System-Scale Network Modeling of Cancer Using EPoC

  • Tobias Abenius
  • Rebecka Jörnsten
  • Teresia Kling
  • Linnéa Schmidt
  • José Sánchez
  • Sven NelanderEmail author
Conference paper
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 736)


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.


Singular Value Decomposition Bayesian Information Criterion Copy Number Aberration Altered Copy Number International Cancer Genome Consortium 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors thank the editors and reviewer for their constructive comments. This project receives funding from Cancerfonden, Barncancerfonden (NB-CNS consortium), Vetenskapsradet (SN,RJ), BioCare (SN).


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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Tobias Abenius
    • 1
  • Rebecka Jörnsten
    • 1
  • Teresia Kling
    • 2
  • Linnéa Schmidt
    • 2
  • José Sánchez
    • 1
  • Sven Nelander
    • 2
    Email author
  1. 1.Mathematical SciencesUniversity of Gothenburg and Chalmers University of TechnologyGothenburgSweden
  2. 2.Cancer Center SahlgrenskaInstitute of MedicineGothenburgSweden

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