Network modeling of the transcriptional effects of copy number aberrations in glioblastoma
2011 (English)In: Molecular Systems Biology, ISSN 1744-4292, Vol. 7, 486- p.Article in journal (Refereed) Published
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.
Place, publisher, year, edition, pages
2011. Vol. 7, 486- p.
cancer biology, cancer genomics, glioblastoma
Medical and Health Sciences
IdentifiersURN: urn:nbn:se:uu:diva-153949DOI: 10.1038/msb.2011.17ISI: 000290411600004PubMedID: 21525872OAI: oai:DiVA.org:uu-153949DiVA: diva2:419195