Network modeling of the transcriptional effects of copy number aberrations in glioblastoma
Journal article, 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

Author

Rebecka Jörnsten

University of Gothenburg

Chalmers, Mathematical Sciences, Mathematical Statistics

Tobias Abenius

University of Gothenburg

Chalmers, Mathematical Sciences, Mathematical Statistics

Teresia Kling

University of Gothenburg

Linnéa Schmidt

University of Gothenburg

Erik Johansson

University of Gothenburg

Torbjörn E M Nordling

Royal Institute of Technology (KTH)

Bodil Nordlander

University of Gothenburg

Chris Sander

Memorial Sloan-Kettering Cancer Center

Peter Gennemark

University of Gothenburg

Chalmers, Mathematical Sciences

Keiko Funa

University of Gothenburg

Björn Nilsson

Lund University

Linda Lindahl

University of Gothenburg

Sven Nelander

University of Gothenburg

Molecular Systems Biology

1744-4292 (ISSN)

Vol. 7 486- 486

Subject Categories

Cell and Molecular Biology

Other Biological Topics

Bioinformatics and Systems Biology

Probability Theory and Statistics

DOI

10.1038/msb.2011.17

PubMed

21525872

More information

Latest update

3/7/2018 1