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

Jornsten, Rebecka; Abenius, Tobias; Kling, Teresia; Schmidt, Linnea; Johansson, Erik; Nordling, Torbjorn E. M.; Nordlander, Bodil; Sander, Chris; Gennemark, Peter and Funa, Keiko, et al. (2011) In Molecular Systems Biology 7.
Abstract
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,... (More)
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 (Less)
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cancer biology, cancer genomics, glioblastoma
in
Molecular Systems Biology
volume
7
publisher
Nature Publishing Group
external identifiers
  • wos:000290411600004
  • scopus:79955562657
ISSN
1744-4292
DOI
10.1038/msb.2011.17
language
English
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yes
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b6c85057-81f2-4700-b9fb-581a985a5c33 (old id 1987685)
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2011-07-01 09:24:35
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2017-11-05 03:50:56
@article{b6c85057-81f2-4700-b9fb-581a985a5c33,
  abstract     = {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},
  author       = {Jornsten, Rebecka and Abenius, Tobias and Kling, Teresia and Schmidt, Linnea and Johansson, Erik and Nordling, Torbjorn E. M. and Nordlander, Bodil and Sander, Chris and Gennemark, Peter and Funa, Keiko and Nilsson, Björn and Lindahl, Linda and Nelander, Sven},
  issn         = {1744-4292},
  keyword      = {cancer biology,cancer genomics,glioblastoma},
  language     = {eng},
  publisher    = {Nature Publishing Group},
  series       = {Molecular Systems Biology},
  title        = {Network modeling of the transcriptional effects of copy number aberrations in glioblastoma},
  url          = {http://dx.doi.org/10.1038/msb.2011.17},
  volume       = {7},
  year         = {2011},
}