In all test datasets, CL showed robust and consistent improvement in prediction performance over other state-of-the-art normalization and classification algorithms.

Classification of breast cancer histology images using Convolutional Neural Networks.

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Teresa Araújo

Full Text Available Breast cancer is one of the main causes of cancer death worldwide. The diagnosis of biopsy tissue with hematoxylin and eosin stained images is non-trivial and specialists often disagree on the final diagnosis. Computer-aided Diagnosis systems contribute to reduce the cost and increase the efficiency of this process. Conventional classification approaches rely on feature extraction methods designed for a specific problem based on field-knowledge. To overcome the many difficulties of the feature-based approaches, deep learning methods are becoming important alternatives. A method for the classification of hematoxylin and eosin stained breast biopsy images using Convolutional Neural Networks (CNNs is proposed. Images are classified in four classes, normal tissue, benign lesion, in situ carcinoma and invasive carcinoma, and in two classes, carcinoma and non-carcinoma. The architecture of the network is designed to retrieve information at different scales, including both nuclei and overall tissue organization. This design allows the extension of the proposed system to whole-slide histology images. The features extracted by the CNN are also used for training a Support Vector Machine classifier. Accuracies of 77.8% for four class and 83.3% for carcinoma/non-carcinoma are achieved. The sensitivity of our method for cancer cases is 95.6%.

Long-term Prostate-specific Antigen Velocity in Improved Classification of Prostate Cancer Risk and Mortality

DEFF Research Database (Denmark)

Ørsted, David Dynnes; Bojesen, Stig E; Kamstrup, Pia R

2013-01-01

BACKGROUND: It remains unclear whether adding long-term prostate-specific antigen velocity (PSAV) to baseline PSA values improves classification of prostate cancer (PCa) risk and mortality in the general population. OBJECTIVE: To determine whether long-term PSAV improves classification of PCa risk...

BCDForest: a boosting cascade deep forest model towards the classification of cancer subtypes based on gene expression data.

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Guo, Yang; Liu, Shuhui; Li, Zhanhuai; Shang, Xuequn

2018-04-11

The classification of cancer subtypes is of great importance to cancer disease diagnosis and therapy. Many supervised learning approaches have been applied to cancer subtype classification in the past few years, especially of deep learning based approaches. Recently, the deep forest model has been proposed as an alternative of deep neural networks to learn hyper-representations by using cascade ensemble decision trees. It has been proved that the deep forest model has competitive or even better performance than deep neural networks in some extent. However, the standard deep forest model may face overfitting and ensemble diversity challenges when dealing with small sample size and high-dimensional biology data. In this paper, we propose a deep learning model, so-called BCDForest, to address cancer subtype classification on small-scale biology datasets, which can be viewed as a modification of the standard deep forest model. The BCDForest distinguishes from the standard deep forest model with the following two main contributions: First, a named multi-class-grained scanning method is proposed to train multiple binary classifiers to encourage diversity of ensemble. Meanwhile, the fitting quality of each classifier is considered in representation learning. Second, we propose a boosting strategy to emphasize more important features in cascade forests, thus to propagate the benefits of discriminative features among cascade layers to improve the classification performance. Systematic comparison experiments on both microarray and RNA-Seq gene expression datasets demonstrate that our method consistently outperforms the state-of-the-art methods in application of cancer subtype classification. The multi-class-grained scanning and boosting strategy in our model provide an effective solution to ease the overfitting challenge and improve the robustness of deep forest model working on small-scale data. Our model provides a useful approach to the classification of cancer subtypes

Cross-Disciplinary Analysis of Lymph Node Classification in Lung Cancer on CT Scanning.

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El-Sherief, Ahmed H; Lau, Charles T; Obuchowski, Nancy A; Mehta, Atul C; Rice, Thomas W; Blackstone, Eugene H

2017-04-01

Accurate and consistent regional lymph node classification is an important element in the staging and multidisciplinary management of lung cancer. Regional lymph node definition sets-lymph node maps-have been created to standardize regional lymph node classification. In 2009, the International Association for the Study of Lung Cancer (IASLC) introduced a lymph node map to supersede all preexisting lymph node maps. Our aim was to study if and how lung cancer specialists apply the IASLC lymph node map when classifying thoracic lymph nodes encountered on CT scans during lung cancer staging. From April 2013 through July 2013, invitations were distributed to all members of the Fleischner Society, Society of Thoracic Radiology, General Thoracic Surgical Club, and the American Association of Bronchology and Interventional Pulmonology to participate in an anonymous online image-based and text-based 20-question survey regarding lymph node classification for lung cancer staging on CT imaging. Three hundred thirty-seven people responded (approximately 25% participation). Respondents consisted of self-reported thoracic radiologists (n = 158), thoracic surgeons (n = 102), and pulmonologists who perform endobronchial ultrasonography (n = 77). Half of the respondents (50%; 95% CI, 44%-55%) reported using the IASLC lymph node map in daily practice, with no significant differences between subspecialties. A disparity was observed between the IASLC definition sets and their interpretation and application on CT scans, in particular for lymph nodes near the thoracic inlet, anterior to the trachea, anterior to the tracheal bifurcation, near the ligamentum arteriosum, between the bronchus intermedius and esophagus, in the internal mammary space, and adjacent to the heart. Use of older lymph node maps and inconsistencies in interpretation and application of definitions in the IASLC lymph node map may potentially lead to misclassification of stage and suboptimal management of lung

Molecular classification of gastric cancer: a new paradigm.

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Shah, Manish A; Khanin, Raya; Tang, Laura; Janjigian, Yelena Y; Klimstra, David S; Gerdes, Hans; Kelsen, David P

2011-05-01

Gastric cancer may be subdivided into 3 distinct subtypes--proximal, diffuse, and distal gastric cancer--based on histopathologic and anatomic criteria. Each subtype is associated with unique epidemiology. Our aim is to test the hypothesis that these distinct gastric cancer subtypes may also be distinguished by gene expression analysis. Patients with localized gastric adenocarcinoma being screened for a phase II preoperative clinical trial (National Cancer Institute, NCI #5917) underwent endoscopic biopsy for fresh tumor procurement. Four to 6 targeted biopsies of the primary tumor were obtained. Macrodissection was carried out to ensure more than 80% carcinoma in the sample. HG-U133A GeneChip (Affymetrix) was used for cDNA expression analysis, and all arrays were processed and analyzed using the Bioconductor R-package. Between November 2003 and January 2006, 57 patients were screened to identify 36 patients with localized gastric cancer who had adequate RNA for expression analysis. Using supervised analysis, we built a classifier to distinguish the 3 gastric cancer subtypes, successfully classifying each into tightly grouped clusters. Leave-one-out cross-validation error was 0.14, suggesting that more than 85% of samples were classified correctly. Gene set analysis with the false discovery rate set at 0.25 identified several pathways that were differentially regulated when comparing each gastric cancer subtype to adjacent normal stomach. Subtypes of gastric cancer that have epidemiologic and histologic distinctions are also distinguished by gene expression data. These preliminary data suggest a new classification of gastric cancer with implications for improving our understanding of disease biology and identification of unique molecular drivers for each gastric cancer subtype. ©2011 AACR.

HCSD: the human cancer secretome database

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Feizi, Amir; Banaei-Esfahani, Amir; Nielsen, Jens

2015-01-01

The human cancer secretome database (HCSD) is a comprehensive database for human cancer secretome data. The cancer secretome describes proteins secreted by cancer cells and structuring information about the cancer secretome will enable further analysis of how this is related with tumor biology...... database is limiting the ability to query the increasing community knowledge. We therefore developed the Human Cancer Secretome Database (HCSD) to fulfil this gap. HCSD contains >80 000 measurements for about 7000 nonredundant human proteins collected from up to 35 high-throughput studies on 17 cancer...

Classification of Breast Cancer Subtypes by combining Gene Expression and DNA Methylation Data

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List Markus

2014-06-01

Full Text Available Selecting the most promising treatment strategy for breast cancer crucially depends on determining the correct subtype. In recent years, gene expression profiling has been investigated as an alternative to histochemical methods. Since databases like TCGA provide easy and unrestricted access to gene expression data for hundreds of patients, the challenge is to extract a minimal optimal set of genes with good prognostic properties from a large bulk of genes making a moderate contribution to classification. Several studies have successfully applied machine learning algorithms to solve this so-called gene selection problem. However, more diverse data from other OMICS technologies are available, including methylation. We hypothesize that combining methylation and gene expression data could already lead to a largely improved classification model, since the resulting model will reflect differences not only on the transcriptomic, but also on an epigenetic level. We compared so-called random forest derived classification models based on gene expression and methylation data alone, to a model based on the combined features and to a model based on the gold standard PAM50. We obtained bootstrap errors of 10-20% and classification error of 1-50%, depending on breast cancer subtype and model. The gene expression model was clearly superior to the methylation model, which was also reflected in the combined model, which mainly selected features from gene expression data. However, the methylation model was able to identify unique features not considered as relevant by the gene expression model, which might provide deeper insights into breast cancer subtype differentiation on an epigenetic level.

Detection and classification of Breast Cancer in Wavelet Sub-bands of Fractal Segmented Cancerous Zones.

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Shirazinodeh, Alireza; Noubari, Hossein Ahmadi; Rabbani, Hossein; Dehnavi, Alireza Mehri

2015-01-01

Recent studies on wavelet transform and fractal modeling applied on mammograms for the detection of cancerous tissues indicate that microcalcifications and masses can be utilized for the study of the morphology and diagnosis of cancerous cases. It is shown that the use of fractal modeling, as applied to a given image, can clearly discern cancerous zones from noncancerous areas. In this paper, for fractal modeling, the original image is first segmented into appropriate fractal boxes followed by identifying the fractal dimension of each windowed section using a computationally efficient two-dimensional box-counting algorithm. Furthermore, using appropriate wavelet sub-bands and image Reconstruction based on modified wavelet coefficients, it is shown that it is possible to arrive at enhanced features for detection of cancerous zones. In this paper, we have attempted to benefit from the advantages of both fractals and wavelets by introducing a new algorithm. By using a new algorithm named F1W2, the original image is first segmented into appropriate fractal boxes, and the fractal dimension of each windowed section is extracted. Following from that, by applying a maximum level threshold on fractal dimensions matrix, the best-segmented boxes are selected. In the next step, the segmented Cancerous zones which are candidates are then decomposed by utilizing standard orthogonal wavelet transform and db2 wavelet in three different resolution levels, and after nullifying wavelet coefficients of the image at the first scale and low frequency band of the third scale, the modified reconstructed image is successfully utilized for detection of breast cancer regions by applying an appropriate threshold. For detection of cancerous zones, our simulations indicate the accuracy of 90.9% for masses and 88.99% for microcalcifications detection results using the F1W2 method. For classification of detected mictocalcification into benign and malignant cases, eight features are identified and

Evaluation Methodology between Globalization and Localization Features Approaches for Skin Cancer Lesions Classification

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Ahmed, H. M.; Al-azawi, R. J.; Abdulhameed, A. A.

2018-05-01

Huge efforts have been put in the developing of diagnostic methods to skin cancer disease. In this paper, two different approaches have been addressed for detection the skin cancer in dermoscopy images. The first approach uses a global method that uses global features for classifying skin lesions, whereas the second approach uses a local method that uses local features for classifying skin lesions. The aim of this paper is selecting the best approach for skin lesion classification. The dataset has been used in this paper consist of 200 dermoscopy images from Pedro Hispano Hospital (PH2). The achieved results are; sensitivity about 96%, specificity about 100%, precision about 100%, and accuracy about 97% for globalization approach while, sensitivity about 100%, specificity about 100%, precision about 100%, and accuracy about 100% for Localization Approach, these results showed that the localization approach achieved acceptable accuracy and better than globalization approach for skin cancer lesions classification.

Does the use of the 2009 FIGO classification of endometrial cancer impact on indications of the sentinel node biopsy?

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Ballester Marcos

2010-08-01

Full Text Available Abstract Background Lymphadenectomy is debated in early stages endometrial cancer. Moreover, a new FIGO classification of endometrial cancer, merging stages IA and IB has been recently published. Therefore, the aims of the present study was to evaluate the relevance of the sentinel node (SN procedure in women with endometrial cancer and to discuss whether the use of the 2009 FIGO classification could modify the indications for SN procedure. Methods Eighty-five patients with endometrial cancer underwent the SN procedure followed by pelvic lymphadenectomy. SNs were detected with a dual or single labelling method in 74 and 11 cases, respectively. All SNs were analysed by both H&E staining and immunohistochemistry. Presumed stage before surgery was assessed for all patients based on MR imaging features using the 1988 FIGO classification and the 2009 FIGO classification. Results An SN was detected in 88.2% of cases (75/85 women. Among the fourteen patients with lymph node metastases one-half were detected by serial sectioning and immunohistochemical analysis. There were no false negative case. Using the 1988 FIGO classification and the 2009 FIGO classification, the correlation between preoperative MRI staging and final histology was moderate with Kappa = 0.24 and Kappa = 0.45, respectively. None of the patients with grade 1 endometrioid carcinoma on biopsy and IA 2009 FIGO stage on MR imaging exhibited positive SN. In patients with grade 2-3 endometrioid carcinoma and stage IA on MR imaging, the rate of positive SN reached 16.6% with an incidence of micrometastases of 50%. Conclusions The present study suggests that sentinel node biopsy is an adequate technique to evaluate lymph node status. The use of the 2009 FIGO classification increases the accuracy of MR imaging to stage patients with early stages of endometrial cancer and contributes to clarify the indication of SN biopsy according to tumour grade and histological type.

Does the use of the 2009 FIGO classification of endometrial cancer impact on indications of the sentinel node biopsy?

International Nuclear Information System (INIS)

Ballester, Marcos; Koskas, Martin; Coutant, Charles; Chéreau, Elisabeth; Seror, Jeremy; Rouzier, Roman; Daraï, Emile

2010-01-01

Lymphadenectomy is debated in early stages endometrial cancer. Moreover, a new FIGO classification of endometrial cancer, merging stages IA and IB has been recently published. Therefore, the aims of the present study was to evaluate the relevance of the sentinel node (SN) procedure in women with endometrial cancer and to discuss whether the use of the 2009 FIGO classification could modify the indications for SN procedure. Eighty-five patients with endometrial cancer underwent the SN procedure followed by pelvic lymphadenectomy. SNs were detected with a dual or single labelling method in 74 and 11 cases, respectively. All SNs were analysed by both H&E staining and immunohistochemistry. Presumed stage before surgery was assessed for all patients based on MR imaging features using the 1988 FIGO classification and the 2009 FIGO classification. An SN was detected in 88.2% of cases (75/85 women). Among the fourteen patients with lymph node metastases one-half were detected by serial sectioning and immunohistochemical analysis. There were no false negative case. Using the 1988 FIGO classification and the 2009 FIGO classification, the correlation between preoperative MRI staging and final histology was moderate with Kappa = 0.24 and Kappa = 0.45, respectively. None of the patients with grade 1 endometrioid carcinoma on biopsy and IA 2009 FIGO stage on MR imaging exhibited positive SN. In patients with grade 2-3 endometrioid carcinoma and stage IA on MR imaging, the rate of positive SN reached 16.6% with an incidence of micrometastases of 50%. The present study suggests that sentinel node biopsy is an adequate technique to evaluate lymph node status. The use of the 2009 FIGO classification increases the accuracy of MR imaging to stage patients with early stages of endometrial cancer and contributes to clarify the indication of SN biopsy according to tumour grade and histological type

An NRG Oncology/GOG study of molecular classification for risk prediction in endometrioid endometrial cancer.

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Cosgrove, Casey M; Tritchler, David L; Cohn, David E; Mutch, David G; Rush, Craig M; Lankes, Heather A; Creasman, William T; Miller, David S; Ramirez, Nilsa C; Geller, Melissa A; Powell, Matthew A; Backes, Floor J; Landrum, Lisa M; Timmers, Cynthia; Suarez, Adrian A; Zaino, Richard J; Pearl, Michael L; DiSilvestro, Paul A; Lele, Shashikant B; Goodfellow, Paul J

2018-01-01

The purpose of this study was to assess the prognostic significance of a simplified, clinically accessible classification system for endometrioid endometrial cancers combining Lynch syndrome screening and molecular risk stratification. Tumors from NRG/GOG GOG210 were evaluated for mismatch repair defects (MSI, MMR IHC, and MLH1 methylation), POLE mutations, and loss of heterozygosity. TP53 was evaluated in a subset of cases. Tumors were assigned to four molecular classes. Relationships between molecular classes and clinicopathologic variables were assessed using contingency tests and Cox proportional methods. Molecular classification was successful for 982 tumors. Based on the NCI consensus MSI panel assessing MSI and loss of heterozygosity combined with POLE testing, 49% of tumors were classified copy number stable (CNS), 39% MMR deficient, 8% copy number altered (CNA) and 4% POLE mutant. Cancer-specific mortality occurred in 5% of patients with CNS tumors; 2.6% with POLE tumors; 7.6% with MMR deficient tumors and 19% with CNA tumors. The CNA group had worse progression-free (HR 2.31, 95%CI 1.53-3.49) and cancer-specific survival (HR 3.95; 95%CI 2.10-7.44). The POLE group had improved outcomes, but the differences were not statistically significant. CNA class remained significant for cancer-specific survival (HR 2.11; 95%CI 1.04-4.26) in multivariable analysis. The CNA molecular class was associated with TP53 mutation and expression status. A simple molecular classification for endometrioid endometrial cancers that can be easily combined with Lynch syndrome screening provides important prognostic information. These findings support prospective clinical validation and further studies on the predictive value of a simplified molecular classification system. Copyright © 2017 Elsevier Inc. All rights reserved.

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

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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.

A Preliminary Classification of Human Functional Sexual Disorders

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Sharpe, Lawrence; And Others

1976-01-01

A preliminary classification is presented for functional human sexual disorders. This system is based on objective behavior and reports of distress. Five categories of sexual disorders are proposed, including the behavioral, psychological and informational components of sexual functioning in the individual and the couple. (Author)

From Molecular Classification to Targeted Therapeutics: The Changing Face of Systemic Therapy in Metastatic Gastroesophageal Cancer

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Adrian Murphy

2015-01-01

Full Text Available Histological classification of adenocarcinoma or squamous cell carcinoma for esophageal cancer or using the Lauren classification for intestinal and diffuse type gastric cancer has limited clinical utility in the management of advanced disease. Germline mutations in E-cadherin (CDH1 or mismatch repair genes (Lynch syndrome were identified many years ago but given their rarity, the identification of these molecular alterations does not substantially impact treatment in the advanced setting. Recent molecular profiling studies of upper GI tumors have added to our knowledge of the underlying biology but have not led to an alternative classification system which can guide clinician’s therapeutic decisions. Recently the Cancer Genome Atlas Research Network has proposed four subtypes of gastric cancer dividing tumors into those positive for Epstein-Barr virus, microsatellite unstable tumors, genomically stable tumors, and tumors with chromosomal instability. Unfortunately to date, many phase III clinical trials involving molecularly targeted agents have failed to meet their survival endpoints due to their use in unselected populations. Future clinical trials should utilize molecular profiling of individual tumors in order to determine the optimal use of targeted therapies in preselected patients.

In vivo subsite classification and diagnosis of oral cancers using Raman spectroscopy

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Aditi Sahu

2016-09-01

Full Text Available Oral cancers suffer from poor disease-free survival rates due to delayed diagnosis. Noninvasive, rapid, objective approaches as adjuncts to visual inspection can help in better management of oral cancers. Raman spectroscopy (RS has shown potential in identification of oral premalignant and malignant conditions and also in the detection of early cancer changes like cancer-field-effects (CFE at buccal mucosa subsite. Anatomic differences between different oral subsites have also been reported using RS. In this study, anatomical differences between subsites and their possible influence on healthy vs pathological classification were evaluated on 85 oral cancer and 72 healthy subjects. Spectra were acquired from buccal mucosa, lip and tongue in healthy, contralateral (internal healthy control, premalignant and cancer conditions using fiber-optic Raman spectrometer. Mean spectra indicate predominance of lipids in healthy buccal mucosa, contribution of both lipids and proteins in lip while major dominance of protein in tongue spectra. From healthy to tumor, changes in protein secondary-structure, DNA and heme-related features were observed. Principal component linear discriminant analysis (PC-LDA followed by leave-one-out-cross-validation (LOOCV was used for data analysis. Findings indicate buccal mucosa and tongue are distinct entities, while lip misclassifies with both these subsites. Additionally, the diagnostic algorithm for individual subsites gave improved classification efficiencies with respect to the pooled subsites model. However, as the pooled subsites model yielded 98% specificity and 100% sensitivity, this model may be more useful for preliminary screening applications. Large-scale validation studies are a pre-requisite before envisaging future clinical applications.

A comprehensive sensitivity analysis of microarray breast cancer classification under feature variability

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Reinders Marcel JT

2009-11-01

Full Text Available Abstract Background Large discrepancies in signature composition and outcome concordance have been observed between different microarray breast cancer expression profiling studies. This is often ascribed to differences in array platform as well as biological variability. We conjecture that other reasons for the observed discrepancies are the measurement error associated with each feature and the choice of preprocessing method. Microarray data are known to be subject to technical variation and the confidence intervals around individual point estimates of expression levels can be wide. Furthermore, the estimated expression values also vary depending on the selected preprocessing scheme. In microarray breast cancer classification studies, however, these two forms of feature variability are almost always ignored and hence their exact role is unclear. Results We have performed a comprehensive sensitivity analysis of microarray breast cancer classification under the two types of feature variability mentioned above. We used data from six state of the art preprocessing methods, using a compendium consisting of eight diferent datasets, involving 1131 hybridizations, containing data from both one and two-color array technology. For a wide range of classifiers, we performed a joint study on performance, concordance and stability. In the stability analysis we explicitly tested classifiers for their noise tolerance by using perturbed expression profiles that are based on uncertainty information directly related to the preprocessing methods. Our results indicate that signature composition is strongly influenced by feature variability, even if the array platform and the stratification of patient samples are identical. In addition, we show that there is often a high level of discordance between individual class assignments for signatures constructed on data coming from different preprocessing schemes, even if the actual signature composition is identical

Automatic classification of ovarian cancer types from cytological images using deep convolutional neural networks.

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Wu, Miao; Yan, Chuanbo; Liu, Huiqiang; Liu, Qian

2018-06-29

Ovarian cancer is one of the most common gynecologic malignancies. Accurate classification of ovarian cancer types (serous carcinoma, mucous carcinoma, endometrioid carcinoma, transparent cell carcinoma) is an essential part in the different diagnosis. Computer-aided diagnosis (CADx) can provide useful advice for pathologists to determine the diagnosis correctly. In our study, we employed a Deep Convolutional Neural Networks (DCNN) based on AlexNet to automatically classify the different types of ovarian cancers from cytological images. The DCNN consists of five convolutional layers, three max pooling layers, and two full reconnect layers. Then we trained the model by two group input data separately, one was original image data and the other one was augmented image data including image enhancement and image rotation. The testing results are obtained by the method of 10-fold cross-validation, showing that the accuracy of classification models has been improved from 72.76 to 78.20% by using augmented images as training data. The developed scheme was useful for classifying ovarian cancers from cytological images. © 2018 The Author(s).

Quantum Cascade Laser-Based Infrared Microscopy for Label-Free and Automated Cancer Classification in Tissue Sections.

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Kuepper, Claus; Kallenbach-Thieltges, Angela; Juette, Hendrik; Tannapfel, Andrea; Großerueschkamp, Frederik; Gerwert, Klaus

2018-05-16

A feasibility study using a quantum cascade laser-based infrared microscope for the rapid and label-free classification of colorectal cancer tissues is presented. Infrared imaging is a reliable, robust, automated, and operator-independent tissue classification method that has been used for differential classification of tissue thin sections identifying tumorous regions. However, long acquisition time by the so far used FT-IR-based microscopes hampered the clinical translation of this technique. Here, the used quantum cascade laser-based microscope provides now infrared images for precise tissue classification within few minutes. We analyzed 110 patients with UICC-Stage II and III colorectal cancer, showing 96% sensitivity and 100% specificity of this label-free method as compared to histopathology, the gold standard in routine clinical diagnostics. The main hurdle for the clinical translation of IR-Imaging is overcome now by the short acquisition time for high quality diagnostic images, which is in the same time range as frozen sections by pathologists.

Human Cancer Models Initiative | Office of Cancer Genomics

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

Oropharyngeal cancer and human papilloma virus: evolving diagnostic and management paradigms.

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Buckley, Lisa; Gupta, Ruta; Ashford, Bruce; Jabbour, Joe; Clark, Jonathan R

2016-06-01

The significant increase in human papilloma virus (HPV)-associated oropharyngeal carcinoma (OPC) over recent years has lead to a surge in research and an improved understanding of the disease. Most patients with HPV-associated OPC present with cystic nodal metastases with a small primary tumour, and respond well to all treatment modalities including primary surgery and primary chemoradiotherapy. Current research is evaluating treatment de-escalation to reduce long-term treatment-associated morbidities. Transoral robotic surgery (TORS) is particularly relevant as the transoral approach allows small primary tumours to be removed with lower morbidity than traditional surgical approaches. The current American Joint Committee on Cancer staging system for oropharyngeal cancer does not appropriately stratify HPV-associated OPC; hence, alternative risk stratification and staging classifications are being proposed. © 2015 Royal Australasian College of Surgeons.

Looking at the ICF and human communication through the lens of classification theory.

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Walsh, Regina

2011-08-01

This paper explores the insights that classification theory can provide about the application of the International Classification of Functioning, Disability and Health (ICF) to communication. It first considers the relationship between conceptual models and classification systems, highlighting that classification systems in speech-language pathology (SLP) have not historically been based on conceptual models of human communication. It then overviews the key concepts and criteria of classification theory. Applying classification theory to the ICF and communication raises a number of issues, some previously highlighted through clinical application. Six focus questions from classification theory are used to explore these issues, and to propose the creation of an ICF-related conceptual model of communicating for the field of communication disability, which would address some of the issues raised. Developing a conceptual model of communication for SLP purposes closely articulated with the ICF would foster productive intra-professional discourse, while at the same time allow the profession to continue to use the ICF for purposes in inter-disciplinary discourse. The paper concludes by suggesting the insights of classification theory can assist professionals to apply the ICF to communication with the necessary rigour, and to work further in developing a conceptual model of human communication.

Side effects of cancer therapies. International classification and documentation systems

International Nuclear Information System (INIS)

Seegenschmiedt, M.H.

1998-01-01

The publication presents and explains verified, international classification and documentation systems for side effects induced by cancer treatments, applicable in general and clinical practice and clinical research, and covers in a clearly arranged manner the whole range of treatments, including acute and chronic side effects of chemotherapy and radiotherapy, surgery, or combined therapies. The book fills a long-felt need in tumor documentation and is a major contribution to quality assurance in clinical oncology in German-speaking countries. As most parts of the book are bilingual, presenting German and English texts and terminology, it satisfies the principles of interdisciplinarity and internationality. The tabulated form chosen for presentation of classification systems and criteria facilitate the user's approach as well as application in daily work. (orig./CB) [de

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

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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.

Alternative Polyadenylation Patterns for Novel Gene Discovery and Classification in Cancer

Directory of Open Access Journals (Sweden)

Oguzhan Begik

2017-07-01

Full Text Available Certain aspects of diagnosis, prognosis, and treatment of cancer patients are still important challenges to be addressed. Therefore, we propose a pipeline to uncover patterns of alternative polyadenylation (APA, a hidden complexity in cancer transcriptomes, to further accelerate efforts to discover novel cancer genes and pathways. Here, we analyzed expression data for 1045 cancer patients and found a significant shift in usage of poly(A signals in common tumor types (breast, colon, lung, prostate, gastric, and ovarian compared to normal tissues. Using machine-learning techniques, we further defined specific subsets of APA events to efficiently classify cancer types. Furthermore, APA patterns were associated with altered protein levels in patients, revealed by antibody-based profiling data, suggesting functional significance. Overall, our study offers a computational approach for use of APA in novel gene discovery and classification in common tumor types, with important implications in basic research, biomarker discovery, and precision medicine approaches.

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.

Examining applying high performance genetic data feature selection and classification algorithms for colon cancer diagnosis.

Science.gov (United States)

Al-Rajab, Murad; Lu, Joan; Xu, Qiang

2017-07-01

This paper examines the accuracy and efficiency (time complexity) of high performance genetic data feature selection and classification algorithms for colon cancer diagnosis. The need for this research derives from the urgent and increasing need for accurate and efficient algorithms. Colon cancer is a leading cause of death worldwide, hence it is vitally important for the cancer tissues to be expertly identified and classified in a rapid and timely manner, to assure both a fast detection of the disease and to expedite the drug discovery process. In this research, a three-phase approach was proposed and implemented: Phases One and Two examined the feature selection algorithms and classification algorithms employed separately, and Phase Three examined the performance of the combination of these. It was found from Phase One that the Particle Swarm Optimization (PSO) algorithm performed best with the colon dataset as a feature selection (29 genes selected) and from Phase Two that the Support Vector Machine (SVM) algorithm outperformed other classifications, with an accuracy of almost 86%. It was also found from Phase Three that the combined use of PSO and SVM surpassed other algorithms in accuracy and performance, and was faster in terms of time analysis (94%). It is concluded that applying feature selection algorithms prior to classification algorithms results in better accuracy than when the latter are applied alone. This conclusion is important and significant to industry and society. Copyright © 2017 Elsevier B.V. All rights reserved.

Development and test of a classification scheme for human factors in incident reports

International Nuclear Information System (INIS)

Miller, R.; Freitag, M.; Wilpert, B.

1997-01-01

The Research Center System Safety of the Berlin University of Technology conducted a research project on the analysis of Human Factors (HF) aspects in incident reported by German Nuclear Power Plants. Based on psychological theories and empirical studies a classification scheme was developed which permits the identification of human involvement in incidents. The classification scheme was applied in an epidemiological study to a selection of more than 600 HF - relevant incidents. The results allow insights into HF related problem areas. An additional study proved that the application of the classification scheme produces results which are reliable and independent from raters. (author). 13 refs, 1 fig

Integrating Human and Machine Intelligence in Galaxy Morphology Classification Tasks

Science.gov (United States)

Beck, Melanie Renee

The large flood of data flowing from observatories presents significant challenges to astronomy and cosmology--challenges that will only be magnified by projects currently under development. Growth in both volume and velocity of astrophysics data is accelerating: whereas the Sloan Digital Sky Survey (SDSS) has produced 60 terabytes of data in the last decade, the upcoming Large Synoptic Survey Telescope (LSST) plans to register 30 terabytes per night starting in the year 2020. Additionally, the Euclid Mission will acquire imaging for 5 x 107 resolvable galaxies. The field of galaxy evolution faces a particularly challenging future as complete understanding often cannot be reached without analysis of detailed morphological galaxy features. Historically, morphological analysis has relied on visual classification by astronomers, accessing the human brains capacity for advanced pattern recognition. However, this accurate but inefficient method falters when confronted with many thousands (or millions) of images. In the SDSS era, efforts to automate morphological classifications of galaxies (e.g., Conselice et al., 2000; Lotz et al., 2004) are reasonably successful and can distinguish between elliptical and disk-dominated galaxies with accuracies of 80%. While this is statistically very useful, a key problem with these methods is that they often cannot say which 80% of their samples are accurate. Furthermore, when confronted with the more complex task of identifying key substructure within galaxies, automated classification algorithms begin to fail. The Galaxy Zoo project uses a highly innovative approach to solving the scalability problem of visual classification. Displaying images of SDSS galaxies to volunteers via a simple and engaging web interface, www.galaxyzoo.org asks people to classify images by eye. Within the first year hundreds of thousands of members of the general public had classified each of the 1 million SDSS galaxies an average of 40 times. Galaxy Zoo

CD147 expression in human gastric cancer is associated with tumor recurrence and prognosis.

Directory of Open Access Journals (Sweden)

Dake Chu

Full Text Available CD147 is correlated with tumor aggressiveness in various human malignancies. Here, we investigated CD147 protein expression in 223 patients with gastric cancer by immunohistochemistry and analyzed its association with disease-free and overall survival. CD147 was increased in gastric cancer compared to normal tissues. Additionally, CD147 expression was associated with gastric cancer invasion, metastasis and TNM stage, whereas it was not related to age, sex, differentiation status, tumor site or Lauren classification. Kaplan-Meier analysis confirmed that CD147 was associated with disease-free and overall survival in patients with gastric cancer; i.e., patients with positive CD147 staining tend to have worse disease-free and overall survival. Moreover, Cox's proportional hazards analysis demonstrated that CD147 was an independent marker of disease-free and overall survival for patients with gastric cancer. These results confirm the association of CD147 with gastric cancer invasion and metastasis and prove that CD147 might be an indicator of tumor recurrence and prognosis in gastric cancer.

Gene Expression Profiles for Predicting Metastasis in Breast Cancer: A Cross-Study Comparison of Classification Methods

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Mark Burton

2012-01-01

Full Text Available Machine learning has increasingly been used with microarray gene expression data and for the development of classifiers using a variety of methods. However, method comparisons in cross-study datasets are very scarce. This study compares the performance of seven classification methods and the effect of voting for predicting metastasis outcome in breast cancer patients, in three situations: within the same dataset or across datasets on similar or dissimilar microarray platforms. Combining classification results from seven classifiers into one voting decision performed significantly better during internal validation as well as external validation in similar microarray platforms than the underlying classification methods. When validating between different microarray platforms, random forest, another voting-based method, proved to be the best performing method. We conclude that voting based classifiers provided an advantage with respect to classifying metastasis outcome in breast cancer patients.

Efficacy of hidden markov model over support vector machine on multiclass classification of healthy and cancerous cervical tissues

Science.gov (United States)

Mukhopadhyay, Sabyasachi; Kurmi, Indrajit; Pratiher, Sawon; Mukherjee, Sukanya; Barman, Ritwik; Ghosh, Nirmalya; Panigrahi, Prasanta K.

2018-02-01

In this paper, a comparative study between SVM and HMM has been carried out for multiclass classification of cervical healthy and cancerous tissues. In our study, the HMM methodology is more promising to produce higher accuracy in classification.

A transient search using combined human and machine classifications

Science.gov (United States)

Wright, Darryl E.; Lintott, Chris J.; Smartt, Stephen J.; Smith, Ken W.; Fortson, Lucy; Trouille, Laura; Allen, Campbell R.; Beck, Melanie; Bouslog, Mark C.; Boyer, Amy; Chambers, K. C.; Flewelling, Heather; Granger, Will; Magnier, Eugene A.; McMaster, Adam; Miller, Grant R. M.; O'Donnell, James E.; Simmons, Brooke; Spiers, Helen; Tonry, John L.; Veldthuis, Marten; Wainscoat, Richard J.; Waters, Chris; Willman, Mark; Wolfenbarger, Zach; Young, Dave R.

2017-12-01

Large modern surveys require efficient review of data in order to find transient sources such as supernovae, and to distinguish such sources from artefacts and noise. Much effort has been put into the development of automatic algorithms, but surveys still rely on human review of targets. This paper presents an integrated system for the identification of supernovae in data from Pan-STARRS1, combining classifications from volunteers participating in a citizen science project with those from a convolutional neural network. The unique aspect of this work is the deployment, in combination, of both human and machine classifications for near real-time discovery in an astronomical project. We show that the combination of the two methods outperforms either one used individually. This result has important implications for the future development of transient searches, especially in the era of Large Synoptic Survey Telescope and other large-throughput surveys.

DBGC: A Database of Human Gastric Cancer

Science.gov (United States)

Wang, Chao; Zhang, Jun; Cai, Mingdeng; Zhu, Zhenggang; Gu, Wenjie; Yu, Yingyan; Zhang, Xiaoyan

2015-01-01

The Database of Human Gastric Cancer (DBGC) is a comprehensive database that integrates various human gastric cancer-related data resources. Human gastric cancer-related transcriptomics projects, proteomics projects, mutations, biomarkers and drug-sensitive genes from different sources were collected and unified in this database. Moreover, epidemiological statistics of gastric cancer patients in China and clinicopathological information annotated with gastric cancer cases were also integrated into the DBGC. We believe that this database will greatly facilitate research regarding human gastric cancer in many fields. DBGC is freely available at http://bminfor.tongji.edu.cn/dbgc/index.do PMID:26566288

A dictionary learning approach for human sperm heads classification.

Science.gov (United States)

Shaker, Fariba; Monadjemi, S Amirhassan; Alirezaie, Javad; Naghsh-Nilchi, Ahmad Reza

2017-12-01

To diagnose infertility in men, semen analysis is conducted in which sperm morphology is one of the factors that are evaluated. Since manual assessment of sperm morphology is time-consuming and subjective, automatic classification methods are being developed. Automatic classification of sperm heads is a complicated task due to the intra-class differences and inter-class similarities of class objects. In this research, a Dictionary Learning (DL) technique is utilized to construct a dictionary of sperm head shapes. This dictionary is used to classify the sperm heads into four different classes. Square patches are extracted from the sperm head images. Columnized patches from each class of sperm are used to learn class-specific dictionaries. The patches from a test image are reconstructed using each class-specific dictionary and the overall reconstruction error for each class is used to select the best matching class. Average accuracy, precision, recall, and F-score are used to evaluate the classification method. The method is evaluated using two publicly available datasets of human sperm head shapes. The proposed DL based method achieved an average accuracy of 92.2% on the HuSHeM dataset, and an average recall of 62% on the SCIAN-MorphoSpermGS dataset. The results show a significant improvement compared to a previously published shape-feature-based method. We have achieved high-performance results. In addition, our proposed approach offers a more balanced classifier in which all four classes are recognized with high precision and recall. In this paper, we use a Dictionary Learning approach in classifying human sperm heads. It is shown that the Dictionary Learning method is far more effective in classifying human sperm heads than classifiers using shape-based features. Also, a dataset of human sperm head shapes is introduced to facilitate future research. Copyright © 2017 Elsevier Ltd. All rights reserved.

Setting a generalized functional linear model (GFLM for the classification of different types of cancer

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Miguel Flores

2016-11-01

Full Text Available This work aims to classify the DNA sequences of healthy and malignant cancer respectively. For this, supervised and unsupervised classification methods from a functional context are used; i.e. each strand of DNA is an observation. The observations are discretized, for that reason different ways to represent these observations with functions are evaluated. In addition, an exploratory study is done: estimating the mean and variance of each functional type of cancer. For the unsupervised classification method, hierarchical clustering with different measures of functional distance is used. On the other hand, for the supervised classification method, a functional generalized linear model is used. For this model the first and second derivatives are used which are included as discriminating variables. It has been verified that one of the advantages of working in the functional context is to obtain a model to correctly classify cancers by 100%. For the implementation of the methods it has been used the fda.usc R package that includes all the techniques of functional data analysis used in this work. In addition, some that have been developed in recent decades. For more details of these techniques can be consulted Ramsay, J. O. and Silverman (2005 and Ferraty et al. (2006.

Classification of Dukes' B and C colorectal cancers using expression arrays

DEFF Research Database (Denmark)

Frederiksen, C.M.; Knudsen, Steen; Laurberg, S.

2003-01-01

Purpose. Colorectal cancer is one of the most common malignancies. Substaging of the cancer is of importance not only to prognosis but also to treatment. Classification of substages based on DNA microarray technology is currently the most promising approach. We therefore investigated if gene...... expression microarrays could be used to classify colorectal tumors. Methods. We used the Affymetrix oligonucleotide arrays to analyze the expression of more than 5,000 genes in samples from the sigmoid and upper rectum of the left colon. Five samples were from normal mucosa and five samples from each...... expression of one of the most common malignancies, colorectal cancer, now seems to be within reach. The data indicates that it is possible at least to classify Dukes' B and C colorectal tumors with microarrays....

AN ADABOOST OPTIMIZED CCFIS BASED CLASSIFICATION MODEL FOR BREAST CANCER DETECTION

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CHANDRASEKAR RAVI

2017-06-01

Full Text Available Classification is a Data Mining technique used for building a prototype of the data behaviour, using which an unseen data can be classified into one of the defined classes. Several researchers have proposed classification techniques but most of them did not emphasis much on the misclassified instances and storage space. In this paper, a classification model is proposed that takes into account the misclassified instances and storage space. The classification model is efficiently developed using a tree structure for reducing the storage complexity and uses single scan of the dataset. During the training phase, Class-based Closed Frequent ItemSets (CCFIS were mined from the training dataset in the form of a tree structure. The classification model has been developed using the CCFIS and a similarity measure based on Longest Common Subsequence (LCS. Further, the Particle Swarm Optimization algorithm is applied on the generated CCFIS, which assigns weights to the itemsets and their associated classes. Most of the classifiers are correctly classifying the common instances but they misclassify the rare instances. In view of that, AdaBoost algorithm has been used to boost the weights of the misclassified instances in the previous round so as to include them in the training phase to classify the rare instances. This improves the accuracy of the classification model. During the testing phase, the classification model is used to classify the instances of the test dataset. Breast Cancer dataset from UCI repository is used for experiment. Experimental analysis shows that the accuracy of the proposed classification model outperforms the PSOAdaBoost-Sequence classifier by 7% superior to other approaches like Naïve Bayes Classifier, Support Vector Machine Classifier, Instance Based Classifier, ID3 Classifier, J48 Classifier, etc.

Human papillomavirus 33 worldwide genetic variation and associated risk of cervical cancer

Science.gov (United States)

Chen, Alyce A.; Heideman, Daniëlle A.M.; Boon, Debby; Chen, Zigui; Burk, Robert D.; De Vuyst, Hugo; Gheit, Tarik; Snijders, Peter J.F.; Tommasino, Massimo; Franceschi, Silvia; Clifford, Gary M.

2014-01-01

Human papillomavirus (HPV) 33, a member of the HPV16-related alpha-9 species group, is found in approximately 5% of cervical cancers worldwide. The current study aimed to characterize the genetic diversity of HPV33 and to explore the association of HPV33 variants with the risk for cervical cancer. Taking advantage of the International Agency for Research on Cancer biobank, we sequenced the entire E6 and E7 open reading frames of 213 HPV33-positive cervical samples from 30 countries. We identified 28 HPV33 variants that formed 5 phylogenetic groups: the previously identified A1, A2, and B (sub) lineages and the novel A3 and C (sub)lineages. The A1 sublineage was strongly over-represented in cervical cases compared to controls in both Africa and Europe. In conclusion, we provide a classification system for HPV33 variants based on the sequence of E6 and E7 and suggest that the association of HPV33 with cervical cancer may differ by variant (sub)lineage. PMID:24314666

Gastric cancer: epidemiology, prevention, classification, and treatment

OpenAIRE

Sitarz, Robert; Skierucha, Małgorzata; Mielko, Jerzy; Offerhaus, G Johan A; Maciejewski, Ryszard; Polkowski, Wojciech P

2018-01-01

Robert Sitarz,1–3 Małgorzata Skierucha,1,2 Jerzy Mielko,1 G Johan A Offerhaus,3 Ryszard Maciejewski,2 Wojciech P Polkowski1 1Department of Surgical Oncology, Medical University of Lublin, Lublin, Poland; 2Department of Human Anatomy, Medical University of Lublin, Lublin, Poland; 3Department of Pathology, University Medical Centre, Utrecht, The Netherlands Abstract: Gastric cancer is the second most common cause of cancer-related deaths in the world, the epidemiology of which has ch...

Classification of prostate cancer grade using temporal ultrasound: in vivo feasibility study

Science.gov (United States)

Ghavidel, Sahar; Imani, Farhad; Khallaghi, Siavash; Gibson, Eli; Khojaste, Amir; Gaed, Mena; Moussa, Madeleine; Gomez, Jose A.; Siemens, D. Robert; Leveridge, Michael; Chang, Silvia; Fenster, Aaron; Ward, Aaron D.; Abolmaesumi, Purang; Mousavi, Parvin

2016-03-01

Temporal ultrasound has been shown to have high classification accuracy in differentiating cancer from benign tissue. In this paper, we extend the temporal ultrasound method to classify lower grade Prostate Cancer (PCa) from all other grades. We use a group of nine patients with mostly lower grade PCa, where cancerous regions are also limited. A critical challenge is to train a classifier with limited aggressive cancerous tissue compared to low grade cancerous tissue. To resolve the problem of imbalanced data, we use Synthetic Minority Oversampling Technique (SMOTE) to generate synthetic samples for the minority class. We calculate spectral features of temporal ultrasound data and perform feature selection using Random Forests. In leave-one-patient-out cross-validation strategy, an area under receiver operating characteristic curve (AUC) of 0.74 is achieved with overall sensitivity and specificity of 70%. Using an unsupervised learning approach prior to proposed method improves sensitivity and AUC to 80% and 0.79. This work represents promising results to classify lower and higher grade PCa with limited cancerous training samples, using temporal ultrasound.

Rough set soft computing cancer classification and network: one stone, two birds.

Science.gov (United States)

Zhang, Yue

2010-07-15

Gene expression profiling provides tremendous information to help unravel the complexity of cancer. The selection of the most informative genes from huge noise for cancer classification has taken centre stage, along with predicting the function of such identified genes and the construction of direct gene regulatory networks at different system levels with a tuneable parameter. A new study by Wang and Gotoh described a novel Variable Precision Rough Sets-rooted robust soft computing method to successfully address these problems and has yielded some new insights. The significance of this progress and its perspectives will be discussed in this article.

North American Magazine Coverage of Skin Cancer and Recreational Tanning Before and After the WHO/IARC 2009 Classification of Indoor Tanning Devices as Carcinogenic.

Science.gov (United States)

McWhirter, Jennifer E; Hoffman-Goetz, Laurie

2015-09-01

The mass media is an influential source of skin cancer information for the public. In 2009, the World Health Organization's International Agency for Research on Cancer classified UV radiation from tanning devices as carcinogenic. Our objective was to determine if media coverage of skin cancer and recreational tanning increased in volume or changed in nature after this classification. We conducted a directed content analysis on 29 North American popular magazines (2007-2012) to investigate the overall volume of articles on skin cancer and recreational tanning and, more specifically, the presence of skin cancer risk factors, UV behaviors, and early detection information in article text (n = 410) and images (n = 714). The volume of coverage on skin cancer and recreational tanning did not increase significantly after the 2009 classification of tanning beds as carcinogenic. Key-related messages, including that UV exposure is a risk factor for skin cancer and that indoor tanning should be avoided, were not reported more frequently after the classification, but the promotion of the tanned look as attractive was conveyed more often in images afterwards (p skin cancer risk factors, other UV behaviors, or early detection information over time. The classification of indoor tanning beds as carcinogenic had no significant impact on the volume or nature of skin cancer and recreational tanning coverage in magazines.

Does glyphosate cause cancer?

OpenAIRE

German Federal Institute for Risk Assessment

2015-01-01

In its recent evaluation from March 2015, the International Agency for Cancer Research (IARC), as the specialized cancer agency of the World Health Organization (WHO), came to the conclusion that glyphosate should now be classified as a carcinogenic substance in Group 2A (probably carcinogenic to humans), based on “limited evidence” in human-experiments and ”sufficient evidence” in animal-experiments. This classification was pub-lished in a short report in the "Lancet" journal on 20 March 201...

Genetic Fuzzy System (GFS based wavelet co-occurrence feature selection in mammogram classification for breast cancer diagnosis

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Meenakshi M. Pawar

2016-09-01

Full Text Available Breast cancer is significant health problem diagnosed mostly in women worldwide. Therefore, early detection of breast cancer is performed with the help of digital mammography, which can reduce mortality rate. This paper presents wrapper based feature selection approach for wavelet co-occurrence feature (WCF using Genetic Fuzzy System (GFS in mammogram classification problem. The performance of GFS algorithm is explained using mini-MIAS database. WCF features are obtained from detail wavelet coefficients at each level of decomposition of mammogram image. At first level of decomposition, 18 features are applied to GFS algorithm, which selects 5 features with an average classification success rate of 39.64%. Subsequently, at second level it selects 9 features from 36 features and the classification success rate is improved to 56.75%. For third level, 16 features are selected from 54 features and average success rate is improved to 64.98%. Lastly, at fourth level 72 features are applied to GFS, which selects 16 features and thereby increasing average success rate to 89.47%. Hence, GFS algorithm is the effective way of obtaining optimal set of feature in breast cancer diagnosis.

miR-134: A Human Cancer Suppressor?

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Jing-Yu Pan

2017-03-01

Full Text Available MicroRNAs (miRNAs are small noncoding RNAs approximately 20–25 nt in length, which play crucial roles through directly binding to corresponding 3′ UTR of targeted mRNAs. It has been reported that miRNAs are involved in numerous of diseases, including cancers. Recently, miR-134 has been identified to dysregulate in handles of human cancers, such as lung cancer, glioma, breast cancer, colorectal cancer, and so on. Increasing evidence indicates that miR-134 is essential for human carcinoma and participates in tumor cell proliferation, apoptosis, invasion and metastasis, drug resistance, as well as cancer diagnosis, treatment, and prognosis. Nevertheless, its roles in human cancer are still ambiguous, and its mechanisms are sophisticated as well, referring to a variety of targets and signal pathways, such as STAT5B, KRAS, MAPK/ERK signal pathway, Notch pathway, etc. Herein, we review the crucial roles of miR-134 in scores of human cancers via analyzing latest investigations, which might provide evidence for cancer diagnose, treatment, prognosis, or further investigations.

Regulations of enzymes in animals: effects of developmental processes, cancer, and radiation. Final report. [Analysis of enzymes in human cancer tissue

Energy Technology Data Exchange (ETDEWEB)

Knox, W.E.

1978-09-01

Low grade tumors of various origins are chemically very different. High grade tumors, whatever their origin, are chemically very similar to one another and to embryonic tissues. Analyses of human tumor tissues and sera from cancer patients were conducted for two new groups of enzymes expected to be informative about the physiological state of the tissue. The enzymes measured in tumors and sera were chosen because they were characteristic of fetal tissues and high grade neoplasms in rats, and could, therefore, be expected to exist in human cancers (and fetuses) and to predominate more in those of higher grade malignancies. Results indicated that the classification of enzymes (or isozymes) as fetal or adult types in the rat could be extended to man. Human cancers do contain most of the enzymes expected, and lack others, as expected. Analyses of the same enzymes in sera gave less clear results. It was recognized at the outset that no simple proportionality existed between tissue and serum levels. The tendency existed in cancer patients to have in serum elevated amounts of those enzymes characteristic of undifferentiated tissues. The abnormalities in a specific patient are conditioned by his physiological state, by the grade of his tumor, and by the mass of tumor present. The tumor mass had a very significant effect, so that monitoring this tumor burden by chemical means should be quite possible. The latest work focused on particular enzymes that have not previously been measured in cancer patients. These studies concentrated on pyrroline-5-carboxylate (P-5-C) reductase and its inhibition and on lysosomal glucosidases and phosphatases. Both groups are relatively high in fetal and neoplastic tissues.

Classification tree analysis of second neoplasms in survivors of childhood cancer

International Nuclear Information System (INIS)

Jazbec, Janez; Todorovski, Ljupčo; Jereb, Berta

2007-01-01

Reports on childhood cancer survivors estimated cumulative probability of developing secondary neoplasms vary from 3,3% to 25% at 25 years from diagnosis, and the risk of developing another cancer to several times greater than in the general population. In our retrospective study, we have used the classification tree multivariate method on a group of 849 first cancer survivors, to identify childhood cancer patients with the greatest risk for development of secondary neoplasms. In observed group of patients, 34 develop secondary neoplasm after treatment of primary cancer. Analysis of parameters present at the treatment of first cancer, exposed two groups of patients at the special risk for secondary neoplasm. First are female patients treated for Hodgkin's disease at the age between 10 and 15 years, whose treatment included radiotherapy. Second group at special risk were male patients with acute lymphoblastic leukemia who were treated at the age between 4,6 and 6,6 years of age. The risk groups identified in our study are similar to the results of studies that used more conventional approaches. Usefulness of our approach in study of occurrence of second neoplasms should be confirmed in larger sample study, but user friendly presentation of results makes it attractive for further studies

Lung cancer gene expression database analysis incorporating prior knowledge with support vector machine-based classification method

Directory of Open Access Journals (Sweden)

Huang Desheng

2009-07-01

Full Text Available Abstract Background A reliable and precise classification is essential for successful diagnosis and treatment of cancer. Gene expression microarrays have provided the high-throughput platform to discover genomic biomarkers for cancer diagnosis and prognosis. Rational use of the available bioinformation can not only effectively remove or suppress noise in gene chips, but also avoid one-sided results of separate experiment. However, only some studies have been aware of the importance of prior information in cancer classification. Methods Together with the application of support vector machine as the discriminant approach, we proposed one modified method that incorporated prior knowledge into cancer classification based on gene expression data to improve accuracy. A public well-known dataset, Malignant pleural mesothelioma and lung adenocarcinoma gene expression database, was used in this study. Prior knowledge is viewed here as a means of directing the classifier using known lung adenocarcinoma related genes. The procedures were performed by software R 2.80. Results The modified method performed better after incorporating prior knowledge. Accuracy of the modified method improved from 98.86% to 100% in training set and from 98.51% to 99.06% in test set. The standard deviations of the modified method decreased from 0.26% to 0 in training set and from 3.04% to 2.10% in test set. Conclusion The method that incorporates prior knowledge into discriminant analysis could effectively improve the capacity and reduce the impact of noise. This idea may have good future not only in practice but also in methodology.

Genetic Bee Colony (GBC) algorithm: A new gene selection method for microarray cancer classification.

Science.gov (United States)

Alshamlan, Hala M; Badr, Ghada H; Alohali, Yousef A

2015-06-01

Naturally inspired evolutionary algorithms prove effectiveness when used for solving feature selection and classification problems. Artificial Bee Colony (ABC) is a relatively new swarm intelligence method. In this paper, we propose a new hybrid gene selection method, namely Genetic Bee Colony (GBC) algorithm. The proposed algorithm combines the used of a Genetic Algorithm (GA) along with Artificial Bee Colony (ABC) algorithm. The goal is to integrate the advantages of both algorithms. The proposed algorithm is applied to a microarray gene expression profile in order to select the most predictive and informative genes for cancer classification. In order to test the accuracy performance of the proposed algorithm, extensive experiments were conducted. Three binary microarray datasets are use, which include: colon, leukemia, and lung. In addition, another three multi-class microarray datasets are used, which are: SRBCT, lymphoma, and leukemia. Results of the GBC algorithm are compared with our recently proposed technique: mRMR when combined with the Artificial Bee Colony algorithm (mRMR-ABC). We also compared the combination of mRMR with GA (mRMR-GA) and Particle Swarm Optimization (mRMR-PSO) algorithms. In addition, we compared the GBC algorithm with other related algorithms that have been recently published in the literature, using all benchmark datasets. The GBC algorithm shows superior performance as it achieved the highest classification accuracy along with the lowest average number of selected genes. This proves that the GBC algorithm is a promising approach for solving the gene selection problem in both binary and multi-class cancer classification. Copyright © 2015 Elsevier Ltd. All rights reserved.

Tumor-Associated Neutrophils in Human Lung Cancer

Science.gov (United States)

2017-10-01

markers in humans. The logistical, ethical , and regulatory difficulties in obtaining human tumor tissue for research also act to discourage such...Mouse models of cancer. Annu. Rev. Pathol 6, 95–119 52. Merlo, L.M. et al. (2006) Cancer as an evolutionary and ecological process. Nat. Rev. Cancer...some effect on the phenotype and function of TANs. The logistical, ethical , and regulatory difficulties in obtaining human tumor tissue for research

Call for a Computer-Aided Cancer Detection and Classification Research Initiative in Oman.

Science.gov (United States)

Mirzal, Andri; Chaudhry, Shafique Ahmad

2016-01-01

Cancer is a major health problem in Oman. It is reported that cancer incidence in Oman is the second highest after Saudi Arabia among Gulf Cooperation Council countries. Based on GLOBOCAN estimates, Oman is predicted to face an almost two-fold increase in cancer incidence in the period 2008-2020. However, cancer research in Oman is still in its infancy. This is due to the fact that medical institutions and infrastructure that play central roles in data collection and analysis are relatively new developments in Oman. We believe the country requires an organized plan and efforts to promote local cancer research. In this paper, we discuss current research progress in cancer diagnosis using machine learning techniques to optimize computer aided cancer detection and classification (CAD). We specifically discuss CAD using two major medical data, i.e., medical imaging and microarray gene expression profiling, because medical imaging like mammography, MRI, and PET have been widely used in Oman for assisting radiologists in early cancer diagnosis and microarray data have been proven to be a reliable source for differential diagnosis. We also discuss future cancer research directions and benefits to Oman economy for entering the cancer research and treatment business as it is a multi-billion dollar industry worldwide.

[Clinical Study of 2014 ISUP New Grade Group Classification for Prostate Cancer Patients Treated by Androgen Deprivation Therapy].

Science.gov (United States)

Uno, Masahiro; Kawase, Makoto; Kato, Daiki; Ishida, Takashi; Kato, Seiichi; Fujimoto, Yoshinori

2018-01-01

The 2014 International Society of Urological Pathology (ISUP) has proposed a new grade group (GG) classification for Gleason scores (GS). The usefulness of the new GG classification was investigated with 518 prostate cancer patients who underwent androgen deprivation therapy. According to the new GG classification, Stages B‒D and the new GG classification relapse-free rate for each stage were calculated using the Kaplan‒Meier method. The new GG classification revealed a significant difference for the relapse-free rate only between some groups. Analysis using the Cox proportional hazards model indicated that the risk of relapse was higher in GGs 4 and 5 than in GG 1. The usefulness about the relapse-free rate in androgen deprivation therapy of the 2014 ISUP new grade group classification a waits future examination.

Early detection of lung cancer from CT images: nodule segmentation and classification using deep learning

Science.gov (United States)

Sharma, Manu; Bhatt, Jignesh S.; Joshi, Manjunath V.

2018-04-01

Lung cancer is one of the most abundant causes of the cancerous deaths worldwide. It has low survival rate mainly due to the late diagnosis. With the hardware advancements in computed tomography (CT) technology, it is now possible to capture the high resolution images of lung region. However, it needs to be augmented by efficient algorithms to detect the lung cancer in the earlier stages using the acquired CT images. To this end, we propose a two-step algorithm for early detection of lung cancer. Given the CT image, we first extract the patch from the center location of the nodule and segment the lung nodule region. We propose to use Otsu method followed by morphological operations for the segmentation. This step enables accurate segmentation due to the use of data-driven threshold. Unlike other methods, we perform the segmentation without using the complete contour information of the nodule. In the second step, a deep convolutional neural network (CNN) is used for the better classification (malignant or benign) of the nodule present in the segmented patch. Accurate segmentation of even a tiny nodule followed by better classification using deep CNN enables the early detection of lung cancer. Experiments have been conducted using 6306 CT images of LIDC-IDRI database. We achieved the test accuracy of 84.13%, with the sensitivity and specificity of 91.69% and 73.16%, respectively, clearly outperforming the state-of-the-art algorithms.

Breast cancer molecular subtype classification using deep features: preliminary results

Science.gov (United States)

Zhu, Zhe; Albadawy, Ehab; Saha, Ashirbani; Zhang, Jun; Harowicz, Michael R.; Mazurowski, Maciej A.

2018-02-01

Radiogenomics is a field of investigation that attempts to examine the relationship between imaging characteris- tics of cancerous lesions and their genomic composition. This could offer a noninvasive alternative to establishing genomic characteristics of tumors and aid cancer treatment planning. While deep learning has shown its supe- riority in many detection and classification tasks, breast cancer radiogenomic data suffers from a very limited number of training examples, which renders the training of the neural network for this problem directly and with no pretraining a very difficult task. In this study, we investigated an alternative deep learning approach referred to as deep features or off-the-shelf network approach to classify breast cancer molecular subtypes using breast dynamic contrast enhanced MRIs. We used the feature maps of different convolution layers and fully connected layers as features and trained support vector machines using these features for prediction. For the feature maps that have multiple layers, max-pooling was performed along each channel. We focused on distinguishing the Luminal A subtype from other subtypes. To evaluate the models, 10 fold cross-validation was performed and the final AUC was obtained by averaging the performance of all the folds. The highest average AUC obtained was 0.64 (0.95 CI: 0.57-0.71), using the feature maps of the last fully connected layer. This indicates the promise of using this approach to predict the breast cancer molecular subtypes. Since the best performance appears in the last fully connected layer, it also implies that breast cancer molecular subtypes may relate to high level image features

Breast Cancer Survival Defined by the ER/PR/HER2 Subtypes and a Surrogate Classification according to Tumor Grade and Immunohistochemical Biomarkers

Directory of Open Access Journals (Sweden)

Carol A. Parise

2014-01-01

Full Text Available Introduction. ER, PR, and HER2 are routinely available in breast cancer specimens. The purpose of this study is to contrast breast cancer-specific survival for the eight ER/PR/HER2 subtypes with survival of an immunohistochemical surrogate for the molecular subtype based on the ER/PR/HER2 subtypes and tumor grade. Methods. We identified 123,780 cases of stages 1–3 primary female invasive breast cancer from California Cancer Registry. The surrogate classification was derived using ER/PR/HER2 and tumor grade. Kaplan-Meier survival analysis and Cox proportional hazards modeling were used to assess differences in survival and risk of mortality for the ER/PR/HER2 subtypes and surrogate classification within each stage. Results. The luminal B/HER2− surrogate classification had a higher risk of mortality than the luminal B/HER2+ for all stages of disease. There was no difference in risk of mortality between the ER+/PR+/HER2− and ER+/PR+/HER2+ in stage 3. With one exception in stage 3, the ER-negative subtypes all had an increased risk of mortality when compared with the ER-positive subtypes. Conclusions. Assessment of survival using ER/PR/HER2 illustrates the heterogeneity of HER2+ subtypes. The surrogate classification provides clear separation in survival and adjusted mortality but underestimates the wide variability within the subtypes that make up the classification.

Breast Cancer Survival Defined by the ER/PR/HER2 Subtypes and a Surrogate Classification according to Tumor Grade and Immunohistochemical Bio markers

International Nuclear Information System (INIS)

Parise, C. A.; Caggiano, V.

2014-01-01

ER, PR, and HER2 are routinely available in breast cancer specimens. The purpose of this study is to contrast breast cancer-specific survival for the eight ER/PR/HER2 subtypes with survival of an immunohistochemical surrogate for the molecular subtype based on the ER/PR/HER2 subtypes and tumor grade. Methods. We identified 123,780 cases of stages 1-3 primary female invasive breast cancer from California Cancer Registry. The surrogate classification was derived using ER/PR/HER2 and tumor grade. Kaplan-Meier survival analysis and Cox proportional hazards modeling were used to assess differences in survival and risk of mortality for the ER/PR/HER2 subtypes and surrogate classification within each stage. Results. The luminal B/HER2− surrogate classification had a higher risk of mortality than the luminal B/HER2+ for all stages of disease. There was no difference in risk of mortality between the ER+/PR+/HER2− and ER+/PR+/HER2+ in stage 3. With one exception in stage 3, the ER-negative subtypes all had an increased risk of mortality when compared with the ER-positive subtypes. Conclusions. Assessment of survival using ER/PR/HER2 illustrates the heterogeneity of HER2+ subtypes. The surrogate classification provides clear separation in survival and adjusted mortality but underestimates the wide variability within the subtypes that make up the classification.

Development of Human Factor Management Requirements and Human Error Classification for the Prevention of Railway Accident

International Nuclear Information System (INIS)

Kwak, Sang Log; Park, Chan Woo; Shin, Seung Ryoung

2008-08-01

Railway accident analysis results show that accidents cased by human factors are not decreasing, whereas H/W related accidents are steadily decreasing. For the efficient management of human factors, many expertise on design, conditions, safety culture and staffing are required. But current safety management activities on safety critical works are focused on training, due to the limited resource and information. In order to improve railway safety, human factors management requirements for safety critical worker and human error classification is proposed in this report. For this accident analysis, status of safety measure on human factor, safety management system on safety critical worker, current safety planning is analysis

Training ANFIS structure using genetic algorithm for liver cancer classification based on microarray gene expression data

Directory of Open Access Journals (Sweden)

Bülent Haznedar

2017-02-01

Full Text Available Classification is an important data mining technique, which is used in many fields mostly exemplified as medicine, genetics and biomedical engineering. The number of studies about classification of the datum on DNA microarray gene expression is specifically increased in recent years. However, because of the reasons as the abundance of gene numbers in the datum as microarray gene expressions and the nonlinear relations mostly across those datum, the success of conventional classification algorithms can be limited. Because of these reasons, the interest on classification methods which are based on artificial intelligence to solve the problem on classification has been gradually increased in recent times. In this study, a hybrid approach which is based on Adaptive Neuro-Fuzzy Inference System (ANFIS and Genetic Algorithm (GA are suggested in order to classify liver microarray cancer data set. Simulation results are compared with the results of other methods. According to the results obtained, it is seen that the recommended method is better than the other methods.

Classification of Cancer-related Death Certificates using Machine Learning

Directory of Open Access Journals (Sweden)

Luke Butt

2013-05-01

Full Text Available BackgroundCancer monitoring and prevention relies on the critical aspect of timely notification of cancer cases. However, the abstraction and classification of cancer from the free-text of pathology reports and other relevant documents, such as death certificates, exist as complex and time-consuming activities.AimsIn this paper, approaches for the automatic detection of notifiable cancer cases as the cause of death from free-text death certificates supplied to Cancer Registries are investigated.Method A number of machine learning classifiers were studied. Features were extracted using natural language techniques and the Medtex toolkit. The numerous features encompassed stemmed words, bi-grams, and concepts from the SNOMED CT medical terminology. The baseline consisted of a keyword spotter using keywords extracted from the long description of ICD-10 cancer related codes.ResultsDeath certificates with notifiable cancer listed as the cause of death can be effectively identified with the methods studied in this paper. A Support Vector Machine (SVM classifier achieved best performance with an overall F-measure of 0.9866 when evaluated on a set of 5,000 free-text death certificates using the token stem feature set. The SNOMED CT concept plus token stem feature set reached the lowest variance (0.0032 and false negative rate (0.0297 while achieving an F-measure of 0.9864. The SVM classifier accounts for the first 18 of the top 40 evaluated runs, and entails the most robust classifier with a variance of 0.001141, half the variance of the other classifiers.ConclusionThe selection of features significantly produced the most influences on the performance of the classifiers, although the type of classifier employed also affects performance. In contrast, the feature weighting schema created a negligible effect on performance. Specifically, it is found that stemmed tokens with or without SNOMED CT concepts create the most effective feature when combined with

Validation of the prognostic gene portfolio, ClinicoMolecular Triad Classification, using an independent prospective breast cancer cohort and external patient populations.

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Wang, Dong-Yu; Done, Susan J; Mc Cready, David R; Leong, Wey L

2014-07-04

Using genome-wide expression profiles of a prospective training cohort of breast cancer patients, ClinicoMolecular Triad Classification (CMTC) was recently developed to classify breast cancers into three clinically relevant groups to aid treatment decisions. CMTC was found to be both prognostic and predictive in a large external breast cancer cohort in that study. This study serves to validate the reproducibility of CMTC and its prognostic value using independent patient cohorts. An independent internal cohort (n = 284) and a new external cohort (n = 2,181) were used to validate the association of CMTC between clinicopathological factors, 12 known gene signatures, two molecular subtype classifiers, and 19 oncogenic signalling pathway activities, and to reproduce the abilities of CMTC to predict clinical outcomes of breast cancer. In addition, we also updated the outcome data of the original training cohort (n = 147). The original training cohort reached a statistically significant difference (p value of the triad classification was reproduced in the second independent internal cohort and the new external validation cohort. CMTC achieved even higher prognostic significance when all available patients were analyzed (n = 4,851). Oncogenic pathways Myc, E2F1, Ras and β-catenin were again implicated in the high-risk groups. Both prospective internal cohorts and the independent external cohorts reproduced the triad classification of CMTC and its prognostic significance. CMTC is an independent prognostic predictor, and it outperformed 12 other known prognostic gene signatures, molecular subtype classifications, and all other standard prognostic clinicopathological factors. Our results support further development of CMTC portfolio into a guide for personalized breast cancer treatments.

Human Prostate Cancer Hallmarks Map

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Datta, Dipamoy; Aftabuddin, Md.; Gupta, Dinesh Kumar; Raha, Sanghamitra; Sen, Prosenjit

2016-01-01

Human prostate cancer is a complex heterogeneous disease that mainly affects elder male population of the western world with a high rate of mortality. Acquisitions of diverse sets of hallmark capabilities along with an aberrant functioning of androgen receptor signaling are the central driving forces behind prostatic tumorigenesis and its transition into metastatic castration resistant disease. These hallmark capabilities arise due to an intense orchestration of several crucial factors, including deregulation of vital cell physiological processes, inactivation of tumor suppressive activity and disruption of prostate gland specific cellular homeostasis. The molecular complexity and redundancy of oncoproteins signaling in prostate cancer demands for concurrent inhibition of multiple hallmark associated pathways. By an extensive manual curation of the published biomedical literature, we have developed Human Prostate Cancer Hallmarks Map (HPCHM), an onco-functional atlas of human prostate cancer associated signaling and events. It explores molecular architecture of prostate cancer signaling at various levels, namely key protein components, molecular connectivity map, oncogenic signaling pathway map, pathway based functional connectivity map etc. Here, we briefly represent the systems level understanding of the molecular mechanisms associated with prostate tumorigenesis by considering each and individual molecular and cell biological events of this disease process. PMID:27476486

Colon cancer associated transcripts in human cancers.

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Chen, Yincong; Xie, Haibiao; Gao, Qunjun; Zhan, Hengji; Xiao, Huizhong; Zou, Yifan; Zhang, Fuyou; Liu, Yuchen; Li, Jianfa

2017-10-01

Long non-coding RNAs serve as important regulators in complicated cellular activities, including cell differentiation, proliferation and death. Dysregulation of long non-coding RNAs occurs in the formation and progression of cancers. The family of colon cancer associated transcripts, long non-coding RNAs colon cancer associated transcript-1 and colon cancer associated transcript-2 are known as oncogenes involved in various cancers. Colon cancer associated transcript-1 is a novel lncRNA located in 8q24.2, and colon cancer associated transcript-2 maps to the 8q24.21 region encompassing rs6983267. Colon cancer associated transcripts have close associations with clinical characteristics, such as lymph node metastasis, high TNM stage and short overall survival. Knockdown of them can reverse the malignant phenotypes of cancer cells, including proliferation, migration, invasion and apoptosis. Moreover, they can increase the expression level of c-MYC and oncogenic microRNAs via activating a series of complex mechanisms. In brief, the family of colon cancer associated transcripts may serve as potential biomarkers or therapeutic targets for human cancers. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

Mycotoxins as human carcinogens-the IARC Monographs classification.

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Ostry, Vladimir; Malir, Frantisek; Toman, Jakub; Grosse, Yann

2017-02-01

Humans are constantly exposed to mycotoxins (e.g. aflatoxins, ochratoxins), mainly via food intake of plant and animal origin. The health risks stemming from mycotoxins may result from their toxicity, in particular their carcinogenicity. In order to prevent these risks, the International Agency for Research on Cancer (IARC) in Lyon (France)-through its IARC Monographs programme-has performed the carcinogenic hazard assessment of some mycotoxins in humans, on the basis of epidemiological data, studies of cancer in experimental animals and mechanistic studies. The present article summarizes the carcinogenic hazard assessments of those mycotoxins, especially aflatoxins (aflatoxin B 1 , B 2 , G 1 , G 2 and M 1 ), fumonisins (fumonisin B 1 and B 2 ) and ochratoxin A (OTA). New information regarding the genotoxicity of OTA (formation of OTA-DNA adducts), the role of OTA in oxidative stress and the identification of epigenetic factors involved in OTA carcinogenesis-should they indeed provide strong evidence that OTA carcinogenicity is mediated by a mechanism that also operates in humans-could lead to the reclassification of OTA.

Laser Raman detection for oral cancer based on an adaptive Gaussian process classification method with posterior probabilities

International Nuclear Information System (INIS)

Du, Zhanwei; Yang, Yongjian; Bai, Yuan; Wang, Lijun; Su, Le; Chen, Yong; Li, Xianchang; Zhou, Xiaodong; Shen, Aiguo; Hu, Jiming; Jia, Jun

2013-01-01

The existing methods for early and differential diagnosis of oral cancer are limited due to the unapparent early symptoms and the imperfect imaging examination methods. In this paper, the classification models of oral adenocarcinoma, carcinoma tissues and a control group with just four features are established by utilizing the hybrid Gaussian process (HGP) classification algorithm, with the introduction of the mechanisms of noise reduction and posterior probability. HGP shows much better performance in the experimental results. During the experimental process, oral tissues were divided into three groups, adenocarcinoma (n = 87), carcinoma (n = 100) and the control group (n = 134). The spectral data for these groups were collected. The prospective application of the proposed HGP classification method improved the diagnostic sensitivity to 56.35% and the specificity to about 70.00%, and resulted in a Matthews correlation coefficient (MCC) of 0.36. It is proved that the utilization of HGP in LRS detection analysis for the diagnosis of oral cancer gives accurate results. The prospect of application is also satisfactory. (paper)

Laser Raman detection for oral cancer based on an adaptive Gaussian process classification method with posterior probabilities

Science.gov (United States)

Du, Zhanwei; Yang, Yongjian; Bai, Yuan; Wang, Lijun; Su, Le; Chen, Yong; Li, Xianchang; Zhou, Xiaodong; Jia, Jun; Shen, Aiguo; Hu, Jiming

2013-03-01

The existing methods for early and differential diagnosis of oral cancer are limited due to the unapparent early symptoms and the imperfect imaging examination methods. In this paper, the classification models of oral adenocarcinoma, carcinoma tissues and a control group with just four features are established by utilizing the hybrid Gaussian process (HGP) classification algorithm, with the introduction of the mechanisms of noise reduction and posterior probability. HGP shows much better performance in the experimental results. During the experimental process, oral tissues were divided into three groups, adenocarcinoma (n = 87), carcinoma (n = 100) and the control group (n = 134). The spectral data for these groups were collected. The prospective application of the proposed HGP classification method improved the diagnostic sensitivity to 56.35% and the specificity to about 70.00%, and resulted in a Matthews correlation coefficient (MCC) of 0.36. It is proved that the utilization of HGP in LRS detection analysis for the diagnosis of oral cancer gives accurate results. The prospect of application is also satisfactory.

Improved prognostic classification of breast cancer defined by antagonistic activation patterns of immune response pathway modules

International Nuclear Information System (INIS)

Teschendorff, Andrew E; Gomez, Sergio; Arenas, Alex; El-Ashry, Dorraya; Schmidt, Marcus; Gehrmann, Mathias; Caldas, Carlos

2010-01-01

Elucidating the activation pattern of molecular pathways across a given tumour type is a key challenge necessary for understanding the heterogeneity in clinical response and for developing novel more effective therapies. Gene expression signatures of molecular pathway activation derived from perturbation experiments in model systems as well as structural models of molecular interactions ('model signatures') constitute an important resource for estimating corresponding activation levels in tumours. However, relatively few strategies for estimating pathway activity from such model signatures exist and only few studies have used activation patterns of pathways to refine molecular classifications of cancer. Here we propose a novel network-based method for estimating pathway activation in tumours from model signatures. We find that although the pathway networks inferred from cancer expression data are highly consistent with the prior information contained in the model signatures, that they also exhibit a highly modular structure and that estimation of pathway activity is dependent on this modular structure. We apply our methodology to a panel of 438 estrogen receptor negative (ER-) and 785 estrogen receptor positive (ER+) breast cancers to infer activation patterns of important cancer related molecular pathways. We show that in ER negative basal and HER2+ breast cancer, gene expression modules reflecting T-cell helper-1 (Th1) and T-cell helper-2 (Th2) mediated immune responses play antagonistic roles as major risk factors for distant metastasis. Using Boolean interaction Cox-regression models to identify non-linear pathway combinations associated with clinical outcome, we show that simultaneous high activation of Th1 and low activation of a TGF-beta pathway module defines a subtype of particularly good prognosis and that this classification provides a better prognostic model than those based on the individual pathways. In ER+ breast cancer, we find that

Survival differences of CIMP subtypes integrated with CNA information in human breast cancer.

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Wang, Huihan; Yan, Weili; Zhang, Shumei; Gu, Yue; Wang, Yihan; Wei, Yanjun; Liu, Hongbo; Wang, Fang; Wu, Qiong; Zhang, Yan

2017-07-25

CpG island methylator phenotype of breast cancer is associated with widespread aberrant methylation at specified CpG islands and distinct patient outcomes. However, the influence of copy number contributing to the prognosis of tumors with different CpG island methylator phenotypes is still unclear. We analyzed both genetic (copy number) and epigenetic alterations in 765 breast cancers from The Cancer Genome Atlas data portal and got a panel of 15 biomarkers for copy number and methylation status evaluation. The gene panel identified two groups corresponding to distinct copy number profiles. In status of mere-loss copy number, patients were faced with a greater risk if they presented a higher CpG islands methylation pattern in biomarker panels. But for samples presenting merely-gained copy number, higher methylation level of CpG islands was associated with improved viability. In all, the integration of copy number alteration and methylation information enhanced the classification power on prognosis. Moreover, we found the molecular subtypes of breast cancer presented different distributions in two CpG island methylation phenotypes. Generated by the same set of human methylation 450K data, additional copy number information could provide insights into survival prediction of cancers with less heterogeneity and might help to determine the biomarkers for diagnosis and treatment for breast cancer patients in a more personalized approach.

Prevalence of Human Papillomavirus in endometrial cancer

DEFF Research Database (Denmark)

Olesen, Tina Bech; Svahn, Malene Frøsig; Faber, Mette Tuxen

2014-01-01

HPV is a common sexually transmitted infection and is considered to be a necessary cause of cervical cancer. The anatomical proximity to the cervix has led researchers to investigate whether Human Papillomavirus (HPV) has a role in the etiology of endometrial cancer.......HPV is a common sexually transmitted infection and is considered to be a necessary cause of cervical cancer. The anatomical proximity to the cervix has led researchers to investigate whether Human Papillomavirus (HPV) has a role in the etiology of endometrial cancer....

Clinical application of a microfluidic chip for immunocapture and quantification of circulating exosomes to assist breast cancer diagnosis and molecular classification.

Science.gov (United States)

Fang, Shimeng; Tian, Hongzhu; Li, Xiancheng; Jin, Dong; Li, Xiaojie; Kong, Jing; Yang, Chun; Yang, Xuesong; Lu, Yao; Luo, Yong; Lin, Bingcheng; Niu, Weidong; Liu, Tingjiao

2017-01-01

Increasing attention has been attracted by exosomes in blood-based diagnosis because cancer cells release more exosomes in serum than normal cells and these exosomes overexpress a certain number of cancer-related biomarkers. However, capture and biomarker analysis of exosomes for clinical application are technically challenging. In this study, we developed a microfluidic chip for immunocapture and quantification of circulating exosomes from small sample volume and applied this device in clinical study. Circulating EpCAM-positive exosomes were measured in 6 cases breast cancer patients and 3 healthy controls to assist diagnosis. A significant increase in the EpCAM-positive exosome level in these patients was detected, compared to healthy controls. Furthermore, we quantified circulating HER2-positive exosomes in 19 cases of breast cancer patients for molecular classification. We demonstrated that the exosomal HER2 expression levels were almost consistent with that in tumor tissues assessed by immunohistochemical staining. The microfluidic chip might provide a new platform to assist breast cancer diagnosis and molecular classification.

Human pancreatic cancer xenografts recapitulate key aspects of cancer cachexia.

Science.gov (United States)

Delitto, Daniel; Judge, Sarah M; Delitto, Andrea E; Nosacka, Rachel L; Rocha, Fernanda G; DiVita, Bayli B; Gerber, Michael H; George, Thomas J; Behrns, Kevin E; Hughes, Steven J; Wallet, Shannon M; Judge, Andrew R; Trevino, Jose G

2017-01-03

Cancer cachexia represents a debilitating syndrome that diminishes quality of life and augments the toxicities of conventional treatments. Cancer cachexia is particularly debilitating in patients with pancreatic cancer (PC). Mechanisms responsible for cancer cachexia are under investigation and are largely derived from observations in syngeneic murine models of cancer which are limited in PC. We evaluate the effect of human PC cells on both muscle wasting and the systemic inflammatory milieu potentially contributing to PC-associated cachexia. Specifically, human PC xenografts were generated by implantation of pancreatic cancer cells, L3.6pl and PANC-1, either in the flank or orthotopically within the pancreas. Mice bearing orthotopic xenografts demonstrated significant muscle wasting and atrophy-associated gene expression changes compared to controls. Further, despite the absence of adaptive immunity, splenic tissue from orthotopically engrafted mice demonstrated elevations in several pro-inflammatory cytokines associated with cancer cachexia, including TNFα, IL1β, IL6 and KC (murine IL8 homologue), when compared to controls. Therefore, data presented here support further investigation into the complexity of cancer cachexia in PC to identify potential targets for this debilitating syndrome.

Validation of the prognostic gene portfolio, ClinicoMolecular Triad Classification, using an independent prospective breast cancer cohort and external patient populations

Science.gov (United States)

2014-01-01

Introduction Using genome-wide expression profiles of a prospective training cohort of breast cancer patients, ClinicoMolecular Triad Classification (CMTC) was recently developed to classify breast cancers into three clinically relevant groups to aid treatment decisions. CMTC was found to be both prognostic and predictive in a large external breast cancer cohort in that study. This study serves to validate the reproducibility of CMTC and its prognostic value using independent patient cohorts. Methods An independent internal cohort (n = 284) and a new external cohort (n = 2,181) were used to validate the association of CMTC between clinicopathological factors, 12 known gene signatures, two molecular subtype classifiers, and 19 oncogenic signalling pathway activities, and to reproduce the abilities of CMTC to predict clinical outcomes of breast cancer. In addition, we also updated the outcome data of the original training cohort (n = 147). Results The original training cohort reached a statistically significant difference (p risk groups. Conclusions Both prospective internal cohorts and the independent external cohorts reproduced the triad classification of CMTC and its prognostic significance. CMTC is an independent prognostic predictor, and it outperformed 12 other known prognostic gene signatures, molecular subtype classifications, and all other standard prognostic clinicopathological factors. Our results support further development of CMTC portfolio into a guide for personalized breast cancer treatments. PMID:24996446

Supervised machine learning and active learning in classification of radiology reports.

Science.gov (United States)

Nguyen, Dung H M; Patrick, Jon D

2014-01-01

This paper presents an automated system for classifying the results of imaging examinations (CT, MRI, positron emission tomography) into reportable and non-reportable cancer cases. This system is part of an industrial-strength processing pipeline built to extract content from radiology reports for use in the Victorian Cancer Registry. In addition to traditional supervised learning methods such as conditional random fields and support vector machines, active learning (AL) approaches were investigated to optimize training production and further improve classification performance. The project involved two pilot sites in Victoria, Australia (Lake Imaging (Ballarat) and Peter MacCallum Cancer Centre (Melbourne)) and, in collaboration with the NSW Central Registry, one pilot site at Westmead Hospital (Sydney). The reportability classifier performance achieved 98.25% sensitivity and 96.14% specificity on the cancer registry's held-out test set. Up to 92% of training data needed for supervised machine learning can be saved by AL. AL is a promising method for optimizing the supervised training production used in classification of radiology reports. When an AL strategy is applied during the data selection process, the cost of manual classification can be reduced significantly. The most important practical application of the reportability classifier is that it can dramatically reduce human effort in identifying relevant reports from the large imaging pool for further investigation of cancer. The classifier is built on a large real-world dataset and can achieve high performance in filtering relevant reports to support cancer registries. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

HUMAN PROSTATE CANCER RISK FACTORS

Science.gov (United States)

Prostate cancer has the highest prevalence of any non-skin cancer in the human body, with similar likelihood of neoplastic foci found within the prostates of men around the world regardless of diet, occupation, lifestyle, or other factors. Essentially all men with circulating an...

A Mouse Model for Human Anal Cancer

Science.gov (United States)

Stelzer, Marie K.; Pitot, Henry C.; Liem, Amy; Schweizer, Johannes; Mahoney, Charles; Lambert, Paul F.

2010-01-01

Human anal cancers are associated with high-risk human papillomaviruses (HPVs) that cause other anogenital cancers and head and neck cancers. As with other cancers, HPV16 is the most common high-risk HPV in anal cancers. We describe the generation and characterization of a mouse model for human anal cancer. This model makes use of K14E6 and K14E7 transgenic mice in which the HPV16 E6 and E7 genes are directed in their expression to stratified squamous epithelia. HPV16 E6 and E7 possess oncogenic properties including but not limited to their capacity to inactivate the cellular tumor suppressors p53 and pRb, respectively. Both E6 and E7 were found to be functionally expressed in the anal epithelia of K14E6/K14E7 transgenic mice. To assess the susceptibility of these mice to anal cancer, mice were treated topically with dimethylbenz[a]anthracene (DMBA), a chemical carcinogen that is known to induce squamous cell carcinomas in other sites. Nearly 50% of DMBA-treated HPV16 E6/E7 transgenic mice showed overt signs of tumors; whereas, none of the like treated non-transgenic mice showed tumors. Histopathological analyses confirmed that the HPV16 transgenic mice were increased in their susceptibility to anal cancers and precancerous lesions. Biomarker analyses demonstrated that these mouse anal cancers exhibit properties that are similar to those observed in HPV-positive precursors to human anal cancer. This is the first mouse model for investigating the contributions of viral and cellular factors in anal carcinogenesis, and should provide a platform for assessing new therapeutic modalities for treating and/or preventing this type of cancer. PMID:20947489

Gene selection for cancer classification with the help of bees.

Science.gov (United States)

Moosa, Johra Muhammad; Shakur, Rameen; Kaykobad, Mohammad; Rahman, Mohammad Sohel

2016-08-10

Development of biologically relevant models from gene expression data notably, microarray data has become a topic of great interest in the field of bioinformatics and clinical genetics and oncology. Only a small number of gene expression data compared to the total number of genes explored possess a significant correlation with a certain phenotype. Gene selection enables researchers to obtain substantial insight into the genetic nature of the disease and the mechanisms responsible for it. Besides improvement of the performance of cancer classification, it can also cut down the time and cost of medical diagnoses. This study presents a modified Artificial Bee Colony Algorithm (ABC) to select minimum number of genes that are deemed to be significant for cancer along with improvement of predictive accuracy. The search equation of ABC is believed to be good at exploration but poor at exploitation. To overcome this limitation we have modified the ABC algorithm by incorporating the concept of pheromones which is one of the major components of Ant Colony Optimization (ACO) algorithm and a new operation in which successive bees communicate to share their findings. The proposed algorithm is evaluated using a suite of ten publicly available datasets after the parameters are tuned scientifically with one of the datasets. Obtained results are compared to other works that used the same datasets. The performance of the proposed method is proved to be superior. The method presented in this paper can provide subset of genes leading to more accurate classification results while the number of selected genes is smaller. Additionally, the proposed modified Artificial Bee Colony Algorithm could conceivably be applied to problems in other areas as well.

FEATURE EXTRACTION BASED WAVELET TRANSFORM IN BREAST CANCER DIAGNOSIS USING FUZZY AND NON-FUZZY CLASSIFICATION

Directory of Open Access Journals (Sweden)

Pelin GORGEL

2013-01-01

Full Text Available This study helps to provide a second eye to the expert radiologists for the classification of manually extracted breast masses taken from 60 digital mammıgrams. These mammograms have been acquired from Istanbul University Faculty of Medicine Hospital and have 78 masses. The diagnosis is implemented with pre-processing by using feature extraction based Fast Wavelet Transform (FWT. Afterwards Adaptive Neuro-Fuzzy Inference System (ANFIS based fuzzy subtractive clustering and Support Vector Machines (SVM methods are used for the classification. It is a comparative study which uses these methods respectively. According to the results of the study, ANFIS based subtractive clustering produces ??% while SVM produces ??% accuracy in malignant-benign classification. The results demonstrate that the developed system could help the radiologists for a true diagnosis and decrease the number of the missing cancerous regions or unnecessary biopsies.

Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin

DEFF Research Database (Denmark)

Hoadley, Katherine A; Yau, Christina; Wolf, Denise M

2014-01-01

Recent genomic analyses of pathologically defined tumor types identify "within-a-tissue" disease subtypes. However, the extent to which genomic signatures are shared across tissues is still unclear. We performed an integrative analysis using five genome-wide platforms and one proteomic platform...... on 3,527 specimens from 12 cancer types, revealing a unified classification into 11 major subtypes. Five subtypes were nearly identical to their tissue-of-origin counterparts, but several distinct cancer types were found to converge into common subtypes. Lung squamous, head and neck, and a subset...

On the classification and evolution of endogenous retrovirus: human endogenous retroviruses may not be 'human' after all.

Science.gov (United States)

Escalera-Zamudio, Marina; Greenwood, Alex D

2016-01-01

Retroviruses, as part of their replication cycle, become integrated into the genome of their host. When this occurs in the germline the integrated proviruses can become an endogenous retrovirus (ERV) which may eventually become fixed in the population. ERVs are present in the genomes of all vertebrates including humans, where more than 50 groups of human endogenous retrovirus (HERVs) have been described within the last 30 years. Despite state-of-the-art genomic tools available for retroviral discovery and the large number of retroviral sequences described to date, there are still gaps in understanding retroviral macroevolutionary patterns and host-retrovirus interactions and a lack of a coherent systematic classification particularly for HERVs. Here, we discuss the current knowledge on ERV (and HERV) classification, distribution and origins focusing on the role of cross-species transmission in retroviral diversity. © 2016 APMIS. Published by John Wiley & Sons Ltd.

Body mass index: different nutritional status according to WHO, OPAS and Lipschitz classifications in gastrointestinal cancer patients.

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Barao, Katia; Forones, Nora Manoukian

2012-01-01

The body mass index (BMI) is the most common marker used on diagnoses of the nutritional status. The great advantage of this index is the easy way to measure, the low cost, the good correlation with the fat mass and the association to morbidity and mortality. To compare the BMI differences according to the WHO, OPAS and Lipschitz classification. A prospective study on 352 patients with esophageal, gastric or colorectal cancer was done. The BMI was calculated and analyzed by the classification of WHO, Lipschitz and OPAS. The mean age was 62.1 ± 12.4 years and 59% of them had more than 59 years. The BMI had not difference between the genders in patients cancer had more than 65 years. A different cut off must be used for this patients, because undernourished patients may be wrongly considered well nourished.

Naïve and Robust: Class-Conditional Independence in Human Classification Learning

Science.gov (United States)

Jarecki, Jana B.; Meder, Björn; Nelson, Jonathan D.

2018-01-01

Humans excel in categorization. Yet from a computational standpoint, learning a novel probabilistic classification task involves severe computational challenges. The present paper investigates one way to address these challenges: assuming class-conditional independence of features. This feature independence assumption simplifies the inference…

Transporter Classification Database (TCDB)

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U.S. Department of Health & Human Services — The Transporter Classification Database details a comprehensive classification system for membrane transport proteins known as the Transporter Classification (TC)...

Human Classification Based on Gestural Motions by Using Components of PCA

International Nuclear Information System (INIS)

Aziz, Azri A; Wan, Khairunizam; Za'aba, S K; Shahriman A B; Asyekin H; Zuradzman M R; Adnan, Nazrul H

2013-01-01

Lately, a study of human capabilities with the aim to be integrated into machine is the famous topic to be discussed. Moreover, human are bless with special abilities that they can hear, see, sense, speak, think and understand each other. Giving such abilities to machine for improvement of human life is researcher's aim for better quality of life in the future. This research was concentrating on human gesture, specifically arm motions for differencing the individuality which lead to the development of the hand gesture database. We try to differentiate the human physical characteristic based on hand gesture represented by arm trajectories. Subjects are selected from different type of the body sizes, and then acquired data undergo resampling process. The results discuss the classification of human based on arm trajectories by using Principle Component Analysis (PCA)

Plasma membrane proteomics of human breast cancer cell lines identifies potential targets for breast cancer diagnosis and treatment.

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Yvonne S Ziegler

Full Text Available The use of broad spectrum chemotherapeutic agents to treat breast cancer results in substantial and debilitating side effects, necessitating the development of targeted therapies to limit tumor proliferation and prevent metastasis. In recent years, the list of approved targeted therapies has expanded, and it includes both monoclonal antibodies and small molecule inhibitors that interfere with key proteins involved in the uncontrolled growth and migration of cancer cells. The targeting of plasma membrane proteins has been most successful to date, and this is reflected in the large representation of these proteins as targets of newer therapies. In view of these facts, experiments were designed to investigate the plasma membrane proteome of a variety of human breast cancer cell lines representing hormone-responsive, ErbB2 over-expressing and triple negative cell types, as well as a benign control. Plasma membranes were isolated by using an aqueous two-phase system, and the resulting proteins were subjected to mass spectrometry analysis. Overall, each of the cell lines expressed some unique proteins, and a number of proteins were expressed in multiple cell lines, but in patterns that did not always follow traditional clinical definitions of breast cancer type. From our data, it can be deduced that most cancer cells possess multiple strategies to promote uncontrolled growth, reflected in aberrant expression of tyrosine kinases, cellular adhesion molecules, and structural proteins. Our data set provides a very rich and complex picture of plasma membrane proteins present on breast cancer cells, and the sorting and categorizing of this data provides interesting insights into the biology, classification, and potential treatment of this prevalent and debilitating disease.

Immunological responses against human papilloma virus and human papilloma virus induced laryngeal cancer.

Science.gov (United States)

Chitose, Shun-ichi; Sakazaki, T; Ono, T; Kurita, T; Mihashi, H; Nakashima, T

2010-06-01

This study aimed to clarify the local immune status in the larynx in the presence of infection or carcinogenesis associated with human papilloma virus. Cytological samples (for human papilloma virus detection) and laryngeal secretions (for immunoglobulin assessment) were obtained from 31 patients with laryngeal disease, during microscopic laryngeal surgery. On histological examination, 12 patients had squamous cell carcinoma, four had laryngeal papilloma and 15 had other benign laryngeal disease. Cytological samples were tested for human papilloma virus DNA using the Hybrid Capture 2 assay. High risk human papilloma virus DNA was detected in 25 per cent of patients (three of 12) with laryngeal cancer. Low risk human papilloma virus DNA was detected only in three laryngeal papilloma patients. The mean laryngeal secretion concentrations of immunoglobulins M, G and A and secretory immunoglobulin A in human papilloma virus DNA positive patients were more than twice those in human papilloma virus DNA negative patients. A statistically significant difference was observed between the secretory immunoglobulin A concentrations in the two groups. Patients with laryngeal cancer had higher laryngeal secretion concentrations of each immunoglobulin type, compared with patients with benign laryngeal disease. The study assessed the mean laryngeal secretion concentrations of each immunoglobulin type in the 12 laryngeal cancer patients, comparing human papilloma virus DNA positive patients (n = 3) and human papilloma virus DNA negative patients (n = 9); the mean concentrations of immunoglobulins M, G and A and secretory immunoglobulin A tended to be greater in human papilloma virus DNA positive cancer patients, compared with human papilloma virus DNA negative cancer patients. These results suggest that the local laryngeal immune response is activated by infection or carcinogenesis due to human papilloma virus. The findings strongly suggest that secretory IgA has inhibitory activity

On the classification techniques in data mining for microarray data classification

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Aydadenta, Husna; Adiwijaya

2018-03-01

Cancer is one of the deadly diseases, according to data from WHO by 2015 there are 8.8 million more deaths caused by cancer, and this will increase every year if not resolved earlier. Microarray data has become one of the most popular cancer-identification studies in the field of health, since microarray data can be used to look at levels of gene expression in certain cell samples that serve to analyze thousands of genes simultaneously. By using data mining technique, we can classify the sample of microarray data thus it can be identified with cancer or not. In this paper we will discuss some research using some data mining techniques using microarray data, such as Support Vector Machine (SVM), Artificial Neural Network (ANN), Naive Bayes, k-Nearest Neighbor (kNN), and C4.5, and simulation of Random Forest algorithm with technique of reduction dimension using Relief. The result of this paper show performance measure (accuracy) from classification algorithm (SVM, ANN, Naive Bayes, kNN, C4.5, and Random Forets).The results in this paper show the accuracy of Random Forest algorithm higher than other classification algorithms (Support Vector Machine (SVM), Artificial Neural Network (ANN), Naive Bayes, k-Nearest Neighbor (kNN), and C4.5). It is hoped that this paper can provide some information about the speed, accuracy, performance and computational cost generated from each Data Mining Classification Technique based on microarray data.

Current understanding of mdig/MINA in human cancers.

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Thakur, Chitra; Chen, Fei

2015-07-01

Mineral dust-induced gene, mdig has recently been identified and is known to be overexpressed in a majority of human cancers and holds predictive power in the poor prognosis of the disease. Mdig is an environmentally expressed gene that is involved in cell proliferation, neoplastic transformation and immune regulation. With the advancement in deciphering the prognostic role of mdig in human cancers, our understanding on how mdig renders a normal cell to undergo malignant transformation is still very limited. This article reviews the current knowledge of the mdig gene in context to human neoplasias and its relation to the clinico-pathologic factors predicting the outcome of the disease in patients. It also emphasizes on the promising role of mdig that can serve as a potential candidate for biomarker discovery and as a therapeutic target in inflammation and cancers. Considering the recent advances in understanding the underlying mechanisms of tumor formation, more preclinical and clinical research is required to validate the potential of using mdig as a novel biological target of therapeutic and diagnostic value. Expression level of mdig influences the prognosis of several human cancers especially cancers of the breast and lung. Evaluation of mdig in cancers can offer novel biomarker with potential therapeutic interventions for the early assessment of cancer development in patients.

Classification of premalignant pancreatic cancer mass-spectrometry data using decision tree ensembles

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Wong G William

2008-06-01

Full Text Available Abstract Background Pancreatic cancer is the fourth leading cause of cancer death in the United States. Consequently, identification of clinically relevant biomarkers for the early detection of this cancer type is urgently needed. In recent years, proteomics profiling techniques combined with various data analysis methods have been successfully used to gain critical insights into processes and mechanisms underlying pathologic conditions, particularly as they relate to cancer. However, the high dimensionality of proteomics data combined with their relatively small sample sizes poses a significant challenge to current data mining methodology where many of the standard methods cannot be applied directly. Here, we propose a novel methodological framework using machine learning method, in which decision tree based classifier ensembles coupled with feature selection methods, is applied to proteomics data generated from premalignant pancreatic cancer. Results This study explores the utility of three different feature selection schemas (Student t test, Wilcoxon rank sum test and genetic algorithm to reduce the high dimensionality of a pancreatic cancer proteomic dataset. Using the top features selected from each method, we compared the prediction performances of a single decision tree algorithm C4.5 with six different decision-tree based classifier ensembles (Random forest, Stacked generalization, Bagging, Adaboost, Logitboost and Multiboost. We show that ensemble classifiers always outperform single decision tree classifier in having greater accuracies and smaller prediction errors when applied to a pancreatic cancer proteomics dataset. Conclusion In our cross validation framework, classifier ensembles generally have better classification accuracies compared to that of a single decision tree when applied to a pancreatic cancer proteomic dataset, thus suggesting its utility in future proteomics data analysis. Additionally, the use of feature selection

Mastectomy or breast conserving surgery? Factors affecting type of surgical treatment for breast cancer – a classification tree approach

International Nuclear Information System (INIS)

Martin, Michael A; Meyricke, Ramona; O'Neill, Terry; Roberts, Steven

2006-01-01

A critical choice facing breast cancer patients is which surgical treatment – mastectomy or breast conserving surgery (BCS) – is most appropriate. Several studies have investigated factors that impact the type of surgery chosen, identifying features such as place of residence, age at diagnosis, tumor size, socio-economic and racial/ethnic elements as relevant. Such assessment of 'propensity' is important in understanding issues such as a reported under-utilisation of BCS among women for whom such treatment was not contraindicated. Using Western Australian (WA) data, we further examine the factors associated with the type of surgical treatment for breast cancer using a classification tree approach. This approach deals naturally with complicated interactions between factors, and so allows flexible and interpretable models for treatment choice to be built that add to the current understanding of this complex decision process. Data was extracted from the WA Cancer Registry on women diagnosed with breast cancer in WA from 1990 to 2000. Subjects' treatment preferences were predicted from covariates using both classification trees and logistic regression. Tumor size was the primary determinant of patient choice, subjects with tumors smaller than 20 mm in diameter preferring BCS. For subjects with tumors greater than 20 mm in diameter factors such as patient age, nodal status, and tumor histology become relevant as predictors of patient choice. Classification trees perform as well as logistic regression for predicting patient choice, but are much easier to interpret for clinical use. The selected tree can inform clinicians' advice to patients

A novel gene signature for molecular diagnosis of human prostate cancer by RT-qPCR.

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Federica Rizzi

Full Text Available Prostate cancer (CaP is one of the most relevant causes of cancer death in Western Countries. Although detection of CaP at early curable stage is highly desirable, actual screening methods present limitations and new molecular approaches are needed. Gene expression analysis increases our knowledge about the biology of CaP and may render novel molecular tools, but the identification of accurate biomarkers for reliable molecular diagnosis is a real challenge. We describe here the diagnostic power of a novel 8-genes signature: ornithine decarboxylase (ODC, ornithine decarboxylase antizyme (OAZ, adenosylmethionine decarboxylase (AdoMetDC, spermidine/spermine N(1-acetyltransferase (SSAT, histone H3 (H3, growth arrest specific gene (GAS1, glyceraldehyde 3-phosphate dehydrogenase (GAPDH and Clusterin (CLU in tumour detection/classification of human CaP.The 8-gene signature was detected by retrotranscription real-time quantitative PCR (RT-qPCR in frozen prostate surgical specimens obtained from 41 patients diagnosed with CaP and recommended to undergo radical prostatectomy (RP. No therapy was given to patients at any time before RP. The bio-bank used for the study consisted of 66 specimens: 44 were benign-CaP paired from the same patient. Thirty-five were classified as benign and 31 as CaP after final pathological examination. Only molecular data were used for classification of specimens. The Nearest Neighbour (NN classifier was used in order to discriminate CaP from benign tissue. Validation of final results was obtained with 10-fold cross-validation procedure. CaP versus benign specimens were discriminated with (80+/-5% accuracy, (81+/-6% sensitivity and (78+/-7% specificity. The method also correctly classified 71% of patients with Gleason score or =7, an important predictor of final outcome.The method showed high sensitivity in a collection of specimens in which a significant portion of the total (13/31, equal to 42% was considered CaP on the basis

Restaging and Survival Analysis of 4036 Ovarian Cancer Patients According to the 2013 FIGO Classification for Ovarian, Fallopian Tube, and Primary Peritoneal Cancer

DEFF Research Database (Denmark)

Rosendahl, Mikkel; Høgdall, Claus Kim; Mosgaard, Berit Jul

2016-01-01

OBJECTIVE: With the 2013 International Federation of Gynecology and Obstetrics (FIGO) staging for ovarian, fallopian tube, and primary peritoneal cancer, the number of substages changed from 10 to 14. Any classification of a malignancy should easily assign patients to prognostic groups, refer....... MATERIALS AND METHODS: Demographic, surgical, histological, and survival data from 4036 ovarian cancer patients were used in the analysis. Five-year survival rates (5YSR) and hazard ratios for the old and revised FIGO staging were calculated using Kaplan-Meier curves and Cox regression. RESULTS: A total...

Defining the cellular precursors to human breast cancer

Science.gov (United States)

Keller, Patricia J.; Arendt, Lisa M.; Skibinski, Adam; Logvinenko, Tanya; Klebba, Ina; Dong, Shumin; Smith, Avi E.; Prat, Aleix; Perou, Charles M.; Gilmore, Hannah; Schnitt, Stuart; Naber, Stephen P.; Garlick, Jonathan A.; Kuperwasser, Charlotte

2012-01-01

Human breast cancers are broadly classified based on their gene-expression profiles into luminal- and basal-type tumors. These two major tumor subtypes express markers corresponding to the major differentiation states of epithelial cells in the breast: luminal (EpCAM+) and basal/myoepithelial (CD10+). However, there are also rare types of breast cancers, such as metaplastic carcinomas, where tumor cells exhibit features of alternate cell types that no longer resemble breast epithelium. Until now, it has been difficult to identify the cell type(s) in the human breast that gives rise to these various forms of breast cancer. Here we report that transformation of EpCAM+ epithelial cells results in the formation of common forms of human breast cancer, including estrogen receptor-positive and estrogen receptor-negative tumors with luminal and basal-like characteristics, respectively, whereas transformation of CD10+ cells results in the development of rare metaplastic tumors reminiscent of the claudin-low subtype. We also demonstrate the existence of CD10+ breast cells with metaplastic traits that can give rise to skin and epidermal tissues. Furthermore, we show that the development of metaplastic breast cancer is attributable, in part, to the transformation of these metaplastic breast epithelial cells. These findings identify normal cellular precursors to human breast cancers and reveal the existence of a population of cells with epidermal progenitor activity within adult human breast tissues. PMID:21940501

Statistical Considerations for Immunohistochemistry Panel Development after Gene Expression Profiling of Human Cancers

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Betensky, Rebecca A.; Nutt, Catherine L.; Batchelor, Tracy T.; Louis, David N.

2005-01-01

In recent years there have been a number of microarray expression studies in which different types of tumors were classified by identifying a panel of differentially expressed genes. Immunohistochemistry is a practical and robust method for extending gene expression data to common pathological specimens with the advantage of being applicable to paraffin-embedded tissues. However, the number of assays required for successful immunohistochemical classification remains unclear. We propose a simulation-based method for assessing sample size for an immunohistochemistry investigation after a promising gene expression study of human tumors. The goals of such an immunohistochemistry study would be to develop and validate a marker panel that yields improved prognostic classification of cancer patients. We demonstrate how the preliminary gene expression data, coupled with certain realistic assumptions, can be used to estimate the number of immunohistochemical assays required for development. These assumptions are more tenable than alternative assumptions that would be required for crude analytic sample size calculations and that may yield underpowered and inefficient studies. We applied our methods to the design of an immunohistochemistry study for glioma classification and estimated the number of assays required to ensure satisfactory technical and prognostic validation. Simulation approaches for computing power and sample size that are based on existing gene expression data provide a powerful tool for efficient design of follow-up genomic studies. PMID:15858152

Human cancer stem cells are a target for cancer prevention using (-)-epigallocatechin gallate.

Science.gov (United States)

Fujiki, Hirota; Sueoka, Eisaburo; Rawangkan, Anchalee; Suganuma, Masami

2017-12-01

Our previous experiments show that the main constituent of green-tea catechins, (-)-epigallocatechin gallate (EGCG), completely prevents tumor promotion on mouse skin initiated with 7,12-dimethylbenz(a)anthracene followed by okadaic acid and that EGCG and green tea extract prevent cancer development in a wide range of target organs in rodents. Therefore, we focused our attention on human cancer stem cells (CSCs) as targets of cancer prevention and treatment with EGCG. The numerous reports concerning anticancer activity of EGCG against human CSCs enriched from cancer cell lines were gathered from a search of PubMed, and we hope our review of the literatures will provide a broad selection for the effects of EGCG on various human CSCs. Based on our theoretical study, we discuss the findings as follows: (1) Compared with the parental cells, human CSCs express increased levels of the stemness markers Nanog, Oct4, Sox2, CD44, CD133, as well as the EMT markers, Twist, Snail, vimentin, and also aldehyde dehydrogenase. They showed decreased levels of E-cadherin and cyclin D1. (2) EGCG inhibits the transcription and translation of genes encoding stemness markers, indicating that EGCG generally inhibits the self-renewal of CSCs. (3) EGCG inhibits the expression of the epithelial-mesenchymal transition phenotypes of human CSCs. (4) The inhibition of EGCG of the stemness of CSCs was weaker compared with parental cells. (5) The weak inhibitory activity of EGCG increased synergistically in combination with anticancer drugs. Green tea prevents human cancer, and the combination of EGCG and anticancer drugs confers cancer treatment with tissue-agnostic efficacy.

Quantitative analysis and classification of AFM images of human hair.

Science.gov (United States)

Gurden, S P; Monteiro, V F; Longo, E; Ferreira, M M C

2004-07-01

The surface topography of human hair, as defined by the outer layer of cellular sheets, termed cuticles, largely determines the cosmetic properties of the hair. The condition of the cuticles is of great cosmetic importance, but also has the potential to aid diagnosis in the medical and forensic sciences. Atomic force microscopy (AFM) has been demonstrated to offer unique advantages for analysis of the hair surface, mainly due to the high image resolution and the ease of sample preparation. This article presents an algorithm for the automatic analysis of AFM images of human hair. The cuticular structure is characterized using a series of descriptors, such as step height, tilt angle and cuticle density, allowing quantitative analysis and comparison of different images. The usefulness of this approach is demonstrated by a classification study. Thirty-eight AFM images were measured, consisting of hair samples from (a) untreated and bleached hair samples, and (b) the root and distal ends of the hair fibre. The multivariate classification technique partial least squares discriminant analysis is used to test the ability of the algorithm to characterize the images according to the properties of the hair samples. Most of the images (86%) were found to be classified correctly.

Clinical significance of combined detection of CYFRA21-1, NSE and CEA in classification and staging of patients with lung cancer

International Nuclear Information System (INIS)

Hu He; Li Yanhua; Liang Weida; Zhang Qin

2011-01-01

To explore clinical value of combined detection of CYFRA21-1, NSE and CEA in classification and staging of patients with lung cancer, the CYFRA21-1, NSE and CEA levels in pleural effusion in 330 patients with lung cancer and in 43 patients with benign were detected by the electrochemiluminescence. The results showed that CYFRA21-1, NSE and CEA levels in pleural effusion in patients with lung cancer group were significantly higher than that of in benign group (P<0.01). The positive rate of tumor markers in different pathological type lung cancer were different,which CYFRA21-1 positive rate in squamous cell cancer group was highest with 65.5%; CEA positive rate in glands cancer group was supreme with 65.0%; the NSE positive rate in differentiation cancer group was highest with 79.5%. The positive rate in three markers combined detection was higher than that in one item detection. The tumor marker levels in lung cancer were positively related with clinical staging. The higher of tumor marker levels and the more late of clinical staging, and the clinical III∼IV period was obviously higher than that I∼II period (P<0.05). The combined detection of CYFRA21-1, NSE and CEA may enhance the positive rate in lung cancer detection, and may have significant clinical value in the classification and staging of patients with lung cancer. (authors)

The correlation study of radiological findings with pathological classification of superficial depressed (IIc type) early gastric cancer

International Nuclear Information System (INIS)

Liu Linxiang; Deng Bingxing; Liu Yujin; Iinuma, G.; Moriyama, N.

2007-01-01

Objective: To investigate the relations between radiological findings and pathological classification of superficial depressed (II c type) early gastric cancer. Methods: Radiological features in subtonic double contrast barium examination and the endoscopic pictures of early gastric cancer compared with the global pathological specimens and micro-pathological features were prospectively studied. Combined with the gastric endoscopic pictures, the sharpness of margin of the lesions, the changes of converging mucosal folds and the changes of the depressed surface on the film of double contrast barium examination were analyzed. The correlation between the radiological features and histological classification of gastric cancer including well differentiated tubular adenocarcinoma (tub1), moderately differentiated tubular adenocarcinoma (tub2), poorly differentiated adenocarcinoma (por) and signet-ring cell carcinoma (sig) were studied. Results: In 102 cases of II c type early gastric cancer, there were tub1 27 cases, tub2 11, por 26 and sig 38 cases histologically. The margin of the depressed lesions of tubl (24 cases) and tub2 (9 cases) cancers were mostly unsharply demarcated or with fine spicular border, while the margin of lesions of por(15 cases) and sig(31 cases) were mostly clearly and sharply demarcated, with statistical significance (P<0.01). The depressed surface of tub1 and tub2 lesions (17 cases) revealed little unevenness, sometimes with evenly granulations, single nodule and scar-like depression, while that of por and sig lesions (41 cases) manifested as nodules of varying sizes, with statistical significance (P<0.01). Conclusion: The radiological findings of superficial depressed early gastric cancer in different histological types were different, the possible histological type could be speculated according to the radiological findings of the lesions. (authors)

Comparison of Computational Algorithms for the Classification of Liver Cancer using SELDI Mass Spectrometry: A Case Study

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Robert J Hickey

2007-01-01

Full Text Available Introduction: As an alternative to DNA microarrays, mass spectrometry based analysis of proteomic patterns has shown great potential in cancer diagnosis. The ultimate application of this technique in clinical settings relies on the advancement of the technology itself and the maturity of the computational tools used to analyze the data. A number of computational algorithms constructed on different principles are available for the classification of disease status based on proteomic patterns. Nevertheless, few studies have addressed the difference in the performance of these approaches. In this report, we describe a comparative case study on the classification accuracy of hepatocellular carcinoma based on the serum proteomic pattern generated from a Surface Enhanced Laser Desorption/Ionization (SELDI mass spectrometer.Methods: Nine supervised classifi cation algorithms are implemented in R software and compared for the classification accuracy.Results: We found that the support vector machine with radial function is preferable as a tool for classification of hepatocellular carcinoma using features in SELDI mass spectra. Among the rest of the methods, random forest and prediction analysis of microarrays have better performance. A permutation-based technique reveals that the support vector machine with a radial function seems intrinsically superior in learning from the training data since it has a lower prediction error than others when there is essentially no differential signal. On the other hand, the performance of the random forest and prediction analysis of microarrays rely on their capability of capturing the signals with substantial differentiation between groups.Conclusions: Our finding is similar to a previous study, where classification methods based on the Matrix Assisted Laser Desorption/Ionization (MALDI mass spectrometry are compared for the prediction accuracy of ovarian cancer. The support vector machine, random forest and prediction

Human Breast Cancer Histoid

Science.gov (United States)

Kaur, Pavinder; Ward, Brenda; Saha, Baisakhi; Young, Lillian; Groshen, Susan; Techy, Geza; Lu, Yani; Atkinson, Roscoe; Taylor, Clive R.; Ingram, Marylou

2011-01-01

Progress in our understanding of heterotypic cellular interaction in the tumor microenvironment, which is recognized to play major roles in cancer progression, has been hampered due to unavailability of an appropriate in vitro co-culture model. The aim of this study was to generate an in vitro 3-dimensional human breast cancer model, which consists of cancer cells and fibroblasts. Breast cancer cells (UACC-893) and fibroblasts at various densities were co-cultured in a rotating suspension culture system to establish co-culture parameters. Subsequently, UACC-893, BT.20, or MDA.MB.453 were co-cultured with fibroblasts for 9 days. Co-cultures resulted in the generation of breast cancer histoid (BCH) with cancer cells showing the invasion of fibroblast spheroids, which were visualized by immunohistochemical (IHC) staining of sections (4 µm thick) of BCH. A reproducible quantitative expression of C-erbB.2 was detected in UACC-893 cancer cells in BCH sections by IHC staining and the Automated Cellular Imaging System. BCH sections also consistently exhibited qualitative expression of pancytokeratins, p53, Ki-67, or E-cadherin in cancer cells and that of vimentin or GSTPi in fibroblasts, fibronectin in the basement membrane and collagen IV in the extracellular matrix. The expression of the protein analytes and cellular architecture of BCH were markedly similar to those of breast cancer tissue. PMID:22034518

The classification of benign and malignant human prostate tissue by multivariate analysis of {sup 1}H magnetic resonance spectra

Energy Technology Data Exchange (ETDEWEB)

Hahn, P.; Smith, I.; Leboldus, L.; Littman, C.; Somorjai, L.; Bezabeh, T. [Institute for Biodiagnostic, National Research Council, Manitoba (Canada)

1998-04-01

{sup 1}H magnetic resonance spectroscopy studies (360 MHz) were performed on specimens of benign (n = 66) and malignant (n = 21) human prostate tissue from 50 patients and the spectral data were subjected to multivariate analysis, specifically linear-discriminant analysis. On the basis of histopathological assessments, an overall classification accuracy of 96.6 % was achieved, with a sensitivity of 100 % and a specificity of 95.5 % in classifying benign prostatic hyperplasia from prostatic cancer. Resonances due to citrate, glutamate, and taurine were among the six spectral subregions identified by our algorithm as having diagnostic potential. Significantly higher levels of citrate were observed in glandular than in stromal benign prostatic hyperplasia (P < 0.05). This method shows excellent promise for the possibility of in vivo assessment of prostate tissue by magnetic resonance. (author)

Towards precise classification of cancers based on robust gene functional expression profiles

Directory of Open Access Journals (Sweden)

Zhu Jing

2005-03-01

Full Text Available Abstract Background Development of robust and efficient methods for analyzing and interpreting high dimension gene expression profiles continues to be a focus in computational biology. The accumulated experiment evidence supports the assumption that genes express and perform their functions in modular fashions in cells. Therefore, there is an open space for development of the timely and relevant computational algorithms that use robust functional expression profiles towards precise classification of complex human diseases at the modular level. Results Inspired by the insight that genes act as a module to carry out a highly integrated cellular function, we thus define a low dimension functional expression profile for data reduction. After annotating each individual gene to functional categories defined in a proper gene function classification system such as Gene Ontology applied in this study, we identify those functional categories enriched with differentially expressed genes. For each functional category or functional module, we compute a summary measure (s for the raw expression values of the annotated genes to capture the overall activity level of the module. In this way, we can treat the gene expressions within a functional module as an integrative data point to replace the multiple values of individual genes. We compare the classification performance of decision trees based on functional expression profiles with the conventional gene expression profiles using four publicly available datasets, which indicates that precise classification of tumour types and improved interpretation can be achieved with the reduced functional expression profiles. Conclusion This modular approach is demonstrated to be a powerful alternative approach to analyzing high dimension microarray data and is robust to high measurement noise and intrinsic biological variance inherent in microarray data. Furthermore, efficient integration with current biological knowledge

Human papillomavirus associated oropharyngeal cancer

International Nuclear Information System (INIS)

Stefanicka, P.

2015-01-01

Recently, there is substantial epidemiological, molecular-pathological and experimental evidence indicating that some of the high-risk human papillomavirus (HR-HPV), especially HPV type 16, are etiologically related to a subset of head and neck squamous cell carcinomas, in particular, those arising from the oropharynx. Incidence of oropharyngeal cancer is increasing in direct opposition to a decreasing incidence of all other head and neck cancers. The prognosis of patients with HPV associated oropharyngeal cancer is significantly better compare to patients with non associated oropharyngeal cancers. Patients with HPV-positive oropharyngeal cancer respond better to radiotherapy, surgery, chemoradiotherapy. Therefore, the presence of HPV in tumor is the most important prognostic factor in patients with oropharyngeal cancers. These findings have prompted the need for change of treatment strategies in these patients. The goal is selective de-intensified treatment stratified for HPV status. (author)

Rule-guided human classification of Volunteered Geographic Information

Science.gov (United States)

Ali, Ahmed Loai; Falomir, Zoe; Schmid, Falko; Freksa, Christian

2017-05-01

During the last decade, web technologies and location sensing devices have evolved generating a form of crowdsourcing known as Volunteered Geographic Information (VGI). VGI acted as a platform of spatial data collection, in particular, when a group of public participants are involved in collaborative mapping activities: they work together to collect, share, and use information about geographic features. VGI exploits participants' local knowledge to produce rich data sources. However, the resulting data inherits problematic data classification. In VGI projects, the challenges of data classification are due to the following: (i) data is likely prone to subjective classification, (ii) remote contributions and flexible contribution mechanisms in most projects, and (iii) the uncertainty of spatial data and non-strict definitions of geographic features. These factors lead to various forms of problematic classification: inconsistent, incomplete, and imprecise data classification. This research addresses classification appropriateness. Whether the classification of an entity is appropriate or inappropriate is related to quantitative and/or qualitative observations. Small differences between observations may be not recognizable particularly for non-expert participants. Hence, in this paper, the problem is tackled by developing a rule-guided classification approach. This approach exploits data mining techniques of Association Classification (AC) to extract descriptive (qualitative) rules of specific geographic features. The rules are extracted based on the investigation of qualitative topological relations between target features and their context. Afterwards, the extracted rules are used to develop a recommendation system able to guide participants to the most appropriate classification. The approach proposes two scenarios to guide participants towards enhancing the quality of data classification. An empirical study is conducted to investigate the classification of grass

ParSel: Parallel Selection of Micro-RNAs for Survival Classification in Cancers.

Science.gov (United States)

Sinha, Debajyoti; Sengupta, Debarka; Bandyopadhyay, Sanghamitra

2017-07-01

It is known that tumor micro-RNAs (miRNA) can define patient survival and treatment response. We present a framework to identify miRNAs which are predictive of cancer survival. The framework attempts to rank the miRNAs by exploring their collaborative role in gene regulation. Our approach tests a significantly large number of combinatorial cases leveraging parallel computation. We carefully avoided parametric assumptions involved in evaluations of miRNA expressions but used rigorous statistical computation to assign an importance score to a miRNA. Experimental results on three cancer types namely, KIRC, OV and GBM verify that the top ranked miRNAs obtained using the proposed framework produce better classification accuracy as compared to some best practice variable selection methods. Some of these top ranked miRNA are also known to be associated with related diseases. © 2017 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim.

Real-time classification of humans versus animals using profiling sensors and hidden Markov tree model

Science.gov (United States)

Hossen, Jakir; Jacobs, Eddie L.; Chari, Srikant

2015-07-01

Linear pyroelectric array sensors have enabled useful classifications of objects such as humans and animals to be performed with relatively low-cost hardware in border and perimeter security applications. Ongoing research has sought to improve the performance of these sensors through signal processing algorithms. In the research presented here, we introduce the use of hidden Markov tree (HMT) models for object recognition in images generated by linear pyroelectric sensors. HMTs are trained to statistically model the wavelet features of individual objects through an expectation-maximization learning process. Human versus animal classification for a test object is made by evaluating its wavelet features against the trained HMTs using the maximum-likelihood criterion. The classification performance of this approach is compared to two other techniques; a texture, shape, and spectral component features (TSSF) based classifier and a speeded-up robust feature (SURF) classifier. The evaluation indicates that among the three techniques, the wavelet-based HMT model works well, is robust, and has improved classification performance compared to a SURF-based algorithm in equivalent computation time. When compared to the TSSF-based classifier, the HMT model has a slightly degraded performance but almost an order of magnitude improvement in computation time enabling real-time implementation.

Novel innate cancer killing activity in humans

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Lovato James

2011-08-01

Full Text Available Abstract Background In this study, we pilot tested an in vitro assay of cancer killing activity (CKA in circulating leukocytes of 22 cancer cases and 25 healthy controls. Methods Using a human cervical cancer cell line, HeLa, as target cells, we compared the CKA in circulating leukocytes, as effector cells, of cancer cases and controls. The CKA was normalized as percentages of total target cells during selected periods of incubation time and at selected effector/target cell ratios in comparison to no-effector-cell controls. Results Our results showed that CKA similar to that of our previous study of SR/CR mice was present in human circulating leukocytes but at profoundly different levels in individuals. Overall, males have a significantly higher CKA than females. The CKA levels in cancer cases were lower than that in healthy controls (mean ± SD: 36.97 ± 21.39 vs. 46.28 ± 27.22. Below-median CKA was significantly associated with case status (odds ratio = 4.36; 95% Confidence Interval = 1.06, 17.88 after adjustment of gender and race. Conclusions In freshly isolated human leukocytes, we were able to detect an apparent CKA in a similar manner to that of cancer-resistant SR/CR mice. The finding of CKA at lower levels in cancer patients suggests the possibility that it may be of a consequence of genetic, physiological, or pathological conditions, pending future studies with larger sample size.

Controversies surrounding human papilloma virus infection, head & neck vs oral cancer, implications for prophylaxis and treatment.

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Campisi, Giuseppina; Giovannelli, Lucia

2009-03-30

Head & Neck Cancer (HNC) represents the sixth most common malignancy worldwide and it is historically linked to well-known behavioural risk factors, i.e., tobacco smoking and/or the alcohol consumption. Recently, substantial evidence has been mounting that Human Papillomavirus (HPV) infection is playing an increasing important role in oral cancer. Because of the attention and clamor surrounding oral HPV infection and related cancers, as well as the use of HPV prophylactic vaccines, in this invited perspective the authors raise some questions and review some controversial issues on HPV infection and its role in HNC, with a particular focus on oral squamous cell carcinoma. The problematic definition and classification of HNC will be discussed, together with the characteristics of oral infection with oncogenic HPV types, the frequency of HPV DNA detection in HNC, the location of HPV-related tumours, the severity and prognosis of HPV-positive HNC, the diagnosis of oral HPV infection, common routes of oral infection and the likelihood of oro-genital HPV transmission, the prevention of HPV infection and novel therapeutic approaches.

Human retroviruses: their role in cancer.

Science.gov (United States)

Blattner, W A

1999-01-01

Viruses are etiologically linked to approximately 20% of all malignancies worldwide. Retroviruses account for approximately 8%-10% of the total. For human T-cell leukemia virus 1 (HTLV-I), the viral regulatory tax gene product is responsible for enhanced transcription of viral and cellular genes that promote cell growth by stimulating various growth factors and through dysregulation of cellular regulatory suppressor genes, such as p53. After a long latent period, adult T-cell leukemia/lymphoma (ATL) occurs in 1 per 1000 carriers per year, resulting in 2500-3000 cases per year worldwide and over half of the adult lymphoid malignancies in endemic areas. Human immunodeficiency virus 1 (HIV-1) accounts for a significant cancer burden, and its transactivating regulatory protein Tat enhances direct and indirect cytokine and immunological dysregulation to cause diverse cancers. Kaposi's sarcoma (KS) is a very rare tumor except after HIV-1 infection, when its incidence is greatly amplified reaching seventy thousand-fold in HIV-infected homosexual men. Human herpesvirus 8 (HHV-8), which is also known as Kaposi's sarcoma-associated virus (KSHV), is a necessary but not sufficient etiological factor in KS. The dramatic decline of KS since the introduction of highly active antiretroviral therapy (HAART) could be due to suppression of HIV-1 tat. B-cell non-Hodgkin's lymphoma occurs as their first acquired immunodeficiency syndrome-defining diagnosis in 3%-4% of HIV-infected patients. Hodgkin's lymphoma is also associated with HIV infection but at a lower risk. Human papillomaviruses are linked to invasive cervical cancer and anogenital cancers among HIV-infected patients. Human retroviruses cause malignancy via direct effects as well as through interactions with other oncogenic herpesviruses and other viruses.

The Future of Asset Management for Human Space Exploration: Supply Classification and an Integrated Database

Science.gov (United States)

Shull, Sarah A.; Gralla, Erica L.; deWeck, Olivier L.; Shishko, Robert

2006-01-01

One of the major logistical challenges in human space exploration is asset management. This paper presents observations on the practice of asset management in support of human space flight to date and discusses a functional-based supply classification and a framework for an integrated database that could be used to improve asset management and logistics for human missions to the Moon, Mars and beyond.

Gene expression classification of colon cancer into molecular subtypes: characterization, validation, and prognostic value.

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Laetitia Marisa

Full Text Available Colon cancer (CC pathological staging fails to accurately predict recurrence, and to date, no gene expression signature has proven reliable for prognosis stratification in clinical practice, perhaps because CC is a heterogeneous disease. The aim of this study was to establish a comprehensive molecular classification of CC based on mRNA expression profile analyses.Fresh-frozen primary tumor samples from a large multicenter cohort of 750 patients with stage I to IV CC who underwent surgery between 1987 and 2007 in seven centers were characterized for common DNA alterations, including BRAF, KRAS, and TP53 mutations, CpG island methylator phenotype, mismatch repair status, and chromosomal instability status, and were screened with whole genome and transcriptome arrays. 566 samples fulfilled RNA quality requirements. Unsupervised consensus hierarchical clustering applied to gene expression data from a discovery subset of 443 CC samples identified six molecular subtypes. These subtypes were associated with distinct clinicopathological characteristics, molecular alterations, specific enrichments of supervised gene expression signatures (stem cell phenotype-like, normal-like, serrated CC phenotype-like, and deregulated signaling pathways. Based on their main biological characteristics, we distinguished a deficient mismatch repair subtype, a KRAS mutant subtype, a cancer stem cell subtype, and three chromosomal instability subtypes, including one associated with down-regulated immune pathways, one with up-regulation of the Wnt pathway, and one displaying a normal-like gene expression profile. The classification was validated in the remaining 123 samples plus an independent set of 1,058 CC samples, including eight public datasets. Furthermore, prognosis was analyzed in the subset of stage II-III CC samples. The subtypes C4 and C6, but not the subtypes C1, C2, C3, and C5, were independently associated with shorter relapse-free survival, even after

Human papillomavirus-associated cancers: A growing global problem

OpenAIRE

Bansal, Anshuma; Singh, Mini P; Rai, Bhavana

2016-01-01

Human papillomavirus (HPV) infection is linked with several cancers such as cancer cervix, vagina, vulva, head and neck, anal, and penile carcinomas. Although there is a proven association of HPV with these cancers, questions regarding HPV testing, vaccination, and treatment of HPV-related cancers continue to remain unanswered. The present article provides an overview of the HPV-associated cancers.

Rhein Induces Apoptosis in Human Breast Cancer Cells

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Ching-Yao Chang

2012-01-01

Full Text Available Human breast cancers cells overexpressing HER2/neu are more aggressive tumors with poor prognosis, and resistance to chemotherapy. This study investigates antiproliferation effects of anthraquinone derivatives of rhubarb root on human breast cancer cells. Of 7 anthraquinone derivatives, only rhein showed antiproliferative and apoptotic effects on both HER2-overexpressing MCF-7 (MCF-7/HER2 and control vector MCF-7 (MCF-7/VEC cells. Rhein induced dose- and time-dependent manners increase in caspase-9-mediated apoptosis correlating with activation of ROS-mediated activation of NF-κB- and p53-signaling pathways in both cell types. Therefore, this study highlighted rhein as processing anti-proliferative activity against HER2 overexpression or HER2-basal expression in breast cancer cells and playing important roles in apoptotic induction of human breast cancer cells.

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

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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.

Esophagus cancer

International Nuclear Information System (INIS)

Anon.

1989-01-01

Ways of metastatic spreading of esophagus cancer, depending on segmental division of esophagus are considered. Classification of esophagus cancer according to morphological structure, domestic clinical classification according to stages and international classification according to TNM system are presented. Diagnosis of esophagus cancer should be complex and based on results of clinical examination of patients, radiological, endoscopic and morphological investigations. Radiological, surgical and combined (preoperative radiotherapy with successive operation) methods of treatment are used in the case of esophagus cancer. Versions of preoperative radiotherapy are given. Favourable results of applying combined surgical treatment with preoperative radiotherapy are shown

Are preoperative histology and MRI useful for classification of endometrial cancer risk?

International Nuclear Information System (INIS)

Body, Noemie; Lavoué, Vincent; De Kerdaniel, Olivier; Foucher, Fabrice; Henno, Sébastien; Cauchois, Aurélie; Laviolle, Bruno; Leblanc, Marc; Levêque, Jean

2016-01-01

The 2010 guidelines of the French National Cancer Institute (INCa) classify patients with endometrial cancer into three risk groups for lymph node invasion and recurrence on the basis of MRI and histological analysis of an endometrial specimen obtained preoperatively. The classification guides therapeutic choices, which may include pelvic and/or para-aortic lymphadenectomy. The purpose of this study was to evaluate the diagnostic performance of preoperative assessment to help identify intermediate- or high-risk patients requiring lymphadenectomy. The study included all patients who underwent surgery for endometrial cancer between January 2010 and December 2013 at either Rennes University Hospital or Vannes Regional Hospital. The criteria for eligibility included a preoperative assessment with MRI and histological examination of an endometrial sample. A histological comparison was made between the preoperative and surgical specimens. Among the 91 patients who underwent a full preoperative assessment, the diagnosis of intermediate- or high-risk endometrial cancer was established by MRI and histology with a sensitivity of 70 %, specificity of 82 %, positive predictive value (PPV) of 87 %, negative predictive value (NPV) of 61 %, positive likelihood ratio (LR+) of 3.8 and negative likelihood ratio (LR-) of 0.3. The risk group was underestimated in 32 % of patients and overestimated in 7 % of patients. MRI underestimated endometrial cancer stage in 20 % of cases, while endometrial sampling underestimated the histological type in 4 % of cases and the grade in 9 % of cases. The preoperative assessment overestimated or underestimated the risk of recurrence in nearly 40 % of cases, with errors in lesion type, grade or stage. Erroneous preoperative risk assessment leads to suboptimal initial surgical management of patients with endometrial cancer

Classification of Ovarian Cancer Surgery Facilitates Treatment Decisions in a Gynecological Multidisciplinary Team

DEFF Research Database (Denmark)

Bjørn, Signe Frahm; Schnack, Tine Henrichsen; Lajer, Henrik

2017-01-01

multidisciplinary team (MDT) decisions. Materials and Methods Four hundred eighteen women diagnosed with ovarian cancers (n = 351) or borderline tumors (n = 66) were selected for primary debulking surgery from January 2008 to July 2013. At an MDT meeting, women were allocated into 3 groups named "pre-COVA" 1 to 3...... classifying the expected extent of the primary surgery and need for postoperative care. On the basis of the operative procedures performed, women were allocated into 1 of the 3 corresponding COVA 1 to 3 groups. The outcome measure was the predictive value of the pre-COVA score compared with the actual COVA......-COVA classification predicted the actual COVA group in 79 (49%) FIGO stages I to IIIB and in 85 (45%) FIGO stages IIIC to IV. Conclusions The COVA classification system is a simple and useful tool in the MDT setting where specialists make treatment decisions based on advanced technology. The use of pre...

Calorimetric signatures of human cancer cells and their nuclei

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Todinova, S. [Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Acad. G. Bonchev Str., Bl. 21, Sofia 1113 (Bulgaria); Stoyanova, E. [Department of Molecular Immunology, Institute of Biology and Immunology of Reproduction, Bulgarian Academy of Sciences, Tzarigradsko shose Blvd. 73, Sofia 1113 (Bulgaria); Krumova, S., E-mail: sakrumo@gmail.com [Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Acad. G. Bonchev Str., Bl. 21, Sofia 1113 (Bulgaria); Iliev, I. [Institute of Experimental Morphology, Pathology and Anthropology with Museum, Acad. G. Bonchev Str., Bl. 25, Sofia 1113 (Bulgaria); Taneva, S.G. [Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Acad. G. Bonchev Str., Bl. 21, Sofia 1113 (Bulgaria)

2016-01-10

Graphical abstract: - Highlights: • Two temperature ranges are distinguished in the thermograms of cells/nuclei. • Different thermodynamic properties of cancer and normal human cells/nuclei. • Dramatic reduction of the enthalpy of the low-temperature range in cancer cells. • Oxaliplatin and 5-FU affect the nuclear matrix proteins and the DNA stability. - Abstract: The human cancer cell lines HeLa, JEG-3, Hep G2, SSC-9, PC-3, HT-29, MCF7 and their isolated nuclei were characterized by differential scanning calorimetry. The calorimetric profiles differed from normal human fibroblast (BJ) cells in the two well distinguished temperature ranges—the high-temperature range (H{sub T}, due to DNA-containing structures) and the low-temperature range (L{sub T}, assigned to the nuclear matrix and cellular proteins). The enthalpy of the L{sub T} range, and, respectively the ratio of the enthalpies of the L{sub T}- vs. H{sub T}-range, ΔH{sub L}/ΔH{sub H}, is strongly reduced for all cancer cells compared to normal fibroblasts. On the contrary, for most of the cancer nuclei this ratio is higher compared to normal nuclei. The HT-29 human colorectal cancer cells/nuclei differed most drastically from normal human fibroblast cells/nuclei. Our data also reveal that the treatment of HT-29 cancer cells with cytostatic drugs affects not only the DNA replication but also the cellular proteome.

Human papillomavirus and genital cancer

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Rapose Alwyn

2009-01-01

Full Text Available Human papillomavirus (HPV is one of the most common sexually transmitted infections world-wide. Low-risk HPV-types are associated with genital warts. Persistent infection with high-risk HPV-types is associated with genital cancers. Smoking and HIV infection have consistently been associated with longer duration of HPV infection and risk for genital cancer. There is an increasing incidence of anal cancers, and a close association with HPV infection has been demonstrated. Receptive anal sex and HIV-positive status are associated with a high risk for anal cancer. Two HPV vaccines are now available and offer protection from infection by the HPV-types included in the vaccine. This benefit is maximally seen in young women who were uninfected prior to vaccination.

Human papillomaviruses and cancer

International Nuclear Information System (INIS)

Haedicke, Juliane; Iftner, Thomas

2013-01-01

Human papillomaviruses (HPV) are small oncogenic DNA viruses of which more than 200 types have been identified to date. A small subset of these is etiologically linked to the development of anogenital malignancies such as cervical cancer. In addition, recent studies established a causative relationship between these high-risk HPV types and tonsillar and oropharyngeal cancer. Clinical management of cervical cancer and head and neck squamous cell carcinomas (HNSCCs) is largely standardized and involves surgical removal of the tumor tissue as well as adjuvant chemoradiation therapy. Notably, the response to therapeutic intervention of HPV-positive HNSCCs has been found to be better as compared to HPV-negative tumors. Although the existing HPV vaccine is solely licensed for the prevention of cervical cancer, it might also have prophylactic potential for the development of high-risk HPV-associated HNSCCs. Another group of viruses, which belongs to the beta-HPV subgroup, has been implicated in nonmelanoma skin cancer, however, the etiology remains to be established. Treatment of HPV-induced nonmelanoma skin cancer is based on local excision. However, topically applied immune-modulating substances represent non-surgical alternatives for the management of smaller cutaneous tumors. In this review we present the current knowledge of the role of HPV in cancer development and discuss clinical management options as well as targets for the development of future intervention therapies

Human papilloma virus in oral cancer

OpenAIRE

Kim, Soung Min

2016-01-01

Cervical cancer is the second most prevalent cancer among women, and it arises from cells that originate in the cervix uteri. Among several causes of cervical malignancies, infection with some types of human papilloma virus (HPV) is well known to be the greatest cervical cancer risk factor. Over 150 subtypes of HPV have been identified; more than 40 types of HPVs are typically transmitted through sexual contact and infect the anogenital region and oral cavity. The recently introduced vaccine ...

Decorin in Human Colon Cancer: Localization In Vivo and Effect on Cancer Cell Behavior In Vitro.

Science.gov (United States)

Nyman, Marie C; Sainio, Annele O; Pennanen, Mirka M; Lund, Riikka J; Vuorikoski, Sanna; Sundström, Jari T T; Järveläinen, Hannu T

2015-09-01

Decorin is generally recognized as a tumor suppressing molecule. Nevertheless, although decorin has been shown to be differentially expressed in malignant tissues, it has often remained unclear whether, in addition to non-malignant stromal cells, cancer cells also express it. Here, we first used two publicly available databases to analyze the current information about decorin expression and immunoreactivity in normal and malignant human colorectal tissue samples. The analyses demonstrated that decorin expression and immunoreactivity may vary in cancer cells of human colorectal tissues. Therefore, we next examined decorin expression in normal, premalignant and malignant human colorectal tissues in more detail using both in situ hybridization and immunohistochemistry for decorin. Our results invariably demonstrate that malignant cells within human colorectal cancer tissues are devoid of both decorin mRNA and immunoreactivity. Identical results were obtained for cells of neuroendocrine tumors of human colon. Using RT-qPCR, we showed that human colon cancer cell lines are also decorin negative, in accordance with the above in vivo results. Finally, we demonstrate that decorin transduction of human colon cancer cell lines causes a significant reduction in their colony forming capability. Thus, strategies to develop decorin-based adjuvant therapies for human colorectal malignancies are highly rational. © The Author(s) 2015.

A taxonomy of epithelial human cancer and their metastases

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De Moor Bart

2009-12-01

. Moreover, a signature was developed based on our unsupervised clustering of breast tumors and this was predictive for disease-specific survival in three independent studies. Next, the metastases from ovarian, breast, lung and vulva cluster with their tissue of origin while metastases from colon showed a bimodal distribution. A significant part clusters with tissue of origin while the remaining tumors cluster with the tissue of destination. Conclusion Our molecular taxonomy of epithelial human cancer indicates surprising correlations over tissues. This may have a significant impact on the classification of many cancer sites and may guide pathologists, both in research and daily practice. Moreover, these results based on unsupervised analysis yielded a signature predictive of clinical outcome in breast cancer. Additionally, we hypothesize that metastases from gastrointestinal origin either remember their tissue of origin or adapt to the tissue of destination. More specifically, colon metastases in the liver show strong evidence for such a bimodal tissue specific profile.

Controlling a human-computer interface system with a novel classification method that uses electrooculography signals.

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Wu, Shang-Lin; Liao, Lun-De; Lu, Shao-Wei; Jiang, Wei-Ling; Chen, Shi-An; Lin, Chin-Teng

2013-08-01

Electrooculography (EOG) signals can be used to control human-computer interface (HCI) systems, if properly classified. The ability to measure and process these signals may help HCI users to overcome many of the physical limitations and inconveniences in daily life. However, there are currently no effective multidirectional classification methods for monitoring eye movements. Here, we describe a classification method used in a wireless EOG-based HCI device for detecting eye movements in eight directions. This device includes wireless EOG signal acquisition components, wet electrodes and an EOG signal classification algorithm. The EOG classification algorithm is based on extracting features from the electrical signals corresponding to eight directions of eye movement (up, down, left, right, up-left, down-left, up-right, and down-right) and blinking. The recognition and processing of these eight different features were achieved in real-life conditions, demonstrating that this device can reliably measure the features of EOG signals. This system and its classification procedure provide an effective method for identifying eye movements. Additionally, it may be applied to study eye functions in real-life conditions in the near future.

Breast cancer surgery and diagnosis-related groups (DRGs): patient classification and hospital reimbursement in 11 European countries.

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Scheller-Kreinsen, David; Quentin, Wilm; Geissler, Alexander; Busse, Reinhard

2013-10-01

Researchers from eleven countries (i.e. Austria, England, Estonia, Finland, France, Germany, Ireland, Netherlands, Poland, Spain, and Sweden) compared how their DRG systems deal with breast cancer surgery patients. DRG algorithms and indicators of resource consumption were assessed for those DRGs that individually contain at least 1% of all breast cancer surgery patients. Six standardised case vignettes were defined and quasi prices according to national DRG-based hospital payment systems were ascertained. European DRG systems classify breast cancer surgery patients according to different sets of classification variables into three to seven DRGs. Quasi prices for an index case treated with partial mastectomy range from €577 in Poland to €5780 in the Netherlands. Countries award their highest payments for very different kinds of patients. Breast cancer specialists and national DRG authorities should consider how other countries' DRG systems classify breast cancer patients in order to identify potential scope for improvement and to ensure fair and appropriate reimbursement. Copyright © 2012 Elsevier Ltd. All rights reserved.

Profiling cancer

DEFF Research Database (Denmark)

Ciro, Marco; Bracken, Adrian P; Helin, Kristian

2003-01-01

In the past couple of years, several very exciting studies have demonstrated the enormous power of gene-expression profiling for cancer classification and prediction of patient survival. In addition to promising a more accurate classification of cancer and therefore better treatment of patients......, gene-expression profiling can result in the identification of novel potential targets for cancer therapy and a better understanding of the molecular mechanisms leading to cancer....

Improving breast cancer classification with mammography, supported on an appropriate variable selection analysis

Science.gov (United States)

Pérez, Noel; Guevara, Miguel A.; Silva, Augusto

2013-02-01

This work addresses the issue of variable selection within the context of breast cancer classification with mammography. A comprehensive repository of feature vectors was used including a hybrid subset gathering image-based and clinical features. It aimed to gather experimental evidence of variable selection in terms of cardinality, type and find a classification scheme that provides the best performance over the Area Under Receiver Operating Characteristics Curve (AUC) scores using the ranked features subset. We evaluated and classified a total of 300 subsets of features formed by the application of Chi-Square Discretization, Information-Gain, One-Rule and RELIEF methods in association with Feed-Forward Backpropagation Neural Network (FFBP), Support Vector Machine (SVM) and Decision Tree J48 (DTJ48) Machine Learning Algorithms (MLA) for a comparative performance evaluation based on AUC scores. A variable selection analysis was performed for Single-View Ranking and Multi-View Ranking groups of features. Features subsets representing Microcalcifications (MCs), Masses and both MCs and Masses lesions achieved AUC scores of 0.91, 0.954 and 0.934 respectively. Experimental evidence demonstrated that classification performance was improved by combining image-based and clinical features. The most important clinical and image-based features were StromaDistortion and Circularity respectively. Other less important but worth to use due to its consistency were Contrast, Perimeter, Microcalcification, Correlation and Elongation.

Environmental and genetic interactions in human cancer

International Nuclear Information System (INIS)

Paterson, M.C.

Humans, depending upon their genetic make-up, differ in their susceptibility to the cancer-causing effects of extrinsic agents. Clinical and laboratory studies on the hereditary disorder, ataxia telangiectasia (AT) show that persons afflicted with this are cancer-prone and unusually sensitive to conventional radiotherapy. Their skin cells, when cultured, are hypersensitive to killing by ionizing radiation, being defective in the enzymatic repair of radiation-induced damange to the genetic material, deoxyribonucleic acid (DNA). This molecular finding implicates DNA damage and its imperfect repair as an early step in the induction of human cancer by radiation and other carcinogens. The parents of AT patients are clincally normal but their cultured cells are often moderately radiosensitive. The increased radiosensitivity of cultured cells offers a means of identifying a presumed cancer-prone subpopulation that should avoid undue exposure to certain carcinogens. The radioresponse of cells from patients with other cancer-associated genetic disorders and persons suspected of being genetically predisposed to radiation-induced cancer has also been measured. Increased cell killing by γ-rays appears in the complex genetic disease, tuberous sclerosis. Cells from cancer-stricken members of a leukemia-prone family are also radiosensitive, as are cells from one patient with radiation-associated breast cancer. These radiobiological data, taken together, strongly suggest that genetic factors can interact with extrinsic agents and thereby play a greater causative role in the development of common cancers in man than previously thought. (L.L.)

Maintenance of prolactin receptors in human breast cancer

International Nuclear Information System (INIS)

Ben-David, M.; Dror, Y.; Biran, S.

1981-01-01

Breast tissue specimens of 110 women with various stages of breast cancer were tested in vitro to determine their specific binding sites for human prolactin. In contrast to the case of steroid receptors, binding sites for prolactin were found in the vast majority of breast cancer tissue. Distribution profiles giving amount of prolactin receptor and their affinity coefficients were found to be similar in the tissues of women whose ages, hormonal status, or stage of breast cancer varied. These findings show that in contrast to steroid receptors, human breast cancer tissue maintains binding sites for prolactin. The findings also indicate that there may be a higher dependency of breast cancer on prolactin than on steroids. Clinical trials must be carried out to determine the role of ''positive'' prolactin receptors in prognosis and prediction of response to future hormone therapy. (author)

Global classification of human facial healthy skin using PLS discriminant analysis and clustering analysis.

Science.gov (United States)

Guinot, C; Latreille, J; Tenenhaus, M; Malvy, D J

2001-04-01

Today's classifications of healthy skin are predominantly based on a very limited number of skin characteristics, such as skin oiliness or susceptibility to sun exposure. The aim of the present analysis was to set up a global classification of healthy facial skin, using mathematical models. This classification is based on clinical, biophysical skin characteristics and self-reported information related to the skin, as well as the results of a theoretical skin classification assessed separately for the frontal and the malar zones of the face. In order to maximize the predictive power of the models with a minimum of variables, the Partial Least Square (PLS) discriminant analysis method was used. The resulting PLS components were subjected to clustering analyses to identify the plausible number of clusters and to group the individuals according to their proximities. Using this approach, four PLS components could be constructed and six clusters were found relevant. So, from the 36 hypothetical combinations of the theoretical skin types classification, we tended to a strengthened six classes proposal. Our data suggest that the association of the PLS discriminant analysis and the clustering methods leads to a valid and simple way to classify healthy human skin and represents a potentially useful tool for cosmetic and dermatological research.

Uncovering growth-suppressive MicroRNAs in lung cancer

DEFF Research Database (Denmark)

Liu, Xi; Sempere, Lorenzo F; Galimberti, Fabrizio

2009-01-01

PURPOSE: MicroRNA (miRNA) expression profiles improve classification, diagnosis, and prognostic information of malignancies, including lung cancer. This study uncovered unique growth-suppressive miRNAs in lung cancer. EXPERIMENTAL DESIGN: miRNA arrays were done on normal lung tissues...... and adenocarcinomas from wild-type and proteasome degradation-resistant cyclin E transgenic mice to reveal repressed miRNAs in lung cancer. Real-time and semiquantitative reverse transcription-PCR as well as in situ hybridization assays validated these findings. Lung cancer cell lines were derived from each......-malignant human lung tissue bank. RESULTS: miR-34c, miR-145, and miR-142-5p were repressed in transgenic lung cancers. Findings were confirmed by real-time and semiquantitative reverse transcription-PCR as well as in situ hybridization assays. Similar miRNA profiles occurred in human normal versus malignant lung...

A Discrete Wavelet Based Feature Extraction and Hybrid Classification Technique for Microarray Data Analysis

Directory of Open Access Journals (Sweden)

Jaison Bennet

2014-01-01

Full Text Available Cancer classification by doctors and radiologists was based on morphological and clinical features and had limited diagnostic ability in olden days. The recent arrival of DNA microarray technology has led to the concurrent monitoring of thousands of gene expressions in a single chip which stimulates the progress in cancer classification. In this paper, we have proposed a hybrid approach for microarray data classification based on nearest neighbor (KNN, naive Bayes, and support vector machine (SVM. Feature selection prior to classification plays a vital role and a feature selection technique which combines discrete wavelet transform (DWT and moving window technique (MWT is used. The performance of the proposed method is compared with the conventional classifiers like support vector machine, nearest neighbor, and naive Bayes. Experiments have been conducted on both real and benchmark datasets and the results indicate that the ensemble approach produces higher classification accuracy than conventional classifiers. This paper serves as an automated system for the classification of cancer and can be applied by doctors in real cases which serve as a boon to the medical community. This work further reduces the misclassification of cancers which is highly not allowed in cancer detection.

Pulsed terahertz imaging of breast cancer in freshly excised murine tumors

Science.gov (United States)

Bowman, Tyler; Chavez, Tanny; Khan, Kamrul; Wu, Jingxian; Chakraborty, Avishek; Rajaram, Narasimhan; Bailey, Keith; El-Shenawee, Magda

2018-02-01

This paper investigates terahertz (THz) imaging and classification of freshly excised murine xenograft breast cancer tumors. These tumors are grown via injection of E0771 breast adenocarcinoma cells into the flank of mice maintained on high-fat diet. Within 1 h of excision, the tumor and adjacent tissues are imaged using a pulsed THz system in the reflection mode. The THz images are classified using a statistical Bayesian mixture model with unsupervised and supervised approaches. Correlation with digitized pathology images is conducted using classification images assigned by a modal class decision rule. The corresponding receiver operating characteristic curves are obtained based on the classification results. A total of 13 tumor samples obtained from 9 tumors are investigated. The results show good correlation of THz images with pathology results in all samples of cancer and fat tissues. For tumor samples of cancer, fat, and muscle tissues, THz images show reasonable correlation with pathology where the primary challenge lies in the overlapping dielectric properties of cancer and muscle tissues. The use of a supervised regression approach shows improvement in the classification images although not consistently in all tissue regions. Advancing THz imaging of breast tumors from mice and the development of accurate statistical models will ultimately progress the technique for the assessment of human breast tumor margins.

Changing Histopathological Diagnostics by Genome-Based Tumor Classification

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Michael Kloth

2014-05-01

Full Text Available Traditionally, tumors are classified by histopathological criteria, i.e., based on their specific morphological appearances. Consequently, current therapeutic decisions in oncology are strongly influenced by histology rather than underlying molecular or genomic aberrations. The increase of information on molecular changes however, enabled by the Human Genome Project and the International Cancer Genome Consortium as well as the manifold advances in molecular biology and high-throughput sequencing techniques, inaugurated the integration of genomic information into disease classification. Furthermore, in some cases it became evident that former classifications needed major revision and adaption. Such adaptations are often required by understanding the pathogenesis of a disease from a specific molecular alteration, using this molecular driver for targeted and highly effective therapies. Altogether, reclassifications should lead to higher information content of the underlying diagnoses, reflecting their molecular pathogenesis and resulting in optimized and individual therapeutic decisions. The objective of this article is to summarize some particularly important examples of genome-based classification approaches and associated therapeutic concepts. In addition to reviewing disease specific markers, we focus on potentially therapeutic or predictive markers and the relevance of molecular diagnostics in disease monitoring.

A Monoclonal Antibody against Wnt-1 Induces Apoptosis in Human Cancer Cells

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Biao He

2004-01-01

Full Text Available Aberrant activation of the Wingless-type (Wnt/β-catenin signaling pathway is associated with a variety of human cancers. Little is known regarding the role that Wnt ligands play in human carcinogenesis. To test whether a Wnt-1 signal is a survival factor in human cancer cells and thus may serve as a potential cancer therapeutic target, we investigated the effect of inhibition of Wnt-1 signaling in a variety of human cancer cell lines, including non small cell lung cancer, breast cancer, mesothelioma, and sarcoma. Both monoclonal antibody and RNA interference (RNAi were used to inhibit Wnt-1 signaling. We found that incubation of a monoclonal anti-Wnt-1 antibody induced apoptosis and caused downstream protein changes in cancer cells overexpressing Wnt-1. In contrast, apoptosis was not detected in cells lacking or having minimal Wnt-1 expression after the antibody incubation. RNAi targeting of Wnt-1 in cancer cells overexpressing Wnt-1 demonstrated similar downstream protein changes and induction of apoptosis. The antibody also suppressed tumor growth in vivo. Our results indicate that both monoclonal anti-Wnt-1 antibody and Wnt-1 siRNA inhibit Wnt-1 signaling and can induce apoptosis in human cancer cells. These findings hold promise as a novel therapeutic strategy for cancer.

Identification of differentially expressed microRNAs in human male breast cancer

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Schipper Elisa

2010-03-01

Full Text Available Abstract Background The discovery of small non-coding RNAs and the subsequent analysis of microRNA expression patterns in human cancer specimens have provided completely new insights into cancer biology. Genetic and epigenetic data indicate oncogenic or tumor suppressor function of these pleiotropic regulators. Therefore, many studies analyzed the expression and function of microRNA in human breast cancer, the most frequent malignancy in females. However, nothing is known so far about microRNA expression in male breast cancer, accounting for approximately 1% of all breast cancer cases. Methods The expression of 319 microRNAs was analyzed in 9 primary human male breast tumors and in epithelial cells from 15 male gynecomastia specimens using fluorescence-labeled bead technology. For identification of differentially expressed microRNAs data were analyzed by cluster analysis and selected statistical methods. Expression levels were validated for the most up- or down-regulated microRNAs in this training cohort using real-time PCR methodology as well as in an independent test cohort comprising 12 cases of human male breast cancer. Results Unsupervised cluster analysis separated very well male breast cancer samples and control specimens according to their microRNA expression pattern indicating cancer-specific alterations of microRNA expression in human male breast cancer. miR-21, miR519d, miR-183, miR-197, and miR-493-5p were identified as most prominently up-regulated, miR-145 and miR-497 as most prominently down-regulated in male breast cancer. Conclusions Male breast cancer displays several differentially expressed microRNAs. Not all of them are shared with breast cancer biopsies from female patients indicating male breast cancer specific alterations of microRNA expression.

A SEMI-AUTOMATIC RULE SET BUILDING METHOD FOR URBAN LAND COVER CLASSIFICATION BASED ON MACHINE LEARNING AND HUMAN KNOWLEDGE

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H. Y. Gu

2017-09-01

Full Text Available Classification rule set is important for Land Cover classification, which refers to features and decision rules. The selection of features and decision are based on an iterative trial-and-error approach that is often utilized in GEOBIA, however, it is time-consuming and has a poor versatility. This study has put forward a rule set building method for Land cover classification based on human knowledge and machine learning. The use of machine learning is to build rule sets effectively which will overcome the iterative trial-and-error approach. The use of human knowledge is to solve the shortcomings of existing machine learning method on insufficient usage of prior knowledge, and improve the versatility of rule sets. A two-step workflow has been introduced, firstly, an initial rule is built based on Random Forest and CART decision tree. Secondly, the initial rule is analyzed and validated based on human knowledge, where we use statistical confidence interval to determine its threshold. The test site is located in Potsdam City. We utilised the TOP, DSM and ground truth data. The results show that the method could determine rule set for Land Cover classification semi-automatically, and there are static features for different land cover classes.

Lnc2Cancer: a manually curated database of experimentally supported lncRNAs associated with various human cancers.

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Ning, Shangwei; Zhang, Jizhou; Wang, Peng; Zhi, Hui; Wang, Jianjian; Liu, Yue; Gao, Yue; Guo, Maoni; Yue, Ming; Wang, Lihua; Li, Xia

2016-01-04

Lnc2Cancer (http://www.bio-bigdata.net/lnc2cancer) is a manually curated database of cancer-associated long non-coding RNAs (lncRNAs) with experimental support that aims to provide a high-quality and integrated resource for exploring lncRNA deregulation in various human cancers. LncRNAs represent a large category of functional RNA molecules that play a significant role in human cancers. A curated collection and summary of deregulated lncRNAs in cancer is essential to thoroughly understand the mechanisms and functions of lncRNAs. Here, we developed the Lnc2Cancer database, which contains 1057 manually curated associations between 531 lncRNAs and 86 human cancers. Each association includes lncRNA and cancer name, the lncRNA expression pattern, experimental techniques, a brief functional description, the original reference and additional annotation information. Lnc2Cancer provides a user-friendly interface to conveniently browse, retrieve and download data. Lnc2Cancer also offers a submission page for researchers to submit newly validated lncRNA-cancer associations. With the rapidly increasing interest in lncRNAs, Lnc2Cancer will significantly improve our understanding of lncRNA deregulation in cancer and has the potential to be a timely and valuable resource. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Oncogenic impact of human papilloma virus in head and neck cancer.

LENUS (Irish Health Repository)

Heffernan, C B

2012-02-01

There is considerable debate within the literature about the significance of human papilloma virus in head and neck squamous cell carcinoma, and its potential influence on the prevention, diagnosis, grading, treatment and prognosis of these cancers. Cigarette smoking and alcohol consumption have traditionally been cited as the main risk factors for head and neck cancers. However, human papilloma virus, normally associated with cervical and other genital carcinomas, has emerged as a possible key aetiological factor in head and neck squamous cell carcinoma, especially oropharyngeal cancers. These cancers pose a significant financial burden on health resources and are increasing in incidence. The recent introduction of vaccines targeted against human papilloma virus types 16 and 18, to prevent cervical cancer, has highlighted the need for ongoing research into the importance of human papilloma virus in head and neck squamous cell carcinoma.

Support vector machine for breast cancer classification using diffusion-weighted MRI histogram features: Preliminary study.

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Vidić, Igor; Egnell, Liv; Jerome, Neil P; Teruel, Jose R; Sjøbakk, Torill E; Østlie, Agnes; Fjøsne, Hans E; Bathen, Tone F; Goa, Pål Erik

2018-05-01

Diffusion-weighted MRI (DWI) is currently one of the fastest developing MRI-based techniques in oncology. Histogram properties from model fitting of DWI are useful features for differentiation of lesions, and classification can potentially be improved by machine learning. To evaluate classification of malignant and benign tumors and breast cancer subtypes using support vector machine (SVM). Prospective. Fifty-one patients with benign (n = 23) and malignant (n = 28) breast tumors (26 ER+, whereof six were HER2+). Patients were imaged with DW-MRI (3T) using twice refocused spin-echo echo-planar imaging with echo time / repetition time (TR/TE) = 9000/86 msec, 90 × 90 matrix size, 2 × 2 mm in-plane resolution, 2.5 mm slice thickness, and 13 b-values. Apparent diffusion coefficient (ADC), relative enhanced diffusivity (RED), and the intravoxel incoherent motion (IVIM) parameters diffusivity (D), pseudo-diffusivity (D*), and perfusion fraction (f) were calculated. The histogram properties (median, mean, standard deviation, skewness, kurtosis) were used as features in SVM (10-fold cross-validation) for differentiation of lesions and subtyping. Accuracies of the SVM classifications were calculated to find the combination of features with highest prediction accuracy. Mann-Whitney tests were performed for univariate comparisons. For benign versus malignant tumors, univariate analysis found 11 histogram properties to be significant differentiators. Using SVM, the highest accuracy (0.96) was achieved from a single feature (mean of RED), or from three feature combinations of IVIM or ADC. Combining features from all models gave perfect classification. No single feature predicted HER2 status of ER + tumors (univariate or SVM), although high accuracy (0.90) was achieved with SVM combining several features. Importantly, these features had to include higher-order statistics (kurtosis and skewness), indicating the importance to account for heterogeneity. Our

Can-CSC-GBE: Developing Cost-sensitive Classifier with Gentleboost Ensemble for breast cancer classification using protein amino acids and imbalanced data.

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Ali, Safdar; Majid, Abdul; Javed, Syed Gibran; Sattar, Mohsin

2016-06-01

Early prediction of breast cancer is important for effective treatment and survival. We developed an effective Cost-Sensitive Classifier with GentleBoost Ensemble (Can-CSC-GBE) for the classification of breast cancer using protein amino acid features. In this work, first, discriminant information of the protein sequences related to breast tissue is extracted. Then, the physicochemical properties hydrophobicity and hydrophilicity of amino acids are employed to generate molecule descriptors in different feature spaces. For comparison, we obtained results by combining Cost-Sensitive learning with conventional ensemble of AdaBoostM1 and Bagging. The proposed Can-CSC-GBE system has effectively reduced the misclassification costs and thereby improved the overall classification performance. Our novel approach has highlighted promising results as compared to the state-of-the-art ensemble approaches. Copyright © 2016 Elsevier Ltd. All rights reserved.

Classification, disease, and diagnosis.

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Jutel, Annemarie

2011-01-01

Classification shapes medicine and guides its practice. Understanding classification must be part of the quest to better understand the social context and implications of diagnosis. Classifications are part of the human work that provides a foundation for the recognition and study of illness: deciding how the vast expanse of nature can be partitioned into meaningful chunks, stabilizing and structuring what is otherwise disordered. This article explores the aims of classification, their embodiment in medical diagnosis, and the historical traditions of medical classification. It provides a brief overview of the aims and principles of classification and their relevance to contemporary medicine. It also demonstrates how classifications operate as social framing devices that enable and disable communication, assert and refute authority, and are important items for sociological study.

BMI-1, a promising therapeutic target for human cancer

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WANG, MIN-CONG; LI, CHUN-LI; CUI, JIE; JIAO, MIN; WU, TAO; JING, LI; NAN, KE-JUN

2015-01-01

BMI-1 oncogene is a member of the polycomb-group gene family and a transcriptional repressor. Overexpression of BMI-1 has been identified in various human cancer tissues and is known to be involved in cancer cell proliferation, cell invasion, distant metastasis, chemosensitivity and patient survival. Accumulating evidence has revealed that BMI-1 is also involved in the regulation of self-renewal, differentiation and tumor initiation of cancer stem cells (CSCs). However, the molecular mechanisms underlying these biological processes remain unclear. The present review summarized the function of BMI-1 in different human cancer types and CSCs, and discussed the signaling pathways in which BMI-1 is potentially involved. In conclusion, BMI-1 may represent a promising target for the prevention and therapy of various cancer types. PMID:26622537

Real-time classification of auditory sentences using evoked cortical activity in humans

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Moses, David A.; Leonard, Matthew K.; Chang, Edward F.

2018-06-01

Objective. Recent research has characterized the anatomical and functional basis of speech perception in the human auditory cortex. These advances have made it possible to decode speech information from activity in brain regions like the superior temporal gyrus, but no published work has demonstrated this ability in real-time, which is necessary for neuroprosthetic brain-computer interfaces. Approach. Here, we introduce a real-time neural speech recognition (rtNSR) software package, which was used to classify spoken input from high-resolution electrocorticography signals in real-time. We tested the system with two human subjects implanted with electrode arrays over the lateral brain surface. Subjects listened to multiple repetitions of ten sentences, and rtNSR classified what was heard in real-time from neural activity patterns using direct sentence-level and HMM-based phoneme-level classification schemes. Main results. We observed single-trial sentence classification accuracies of 90% or higher for each subject with less than 7 minutes of training data, demonstrating the ability of rtNSR to use cortical recordings to perform accurate real-time speech decoding in a limited vocabulary setting. Significance. Further development and testing of the package with different speech paradigms could influence the design of future speech neuroprosthetic applications.

Micro-Doppler Based Classification of Human Aquatic Activities via Transfer Learning of Convolutional Neural Networks

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Jinhee Park

2016-11-01

Full Text Available Accurate classification of human aquatic activities using radar has a variety of potential applications such as rescue operations and border patrols. Nevertheless, the classification of activities on water using radar has not been extensively studied, unlike the case on dry ground, due to its unique challenge. Namely, not only is the radar cross section of a human on water small, but the micro-Doppler signatures are much noisier due to water drops and waves. In this paper, we first investigate whether discriminative signatures could be obtained for activities on water through a simulation study. Then, we show how we can effectively achieve high classification accuracy by applying deep convolutional neural networks (DCNN directly to the spectrogram of real measurement data. From the five-fold cross-validation on our dataset, which consists of five aquatic activities, we report that the conventional feature-based scheme only achieves an accuracy of 45.1%. In contrast, the DCNN trained using only the collected data attains 66.7%, and the transfer learned DCNN, which takes a DCNN pre-trained on a RGB image dataset and fine-tunes the parameters using the collected data, achieves a much higher 80.3%, which is a significant performance boost.

Transposable Elements in Human Cancer: Causes and Consequences of Deregulation

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Anwar, Sumadi Lukman; Wulaningsih, Wahyu; Lehmann, Ulrich

2017-01-01

Transposable elements (TEs) comprise nearly half of the human genome and play an essential role in the maintenance of genomic stability, chromosomal architecture, and transcriptional regulation. TEs are repetitive sequences consisting of RNA transposons, DNA transposons, and endogenous retroviruses that can invade the human genome with a substantial contribution in human evolution and genomic diversity. TEs are therefore firmly regulated from early embryonic development and during the entire course of human life by epigenetic mechanisms, in particular DNA methylation and histone modifications. The deregulation of TEs has been reported in some developmental diseases, as well as for different types of human cancers. To date, the role of TEs, the mechanisms underlying TE reactivation, and the interplay with DNA methylation in human cancers remain largely unexplained. We reviewed the loss of epigenetic regulation and subsequent genomic instability, chromosomal aberrations, transcriptional deregulation, oncogenic activation, and aberrations of non-coding RNAs as the potential mechanisms underlying TE deregulation in human cancers. PMID:28471386

Transposable Elements in Human Cancer: Causes and Consequences of Deregulation

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Sumadi Lukman Anwar

2017-05-01

Full Text Available Transposable elements (TEs comprise nearly half of the human genome and play an essential role in the maintenance of genomic stability, chromosomal architecture, and transcriptional regulation. TEs are repetitive sequences consisting of RNA transposons, DNA transposons, and endogenous retroviruses that can invade the human genome with a substantial contribution in human evolution and genomic diversity. TEs are therefore firmly regulated from early embryonic development and during the entire course of human life by epigenetic mechanisms, in particular DNA methylation and histone modifications. The deregulation of TEs has been reported in some developmental diseases, as well as for different types of human cancers. To date, the role of TEs, the mechanisms underlying TE reactivation, and the interplay with DNA methylation in human cancers remain largely unexplained. We reviewed the loss of epigenetic regulation and subsequent genomic instability, chromosomal aberrations, transcriptional deregulation, oncogenic activation, and aberrations of non-coding RNAs as the potential mechanisms underlying TE deregulation in human cancers.

Viruses and human cancers: challenges for preventive strategies.

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de The, G

1995-01-01

Virus-associated human cancers provide unique opportunities for preventive strategies. The role of human papilloma viruses (HPV 16 and 18), hepatitis B virus (HBV), Epstein-Barr herpes virus (EBV), and retroviruses (human immunodeficiency virus [HIV] and human T-cell leukemia/lymphoma virus [HTLV]) in the development of common carcinomas and lymphomas represents a major cancer threat, particularly among individuals residing in developing countries, which account for 80% of the world's population. Even though these viruses are not the sole etiological agents of these cancers (as would be the case for infectious diseases), different approaches can be implemented to significantly decrease the incidence of virus-associated malignancies. The first approach is vaccination, which is available for HBV and possibly soon for EBV. The long delay between primary viral infection and development of associated tumors as well as the cost involved with administering vaccinations detracts from the feasibility of such an approach within developing countries. The second approach is to increase efforts to detect pre-cancerous lesions or early tumors using immunovirological means. This would allow early diagnosis and better treatment. The third strategy is linked to the existence of disease susceptibility genes, and suggests that counseling be provided for individuals carrying these genes to encourage them to modify their lifestyles and other conditions associated with increased cancer risks (predictive oncology). Specific recommendations include: a) increase international studies that explore the causes of the large variations in prevalence of common cancers throughout the world; b) conduct interdisciplinary studies involving laboratory investigation and social sciences, which may suggest hypotheses that may then be tested experimentally; and c) promote more preventive and health enhancement strategies in addition to curative and replacement therapies. PMID:8741797

Impact of full field digital mammography on the classification and mammographic characteristics of interval breast cancers

Energy Technology Data Exchange (ETDEWEB)

Knox, Mark, E-mail: marktknox@gmail.com; O’Brien, Angela, E-mail: angelaobrien@doctors.org.uk; Szabó, Endre, E-mail: endrebacsi@freemail.hu; Smith, Clare S., E-mail: csmith@mater.ie; Fenlon, Helen M., E-mail: helen.fenlon@cancerscreening.ie; McNicholas, Michelle M., E-mail: michelle.mcnicholas@cancerscreening.ie; Flanagan, Fidelma L., E-mail: fidelma.flanagan@cancerscreening.ie

2015-06-15

Highlights: • Digital mammography has changed the presentation of interval breast cancer. • Less interval breast cancers are associated with microcalcifications following FFDM. • Interval breast cancer audit remains a key feature of any breast screening program. - Abstract: Objective: Full field digital mammography (FFDM) is increasingly replacing screen film mammography (SFM) in breast screening programs. Interval breast cancers are an issue in all screening programs and the purpose of our study is to assess the impact of FFDM on the classification of interval breast cancers at independent blind review and to compare the mammographic features of interval cancers at FFDM and SFM. Materials and methods: This study included 138 cases of interval breast cancer, 76 following an FFDM screening examination and 62 following screening with SFM. The prior screening mammogram was assessed by each of five consultant breast radiologists who were blinded to the site of subsequent cancer. Subsequent review of the diagnostic mammogram was performed and cases were classified as missed, minimal signs, occult or true interval. Mammographic features of the interval cancer at diagnosis and any abnormality identified on the prior screening mammogram were recorded. Results: The percentages of cancers classified as missed at FFDM and SFM did not differ significantly, 10.5% (8 of 76) at FFDM and 8.1% (5 of 62) at SFM (p = .77). There were significantly less interval cancers presenting as microcalcifications (alone or in association with another abnormality) following screening with FFDM, 16% (12 of 76) than following a SFM examination, 32% (20 of 62) (p = .02). Conclusion: Interval breast cancers continue to pose a problem at FFDM. The switch to FFDM has changed the mammographic presentation of interval breast cancer, with less interval cancers presenting in association with microcalcifications.

Impact of full field digital mammography on the classification and mammographic characteristics of interval breast cancers

International Nuclear Information System (INIS)

Knox, Mark; O’Brien, Angela; Szabó, Endre; Smith, Clare S.; Fenlon, Helen M.; McNicholas, Michelle M.; Flanagan, Fidelma L.

2015-01-01

Highlights: • Digital mammography has changed the presentation of interval breast cancer. • Less interval breast cancers are associated with microcalcifications following FFDM. • Interval breast cancer audit remains a key feature of any breast screening program. - Abstract: Objective: Full field digital mammography (FFDM) is increasingly replacing screen film mammography (SFM) in breast screening programs. Interval breast cancers are an issue in all screening programs and the purpose of our study is to assess the impact of FFDM on the classification of interval breast cancers at independent blind review and to compare the mammographic features of interval cancers at FFDM and SFM. Materials and methods: This study included 138 cases of interval breast cancer, 76 following an FFDM screening examination and 62 following screening with SFM. The prior screening mammogram was assessed by each of five consultant breast radiologists who were blinded to the site of subsequent cancer. Subsequent review of the diagnostic mammogram was performed and cases were classified as missed, minimal signs, occult or true interval. Mammographic features of the interval cancer at diagnosis and any abnormality identified on the prior screening mammogram were recorded. Results: The percentages of cancers classified as missed at FFDM and SFM did not differ significantly, 10.5% (8 of 76) at FFDM and 8.1% (5 of 62) at SFM (p = .77). There were significantly less interval cancers presenting as microcalcifications (alone or in association with another abnormality) following screening with FFDM, 16% (12 of 76) than following a SFM examination, 32% (20 of 62) (p = .02). Conclusion: Interval breast cancers continue to pose a problem at FFDM. The switch to FFDM has changed the mammographic presentation of interval breast cancer, with less interval cancers presenting in association with microcalcifications

Prognostic Performance and Reproducibility of the 1973 and 2004/2016 World Health Organization Grading Classification Systems in Non-muscle-invasive Bladder Cancer: A European Association of Urology Non-muscle Invasive Bladder Cancer Guidelines Panel Systematic Review.

Science.gov (United States)

Soukup, Viktor; Čapoun, Otakar; Cohen, Daniel; Hernández, Virginia; Babjuk, Marek; Burger, Max; Compérat, Eva; Gontero, Paolo; Lam, Thomas; MacLennan, Steven; Mostafid, A Hugh; Palou, Joan; van Rhijn, Bas W G; Rouprêt, Morgan; Shariat, Shahrokh F; Sylvester, Richard; Yuan, Yuhong; Zigeuner, Richard

2017-11-01

Tumour grade is an important prognostic indicator in non-muscle-invasive bladder cancer (NMIBC). Histopathological classifications are limited by interobserver variability (reproducibility), which may have prognostic implications. European Association of Urology NMIBC guidelines suggest concurrent use of both 1973 and 2004/2016 World Health Organization (WHO) classifications. To compare the prognostic performance and reproducibility of the 1973 and 2004/2016 WHO grading systems for NMIBC. A systematic literature search was undertaken incorporating Medline, Embase, and the Cochrane Library. Studies were critically appraised for risk of bias (QUIPS). For prognosis, the primary outcome was progression to muscle-invasive or metastatic disease. Secondary outcomes were disease recurrence, and overall and cancer-specific survival. For reproducibility, the primary outcome was interobserver variability between pathologists. Secondary outcome was intraobserver variability (repeatability) by the same pathologist. Of 3593 articles identified, 20 were included in the prognostic review; three were eligible for the reproducibility review. Increasing tumour grade in both classifications was associated with higher disease progression and recurrence rates. Progression rates in grade 1 patients were similar to those in low-grade patients; progression rates in grade 3 patients were higher than those in high-grade patients. Survival data were limited. Reproducibility of the 2004/2016 system was marginally better than that of the 1973 system. Two studies on repeatability showed conflicting results. Most studies had a moderate to high risk of bias. Current grading classifications in NMIBC are suboptimal. The 1973 system identifies more aggressive tumours. Intra- and interobserver variability was slightly less in the 2004/2016 classification. We could not confirm that the 2004/2016 classification outperforms the 1973 classification in prediction of recurrence and progression. This article

Compensatory neurofuzzy model for discrete data classification in biomedical

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Ceylan, Rahime

2015-03-01

Biomedical data is separated to two main sections: signals and discrete data. So, studies in this area are about biomedical signal classification or biomedical discrete data classification. There are artificial intelligence models which are relevant to classification of ECG, EMG or EEG signals. In same way, in literature, many models exist for classification of discrete data taken as value of samples which can be results of blood analysis or biopsy in medical process. Each algorithm could not achieve high accuracy rate on classification of signal and discrete data. In this study, compensatory neurofuzzy network model is presented for classification of discrete data in biomedical pattern recognition area. The compensatory neurofuzzy network has a hybrid and binary classifier. In this system, the parameters of fuzzy systems are updated by backpropagation algorithm. The realized classifier model is conducted to two benchmark datasets (Wisconsin Breast Cancer dataset and Pima Indian Diabetes dataset). Experimental studies show that compensatory neurofuzzy network model achieved 96.11% accuracy rate in classification of breast cancer dataset and 69.08% accuracy rate was obtained in experiments made on diabetes dataset with only 10 iterations.

Classification between normal and tumor tissues based on the pair-wise gene expression ratio

International Nuclear Information System (INIS)

Yap, YeeLeng; Zhang, XueWu; Ling, MT; Wang, XiangHong; Wong, YC; Danchin, Antoine

2004-01-01

Precise classification of cancer types is critically important for early cancer diagnosis and treatment. Numerous efforts have been made to use gene expression profiles to improve precision of tumor classification. However, reliable cancer-related signals are generally lacking. Using recent datasets on colon and prostate cancer, a data transformation procedure from single gene expression to pair-wise gene expression ratio is proposed. Making use of the internal consistency of each expression profiling dataset this transformation improves the signal to noise ratio of the dataset and uncovers new relevant cancer-related signals (features). The efficiency in using the transformed dataset to perform normal/tumor classification was investigated using feature partitioning with informative features (gene annotation) as discriminating axes (single gene expression or pair-wise gene expression ratio). Classification results were compared to the original datasets for up to 10-feature model classifiers. 82 and 262 genes that have high correlation to tissue phenotype were selected from the colon and prostate datasets respectively. Remarkably, data transformation of the highly noisy expression data successfully led to lower the coefficient of variation (CV) for the within-class samples as well as improved the correlation with tissue phenotypes. The transformed dataset exhibited lower CV when compared to that of single gene expression. In the colon cancer set, the minimum CV decreased from 45.3% to 16.5%. In prostate cancer, comparable CV was achieved with and without transformation. This improvement in CV, coupled with the improved correlation between the pair-wise gene expression ratio and tissue phenotypes, yielded higher classification efficiency, especially with the colon dataset – from 87.1% to 93.5%. Over 90% of the top ten discriminating axes in both datasets showed significant improvement after data transformation. The high classification efficiency achieved suggested

Discrimination of rectal cancer through human serum using surface-enhanced Raman spectroscopy

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Li, Xiaozhou; Yang, Tianyue; Li, Siqi; Zhang, Su; Jin, Lili

2015-05-01

In this paper, surface-enhanced Raman spectroscopy (SERS) was used to detect the changes in blood serum components that accompany rectal cancer. The differences in serum SERS data between rectal cancer patients and healthy controls were examined. Postoperative rectal cancer patients also participated in the comparison to monitor the effects of cancer treatments. The results show that there are significant variations at certain wavenumbers which indicates alteration of corresponding biological substances. Principal component analysis (PCA) and parameters of intensity ratios were used on the original SERS spectra for the extraction of featured variables. These featured variables then underwent linear discriminant analysis (LDA) and classification and regression tree (CART) for the discrimination analysis. Accuracies of 93.5 and 92.4 % were obtained for PCA-LDA and parameter-CART, respectively.

Identification of hormonal receptors in human breast cancer

International Nuclear Information System (INIS)

Rosa Pascual, M.; Lage, A.; Diaz, J.W.; Moreno, L.; Marta Diaz, T.

1981-01-01

The experience in the implementation of a technique for determining hormono-dependence of human breast cancer is presented. The results found with the use of the technique in 50 patients with malignant breast cancer treated at IOR are examined and discussed. (author)

Autophagy Therapeutic Potential of Garlic in Human Cancer Therapy

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Yung-Lin Chu

2013-07-01

Full Text Available Cancer is one of the deadliest diseases against humans. To tackle this menace, humans have developed several high-technology therapies, such as chemotherapy, tomotherapy, targeted therapy, and antibody therapy. However, all these therapies have their own adverse side effects. Therefore, recent years have seen increased attention being given to the natural food for complementary therapy, which have less side effects. Garlic 大 蒜 Dà Suàn; Allium sativum, is one of most powerful food used in many of the civilizations for both culinary and medicinal purpose. In general, these foods induce cancer cell death by apoptosis, autophagy, or necrosis. Studies have discussed how natural food factors regulate cell survival or death by autophagy in cancer cells. From many literature reviews, garlic could not only induce apoptosis but also autophagy in cancer cells. Autophagy, which is called type-II programmed cell death, provides new strategy in cancer therapy. In conclusion, we wish that garlic could be the pioneer food of complementary therapy in clinical cancer treatment and increase the life quality of cancer patients.

Histopathological Breast Cancer Image Classification by Deep Neural Network Techniques Guided by Local Clustering.

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Nahid, Abdullah-Al; Mehrabi, Mohamad Ali; Kong, Yinan

2018-01-01

Breast Cancer is a serious threat and one of the largest causes of death of women throughout the world. The identification of cancer largely depends on digital biomedical photography analysis such as histopathological images by doctors and physicians. Analyzing histopathological images is a nontrivial task, and decisions from investigation of these kinds of images always require specialised knowledge. However, Computer Aided Diagnosis (CAD) techniques can help the doctor make more reliable decisions. The state-of-the-art Deep Neural Network (DNN) has been recently introduced for biomedical image analysis. Normally each image contains structural and statistical information. This paper classifies a set of biomedical breast cancer images (BreakHis dataset) using novel DNN techniques guided by structural and statistical information derived from the images. Specifically a Convolutional Neural Network (CNN), a Long-Short-Term-Memory (LSTM), and a combination of CNN and LSTM are proposed for breast cancer image classification. Softmax and Support Vector Machine (SVM) layers have been used for the decision-making stage after extracting features utilising the proposed novel DNN models. In this experiment the best Accuracy value of 91.00% is achieved on the 200x dataset, the best Precision value 96.00% is achieved on the 40x dataset, and the best F -Measure value is achieved on both the 40x and 100x datasets.

Enigmatic human tails: A review of their history, embryology, classification, and clinical manifestations.

Science.gov (United States)

Tubbs, R Shane; Malefant, Jason; Loukas, Marios; Jerry Oakes, W; Oskouian, Rod J; Fries, Fabian N

2016-05-01

The presence of a human tail is a rare and intriguing phenomenon. While cases have been reported in the literature, confusion remains with respect to the proper classification, definition, and treatment methods. We review the literature concerning this anatomical derailment. We also consider the importance of excluding underlying congenital anomalies in these patients to prevent neurological deficits and other abnormal manifestations. © 2016 Wiley Periodicals, Inc.

Human endogenous retroviruses and cancer prevention: evidence and prospects.

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Cegolon, Luca; Salata, Cristiano; Weiderpass, Elisabete; Vineis, Paolo; Palù, Giorgio; Mastrangelo, Giuseppe

2013-01-03

Cancer is a significant and growing problem worldwide. While this increase may, in part, be attributed to increasing longevity, improved case notifications and risk-enhancing lifestyle (such as smoking, diet and obesity), hygiene-related factors resulting in immuno-regulatory failure may also play a major role and call for a revision of vaccination strategies to protect against a range of cancers in addition to infections. Human endogenous retroviruses (HERVs) are a significant component of a wider family of retroelements that constitutes part of the human genome. They were originated by the integration of exogenous retroviruses into the human genome millions of years ago. HERVs are estimated to comprise about 8% of human DNA and are ubiquitous in somatic and germinal tissues.Physiologic and pathologic processes are influenced by some biologically active HERV families. HERV antigens are only expressed at low levels by the host, but in circumstances of inappropriate control their genes may initiate or maintain pathological processes. Although the precise mechanism leading to abnormal HERVs gene expression has yet to be clearly elucidated, environmental factors seem to be involved by influencing the human immune system.HERV-K expression has been detected in different types of tumors.Among the various human endogenous retroviral families, the K series was the latest acquired by the human species. Probably because of its relatively recent origin, the HERV-K is the most complete and biologically active family.The abnormal expression of HERV-K seemingly triggers pathological processes leading to melanoma onset, but also contributes to the morphological and functional cellular modifications implicated in melanoma maintenance and progression.The HERV-K-MEL antigen is encoded by a pseudo-gene incorporated in the HERV-K env-gene. HERV-K-MEL is significantly expressed in the majority of dysplastic and normal naevi, as well as other tumors like sarcoma, lymphoma, bladder and

Human endogenous retroviruses and cancer prevention: evidence and prospects

International Nuclear Information System (INIS)

Cegolon, Luca; Salata, Cristiano; Weiderpass, Elisabete; Vineis, Paolo; Palù, Giorgio; Mastrangelo, Giuseppe

2013-01-01

Cancer is a significant and growing problem worldwide. While this increase may, in part, be attributed to increasing longevity, improved case notifications and risk-enhancing lifestyle (such as smoking, diet and obesity), hygiene-related factors resulting in immuno-regulatory failure may also play a major role and call for a revision of vaccination strategies to protect against a range of cancers in addition to infections. Human endogenous retroviruses (HERVs) are a significant component of a wider family of retroelements that constitutes part of the human genome. They were originated by the integration of exogenous retroviruses into the human genome millions of years ago. HERVs are estimated to comprise about 8% of human DNA and are ubiquitous in somatic and germinal tissues. Physiologic and pathologic processes are influenced by some biologically active HERV families. HERV antigens are only expressed at low levels by the host, but in circumstances of inappropriate control their genes may initiate or maintain pathological processes. Although the precise mechanism leading to abnormal HERVs gene expression has yet to be clearly elucidated, environmental factors seem to be involved by influencing the human immune system. HERV-K expression has been detected in different types of tumors. Among the various human endogenous retroviral families, the K series was the latest acquired by the human species. Probably because of its relatively recent origin, the HERV-K is the most complete and biologically active family. The abnormal expression of HERV-K seemingly triggers pathological processes leading to melanoma onset, but also contributes to the morphological and functional cellular modifications implicated in melanoma maintenance and progression. The HERV-K-MEL antigen is encoded by a pseudo-gene incorporated in the HERV-K env-gene. HERV-K-MEL is significantly expressed in the majority of dysplastic and normal naevi, as well as other tumors like sarcoma, lymphoma, bladder

Exploring human error in military aviation flight safety events using post-incident classification systems.

Science.gov (United States)

Hooper, Brionny J; O'Hare, David P A

2013-08-01

Human error classification systems theoretically allow researchers to analyze postaccident data in an objective and consistent manner. The Human Factors Analysis and Classification System (HFACS) framework is one such practical analysis tool that has been widely used to classify human error in aviation. The Cognitive Error Taxonomy (CET) is another. It has been postulated that the focus on interrelationships within HFACS can facilitate the identification of the underlying causes of pilot error. The CET provides increased granularity at the level of unsafe acts. The aim was to analyze the influence of factors at higher organizational levels on the unsafe acts of front-line operators and to compare the errors of fixed-wing and rotary-wing operations. This study analyzed 288 aircraft incidents involving human error from an Australasian military organization occurring between 2001 and 2008. Action errors accounted for almost twice (44%) the proportion of rotary wing compared to fixed wing (23%) incidents. Both classificatory systems showed significant relationships between precursor factors such as the physical environment, mental and physiological states, crew resource management, training and personal readiness, and skill-based, but not decision-based, acts. The CET analysis showed different predisposing factors for different aspects of skill-based behaviors. Skill-based errors in military operations are more prevalent in rotary wing incidents and are related to higher level supervisory processes in the organization. The Cognitive Error Taxonomy provides increased granularity to HFACS analyses of unsafe acts.

Cancer in human immunodeficiency virus-infected children : A case series from the Children's Cancer Group and the National Cancer Institute

NARCIS (Netherlands)

Granovsky, MO; Mueller, BU; Nicholson, HS; Rosenberg, PS; Rabkin, CS

Purpose: To describe the spectrum of malignancies in human immunodeficiency virus (HIV)-infected children and the clinical outcome of patients with these tumors. Methods: We retrospectively surveyed the Children's Cancer Group (CCG) and the National Cancer institute (NCI) for cases of cancer that

Human papillomavirus in cervical cancer and oropharyngeal cancer: One cause, two diseases.

Science.gov (United States)

Berman, Tara A; Schiller, John T

2017-06-15

Human papillomavirus (HPV) causes greater than 5% of cancers worldwide, including all cervical cancers and an alarmingly increasing proportion of oropharyngeal cancers (OPCs). Despite markedly reduced cervical cancer incidence in industrialized nations with organized screening programs, cervical cancer remains the second most common cause of death from cancer in women worldwide, as developing countries lack resources for universal, high-quality screening. In the United States, HPV-related OPC is only 1 of 5 cancers with a rising incidence since 1975 and now has taken over the cervix as the most common site of HPV-related cancer. Similar trends follow throughout North America and Europe. The need for early detection and prevention is paramount. Despite the common etiologic role of HPV in the development of cervical cancer and HPV-associated OPC, great disparity exists between incidence, screening modalities (or lack thereof), treatment, and prevention in these 2 very distinct cohorts. These differences in cervical cancer and HPV-associated OPC and their impact are discussed here. Cancer 2017;123:2219-2229. © 2017 American Cancer Society. Published 2017. This article is a U.S. Government work and is in the public domain in the USA.

Crowdsourcing the General Public for Large Scale Molecular Pathology Studies in Cancer

Directory of Open Access Journals (Sweden)

Francisco J. Candido dos Reis

2015-07-01

Interpretation: Crowdsourcing of the general public to classify cancer pathology data for research is viable, engages the public and provides accurate ER data. Crowdsourced classification of research data may offer a valid solution to problems of throughput requiring human input.

Colombia: Territorial classification

International Nuclear Information System (INIS)

Mendoza Morales, Alberto

1998-01-01

The article is about the approaches of territorial classification, thematic axes, handling principles and territorial occupation, politician and administrative units and administration regions among other topics. Understanding as Territorial Classification the space distribution on the territory of the country, of the geographical configurations, the human communities, the political-administrative units and the uses of the soil, urban and rural, existent and proposed

[Molecular classification of breast cancer patients obtained through the technique of chromogenic in situ hybridization (CISH)].

Science.gov (United States)

Fernández, Angel; Reigosa, Aldo

2013-12-01

Breast cancer is a heterogeneous disease composed of a growing number of biological subtypes, with substantial variability of the disease progression within each category. The aim of this research was to classify the samples object of study according to the molecular classes of breast cancer: luminal A, luminal B, HER2 and triple negative, as a result of the state of HER2 amplification obtained by the technique of chromogenic in situ hybridization (CISH). The sample consisted of 200 biopsies fixed in 10% formalin, processed by standard techniques up to paraffin embedding, corresponding to patients diagnosed with invasive ductal carcinoma of the breast. These biopsies were obtained from patients from private practice and the Institute of Oncology "Dr. Miguel Pérez Carreño", for immunohistochemistry (IHC) of hormone receptors and HER2 made in the Hospital Metropolitano del Norte, Valencia, Venezuela. The molecular classification of the patient's tumors considering the expression of estrogen and progesterone receptors by IHC and HER2 amplification by CISH, allowed those cases originally classified as unknown, since they had an indeterminate (2+) outcome for HER2 expression by IHC, to be grouped into the different molecular classes. Also, this classification permitted that some cases, initially considered as belonging to a molecular class, were assigned to another class, after the revaluation of the HER2 status by CISH.

CDX2 prognostic value in stage II/III resected colon cancer is related to CMS classification.

Science.gov (United States)

Pilati, C; Taieb, J; Balogoun, R; Marisa, L; de Reyniès, A; Laurent-Puig, P

2017-05-01

Caudal-type homeobox transcription factor 2 (CDX2) is involved in colon cancer (CC) oncogenesis and has been proposed as a prognostic biomarker in patients with stage II or III CC. We analyzed CDX2 expression in a series of 469 CC typed for the new international consensus molecular subtype (CMS) classification, and we confirmed results in a series of 90 CC. Here, we show that lack of CDX2 expression is only present in the mesenchymal subgroup (CMS4) and in MSI-immune tumors (CMS1) and not in CMS2 and CMS3 colon cancer. Although CDX2 expression was a globally independent prognostic factor, loss of CDX2 expression is not associated with a worse prognosis in the CMS1 group, but is highly prognostic in CMS4 patients for both relapse free and overall survival. Similarly, lack of CDX2 expression was a bad prognostic factor in MSS patients, but not in MSI. Our work suggests that combination of the consensual CMS classification and lack of CDX2 expression could be a useful marker to identify CMS4/CDX2-negative patients with a very poor prognosis. © The Author 2017. 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.

Effect of primarily cultured human lung cancer-associated fibroblasts on radiosensitivity of lung cancer cells

International Nuclear Information System (INIS)

Ji Xiaoqin; Ji Jiang; Chen Yongbing; Shan Fang; Lu Xueguan

2014-01-01

Objective: To investigate the effect of human lung cancer-associated fibroblasts (CAF) on the radiosensitivity of lung cancer cells when CAF is placed in direct contact co-culture with lung cancer cells. Methods: Human lung CAF was obtained from fresh human lung adenocarcinoma tissue specimens by primary culture and subculture and was then identified by immunofluorescence staining. The CAF was placed in direct contact co-culture with lung cancer A 549 and H 1299 cells, and the effects of CAF on the radiosensitivity of A 549 and H 1299 cells were evaluated by colony-forming assay. Results: The human lung CAF obtained by adherent culture could stably grow and proliferate, and it had specific expression of α-smooth muscle actin, vimentin, and fibroblast activation protein,but without expression of cytokeratin-18. The plating efficiency (PE, %) of A 549 cells at 0 Gy irradiation was (20.0 ± 3.9)% when cultured alone versus (32.3 ± 5.5)% when co-cultured with CAF (t=3.16, P<0.05), and the PE of H 1299 cells at 0 Gy irradiation was (20.6 ± 3.1)% when cultured alone versus (35.2 ± 2.3)% when co-cultured with CAF (t=6.55, P<0.05). The cell survival rate at 2 Gy irradiation (SF 2 ) of A 549 cells was 0.727 ±0.061 when cultured alone versus 0.782 ± 0.089 when co-cultured with CAF (t=0.88, P>0.05), and the SF 2 of H 1299 cells was 0.692 ±0.065 when cultured alone versus 0.782 ± 0.037 when co-cultured with CAF (t=2.08, P>0.05). The protection enhancement ratios of human lung CAF for A 549 cells and H 1299 cells were 1.29 and 1.25, respectively. Conclusions: Human lung CAF reduces the radiosensitivity of lung cancer cells when placed in direct contact co-culture with them, and the radioprotective effect may be attributed to CAF promoting the proliferation of lung cancer cells. (authors)

An approach for leukemia classification based on cooperative game theory.

Science.gov (United States)

Torkaman, Atefeh; Charkari, Nasrollah Moghaddam; Aghaeipour, Mahnaz

2011-01-01

Hematological malignancies are the types of cancer that affect blood, bone marrow and lymph nodes. As these tissues are naturally connected through the immune system, a disease affecting one of them will often affect the others as well. The hematological malignancies include; Leukemia, Lymphoma, Multiple myeloma. Among them, leukemia is a serious malignancy that starts in blood tissues especially the bone marrow, where the blood is made. Researches show, leukemia is one of the common cancers in the world. So, the emphasis on diagnostic techniques and best treatments would be able to provide better prognosis and survival for patients. In this paper, an automatic diagnosis recommender system for classifying leukemia based on cooperative game is presented. Through out this research, we analyze the flow cytometry data toward the classification of leukemia into eight classes. We work on real data set from different types of leukemia that have been collected at Iran Blood Transfusion Organization (IBTO). Generally, the data set contains 400 samples taken from human leukemic bone marrow. This study deals with cooperative game used for classification according to different weights assigned to the markers. The proposed method is versatile as there are no constraints to what the input or output represent. This means that it can be used to classify a population according to their contributions. In other words, it applies equally to other groups of data. The experimental results show the accuracy rate of 93.12%, for classification and compared to decision tree (C4.5) with (90.16%) in accuracy. The result demonstrates that cooperative game is very promising to be used directly for classification of leukemia as a part of Active Medical decision support system for interpretation of flow cytometry readout. This system could assist clinical hematologists to properly recognize different kinds of leukemia by preparing suggestions and this could improve the treatment of leukemic

Frequency of human papillomavirus infection in patients with gastrointestinal cancer.

Science.gov (United States)

Roesch-Dietlen, F; Cano-Contreras, A D; Sánchez-Maza, Y J; Espinosa-González, J M; Vázquez-Prieto, M Á; Valdés-de la O, E J; Díaz-Roesch, F; Carrasco-Arroniz, M Á; Cruz-Palacios, A; Grube-Pagola, P; Sumoza-Toledo, A; Vivanco-Cid, H; Mellado-Sánchez, G; Meixueiro-Daza, A; Silva-Cañetas, C S; Carrillo-Toledo, M G; Lagunes-Torres, R; Amieva-Balmori, M; Gómez-Castaño, P C; Reyes-Huerta, J U; Remes-Troche, J M

2018-02-15

Cancer is the result of the interaction of genetic and environmental factors. It has recently been related to viral infections, one of which is human papillomavirus. The aim of the present study was to describe the frequency of human papillomavirus infection in patients with digestive system cancers. A prospective, multicenter, observational study was conducted on patients with gastrointestinal cancer at 2public healthcare institutes in Veracruz. Two tumor samples were taken, one for histologic study and the other for DNA determination of human papillomavirus and its genotypes. Anthropometric variables, risk factors, sexual habits, tumor location, and histologic type of the cancer were analyzed. Absolute and relative frequencies were determined using the SPSS version 24.0 program. Fifty-three patients were studied. They had gastrointestinal cancer located in: the colon (62.26%), stomach (18.87%), esophagus (7.55%), rectum (7.55%), and small bowel (3.77%). Human papillomavirus was identified in 11.32% of the patients, 66.7% of which corresponded to squamous cell carcinoma and 33.3% to adenocarcinoma. Only genotype 18 was identified. Mean patient age was 61.8±15.2 years, 56.60% of the patients were men, and 43.40% were women. A total of 15.8% of the patients had a family history of cancer and 31.6% had a personal history of the disease, 38.6% were tobacco smokers, and 61.4% consumed alcohol. Regarding sex, 5.3% of the patients said they were homosexual, 3.5% were bisexual, 29.8% engaged in oral sex, and 24.6% in anal sex. Our study showed that human papillomavirus infection was a risk factor for the development of gastrointestinal cancer, especially of squamous cell origin. Copyright © 2018 Asociación Mexicana de Gastroenterología. Publicado por Masson Doyma México S.A. All rights reserved.

Human Nanog pseudogene8 promotes the proliferation of gastrointestinal cancer cells

International Nuclear Information System (INIS)

Uchino, Keita; Hirano, Gen; Hirahashi, Minako; Isobe, Taichi; Shirakawa, Tsuyoshi; Kusaba, Hitoshi; Baba, Eishi; Tsuneyoshi, Masazumi; Akashi, Koichi

2012-01-01

There is emerging evidence that human solid tumor cells originate from cancer stem cells (CSCs). In cancer cell lines, tumor-initiating CSCs are mainly found in the side population (SP) that has the capacity to extrude dyes such as Hoechst 33342. We found that Nanog is expressed specifically in SP cells of human gastrointestinal (GI) cancer cells. Nucleotide sequencing revealed that NanogP8 but not Nanog was expressed in GI cancer cells. Transfection of NanogP8 into GI cancer cell lines promoted cell proliferation, while its inhibition by anti-Nanog siRNA suppressed the proliferation. Immunohistochemical staining of primary GI cancer tissues revealed NanogP8 protein to be strongly expressed in 3 out of 60 cases. In these cases, NanogP8 was found especially in an infiltrative part of the tumor, in proliferating cells with Ki67 expression. These data suggest that NanogP8 is involved in GI cancer development in a fraction of patients, in whom it presumably acts by supporting CSC proliferation. -- Highlights: ► Nanog maintains pluripotency by regulating embryonic stem cells differentiation. ► Nanog is expressed in cancer stem cells of human gastrointestinal cancer cells. ► Nucleotide sequencing revealed that Nanog pseudogene8 but not Nanog was expressed. ► Nanog pseudogene8 promotes cancer stem cells proliferation. ► Nanog pseudogene8 is involved in gastrointestinal cancer development.

Prediction of Depression in Cancer Patients With Different Classification Criteria, Linear Discriminant Analysis versus Logistic Regression.

Science.gov (United States)

Shayan, Zahra; Mohammad Gholi Mezerji, Naser; Shayan, Leila; Naseri, Parisa

2015-11-03

Logistic regression (LR) and linear discriminant analysis (LDA) are two popular statistical models for prediction of group membership. Although they are very similar, the LDA makes more assumptions about the data. When categorical and continuous variables used simultaneously, the optimal choice between the two models is questionable. In most studies, classification error (CE) is used to discriminate between subjects in several groups, but this index is not suitable to predict the accuracy of the outcome. The present study compared LR and LDA models using classification indices. This cross-sectional study selected 243 cancer patients. Sample sets of different sizes (n = 50, 100, 150, 200, 220) were randomly selected and the CE, B, and Q classification indices were calculated by the LR and LDA models. CE revealed the a lack of superiority for one model over the other, but the results showed that LR performed better than LDA for the B and Q indices in all situations. No significant effect for sample size on CE was noted for selection of an optimal model. Assessment of the accuracy of prediction of real data indicated that the B and Q indices are appropriate for selection of an optimal model. The results of this study showed that LR performs better in some cases and LDA in others when based on CE. The CE index is not appropriate for classification, although the B and Q indices performed better and offered more efficient criteria for comparison and discrimination between groups.

An immunologic portrait of cancer

Directory of Open Access Journals (Sweden)

Stroncek David F

2011-08-01

Full Text Available Abstract The advent of high-throughput technology challenges the traditional histopathological classification of cancer, and proposes new taxonomies derived from global transcriptional patterns. Although most of these molecular re-classifications did not endure the test of time, they provided bulk of new information that can reframe our understanding of human cancer biology. Here, we focus on an immunologic interpretation of cancer that segregates oncogenic processes independent from their tissue derivation into at least two categories of which one bears the footprints of immune activation. Several observations describe a cancer phenotype where the expression of interferon stimulated genes and immune effector mechanisms reflect patterns commonly observed during the inflammatory response against pathogens, which leads to elimination of infected cells. As these signatures are observed in growing cancers, they are not sufficient to entirely clear the organism of neoplastic cells but they sustain, as in chronic infections, a self-perpetuating inflammatory process. Yet, several studies determined an association between this inflammatory status and a favorable natural history of the disease or a better responsiveness to cancer immune therapy. Moreover, these signatures overlap with those observed during immune-mediated cancer rejection and, more broadly, immune-mediated tissue-specific destruction in other immune pathologies. Thus, a discussion concerning this cancer phenotype is warranted as it remains unknown why it occurs in immune competent hosts. It also remains uncertain whether a genetically determined response of the host to its own cancer, the genetic makeup of the neoplastic process or a combination of both drives the inflammatory process. Here we reflect on commonalities and discrepancies among studies and on the genetic or somatic conditions that may cause this schism in cancer behavior.

Human Papilloma Virus Vaccination for Control of Cervical Cancer ...

African Journals Online (AJOL)

Human Papilloma Virus Vaccination for Control of Cervical Cancer: A ... Primary HPV prevention may be the key to reducing incidence and burden of cervical cancer ... Other resources included locally-published articles and additional internet ...

Modulation of TIP60 by Human Papilloma Virus in Breast Cancer

Science.gov (United States)

2013-04-01

1 AG________ Award Number: W81XWH-11-1-0687 Title Modulation of TIP60 by Human Papilloma Virus in Breast Cancer... Human Papilloma Virus in Breast Cancer 5b. GRANT NUMBER 1 H 11 1 06 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Betty Diamond 5d. PROJECT...virus (EBV), Hepatitis B Virus (HBV), Hepatitis C virus (HCV), Human Papilloma virus (HPV), Human T-cell lymphotropic virus (HTLV-1) and Kaposi’s

Low-risk susceptibility alleles in 40 human breast cancer cell lines

International Nuclear Information System (INIS)

Riaz, Muhammad; Elstrodt, Fons; Hollestelle, Antoinette; Dehghan, Abbas; Klijn, Jan GM; Schutte, Mieke

2009-01-01

Low-risk breast cancer susceptibility alleles or SNPs confer only modest breast cancer risks ranging from just over 1.0 to1.3 fold. Yet, they are common among most populations and therefore are involved in the development of essentially all breast cancers. The mechanism by which the low-risk SNPs confer breast cancer risks is currently unclear. The breast cancer association consortium BCAC has hypothesized that the low-risk SNPs modulate expression levels of nearby located genes. Genotypes of five low-risk SNPs were determined for 40 human breast cancer cell lines, by direct sequencing of PCR-amplified genomic templates. We have analyzed expression of the four genes that are located nearby the low-risk SNPs, by using real-time RT-PCR and Human Exon microarrays. The SNP genotypes and additional phenotypic data on the breast cancer cell lines are presented. We did not detect any effect of the SNP genotypes on expression levels of the nearby-located genes MAP3K1, FGFR2, TNRC9 and LSP1. The SNP genotypes provide a base line for functional studies in a well-characterized cohort of 40 human breast cancer cell lines. Our expression analyses suggest that a putative disease mechanism through gene expression modulation is not operative in breast cancer cell lines

Drug-selected human lung cancer stem cells: cytokine network, tumorigenic and metastatic properties.

Directory of Open Access Journals (Sweden)

Vera Levina

2008-08-01

Full Text Available Cancer stem cells (CSCs are thought to be responsible for tumor regeneration after chemotherapy, although direct confirmation of this remains forthcoming. We therefore investigated whether drug treatment could enrich and maintain CSCs and whether the high tumorogenic and metastatic abilities of CSCs were based on their marked ability to produce growth and angiogenic factors and express their cognate receptors to stimulate tumor cell proliferation and stroma formation.Treatment of lung tumor cells with doxorubicin, cisplatin, or etoposide resulted in the selection of drug surviving cells (DSCs. These cells expressed CD133, CD117, SSEA-3, TRA1-81, Oct-4, and nuclear beta-catenin and lost expression of the differentiation markers cytokeratins 8/18 (CK 8/18. DSCs were able to grow as tumor spheres, maintain self-renewal capacity, and differentiate. Differentiated progenitors lost expression of CD133, gained CK 8/18 and acquired drug sensitivity. In the presence of drugs, differentiation of DSCs was abrogated allowing propagation of cells with CSC-like characteristics. Lung DSCs demonstrated high tumorogenic and metastatic potential following inoculation into SCID mice, which supported their classification as CSCs. Luminex analysis of human and murine cytokines in sonicated lysates of parental- and CSC-derived tumors revealed that CSC-derived tumors contained two- to three-fold higher levels of human angiogenic and growth factors (VEGF, bFGF, IL-6, IL-8, HGF, PDGF-BB, G-CSF, and SCGF-beta. CSCs also showed elevated levels of expression of human VEGFR2, FGFR2, CXCR1, 2 and 4 receptors. Moreover, human CSCs growing in SCID mice stimulated murine stroma to produce elevated levels of angiogenic and growth factors.These findings suggest that chemotherapy can lead to propagation of CSCs and prevention of their differentiation. The high tumorigenic and metastatic potentials of CSCs are associated with efficient cytokine network production that may represent

Socializing the human factors analysis and classification system: incorporating social psychological phenomena into a human factors error classification system.

Science.gov (United States)

Paletz, Susannah B F; Bearman, Christopher; Orasanu, Judith; Holbrook, Jon

2009-08-01

The presence of social psychological pressures on pilot decision making was assessed using qualitative analyses of critical incident interviews. Social psychological phenomena have long been known to influence attitudes and behavior but have not been highlighted in accident investigation models. Using a critical incident method, 28 pilots who flew in Alaska were interviewed. The participants were asked to describe a situation involving weather when they were pilot in command and found their skills challenged. They were asked to describe the incident in detail but were not explicitly asked to identify social pressures. Pressures were extracted from transcripts in a bottom-up manner and then clustered into themes. Of the 28 pilots, 16 described social psychological pressures on their decision making, specifically, informational social influence, the foot-in-the-door persuasion technique, normalization of deviance, and impression management and self-consistency motives. We believe accident and incident investigations can benefit from explicit inclusion of common social psychological pressures. We recommend specific ways of incorporating these pressures into theHuman Factors Analysis and Classification System.

The 2015 World Health Organization Classification of Lung Tumors: Impact of Genetic, Clinical and Radiologic Advances Since the 2004 Classification.

Science.gov (United States)

Travis, William D; Brambilla, Elisabeth; Nicholson, Andrew G; Yatabe, Yasushi; Austin, John H M; Beasley, Mary Beth; Chirieac, Lucian R; Dacic, Sanja; Duhig, Edwina; Flieder, Douglas B; Geisinger, Kim; Hirsch, Fred R; Ishikawa, Yuichi; Kerr, Keith M; Noguchi, Masayuki; Pelosi, Giuseppe; Powell, Charles A; Tsao, Ming Sound; Wistuba, Ignacio

2015-09-01

The 2015 World Health Organization (WHO) Classification of Tumors of the Lung, Pleura, Thymus and Heart has just been published with numerous important changes from the 2004 WHO classification. The most significant changes in this edition involve (1) use of immunohistochemistry throughout the classification, (2) a new emphasis on genetic studies, in particular, integration of molecular testing to help personalize treatment strategies for advanced lung cancer patients, (3) a new classification for small biopsies and cytology similar to that proposed in the 2011 Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society classification, (4) a completely different approach to lung adenocarcinoma as proposed by the 2011 Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society classification, (5) restricting the diagnosis of large cell carcinoma only to resected tumors that lack any clear morphologic or immunohistochemical differentiation with reclassification of the remaining former large cell carcinoma subtypes into different categories, (6) reclassifying squamous cell carcinomas into keratinizing, nonkeratinizing, and basaloid subtypes with the nonkeratinizing tumors requiring immunohistochemistry proof of squamous differentiation, (7) grouping of neuroendocrine tumors together in one category, (8) adding NUT carcinoma, (9) changing the term sclerosing hemangioma to sclerosing pneumocytoma, (10) changing the name hamartoma to "pulmonary hamartoma," (11) creating a group of PEComatous tumors that include (a) lymphangioleiomyomatosis, (b) PEComa, benign (with clear cell tumor as a variant) and (c) PEComa, malignant, (12) introducing the entity pulmonary myxoid sarcoma with an EWSR1-CREB1 translocation, (13) adding the entities myoepithelioma and myoepithelial carcinomas, which can show EWSR1 gene rearrangements, (14) recognition of usefulness of WWTR1-CAMTA1 fusions in diagnosis of epithelioid

Altered serotonin physiology in human breast cancers favors paradoxical growth and cell survival.

Science.gov (United States)

Pai, Vaibhav P; Marshall, Aaron M; Hernandez, Laura L; Buckley, Arthur R; Horseman, Nelson D

2009-01-01

The breast microenvironment can either retard or accelerate the events associated with progression of latent cancers. However, the actions of local physiological mediators in the context of breast cancers are poorly understood. Serotonin (5-HT) is a critical local regulator of epithelial homeostasis in the breast and other organs. Herein, we report complex alterations in the intrinsic mammary gland serotonin system of human breast cancers. Serotonin biosynthetic capacity was analyzed in human breast tumor tissue microarrays using immunohistochemistry for tryptophan hydroxylase 1 (TPH1). Serotonin receptors (5-HT1-7) were analyzed in human breast tumors using the Oncomine database. Serotonin receptor expression, signal transduction, and 5-HT effects on breast cancer cell phenotype were compared in non-transformed and transformed human breast cells. In the context of the normal mammary gland, 5-HT acts as a physiological regulator of lactation and involution, in part by favoring growth arrest and cell death. This tightly regulated 5-HT system is subverted in multiple ways in human breast cancers. Specifically, TPH1 expression undergoes a non-linear change during progression, with increased expression during malignant progression. Correspondingly, the tightly regulated pattern of 5-HT receptors becomes dysregulated in human breast cancer cells, resulting in both ectopic expression of some isoforms and suppression of others. The receptor expression change is accompanied by altered downstream signaling of 5-HT receptors in human breast cancer cells, resulting in resistance to 5-HT-induced apoptosis, and stimulated proliferation. Our data constitutes the first report of direct involvement of 5-HT in human breast cancer. Increased 5-HT biosynthetic capacity accompanied by multiple changes in 5-HT receptor expression and signaling favor malignant progression of human breast cancer cells (for example, stimulated proliferation, inappropriate cell survival). This occurs

Potential Therapeutic Roles of Tanshinone IIA in Human Bladder Cancer Cells

Directory of Open Access Journals (Sweden)

Sheng-Chun Chiu

2014-09-01

Full Text Available Tanshinone IIA (Tan-IIA, one of the major lipophilic components isolated from the root of Salviae Miltiorrhizae, has been found to exhibit anticancer activity in various cancer cells. We have demonstrated that Tan-IIA induces apoptosis in several human cancer cells through caspase- and mitochondria-dependent pathways. Here we explored the anticancer effect of Tan-IIA in human bladder cancer cell lines. Our results showed that Tan-IIA caused bladder cancer cell death in a time- and dose-dependent manner. Tan-IIA induced apoptosis through the mitochondria-dependent pathway in these bladder cancer cells. Tan-IIA also suppressed the migration of bladder cancer cells as revealed by the wound healing and transwell assays. Finally, combination therapy of Tan-IIA with a lower dose of cisplatin successfully killed bladder cancer cells, suggesting that Tan-IIA can serve as a potential anti-cancer agent in bladder cancer.

hemaClass.org: Online One-By-One Microarray Normalization and Classification of Hematological Cancers for Precision Medicine.

Science.gov (United States)

Falgreen, Steffen; Ellern Bilgrau, Anders; Brøndum, Rasmus Froberg; Hjort Jakobsen, Lasse; Have, Jonas; Lindblad Nielsen, Kasper; El-Galaly, Tarec Christoffer; Bødker, Julie Støve; Schmitz, Alexander; H Young, Ken; Johnsen, Hans Erik; Dybkær, Karen; Bøgsted, Martin

2016-01-01

Dozens of omics based cancer classification systems have been introduced with prognostic, diagnostic, and predictive capabilities. However, they often employ complex algorithms and are only applicable on whole cohorts of patients, making them difficult to apply in a personalized clinical setting. This prompted us to create hemaClass.org, an online web application providing an easy interface to one-by-one RMA normalization of microarrays and subsequent risk classifications of diffuse large B-cell lymphoma (DLBCL) into cell-of-origin and chemotherapeutic sensitivity classes. Classification results for one-by-one array pre-processing with and without a laboratory specific RMA reference dataset were compared to cohort based classifiers in 4 publicly available datasets. Classifications showed high agreement between one-by-one and whole cohort pre-processsed data when a laboratory specific reference set was supplied. The website is essentially the R-package hemaClass accompanied by a Shiny web application. The well-documented package can be used to run the website locally or to use the developed methods programmatically. The website and R-package is relevant for biological and clinical lymphoma researchers using affymetrix U-133 Plus 2 arrays, as it provides reliable and swift methods for calculation of disease subclasses. The proposed one-by-one pre-processing method is relevant for all researchers using microarrays.

Human endogenous retroviruses and cancer prevention: evidence and prospects

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Cegolon Luca

2013-01-01

Full Text Available Abstract Background Cancer is a significant and growing problem worldwide. While this increase may, in part, be attributed to increasing longevity, improved case notifications and risk-enhancing lifestyle (such as smoking, diet and obesity, hygiene-related factors resulting in immuno-regulatory failure may also play a major role and call for a revision of vaccination strategies to protect against a range of cancers in addition to infections. Discussion Human endogenous retroviruses (HERVs are a significant component of a wider family of retroelements that constitutes part of the human genome. They were originated by the integration of exogenous retroviruses into the human genome millions of years ago. HERVs are estimated to comprise about 8% of human DNA and are ubiquitous in somatic and germinal tissues. Physiologic and pathologic processes are influenced by some biologically active HERV families. HERV antigens are only expressed at low levels by the host, but in circumstances of inappropriate control their genes may initiate or maintain pathological processes. Although the precise mechanism leading to abnormal HERVs gene expression has yet to be clearly elucidated, environmental factors seem to be involved by influencing the human immune system. HERV-K expression has been detected in different types of tumors. Among the various human endogenous retroviral families, the K series was the latest acquired by the human species. Probably because of its relatively recent origin, the HERV-K is the most complete and biologically active family. The abnormal expression of HERV-K seemingly triggers pathological processes leading to melanoma onset, but also contributes to the morphological and functional cellular modifications implicated in melanoma maintenance and progression. The HERV-K-MEL antigen is encoded by a pseudo-gene incorporated in the HERV-K env-gene. HERV-K-MEL is significantly expressed in the majority of dysplastic and normal naevi, as well

Feature Importance for Human Epithelial (HEp-2 Cell Image Classification

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Vibha Gupta

2018-02-01

Full Text Available Indirect Immuno-Fluorescence (IIF microscopy imaging of human epithelial (HEp-2 cells is a popular method for diagnosing autoimmune diseases. Considering large data volumes, computer-aided diagnosis (CAD systems, based on image-based classification, can help in terms of time, effort, and reliability of diagnosis. Such approaches are based on extracting some representative features from the images. This work explores the selection of the most distinctive features for HEp-2 cell images using various feature selection (FS methods. Considering that there is no single universally optimal feature selection technique, we also propose hybridization of one class of FS methods (filter methods. Furthermore, the notion of variable importance for ranking features, provided by another type of approaches (embedded methods such as Random forest, Random uniform forest is exploited to select a good subset of features from a large set, such that addition of new features does not increase classification accuracy. In this work, we have also, with great consideration, designed class-specific features to capture morphological visual traits of the cell patterns. We perform various experiments and discussions to demonstrate the effectiveness of FS methods along with proposed and a standard feature set. We achieve state-of-the-art performance even with small number of features, obtained after the feature selection.

Human tissue models in cancer research: looking beyond the mouse

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Samuel J. Jackson

2017-08-01

Full Text Available Mouse models, including patient-derived xenograft mice, are widely used to address questions in cancer research. However, there are documented flaws in these models that can result in the misrepresentation of human tumour biology and limit the suitability of the model for translational research. A coordinated effort to promote the more widespread development and use of ‘non-animal human tissue’ models could provide a clinically relevant platform for many cancer studies, maximising the opportunities presented by human tissue resources such as biobanks. A number of key factors limit the wide adoption of non-animal human tissue models in cancer research, including deficiencies in the infrastructure and the technical tools required to collect, transport, store and maintain human tissue for lab use. Another obstacle is the long-standing cultural reliance on animal models, which can make researchers resistant to change, often because of concerns about historical data compatibility and losing ground in a competitive environment while new approaches are embedded in lab practice. There are a wide range of initiatives that aim to address these issues by facilitating data sharing and promoting collaborations between organisations and researchers who work with human tissue. The importance of coordinating biobanks and introducing quality standards is gaining momentum. There is an exciting opportunity to transform cancer drug discovery by optimising the use of human tissue and reducing the reliance on potentially less predictive animal models.

Human tissue models in cancer research: looking beyond the mouse.

Science.gov (United States)

Jackson, Samuel J; Thomas, Gareth J

2017-08-01

Mouse models, including patient-derived xenograft mice, are widely used to address questions in cancer research. However, there are documented flaws in these models that can result in the misrepresentation of human tumour biology and limit the suitability of the model for translational research. A coordinated effort to promote the more widespread development and use of 'non-animal human tissue' models could provide a clinically relevant platform for many cancer studies, maximising the opportunities presented by human tissue resources such as biobanks. A number of key factors limit the wide adoption of non-animal human tissue models in cancer research, including deficiencies in the infrastructure and the technical tools required to collect, transport, store and maintain human tissue for lab use. Another obstacle is the long-standing cultural reliance on animal models, which can make researchers resistant to change, often because of concerns about historical data compatibility and losing ground in a competitive environment while new approaches are embedded in lab practice. There are a wide range of initiatives that aim to address these issues by facilitating data sharing and promoting collaborations between organisations and researchers who work with human tissue. The importance of coordinating biobanks and introducing quality standards is gaining momentum. There is an exciting opportunity to transform cancer drug discovery by optimising the use of human tissue and reducing the reliance on potentially less predictive animal models. © 2017. Published by The Company of Biologists Ltd.

An Entropy-based gene selection method for cancer classification using microarray data

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Krishnan Arun

2005-03-01

Full Text Available Abstract Background Accurate diagnosis of cancer subtypes remains a challenging problem. Building classifiers based on gene expression data is a promising approach; yet the selection of non-redundant but relevant genes is difficult. The selected gene set should be small enough to allow diagnosis even in regular clinical laboratories and ideally identify genes involved in cancer-specific regulatory pathways. Here an entropy-based method is proposed that selects genes related to the different cancer classes while at the same time reducing the redundancy among the genes. Results The present study identifies a subset of features by maximizing the relevance and minimizing the redundancy of the selected genes. A merit called normalized mutual information is employed to measure the relevance and the redundancy of the genes. In order to find a more representative subset of features, an iterative procedure is adopted that incorporates an initial clustering followed by data partitioning and the application of the algorithm to each of the partitions. A leave-one-out approach then selects the most commonly selected genes across all the different runs and the gene selection algorithm is applied again to pare down the list of selected genes until a minimal subset is obtained that gives a satisfactory accuracy of classification. The algorithm was applied to three different data sets and the results obtained were compared to work done by others using the same data sets Conclusion This study presents an entropy-based iterative algorithm for selecting genes from microarray data that are able to classify various cancer sub-types with high accuracy. In addition, the feature set obtained is very compact, that is, the redundancy between genes is reduced to a large extent. This implies that classifiers can be built with a smaller subset of genes.

Differential expression of carbohydrate antigen 19-9 in human colorectal cancer: A comparison with colon and rectal cancers

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ZHANG, SHUAI; CHEN, YIJUN; ZHU, ZHANMENG; DING, YUNLONG; REN, SHUANGYI; ZUO, YUNFEI

2013-01-01

Colorectal cancer is one of the leading causes of cancer-related mortality, being the third most commonly diagnosed cancer among men and the second among women. Accumulating evidence regarding carbohydrate antigen (CA) demonstrated that tumor-associated antigens are clinically useful for the diagnosis, staging and monitoring of human gastrointestinal cancers, particularly colorectal cancer. There has been an extensive investigation for sensitive and specific markers of this disease. Currently, the gastrointestinal cancer-associated carbohydrate antigen 19-9 (CA19-9) is the most widely applied tumor marker in cancer diagnosis. Despite a similar etiology and cancer incidence rates, there are anatomical and clinical differences between colon and rectal cancer, as well as differences regarding tumor progression and adjuvant treatments. To investigate whether CA19-9 is differentially expressed between colon and rectal cancer, we conducted a differential analysis of serum CA19-9 levels among 227 cases of colorectal cancer, analyzing gender, age, Dukes’ stage and distant metastasis for human colon and rectal cancer as a single entity, separately and as matched pairs. We demonstrated that the serum CA19-9 levels in colorectal cancer were upregulated in advanced stages with distant metastasis. By contrast, the serum CA19-9 levels in colon cancer displayed a differential and upregulated behavior in advanced stages with distant metastasis. By analyzing as matched pairs, the upregulated serum CA19-9 levels in rectal cancer during the early stages without distant metastasis further supported our hypothesis that the expression of CA19-9 displays a site-specific differential behavior. The integrative analysis suggested a significant difference between human colon and rectal cancer, justifying individualized therapy for these two types of cancer. PMID:24649295

Acromioclavicular joint dislocations: radiological correlation between Rockwood classification system and injury patterns in human cadaver species.

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Eschler, Anica; Rösler, Klaus; Rotter, Robert; Gradl, Georg; Mittlmeier, Thomas; Gierer, Philip

2014-09-01

The classification system of Rockwood and Young is a commonly used classification for acromioclavicular joint separations subdividing types I-VI. This classification hypothesizes specific lesions to anatomical structures (acromioclavicular and coracoclavicular ligaments, capsule, attached muscles) leading to the injury. In recent literature, our understanding for anatomical correlates leading to the radiological-based Rockwood classification is questioned. The goal of this experimental-based investigation was to approve the correlation between the anatomical injury pattern and the Rockwood classification. In four human cadavers (seven shoulders), the acromioclavicular and coracoclavicular ligaments were transected stepwise. Radiological correlates were recorded (Zanca view) with 15-kg longitudinal tension applied at the wrist. The resulting acromio- and coracoclavicular distances were measured. Radiographs after acromioclavicular ligament transection showed joint space enlargement (8.6 ± 0.3 vs. 3.1 ± 0.5 mm, p acromioclavicular joint space width increased to 16.7 ± 2.7 vs. 8.6 ± 0.3 mm, p acromioclavicular joint lesions higher than Rockwood type I and II. The clinical consequence for reconstruction of low-grade injuries might be a solely surgical approach for the acromioclavicular ligaments or conservative treatment. High-grade injuries were always based on additional structural damage to the coracoclavicular ligaments. Rockwood type V lesions occurred while muscle attachments were intact.

Human Nanog pseudogene8 promotes the proliferation of gastrointestinal cancer cells

Energy Technology Data Exchange (ETDEWEB)

Uchino, Keita, E-mail: uchino13@intmed1.med.kyushu-u.ac.jp [Department of Medicine and Biosystemic Science, Kyushu University Graduate School of Medical Sciences, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582 (Japan); Hirano, Gen [Department of Medicine and Biosystemic Science, Kyushu University Graduate School of Medical Sciences, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582 (Japan); Hirahashi, Minako [Department of Anatomic Pathology, Graduate School of Medical Sciences, Kyushu University, Fukuoka (Japan); Isobe, Taichi; Shirakawa, Tsuyoshi; Kusaba, Hitoshi; Baba, Eishi [Department of Medicine and Biosystemic Science, Kyushu University Graduate School of Medical Sciences, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582 (Japan); Tsuneyoshi, Masazumi [Department of Anatomic Pathology, Graduate School of Medical Sciences, Kyushu University, Fukuoka (Japan); Akashi, Koichi [Department of Medicine and Biosystemic Science, Kyushu University Graduate School of Medical Sciences, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582 (Japan)

2012-09-10

There is emerging evidence that human solid tumor cells originate from cancer stem cells (CSCs). In cancer cell lines, tumor-initiating CSCs are mainly found in the side population (SP) that has the capacity to extrude dyes such as Hoechst 33342. We found that Nanog is expressed specifically in SP cells of human gastrointestinal (GI) cancer cells. Nucleotide sequencing revealed that NanogP8 but not Nanog was expressed in GI cancer cells. Transfection of NanogP8 into GI cancer cell lines promoted cell proliferation, while its inhibition by anti-Nanog siRNA suppressed the proliferation. Immunohistochemical staining of primary GI cancer tissues revealed NanogP8 protein to be strongly expressed in 3 out of 60 cases. In these cases, NanogP8 was found especially in an infiltrative part of the tumor, in proliferating cells with Ki67 expression. These data suggest that NanogP8 is involved in GI cancer development in a fraction of patients, in whom it presumably acts by supporting CSC proliferation. -- Highlights: Black-Right-Pointing-Pointer Nanog maintains pluripotency by regulating embryonic stem cells differentiation. Black-Right-Pointing-Pointer Nanog is expressed in cancer stem cells of human gastrointestinal cancer cells. Black-Right-Pointing-Pointer Nucleotide sequencing revealed that Nanog pseudogene8 but not Nanog was expressed. Black-Right-Pointing-Pointer Nanog pseudogene8 promotes cancer stem cells proliferation. Black-Right-Pointing-Pointer Nanog pseudogene8 is involved in gastrointestinal cancer development.

World Health Organisation Classification of Lymphoid Tumours in Veterinary and Human Medicine: a Comparative Evaluation of Gastrointestinal Lymphomas in 61 Cats.

Science.gov (United States)

Wolfesberger, B; Fuchs-Baumgartinger, A; Greß, V; Hammer, S E; Gradner, G; Knödl, K; Tichy, A; Rütgen, B C; Beham-Schmid, C

2018-02-01

To diagnose and classify the various entities of lymphomas, the World Health Organisation (WHO) classification is applied in human as well as in veterinary medicine. We validated the concordance of these classification systems by having a veterinary and human pathologist evaluate gastrointestinal lymphoma tissue from 61 cats. In 59% of all cases, there was a match between their respective diagnoses of the lymphoma subtype. A complete consensus between the two evaluators was obtained for all samples with a diagnosis of diffuse large B-cell lymphoma, T-cell anaplastic large cell lymphoma and extranodal marginal zone lymphoma of mucosa-associated lymphoid tissue. A corresponding diagnosis was also made in the majority of samples with enteropathy associated T-cell lymphoma (EATL) type II, although this subtype in cats has similarities to the 'indolent T-cell lymphoproliferative disorder of the gastrointestinal tract', a provisional entity newly added to the revised human WHO classification in 2016. Very little consensus has been found with cases of EATL type I due to the fact that most did not meet all of the criteria of human EATL I. Hence, the human pathologist assigned them to the heterogeneous group of peripheral T-cell lymphomas (not otherwise specified). Consequently, concrete guidelines and advanced immunophenotyping based on the model of human medicine are essential to differentiate these challenging entities in veterinary medicine. Copyright © 2017 Elsevier Ltd. All rights reserved.

Cancer cell detection and classification using transformation invariant template learning methods

International Nuclear Information System (INIS)

Talware, Rajendra; Abhyankar, Aditya

2011-01-01

In traditional cancer cell detection, pathologists examine biopsies to make diagnostic assessments, largely based on cell morphology and tissue distribution. The process of image acquisition is very much subjective and the pattern undergoes unknown or random transformations during data acquisition (e.g. variation in illumination, orientation, translation and perspective) results in high degree of variability. Transformed Component Analysis (TCA) incorporates a discrete, hidden variable that accounts for transformations and uses the Expectation Maximization (EM) algorithm to jointly extract components and normalize for transformations. Further the TEMPLAR framework developed takes advantage of hierarchical pattern models and adds probabilistic modeling for local transformations. Pattern classification is based on Expectation Maximization algorithm and General Likelihood Ratio Tests (GLRT). Performance of TEMPLAR is certainly improved by defining area of interest on slide a priori. Performance can be further enhanced by making the kernel function adaptive during learning. (author)

Advances in human genetics

Energy Technology Data Exchange (ETDEWEB)

Harris, H.; Hirschhorn, K. (eds.)

1993-01-01

This book has five chapters covering peroxisomal diseases, X-linked immunodeficiencies, genetic mutations affecting human lipoproteins and their receptors and enzymes, genetic aspects of cancer, and Gaucher disease. The chapter on peroxisomes covers their discovery, structure, functions, disorders, etc. The chapter on X-linked immunodeficiencies discusses such diseases as agammaglobulinemia, severe combined immunodeficiency, Wiskott-Aldrich syndrome, animal models, linkage analysis, etc. Apolipoprotein formation, synthesis, gene regulation, proteins, etc. are the main focus of chapter 3. The chapter on cancer covers such topics as oncogene mapping and the molecular characterization of some recessive oncogenes. Gaucher disease is covered from its diagnosis, classification, and prevention, to its organ system involvement and molecular biology.

Thermographic image analysis for classification of ACL rupture disease, bone cancer, and feline hyperthyroid, with Gabor filters

Science.gov (United States)

Alvandipour, Mehrdad; Umbaugh, Scott E.; Mishra, Deependra K.; Dahal, Rohini; Lama, Norsang; Marino, Dominic J.; Sackman, Joseph

2017-05-01

Thermography and pattern classification techniques are used to classify three different pathologies in veterinary images. Thermographic images of both normal and diseased animals were provided by the Long Island Veterinary Specialists (LIVS). The three pathologies are ACL rupture disease, bone cancer, and feline hyperthyroid. The diagnosis of these diseases usually involves radiology and laboratory tests while the method that we propose uses thermographic images and image analysis techniques and is intended for use as a prescreening tool. Images in each category of pathologies are first filtered by Gabor filters and then various features are extracted and used for classification into normal and abnormal classes. Gabor filters are linear filters that can be characterized by the two parameters wavelength λ and orientation θ. With two different wavelength and five different orientations, a total of ten different filters were studied. Different combinations of camera views, filters, feature vectors, normalization methods, and classification methods, produce different tests that were examined and the sensitivity, specificity and success rate for each test were produced. Using the Gabor features alone, sensitivity, specificity, and overall success rates of 85% for each of the pathologies was achieved.

A Novel Phosphatase Gene on 10q23, MINNP, in Hereditary and Sporadic Breast Cancer

Science.gov (United States)

2002-08-01

surrounding stroma. 14. SUBJECT TERMS 15. NUMBER OF PAGES human cancer genetics, breast cancer 28 16. PRICE CODE 17. SECURITY CLASSIFICATION 18...Genetics. All rights reserved. et al. 1998). CS is a poorly recognized autosomal dom- 0002-929712001/6904-0005$02.00 inant cancer syndrome...58, 1348-1352. 24. Coles, C., Condie, A., Chetty, U., Steel. C.M., Evans, H.J. and Prosser, J. 36. Wolf, C., Rouyer, N., Lutz, Y.. Adida . C., Loriot, M

Establishing the pig as a large animal model for vaccine development against human cancer

DEFF Research Database (Denmark)

Overgaard, Nana Haahr; Frøsig, Thomas Mørch; Welner, Simon

2015-01-01

Immunotherapy has increased overall survival of metastatic cancer patients, and cancer antigens are promising vaccine targets. To fulfill the promise, appropriate tailoring of the vaccine formulations to mount in vivo cytotoxic T cell (CTL) responses toward co-delivered cancer antigens is essential...... and the porcine immunome is closer related to the human counterpart, we here introduce pigs as a supplementary large animal model for human cancer vaccine development. IDO and RhoC, both important in human cancer development and progression, were used as vaccine targets and 12 pigs were immunized with overlapping......C-derived peptides across all groups with no adjuvant being superior. These findings support the further use of pigs as a large animal model for vaccine development against human cancer....

Classification across gene expression microarray studies

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Kuner Ruprecht

2009-12-01

Full Text Available Abstract Background The increasing number of gene expression microarray studies represents an important resource in biomedical research. As a result, gene expression based diagnosis has entered clinical practice for patient stratification in breast cancer. However, the integration and combined analysis of microarray studies remains still a challenge. We assessed the potential benefit of data integration on the classification accuracy and systematically evaluated the generalization performance of selected methods on four breast cancer studies comprising almost 1000 independent samples. To this end, we introduced an evaluation framework which aims to establish good statistical practice and a graphical way to monitor differences. The classification goal was to correctly predict estrogen receptor status (negative/positive and histological grade (low/high of each tumor sample in an independent study which was not used for the training. For the classification we chose support vector machines (SVM, predictive analysis of microarrays (PAM, random forest (RF and k-top scoring pairs (kTSP. Guided by considerations relevant for classification across studies we developed a generalization of kTSP which we evaluated in addition. Our derived version (DV aims to improve the robustness of the intrinsic invariance of kTSP with respect to technologies and preprocessing. Results For each individual study the generalization error was benchmarked via complete cross-validation and was found to be similar for all classification methods. The misclassification rates were substantially higher in classification across studies, when each single study was used as an independent test set while all remaining studies were combined for the training of the classifier. However, with increasing number of independent microarray studies used in the training, the overall classification performance improved. DV performed better than the average and showed slightly less variance. In

Human Papillomavirus and Vaccination in Cervical Cancer

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Kung-Liahng Wang

2007-12-01

Full Text Available Cervical cancer is not only the most frequently reported cancer among women, but also the most common female genital tract neoplasm in Taiwan. Early detection is effective, because the development, maintenance and progression of precursor lesions (cervical intraepithelial neoplasia [CIN] evolve slowly into invasive cancer, typically over a period of more than 10 years. It is now recognized that human papillomavirus (HPV infection is a necessary cause for over 99% of cervical cancer cases. Advances in the understanding of the causative role of HPV in the etiology of high-grade cervical lesions (CIN 2/3 and cervical cancer have led to the development, evaluation and recommendation of HPV-based technologies for cervical cancer prevention and control. The prevention of HPV infection before the onset of CIN is now possible with recently available prophylactic HPV vaccines, e.g. the quadrivalent Gardasil (Merck & Co., NJ, USA and bivalent Cervarix (GlaxoSmithKline, London, UK. This review article provides an up-to-date summary of recent studies and available information concerning HPV and vaccination in cervical cancer.

Apoptosis induced by GanoPoly in human gastric cancer cell line ...

African Journals Online (AJOL)

In order to investigate polysaccharide effect on the cultured human gastric cancer cells (SGC7901), DNA ladder, flow cytometry and western blot were used to examine the morpholog, proliferation and apoptosis of human gastric cancer SGC-7901 cells when they were affected by polysaccharide. Results show that ...

Anti-human SIRPα antibody is a new tool for cancer immunotherapy.

Science.gov (United States)

Murata, Yoji; Tanaka, Daisuke; Hazama, Daisuke; Yanagita, Tadahiko; Saito, Yasuyuki; Kotani, Takenori; Oldenborg, Per-Arne; Matozaki, Takashi

2018-02-23

Interaction of signal regulatory protein α (SIRPα) expressed on the surface of macrophages with its ligand CD47 expressed on target cells negatively regulates phagocytosis of the latter cells by the former. We recently showed that blocking Abs to mouse SIRPα enhanced both the Ab-dependent cellular phagocytosis (ADCP) activity of mouse macrophages for Burkitt's lymphoma Raji cells opsonized with an Ab to CD20 (rituximab) in vitro as well as the inhibitory effect of rituximab on the growth of tumors formed by Raji cells in nonobese diabetic (NOD)/SCID mice. However, the effects of blocking Abs to human SIRPα in preclinical cancer models have remained unclear given that such Abs have failed to interact with endogenous SIRPα expressed on macrophages of immunodeficient mice. With the use of Rag2 -/- γ c -/- mice harboring a transgene for human SIRPα under the control of human regulatory elements (hSIRPα-DKO mice), we here show that a blocking Ab to human SIRPα significantly enhanced the ADCP activity of macrophages derived from these mice for human cancer cells. The anti-human SIRPα Ab also markedly enhanced the inhibitory effect of rituximab on the growth of tumors formed by Raji cells in hSIRPα-DKO mice. Our results thus suggest that the combination of Abs to human SIRPα with therapeutic Abs specific for tumor antigens warrants further investigation for potential application to cancer immunotherapy. In addition, humanized mice, such as hSIRPα-DKO mice, should prove useful for validation of the antitumor effects of checkpoint inhibitors before testing in clinical trials. © 2018 The Authors. Cancer Science published by John Wiley & Sons Australia, Ltd on behalf of Japanese Cancer Association.

A transcriptome anatomy of human colorectal cancers

International Nuclear Information System (INIS)

Lü, Bingjian; Xu, Jing; Lai, Maode; Zhang, Hao; Chen, Jian

2006-01-01

Accumulating databases in human genome research have enabled integrated genome-wide study on complicated diseases such as cancers. A practical approach is to mine a global transcriptome profile of disease from public database. New concepts of these diseases might emerge by landscaping this profile. In this study, we clustered human colorectal normal mucosa (N), inflammatory bowel disease (IBD), adenoma (A) and cancer (T) related expression sequence tags (EST) into UniGenes via an in-house GetUni software package and analyzed the transcriptome overview of these libraries by GOTree Machine (GOTM). Additionally, we downloaded UniGene based cDNA libraries of colon and analyzed them by Xprofiler to cross validate the efficiency of GetUni. Semi-quantitative RT-PCR was used to validate the expression of β-catenin and. 7 novel genes in colorectal cancers. The efficiency of GetUni was successfully validated by Xprofiler and RT-PCR. Genes in library N, IBD and A were all found in library T. A total of 14,879 genes were identified with 2,355 of them having at least 2 transcripts. Differences in gene enrichment among these libraries were statistically significant in 50 signal transduction pathways and Pfam protein domains by GOTM analysis P < 0.01 Hypergeometric Test). Genes in two metabolic pathways, ribosome and glycolysis, were more enriched in the expression profiles of A and IBD than in N and T. Seven transmembrane receptor superfamily genes were typically abundant in cancers. Colorectal cancers are genetically heterogeneous. Transcription variants are common in them. Aberrations of ribosome and glycolysis pathway might be early indicators of precursor lesions in colon cancers. The electronic gene expression profile could be used to highlight the integral molecular events in colorectal cancers

Interval-value Based Particle Swarm Optimization algorithm for cancer-type specific gene selection and sample classification

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D. Ramyachitra

2015-09-01

Full Text Available Microarray technology allows simultaneous measurement of the expression levels of thousands of genes within a biological tissue sample. The fundamental power of microarrays lies within the ability to conduct parallel surveys of gene expression using microarray data. The classification of tissue samples based on gene expression data is an important problem in medical diagnosis of diseases such as cancer. In gene expression data, the number of genes is usually very high compared to the number of data samples. Thus the difficulty that lies with data are of high dimensionality and the sample size is small. This research work addresses the problem by classifying resultant dataset using the existing algorithms such as Support Vector Machine (SVM, K-nearest neighbor (KNN, Interval Valued Classification (IVC and the improvised Interval Value based Particle Swarm Optimization (IVPSO algorithm. Thus the results show that the IVPSO algorithm outperformed compared with other algorithms under several performance evaluation functions.

Interval-value Based Particle Swarm Optimization algorithm for cancer-type specific gene selection and sample classification.

Science.gov (United States)

Ramyachitra, D; Sofia, M; Manikandan, P

2015-09-01

Microarray technology allows simultaneous measurement of the expression levels of thousands of genes within a biological tissue sample. The fundamental power of microarrays lies within the ability to conduct parallel surveys of gene expression using microarray data. The classification of tissue samples based on gene expression data is an important problem in medical diagnosis of diseases such as cancer. In gene expression data, the number of genes is usually very high compared to the number of data samples. Thus the difficulty that lies with data are of high dimensionality and the sample size is small. This research work addresses the problem by classifying resultant dataset using the existing algorithms such as Support Vector Machine (SVM), K-nearest neighbor (KNN), Interval Valued Classification (IVC) and the improvised Interval Value based Particle Swarm Optimization (IVPSO) algorithm. Thus the results show that the IVPSO algorithm outperformed compared with other algorithms under several performance evaluation functions.

[Magnetic resonance semiotics of prostate cancer according to the PI-RADS classification. The clinical diagnostic algorithm of a study].

Science.gov (United States)

Korobkin, A S; Shariya, M A; Chaban, A S; Voskanvan, G A; Vinarov, A Z

2015-01-01

to elaborate the magnetic resonance imaging (MRI) signs of prostate cancer (PC) in accordance with the PI-RADS classification during multiparametric MRI (mpMRI). A total of 89 men aged 20 to 82 years were examined. A control group consisted of 8 (9%) healthy volunteers younger than 30 years of age with no urological history to obtain control images and MRI plots and 20 (22.5%) men aged 26-76 years, whose morphological changes were inflammatory and hyperplastic. The second age-matched group included 61 (68.5%) patients diagnosed with prostate cancer at morphological examination. A set of studies included digital rectal examination, serum prostate-specific antigen, and transrectal ultrasound-guided prostate biopsy. All the patients underwent prostate mpMRI applying a 3.0 T Achieva MRI scanner (Philips, the Netherlands). The patients have been found to have mpMRI signs that were typical of PC; its MRI semiotics according to the PI-RADS classification is presented. Each mpMRI procedure has been determined to be of importance and informative value in detecting PC. The comprehensive mpMRI approach to diagnosing PC improves the quality and diagnostic value of prostate MRI.

Evidence for widespread dysregulation of circadian clock progression in human cancer

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Jarrod Shilts

2018-01-01

Full Text Available The ubiquitous daily rhythms in mammalian physiology are guided by progression of the circadian clock. In mice, systemic disruption of the clock can promote tumor growth. In vitro, multiple oncogenes can disrupt the clock. However, due to the difficulties of studying circadian rhythms in solid tissues in humans, whether the clock is disrupted within human tumors has remained unknown. We sought to determine the state of the circadian clock in human cancer using publicly available transcriptome data. We developed a method, called the clock correlation distance (CCD, to infer circadian clock progression in a group of samples based on the co-expression of 12 clock genes. Our method can be applied to modestly sized datasets in which samples are not labeled with time of day and coverage of the circadian cycle is incomplete. We used the method to define a signature of clock gene co-expression in healthy mouse organs, then validated the signature in healthy human tissues. By then comparing human tumor and non-tumor samples from twenty datasets of a range of cancer types, we discovered that clock gene co-expression in tumors is consistently perturbed. Subsequent analysis of data from clock gene knockouts in mice suggested that perturbed clock gene co-expression in human cancer is not caused solely by the inactivation of clock genes. Furthermore, focusing on lung cancer, we found that human lung tumors showed systematic changes in expression in a large set of genes previously inferred to be rhythmic in healthy lung. Our findings suggest that clock progression is dysregulated in many solid human cancers and that this dysregulation could have broad effects on circadian physiology within tumors. In addition, our approach opens the door to using publicly available data to infer circadian clock progression in a multitude of human phenotypes.

Identifying molecular subtypes in human colon cancer using gene expression and DNA methylation microarray data

OpenAIRE

REN, ZHONGLU; WANG, WENHUI; LI, JINMING

2015-01-01

Identifying colon cancer subtypes based on molecular signatures may allow for a more rational, patient-specific approach to therapy in the future. Classifications using gene expression data have been attempted before with little concordance between the different studies carried out. In this study we aimed to uncover subtypes of colon cancer that have distinct biological characteristics and identify a set of novel biomarkers which could best reflect the clinical and/or biological characteristi...

Recurrent chimeric RNAs enriched in human prostate cancer identified by deep sequencing

Science.gov (United States)

Kannan, Kalpana; Wang, Liguo; Wang, Jianghua; Ittmann, Michael M.; Li, Wei; Yen, Laising

2011-01-01

Transcription-induced chimeric RNAs, possessing sequences from different genes, are expected to increase the proteomic diversity through chimeric proteins or altered regulation. Despite their importance, few studies have focused on chimeric RNAs especially regarding their presence/roles in human cancers. By deep sequencing the transcriptome of 20 human prostate cancer and 10 matched benign prostate tissues, we obtained 1.3 billion sequence reads, which led to the identification of 2,369 chimeric RNA candidates. Chimeric RNAs occurred in significantly higher frequency in cancer than in matched benign samples. Experimental investigation of a selected 46 set led to the confirmation of 32 chimeric RNAs, of which 27 were highly recurrent and previously undescribed in prostate cancer. Importantly, a subset of these chimeras was present in prostate cancer cell lines, but not detectable in primary human prostate epithelium cells, implying their associations with cancer. These chimeras contain discernable 5′ and 3′ splice sites at the RNA junction, indicating that their formation is mediated by splicing. Their presence is also largely independent of the expression of parental genes, suggesting that other factors are involved in their production and regulation. One chimera, TMEM79-SMG5, is highly differentially expressed in human cancer samples and therefore a potential biomarker. The prevalence of chimeric RNAs may allow the limited number of human genes to encode a substantially larger number of RNAs and proteins, forming an additional layer of cellular complexity. Together, our results suggest that chimeric RNAs are widespread, and increased chimeric RNA events could represent a unique class of molecular alteration in cancer. PMID:21571633

Comparison of breast cancer mucin (BCM) and CA 15-3 in human breast cancer

NARCIS (Netherlands)

Garcia, M.B.; Blankenstein, M.A.; Wall, E. van der; Nortier, J.W.R.; Schornagel, J.H.; Thijssen, J.H.H.

1990-01-01

The Breast Cancer Mucin (BCM) enzyme immunoassay utilizes two monoclonal antibodies (Mab), M85/34 and F36/22, for the identification of a mucin-like glycoprotein in serum of breast cancer patients. We have compared BCM with CA 15-3, another member of the human mammary epithelial antigen

Implementation of several mathematical algorithms to breast tissue density classification

International Nuclear Information System (INIS)

Quintana, C.; Redondo, M.; Tirao, G.

2014-01-01

The accuracy of mammographic abnormality detection methods is strongly dependent on breast tissue characteristics, where a dense breast tissue can hide lesions causing cancer to be detected at later stages. In addition, breast tissue density is widely accepted to be an important risk indicator for the development of breast cancer. This paper presents the implementation and the performance of different mathematical algorithms designed to standardize the categorization of mammographic images, according to the American College of Radiology classifications. These mathematical techniques are based on intrinsic properties calculations and on comparison with an ideal homogeneous image (joint entropy, mutual information, normalized cross correlation and index Q) as categorization parameters. The algorithms evaluation was performed on 100 cases of the mammographic data sets provided by the Ministerio de Salud de la Provincia de Córdoba, Argentina—Programa de Prevención del Cáncer de Mama (Department of Public Health, Córdoba, Argentina, Breast Cancer Prevention Program). The obtained breast classifications were compared with the expert medical diagnostics, showing a good performance. The implemented algorithms revealed a high potentiality to classify breasts into tissue density categories. - Highlights: • Breast density classification can be obtained by suitable mathematical algorithms. • Mathematical processing help radiologists to obtain the BI-RADS classification. • The entropy and joint entropy show high performance for density classification

Long interspersed element-1 protein expression is a hallmark of many human cancers.

Science.gov (United States)

Rodić, Nemanja; Sharma, Reema; Sharma, Rajni; Zampella, John; Dai, Lixin; Taylor, Martin S; Hruban, Ralph H; Iacobuzio-Donahue, Christine A; Maitra, Anirban; Torbenson, Michael S; Goggins, Michael; Shih, Ie-Ming; Duffield, Amy S; Montgomery, Elizabeth A; Gabrielson, Edward; Netto, George J; Lotan, Tamara L; De Marzo, Angelo M; Westra, William; Binder, Zev A; Orr, Brent A; Gallia, Gary L; Eberhart, Charles G; Boeke, Jef D; Harris, Chris R; Burns, Kathleen H

2014-05-01

Cancers comprise a heterogeneous group of human diseases. Unifying characteristics include unchecked abilities of tumor cells to proliferate and spread anatomically, and the presence of clonal advantageous genetic changes. However, universal and highly specific tumor markers are unknown. Herein, we report widespread long interspersed element-1 (LINE-1) repeat expression in human cancers. We show that nearly half of all human cancers are immunoreactive for a LINE-1-encoded protein. LINE-1 protein expression is a common feature of many types of high-grade malignant cancers, is rarely detected in early stages of tumorigenesis, and is absent from normal somatic tissues. Studies have shown that LINE-1 contributes to genetic changes in cancers, with somatic LINE-1 insertions seen in selected types of human cancers, particularly colon cancer. We sought to correlate this observation with expression of the LINE-1-encoded protein, open reading frame 1 protein, and found that LINE-1 open reading frame 1 protein is a surprisingly broad, yet highly tumor-specific, antigen. Copyright © 2014 American Society for Investigative Pathology. Published by Elsevier Inc. All rights reserved.

Microbiota dysbiosis in select human cancers: Evidence of association and causality.

Science.gov (United States)

Chen, Jie; Domingue, Jada C; Sears, Cynthia L

2017-08-01

The human microbiota is a complex ecosystem of diverse microorganisms consisting of bacteria, viruses, and fungi residing predominantly in epidermal and mucosal habitats across the body, such as skin, oral cavity, lung, intestine and vagina. These symbiotic communities in health, or dysbiotic communities in disease, display tremendous interaction with the local environment and systemic responses, playing a critical role in the host's nutrition, immunity, metabolism and diseases including cancers. While the profiling of normal microbiota in healthy populations is useful and necessary, more recent studies have focused on the microbiota associated with disease, particularly cancers. In this paper, we review current evidence on the role of the human microbiota in four cancer types (colorectal cancer, head and neck cancer, pancreatic cancer, and lung cancer) proposed as affected by both the oral and gut microbiota, and provide a perspective on current gaps in the knowledge of the microbiota and cancer. Copyright © 2017 Elsevier Ltd. All rights reserved.

Cyclin A1 promoter hypermethylation in human papillomavirus-associated cervical cancer

International Nuclear Information System (INIS)

Kitkumthorn, Nakarin; Mutirangura, Apiwat; Yanatatsanajit, Pattamawadee; Kiatpongsan, Sorapop; Phokaew, Chureerat; Triratanachat, Surang; Trivijitsilp, Prasert; Termrungruanglert, Wichai; Tresukosol, Damrong; Niruthisard, Somchai

2006-01-01

The aim of this study was to evaluate epigenetic status of cyclin A1 in human papillomavirus-associated cervical cancer. Y. Tokumaru et al., Cancer Res 64, 5982-7 (Sep 1, 2004)demonstrated in head and neck squamous-cell cancer an inverse correlation between cyclin A1 promoter hypermethylation and TP53 mutation. Human papillomavirus-associated cervical cancer, however, is deprived of TP53 function by a different mechanism. Therefore, it was of interest to investigate the epigenetic alterations during multistep cervical cancer development. In this study, we performed duplex methylation-specific PCR and reverse transcriptase PCR on several cervical cancer cell lines and microdissected cervical cancers. Furthermore, the incidence of cyclin A1 methylation was studied in 43 samples of white blood cells, 25 normal cervices, and 24, 5 and 30 human papillomavirus-associated premalignant, microinvasive and invasive cervical lesions, respectively. We demonstrated cyclin A1 methylation to be commonly found in cervical cancer, both in vitro and in vivo, with its physiological role being to decrease gene expression. More important, this study demonstrated that not only is cyclin A1 promoter hypermethylation strikingly common in cervical cancer, but is also specific to the invasive phenotype in comparison with other histopathological stages during multistep carcinogenesis. None of the normal cells and low-grade squamous intraepithelial lesions exhibited methylation. In contrast, 36.6%, 60% and 93.3% of high-grade squamous intraepithelial lesions, microinvasive and invasive cancers, respectively, showed methylation. This methylation study indicated that cyclin A1 is a potential tumor marker for early diagnosis of invasive cervical cancer

An integrative -omics approach to identify functional sub-networks in human colorectal cancer.

Directory of Open Access Journals (Sweden)

Rod K Nibbe

2010-01-01

Full Text Available Emerging evidence indicates that gene products implicated in human cancers often cluster together in "hot spots" in protein-protein interaction (PPI networks. Additionally, small sub-networks within PPI networks that demonstrate synergistic differential expression with respect to tumorigenic phenotypes were recently shown to be more accurate classifiers of disease progression when compared to single targets identified by traditional approaches. However, many of these studies rely exclusively on mRNA expression data, a useful but limited measure of cellular activity. Proteomic profiling experiments provide information at the post-translational level, yet they generally screen only a limited fraction of the proteome. Here, we demonstrate that integration of these complementary data sources with a "proteomics-first" approach can enhance the discovery of candidate sub-networks in cancer that are well-suited for mechanistic validation in disease. We propose that small changes in the mRNA expression of multiple genes in the neighborhood of a protein-hub can be synergistically associated with significant changes in the activity of that protein and its network neighbors. Further, we hypothesize that proteomic targets with significant fold change between phenotype and control may be used to "seed" a search for small PPI sub-networks that are functionally associated with these targets. To test this hypothesis, we select proteomic targets having significant expression changes in human colorectal cancer (CRC from two independent 2-D gel-based screens. Then, we use random walk based models of network crosstalk and develop novel reference models to identify sub-networks that are statistically significant in terms of their functional association with these proteomic targets. Subsequently, using an information-theoretic measure, we evaluate synergistic changes in the activity of identified sub-networks based on genome-wide screens of mRNA expression in CRC

Oral epithelial dysplasia classification systems

DEFF Research Database (Denmark)

Warnakulasuriya, S; Reibel, J; Bouquot, J

2008-01-01

At a workshop coordinated by the WHO Collaborating Centre for Oral Cancer and Precancer in the United Kingdom issues related to potentially malignant disorders of the oral cavity were discussed by an expert group. The consensus views of the Working Group are presented in a series of papers....... In this report, we review the oral epithelial dysplasia classification systems. The three classification schemes [oral epithelial dysplasia scoring system, squamous intraepithelial neoplasia and Ljubljana classification] were presented and the Working Group recommended epithelial dysplasia grading for routine...... use. Although most oral pathologists possibly recognize and accept the criteria for grading epithelial dysplasia, firstly based on architectural features and then of cytology, there is great variability in their interpretation of the presence, degree and significance of the individual criteria...

mRNA/microRNA gene expression profile in microsatellite unstable colorectal cancer

Directory of Open Access Journals (Sweden)

Calin George A

2007-08-01

Full Text Available Abstract Background Colorectal cancer develops through two main genetic instability pathways characterized by distinct pathologic features and clinical outcome. Results We investigated colon cancer samples (23 characterized by microsatellite stability, MSS, and 16 by high microsatellite instability, MSI-H for genome-wide expression of microRNA (miRNA and mRNA. Based on combined miRNA and mRNA gene expression, a molecular signature consisting of twenty seven differentially expressed genes, inclusive of 8 miRNAs, could correctly distinguish MSI-H versus MSS colon cancer samples. Among the differentially expressed miRNAs, various members of the oncogenic miR-17-92 family were significantly up-regulated in MSS cancers. The majority of protein coding genes were also up-regulated in MSS cancers. Their functional classification revealed that they were most frequently associated with cell cycle, DNA replication, recombination, repair, gastrointestinal disease and immune response. Conclusion This is the first report that indicates the existence of differences in miRNA expression between MSS versus MSI-H colorectal cancers. In addition, the work suggests that the combination of mRNA/miRNA expression signatures may represent a general approach for improving bio-molecular classification of human cancer.

Rhein induces apoptosis of HCT-116 human colon cancer cells via ...

African Journals Online (AJOL)

Rhein, a major compound in rhubarb, has been found to have anti-tumor properties in many human cancer cells. However, the details about rhein suppressing the growth of human colon cancer cells remained elusive. In this paper, we explored the potential of rhein as a chemotherapeutic agent on HCT- 116 cells and ...

Deoxyribonucleic-binding homeobox proteins are augmented in human cancer

DEFF Research Database (Denmark)

Wewer, U M; Mercurio, A M; Chung, S Y

1990-01-01

Homeobox genes encode sequence-specific DNA-binding proteins that are involved in the regulation of gene expression during embryonic development. In this study, we examined the expression of homeobox proteins in human cancer. Antiserum was obtained against a synthetic peptide derived from...... was then isolated and used to elicit a rabbit antiserum. In immunostaining, both antisera reacted with the nuclei of cultured tumor cells. In tissue sections of human carcinoma, nuclear immunoreactivity was observed in the tumor cells in 40 of 42 cases examined. Adjacent normal epithelial tissue obtained from......, the presence of the homeobox transcript in human carcinoma was documented by in situ hybridization and RNase protection mapping. These results demonstrate that human cancer is associated with the expression of homeobox proteins. Such homeobox proteins, as well as other regulatory proteins, could be involved...

Automatic Classification of Normal and Cancer Lung CT Images Using Multiscale AM-FM Features

Directory of Open Access Journals (Sweden)

Eman Magdy

2015-01-01

Full Text Available Computer-aided diagnostic (CAD systems provide fast and reliable diagnosis for medical images. In this paper, CAD system is proposed to analyze and automatically segment the lungs and classify each lung into normal or cancer. Using 70 different patients’ lung CT dataset, Wiener filtering on the original CT images is applied firstly as a preprocessing step. Secondly, we combine histogram analysis with thresholding and morphological operations to segment the lung regions and extract each lung separately. Amplitude-Modulation Frequency-Modulation (AM-FM method thirdly, has been used to extract features for ROIs. Then, the significant AM-FM features have been selected using Partial Least Squares Regression (PLSR for classification step. Finally, K-nearest neighbour (KNN, support vector machine (SVM, naïve Bayes, and linear classifiers have been used with the selected AM-FM features. The performance of each classifier in terms of accuracy, sensitivity, and specificity is evaluated. The results indicate that our proposed CAD system succeeded to differentiate between normal and cancer lungs and achieved 95% accuracy in case of the linear classifier.

Effect of Training on Knowledge about Cervical Cancer and Human ...

African Journals Online (AJOL)

UNIBEN

Effect of Training on Knowledge about Cervical Cancer and Human. Papiloma Virus Vaccine ... debut, multiple sexual partners, smoking, history of sexually ... prevent cervical cancer. These include ..... needed to understand and explain the.

Saudi anti-human cancer plants database (SACPD): A collection of plants with anti-human cancer activities.

Science.gov (United States)

Al-Zahrani, Ateeq Ahmed

2018-01-30

Several anticancer drugs have been developed from natural products such as plants. Successful experiments in inhibiting the growth of human cancer cell lines using Saudi plants were published over the last three decades. Up to date, there is no Saudi anticancer plants database as a comprehensive source for the interesting data generated from these experiments. Therefore, there was a need for creating a database to collect, organize, search and retrieve such data. As a result, the current paper describes the generation of the Saudi anti-human cancer plants database (SACPD). The database contains most of the reported information about the naturally growing Saudi anticancer plants. SACPD comprises the scientific and local names of 91 plant species that grow naturally in Saudi Arabia. These species belong to 38 different taxonomic families. In Addition, 18 species that represent16 family of medicinal plants and are intensively sold in the local markets in Saudi Arabia were added to the database. The website provides interesting details, including plant part containing the anticancer bioactive compounds, plants locations and cancer/cell type against which they exhibit their anticancer activity. Our survey revealed that breast, liver and leukemia were the most studied cancer cell lines in Saudi Arabia with percentages of 27%, 19% and 15%, respectively. The current SACPD represents a nucleus around which more development efforts can expand to accommodate all future submissions about new Saudi plant species with anticancer activities. SACPD will provide an excellent starting point for researchers and pharmaceutical companies who are interested in developing new anticancer drugs. SACPD is available online at https://teeqrani1.wixsite.com/sapd.

IMS2 – An integrated medical software system for early lung cancer detection using ion mobility spectrometry data of human breath

Directory of Open Access Journals (Sweden)

Baumbach Jan

2007-12-01

Full Text Available IMS2 is an Integrated Medical Software system for the analysis of Ion Mobility Spectrometry (IMS data. It assists medical staff with the following IMS data processing steps: acquisition, visualization, classification, and annotation. IMS2 provides data analysis and interpretation features on the one hand, and also helps to improve the classification by increasing the number of the pre-classified datasets on the other hand. It is designed to facilitate early detection of lung cancer, one of the most common cancer types with one million deaths each year around the world.

Oncogenic KRAS activates an embryonic stem cell-like program in human colon cancer initiation.

Science.gov (United States)

Le Rolle, Anne-France; Chiu, Thang K; Zeng, Zhaoshi; Shia, Jinru; Weiser, Martin R; Paty, Philip B; Chiu, Vi K

2016-01-19

Colorectal cancer is the third most frequently diagnosed cancer worldwide. Prevention of colorectal cancer initiation represents the most effective overall strategy to reduce its associated morbidity and mortality. Activating KRAS mutation (KRASmut) is the most prevalent oncogenic driver in colorectal cancer development, and KRASmut inhibition represents an unmet clinical need. We apply a systems-level approach to study the impact of KRASmut on stem cell signaling during human colon cancer initiation by performing gene set enrichment analysis on gene expression from human colon tissues. We find that KRASmut imposes the embryonic stem cell-like program during human colon cancer initiation from colon adenoma to stage I carcinoma. Expression of miR145, an embryonic SC program inhibitor, promotes cell lineage differentiation marker expression in KRASmut colon cancer cells and significantly suppresses their tumorigenicity. Our data support an in vivo plasticity model of human colon cancer initiation that merges the intrinsic stem cell properties of aberrant colon stem cells with the embryonic stem cell-like program induced by KRASmut to optimize malignant transformation. Inhibition of the embryonic SC-like program in KRASmut colon cancer cells reveals a novel therapeutic strategy to programmatically inhibit KRASmut tumors and prevent colon cancer.

Learning classification models with soft-label information.

Science.gov (United States)

Nguyen, Quang; Valizadegan, Hamed; Hauskrecht, Milos

2014-01-01

Learning of classification models in medicine often relies on data labeled by a human expert. Since labeling of clinical data may be time-consuming, finding ways of alleviating the labeling costs is critical for our ability to automatically learn such models. In this paper we propose a new machine learning approach that is able to learn improved binary classification models more efficiently by refining the binary class information in the training phase with soft labels that reflect how strongly the human expert feels about the original class labels. Two types of methods that can learn improved binary classification models from soft labels are proposed. The first relies on probabilistic/numeric labels, the other on ordinal categorical labels. We study and demonstrate the benefits of these methods for learning an alerting model for heparin induced thrombocytopenia. The experiments are conducted on the data of 377 patient instances labeled by three different human experts. The methods are compared using the area under the receiver operating characteristic curve (AUC) score. Our AUC results show that the new approach is capable of learning classification models more efficiently compared to traditional learning methods. The improvement in AUC is most remarkable when the number of examples we learn from is small. A new classification learning framework that lets us learn from auxiliary soft-label information provided by a human expert is a promising new direction for learning classification models from expert labels, reducing the time and cost needed to label data.

Characteristics of [18F] fluorodeoxyglucose uptake in human colon cancer cells

International Nuclear Information System (INIS)

Kim, Chae Kyun; Chung, June Key; Jeong, Jae Min; Lee, Myung Chul; Koh, Chang Soon

1997-01-01

Cancer tissues are characterized by increased glucose uptake. 18 F-fluorodeoxyglucose(FDG), a glucose analogue is used for the diagnosis of cancer in PET studies. This study was aimed to compare the glucose uptake and glucose transporter 1(GLUT1) expression in various human colon cancer cells. We measured FDG uptake by cell retention study and expression of GLUT1 using Western blotting. Human colon cancer cells, SNU-C2A, SNU-C4 and SNU-C5, were used. The cells were incubated with 1μ Ci/ml of FDG in HEPES- buffered saline for one hour. The FDG uptake of SNU-C2A, SNU-C4 and SNU-C5 were 16.8±1.36, 12.3±5.55 and 61.0±2.17 cpm/μg of protein, respectively. Dose-response and time-course studies represent that FDG uptake of cancer cells were dose dependent and time dependent. The rate of FDG uptake of SNU-C2A, SNU-C4 and SNU-C5 were 0.29±0.03, 0.21±0.09 and 1.07±0.07 cpm/min/μg of protein, respectively. Western blot analysis showed that the GLUT1 expression of SNU-C5 was significantly higher than those of SNU-C2A and SNU-C4. These results represent that FDG uptake into human colon cancer cells are different from each other. In addition, FDG uptake and expression of GLUT1 are closely related in human colon cancer cells

Anti-cancer effects of bioactive compounds from rose hip fruit in human breast cancer cell lines

OpenAIRE

Zhong, Lijie

2017-01-01

Rose hips have long been used in human diets as a food ingredient and supplement. Their multiple medical properties, which have been attributed to their abundant carotenoid composition, have attracted widespread scientific attention. This thesis examined the carotenoid composition in rose hips from five rose species. The anti-cancer effect of different carotenoid fractions from rose hips was investigated in human breast cancer cell lines, using the natural variation in carotenoid content in h...

Rel/Nuclear factor-kappa B apoptosis pathways in human cervical cancer cells

Science.gov (United States)

Shehata, Marlene F

2005-01-01

Cervical cancer is considered a common yet preventable cause of death in women. It has been estimated that about 420 women out of the 1400 women diagnosed with cervical cancer will die during 5 years from diagnosis. This review addresses the pathogenesis of cervical cancer in humans with a special emphasis on the human papilloma virus as a predominant cause of cervical cancer in humans. The current understanding of apoptosis and regulators of apoptosis as well as their implication in carcinogenesis will follow. A special focus will be given to the role of Rel/NF-κB family of genes in the growth and chemotherapeutic treatment of the malignant HeLa cervical cells emphasizing on Xrel3, a cRel homologue. PMID:15857509

Genetic and environmental factors in experimental and human cancer

Energy Technology Data Exchange (ETDEWEB)

Takayama, S.; Takebe, H.; Gelboin, H.V.; MaChahon, B.; Matsushima, T.; Sugimura, T.

1980-01-01

Recently technological advances in assaying mutagenic principles have revealed that there are many mutagens in the environment, some of which might be carcinogenic to human beings. Other advances in genetics have shown that genetic factors might play an important role in the induction of cancer in human beings, e.g., the high incidence of skin cancers in patients with xeroderma pigmentosum. These proceedings deal with the relationships between genetic and environmental factors in carcinogenesis. The contributors cover mixed-function oxidases, pharmacogenetics, twin studies, DNA repair, immunology, and epidemiology.

Stratification and Prognostic Relevance of Jass’s Molecular Classification of Colorectal Cancer

International Nuclear Information System (INIS)

Zlobec, Inti; Bihl, Michel P.; Foerster, Anja; Rufle, Alex; Terracciano, Luigi; Lugli, Alessandro

2012-01-01

Background: The current proposed model of colorectal tumorigenesis is based primarily on CpG island methylator phenotype (CIMP), microsatellite instability (MSI), KRAS, BRAF, and methylation status of 0-6-Methylguanine DNA Methyltransferase (MGMT) and classifies tumors into five subgroups. The aim of this study is to validate this molecular classification and test its prognostic relevance. Methods: Three hundred two patients were included in this study. Molecular analysis was performed for five CIMP-related promoters (CRABP1, MLH1, p16INK4a, CACNA1G, NEUROG1), MGMT, MSI, KRAS, and BRAF. Methylation in at least 4 promoters or in one to three promoters was considered CIMP-high and CIMP-low (CIMP-H/L), respectively. Results: CIMP-H, CIMP-L, and CIMP-negative were found in 7.1, 43, and 49.9% cases, respectively. One hundred twenty-three tumors (41%) could not be classified into any one of the proposed molecular subgroups, including 107 CIMP-L, 14 CIMP-H, and two CIMP-negative cases. The 10 year survival rate for CIMP-high patients [22.6% (95%CI: 7–43)] was significantly lower than for CIMP-L or CIMP-negative (p = 0.0295). Only the combined analysis of BRAF and CIMP (negative versus L/H) led to distinct prognostic subgroups. Conclusion: Although CIMP status has an effect on outcome, our results underline the need for standardized definitions of low- and high-level CIMP, which clearly hinders an effective prognostic and molecular classification of colorectal cancer.

Stratification and Prognostic Relevance of Jass’s Molecular Classification of Colorectal Cancer

Energy Technology Data Exchange (ETDEWEB)

Zlobec, Inti [Institute of Pathology, University of Bern, Bern (Switzerland); Institute for Pathology, University Hospital Basel, Basel (Switzerland); Bihl, Michel P.; Foerster, Anja; Rufle, Alex; Terracciano, Luigi [Institute for Pathology, University Hospital Basel, Basel (Switzerland); Lugli, Alessandro, E-mail: inti.zlobec@pathology.unibe.ch [Institute of Pathology, University of Bern, Bern (Switzerland); Institute for Pathology, University Hospital Basel, Basel (Switzerland)

2012-02-27

Background: The current proposed model of colorectal tumorigenesis is based primarily on CpG island methylator phenotype (CIMP), microsatellite instability (MSI), KRAS, BRAF, and methylation status of 0-6-Methylguanine DNA Methyltransferase (MGMT) and classifies tumors into five subgroups. The aim of this study is to validate this molecular classification and test its prognostic relevance. Methods: Three hundred two patients were included in this study. Molecular analysis was performed for five CIMP-related promoters (CRABP1, MLH1, p16INK4a, CACNA1G, NEUROG1), MGMT, MSI, KRAS, and BRAF. Methylation in at least 4 promoters or in one to three promoters was considered CIMP-high and CIMP-low (CIMP-H/L), respectively. Results: CIMP-H, CIMP-L, and CIMP-negative were found in 7.1, 43, and 49.9% cases, respectively. One hundred twenty-three tumors (41%) could not be classified into any one of the proposed molecular subgroups, including 107 CIMP-L, 14 CIMP-H, and two CIMP-negative cases. The 10 year survival rate for CIMP-high patients [22.6% (95%CI: 7–43)] was significantly lower than for CIMP-L or CIMP-negative (p = 0.0295). Only the combined analysis of BRAF and CIMP (negative versus L/H) led to distinct prognostic subgroups. Conclusion: Although CIMP status has an effect on outcome, our results underline the need for standardized definitions of low- and high-level CIMP, which clearly hinders an effective prognostic and molecular classification of colorectal cancer.

Stratification and prognostic relevance of Jass’s molecular classification of colorectal cancer

Directory of Open Access Journals (Sweden)

Inti eZlobec

2012-02-01

Full Text Available Background: The current proposed model of colorectal tumorigenesis is based primarily on CpG island methylator phenotype (CIMP, microsatellite instability (MSI, KRAS, BRAF, and methylation status of 0-6-Methylguanine DNA Methyltransferase (MGMT and classifies tumors into 5 subgroups. The aim of this study is to validate this molecular classification and test its prognostic relevance. Methods: 302 patients were included in this study. Molecular analysis was performed for 5 CIMP-related promoters (CRABP1, MLH1, p16INK4a, CACNA1G, NEUROG1, MGMT, MSI, KRAS and BRAF. Tumors were CIMP-high or CIMP-low if ≥4 and 1-3 promoters were methylated, respectively. Results: CIMP-high, CIMP-low and CIMP–negative were found in 7.1%, 43% and 49.9% cases, respectively. 123 tumors (41% could not be classified into any one of the proposed molecular subgroups, including 107 CIMP-low, 14 CIMP-high and 2 CIMP-negative cases. The 10-year survival rate for CIMP-high patients (22.6% (95%CI: 7-43 was significantly lower than for CIMP-low or CIMP-negative (p=0.0295. Only the combined analysis of BRAF and CIMP (negative versus low/high led to distinct prognostic subgroups. Conclusion: Although CIMP status has an effect on outcome, our results underline the need for standardized definitions of low- and high-level CIMP, which clearly hinders an effective prognostic and molecular classification of colorectal cancer.

Characterization of human breast cancer by scanning acoustic microscopy

Science.gov (United States)

Chen, Di; Malyarenko, Eugene; Seviaryn, Fedar; Yuan, Ye; Sherman, Mark; Bandyopadhyay, Sudeshna; Gierach, Gretchen; Greenway, Christopher W.; Maeva, Elena; Strumban, Emil; Duric, Neb; Maev, Roman

2013-03-01

Objectives: The purpose of this study was to characterize human breast cancer tissues by the measurement of microacoustic properties. Methods: We investigated eight breast cancer patients using acoustic microscopy. For each patient, seven blocks of tumor tissue were collected from seven different positions around a tumor mass. Frozen sections (10 micrometer, μm) of human breast cancer tissues without staining and fixation were examined in a scanning acoustic microscope with focused transducers at 80 and 200 MHz. Hematoxylin and Eosin (H and E) stained sections from the same frozen breast cancer tissues were imaged by optical microscopy for comparison. Results: The results of acoustic imaging showed that acoustic attenuation and sound speed in cancer cell-rich tissue regions were significantly decreased compared with the surrounding tissue regions, where most components are normal cells/tissues, such as fibroblasts, connective tissue and lymphocytes. Our observation also showed that the ultrasonic properties were influenced by arrangements of cells and tissue patterns. Conclusions: Our data demonstrate that attenuation and sound speed imaging can provide biomechanical information of the tumor and normal tissues. The results also demonstrate the potential of acoustic microscopy as an auxiliary method for operative detection and localization of cancer affected regions.

Concepts in causality: chemically induced human urinary bladder cancer

International Nuclear Information System (INIS)

Lower, G.M. Jr.

1982-01-01

A significant portion of the incidence of human urinary bladder cancer can be attributed to occupational and cultural (tobacco smoking) situations associated with exposures to various arylamines, many of which represent established human carcinogens. A brief historical overview of research in bladder cancer causality indicates that the identification of causal agents and causal mechanism has been approached and rests upon information gathered at the organismal (geographical/historical), cellular, and molecular levels of biologic organization. This viewpoint speaks of a natural evolution within the biomedical sciences; a natural evolution from descriptive approaches to mechanistic approaches; and a natural evolution from more or less independent discipline-oriented approaches to hierarchically organized multidisciplinary approaches. Available information relevant to bladder cancer causality can be readily integrated into general conceptual frameworks to yield a hierarchial view of the natural history of urinary bladder cancer, a view consistent with contemporary natural systems and information theory and perhaps relevant also to other chemically induced epithelial cancers. Such frameworks are useful in appreciating the spatial and temporal boundaries and interrelationships in causality and the conceptual interrelationships within the biomedical sciences. Recent approaches in molecular epidemiology and the assessment of relative individual susceptibility to bladder cancer indicate that such frameworks are useful in forming hypotheses

Semi-synthetic salinomycin analogs exert cytotoxic activity against human colorectal cancer stem cells.

Science.gov (United States)

Klose, Johannes; Kattner, Sarah; Borgström, Björn; Volz, Claudia; Schmidt, Thomas; Schneider, Martin; Oredsson, Stina; Strand, Daniel; Ulrich, Alexis

2018-01-01

Salinomycin, a polyether antibiotic, is a well-known inhibitor of human cancer stem cells. Chemical modification of the allylic C20 hydroxyl of salinomycin has enabled access to synthetic analogs that display increased cytotoxic activity compared to the native structure. The aim of this study was to investigate the activity of a cohort of C20-O-acyl analogs of salinomycin on human colorectal cancer cell lines in vitro. Two human colorectal cancer cell lines (SW480 and SW620) were exposed to three C20-O-acylated analogs and salinomycin. The impact of salinomycin and its analogs on tumor cell number, migration, cell death, and cancer stem cell specifity was analyzed. Exposure of human colorectal cancer cells to the C20-O-acylated analogs of salinomycin resulted in reduced tumor cell number and impaired tumor cell migration at lower concentrations than salinomycin. When used at higher (micromolar) concentrations, these effects were accompanied by induction of apoptotic cell death. Salinomycin analogs further expose improved activity against cancer stem cells compared to salinomycin. Copyright © 2017 Elsevier Inc. All rights reserved.

The detection, diagnosis and therapy of human lung cancer

International Nuclear Information System (INIS)

1978-01-01

The Cancergram covers clinical aspects of cancers of the lung and tracheo-bronchial tree, i.e., the lower respiratory tract. This includes primary lung cancer in both early and advanced disease status. The topic includes clinically relevant aspects of the prevention, detection, diagnosis, evaluation, and therapy of lung cancer. Certain aspects of metastatic lung disease treatment or therapy which involve aspects of interest to primary lung cancer are included. With certain exceptions, general pre-clinical or animal studies not directly related to the primary human disease are excluded

The differentiation of malignant and benign human breast tissue at surgical margins and biopsy using x-ray interaction data and Bayesian classification

International Nuclear Information System (INIS)

Mersov, A.; Mersov, G.; Al-Ebraheem, A.; Cornacchi, S.; Gohla, G.; Lovrics, P.; Farquharson, M.J.

2014-01-01

Worldwide, about 1.3 million women are diagnosed with breast cancer annually with an estimated 465,000 deaths. Accordingly, there is a need for high accuracy and speed in diagnosis of lesions suspected of being cancerous. This study assesses the interaction data collected from low energy x-rays within breast tissue samples. Trace element concentrations are assessed using x-ray fluorescence, as well as electron density, and molecular structure which are examined using incoherent and coherent scatter, respectively. Our work to date has shown that such data can provide a quantitative measure of certain tissue characterising parameters and hence, through appropriate modelling, could be used to classify samples for uses such as surgical margin detection and biopsy examination. The parameters used in this study for comparing the normal and tumour tissue sample populations are: levels of elements Ca, Cu, Fe, Br, Zn, Rb, K; the area, FWHM and amplitude from peaks fitted to the coherent scatter profile that are associated with fat, fibre and water content; the ratio of the Compton and coherent scatter peak area, FWHM and amplitude from the incoherent scatter profile. The novelty of the approach to this work lies in the fact that the classification process does not rely on one source of data but combines several measurements, the data from which in this application are modelled using a method based on Bayesian classification. The reliability of the classifications was assessed by its application to diagnostically known data that was not itself included in the thresholds determination. The results of the classification of over 70 breast tissue samples will be presented in this study. Bayesian modelling was carried out using selected significant parameters for classification resulting in 71% of normal tissue samples (n=35) and 66% of tumour tissue samples (n=35) being correctly classified when using all the samples. Bayesian classification using the same variables on all

mRMR-ABC: A Hybrid Gene Selection Algorithm for Cancer Classification Using Microarray Gene Expression Profiling

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Hala Alshamlan

2015-01-01

Full Text Available An artificial bee colony (ABC is a relatively recent swarm intelligence optimization approach. In this paper, we propose the first attempt at applying ABC algorithm in analyzing a microarray gene expression profile. In addition, we propose an innovative feature selection algorithm, minimum redundancy maximum relevance (mRMR, and combine it with an ABC algorithm, mRMR-ABC, to select informative genes from microarray profile. The new approach is based on a support vector machine (SVM algorithm to measure the classification accuracy for selected genes. We evaluate the performance of the proposed mRMR-ABC algorithm by conducting extensive experiments on six binary and multiclass gene expression microarray datasets. Furthermore, we compare our proposed mRMR-ABC algorithm with previously known techniques. We reimplemented two of these techniques for the sake of a fair comparison using the same parameters. These two techniques are mRMR when combined with a genetic algorithm (mRMR-GA and mRMR when combined with a particle swarm optimization algorithm (mRMR-PSO. The experimental results prove that the proposed mRMR-ABC algorithm achieves accurate classification performance using small number of predictive genes when tested using both datasets and compared to previously suggested methods. This shows that mRMR-ABC is a promising approach for solving gene selection and cancer classification problems.

mRMR-ABC: A Hybrid Gene Selection Algorithm for Cancer Classification Using Microarray Gene Expression Profiling.

Science.gov (United States)

Alshamlan, Hala; Badr, Ghada; Alohali, Yousef

2015-01-01

An artificial bee colony (ABC) is a relatively recent swarm intelligence optimization approach. In this paper, we propose the first attempt at applying ABC algorithm in analyzing a microarray gene expression profile. In addition, we propose an innovative feature selection algorithm, minimum redundancy maximum relevance (mRMR), and combine it with an ABC algorithm, mRMR-ABC, to select informative genes from microarray profile. The new approach is based on a support vector machine (SVM) algorithm to measure the classification accuracy for selected genes. We evaluate the performance of the proposed mRMR-ABC algorithm by conducting extensive experiments on six binary and multiclass gene expression microarray datasets. Furthermore, we compare our proposed mRMR-ABC algorithm with previously known techniques. We reimplemented two of these techniques for the sake of a fair comparison using the same parameters. These two techniques are mRMR when combined with a genetic algorithm (mRMR-GA) and mRMR when combined with a particle swarm optimization algorithm (mRMR-PSO). The experimental results prove that the proposed mRMR-ABC algorithm achieves accurate classification performance using small number of predictive genes when tested using both datasets and compared to previously suggested methods. This shows that mRMR-ABC is a promising approach for solving gene selection and cancer classification problems.

Computed aided system for separation and classification of the abnormal erythrocytes in human blood

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Wąsowicz, Michał; Grochowski, Michał; Kulka, Marek; Mikołajczyk, Agnieszka; Ficek, Mateusz; Karpieńko, Katarzyna; Cićkiewicz, Maciej

2017-12-01

The human peripheral blood consists of cells (red cells, white cells, and platelets) suspended in plasma. In the following research the team assessed an influence of nanodiamond particles on blood elements over various periods of time. The material used in the study consisted of samples taken from ten healthy humans of various age, different blood types and both sexes. The markings were leaded by adding to the blood unmodified diamonds and oxidation modified. The blood was put under an impact of two diamond concentrations: 20μl and 100μl. The amount of abnormal cells increased with time. The percentage of echinocytes as a result of interaction with nanodiamonds in various time intervals for individual specimens was scarce. The impact of the two diamond types had no clinical importance on red blood cells. It is supposed that as a result of longlasting exposure a dehydratation of red cells takes place, because of the function of the cells. The analysis of an influence of nanodiamond particles on blood elements was supported by computer system designed for automatic counting and classification of the Red Blood Cells (RBC). The system utilizes advanced image processing methods for RBCs separation and counting and Eigenfaces method coupled with the neural networks for RBCs classification into normal and abnormal cells purposes.

International association for the study of lung cancer/american thoracic society/european respiratory society international multidisciplinary classification of lung adenocarcinoma.

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Travis, William D; Brambilla, Elisabeth; Noguchi, Masayuki; Nicholson, Andrew G; Geisinger, Kim R; Yatabe, Yasushi; Beer, David G; Powell, Charles A; Riely, Gregory J; Van Schil, Paul E; Garg, Kavita; Austin, John H M; Asamura, Hisao; Rusch, Valerie W; Hirsch, Fred R; Scagliotti, Giorgio; Mitsudomi, Tetsuya; Huber, Rudolf M; Ishikawa, Yuichi; Jett, James; Sanchez-Cespedes, Montserrat; Sculier, Jean-Paul; Takahashi, Takashi; Tsuboi, Masahiro; Vansteenkiste, Johan; Wistuba, Ignacio; Yang, Pan-Chyr; Aberle, Denise; Brambilla, Christian; Flieder, Douglas; Franklin, Wilbur; Gazdar, Adi; Gould, Michael; Hasleton, Philip; Henderson, Douglas; Johnson, Bruce; Johnson, David; Kerr, Keith; Kuriyama, Keiko; Lee, Jin Soo; Miller, Vincent A; Petersen, Iver; Roggli, Victor; Rosell, Rafael; Saijo, Nagahiro; Thunnissen, Erik; Tsao, Ming; Yankelewitz, David

2011-02-01

Adenocarcinoma is the most common histologic type of lung cancer. To address advances in oncology, molecular biology, pathology, radiology, and surgery of lung adenocarcinoma, an international multidisciplinary classification was sponsored by the International Association for the Study of Lung Cancer, American Thoracic Society, and European Respiratory Society. This new adenocarcinoma classification is needed to provide uniform terminology and diagnostic criteria, especially for bronchioloalveolar carcinoma (BAC), the overall approach to small nonresection cancer specimens, and for multidisciplinary strategic management of tissue for molecular and immunohistochemical studies. An international core panel of experts representing all three societies was formed with oncologists/pulmonologists, pathologists, radiologists, molecular biologists, and thoracic surgeons. A systematic review was performed under the guidance of the American Thoracic Society Documents Development and Implementation Committee. The search strategy identified 11,368 citations of which 312 articles met specified eligibility criteria and were retrieved for full text review. A series of meetings were held to discuss the development of the new classification, to develop the recommendations, and to write the current document. Recommendations for key questions were graded by strength and quality of the evidence according to the Grades of Recommendation, Assessment, Development, and Evaluation approach. The classification addresses both resection specimens, and small biopsies and cytology. The terms BAC and mixed subtype adenocarcinoma are no longer used. For resection specimens, new concepts are introduced such as adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) for small solitary adenocarcinomas with either pure lepidic growth (AIS) or predominant lepidic growth with ≤ 5 mm invasion (MIA) to define patients who, if they undergo complete resection, will have 100% or near 100

Current Trends in the Molecular Classification of Renal Neoplasms

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Andrew N. Young

2006-01-01

Full Text Available Renal cell carcinoma (RCC is the most common form of kidney cancer in adults. RCC is a significant challenge for pathologic diagnosis and clinical management. The primary approach to diagnosis is by light microscopy, using the World Health Organization (WHO classification system, which defines histopathologic tumor subtypes with distinct clinical behavior and underlying genetic mutations. However, light microscopic diagnosis of RCC subtypes is often difficult due to variable histology. In addition, the clinical behavior of RCC is highly variable and therapeutic response rates are poor. Few clinical assays are available to predict outcome in RCC or correlate behavior with histology. Therefore, novel RCC classification systems based on gene expression should be useful for diagnosis, prognosis, and treatment. Recent microarray studies have shown that renal tumors are characterized by distinct gene expression profiles, which can be used to discover novel diagnostic and prognostic biomarkers. Here, we review clinical features of kidney cancer, the WHO classification system, and the growing role of molecular classification for diagnosis, prognosis, and therapy of this disease.

Assessing Unmet Information Needs of Breast Cancer Survivors: Exploratory Study of Online Health Forums Using Text Classification and Retrieval.

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McRoy, Susan; Rastegar-Mojarad, Majid; Wang, Yanshan; Ruddy, Kathryn J; Haddad, Tufia C; Liu, Hongfang

2018-05-15

Patient education materials given to breast cancer survivors may not be a good fit for their information needs. Needs may change over time, be forgotten, or be misreported, for a variety of reasons. An automated content analysis of survivors' postings to online health forums can identify expressed information needs over a span of time and be repeated regularly at low cost. Identifying these unmet needs can guide improvements to existing education materials and the creation of new resources. The primary goals of this project are to assess the unmet information needs of breast cancer survivors from their own perspectives and to identify gaps between information needs and current education materials. This approach employs computational methods for content modeling and supervised text classification to data from online health forums to identify explicit and implicit requests for health-related information. Potential gaps between needs and education materials are identified using techniques from information retrieval. We provide a new taxonomy for the classification of sentences in online health forum data. 260 postings from two online health forums were selected, yielding 4179 sentences for coding. After annotation of data and training alternative one-versus-others classifiers, a random forest-based approach achieved F1 scores from 66% (Other, dataset2) to 90% (Medical, dataset1) on the primary information types. 136 expressions of need were used to generate queries to indexed education materials. Upon examination of the best two pages retrieved for each query, 12% (17/136) of queries were found to have relevant content by all coders, and 33% (45/136) were judged to have relevant content by at least one. Text from online health forums can be analyzed effectively using automated methods. Our analysis confirms that breast cancer survivors have many information needs that are not covered by the written documents they typically receive, as our results suggest that at most

The classification of lung cancers and their degree of malignancy by FTIR, PCA-LDA analysis, and a physics-based computational model.

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Kaznowska, E; Depciuch, J; Łach, K; Kołodziej, M; Koziorowska, A; Vongsvivut, J; Zawlik, I; Cholewa, M; Cebulski, J

2018-08-15

Lung cancer has the highest mortality rate of all malignant tumours. The current effects of cancer treatment, as well as its diagnostics, are unsatisfactory. Therefore it is very important to introduce modern diagnostic tools, which will allow for rapid classification of lung cancers and their degree of malignancy. For this purpose, the authors propose the use of Fourier Transform InfraRed (FTIR) spectroscopy combined with Principal Component Analysis-Linear Discriminant Analysis (PCA-LDA) and a physics-based computational model. The results obtained for lung cancer tissues, adenocarcinoma and squamous cell carcinoma FTIR spectra, show a shift in wavenumbers compared to control tissue FTIR spectra. Furthermore, in the FTIR spectra of adenocarcinoma there are no peaks corresponding to glutamate or phospholipid functional groups. Moreover, in the case of G2 and G3 malignancy of adenocarcinoma lung cancer, the absence of an OH groups peak was noticed. Thus, it seems that FTIR spectroscopy is a valuable tool to classify lung cancer and to determine the degree of its malignancy. Copyright © 2018 Elsevier B.V. All rights reserved.

Role of MicroRNA-1 in Human Cancer and Its Therapeutic Potentials

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Chao Han

2014-01-01

Full Text Available While the mechanisms of human cancer development are not fully understood, evidence of microRNA (miRNA, miR dysregulation has been reported in many human diseases, including cancer. miRs are small noncoding RNA molecules that regulate posttranscriptional gene expression by binding to complementary sequences in the specific region of gene mRNAs, resulting in downregulation of gene expression. Not only are certain miRs consistently dysregulated across many cancers, but they also play critical roles in many aspects of cell growth, proliferation, metastasis, apoptosis, and drug resistance. Recent studies from our group and others revealed that miR-1 is frequently downregulated in various types of cancer. Through targeting multiple oncogenes and oncogenic pathways, miR-1 has been demonstrated to be a tumor suppressor gene that represses cancer cell proliferation and metastasis and promotes apoptosis by ectopic expression. In this review, we highlight recent findings on the aberrant expression and functional significance of miR-1 in human cancers and emphasize its significant values for therapeutic potentials.

Onco-GPCR signaling and dysregulated expression of microRNAs in human cancer.

Science.gov (United States)

Nohata, Nijiro; Goto, Yusuke; Gutkind, J Silvio

2017-01-01

The G-protein-coupled receptor (GPCR) family is the largest family of cell-surface receptors involved in signal transduction. Aberrant expression of GPCRs and G proteins are frequently associated with prevalent human diseases, including cancer. In fact, GPCRs represent the therapeutic targets of more than a quarter of the clinical drugs currently on the market. MiRNAs (miRNAs) are also aberrantly expressed in many human cancers, and they have significant roles in the initiation, development and metastasis of human malignancies. Recent studies have revealed that dysregulation of miRNAs and their target genes expression are associated with cancer progression. The emerging information suggests that miRNAs play an important role in the fine tuning of many signaling pathways, including GPCR signaling. We summarize our current knowledge of the individual functions of miRNAs regulated by GPCRs and GPCR signaling-associated molecules, and miRNAs that regulate the expression and activity of GPCRs, their endogenous ligands and their coupled heterotrimeric G proteins in human cancer.

Confronting human papilloma virus/oropharyngeal cancer: a model for interprofessional collaboration.

Science.gov (United States)

Fried, Jacquelyn L

2014-06-01

A collaborative practice model related to Human Papilloma Virus (HPV) associated oropharyngeal cancer highlights the role of the dental hygienist in addressing this condition. The incidence of HPV associated head and neck cancer is rising. Multiple professionals including the dental hygienist can work collaboratively to confront this growing public health concern. A critical review applies the growth and utilization of interprofessional education (IPE) and interprofessional collaboration (IPC) to multi-disciplinary models addressing the human papilloma virus and oropharyngeal cancers. A model related to HPV associated oropharyngeal cancer addresses an oral systemic condition that supports the inclusion of a dental hygienist on collaborative teams addressing prevention, detection, treatment and cure of OPC. Copyright © 2014 Elsevier Inc. All rights reserved.

Fenton reaction induced cancer in wild type rats recapitulates genomic alterations observed in human cancer.

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Shinya Akatsuka

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

Decreased glucose uptake by hyperglycemia is regulated by different mechanisms in human cancer cells and monocytes

International Nuclear Information System (INIS)

Kim, Chae Kyun; Chung, June Key; Lee, Yong Jin; Hong, Mee Kyoung; Jeong, Jae Min; Lee, Dong Soo; Lee, Myung Chul

2002-01-01

To clarify the difference in glucose uptake between human cancer cells and monocytes, we studied ( 18 F) fluorodeoxyglucose (FDG) uptake in three human colon cancer cell lines (SNU-C2A, SNU-C4, SNU-C5), one human lung cancer cell line (NCI-H522), and human peripheral blood monocytes. The FDG uptake of both cancer cells and monocytes was increased in glucose-free medium, but decreased in the medium containing 16.7 mM glucose (hyperglycemic). The level of Glut1 mRNA decreased in human colon cancer cells and NCI-H522 under hyperglycemic condition. Glut1 protein expression was also decreased in the four human cancer cell lines under hyperglycemic condition, whereas it was consistently undetectable in monocytes. SNU-C2A, SNU-C4 and NCI-H522 showed a similar level of hexokinase activity (7.5-10.8 mU/mg), while SNU-C5 and moncytes showed lower range of hexokinase activity (4.3-6.5 mU/mg). These data suggest that glucose uptake is regulated by different mechanisms in human cancer cells and monocytes

Human cancer protein-protein interaction network: a structural perspective.

Directory of Open Access Journals (Sweden)

Gozde Kar

2009-12-01

Full Text Available Protein-protein interaction networks provide a global picture of cellular function and biological processes. Some proteins act as hub proteins, highly connected to others, whereas some others have few interactions. The dysfunction of some interactions causes many diseases, including cancer. Proteins interact through their interfaces. Therefore, studying the interface properties of cancer-related proteins will help explain their role in the interaction networks. Similar or overlapping binding sites should be used repeatedly in single interface hub proteins, making them promiscuous. Alternatively, multi-interface hub proteins make use of several distinct binding sites to bind to different partners. We propose a methodology to integrate protein interfaces into cancer interaction networks (ciSPIN, cancer structural protein interface network. The interactions in the human protein interaction network are replaced by interfaces, coming from either known or predicted complexes. We provide a detailed analysis of cancer related human protein-protein interfaces and the topological properties of the cancer network. The results reveal that cancer-related proteins have smaller, more planar, more charged and less hydrophobic binding sites than non-cancer proteins, which may indicate low affinity and high specificity of the cancer-related interactions. We also classified the genes in ciSPIN according to phenotypes. Within phenotypes, for breast cancer, colorectal cancer and leukemia, interface properties were found to be discriminating from non-cancer interfaces with an accuracy of 71%, 67%, 61%, respectively. In addition, cancer-related proteins tend to interact with their partners through distinct interfaces, corresponding mostly to multi-interface hubs, which comprise 56% of cancer-related proteins, and constituting the nodes with higher essentiality in the network (76%. We illustrate the interface related affinity properties of two cancer-related hub

Clinical Effect of Human Papillomavirus Genotypes in Patients With Cervical Cancer Undergoing Primary Radiotherapy

International Nuclear Information System (INIS)

Wang, Chun-Chieh; Lai, Chyong-Huey; Huang, Huei-Jean; Chao, Angel; Chang, Chee-Jen; Chang, Ting-Chang; Chou, Hung-Hsueh; Hong, Ji-Hong

2010-01-01

Purpose: To study the prognostic value of the human papillomavirus (HPV) genotypes in cervical cancer patients undergoing radiotherapy. Patients and Methods: A total of 1,010 patients with cervical cancer after radiotherapy between 1993 and 2000 were eligible for this study. The HPV genotypes were determined by a genechip, which detects 38 types of HPV. The patient characteristics and treatment outcomes were analyzed using the Cox regression hazard model and classification and regression tree decision tree method. Results: A total of 25 genotypes of HPV were detected in 992 specimens (98.2%). The leading 8 types were HPV16, 58, 18, 33, 52, 39, 31, and 45. These types belong to two high-risk HPV species: alpha-7 (HPV18, 39, 45) and alpha-9 (HPV16, 31, 33, 52, 58). Three HPV-based risk groups, which were independent of established prognostic factors, such as International Federation of Gynecology and Obstetrics stage, age, pathologic features, squamous cell carcinoma antigen, and lymph node metastasis, were associated with the survival outcomes. The high-risk group consisted of the patients without HPV infection or the ones infected with the alpha-7 species only. Patients co-infected with the alpha-7 and alpha-9 species belonged to the medium-risk group, and the others were included in the low-risk group. Conclusion: The results of the present study have confirmed the prognostic value of HPV genotypes in cervical cancer treated with radiotherapy. The different effect of the alpha-7 and alpha-9 species on the radiation response deserves additional exploration.

Clinical and Pathological Staging Validation in the Eighth Edition of the TNM Classification for Lung Cancer: Correlation between Solid Size on Thin-Section Computed Tomography and Invasive Size in Pathological Findings in the New T Classification.

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Aokage, Keiju; Miyoshi, Tomohiro; Ishii, Genichiro; Kusumoto, Masahiro; Nomura, Shogo; Katsumata, Shinya; Sekihara, Keigo; Hishida, Tomoyuki; Tsuboi, Masahiro

2017-09-01

The aim of this study was to validate the new eighth edition of the TNM classification and to elucidate whether radiological solid size corresponds to pathological invasive size incorporated in this T factor. We analyzed the data on 1792 patients who underwent complete resection from 2003 to 2011 at the National Cancer Center Hospital East, Japan. We reevaluated preoperative thin-section computed tomography (TSCT) to determine solid size and pathological invasive size using the fourth edition of the WHO classification and reclassified them according to the new TNM classification. The discriminative power of survival curves by the seventh edition was compared with that by the eighth edition by using concordance probability estimates and Akaike's information criteria calculated using a univariable Cox regression model. Pearson's correlation coefficient was calculated to elucidate the correlation between radiological solid size using TSCT and pathological invasive size. The overall survival curves in the eighth edition were well distinct at each clinical and pathological stage. The 5-year survival rates of patients with clinical and pathological stage 0 newly defined were both 100%. The concordance probability estimate and Akaike's information criterion values of the eighth edition were higher than those of the seventh edition in discriminatory power for overall survival. Solid size on TSCT scan and pathological invasive size showed a positive linear relationship, and Pearson's correlation coefficient was calculated as 0.83, which indicated strong correlation. This TNM classification will be feasible regarding patient survival, and radiological solid size correlates significantly with pathological invasive size as a new T factor. Copyright © 2017 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.

An early history of human breast cancer: West meets East.

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Yan, Shou-He

2013-09-01

Cancer has been increasingly recognized as a global issue. This is especially true in countries like China, where cancer incidence has increased likely because of changes in environment and lifestyle. However, cancer is not a modern disease; early cases have been recorded in ancient medical books in the West and in China. Here, we provide a brief history of cancer, focusing on cancer of the breast, and review the etymology of ai, the Chinese character for cancer. Notable findings from both Western and Chinese traditional medicine are presented to give an overview of the most important, early contributors to our evolving understanding of human breast cancer. We also discuss the earliest historical documents to record patients with breast cancer.

Polymeric nanoparticles as cancer-specific DNA delivery vectors to human hepatocellular carcinoma.

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Zamboni, Camila G; Kozielski, Kristen L; Vaughan, Hannah J; Nakata, Maisa M; Kim, Jayoung; Higgins, Luke J; Pomper, Martin G; Green, Jordan J

2017-10-10

Hepatocellular carcinoma (HCC) is the third most deadly cancer in the US, with a meager 5-year survival rate of effective and cancer-specific DNA delivery to human HCC using biodegradable poly(beta-amino ester) (PBAE) nanoparticles (NPs). Varied PBAE NP formulations were evaluated for transfection efficacy and cytotoxicity to a range of human HCC cells as well as healthy human hepatocytes. To address HCC heterogeneity, nine different sources of human HCC cells were utilized. The polymeric NPs composed of 2-((3-aminopropyl)amino) ethanol end-modified poly(1,5-pentanediol diacrylate-co-3-amino-1-propanol) ('536') at a 25 polymer-to-DNA weight-to-weight ratio led to high transfection efficacy to all of the liver cancer lines, but not to hepatocytes. Each individual HCC line had a significantly higher percentage of exogenous gene expression than the healthy liver cells (Peffective DNA transfection in vivo. PBAE-based NPs enabled high and preferential DNA delivery to HCC cells, sparing healthy hepatocytes. These biodegradable and liver cancer-selective NPs are a promising technology to deliver therapeutic genes to liver cancer. Copyright © 2017 Elsevier B.V. All rights reserved.

Epigenetic modulation of cancer-germline antigen gene expression in tumorigenic human mesenchymal stem cells: implications for cancer therapy

DEFF Research Database (Denmark)

Gjerstorff, Morten; Burns, Jorge S; Nielsen, Ole

2009-01-01

Cancer-germline antigens are promising targets for cancer immunotherapy, but whether such therapies will also eliminate the primary tumor stem cell population remains undetermined. We previously showed that long-term cultures of telomerized adult human bone marrow mesenchymal stem cells can...... spontaneously evolve into tumor-initiating, mesenchymal stem cells (hMSC-TERT20), which have characteristics of clinical sarcoma cells. In this study, we used the hMSC-TERT20 tumor stem cell model to investigate the potential of cancer-germline antigens to serve as tumor stem cell targets. We found...... of cancer-germline antigens in hMSC-TERT20 cells, while their expression levels in primary human mesenchymal stem cells remained unaffected. The expression pattern of cancer-germline antigens in tumorigenic mesenchymal stem cells and sarcomas, plus their susceptibility to enhancement by epigenetic...

Risk factors and classifications of hilar cholangiocarcinoma.

Science.gov (United States)

Suarez-Munoz, Miguel Angel; Fernandez-Aguilar, Jose Luis; Sanchez-Perez, Belinda; Perez-Daga, Jose Antonio; Garcia-Albiach, Beatriz; Pulido-Roa, Ysabel; Marin-Camero, Naiara; Santoyo-Santoyo, Julio

2013-07-15

Cholangiocarcinoma is the second most common primary malignant tumor of the liver. Perihilar cholangiocarcinoma or Klatskin tumor represents more than 50% of all biliary tract cholangiocarcinomas. A wide range of risk factors have been identified among patients with Perihilar cholangiocarcinoma including advanced age, male gender, primary sclerosing cholangitis, choledochal cysts, cholelithiasis, cholecystitis, parasitic infection (Opisthorchis viverrini and Clonorchis sinensis), inflammatory bowel disease, alcoholic cirrhosis, nonalcoholic cirrhosis, chronic pancreatitis and metabolic syndrome. Various classifications have been used to describe the pathologic and radiologic appearance of cholangiocarcinoma. The three systems most commonly used to evaluate Perihilar cholangiocarcinoma are the Bismuth-Corlette (BC) system, the Memorial Sloan-Kettering Cancer Center and the TNM classification. The BC classification provides preoperative assessment of local spread. The Memorial Sloan-Kettering cancer center proposes a staging system according to three factors related to local tumor extent: the location and extent of bile duct involvement, the presence or absence of portal venous invasion, and the presence or absence of hepatic lobar atrophy. The TNM classification, besides the usual descriptors, tumor, node and metastases, provides additional information concerning the possibility for the residual tumor (R) and the histological grade (G). Recently, in 2011, a new consensus classification for the Perihilar cholangiocarcinoma had been published. The consensus was organised by the European Hepato-Pancreato-Biliary Association which identified the need for a new staging system for this type of tumors. The classification includes information concerning biliary or vascular (portal or arterial) involvement, lymph node status or metastases, but also other essential aspects related to the surgical risk, such as remnant hepatic volume or the possibility of underlying disease.

Isorhapontigenin (ISO) Inhibits Invasive Bladder Cancer Formation In Vivo and Human Bladder Cancer Invasion In Vitro by Targeting STAT1/FOXO1 Axis.

Science.gov (United States)

Jiang, Guosong; Wu, Amy D; Huang, Chao; Gu, Jiayan; Zhang, Liping; Huang, Haishan; Liao, Xin; Li, Jingxia; Zhang, Dongyun; Zeng, Xingruo; Jin, Honglei; Huang, Haojie; Huang, Chuanshu

2016-07-01

Although our most recent studies have identified Isorhapontigenin (ISO), a novel derivative of stilbene that isolated from a Chinese herb Gnetum cleistostachyum, for its inhibition of human bladder cancer growth, nothing is known whether ISO possesses an inhibitory effect on bladder cancer invasion. Thus, we addressed this important question in current study and discovered that ISO treatment could inhibit mouse-invasive bladder cancer development following bladder carcinogen N-butyl-N-(4-hydroxybutyl) nitrosamine (BBN) exposure in vivo We also found that ISO suppressed human bladder cancer cell invasion accompanied by upregulation of the forkhead box class O 1 (FOXO1) mRNA transcription in vitro Accordingly, FOXO1 was profoundly downregulated in human bladder cancer tissues and was negatively correlated with bladder cancer invasion. Forced expression of FOXO1 specifically suppressed high-grade human bladder cancer cell invasion, whereas knockdown of FOXO1 promoted noninvasive bladder cancer cells becoming invasive bladder cancer cells. Moreover, knockout of FOXO1 significantly increased bladder cancer cell invasion and abolished the ISO inhibition of invasion in human bladder cancer cells. Further studies showed that the inhibition of Signal transducer and activator of transcription 1 (STAT1) phosphorylation at Tyr701 was crucial for ISO upregulation of FOXO1 transcription. Furthermore, this study revealed that metalloproteinase-2 (MMP-2) was a FOXO1 downstream effector, which was also supported by data obtained from mouse model of ISO inhibition BBN-induced mouse-invasive bladder cancer formation. These findings not only provide a novel insight into the understanding of mechanism of bladder cancer's propensity to invasion, but also identify a new role and mechanisms underlying the natural compound ISO that specifically suppresses such bladder cancer invasion through targeting the STAT1-FOXO1-MMP-2 axis. Cancer Prev Res; 9(7); 567-80. ©2016 AACR. ©2016 American

Using rule-based machine learning for candidate disease gene prioritization and sample classification of cancer gene expression data.

Science.gov (United States)

Glaab, Enrico; Bacardit, Jaume; Garibaldi, Jonathan M; Krasnogor, Natalio

2012-01-01

Microarray data analysis has been shown to provide an effective tool for studying cancer and genetic diseases. Although classical machine learning techniques have successfully been applied to find informative genes and to predict class labels for new samples, common restrictions of microarray analysis such as small sample sizes, a large attribute space and high noise levels still limit its scientific and clinical applications. Increasing the interpretability of prediction models while retaining a high accuracy would help to exploit the information content in microarray data more effectively. For this purpose, we evaluate our rule-based evolutionary machine learning systems, BioHEL and GAssist, on three public microarray cancer datasets, obtaining simple rule-based models for sample classification. A comparison with other benchmark microarray sample classifiers based on three diverse feature selection algorithms suggests that these evolutionary learning techniques can compete with state-of-the-art methods like support vector machines. The obtained models reach accuracies above 90% in two-level external cross-validation, with the added value of facilitating interpretation by using only combinations of simple if-then-else rules. As a further benefit, a literature mining analysis reveals that prioritizations of informative genes extracted from BioHEL's classification rule sets can outperform gene rankings obtained from a conventional ensemble feature selection in terms of the pointwise mutual information between relevant disease terms and the standardized names of top-ranked genes.

Finding Combination of Features from Promoter Regions for Ovarian Cancer-related Gene Group Classification

KAUST Repository

Olayan, Rawan S.

2012-01-01

In classification problems, it is always important to use the suitable combination of features that will be employed by classifiers. Generating the right combination of features usually results in good classifiers. In the situation when the problem is not well understood, data items are usually described by many features in the hope that some of these may be the relevant or most relevant ones. In this study, we focus on one such problem related to genes implicated in ovarian cancer (OC). We try to recognize two important OC-related gene groups: oncogenes, which support the development and progression of OC, and oncosuppressors, which oppose such tendencies. For this, we use the properties of promoters of these genes. We identified potential “regulatory features” that characterize OC-related oncogenes and oncosuppressors promoters. In our study, we used 211 oncogenes and 39 oncosuppressors. For these, we identified 538 characteristic sequence motifs from their promoters. Promoters are annotated by these motifs and derived feature vectors used to develop classification models. We made a comparison of a number of classification models in their ability to distinguish oncogenes from oncosuppressors. Based on 10-fold cross-validation, the resultant model was able to separate the two classes with sensitivity of 96% and specificity of 100% with the complete set of features. Moreover, we developed another recognition model where we attempted to distinguish oncogenes and oncosuppressors as one group from other OC-related genes. That model achieved accuracy of 82%. We believe that the results of this study will help in discovering other OC-related oncogenes and oncosuppressors not identified as yet.

Finding Combination of Features from Promoter Regions for Ovarian Cancer-related Gene Group Classification

KAUST Repository

Olayan, Rawan S.

2012-12-01

In classification problems, it is always important to use the suitable combination of features that will be employed by classifiers. Generating the right combination of features usually results in good classifiers. In the situation when the problem is not well understood, data items are usually described by many features in the hope that some of these may be the relevant or most relevant ones. In this study, we focus on one such problem related to genes implicated in ovarian cancer (OC). We try to recognize two important OC-related gene groups: oncogenes, which support the development and progression of OC, and oncosuppressors, which oppose such tendencies. For this, we use the properties of promoters of these genes. We identified potential “regulatory features” that characterize OC-related oncogenes and oncosuppressors promoters. In our study, we used 211 oncogenes and 39 oncosuppressors. For these, we identified 538 characteristic sequence motifs from their promoters. Promoters are annotated by these motifs and derived feature vectors used to develop classification models. We made a comparison of a number of classification models in their ability to distinguish oncogenes from oncosuppressors. Based on 10-fold cross-validation, the resultant model was able to separate the two classes with sensitivity of 96% and specificity of 100% with the complete set of features. Moreover, we developed another recognition model where we attempted to distinguish oncogenes and oncosuppressors as one group from other OC-related genes. That model achieved accuracy of 82%. We believe that the results of this study will help in discovering other OC-related oncogenes and oncosuppressors not identified as yet.

An Approach for Leukemia Classification Based on Cooperative Game Theory

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Atefeh Torkaman

2011-01-01

Full Text Available Hematological malignancies are the types of cancer that affect blood, bone marrow and lymph nodes. As these tissues are naturally connected through the immune system, a disease affecting one of them will often affect the others as well. The hematological malignancies include; Leukemia, Lymphoma, Multiple myeloma. Among them, leukemia is a serious malignancy that starts in blood tissues especially the bone marrow, where the blood is made. Researches show, leukemia is one of the common cancers in the world. So, the emphasis on diagnostic techniques and best treatments would be able to provide better prognosis and survival for patients. In this paper, an automatic diagnosis recommender system for classifying leukemia based on cooperative game is presented. Through out this research, we analyze the flow cytometry data toward the classification of leukemia into eight classes. We work on real data set from different types of leukemia that have been collected at Iran Blood Transfusion Organization (IBTO. Generally, the data set contains 400 samples taken from human leukemic bone marrow. This study deals with cooperative game used for classification according to different weights assigned to the markers. The proposed method is versatile as there are no constraints to what the input or output represent. This means that it can be used to classify a population according to their contributions. In other words, it applies equally to other groups of data. The experimental results show the accuracy rate of 93.12%, for classification and compared to decision tree (C4.5 with (90.16% in accuracy. The result demonstrates that cooperative game is very promising to be used directly for classification of leukemia as a part of Active Medical decision support system for interpretation of flow cytometry readout. This system could assist clinical hematologists to properly recognize different kinds of leukemia by preparing suggestions and this could improve the treatment

Cervical Cancer Incidence in Young U.S. Females After Human Papillomavirus Vaccine Introduction.

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Guo, Fangjian; Cofie, Leslie E; Berenson, Abbey B

2018-05-30

Since 2006, human papillomavirus vaccine has been recommended for young females in the U.S. This study aimed to compare cervical cancer incidence among young women before and after the human papillomavirus vaccine was introduced. This cross-sectional study used data from the National Program for Cancer Registries and Surveillance, Epidemiology, and End Results Incidence-U.S. Cancer Statistics 2001-2014 database for U.S. females aged 15-34 years. This study compared the 4-year average annual incidence of invasive cervical cancer in the 4 years before human papillomavirus vaccine was introduced (2003-2006) and the 4 most recent years in the vaccine era (2011-2014). Joinpoint regression models of cervical incidence from 2001 to 2014 were fitted to identify the discrete joints (year) that represent statistically significant changes in the direction of the trend after the introduction of human papillomavirus vaccination in 2006. Data were collected in 2001-2014, released, and analyzed in 2017. The 4-year average annual incidence rates for cervical cancer in 2011-2014 were 29% lower than that in 2003-2006 (6.0 vs 8.4 per 1,000,000 people, rate ratio=0.71, 95% CI=0.64, 0.80) among females aged 15-24 years, and 13.0% lower among females aged 25-34 years. Joinpoint analyses of cervical cancer incidence among females aged 15-24 years revealed a significant joint at 2009 for both squamous cell carcinoma and non-squamous cell carcinoma. Among females aged 25-34 years, there was no significant decrease in cervical cancer incidence after 2006. A significant decrease in the incidence of cervical cancer among young females after the introduction of human papillomavirus vaccine may indicate early effects of human papillomavirus vaccination. Copyright © 2018 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.

A systematic atlas of chaperome deregulation topologies across the human cancer landscape

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Sverchkova, Angelina

2018-01-01

Proteome balance is safeguarded by the proteostasis network (PN), an intricately regulated network of conserved processes that evolved to maintain native function of the diverse ensemble of protein species, ensuring cellular and organismal health. Proteostasis imbalances and collapse are implicated in a spectrum of human diseases, from neurodegeneration to cancer. The characteristics of PN disease alterations however have not been assessed in a systematic way. Since the chaperome is among the central components of the PN, we focused on the chaperome in our study by utilizing a curated functional ontology of the human chaperome that we connect in a high-confidence physical protein-protein interaction network. Challenged by the lack of a systems-level understanding of proteostasis alterations in the heterogeneous spectrum of human cancers, we assessed gene expression across more than 10,000 patient biopsies covering 22 solid cancers. We derived a novel customized Meta-PCA dimension reduction approach yielding M-scores as quantitative indicators of disease expression changes to condense the complexity of cancer transcriptomics datasets into quantitative functional network topographies. We confirm upregulation of the HSP90 family and also highlight HSP60s, Prefoldins, HSP100s, ER- and mitochondria-specific chaperones as pan-cancer enriched. Our analysis also reveals a surprisingly consistent strong downregulation of small heat shock proteins (sHSPs) and we stratify two cancer groups based on the preferential upregulation of ATP-dependent chaperones. Strikingly, our analyses highlight similarities between stem cell and cancer proteostasis, and diametrically opposed chaperome deregulation between cancers and neurodegenerative diseases. We developed a web-based Proteostasis Profiler tool (Pro2) enabling intuitive analysis and visual exploration of proteostasis disease alterations using gene expression data. Our study showcases a comprehensive profiling of chaperome shifts

A systematic atlas of chaperome deregulation topologies across the human cancer landscape.

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Ali Hadizadeh Esfahani

2018-01-01

Full Text Available Proteome balance is safeguarded by the proteostasis network (PN, an intricately regulated network of conserved processes that evolved to maintain native function of the diverse ensemble of protein species, ensuring cellular and organismal health. Proteostasis imbalances and collapse are implicated in a spectrum of human diseases, from neurodegeneration to cancer. The characteristics of PN disease alterations however have not been assessed in a systematic way. Since the chaperome is among the central components of the PN, we focused on the chaperome in our study by utilizing a curated functional ontology of the human chaperome that we connect in a high-confidence physical protein-protein interaction network. Challenged by the lack of a systems-level understanding of proteostasis alterations in the heterogeneous spectrum of human cancers, we assessed gene expression across more than 10,000 patient biopsies covering 22 solid cancers. We derived a novel customized Meta-PCA dimension reduction approach yielding M-scores as quantitative indicators of disease expression changes to condense the complexity of cancer transcriptomics datasets into quantitative functional network topographies. We confirm upregulation of the HSP90 family and also highlight HSP60s, Prefoldins, HSP100s, ER- and mitochondria-specific chaperones as pan-cancer enriched. Our analysis also reveals a surprisingly consistent strong downregulation of small heat shock proteins (sHSPs and we stratify two cancer groups based on the preferential upregulation of ATP-dependent chaperones. Strikingly, our analyses highlight similarities between stem cell and cancer proteostasis, and diametrically opposed chaperome deregulation between cancers and neurodegenerative diseases. We developed a web-based Proteostasis Profiler tool (Pro2 enabling intuitive analysis and visual exploration of proteostasis disease alterations using gene expression data. Our study showcases a comprehensive profiling of

An Efficient Ensemble Learning Method for Gene Microarray Classification

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Alireza Osareh

2013-01-01

Full Text Available The gene microarray analysis and classification have demonstrated an effective way for the effective diagnosis of diseases and cancers. However, it has been also revealed that the basic classification techniques have intrinsic drawbacks in achieving accurate gene classification and cancer diagnosis. On the other hand, classifier ensembles have received increasing attention in various applications. Here, we address the gene classification issue using RotBoost ensemble methodology. This method is a combination of Rotation Forest and AdaBoost techniques which in turn preserve both desirable features of an ensemble architecture, that is, accuracy and diversity. To select a concise subset of informative genes, 5 different feature selection algorithms are considered. To assess the efficiency of the RotBoost, other nonensemble/ensemble techniques including Decision Trees, Support Vector Machines, Rotation Forest, AdaBoost, and Bagging are also deployed. Experimental results have revealed that the combination of the fast correlation-based feature selection method with ICA-based RotBoost ensemble is highly effective for gene classification. In fact, the proposed method can create ensemble classifiers which outperform not only the classifiers produced by the conventional machine learning but also the classifiers generated by two widely used conventional ensemble learning methods, that is, Bagging and AdaBoost.

Mechanism-based classification and physical therapy management of persons with cancer pain: A prospective case series

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Senthil P Kumar

2013-01-01

Full Text Available Context: Mechanism-based classification (MBC was established with current evidence and physical therapy (PT management methods for both cancer and for noncancer pain. Aims: This study aims to describe the efficacy of MBC-based PT in persons with primary complaints of cancer pain. Settings and Design: A prospective case series of patients who attended the physiotherapy department of a multispecialty university-affiliated teaching hospital. Material and Methods: A total of 24 adults (18 female, 6 male aged 47.5 ± 10.6 years, with primary diagnosis of heterogeneous group of cancer, chief complaints of chronic disabling pain were included in the study on their consent for participation The patients were evaluated and classified on the basis of five predominant mechanisms for pain. Physical therapy interventions were recommended based on mechanisms identified and home program was prescribed with a patient log to ensure compliance. Treatments were given in five consecutive weekly sessions for five weeks each of 30 min duration. Statistical Analysis Used: Pre-post comparisons for pain severity (PS and pain interference (PI subscales of Brief pain inventory-Cancer pain (BPI-CP and, European organization for research and treatment in cancer-quality of life questionnaire (EORTC-QLQ-C30 were done using Wilcoxon signed-rank test at 95% confidence interval using SPSS for Windows version 16.0 (SPSS Inc, Chicago, IL. Results: There were statistically significant ( P < 0.05 reduction in pain severity, pain interference and total BPI-CP scores, and the EORTC-QLQ-C30. Conclusion: MBC-PT was effective for improving BPI-CP and EORTC-QLQ-C30 scores in people with cancer pain.

¹H NMR-based metabolic profiling of human rectal cancer tissue

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2013-01-01

Background Rectal cancer is one of the most prevalent tumor types. Understanding the metabolic profile of rectal cancer is important for developing therapeutic approaches and molecular diagnosis. Methods Here, we report a metabonomics profiling of tissue samples on a large cohort of human rectal cancer subjects (n = 127) and normal controls (n = 43) using 1H nuclear magnetic resonance (1H NMR) based metabonomics assay, which is a highly sensitive and non-destructive method for the biomarker identification in biological systems. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and orthogonal projection to latent structure with discriminant analysis (OPLS-DA) were applied to analyze the 1H-NMR profiling data to identify the distinguishing metabolites of rectal cancer. Results Excellent separation was obtained and distinguishing metabolites were observed among the different stages of rectal cancer tissues (stage I = 35; stage II = 37; stage III = 37 and stage IV = 18) and normal controls. A total of 38 differential metabolites were identified, 16 of which were closely correlated with the stage of rectal cancer. The up-regulation of 10 metabolites, including lactate, threonine, acetate, glutathione, uracil, succinate, serine, formate, lysine and tyrosine, were detected in the cancer tissues. On the other hand, 6 metabolites, including myo-inositol, taurine, phosphocreatine, creatine, betaine and dimethylglycine were decreased in cancer tissues. These modified metabolites revealed disturbance of energy, amino acids, ketone body and choline metabolism, which may be correlated with the progression of human rectal cancer. Conclusion Our findings firstly identify the distinguishing metabolites in different stages of rectal cancer tissues, indicating possibility of the attribution of metabolites disturbance to the progression of rectal cancer. The altered metabolites may be as potential biomarkers, which would

Evaluating human cancer cell metastasis in zebrafish

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Teng, Yong; Xie, Xiayang; Walker, Steven; White, David T; Mumm, Jeff S; Cowell, John K

2013-01-01

In vivo metastasis assays have traditionally been performed in mice, but the process is inefficient and costly. However, since zebrafish do not develop an adaptive immune system until 14 days post-fertilization, human cancer cells can survive and metastasize when transplanted into zebrafish larvae. Despite isolated reports, there has been no systematic evaluation of the robustness of this system to date. Individual cell lines were stained with CM-Dil and injected into the perivitelline space of 2-day old zebrafish larvae. After 2-4 days fish were imaged using confocal microscopy and the number of metastatic cells was determined using Fiji software. To determine whether zebrafish can faithfully report metastatic potential in human cancer cells, we injected a series of cells with different metastatic potential into the perivitelline space of 2 day old embryos. Using cells from breast, prostate, colon and pancreas we demonstrated that the degree of cell metastasis in fish is proportional to their invasion potential in vitro. Highly metastatic cells such as MDA231, DU145, SW620 and ASPC-1 are seen in the vasculature and throughout the body of the fish after only 24–48 hours. Importantly, cells that are not invasive in vitro such as T47D, LNCaP and HT29 do not metastasize in fish. Inactivation of JAK1/2 in fibrosarcoma cells leads to loss of invasion in vitro and metastasis in vivo, and in zebrafish these cells show limited spread throughout the zebrafish body compared with the highly metastatic parental cells. Further, knockdown of WASF3 in DU145 cells which leads to loss of invasion in vitro and metastasis in vivo also results in suppression of metastasis in zebrafish. In a cancer progression model involving normal MCF10A breast epithelial cells, the degree of invasion/metastasis in vitro and in mice is mirrored in zebrafish. Using a modified version of Fiji software, it is possible to quantify individual metastatic cells in the transparent larvae to correlate with

Support vector machine and principal component analysis for microarray data classification

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Astuti, Widi; Adiwijaya

2018-03-01

Cancer is a leading cause of death worldwide although a significant proportion of it can be cured if it is detected early. In recent decades, technology called microarray takes an important role in the diagnosis of cancer. By using data mining technique, microarray data classification can be performed to improve the accuracy of cancer diagnosis compared to traditional techniques. The characteristic of microarray data is small sample but it has huge dimension. Since that, there is a challenge for researcher to provide solutions for microarray data classification with high performance in both accuracy and running time. This research proposed the usage of Principal Component Analysis (PCA) as a dimension reduction method along with Support Vector Method (SVM) optimized by kernel functions as a classifier for microarray data classification. The proposed scheme was applied on seven data sets using 5-fold cross validation and then evaluation and analysis conducted on term of both accuracy and running time. The result showed that the scheme can obtained 100% accuracy for Ovarian and Lung Cancer data when Linear and Cubic kernel functions are used. In term of running time, PCA greatly reduced the running time for every data sets.

Effect of cyclophilin A on gene expression in human pancreatic cancer cells.

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Li, Min; Wang, Hao; Li, Fei; Fisher, William E; Chen, Changyi; Yao, Qizhi

2005-11-01

We previously found that cyclophilin A (CypA) is overexpressed in human pancreatic cancer cells and stimulates cell proliferation through CD147. In this study, we further investigated the effect of CypA on gene expression of several key molecules that are involved in pancreatic cancer cell proliferation. Human pancreatic cancer cell lines (Panc-1, MIA PaCa-2, and BxPC-3) and human pancreatic ductal epithelial (HPDE) cells were used. The messenger RNA (mRNA) levels of CypA, CypB, CD147, neuropilins (NRPs), vascular endothelial growth factor (VEGF), and VEGF receptors upon the treatment of exogenous recombinant human CypA were determined by real-time reverse-transcription polymerase chain reaction. Exogenous human recombinant CypA reduced the mRNA levels of NRP-1 and VEGF, but not endogenous CypA, CypB, and CD147, in Panc-1, MIA PaCa-2, and BxPC-3 cells. In contrast, HPDE cells showed a decrease of endogenous CypA and CD147 mRNA, but not detectable changes of CypB, NRPs, and VEGF mRNA levels upon exogenous CypA treatment. These data show that exogenous CypA downregulates NRP-1 and VEGF expression in pancreatic cancer cells. This effect is different in normal HPDE cells. Thus, soluble CypA may affect cell growth of pancreatic cancer.

Hybrid clone cells derived from human breast epithelial cells and human breast cancer cells exhibit properties of cancer stem/initiating cells.

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Gauck, Daria; Keil, Silvia; Niggemann, Bernd; Zänker, Kurt S; Dittmar, Thomas

2017-08-02

The biological phenomenon of cell fusion has been associated with cancer progression since it was determined that normal cell × tumor cell fusion-derived hybrid cells could exhibit novel properties, such as enhanced metastatogenic capacity or increased drug resistance, and even as a mechanism that could give rise to cancer stem/initiating cells (CS/ICs). CS/ICs have been proposed as cancer cells that exhibit stem cell properties, including the ability to (re)initiate tumor growth. Five M13HS hybrid clone cells, which originated from spontaneous cell fusion events between M13SV1-EGFP-Neo human breast epithelial cells and HS578T-Hyg human breast cancer cells, and their parental cells were analyzed for expression of stemness and EMT-related marker proteins by Western blot analysis and confocal laser scanning microscopy. The frequency of ALDH1-positive cells was determined by flow cytometry using AldeRed fluorescent dye. Concurrently, the cells' colony forming capabilities as well as the cells' abilities to form mammospheres were investigated. The migratory activity of the cells was analyzed using a 3D collagen matrix migration assay. M13HS hybrid clone cells co-expressed SOX9, SLUG, CK8 and CK14, which were differently expressed in parental cells. A variation in the ALDH1-positive putative stem cell population was observed among the five hybrids ranging from 1.44% (M13HS-7) to 13.68% (M13HS-2). In comparison to the parental cells, all five hybrid clone cells possessed increased but also unique colony formation and mammosphere formation capabilities. M13HS-4 hybrid clone cells exhibited the highest colony formation capacity and second highest mammosphere formation capacity of all hybrids, whereby the mean diameter of the mammospheres was comparable to the parental cells. In contrast, the largest mammospheres originated from the M13HS-2 hybrid clone cells, whereas these cells' mammosphere formation capacity was comparable to the parental breast cancer cells. All M13HS

Low expression of Mel-18 predicts poor prognosis in patients with breast cancer.

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Guo, B-H; Zhang, X; Zhang, H-Z; Lin, H-L; Feng, Y; Shao, J-Y; Huang, W-L; Kung, H-F; Zeng, M-S

2010-12-01

Our previous study suggested that melanoma nuclear protein 18 (Mel-18) acted as a tumor suppressor in human breast cancer. This study was designed to investigate the clinical and prognostic significance of Mel-18 in breast cancer patients. Mel-18 was detected by immunohistochemistry in paraffin-embedded tissues from 287 breast cancer patients, of which 287 were from primary cancer sites, 63 from matched adjacent noncancerous sites, and 35 from metastatic lymph nodes. Differences in Mel-18 expression and clinical characteristics were compared by χ² test. Prognostic outcomes correlated with Mel-18 were examined using Kaplan-Meier analysis and Cox proportional hazards model. The decreased Mel-18 expression is incremental depending upon the magnitude of cancer progression (P Mel-18 was conversely correlated with the pathological classifications (P Mel-18 showed prolonged overall survivals (P Mel-18 expression may be a risk factor for the patients' survival (P Mel-18 expression is correlated with advanced clinicopathologic classifications and a poor overall survival in breast cancer patients. These findings suggest that Mel-18 may serve as a useful marker in prognostic evaluation for patients.

Expression profile of the N-myc Downstream Regulated Gene 2 (NDRG2 in human cancers with focus on breast cancer

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Vogel Lotte K

2011-01-01

Full Text Available Abstract Background Several studies have shown that NDRG2 mRNA is down-regulated or undetectable in various human cancers and cancer cell-lines. Although the function of NDRG2 is currently unknown, high NDRG2 expression correlates with improved prognosis in high-grade gliomas, gastric cancer and hepatocellular carcinomas. Furthermore, in vitro studies have revealed that over-expression of NDRG2 in cell-lines causes a significant reduction in their growth. The aim of this study was to examine levels of NDRG2 mRNA in several human cancers, with focus on breast cancer, by examining affected and normal tissue. Methods By labelling a human Cancer Profiling Array with a radioactive probe against NDRG2, we evaluated the level of NDRG2 mRNA in 154 paired normal and tumor samples encompassing 19 different human cancers. Furthermore, we used quantitative real-time RT-PCR to quantify the levels of NDRG2 and MYC mRNA in thyroid gland cancer and breast cancer, using a distinct set of normal and tumor samples. Results From the Cancer Profiling Array, we saw that the level of NDRG2 mRNA was reduced by at least 2-fold in almost a third of the tumor samples, compared to the normal counterpart, and we observed a marked decreased level in colon, cervix, thyroid gland and testis. However, a Benjamini-Hochberg correction showed that none of the tissues showed a significant reduction in NDRG2 mRNA expression in tumor tissue compared to normal tissue. Using quantitative RT-PCR, we observed a significant reduction in the level of NDRG2 mRNA in a distinct set of tumor samples from both thyroid gland cancer (p = 0.02 and breast cancer (p = 0.004, compared with normal tissue. MYC mRNA was not significantly altered in breast cancer or in thyroid gland cancer, compared with normal tissue. In thyroid gland, no correlation was found between MYC and NDRG2 mRNA levels, but in breast tissue we found a weakly significant correlation with a positive r-value in both normal and

Expression profile of the N-myc Downstream Regulated Gene 2 (NDRG2) in human cancers with focus on breast cancer

International Nuclear Information System (INIS)

Lorentzen, Anders; Lewinsky, Rikke H; Bornholdt, Jette; Vogel, Lotte K; Mitchelmore, Cathy

2011-01-01

Several studies have shown that NDRG2 mRNA is down-regulated or undetectable in various human cancers and cancer cell-lines. Although the function of NDRG2 is currently unknown, high NDRG2 expression correlates with improved prognosis in high-grade gliomas, gastric cancer and hepatocellular carcinomas. Furthermore, in vitro studies have revealed that over-expression of NDRG2 in cell-lines causes a significant reduction in their growth. The aim of this study was to examine levels of NDRG2 mRNA in several human cancers, with focus on breast cancer, by examining affected and normal tissue. By labelling a human Cancer Profiling Array with a radioactive probe against NDRG2, we evaluated the level of NDRG2 mRNA in 154 paired normal and tumor samples encompassing 19 different human cancers. Furthermore, we used quantitative real-time RT-PCR to quantify the levels of NDRG2 and MYC mRNA in thyroid gland cancer and breast cancer, using a distinct set of normal and tumor samples. From the Cancer Profiling Array, we saw that the level of NDRG2 mRNA was reduced by at least 2-fold in almost a third of the tumor samples, compared to the normal counterpart, and we observed a marked decreased level in colon, cervix, thyroid gland and testis. However, a Benjamini-Hochberg correction showed that none of the tissues showed a significant reduction in NDRG2 mRNA expression in tumor tissue compared to normal tissue. Using quantitative RT-PCR, we observed a significant reduction in the level of NDRG2 mRNA in a distinct set of tumor samples from both thyroid gland cancer (p = 0.02) and breast cancer (p = 0.004), compared with normal tissue. MYC mRNA was not significantly altered in breast cancer or in thyroid gland cancer, compared with normal tissue. In thyroid gland, no correlation was found between MYC and NDRG2 mRNA levels, but in breast tissue we found a weakly significant correlation with a positive r-value in both normal and tumor tissues, suggesting that MYC and NDRG2 mRNA are

Psychological impact of genetic counseling for hereditary nonpolyposis colorectal cancer: the role of cancer history, gender, age, and psychological distress.

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Hasenbring, Monika I; Kreddig, Nina; Deges, Gabriele; Epplen, Joerg T; Kunstmann, Erdmute; Stemmler, Susanne; Schulmann, Karsten; Willert, Joerg; Schmiegel, Wolf

2011-04-01

We prospectively examined the impact of an initial interdisciplinary genetic counseling (human geneticist, oncologist, and psycho-oncologist) on feelings of anxiety with a special focus on subgroups related to personal cancer history, gender, age, and education. At baseline, cancer-affected men revealed a significantly higher level of anxiety than unaffected men (pDepression Scale-A cases can be predicted by general distress (Brief Symptom Inventory) as well as by hereditary nonpolyposis colorectal cancer-related cognitions of intrusion and avoidance (impact of event scale) with a correct classification of 86%. Although initial hereditary nonpolyposis colorectal cancer counseling leads to an overall reduction of anxiety, differential effects of cancer history, gender, and age focus on subgroups of cancer-affected men, who may display unexpectedly high anxiety scores at baseline. Especially younger men do not seem to reduce this high anxiety level. Baseline anxiety was mainly determined by maladaptive situation-specific cognitions. Therefore, consulters should be more aware of anxiety-related cognitions in cancer-affected younger men.

Decreased glucose uptake by hyperglycemia is regulated by different mechanisms in human cancer cells and monocytes

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Kim, Chae Kyun; Chung, June Key; Lee, Yong Jin; Hong, Mee Kyoung; Jeong, Jae Min; Lee, Dong Soo; Lee, Myung Chul [College of Medicine, Seoul National Univ., Seoul (Korea, Republic of)

2002-04-01

To clarify the difference in glucose uptake between human cancer cells and monocytes, we studied ({sup 18}F) fluorodeoxyglucose (FDG) uptake in three human colon cancer cell lines (SNU-C2A, SNU-C4, SNU-C5), one human lung cancer cell line (NCI-H522), and human peripheral blood monocytes. The FDG uptake of both cancer cells and monocytes was increased in glucose-free medium, but decreased in the medium containing 16.7 mM glucose (hyperglycemic). The level of Glut1 mRNA decreased in human colon cancer cells and NCI-H522 under hyperglycemic condition. Glut1 protein expression was also decreased in the four human cancer cell lines under hyperglycemic condition, whereas it was consistently undetectable in monocytes. SNU-C2A, SNU-C4 and NCI-H522 showed a similar level of hexokinase activity (7.5-10.8 mU/mg), while SNU-C5 and moncytes showed lower range of hexokinase activity (4.3-6.5 mU/mg). These data suggest that glucose uptake is regulated by different mechanisms in human cancer cells and monocytes.

Classification of samples into two or more ordered populations with application to a cancer trial.

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Conde, D; Fernández, M A; Rueda, C; Salvador, B

2012-12-10

In many applications, especially in cancer treatment and diagnosis, investigators are interested in classifying patients into various diagnosis groups on the basis of molecular data such as gene expression or proteomic data. Often, some of the diagnosis groups are known to be related to higher or lower values of some of the predictors. The standard methods of classifying patients into various groups do not take into account the underlying order. This could potentially result in high misclassification rates, especially when the number of groups is larger than two. In this article, we develop classification procedures that exploit the underlying order among the mean values of the predictor variables and the diagnostic groups by using ideas from order-restricted inference. We generalize the existing methodology on discrimination under restrictions and provide empirical evidence to demonstrate that the proposed methodology improves over the existing unrestricted methodology. The proposed methodology is applied to a bladder cancer data set where the researchers are interested in classifying patients into various groups. Copyright © 2012 John Wiley & Sons, Ltd.

Cell of origin associated classification of B-cell malignancies by gene signatures of the normal B-cell hierarchy.

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Johnsen, Hans Erik; Bergkvist, Kim Steve; Schmitz, Alexander; Kjeldsen, Malene Krag; Hansen, Steen Møller; Gaihede, Michael; Nørgaard, Martin Agge; Bæch, John; Grønholdt, Marie-Louise; Jensen, Frank Svendsen; Johansen, Preben; Bødker, Julie Støve; Bøgsted, Martin; Dybkær, Karen

2014-06-01

Recent findings have suggested biological classification of B-cell malignancies as exemplified by the "activated B-cell-like" (ABC), the "germinal-center B-cell-like" (GCB) and primary mediastinal B-cell lymphoma (PMBL) subtypes of diffuse large B-cell lymphoma and "recurrent translocation and cyclin D" (TC) classification of multiple myeloma. Biological classification of B-cell derived cancers may be refined by a direct and systematic strategy where identification and characterization of normal B-cell differentiation subsets are used to define the cancer cell of origin phenotype. Here we propose a strategy combining multiparametric flow cytometry, global gene expression profiling and biostatistical modeling to generate B-cell subset specific gene signatures from sorted normal human immature, naive, germinal centrocytes and centroblasts, post-germinal memory B-cells, plasmablasts and plasma cells from available lymphoid tissues including lymph nodes, tonsils, thymus, peripheral blood and bone marrow. This strategy will provide an accurate image of the stage of differentiation, which prospectively can be used to classify any B-cell malignancy and eventually purify tumor cells. This report briefly describes the current models of the normal B-cell subset differentiation in multiple tissues and the pathogenesis of malignancies originating from the normal germinal B-cell hierarchy.

Feature Extraction and Classification on Esophageal X-Ray Images of Xinjiang Kazak Nationality

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Fang Yang

2017-01-01

Full Text Available Esophageal cancer is one of the fastest rising types of cancers in China. The Kazak nationality is the highest-risk group in Xinjiang. In this work, an effective computer-aided diagnostic system is developed to assist physicians in interpreting digital X-ray image features and improving the quality of diagnosis. The modules of the proposed system include image preprocessing, feature extraction, feature selection, image classification, and performance evaluation. 300 original esophageal X-ray images were resized to a region of interest and then enhanced by the median filter and histogram equalization method. 37 features from textural, frequency, and complexity domains were extracted. Both sequential forward selection and principal component analysis methods were employed to select the discriminative features for classification. Then, support vector machine and K-nearest neighbors were applied to classify the esophageal cancer images with respect to their specific types. The classification performance was evaluated in terms of the area under the receiver operating characteristic curve, accuracy, precision, and recall, respectively. Experimental results show that the classification performance of the proposed system outperforms the conventional visual inspection approaches in terms of diagnostic quality and processing time. Therefore, the proposed computer-aided diagnostic system is promising for the diagnostics of esophageal cancer.

The rise of a novel classification system for endometrial carcinoma; integration of molecular subclasses.

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McAlpine, Jessica; Leon-Castillo, Alicia; Bosse, Tjalling

2018-04-01

Endometrial cancer is a clinically heterogeneous disease and it is becoming increasingly clear that this heterogeneity may be a function of the diversity of the underlying molecular alterations. Recent large-scale genomic studies have revealed that endometrial cancer can be divided into at least four distinct molecular subtypes, with well-described underlying genomic aberrations. These subtypes can be reliably delineated and carry significant prognostic as well as predictive information; embracing and incorporating them into clinical practice is thus attractive. The road towards the integration of molecular features into current classification systems is not without obstacles. Collaborative studies engaging research teams from across the world are working to define pragmatic assays, improve risk stratification systems by combining molecular features and traditional clinicopathological parameters, and determine how molecular classification can be optimally utilized to direct patient care. Pathologists and clinicians caring for women with endometrial cancer need to engage with and understand the possibilities and limitations of this new approach, because integration of molecular classification of endometrial cancers is anticipated to become an essential part of gynaecological pathology practice. This review will describe the challenges in current systems of endometrial carcinoma classification, the evolution of new molecular technologies that define prognostically distinct molecular subtypes, and potential applications of molecular classification as a step towards precision medicine and refining care for individuals with the most common gynaecological cancer in the developed world. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Cytotoxic activity of kenaf (Hibiscus cannabinus L.) seed extract and oil against human cancer cell lines

Science.gov (United States)

Wong, Yu Hua; Tan, Wai Yan; Tan, Chin Ping; Long, Kamariah; Nyam, Kar Lin

2014-01-01

Objective To examine the cytotoxic properties of both the kenaf (Hibiscus cannabinus L.) seed extract and kenaf seed oil on human cervical cancer, human breast cancer, human colon cancer and human lung cancer cell lines. Methods The in vitro cytotoxic activity of the kenaf (Hibiscus cannabinus L.) seed extract and kenaf seed oil on human cancer cell lines was evaluated by using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide and sulforhodamine B assays. Cell morphological changes were observed by using an inverted light microscope. Results The kenaf seed extract (KSE) exhibited a lower IC50 than kenaf seed oil (KSO) in all of the cancer cell lines. Morphological alterations in the cell lines after KSE and KSO treatment were observed. KSE and KSO possessed effective cytotoxic activities against all the cell lines been selected. Conclusions KSE and KSO could be potential sources of natural anti-cancer agents. Further investigations on using kenaf seeds for anti-proliferative properties are warranted. PMID:25183141

A joint model of persistent human papillomavirus infection and cervical cancer risk: Implications for cervical cancer screening

OpenAIRE

Katki, Hormuzd A.; Cheung, Li C.; Fetterman, Barbara; Castle, Philip E.; Sundaram, Rajeshwari

2015-01-01

New cervical cancer screening guidelines in the US and many European countries recommend that women get tested for human papillomavirus (HPV). To inform decisions about screening intervals, we calculate the increase in precancer/cancer risk per year of continued HPV infection. However, both time to onset of precancer/cancer and time to HPV clearance are interval-censored, and onset of precancer/cancer strongly informatively censors HPV clearance. We analyze this bivariate informatively interv...

Muscarinic receptor agonists stimulate matrix metalloproteinase 1-dependent invasion of human colon cancer cells

International Nuclear Information System (INIS)

Raufman, Jean-Pierre; Cheng, Kunrong; Saxena, Neeraj; Chahdi, Ahmed; Belo, Angelica; Khurana, Sandeep; Xie, Guofeng

2011-01-01

Highlights: ► Muscarinic receptor agonists stimulated robust human colon cancer cell invasion. ► Anti-matrix metalloproteinase1 antibody pre-treatment blocks cell invasion. ► Bile acids stimulate MMP1 expression, cell migration and MMP1-dependent invasion. -- Abstract: Mammalian matrix metalloproteinases (MMPs) which degrade extracellular matrix facilitate colon cancer cell invasion into the bloodstream and extra-colonic tissues; in particular, MMP1 expression correlates strongly with advanced colon cancer stage, hematogenous metastasis and poor prognosis. Likewise, muscarinic receptor signaling plays an important role in colon cancer; muscarinic receptors are over-expressed in colon cancer compared to normal colon epithelial cells. Muscarinic receptor activation stimulates proliferation, migration and invasion of human colon cancer cells. In mouse intestinal neoplasia models genetic ablation of muscarinic receptors attenuates carcinogenesis. In the present work, we sought to link these observations by showing that MMP1 expression and activation plays a mechanistic role in muscarinic receptor agonist-induced colon cancer cell invasion. We show that acetylcholine, which robustly increases MMP1 expression, stimulates invasion of HT29 and H508 human colon cancer cells into human umbilical vein endothelial cell monolayers – this was abolished by pre-incubation with atropine, a non-selective muscarinic receptor inhibitor, and by pre-incubation with anti-MMP1 neutralizing antibody. Similar results were obtained using a Matrigel chamber assay and deoxycholyltaurine (DCT), an amidated dihydroxy bile acid associated with colon neoplasia in animal models and humans, and previously shown to interact functionally with muscarinic receptors. DCT treatment of human colon cancer cells resulted in time-dependent, 10-fold increased MMP1 expression, and DCT-induced cell invasion was also blocked by pre-treatment with anti-MMP1 antibody. This study contributes to understanding

Muscarinic receptor agonists stimulate matrix metalloproteinase 1-dependent invasion of human colon cancer cells

Energy Technology Data Exchange (ETDEWEB)

Raufman, Jean-Pierre, E-mail: jraufman@medicine.umaryland.edu [Division of Gastroenterology and Hepatology, University of Maryland School of Medicine, Baltimore, MD (United States); Cheng, Kunrong; Saxena, Neeraj; Chahdi, Ahmed; Belo, Angelica; Khurana, Sandeep; Xie, Guofeng [Division of Gastroenterology and Hepatology, University of Maryland School of Medicine, Baltimore, MD (United States)

2011-11-18

Highlights: Black-Right-Pointing-Pointer Muscarinic receptor agonists stimulated robust human colon cancer cell invasion. Black-Right-Pointing-Pointer Anti-matrix metalloproteinase1 antibody pre-treatment blocks cell invasion. Black-Right-Pointing-Pointer Bile acids stimulate MMP1 expression, cell migration and MMP1-dependent invasion. -- Abstract: Mammalian matrix metalloproteinases (MMPs) which degrade extracellular matrix facilitate colon cancer cell invasion into the bloodstream and extra-colonic tissues; in particular, MMP1 expression correlates strongly with advanced colon cancer stage, hematogenous metastasis and poor prognosis. Likewise, muscarinic receptor signaling plays an important role in colon cancer; muscarinic receptors are over-expressed in colon cancer compared to normal colon epithelial cells. Muscarinic receptor activation stimulates proliferation, migration and invasion of human colon cancer cells. In mouse intestinal neoplasia models genetic ablation of muscarinic receptors attenuates carcinogenesis. In the present work, we sought to link these observations by showing that MMP1 expression and activation plays a mechanistic role in muscarinic receptor agonist-induced colon cancer cell invasion. We show that acetylcholine, which robustly increases MMP1 expression, stimulates invasion of HT29 and H508 human colon cancer cells into human umbilical vein endothelial cell monolayers - this was abolished by pre-incubation with atropine, a non-selective muscarinic receptor inhibitor, and by pre-incubation with anti-MMP1 neutralizing antibody. Similar results were obtained using a Matrigel chamber assay and deoxycholyltaurine (DCT), an amidated dihydroxy bile acid associated with colon neoplasia in animal models and humans, and previously shown to interact functionally with muscarinic receptors. DCT treatment of human colon cancer cells resulted in time-dependent, 10-fold increased MMP1 expression, and DCT-induced cell invasion was also blocked by pre

AR Signaling in Human Malignancies: Prostate Cancer and Beyond.

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Antonarakis, Emmanuel S

2018-01-18

The notion that androgens and androgen receptor (AR) signaling are the hallmarks of prostate cancer oncogenesis and disease progression is generally well accepted. What is more poorly understood is the role of AR signaling in other human malignancies. This special issue of Cancers initially reviews the role of AR in advanced prostate cancer, and then explores the potential importance of AR signaling in other epithelial malignancies. The first few articles focus on the use of novel AR-targeting therapies in castration-resistant prostate cancer and the mechanisms of resistance to novel antiandrogens, and they also outline the interaction between AR and other cellular pathways, including PI3 kinase signaling, transcriptional regulation, angiogenesis, stromal factors, Wnt signaling, and epigenetic regulation in prostate cancer. The next several articles review the possible role of androgens and AR signaling in breast cancer, bladder cancer, salivary gland cancer, and hepatocellular carcinoma, as well as the potential treatment implications of using antiandrogen therapies in these non-prostatic malignancies.

Dihydrochalcone Compounds Isolated from Crabapple Leaves Showed Anticancer Effects on Human Cancer Cell Lines

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Xiaoxiao Qin

2015-11-01

Full Text Available Seven dihydrochalcone compounds were isolated from the leaves of Malus crabapples, cv. “Radiant”, and their chemical structures were elucidated by UV, IR, ESI-MS, 1H-NMR and 13C-NMR analyses. These compounds, which include trilobatin (A1, phloretin (A2, 3-hydroxyphloretin (A3, phloretin rutinoside (A4, phlorizin (A5, 6′′-O-coumaroyl-4′-O-glucopyranosylphloretin (A6, and 3′′′-methoxy-6′′-O-feruloy-4′-O-glucopyranosyl-phloretin (A7, all belong to the phloretin class and its derivatives. Compounds A6 and A7 are two new rare dihydrochalcone compounds. The results of a MTT cancer cell growth inhibition assay demonstrated that phloretin and these derivatives showed significant positive anticancer activities against several human cancer cell lines, including the A549 human lung cancer cell line, Bel 7402 liver cancer cell line, HepG2 human ileocecal cancer cell line, and HT-29 human colon cancer cell line. A7 had significant effects on all cancer cell lines, suggesting potential applications for phloretin and its derivatives. Adding a methoxyl group to phloretin dramatically increases phloretin’s anticancer activity.

Apoptosis induced by GanoPoly in human gastric cancer cell line ...

African Journals Online (AJOL)

use

2011-12-12

Dec 12, 2011 ... ... cancer's active therapy. Key words: Apoptosis, polysaccharide, human gastric cancer cells. ... The active components of polysaccharides are all glucans, which have a ... for the treatment of alleviated fatigue, night sweating,.

Objects Classification by Learning-Based Visual Saliency Model and Convolutional Neural Network.

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Li, Na; Zhao, Xinbo; Yang, Yongjia; Zou, Xiaochun

2016-01-01

Humans can easily classify different kinds of objects whereas it is quite difficult for computers. As a hot and difficult problem, objects classification has been receiving extensive interests with broad prospects. Inspired by neuroscience, deep learning concept is proposed. Convolutional neural network (CNN) as one of the methods of deep learning can be used to solve classification problem. But most of deep learning methods, including CNN, all ignore the human visual information processing mechanism when a person is classifying objects. Therefore, in this paper, inspiring the completed processing that humans classify different kinds of objects, we bring forth a new classification method which combines visual attention model and CNN. Firstly, we use the visual attention model to simulate the processing of human visual selection mechanism. Secondly, we use CNN to simulate the processing of how humans select features and extract the local features of those selected areas. Finally, not only does our classification method depend on those local features, but also it adds the human semantic features to classify objects. Our classification method has apparently advantages in biology. Experimental results demonstrated that our method made the efficiency of classification improve significantly.

Detection and Classification of Human Body Odor Using an Electronic Nose

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Teerakiat Kerdcharoen

2009-09-01

Full Text Available An electronic nose (E-nose has been designed and equipped with software that can detect and classify human armpit body odor. An array of metal oxide sensors was used for detecting volatile organic compounds. The measurement circuit employs a voltage divider resistor to measure the sensitivity of each sensor. This E-nose was controlled by in-house developed software through a portable USB data acquisition card with a principle component analysis (PCA algorithm implemented for pattern recognition and classification. Because gas sensor sensitivity in the detection of armpit odor samples is affected by humidity, we propose a new method and algorithms combining hardware/software for the correction of the humidity noise. After the humidity correction, the E-nose showed the capability of detecting human body odor and distinguishing the body odors from two persons in a relative manner. The E-nose is still able to recognize people, even after application of deodorant. In conclusion, this is the first report of the application of an E-nose for armpit odor recognition.

Detection and classification of human body odor using an electronic nose.

Science.gov (United States)

Wongchoosuk, Chatchawal; Lutz, Mario; Kerdcharoen, Teerakiat

2009-01-01

An electronic nose (E-nose) has been designed and equipped with software that can detect and classify human armpit body odor. An array of metal oxide sensors was used for detecting volatile organic compounds. The measurement circuit employs a voltage divider resistor to measure the sensitivity of each sensor. This E-nose was controlled by in-house developed software through a portable USB data acquisition card with a principle component analysis (PCA) algorithm implemented for pattern recognition and classification. Because gas sensor sensitivity in the detection of armpit odor samples is affected by humidity, we propose a new method and algorithms combining hardware/software for the correction of the humidity noise. After the humidity correction, the E-nose showed the capability of detecting human body odor and distinguishing the body odors from two persons in a relative manner. The E-nose is still able to recognize people, even after application of deodorant. In conclusion, this is the first report of the application of an E-nose for armpit odor recognition.

Imaging Proteolysis by Living Human Breast Cancer Cells

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Mansoureh Sameni

2000-01-01

Full Text Available Malignant progression is accompanied by degradation of extracellular matrix proteins. Here we describe a novel confocal assay in which we can observe proteolysis by living human breast cancer cells (BT20 and BT549 through the use of quenchedfluorescent protein substrates. Degradation thus was imaged, by confocal optical sectioning, as an accumulation of fluorescent products. With the BT20 cells, fluorescence was localized to pericellular focal areas that coincide with pits in the underlying matrix. In contrast, fluorescence was localized to intracellular vesicles in the BT549 cells, vesicles that also label for lysosomal markers. Neither intracellular nor pericellular fluorescence was observed in the BT549 cells in the presence of cytochalasin B, suggesting that degradation occurred intracellularly and was dependent on endocytic uptake of substrate. In the presence of a cathepsin 13-selective cysteine protease inhibitor, intracellular fluorescence was decreased ~90% and pericellular fluorescence decreased 67% to 96%, depending on the protein substrate. Matrix metallo protease inhibitors reduced pericellular fluorescence ~50%, i.e., comparably to a serine and a broad spectrum cysteine protease inhibitor. Our results suggest that: 1 a proteolytic cascade participates in pericellular digestion of matrix proteins by living human breast cancer cells, and 2 the cysteine protease cathepsin B participates in both pericellular and intracellular digestion of matrix proteins by living human breast cancer cells.

Degradation of endothelial basement membrane by human breast cancer cell lines

International Nuclear Information System (INIS)

Yee, C.; Shiu, R.P.

1986-01-01

During metastasis, it is believed that tumor cells destroy the basement membrane (BM) of blood vessels in order to disseminate through the circulatory system. By radioactively labeling the extracellular matrix produced by primary endothelial cells in vitro, the ability of human breast cancer cells to degrade BM components was studied. We found that T-47D, a human breast cancer line, was able to degrade significant amounts of [35S]methionine-labeled and [3H]proline-labeled BM, but not 35SO4-labeled BM. Six other tumor cell lines of human breast origin were assayed in the same manner and were found to degrade BM to varying degrees. Several non-tumor cell lines tested showed relatively little degrading activity. The use of serum-free medium greatly enhanced degradation of the BM by tumor cells, suggesting a role for naturally occurring enzyme inhibitors in the serum. Direct cell contact with the BM was required for BM degradation, suggesting that the active enzymes are cell associated. The addition of hormones implicated in the etiology of breast cancer did not significantly alter the ability of T-47D cells to degrade the BM. The use of this assay affords future studies on the mechanism of invasion and metastasis of human breast cancer

Comparing the DNA hypermethylome with gene mutations in human colorectal cancer.

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Kornel E Schuebel

2007-09-01

Full Text Available We have developed a transcriptome-wide approach to identify genes affected by promoter CpG island DNA hypermethylation and transcriptional silencing in colorectal cancer. By screening cell lines and validating tumor-specific hypermethylation in a panel of primary human colorectal cancer samples, we estimate that nearly 5% or more of all known genes may be promoter methylated in an individual tumor. When directly compared to gene mutations, we find larger numbers of genes hypermethylated in individual tumors, and a higher frequency of hypermethylation within individual genes harboring either genetic or epigenetic changes. Thus, to enumerate the full spectrum of alterations in the human cancer genome, and to facilitate the most efficacious grouping of tumors to identify cancer biomarkers and tailor therapeutic approaches, both genetic and epigenetic screens should be undertaken.

Biologic activities of recombinant human-beta-defensin-4 toward cultured human cancer cells.

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Gerashchenko, O L; Zhuravel, E V; Skachkova, O V; Khranovska, N N; Filonenko, V V; Pogrebnoy, P V; Soldatkina, M A

2013-06-01

The aim of the study was in vitro analysis of biological activity of recombinant human beta-defensin-4 (rec-hBD-4). hBD-4 cDNA was cloned into pGEX-2T vector, and recombinant plasmid was transformed into E. coli BL21(DE3) cells. To purify soluble fusion GST-hBD-4 protein, affinity chromatography was applied. Rec-hBD-4 was cleaved from the fusion protein with thrombin, and purified by reverse phase chromatography on Sep-Pack C18. Effects of rec-hBD-4 on proliferation, viability, cell cycle distribution, substrate-independent growth, and mobility of cultured human cancer cells of A431, A549, and TPC-1 lines were analyzed by direct cell counting technique, MTT assay, flow cytofluorometry, colony forming assay in semi-soft medium, and wound healing assay. Rec-hBD-4 was expressed in bacterial cells as GST-hBD-4 fusion protein, and purified by routine 3-step procedure (affine chromatography on glutathione-agarose, cleavage of fusion protein by thrombin, and reverse phase chromatography). Analysis of in vitro activity of rec-hBD-4 toward three human cancer cell lines has demonstrated that the defensin is capable to affect cell behaviour in concentration-dependent manner. In 1-100 nM concentrations rec-hBD-4 significantly stimulates cancer cell proliferation and viability, and promotes cell cycle progression through G2/M checkpoint, greatly enhances colony-forming activity and mobility of the cells. Treatment of the cells with 500 nM of rec-hBD-4 resulted in opposite effects: significant suppression of cell proliferation and viability, blockage of cell cycle in G1/S checkpoint, significant inhibition of cell migration and colony forming activity. Recombinant human beta-defensin-4 is biologically active peptide capable to cause oppositely directed effects toward biologic features of cancer cells in vitro dependent on its concentration.

Prevalence of Telomerase Activity in Human Cancer

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Chi-Hau Chen

2011-05-01

Full Text Available Telomerase activity has been measured in a wide variety of cancerous and non-cancerous tissue types, and the vast majority of clinical studies have shown a direct correlation between it and the presence of cancerous cells. Telomerase plays a key role in cellular immortality and tumorigenesis. Telomerase is activated in 80–90% of human carcinomas, but not in normal somatic cells, therefore, its detection holds promise as a diagnostic marker for cancer. Measurable levels of telomerase have been detected in malignant cells from various samples: tissue from gestational trophoblastic neoplasms; squamous carcinoma cells from oral rinses; lung carcinoma cells from bronchial washings; colorectal carcinoma cells from colonic luminal washings; bladder carcinoma cells from urine or bladder washings; and breast carcinoma or thyroid cancer cells from fine needle aspirations. Such clinical tests for telomerase can be useful as non-invasive and cost-effective methods for early detection and monitoring of cancer. In addition, telomerase activity has been shown to correlate with poor clinical outcome in late-stage diseases such as non-small cell lung cancer, colorectal cancer, and soft tissue sarcomas. In such cases, testing for telomerase activity can be used to identify patients with a poor prognosis and to select those who might benefit from adjuvant treatment. Our review of the latest medical advances in this field reveals that telomerase holds great promise as a biomarker for early cancer detection and monitoring, and has considerable potential as the basis for developing new anticancer therapies.

Human papilloma viruses (HPV and breast cancer.

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James Sutherland Lawson

2015-12-01

Full Text Available Purpose: Human papillomaviruses (HPV may have a role in some breast cancers. The purpose of this study is to fill important gaps in the evidence. These gaps are: (i confirmation of the presence of high risk for cancer HPVs in breast cancers, (ii evidence of HPV infections in benign breast tissues prior to the development of HPV positive breast cancer in the same patients, (iii evidence that HPVs are biologically active and not harmless passengers in breast cancer.Methods: RNA-seq data from The Cancer Genome Atlas (TCGA was used to identify HPV RNA sequences in breast cancers. We also conducted a retrospective cohort study based on polymerase chain reaction (PCR analyses to identify HPVs in archival specimens from Australian women with benign breast biopsies who later developed breast cancer. To assess whether HPVs in breast cancer were biologically active, the expression of the oncogenic protein HPV E7 was assessed by immunohistochemistry (IHC.Results: Thirty (3.5% low risk and 20 (2.3% high risk HPV types were identified in 855 breast cancers from the TCGA data base. The high risk types were HPV 18 (48%, HPV 113 (24%, HPV 16 (10%, HPV 52 (10%. Data from the PCR cohort study, indicated that HPV type 18 was the most common type identified in breast cancer specimens (55% of 40 breast cancer specimens followed by HPV 16 (13%. The same HPV type was identified in both the benign and subsequent breast cancer in 15 patients. HPV E7 proteins were identified in 72% of benign breast specimens and 59% of invasive breast cancer specimens.Conclusions: There were 4 observations of particular interest: (i confirmation by both NGS and PCR of the presence of high risk HPV gene sequences in breast cancers, (ii a correlation between high risk HPV in benign breast specimens and subsequent HPV positive breast cancer in the same patient, (iii HPVs in breast cancer are likely to be biologically active (as shown by transcription of HPV DNA to RNA plus the expression of

Comparative oncology: what dogs and other species can teach us about humans with cancer

Science.gov (United States)

Schiffman, Joshua D.; Breen, Matthew

2015-01-01

Over 1.66 million humans (approx. 500/100 000 population rate) and over 4.2 million dogs (approx. 5300/100 000 population rate) are diagnosed with cancer annually in the USA. The interdisciplinary field of comparative oncology offers a unique and strong opportunity to learn more about universal cancer risk and development through epidemiology, genetic and genomic investigations. Working across species, researchers from human and veterinary medicine can combine scientific findings to understand more quickly the origins of cancer and translate these findings to novel therapies to benefit both human and animals. This review begins with the genetic origins of canines and their advantage in cancer research. We next focus on recent findings in comparative oncology related to inherited, or genetic, risk for tumour development. We then detail the somatic, or genomic, changes within tumours and the similarities between species. The shared cancers between humans and dogs that we discuss include sarcoma (osteosarcoma, soft tissue sarcoma, histiocytic sarcoma, hemangiosarcoma), haematological malignancies (lymphoma, leukaemia), bladder cancer, intracranial neoplasms (meningioma, glioma) and melanoma. Tumour risk in other animal species is also briefly discussed. As the field of genomics advances, we predict that comparative oncology will continue to benefit both humans and the animals that live among us. PMID:26056372

Predicting decisions in human social interactions using real-time fMRI and pattern classification.

Science.gov (United States)

Hollmann, Maurice; Rieger, Jochem W; Baecke, Sebastian; Lützkendorf, Ralf; Müller, Charles; Adolf, Daniela; Bernarding, Johannes

2011-01-01

Negotiation and trade typically require a mutual interaction while simultaneously resting in uncertainty which decision the partner ultimately will make at the end of the process. Assessing already during the negotiation in which direction one's counterpart tends would provide a tremendous advantage. Recently, neuroimaging techniques combined with multivariate pattern classification of the acquired data have made it possible to discriminate subjective states of mind on the basis of their neuronal activation signature. However, to enable an online-assessment of the participant's mind state both approaches need to be extended to a real-time technique. By combining real-time functional magnetic resonance imaging (fMRI) and online pattern classification techniques, we show that it is possible to predict human behavior during social interaction before the interacting partner communicates a specific decision. Average accuracy reached approximately 70% when we predicted online the decisions of volunteers playing the ultimatum game, a well-known paradigm in economic game theory. Our results demonstrate the successful online analysis of complex emotional and cognitive states using real-time fMRI, which will enable a major breakthrough for social fMRI by providing information about mental states of partners already during the mutual interaction. Interestingly, an additional whole brain classification across subjects confirmed the online results: anterior insula, ventral striatum, and lateral orbitofrontal cortex, known to act in emotional self-regulation and reward processing for adjustment of behavior, appeared to be strong determinants of later overt behavior in the ultimatum game. Using whole brain classification we were also able to discriminate between brain processes related to subjective emotional and motivational states and brain processes related to the evaluation of objective financial incentives.

Predicting decisions in human social interactions using real-time fMRI and pattern classification.

Directory of Open Access Journals (Sweden)

Maurice Hollmann

Full Text Available Negotiation and trade typically require a mutual interaction while simultaneously resting in uncertainty which decision the partner ultimately will make at the end of the process. Assessing already during the negotiation in which direction one's counterpart tends would provide a tremendous advantage. Recently, neuroimaging techniques combined with multivariate pattern classification of the acquired data have made it possible to discriminate subjective states of mind on the basis of their neuronal activation signature. However, to enable an online-assessment of the participant's mind state both approaches need to be extended to a real-time technique. By combining real-time functional magnetic resonance imaging (fMRI and online pattern classification techniques, we show that it is possible to predict human behavior during social interaction before the interacting partner communicates a specific decision. Average accuracy reached approximately 70% when we predicted online the decisions of volunteers playing the ultimatum game, a well-known paradigm in economic game theory. Our results demonstrate the successful online analysis of complex emotional and cognitive states using real-time fMRI, which will enable a major breakthrough for social fMRI by providing information about mental states of partners already during the mutual interaction. Interestingly, an additional whole brain classification across subjects confirmed the online results: anterior insula, ventral striatum, and lateral orbitofrontal cortex, known to act in emotional self-regulation and reward processing for adjustment of behavior, appeared to be strong determinants of later overt behavior in the ultimatum game. Using whole brain classification we were also able to discriminate between brain processes related to subjective emotional and motivational states and brain processes related to the evaluation of objective financial incentives.

Immune and Inflammatory Cell Composition of Human Lung Cancer Stroma.

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G-Andre Banat

Full Text Available Recent studies indicate that the abnormal microenvironment of tumors may play a critical role in carcinogenesis, including lung cancer. We comprehensively assessed the number of stromal cells, especially immune/inflammatory cells, in lung cancer and evaluated their infiltration in cancers of different stages, types and metastatic characteristics potential. Immunohistochemical analysis of lung cancer tissue arrays containing normal and lung cancer sections was performed. This analysis was combined with cyto-/histomorphological assessment and quantification of cells to classify/subclassify tumors accurately and to perform a high throughput analysis of stromal cell composition in different types of lung cancer. In human lung cancer sections we observed a significant elevation/infiltration of total-T lymphocytes (CD3+, cytotoxic-T cells (CD8+, T-helper cells (CD4+, B cells (CD20+, macrophages (CD68+, mast cells (CD117+, mononuclear cells (CD11c+, plasma cells, activated-T cells (MUM1+, B cells, myeloid cells (PD1+ and neutrophilic granulocytes (myeloperoxidase+ compared with healthy donor specimens. We observed all of these immune cell markers in different types of lung cancers including squamous cell carcinoma, adenocarcinoma, adenosquamous cell carcinoma, small cell carcinoma, papillary adenocarcinoma, metastatic adenocarcinoma, and bronchioloalveolar carcinoma. The numbers of all tumor-associated immune cells (except MUM1+ cells in stage III cancer specimens was significantly greater than those in stage I samples. We observed substantial stage-dependent immune cell infiltration in human lung tumors suggesting that the tumor microenvironment plays a critical role during lung carcinogenesis. Strategies for therapeutic interference with lung cancer microenvironment should consider the complexity of its immune cell composition.

Urinary acylcarnitines are altered in human kidney cancer.

Science.gov (United States)

Ganti, Sheila; Taylor, Sandra L; Kim, Kyoungmi; Hoppel, Charles L; Guo, Lining; Yang, Joy; Evans, Christopher; Weiss, Robert H

2012-06-15

Kidney cancer often diagnosed at late stages when treatment options are severely limited. Thus, greater understanding of tumor metabolism leading ultimately to novel approaches to diagnosis is needed. Our laboratory has been utilizing metabolomics to evaluate compounds appearing in kidney cancer patients' biofluids at concentrations different from control patients. Here, we collected urine samples from kidney cancer patients and analyzed them by chromatography coupled to mass spectrometry. Once normalized to control for urinary concentration, samples were analyzed by two independent laboratories. After technical validation, we now show differential urinary concentrations of several acylcarnitines as a function of both cancer status and kidney cancer grade, with most acylcarnitines being increased in the urine of cancer patients and in those patients with high cancer grades. This finding was validated in a mouse xenograft model of human kidney cancer. Biological validation shows carbon chain length-dependent effects of the acylcarnitines on cytotoxicity in vitro, and higher chain length acylcarnitines demonstrated inhibitory effects on NF-κB activation, suggesting an immune modulatory effect of these compounds. Thus, acylcarnitines in the kidney cancer urine may reflect alterations in metabolism, cell component synthesis and/or immune surveillance, and may help explain the profound chemotherapy resistance seen with this cancer. This study shows for the first time the value of a novel class of metabolites which may lead to new therapeutic approaches for cancer and may prove useful in cancer biomarker studies. Furthermore, these findings open up a new area of investigation into the metabolic basis of kidney cancer. Copyright © 2011 UICC.

Breast Cancer Cell Colonization of the Human Bone Marrow Adipose Tissue Niche.

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Templeton, Zach S; Lie, Wen-Rong; Wang, Weiqi; Rosenberg-Hasson, Yael; Alluri, Rajiv V; Tamaresis, John S; Bachmann, Michael H; Lee, Kitty; Maloney, William J; Contag, Christopher H; King, Bonnie L

2015-12-01

Bone is a preferred site of breast cancer metastasis, suggesting the presence of tissue-specific features that attract and promote the outgrowth of breast cancer cells. We sought to identify parameters of human bone tissue associated with breast cancer cell osteotropism and colonization in the metastatic niche. Migration and colonization patterns of MDA-MB-231-fLuc-EGFP (luciferase-enhanced green fluorescence protein) and MCF-7-fLuc-EGFP breast cancer cells were studied in co-culture with cancellous bone tissue fragments isolated from 14 hip arthroplasties. Breast cancer cell migration into tissues and toward tissue-conditioned medium was measured in Transwell migration chambers using bioluminescence imaging and analyzed as a function of secreted factors measured by multiplex immunoassay. Patterns of breast cancer cell colonization were evaluated with fluorescence microscopy and immunohistochemistry. Enhanced MDA-MB-231-fLuc-EGFP breast cancer cell migration to bone-conditioned versus control medium was observed in 12/14 specimens (P = .0014) and correlated significantly with increasing levels of the adipokines/cytokines leptin (P = .006) and IL-1β (P = .001) in univariate and multivariate regression analyses. Fluorescence microscopy and immunohistochemistry of fragments underscored the extreme adiposity of adult human bone tissues and revealed extensive breast cancer cell colonization within the marrow adipose tissue compartment. Our results show that breast cancer cells migrate to human bone tissue-conditioned medium in association with increasing levels of leptin and IL-1β, and colonize the bone marrow adipose tissue compartment of cultured fragments. Bone marrow adipose tissue and its molecular signals may be important but understudied components of the breast cancer metastatic niche. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Regulatory T Cells in Human Ovarian Cancer

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Dong-Jun Peng

2012-01-01

Full Text Available Multiple layers of suppressive components including regulatory T (TReg cells, suppressive antigen-presenting cells, and inhibitory cytokines form suppressive networks in the ovarian cancer microenvironment. It has been demonstrated that as a major suppressive element, TReg cells infiltrate tumor, interact with several types of immune cells, and mediate immune suppression through different molecular and cellular mechanisms. In this paper, we focus on human ovarian cancer and will discuss the nature of TReg cells including their subsets, trafficking, expansion, and function. We will briefly review the development of manipulation of TReg cells in preclinical and clinical settings.

Molecular concept in human oral cancer.

Science.gov (United States)

Krishna, Akhilesh; Singh, Shraddha; Kumar, Vijay; Pal, U S

2015-01-01

The incidence of oral cancer remains high in both Asian and Western countries. Several risk factors associated with development of oral cancer are now well-known, including tobacco chewing, smoking, and alcohol consumption. Cancerous risk factors may cause many genetic events through chromosomal alteration or mutations in genetic material and lead to progression and development of oral cancer through histological progress, carcinogenesis. Oral squamous carcinogenesis is a multistep process in which multiple genetic events occur that alter the normal functions of proto-oncogenes/oncogenes and tumor suppressor genes. Furthermore, these gene alterations can deregulate the normal activity such as increase in the production of growth factors (transforming growth factor-α [TGF-α], TGF-β, platelet-derived growth factor, etc.) or numbers of cell surface receptors (epidermal growth factor receptor, G-protein-coupled receptor, etc.), enhanced intracellular messenger signaling and mutated production of transcription factors (ras gene family, c-myc gene) which results disturb to tightly regulated signaling pathways of normal cell. Several oncogenes and tumor suppressor genes have been implicated in oral cancer especially cyclin family, ras, PRAD-1, cyclin-dependent kinase inhibitors, p53 and RB1. Viral infections, particularly with oncogenic human papilloma virus subtype (16 and 18) and Epstein-Barr virus have tumorigenic effect on oral epithelia. Worldwide, this is an urgent need to initiate oral cancer research programs at molecular and genetic level which investigates the causes of genetic and molecular defect, responsible for malignancy. This approach may lead to development of target dependent tumor-specific drugs and appropriate gene therapy.

Polarimetry based partial least square classification of ex vivo healthy and basal cell carcinoma human skin tissues.

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Ahmad, Iftikhar; Ahmad, Manzoor; Khan, Karim; Ikram, Masroor

2016-06-01

Optical polarimetry was employed for assessment of ex vivo healthy and basal cell carcinoma (BCC) tissue samples from human skin. Polarimetric analyses revealed that depolarization and retardance for healthy tissue group were significantly higher (ppolarimetry together with PLS statistics hold promise for automated pathology classification. Copyright © 2016 Elsevier B.V. All rights reserved.

Using rule-based machine learning for candidate disease gene prioritization and sample classification of cancer gene expression data.

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Enrico Glaab

Full Text Available Microarray data analysis has been shown to provide an effective tool for studying cancer and genetic diseases. Although classical machine learning techniques have successfully been applied to find informative genes and to predict class labels for new samples, common restrictions of microarray analysis such as small sample sizes, a large attribute space and high noise levels still limit its scientific and clinical applications. Increasing the interpretability of prediction models while retaining a high accuracy would help to exploit the information content in microarray data more effectively. For this purpose, we evaluate our rule-based evolutionary machine learning systems, BioHEL and GAssist, on three public microarray cancer datasets, obtaining simple rule-based models for sample classification. A comparison with other benchmark microarray sample classifiers based on three diverse feature selection algorithms suggests that these evolutionary learning techniques can compete with state-of-the-art methods like support vector machines. The obtained models reach accuracies above 90% in two-level external cross-validation, with the added value of facilitating interpretation by using only combinations of simple if-then-else rules. As a further benefit, a literature mining analysis reveals that prioritizations of informative genes extracted from BioHEL's classification rule sets can outperform gene rankings obtained from a conventional ensemble feature selection in terms of the pointwise mutual information between relevant disease terms and the standardized names of top-ranked genes.

Clonogenic growth of human breast cancer cells co-cultured in direct contact with serum-activated fibroblasts

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Samoszuk, Michael; Tan, Jenny; Chorn, Guillaume

2005-01-01

Accumulating evidence suggests that fibroblasts play a pivotal role in promoting the growth of breast cancer cells. The objective of the present study was to characterize and validate an in vitro model of the interaction between small numbers of human breast cancer cells and human fibroblasts. We measured the clonogenic growth of small numbers of human breast cancer cells co-cultured in direct contact with serum-activated, normal human fibroblasts. Using DNA microarrays, we also characterized the gene expression profile of the serum-activated fibroblasts. In order to validate the in vivo relevance of our experiments, we then analyzed clinical samples of metastatic breast cancer for the presence of myofibroblasts expressing α-smooth muscle actin. Clonogenic growth of human breast cancer cells obtained directly from in situ and invasive tumors was dramatically and consistently enhanced when the tumor cells were co-cultured in direct contact with serum-activated fibroblasts. This effect was abolished when the cells were co-cultured in transwells separated by permeable inserts. The fibroblasts in our experimental model exhibited a gene expression signature characteristic of 'serum response' (i.e. myofibroblasts). Immunostaining of human samples of metastatic breast cancer tissue confirmed that myofibroblasts are in direct contact with breast cancer cells. Serum-activated fibroblasts promote the clonogenic growth of human breast cancer cells in vitro through a mechanism that involves direct physical contact between the cells. This model shares many important molecular and phenotypic similarities with the fibroblasts that are naturally found in breast cancers

AAPT Diagnostic Criteria for Chronic Cancer Pain Conditions

OpenAIRE

Paice, Judith A.; Mulvey, Matt; Bennett, Michael; Dougherty, Patrick M.; Farrar, John T.; Mantyh, Patrick W.; Miaskowski, Christine; Schmidt, Brian; Smith, Thomas J.

2016-01-01

Chronic cancer pain is a serious complication of malignancy or its treatment. Currently, no comprehensive, universally accepted cancer pain classification system exists. Clarity in classification of common cancer pain syndromes would improve clinical assessment and management. Moreover, an evidence-based taxonomy would enhance cancer pain research efforts by providing consistent diagnostic criteria, ensuring comparability across clinical trials. As part of a collaborative effort between the A...

Multiclass classification for skin cancer profiling based on the integration of heterogeneous gene expression series.

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Gálvez, Juan Manuel; Castillo, Daniel; Herrera, Luis Javier; San Román, Belén; Valenzuela, Olga; Ortuño, Francisco Manuel; Rojas, Ignacio

2018-01-01

)-based classification including feature ranking was performed. The accuracy attained exceeded the 92% in overall recognition of the 7 different cancer-related skin states. The proposed integration scheme is expected to allow the co-integration with other state-of-the-art technologies such as RNA-seq.

Gallic acid reduces cell viability, proliferation, invasion and angiogenesis in human cervical cancer cells

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ZHAO, BING; HU, MENGCAI

2013-01-01

Gallic acid is a trihydroxybenzoic acid, also known as 3,4,5-trihydroxybenzoic acid, which is present in plants worldwide, including Chinese medicinal herbs. Gallic acid has been shown to have cytotoxic effects in certain cancer cells, without damaging normal cells. The objective of the present study was to determine whether gallic acid is able to inhibit human cervical cancer cell viability, proliferation and invasion and suppress cervical cancer cell-mediated angiogenesis. Treatment of HeLa and HTB-35 human cancer cells with gallic acid decreased cell viability in a dose-dependent manner. BrdU proliferation and tube formation assays indicated that gallic acid significantly decreased human cervical cancer cell proliferation and tube formation in human umbilical vein endothelial cells, respectively. Additionally, gallic acid decreased HeLa and HTB-35 cell invasion in vitro. Western blot analysis demonstrated that the expression of ADAM17, EGFR, p-Akt and p-Erk was suppressed by gallic acid in the HeLa and HTB-35 cell lines. These data indicate that the suppression of ADAM17 and the downregulation of the EGFR, Akt/p-Akt and Erk/p-Erk signaling pathways may contribute to the suppression of cancer progression by Gallic acid. Gallic acid may be a valuable candidate for the treatment of cervical cancer. PMID:24843386