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System-scale network modeling of cancer using EPoC

Journal article
Authors Tobias Abenius
Rebecka Jörnsten
Teresia Kling
Linnéa Schmidt
José Sánchez
Sven Nelander
Published in Advances in Experimental Medicine and Biology
Volume 736
Issue 5
Pages 617-643
ISSN 0065-2598
Publication year 2012
Published at Department of Mathematical Sciences, Mathematical Statistics
Institute of Medicine
Pages 617-643
Language en
Links dx.doi.org/10.1007/978-1-4419-7210-...
Subject categories Mathematical statistics, Molecular biology, Bioinformatics and Systems Biology, Functional genomics, Cell biology, Tumour biology, Molecular medicine, Medical microbiology

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.

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