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

Molecular Systems Biology

Rebecka Jörnsten²  Tobias Abenius²  Teresia Kling¹  Linnéa Schmidt¹  Erik Johansson¹  Torbjörn E M Nordling⁵  Bodil Nordlander¹  Chris Sander⁴  Peter Gennemark²  Keiko Funa¹  Björn Nilsson³  Linda Lindahl¹ 

[1] Sahlgrenska Cancer Center, Institute of Medicine, Gothenburg, Sweden;Mathematical Sciences, University of Gothenburg and Chalmers University of Technology, Gothenburg, Sweden;Department of Laboratory Medicine, Lund University, Lund, Sweden;Memorial Sloan-Kettering Cancer Center, Computational Biology Center, New York, NY, USA;Automatic Control, School of Electrical Engineering, KTH Royal Institute of Technology, Stockholm, Sweden

关键词: cancer biology;    cancer genomics;    glioblastoma;   

DOI  :  10.1038/msb.2011.17

来源: Wiley

【 摘 要 】

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

【 授权许可】

CC BY-NC-SA   
Copyright © 2011 EMBO and Macmillan Publishers Limited

Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.