Network modeling of the transcriptional effects of copy number aberrations in glioblastoma
Journal article, 2011
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.
Models
Gene Expression Profiling
metabolism
pathology
Gene Dosage
Nuclear Proteins
genetics
Genetic
Genome-Wide Association Study
metabolism
Databases
Factual
genetics
pathology
Transcriptional Activation
genetics
Human
Cell Line
genetics
Glioblastoma
metabolism
Gene Regulatory Networks
mortality
Chromosome Aberrations
Nerve Tissue Proteins
Prognosis
Software
genetics
metabolism
Neoplastic
genetics
Tumor Suppressor Protein p53
Humans
mortality
Genome
Tumor
Nervous System Neoplasms
Gene Expression Regulation
metabolism
Author
Rebecka Jörnsten
University of Gothenburg
Chalmers, Mathematical Sciences, Mathematical Statistics
Tobias Abenius
University of Gothenburg
Chalmers, Mathematical Sciences, Mathematical Statistics
Teresia Kling
University of Gothenburg
Linnéa Schmidt
University of Gothenburg
Erik Johansson
University of Gothenburg
Torbjörn E M Nordling
Royal Institute of Technology (KTH)
Bodil Nordlander
University of Gothenburg
Chris Sander
Memorial Sloan-Kettering Cancer Center
Peter Gennemark
University of Gothenburg
Chalmers, Mathematical Sciences
Keiko Funa
University of Gothenburg
Björn Nilsson
Lund University
Linda Lindahl
University of Gothenburg
Sven Nelander
University of Gothenburg
Molecular Systems Biology
17444292 (eISSN)
Vol. 7 486- 486Subject Categories
Cell and Molecular Biology
Other Biological Topics
Bioinformatics and Systems Biology
Probability Theory and Statistics
DOI
10.1038/msb.2011.17
PubMed
21525872