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.

Biological Networks for Cancer Candidate Biomarkers Discovery

“…Endogenous perturbation analysis of cancer is a causal network model that explains the transcriptional consequences of DNA copy number alterations to detect survival markers for glioblastoma. 12,13 Shi et al 14 also developed a network model that combined copy number alteration and mRNA expression data using a sparse double Laplacian shrinkage (SDLS) method. The advantage of the SDLS is that it effectively accommodates correlations on both sides of the gene expression and copy number alteration regression.…”