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.

metabolism

Gene Expression Regulation

Models

Nervous System Neoplasms

Tumor

Genome

mortality

Humans

Tumor Suppressor Protein p53

genetics

Neoplastic

metabolism

Gene Expression Profiling

metabolism

pathology

genetics

Gene Dosage

Software

Prognosis

Nerve Tissue Proteins

Chromosome Aberrations

mortality

Gene Regulatory Networks

metabolism

Glioblastoma

genetics

Cell Line

Nuclear Proteins

Human

genetics

Transcriptional Activation

pathology

genetics

Factual

genetics

Databases

metabolism

Genome-Wide Association Study

Genetic

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

1744-4292 (ISSN)

Vol. 7 486- 486

Ämneskategorier

Cell- och molekylärbiologi

Annan biologi

Bioinformatik och systembiologi

Sannolikhetsteori och statistik

DOI

10.1038/msb.2011.17

PubMed

21525872