automated network analysis identifies core pathways in glioblastoma自动化网络分析识别核心通路在胶质母细胞瘤.pdf

Automated Network Analysis Identifies Core Pathways in Glioblastoma 1,2 1 1 1 1 Ethan Cerami *, Emek Demir , Nikolaus Schultz , Barry S. Taylor , Chris Sander 1 Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America, 2 Tri-Institutional Training Program in Computational Biology and Medicine, New York, New York, United States of America Abstract Background: Glioblastoma multiforme (GBM) is the most common and aggressive type of brain tumor in humans and the first cancer with comprehensive genomic profiles mapped by The Cancer Genome Atlas (TCGA) project. A central challenge in large-scale genome projects, such as the TCGA GBM project, is the ability to distinguish cancer-causing ‘‘driver’’ mutations from passively selected ‘‘passenger’’ mutations. Principal Findings: In contrast to a purely frequency based approach to identifying driver mutations in cancer, we propose an automated network-based approach for identifying candidate oncogenic processes and driver genes. The approach is based on the hypothesis that cellular networks contain functional modules, and that tumors target specific modules critical to their growth. Key elements in the approach include combined analysis of sequence mutations and DNA copy number alterations; use of a unified molecular interaction network consisting of both protein-protein interactions and signaling pathways; and identification and statistical assessment of network modules, i.e. cohesive groups of genes of interest with a higher density of interactions within groups than between groups. Conclusions: We confirm and extend the observ