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

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 e...

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Detalles Bibliográficos
Autores principales: Jörnsten, Rebecka, Abenius, Tobias, Kling, Teresia, Schmidt, Linnéa, Johansson, Erik, Nordling, Torbjörn E M, Nordlander, Bodil, Sander, Chris, Gennemark, Peter, Funa, Keiko, Nilsson, Björn, Lindahl, Linda, Nelander, Sven
Formato: Texto
Lenguaje:English
Publicado: European Molecular Biology Organization 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3101951/
https://www.ncbi.nlm.nih.gov/pubmed/21525872
http://dx.doi.org/10.1038/msb.2011.17
Descripción
Sumario: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.