Normal Cell-Type Epigenetics and Breast Cancer Classification: A Case Study of Cell Mixture–Adjusted Analysis of DNA Methylation Data from Tumors
Historically, breast cancer classification has relied on prognostic subtypes. Thus, unlike hematopoietic cancers, breast tumor classification lacks phylogenetic rationale. The feasibility of phylogenetic classification of breast tumors has recently been demonstrated based on estrogen receptor (ER), androgen receptor (AR), vitamin D receptor (VDR) and Keratin 5 expression. Four hormonal states (HR0–3) comprising 11 cellular subtypes of breast cells have been proposed. This classification scheme has been shown to have relevance to clinical prognosis. We examine the implications of such phylogenetic classification on DNA me...
Source: Cancer Informatics - December 9, 2014 Category: Cancer & Oncology Authors: Eugene Andrés HousemanTan A. Ince Source Type: research
Overcome Support Vector Machine Diagnosis Overfitting
Support vector machines (SVMs) are widely employed in molecular diagnosis of disease for their efficiency and robustness. However, there is no previous research to analyze their overfitting in high-dimensional omics data based disease diagnosis, which is essential to avoid deceptive diagnostic results and enhance clinical decision making. In this work, we comprehensively investigate this problem from both theoretical and practical standpoints to unveil the special characteristics of SVM overfitting. We found that disease diagnosis under an SVM classifier would inevitably encounter overfitting under a Gaussian kernel becaus...
Source: Cancer Informatics - December 9, 2014 Category: Cancer & Oncology Authors: Henry HanXiaoqian Jiang Source Type: research
Semantically Linking In Silico Cancer Models
Multiscale models are commonplace in cancer modeling, where individual models acting on different biological scales are combined within a single, cohesive modeling framework. However, model composition gives rise to challenges in understanding interfaces and interactions between them. Based on specific domain expertise, typically these computational models are developed by separate research groups using different methodologies, programming languages, and parameters. This paper introduces a graph-based model for semantically linking computational cancer models via domain graphs that can help us better understand and explore...
Source: Cancer Informatics - December 8, 2014 Category: Cancer & Oncology Authors: David JohnsonAnthony J. ConnorSteve McKeeverZhihui WangThomas S. DeisboeckTom QuaiserEliezer Shochat Source Type: research
Computational Construction of Antibody–Drug Conjugates Using Surface Lysines as the Antibody Conjugation Site and a Non-cleavable Linker
Antibody–drug conjugates (ADCs) constitute a category of anticancer targeted therapy that has gathered great interest during the last few years because of their potential to kill cancer cells while causing significantly fewer side effects than traditional chemotherapy. In this paper, a process of computational construction of ADCs is described, using the surface lysines of an antibody and a non-covalent linker molecule, as well as a cytotoxic substance, as files in Protein Data Bank format. Also, aspects related to the function, properties, and development of ADCs are discussed. (Source: Cancer Informatics)
Source: Cancer Informatics - December 8, 2014 Category: Cancer & Oncology Authors: Arianna FilntisiDimitrios VlachakisGeorge K. MatsopoulosSophia Kossida Source Type: research
Bayesian Joint Selection of Genes and Pathways: Applications in Multiple Myeloma Genomics
It is well-established that the development of a disease, especially cancer, is a complex process that results from the joint effects of multiple genes involved in various molecular signaling pathways. In this article, we propose methods to discover genes and molecular pathways significantly associated with clinical outcomes in cancer samples. We exploit the natural hierarchal structure of genes related to a given pathway as a group of interacting genes to conduct selection of both pathways and genes. We posit the problem in a hierarchical structured variable selection (HSVS) framework to analyze the corresponding gene exp...
Source: Cancer Informatics - December 7, 2014 Category: Cancer & Oncology Authors: Lin ZhangJeffrey S. MorrisJiexin ZhangRobert Z. OrlowskiVeerabhadran Baladandayuthapani Source Type: research
A Pan-Cancer Analysis of Alternative Splicing Events Reveals Novel Tumor-Associated Splice Variants of Matriptase
High-throughput transcriptome sequencing allows identification of cancer-related changes that occur at the stages of transcription, pre-messenger RNA (mRNA), and splicing. In the current study, we devised a pipeline to predict novel alternative splicing (AS) variants from high-throughput transcriptome sequencing data and applied it to large sets of tumor transcriptomes from The Cancer Genome Atlas (TCGA). We identified two novel tumor-associated splice variants of matriptase, a known cancer-associated gene, in the transcriptome data from epithelial-derived tumors but not normal tissue. Most notably, these variants were fou...
Source: Cancer Informatics - December 4, 2014 Category: Cancer & Oncology Authors: Daryanaz DargahiRichard D. SwayzeLeanna YeePeter J. BergqvistBradley J. HedbergAlireza Heravi-MoussaviEdie M. DullaghanRyan DerchoJianghong AnJohn S. Babcookand Steven J.M. Jones Source Type: research
Trial Prospector: Matching Patients with Cancer Research Studies Using an Automated and Scalable Approach
We report the results from deployment of Trial Prospector at the National Cancer Institute (NCI)-designated Case Comprehensive Cancer Center (Case CCC) with 1,367 clinical trial eligibility evaluations performed with 100% accuracy. (Source: Cancer Informatics)
Source: Cancer Informatics - December 4, 2014 Category: Cancer & Oncology Authors: Satya S. SahooShiqiang TaoAndrew ParchmanZhihui LuoLicong CuiPatrick MerglerRobert LaneseJill S. Barnholtz-SloanNeal J. MeropolGuo-Qiang Zhang Source Type: research
Assessment of Subnetwork Detection Methods for Breast Cancer
Subnetwork detection is often used with differential expression analysis to identify modules or pathways associated with a disease or condition. Many computational methods are available for subnetwork analysis. Here, we compare the results of eight methods: simulated annealing–based jActiveModules, greedy search–based jActiveModules, DEGAS, BioNet, NetBox, ClustEx, OptDis, and NetWalker. These methods represent distinctly different computational strategies and are among the most widely used. Each of these methods was used to analyze gene expression data consisting of paired tumor and normal samples from 50 breast cance...
Source: Cancer Informatics - December 2, 2014 Category: Cancer & Oncology Authors: Biaobin JiangMichael Gribskov Source Type: research
Streamlined Genome Sequence Compression using Distributed Source Coding
We aim at developing a streamlined genome sequence compression algorithm to support alternative miniaturized sequencing devices, which have limited communication, storage, and computation power. Existing techniques that require heavy client (encoder side) cannot be applied. To tackle this challenge, we carefully examined distributed source coding theory and developed a customized reference-based genome compression protocol to meet the low-complexity need at the client side. Based on the variation between source and reference, our protocol will pick adaptively either syndrome coding or hash coding to compress subsequences o...
Source: Cancer Informatics - December 2, 2014 Category: Cancer & Oncology Authors: Shuang WangXiaoqian JiangFeng ChenLijuan CuiSamuel Cheng Source Type: research
Revealing Biological Pathways Implicated in Lung Cancer from TCGA Gene Expression Data Using Gene Set Enrichment Analysis
Analyzing biological system abnormalities in cancer patients based on measures of biological entities, such as gene expression levels, is an important and challenging problem. This paper applies existing methods, Gene Set Enrichment Analysis and Signaling Pathway Impact Analysis, to pathway abnormality analysis in lung cancer using microarray gene expression data. Gene expression data from studies of Lung Squamous Cell Carcinoma (LUSC) in The Cancer Genome Atlas project, and pathway gene set data from the Kyoto Encyclopedia of Genes and Genomes were used to analyze the relationship between pathways and phenotypes. Results,...
Source: Cancer Informatics - December 1, 2014 Category: Cancer & Oncology Authors: Binghuang CaiXia Jiang Source Type: research
Health Information Technology in Oncology Practice: A Literature Review
The adoption and implementation of information technology are dramatically remodeling healthcare services all over the world, resulting in an unstoppable and sometimes overwhelming process. After the introduction of the main elements of electronic health records and a description of what every cancer-care professional should be familiar with, we present a narrative review focusing on the current use of computerized clinical information and decision systems in oncology practice. Following a detailed analysis of the many coveted goals that oncologists have reached while embracing informatics progress, the authors suggest how...
Source: Cancer Informatics - December 1, 2014 Category: Cancer & Oncology Authors: G. FasolaM. MacerelliA. FolladorK. RihawiG. AprileV. Della Mea Source Type: research
Supervised Classification by Filter Methods and Recursive Feature Elimination Predicts Risk of Radiotherapy-Related Fatigue in Patients with Prostate Cancer
Conclusion: The results suggest that a novel analytical algorithm that incorporates fold-change differential analysis, LDA, and a k-NN may have applicability in predicting regimen-related toxicity in cancer patients with high reliability, if we take into account these results and the limited amount of data that we had at disposal. It is expected that the accuracy will be improved by increasing data sampling in the learning phase. (Source: Cancer Informatics)
Source: Cancer Informatics - December 1, 2014 Category: Cancer & Oncology Authors: Leorey N. SaliganJuan Luis Fernández-MartínezEnrique J. deAndrés-GalianaStephen Sonis Source Type: research
FocalCall: An R Package for the Annotation of Focal Copy Number Aberrations
In order to identify somatic focal copy number aberrations (CNAs) in cancer specimens and to distinguish them from germ-line copy number variations (CNVs), we developed the software package FocalCall. FocalCall enables user-defined size cutoffs to recognize focal aberrations and builds on established array comparative genomic hybridization segmentation and calling algorithms. To distinguish CNAs from CNVs, the algorithm uses matched patient normal signals as references or, if this is not available, a list with known CNVs in a population. Furthermore, FocalCall differentiates between homozygous and heterozygous deletions as...
Source: Cancer Informatics - December 1, 2014 Category: Cancer & Oncology Authors: Oscar KrijgsmanChristian BennerGerrit A. MeijerMark A. van de WielBauke Ylstra Source Type: research
Correction to “Master Regulators, Regulatory Networks, and Pathways of Glioblastoma Subtypes”
(Source: Cancer Informatics)
Source: Cancer Informatics - November 30, 2014 Category: Cancer & Oncology Authors: Serdar BozdagAiguo LiMehmet Baysanand Howard A. Fine Source Type: research
Correction to "Network Analysis of Cancer-focused Association Network Reveals Distinct Network Association Patterns"
No abstract supplied. (Source: Cancer Informatics)
Source: Cancer Informatics - November 20, 2014 Category: Cancer & Oncology Authors: Yuji ZhangCui Tao Source Type: research
