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.

mortality

metabolism

Glioblastoma

Prognosis

Chromosome Aberrations

pathology

genetics

Genome-Wide Association Study

Gene Dosage

Tumor

Factual

Models

metabolism

metabolism

Gene Regulatory Networks

genetics

Transcriptional Activation

Cell Line

Databases

Genetic

pathology

metabolism

Gene Expression Regulation

Gene Expression Profiling

Neoplastic

mortality

genetics

genetics

Nervous System Neoplasms

Software

genetics

Nuclear Proteins

Genome

Human

Nerve Tissue Proteins

metabolism

genetics

Humans

Tumor Suppressor Protein p53

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

The Royal Institute of Technology (KTH)

Bodil Nordlander

Göteborgs universitet

Chris Sander

Memorial Sloan-Kettering Cancer Center

Peter Gennemark

Chalmers, Matematiska vetenskaper

Göteborgs universitet

Keiko Funa

Goteborgs Universitet

Goteborgs universitet, Institutionen for biomedicin

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