Toward Signaling-Driven Biomarkers Immune to Normal Tissue Contamination
The goal of this study was to discover a minimally invasive pathway-specific biomarker that is immune to normal cell mRNA contamination for diagnosing head and neck squamous cell carcinoma (HNSCC). Using Elsevier’s MedScan natural language processing component of the Pathway Studio software and the TRANSFAC database, we produced a curated set of genes regulated by the signaling networks driving the development of HNSCC. The network and its gene targets provided prior probabilities for gene expression, which guided our CoGAPS matrix factorization algorithm to isolate patterns related to HNSCC signaling activity from a mic...
Source: Cancer Informatics - February 10, 2016 Category: Cancer & Oncology Authors: John C. StansfieldMatthew RusayRoger ShanConor KeltonDaria A. GaykalovaElana J. FertigJoseph A. Califanoand Michael F. Ochs Source Type: research
Correlation between Gene Variants, Signaling Pathways, and Efficacy of Chemotherapy Drugs against Colon Cancers
Efficacies, toxicities, and resistance mechanisms of chemotherapy drugs, such as oxaliplatin and 5-fluorouracil (5-FU), vary widely among various categories and subcategories of colon cancers. By understanding the differences in the drug efficacy and resistance at the level of protein–protein networks, we identified the correlation between the drug activity of oxaliplatin/5-FU and gene variations from the US National Cancer Institute-60 human cancer cell lines. The activity of either of these drugs is correlated with specific amino acid variant(s) of KRAS and other genes from the signaling pathways of colon cancer progre...
Source: Cancer Informatics - January 20, 2016 Category: Cancer & Oncology Authors: Swarnendu TripathiLouiza BelkacemiMargaret S. CheungRathindra N. Bose Source Type: research
Integrating Multiscale Modeling with Drug Effects for Cancer Treatment
In this paper, we review multiscale modeling for cancer treatment with the incorporation of drug effects from an applied system’s pharmacology perspective. Both the classical pharmacology and systems biology are inherently quantitative; however, systems biology focuses more on networks and multifactorial controls over biological processes rather than on drugs and targets in isolation, whereas systems pharmacology has a strong focus on studying drugs with regard to the pharmacokinetic (PK) and pharmacodynamic (PD) relations accompanying drug interactions with multiscale physiology as well as the prediction of dosage-expos...
Source: Cancer Informatics - January 13, 2016 Category: Cancer & Oncology Authors: Xiangfang L. LiWasiu O. OduolaLijun QianEdward R. Dougherty Source Type: research
Introductory Editorial: Computer Simulation, Visualization, and Image Processing of Cancer Data and Processes
No abstract supplied. (Source: Cancer Informatics)
Source: Cancer Informatics - January 12, 2016 Category: Cancer & Oncology Authors: David JohnsonJames OsborneZhihui WangKostas Marias Source Type: research
Simulation of the Protein-Shedding Kinetics of a Fully Vascularized Tumor
Circulating biomarkers are of significant interest for cancer detection and treatment personalization. However, the biophysical processes that determine how proteins are shed from cancer cells or their microenvironment, diffuse through tissue, enter blood vasculature, and persist in circulation remain poorly understood. Since approaches primarily focused on experimental evaluation are incapable of measuring the shedding and persistence for every possible marker candidate, we propose an interdisciplinary computational/experimental approach that includes computational modeling of tumor tissue heterogeneity. The model impleme...
Source: Cancer Informatics - December 20, 2015 Category: Cancer & Oncology Authors: Hermann B. FrieboesLouis T. CurtisMin WuKian KaniParag Mallick Source Type: research
Improving Cancer Gene Expression Data Quality through a TCGA Data-Driven Evaluation of Identifier Filtering
Data quality is a recognized problem for high-throughput genomics platforms, as evinced by the proliferation of methods attempting to filter out lower quality data points. Different filtering methods lead to discordant results, raising the question, which methods are best? Astonishingly, little computational support is offered to analysts to decide which filtering methods are optimal for the research question at hand. To evaluate them, we begin with a pair of expression data sets, transcriptomic and proteomic, on the same samples. The pair of data sets form a test-bed for the evaluation. Identifier mapping between the data...
Source: Cancer Informatics - December 16, 2015 Category: Cancer & Oncology Authors: Kevin K. McDadeUma ChandranRoger S. Day Source Type: research
Differential Expression Analysis for RNA-Seq: An Overview of Statistical Methods and Computational Software
Deep sequencing has recently emerged as a powerful alternative to microarrays for the high-throughput profiling of gene expression. In order to account for the discrete nature of RNA sequencing data, new statistical methods and computational tools have been developed for the analysis of differential expression to identify genes that are relevant to a disease such as cancer. In this paper, it is thus timely to provide an overview of these analysis methods and tools. For readers with statistical background, we also review the parameter estimation algorithms and hypothesis testing strategies used in these methods. (Source: Cancer Informatics)
Source: Cancer Informatics - December 13, 2015 Category: Cancer & Oncology Authors: Huei-Chung HuangYi Niuand Li-Xuan Qin Source Type: research
The Impact of Age and Sex in DLBCL: Systems Biology Analyses Identify Distinct Molecular Changes and Signaling Networks
We examined global transcriptome DLBCL data from The Cancer Genome Atlas (TCGA) via a systems biology approach to determine the molecular differences associated with age and sex. Collectively, sex and age revealed striking transcriptional differences with older age associated with decreased metabolism and telomere functions and female sex was associated with decreased interferon signaling, transcription, cell cycle, and PD-1 signaling. We discovered that the key genes for most groups strongly regulated immune function activity. Furthermore, older females were predicted to have less DLBCL progression versus older males and ...
Source: Cancer Informatics - December 10, 2015 Category: Cancer & Oncology Authors: Afshin BeheshtiDonna NeubergJ. Tyson McDonaldCharles R. VanderburgAndrew M. Evens Source Type: research
Predictive Modeling of Drug Treatment in the Area of Personalized Medicine
Despite a growing body of knowledge on the mechanisms underlying the onset and progression of cancer, treatment success rates in oncology are at best modest. Current approaches use statistical methods that fail to embrace the inherent and expansive complexity of the tumor/patient/drug interaction. Computational modeling, in particular mechanistic modeling, has the power to resolve this complexity. Using fundamental knowledge on the interactions occurring between the components of a complex biological system, large-scale in silico models with predictive capabilities can be generated. Here, we describe how mechanistic virtua...
Source: Cancer Informatics - December 6, 2015 Category: Cancer & Oncology Authors: Lesley A. OgilvieChristoph WierlingThomas KesslerHans Lehrachand Bodo M. H. Lange Source Type: research
Multigroup Equivalence Analysis for High-Dimensional Expression Data
Hypothesis tests of equivalence are typically known for their application in bioequivalence studies and acceptance sampling. Their application to gene expression data, in particular high-dimensional gene expression data, has only recently been studied. In this paper, we examine how two multigroup equivalence tests, the F-test and the range test, perform when applied to microarray expression data. We adapted these tests to a well-known equivalence criterion, the difference ratio. Our simulation results showed that both tests can achieve moderate power while controlling the type I error at nominal level for typical expressio...
Source: Cancer Informatics - November 23, 2015 Category: Cancer & Oncology Authors: Celeste YangAlfred A. BartolucciXiangqin Cui Source Type: research
Cancer Bioinformatic Methods to Infer Meaningful Data From Small-Size Cohorts
Whole-genome analyses have uncovered that most cancer-relevant genes cluster into 12 signaling pathways. Knowledge of the signaling pathways and associated gene signatures not only allows us to understand the mechanisms of oncogenesis inherent to specific cancers but also provides us with drug targets, molecular diagnostic and prognosis factors, as well as biomarkers for patient risk stratification and treatment. Publicly available genomic data sets constitute a wealth of gene mining opportunities for hypothesis generation and testing. However, the increasingly recognized genetic and epigenetic inter- and intratumor hetero...
Source: Cancer Informatics - November 2, 2015 Category: Cancer & Oncology Authors: Nabila Bennani-BaitiIdriss M. Bennani-Baiti Source Type: research
Effectiveness and Usability of Bioinformatics Tools to Analyze Pathways Associated with miRNA Expression
MiRNAs are small, nonprotein-coding RNA molecules involved in gene regulation. While bioinformatics help guide miRNA research, it is less clear how they perform when studying biological pathways. We used 13 criteria to evaluate effectiveness and usability of existing bioinformatics tools. We evaluated the performance of six bioinformatics tools with a cluster of 12 differentially expressed miRNAs in colorectal tumors and three additional sets of 12 miRNAs that are not part of a known cluster. MiRPath performed the best of all the tools in linking miRNAs, with 92% of all miRNAs linked as well as the highest based on our est...
Source: Cancer Informatics - October 29, 2015 Category: Cancer & Oncology Authors: Lila E. MullanyRoger K. WolffMartha L. Slattery Source Type: research
Publication Bias in Methodological Computational Research
We report an exemplary pilot study that aims at gaining experiences with the collection and analysis of information on unpublished research efforts with respect to publication bias, and we outline the encountered problems. Based on these experiences, we try to formalize the notion of publication bias. (Source: Cancer Informatics)
Source: Cancer Informatics - October 15, 2015 Category: Cancer & Oncology Authors: Anne-Laure BoulesteixVeronika StierleAlexander Hapfelmeier Source Type: research
FXYD5 is a Marker for Poor Prognosis and a Potential Driver for Metastasis in Ovarian Carcinomas
Ovarian cancer (OC) is a leading cause of cancer mortality, but aside from a few well-studied mutations, very little is known about its underlying causes. As such, we performed survival analysis on ovarian copy number amplifications and gene expression datasets presented by The Cancer Genome Atlas in order to identify potential drivers and markers of aggressive OC. Additionally, two independent datasets from the Gene Expression Omnibus web platform were used to validate the identified markers. Based on our analysis, we identified FXYD5, a glycoprotein known to reduce cell adhesion, as a potential driver of metastasis and a...
Source: Cancer Informatics - October 12, 2015 Category: Cancer & Oncology Authors: Pichai RamanTimothy PurwinRichard PestellAydin Tozeren Source Type: research
Multiscale Model of Colorectal Cancer Using the Cellular Potts Framework
Colorectal cancer (CRC) is one of the major causes of death in the developed world and forms a canonical example of tumorigenesis. CRC arises from a string of mutations of individual cells in the colorectal crypt, making it particularly suited for multiscale multicellular modeling, where mutations of individual cells can be clearly represented and their effects readily tracked. In this paper, we present a multicellular model of the onset of colorectal cancer, utilizing the cellular Potts model (CPM). We use the model to investigate how, through the modification of their mechanical properties, mutant cells colonize the cryp...
Source: Cancer Informatics - October 4, 2015 Category: Cancer & Oncology Authors: James M. Osborne Source Type: research
