System-scale network modeling of cancer using EPoC
Journal article, 2012

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.


Tobias Abenius

University of Gothenburg

Chalmers, Mathematical Sciences, Mathematical Statistics

Rebecka Jörnsten

University of Gothenburg

Chalmers, Mathematical Sciences, Mathematical Statistics

Teresia Kling

University of Gothenburg

Linnéa Schmidt

University of Gothenburg

José Sánchez

Chalmers, Mathematical Sciences, Mathematical Statistics

University of Gothenburg

Sven Nelander

University of Gothenburg

Advances in Experimental Medicine and Biology

0065-2598 (ISSN)

Vol. 736 5 617-643

Subject Categories

Biochemistry and Molecular Biology

Cell and Molecular Biology

Other Biological Topics

Microbiology in the medical area

Bioinformatics and Systems Biology

Probability Theory and Statistics

Areas of Advance

Life Science Engineering (2010-2018)







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