Network modeling of the transcriptional effects of copy number aberrations in glioblastoma
Artikel i vetenskaplig tidskrift, 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
Författare
Rebecka Jörnsten
Göteborgs universitet
Chalmers, Matematiska vetenskaper, Matematisk statistik
Tobias Abenius
Göteborgs universitet
Chalmers, Matematiska vetenskaper, Matematisk statistik
Teresia Kling
Göteborgs universitet
Linnéa Schmidt
Göteborgs universitet
Erik Johansson
Göteborgs universitet
Torbjörn E M Nordling
Kungliga Tekniska Högskolan (KTH)
Bodil Nordlander
Göteborgs universitet
Chris Sander
Memorial Sloan-Kettering Cancer Center
Peter Gennemark
Göteborgs universitet
Chalmers, Matematiska vetenskaper
Keiko Funa
Göteborgs universitet
Björn Nilsson
Lunds universitet
Linda Lindahl
Göteborgs universitet
Sven Nelander
Göteborgs universitet
Molecular Systems Biology
17444292 (eISSN)
Vol. 7 486- 486Ämneskategorier
Cell- och molekylärbiologi
Annan biologi
Bioinformatik och systembiologi
Sannolikhetsteori och statistik
DOI
10.1038/msb.2011.17
PubMed
21525872