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Network modeling of the transcriptional effects of copy number aberrations in glioblastoma

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Author

  • Rebecka Jornsten
  • Tobias Abenius
  • Teresia Kling
  • Linnea Schmidt
  • Erik Johansson
  • Torbjorn E. M. Nordling
  • Bodil Nordlander
  • Chris Sander
  • Peter Gennemark
  • Keiko Funa
  • Björn Nilsson
  • Linda Lindahl
  • Sven Nelander
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Summary, in English

DNA copy number aberrations (CNAs) are a hallmark of cancer genomes. However, little is known about how such changes affect global gene expression. We develop a modeling framework, EPoC (Endogenous Perturbation analysis of Cancer), to (1) detect disease-driving CNAs and their effect on target mRNA expression, and to (2) stratify cancer patients into long-and short-term survivors. Our method constructs causal network models of gene expression by combining genome-wide DNA-and RNA-level data. Prognostic scores are obtained from a singular value decomposition of the networks. By applying EPoC to glioblastoma data from The Cancer Genome Atlas consortium, we demonstrate that the resulting network models contain known disease-relevant hub genes, reveal interesting candidate hubs, and uncover predictors of patient survival. Targeted validations in four glioblastoma cell lines support selected predictions, and implicate the p53-interacting protein Necdin in suppressing glioblastoma cell growth. We conclude that large-scale network modeling of the effects of CNAs on gene expression may provide insights into the biology of human cancer. Free software in MATLAB and R is provided. Molecular Systems Biology 7: 486; published online 26 April 2011; doi:10.1038/msb.2011.17

Department/s

Publishing year

2011

Language

English

Publication/Series

Molecular Systems Biology

Volume

7

Document type

Journal article

Publisher

Nature Publishing Group

Topic

  • Hematology

Keywords

  • cancer biology
  • cancer genomics
  • glioblastoma

Status

Published

ISBN/ISSN/Other

  • ISSN: 1744-4292