title: System-scale network modeling of cancer using EPoC.
abstract: One of the central problems of cancer systems biology is to understand the complex molecular changes of cancerous cells and tissues, and use this understanding to support the development of new targeted therapies. EPoC (Endogenous Perturbation analysis of Cancer) is a network modeling technique for tumor molecular profiles. EPoC models are constructed from combined copy number aberration (CNA) and mRNA data and aim to (1) identify genes whose copy number aberrations significantly affect target mRNA expression and (2) generate markers for long- and short-term survival of cancer patients. Models are constructed by a combination of regression and bootstrapping methods. Prognostic scores are obtained from a singular value decomposition of the networks. We have previously analyzed the performance of EPoC using glioblastoma data from The Cancer Genome Atlas (TCGA) consortium, and have shown that resulting network models contain both known and candidate disease-relevant genes as network hubs, as well as uncover predictors of patient survival. Here, we give a practical guide how to perform EPoC modeling in practice using R, and present a set of alternative modeling frameworks.
authors.affiliation: Mathematical Sciences, University of Gothenburg and Chalmers University of Technology, 412 96 Gothenburg, Sweden. Tobias.Abenius@chalmers.se
authors.forename: Tobias
authors.initials: T
authors.lastname: Abenius
authors.forename: Rebecka
authors.initials: R
authors.lastname: Jörnsten
authors.forename: Teresia
authors.initials: T
authors.lastname: Kling
authors.forename: Linnéa
authors.initials: L
authors.lastname: Schmidt
authors.forename: José
authors.initials: J
authors.lastname: Sánchez
authors.forename: Sven
authors.initials: S
authors.lastname: Nelander
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000145:classification / Q000379:methods*; D018628:Gene Dosage; D015972:Gene Expression Regulation, Neoplastic; D053263:Gene Regulatory Networks / Q000187:drug effects / Q000235:genetics*; D020022:Genetic Predisposition to Disease / Q000235:genetics; D005909:Glioblastoma / Q000188:drug therapy / Q000235:genetics; D006801:Humans; D008957:Models, Genetic; D009369:Neoplasms / Q000188:drug therapy / Q000235:genetics*; D011379:Prognosis; D015203:Reproducibility of Results; D016019:Survival Analysis; D049490:Systems Biology / Q000379:methods*