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    467 results match your criteria Cancer Informatics [Journal]

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    Nonmonotonic Pathway Gene Expression Analysis Reveals Oncogenic Role of p27/Kip1 at Intermediate Dose.
    Cancer Inform 2017 13;16:1176935117740132. Epub 2017 Nov 13.
    Department of Computer Science, New Mexico State University, Las Cruces, NM, USA.
    The mechanistic basis by which the level of p27(Kip1) expression influences tumor aggressiveness and patient mortality remains unclear. To elucidate the competing tumor-suppressing and oncogenic effects of p27(Kip1) on gene expression in tumors, we analyzed the transcriptomes of squamous cell papilloma derived from Cdkn1b nullizygous, heterozygous, and wild-type mice. We developed a novel functional pathway analysis method capable of testing directional and nonmonotonic dose response. Read More

    Gene-Set Reduction for Analysis of Major and Minor Gleason Scores Based on Differential Gene-Set Expressions and Biological Pathways in Prostate Cancer.
    Cancer Inform 2017 11;16:1176935117730016. Epub 2017 Sep 11.
    Indian Institute of Public Health, Public Health Foundation of India, Hyderabad, India.
    The Gleason score (GS) plays an important role in prostate cancer detection and treatment. It is calculated based on a sum between its major and minor components, each ranging from 1 to 5, assigned after examination of sample cells taken from each side of the prostate gland during biopsy. A total GS of at least 7 is associated with more aggressive prostate cancer. Read More

    xSyn: A Software Tool for Identifying Sophisticated 3-Way Interactions From Cancer Expression Data.
    Cancer Inform 2017 28;16:1176935117728516. Epub 2017 Aug 28.
    BDX Research & Consulting LLC, Fairfax, VA, USA.
    Background: Constructing gene co-expression networks from cancer expression data is important for investigating the genetic mechanisms underlying cancer. However, correlation coefficients or linear regression models are not able to model sophisticated relationships among gene expression profiles. Here, we address the 3-way interaction that 2 genes' expression levels are clustered in different space locations under the control of a third gene's expression levels. Read More

    Adaptive Multiview Nonnegative Matrix Factorization Algorithm for Integration of Multimodal Biomedical Data.
    Cancer Inform 2017 18;16:1176935117725727. Epub 2017 Aug 18.
    Institute for Systems Genetics and Department of Biochemistry and Molecular Pharmacology, NYU School of Medicine, New York, NY, USA.
    The amounts and types of available multimodal tumor data are rapidly increasing, and their integration is critical for fully understanding the underlying cancer biology and personalizing treatment. However, the development of methods for effectively integrating multimodal data in a principled manner is lagging behind our ability to generate the data. In this article, we introduce an extension to a multiview nonnegative matrix factorization algorithm (NNMF) for dimensionality reduction and integration of heterogeneous data types and compare the predictive modeling performance of the method on unimodal and multimodal data. Read More

    Immuno-Oncology Integrative Networks: Elucidating the Influences of Osteosarcoma Phenotypes.
    Cancer Inform 2017 26;16:1176935117721691. Epub 2017 Jul 26.
    Center for Computational Science, University of Miami, Miami, FL, USA.
    In vivo and in vitro functional phenotyping characterization was recently obtained with reference to an experimental pan-cancer study of 22 osteosarcoma (OS) cell lines. Here, differentially expressed gene (DEG) profiles were recomputed from the publicly available data to conduct network inference on the immune system regulatory activity across the characterized OS phenotypes. Based on such DEG profiles, and for each phenotype that was analyzed, we obtained coexpression networks and bio-annotations for them. Read More

    Prediction With Dimension Reduction of Multiple Molecular Data Sources for Patient Survival.
    Cancer Inform 2017 11;16:1176935117718517. Epub 2017 Jul 11.
    Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA.
    Predictive modeling from high-dimensional genomic data is often preceded by a dimension reduction step, such as principal component analysis (PCA). However, the application of PCA is not straightforward for multisource data, wherein multiple sources of 'omics data measure different but related biological components. In this article, we use recent advances in the dimension reduction of multisource data for predictive modeling. Read More

    An Assessment of Database-Validated microRNA Target Genes in Normal Colonic Mucosa: Implications for Pathway Analysis.
    Cancer Inform 2017 23;16:1176935117716405. Epub 2017 Jun 23.
    Department of Internal Medicine, The University of Utah, Salt Lake City, UT, USA.
    Background: Determination of functional pathways regulated by microRNAs (miRNAs), while an essential step in developing therapeutics, is challenging. Some miRNAs have been studied extensively; others have limited information. In this study, we focus on 254 miRNAs previously identified as being associated with colorectal cancer and their database-identified validated target genes. Read More

    A Software Application for Mining and Presenting Relevant Cancer Clinical Trials per Cancer Mutation.
    Cancer Inform 2017 22;16:1176935117711940. Epub 2017 Jun 22.
    Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
    ClinicalTrials.org is a popular portal which physicians use to find clinical trials for their patients. However, the current setup of ClinicalTrials. Read More

    The Model-Based Study of the Effectiveness of Reporting Lists of Small Feature Sets Using RNA-Seq Data.
    Cancer Inform 2017 12;16:1176935117710530. Epub 2017 Jun 12.
    Department of Electrical & Computer Engineering, Texas A&M University, College Station, TX, USA.
    Ranking feature sets for phenotype classification based on gene expression is a challenging issue in cancer bioinformatics. When the number of samples is small, all feature selection algorithms are known to be unreliable, producing significant error, and error estimators suffer from different degrees of imprecision. The problem is compounded by the fact that the accuracy of classification depends on the manner in which the phenomena are transformed into data by the measurement technology. Read More

    Tumor RAS Gene Expression Levels Are Influenced by the Mutational Status of RAS Genes and Both Upstream and Downstream RAS Pathway Genes.
    Cancer Inform 2017 8;16:1176935117711944. Epub 2017 Jun 8.
    Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, Frederick, MD, USA.
    The 3 human RAS genes play pivotal roles regulating proliferation, differentiation, and survival in normal cells and become mutated in 15% to 20% of all human tumors and amplified in many others. In this report, we examined data from The Cancer Genome Atlas to investigate the relationship between RAS gene mutational status and messenger RNA expression. We show that all 3 RAS genes exhibit increased expression when they are mutated in a context-dependent manner. Read More

    Immune Checkpoint Inhibition and the Prevalence of Autoimmune Disorders Among Patients With Lung and Renal Cancer.
    Cancer Inform 2017 1;16:1176935117712520. Epub 2017 Jun 1.
    Institute for Pharmaceutical Outcomes & Policy, University of Kentucky, Lexington, KY, USA.
    Purpose: Immune checkpoint inhibition reactivates the immune response against cancer cells in multiple tissue types and has been shown to induce durable responses. However, for patients with autoimmune disorders, their conditions can worsen with this reactivation. We sought to identify, among patients with lung and renal cancer, how many harbor a comorbid autoimmune condition and may be at risk of worsening their condition while on immune checkpoint inhibitors such as nivolumab and pembrolizumab. Read More

    Sequence Analysis and Phylogenetic Studies of Hypoxia-Inducible Factor-1α.
    Cancer Inform 2017 31;16:1176935117712242. Epub 2017 May 31.
    Department of Biotechnology, Sir M. Visvesvaraya Institute of Technology, Bangalore, India.
    Hypoxia-inducible factors (HIF) belong to the basic helix loop helix-PER ARNT SIM (bHLH-PAS) family of transcription factors that induce metabolic reprogramming under hypoxic condition. The phylogenetic studies of hypoxia-inducible factor-1α (HIF-1α) sequences across different organisms/species may leave a clue on the evolutionary relationships and its probable correlation to tumorigenesis and adaptation to low oxygen environments. In this study, we have aimed at the evolutionary investigation of the protein HIF-1α across different species to decipher their sequence variations/mutations and look into the probable causes and abnormal behaviour of this molecule under exotic conditions. Read More

    Lung Cancer Pathological Image Analysis Using a Hidden Potts Model.
    Cancer Inform 2017 5;16:1176935117711910. Epub 2017 Jun 5.
    Department of Biostatistics, University of Florida, Gainesville, FL, USA.
    Nowadays, many biological data are acquired via images. In this article, we study the pathological images scanned from 205 patients with lung cancer with the goal to find out the relationship between the survival time and the spatial distribution of different types of cells, including lymphocyte, stroma, and tumor cells. Toward this goal, we model the spatial distribution of different types of cells using a modified Potts model for which the parameters represent interactions between different types of cells and estimate the parameters of the Potts model using the double Metropolis-Hastings algorithm. Read More

    Time-Based Switching Control of Genetic Regulatory Networks: Toward Sequential Drug Intake for Cancer Therapy.
    Cancer Inform 2017 10;16:1176935117706888. Epub 2017 May 10.
    Department of Electrical & Computer Engineering, Texas A&M University, College Station, TX, USA.
    As cancer growth and development typically involves multiple genes and pathways, combination therapy has been touted as the standard of care in the treatment of cancer. However, drug toxicity becomes a major concern whenever a patient takes 2 or more drugs simultaneously at the maximum tolerable dosage. A potential solution would be administering the drugs in a sequential or alternating manner rather than concurrently. Read More

    Applying Multivariate Adaptive Splines to Identify Genes With Expressions Varying After Diagnosis in Microarray Experiments.
    Cancer Inform 2017 4;16:1176935117705381. Epub 2017 May 4.
    StubHub, San Francisco, CA, USA.
    Purpose: To analyze a microarray experiment to identify the genes with expressions varying after the diagnosis of breast cancer.

    Methods: A total of 44 928 probe sets in an Affymetrix microarray data publicly available on Gene Expression Omnibus from 249 patients with breast cancer were analyzed by the nonparametric multivariate adaptive splines. Then, the identified genes with turning points were grouped by K-means clustering, and their network relationship was subsequently analyzed by the Ingenuity Pathway Analysis. Read More

    Quantitative Study of Thermal Disturbances Due to Nonuniformly Perfused Tumors in Peripheral Regions of Women's Breast.
    Cancer Inform 2017 15;16:1176935117700894. Epub 2017 May 15.
    Department of Applied Mathematics & Humanities, Sardar Vallabhbhai National Institute of Technology (SVNIT), Surat, Surat, India.
    Background: Mathematical modeling of biothermal processes is widely used to enhance the quantitative understanding of thermoregulation system of human body organs. This quantitative knowledge of thermal information of various human body organs can be used for developing clinical applications. In the past, investigators have studied thermal distribution in hemisphere-shaped human breast in the presence of sphere-shaped tumor. Read More

    A Novel Optical Bioimaging Method for Direct Assessment of Ovarian Cancer Chemotherapy Response at Laparoscopy.
    Cancer Inform 2016 8;15:243-245. Epub 2017 Jan 8.
    Nuffield Department of Obstetrics and Gynaecology, Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.
    In patients with advanced ovarian cancer (AOC), additional imaging of disseminated disease at laparoscopy could complement conventional imaging for estimation of chemotherapy response. We developed an image segmentation method and evaluated its use in making accurate and objective measurements of peritoneal metastases in comparison to Response Evaluation Criteria In Solid Tumors (RECIST) criteria. A software tool using a custom ImageJ macro-based approach was employed to estimate lesion size by converting image pixels into unit length. Read More

    Bioinformatics Education in Pathology Training: Current Scope and Future Direction.
    Cancer Inform 2017 10;16:1176935117703389. Epub 2017 Apr 10.
    Department of Pathology & Immunology, Baylor College of Medicine, Houston, TX, USA.
    Training anatomic and clinical pathology residents in the principles of bioinformatics is a challenging endeavor. Most residents receive little to no formal exposure to bioinformatics during medical education, and most of the pathology training is spent interpreting histopathology slides using light microscopy or focused on laboratory regulation, management, and interpretation of discrete laboratory data. At a minimum, residents should be familiar with data structure, data pipelines, data manipulation, and data regulations within clinical laboratories. Read More

    miR-10a and miR-204 as a Potential Prognostic Indicator in Low-Grade Gliomas.
    Cancer Inform 2017 12;16:1176935117702878. Epub 2017 Apr 12.
    Interdisciplinary Research Program of Bioinformatics and Longevity Science, Pusan National University, Busan, Republic of Korea.
    This study aimed to identify and characterize microRNAs (miRNAs) that are related to radiosensitivity in low-grade gliomas (LGGs). The miRNA expression levels in radiosensitive and radioresistant LGGs were compared using The Cancer Genome Atlas database, and differentially expressed miRNAs were identified using the EBSeq package. The miRNA target genes were predicted using Web databases. Read More

    A mixture copula Bayesian network model for multimodal genomic data.
    Cancer Inform 2017 12;16:1176935117702389. Epub 2017 Apr 12.
    Department of Geosciences, University of Arkansas, USA.
    Gaussian Bayesian networks have become a widely used framework to estimate directed associations between joint Gaussian variables, where the network structure encodes the decomposition of multivariate normal density into local terms. However, the resulting estimates can be inaccurate when the normality assumption is moderately or severely violated, making it unsuitable for dealing with recent genomic data such as the Cancer Genome Atlas data. In the present paper, we propose a mixture copula Bayesian network model which provides great flexibility in modeling non-Gaussian and multimodal data for causal inference. Read More

    Therapeutic Interventions of Cancers Using Intrinsically Disordered Proteins as Drug Targets: c-Myc as Model System.
    Cancer Inform 2017 16;16:1176935117699408. Epub 2017 Mar 16.
    School of Basic Sciences, Indian Institute of Technology Mandi, Mandi, India.
    The concept of protein intrinsic disorder has taken the driving seat to understand regulatory proteins in general. Reports suggest that in mammals nearly 75% of signalling proteins contain long disordered regions with greater than 30 amino acid residues. Therefore, intrinsically disordered proteins (IDPs) have been implicated in several human diseases and should be considered as potential novel drug targets. Read More

    Roadmap to a Comprehensive Clinical Data Warehouse for Precision Medicine Applications in Oncology.
    Cancer Inform 2017 2;16:1176935117694349. Epub 2017 Mar 2.
    College of Medicine, University of Kentucky, Lexington, KY, USA.
    Leading institutions throughout the country have established Precision Medicine programs to support personalized treatment of patients. A cornerstone for these programs is the establishment of enterprise-wide Clinical Data Warehouses. Working shoulder-to-shoulder, a team of physicians, systems biologists, engineers, and scientists at Rutgers Cancer Institute of New Jersey have designed, developed, and implemented the Warehouse with information originating from data sources, including Electronic Medical Records, Clinical Trial Management Systems, Tumor Registries, Biospecimen Repositories, Radiology and Pathology archives, and Next Generation Sequencing services. Read More

    Significant Prognostic Features and Patterns of Somatic TP53 Mutations in Human Cancers.
    Cancer Inform 2017 20;16:1176935117691267. Epub 2017 Feb 20.
    Department of Computer Science and Bioinformatics Facility of Xavier RCMI Center for Cancer Research, Xavier University of Louisiana, New Orleans, LA, USA.
    TP53 is the most frequently altered gene in human cancers. Numerous retrospective studies have related its mutation and abnormal p53 protein expression to poor patient survival. Nonetheless, the clinical significance of TP53 (p53) status has been a controversial issue. Read More

    Integrative Analysis of Gene Networks and Their Application to Lung Adenocarcinoma Studies.
    Cancer Inform 2017 23;16:1176935117690778. Epub 2017 Feb 23.
    Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA.
    The construction of gene regulatory networks (GRNs) is an essential component of biomedical research to determine disease mechanisms and identify treatment targets. Gaussian graphical models (GGMs) have been widely used for constructing GRNs by inferring conditional dependence among a set of gene expressions. In practice, GRNs obtained by the analysis of a single data set may not be reliable due to sample limitations. Read More

    Epithelial Ovarian Cancer Diagnosis of Second-Harmonic Generation Images: A Semiautomatic Collagen Fibers Quantification Protocol.
    Cancer Inform 2017 3;16:1176935117690162. Epub 2017 Feb 3.
    Biofotónica y Procesamiento de Información Biológica (ByPIB), Centro de Investigación y Transferencia de Entre Ríos (CITER), CONICET-UNER, Entre Ríos, Argentina.
    A vast number of human pathologic conditions are directly or indirectly related to tissular collagen structure remodeling. The nonlinear optical microscopy second-harmonic generation has become a powerful tool for imaging biological tissues with anisotropic hyperpolarized structures, such as collagen. During the past years, several quantification methods to analyze and evaluate these images have been developed. Read More

    Unified Least Squares Methods for the Evaluation of Diagnostic Tests With the Gold Standard.
    Cancer Inform 2017 3;16:1176935116686063. Epub 2017 Feb 3.
    Rehabilitation Medicine Department, NIH Clinical Center, Bethesda, MD, USA.
    The article proposes a unified least squares method to estimate the receiver operating characteristic (ROC) parameters for continuous and ordinal diagnostic tests, such as cancer biomarkers. The method is based on a linear model framework using the empirically estimated sensitivities and specificities as input "data." It gives consistent estimates for regression and accuracy parameters when the underlying continuous test results are normally distributed after some monotonic transformation. Read More

    Improving Gastric Cancer Outcome Prediction Using Single Time-Point Artificial Neural Network Models.
    Cancer Inform 2017 16;16:1176935116686062. Epub 2017 Feb 16.
    Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and liver Diseases, Shahid Beheshti University of Medical Sciences,Tehran, Iran.
    In cancer studies, the prediction of cancer outcome based on a set of prognostic variables has been a long-standing topic of interest. Current statistical methods for survival analysis offer the possibility of modelling cancer survivability but require unrealistic assumptions about the survival time distribution or proportionality of hazard. Therefore, attention must be paid in developing nonlinear models with less restrictive assumptions. Read More

    A Numerical Handling of the Boundary Conditions Imposed by the Skull on an Inhomogeneous Diffusion-Reaction Model of Glioblastoma Invasion Into the Brain: Clinical Validation Aspects.
    Cancer Inform 2017 3;16:1176935116684824. Epub 2017 Feb 3.
    In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, National Technical University of Athens, Zografou, Greece.
    A novel explicit triscale reaction-diffusion numerical model of glioblastoma multiforme tumor growth is presented. The model incorporates the handling of Neumann boundary conditions imposed by the cranium and takes into account both the inhomogeneous nature of human brain and the complexity of the skull geometry. The finite-difference time-domain method is adopted. Read More

    Identification of Genetic and Epigenetic Variants Associated with Breast Cancer Prognosis by Integrative Bioinformatics Analysis.
    Cancer Inform 2017 9;16:1-13. Epub 2017 Jan 9.
    Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
    Introduction: Breast cancer being a multifaceted disease constitutes a wide spectrum of histological and molecular variability in tumors. However, the task for the identification of these variances is complicated by the interplay between inherited genetic and epigenetic aberrations. Therefore, this study provides an extrapolate outlook to the sinister partnership between DNA methylation and single-nucleotide polymorphisms (SNPs) in relevance to the identification of prognostic markers in breast cancer. Read More

    Bayesian ABC-MCMC Classification of Liquid Chromatography-Mass Spectrometry Data.
    Cancer Inform 2015 9;14(Suppl 5):175-182. Epub 2017 Jan 9.
    Department of Electrical and Computer Engineering, Center for Bioinformatics and Genomics Systems Engineering, Texas A&M University, College Station, TX, USA.
    Proteomics promises to revolutionize cancer treatment and prevention by facilitating the discovery of molecular biomarkers. Progress has been impeded, however, by the small-sample, high-dimensional nature of proteomic data. We propose the application of a Bayesian approach to address this issue in classification of proteomic profiles generated by liquid chromatography-mass spectrometry (LC-MS). Read More

    ROC Estimation from Clustered Data with an Application to Liver Cancer Data.
    Cancer Inform 2016 22;15(Suppl 4):19-26. Epub 2016 Dec 22.
    Department of Mathematics and Statistics, University of Maryland, Baltimore County, Baltimore, MD, USA.
    In this article, we propose a regression model to compare the performances of different diagnostic methods having clustered ordinal test outcomes. The proposed model treats ordinal test outcomes (an ordinal categorical variable) as grouped-survival time data and uses random effects to explain correlation among outcomes from the same cluster. To compare different diagnostic methods, we introduce a set of covariates indicating diagnostic methods and compare their coefficients. Read More

    Comparison of Three Information Sources for Smoking Information in Electronic Health Records.
    Cancer Inform 2016 8;15:237-242. Epub 2016 Dec 8.
    Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
    Objective: The primary aim was to compare independent and joint performance of retrieving smoking status through different sources, including narrative text processed by natural language processing (NLP), patient-provided information (PPI), and diagnosis codes (ie, International Classification of Diseases, Ninth Revision [ICD-9]). We also compared the performance of retrieving smoking strength information (ie, heavy/light smoker) from narrative text and PPI.

    Materials And Methods: Our study leveraged an existing lung cancer cohort for smoking status, amount, and strength information, which was manually chart-reviewed. Read More

    Quantitative Proteomic Approach for MicroRNA Target Prediction Based on (18)O/(16)O Labeling.
    Cancer Inform 2015 8;14(Suppl 5):163-173. Epub 2016 Dec 8.
    Department of Electrical and Computer Engineering, The University of Texas at San Antonio, San Antonio, TX, USA.
    Motivation: Among many large-scale proteomic quantification methods, (18)O/(16)O labeling requires neither specific amino acid in peptides nor label incorporation through several cell cycles, as in metabolic labeling; it does not cause significant elution time shifts between heavy- and light-labeled peptides, and its dynamic range of quantification is larger than that of tandem mass spectrometry-based quantification methods. These properties offer (18)O/(16)O labeling the maximum flexibility in application. However, (18)O/(16)O labeling introduces large quantification variations due to varying labeling efficiency. Read More

    A Modular Repository-based Infrastructure for Simulation Model Storage and Execution Support in the Context of In Silico Oncology and In Silico Medicine.
    Cancer Inform 2016 27;15:219-235. Epub 2016 Oct 27.
    In Silico Oncology and In Silico Medicine Group, Institute of Communication and Computer Systems, National Technical University of Athens, Zografos, Greece.
    The plethora of available disease prediction models and the ongoing process of their application into clinical practice - following their clinical validation - have created new needs regarding their efficient handling and exploitation. Consolidation of software implementations, descriptive information, and supportive tools in a single place, offering persistent storage as well as proper management of execution results, is a priority, especially with respect to the needs of large healthcare providers. At the same time, modelers should be able to access these storage facilities under special rights, in order to upgrade and maintain their work. Read More

    Discovering Outliers of Potential Drug Toxicities Using a Large-scale Data-driven Approach.
    Cancer Inform 2016 26;15:211-217. Epub 2016 Oct 26.
    Center for Biomedical Data and Language Processing, Department of Health Informatics and Administration, University of Wisconsin-Milwaukee, Milwaukee, WI, USA.; Department of Health Informatics and Administration, College of Health Sciences, University of Wisconsin-Milwaukee, Milwaukee, WI, USA.; College of Health Sciences, University of Wisconsin-Milwaukee, Milwaukee, WI, USA.; Center for Urban Population Health, Milwaukee, WI, USA.; Joseph J. Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, WI, USA.
    We systematically compared the adverse effects of cancer drugs to detect event outliers across different clinical trials using a data-driven approach. Because many cancer drugs are toxic to patients, better understanding of adverse events of cancer drugs is critical for developing therapies that could minimize the toxic effects. However, due to the large variabilities of adverse events across different cancer drugs, methods to efficiently compare adverse effects across different cancer drugs are lacking. Read More

    Cluster Analysis of p53 Binding Site Sequences Reveals Subsets with Different Functions.
    Cancer Inform 2016 25;15:199-209. Epub 2016 Oct 25.
    School of Biology, University of St Andrews, St Andrews, UK.; Current address: Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK.
    p53 is an important regulator of cell cycle arrest, senescence, apoptosis and metabolism, and is frequently mutated in tumors. It functions as a tetramer, where each component dimer binds to a decameric DNA region known as a response element. We identify p53 binding site subtypes and examine the functional and evolutionary properties of these subtypes. Read More

    A Novel Graph-based Algorithm to Infer Recurrent Copy Number Variations in Cancer.
    Cancer Inform 2016 9;15(Suppl 2):43-50. Epub 2016 Oct 9.
    Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, Canada; Centre for Healthcare Innovation, Winnipeg Regional Health Authority/University of Manitoba, Winnipeg, Canada; Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, Canada.
    Many cancers have been linked to copy number variations (CNVs) in the genomic DNA. Although there are existing methods to analyze CNVs from individual samples, cancer-causing genes are more frequently discovered in regions where CNVs are common among tumor samples, also known as recurrent CNVs. Integrating multiple samples and locating recurrent CNV regions remain a challenge, both computationally and conceptually. Read More

    Discovering MicroRNA-Regulatory Modules in Multi-Dimensional Cancer Genomic Data: A Survey of Computational Methods.
    Cancer Inform 2016 3;15(Suppl 2):25-42. Epub 2016 Oct 3.
    Keenan and Li Ka Shing Knowledge Institute of Saint Michael's Hospital, Toronto, ON, Canada.; Institute of Medical Sciences and Department of Medicine, University of Toronto, Toronto, ON, Canada.
    MicroRNAs (miRs) are small single-stranded noncoding RNA that function in RNA silencing and post-transcriptional regulation of gene expression. An increasing number of studies have shown that miRs play an important role in tumorigenesis, and understanding the regulatory mechanism of miRs in this gene regulatory network will help elucidate the complex biological processes at play during malignancy. Despite advances, determination of miR-target interactions (MTIs) and identification of functional modules composed of miRs and their specific targets remain a challenge. Read More

    DNA Methylation Heterogeneity Patterns in Breast Cancer Cell Lines.
    Cancer Inform 2016 7;15(Supple 4):1-9. Epub 2016 Sep 7.
    Department of Mathematics, Texas State University, San Marcos, TX, USA.
    Heterogeneous DNA methylation patterns are linked to tumor growth. In order to study DNA methylation heterogeneity patterns for breast cancer cell lines, we comparatively study four metrics: variance, I (2) statistic, entropy, and methylation state. Using the categorical metric methylation state, we select the two most heterogeneous states to identify genes that directly affect tumor suppressor genes and high- or moderate-risk breast cancer genes. Read More

    Novel Biomarker Candidates for Colorectal Cancer Metastasis: A Meta-analysis of In Vitro Studies.
    Cancer Inform 2016 22;15(Suppl 4):11-7. Epub 2016 Sep 22.
    College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul, Korea.
    Colorectal cancer (CRC) is one of the most common and lethal cancers. Although numerous studies have evaluated potential biomarkers for early diagnosis, current biomarkers have failed to reach an acceptable level of accuracy for distant metastasis. In this paper, we performed a gene set meta-analysis of in vitro microarray studies and combined the results from this study with previously published proteomic data to validate and suggest prognostic candidates for CRC metastasis. Read More

    Identifying Significant Features in Cancer Methylation Data Using Gene Pathway Segmentation.
    Cancer Inform 2016 20;15:189-98. Epub 2016 Sep 20.
    Department of Computing, Imperial College London, London, UK.
    In order to provide the most effective therapy for cancer, it is important to be able to diagnose whether a patient's cancer will respond to a proposed treatment. Methylation profiling could contain information from which such predictions could be made. Currently, hypothesis testing is used to determine whether possible biomarkers for cancer progression produce statistically significant results. Read More

    Overlapping Group Logistic Regression with Applications to Genetic Pathway Selection.
    Cancer Inform 2016 15;15:179-87. Epub 2016 Sep 15.
    Department of Biostatistics, University of Iowa, Iowa City, IA, USA.
    Discovering important genes that account for the phenotype of interest has long been a challenge in genome-wide expression analysis. Analyses such as gene set enrichment analysis (GSEA) that incorporate pathway information have become widespread in hypothesis testing, but pathway-based approaches have been largely absent from regression methods due to the challenges of dealing with overlapping pathways and the resulting lack of available software. The R package grpreg is widely used to fit group lasso and other group-penalized regression models; in this study, we develop an extension, grpregOverlap, to allow for overlapping group structure using a latent variable approach. Read More

    Biological Networks for Cancer Candidate Biomarkers Discovery.
    Cancer Inform 2016 4;15(Suppl 3):1-7. Epub 2016 Sep 4.
    Center for Systems Biology, Soochow University, Suzhou, Jiangsu, China.
    Due to its extraordinary heterogeneity and complexity, cancer is often proposed as a model case of a systems biology disease or network disease. There is a critical need of effective biomarkers for cancer diagnosis and/or outcome prediction from system level analyses. Methods based on integrating omics data into networks have the potential to revolutionize the identification of cancer biomarkers. Read More

    Characterization of Gene Expression Patterns among Artificially Developed Cancer Stem Cells Using Spherical Self-Organizing Map.
    Cancer Inform 2016 16;15:163-78. Epub 2016 Aug 16.
    Laboratory of Nano-Biotechnology, Department of Medical Bioengineering Science, Graduate School of Natural Science and Technology, Okayama University, Kita-ku, Okayama, Japan.
    We performed gene expression microarray analysis coupled with spherical self-organizing map (sSOM) for artificially developed cancer stem cells (CSCs). The CSCs were developed from human induced pluripotent stem cells (hiPSCs) with the conditioned media of cancer cell lines, whereas the CSCs were induced from primary cell culture of human cancer tissues with defined factors (OCT3/4, SOX2, and KLF4). These cells commonly expressed human embryonic stem cell (hESC)/hiPSC-specific genes (POU5F1, SOX2, NANOG, LIN28, and SALL4) at a level equivalent to those of control hiPSC 201B7. Read More

    ExSurv: A Web Resource for Prognostic Analyses of Exons Across Human Cancers Using Clinical Transcriptomes.
    Cancer Inform 2016 7;15(Suppl 2):17-24. Epub 2016 Aug 7.
    Department of Biohealth Informatics, School of Informatics and Computing, Indiana University Purdue University, Indianapolis, IN, USA.; Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, 5021 Health Information and Translational Sciences (HITS), Indianapolis, IN, USA.; Department of Medical and Molecular Genetics, Indiana University School of Medicine, Medical Research and Library Building, Indianapolis, IN, USA.
    Survival analysis in biomedical sciences is generally performed by correlating the levels of cellular components with patients' clinical features as a common practice in prognostic biomarker discovery. While the common and primary focus of such analysis in cancer genomics so far has been to identify the potential prognostic genes, alternative splicing - a posttranscriptional regulatory mechanism that affects the functional form of a protein due to inclusion or exclusion of individual exons giving rise to alternative protein products, has increasingly gained attention due to the prevalence of splicing aberrations in cancer transcriptomes. Hence, uncovering the potential prognostic exons can not only help in rationally designing exon-specific therapeutics but also increase specificity toward more personalized treatment options. Read More

    Recursive Partitioning Method on Competing Risk Outcomes.
    Cancer Inform 2016 26;15(Suppl 2):9-16. Epub 2016 Jul 26.
    Department of Biostatistics, Princess Margaret Cancer Centre, Toronto, ON, Canada.; Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, Canada.
    In some cancer clinical studies, researchers have interests to explore the risk factors associated with competing risk outcomes such as recurrence-free survival. We develop a novel recursive partitioning framework on competing risk data for both prognostic and predictive model constructions. We define specific splitting rules, pruning algorithm, and final tree selection algorithm for the competing risk tree models. Read More

    Pathway-Informed Classification System (PICS) for Cancer Analysis Using Gene Expression Data.
    Cancer Inform 2016 27;15:151-61. Epub 2016 Jul 27.
    Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
    We introduce Pathway-Informed Classification System (PICS) for classifying cancers based on tumor sample gene expression levels. PICS is a computational method capable of expeditiously elucidating both known and novel biological pathway involvement specific to various cancers and uses that learned pathway information to separate patients into distinct classes. The method clearly separates a pan-cancer dataset by tissue of origin and also sub-classifies individual cancer datasets into distinct survival classes. Read More

    TMAinspiration: Decode Interdependencies in Multifactorial Tissue Microarray Data.
    Cancer Inform 2016 29;15:143-9. Epub 2016 Jun 29.
    Institute of Bioinformatics, University of Münster, Münster, Germany.
    There are no satisfying tools in tissue microarray (TMA) data analysis up to now to analyze the cooperative behavior of all measured markers in a multifactorial TMA approach. The developed tool TMAinspiration is not only offering an analysis option to close this gap but also offering an ecosystem consisting of quality control concepts and supporting scripts to make this approach a platform for informed practice and further research. The TMAinspiration method is specifically focusing on the demands of the TMA analysis by controlling errors and noise by a generalized regression scheme while at the same time avoiding to introduce a priori too many constraints into the analysis of the data. Read More

    An Integrated Approach for RNA-seq Data Normalization.
    Cancer Inform 2016 27;15:129-41. Epub 2016 Jun 27.
    Biostatistics Program, School of Public Health, LSU Health Sciences Center, New Orleans, LA, USA.
    Background: DNA copy number alteration is common in many cancers. Studies have shown that insertion or deletion of DNA sequences can directly alter gene expression, and significant correlation exists between DNA copy number and gene expression. Data normalization is a critical step in the analysis of gene expression generated by RNA-seq technology. Read More

    The Melanoma MAICare Framework: A Microsimulation Model for the Assessment of Individualized Cancer Care.
    Cancer Inform 2016 15;15:115-27. Epub 2016 Jun 15.
    Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, the Netherlands.
    Recently, new but expensive treatments have become available for metastatic melanoma. These improve survival, but in view of the limited funds available, cost-effectiveness needs to be evaluated. Most cancer cost-effectiveness models are based on the observed clinical events such as recurrence- free and overall survival. Read More

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