System-scale network modeling of cancer using EPoC
Artikel i vetenskaplig tidskrift, 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.

Författare

Tobias Abenius

Göteborgs universitet

Chalmers, Matematiska vetenskaper, matematisk statistik

Rebecka Jörnsten

Göteborgs universitet

Chalmers, Matematiska vetenskaper, matematisk statistik

Teresia Kling

Göteborgs universitet

Linnéa Schmidt

Göteborgs universitet

José Sánchez

Chalmers, Matematiska vetenskaper, matematisk statistik

Göteborgs universitet

Sven Nelander

Göteborgs universitet

Advances in Experimental Medicine and Biology

0065-2598 (ISSN)

Vol. 736 5 617-643

Ämneskategorier

Biokemi och molekylärbiologi

Cell- och molekylärbiologi

Annan biologi

Mikrobiologi inom det medicinska området

Bioinformatik och systembiologi

Sannolikhetsteori och statistik

Styrkeområden

Livsvetenskaper och teknik

DOI

10.1007/978-1-4419-7210-1_37

PubMed

22161356

ISBN

9781441972095