Journal Article

Automated Network Analysis Identifies Core Pathways in Glioblastoma

Demir, Emek, Schultz, Nikolaus, Sander, Chris, Cerami, Ethan, Taylor, Barry S.

2010

Overview

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. 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. We confirm and extend the observation that GBM alterations tend to occur within specific functional modules, in spite of considerable patient-to-patient variation, and that two of the largest modules involve signaling via p53, Rb, PI3K and receptor protein kinases. We also identify new candidate drivers in GBM, including AGAP2/CENTG1, a putative oncogene and an activator of the PI3K pathway; and, three additional significantly altered modules, including one involved in microtubule organization. To facilitate the application of our network-based approach to additional cancer types, we make the method freely available as part of a software tool called NetBox.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: 1-Phosphatidylinositol 3-kinase / Algorithms / Analysis / Bioinformatics / Biology / Brain / Brain cancer / Brain Neoplasms - genetics / Brain tumors / Cancer / Cancer genetics / Cartography / Cellular communication / Computational Biology/Genomics / Computational Biology/Systems Biology / Copy number / Deoxyribonucleic acid / DNA / Endothelium / Gene expression / Gene Regulatory Networks / Genes / Genetic aspects / Genetic Predisposition to Disease / Genetics and Genomics/Cancer Genetics / Genetics and Genomics/Genetics of Disease / Genomes / Genomics / Glioblastoma / Glioblastoma - genetics / Glioblastoma multiforme / GTP-Binding Proteins - genetics / GTPase-Activating Proteins - genetics / Health aspects / Humans / Hypotheses / Kinases / Metabolism / Metastasis / Models, Genetic / Modules / Mutation / Natural language processing / Network analysis / Nucleotide sequence / p53 Protein / Passengers / Phosphatidylinositol 3-Kinases - genetics / Phylogenetics / Protein interaction / Protein kinases / Protein-protein interactions / Proteins / Retinoblastoma protein / Retinoblastoma Protein - genetics / Signal transduction / Signal Transduction - genetics / Signaling / Software / Software development tools / Technology application / Tumor proteins / Tumor Suppressor Protein p53 - genetics / Tumors / Wireless communications