title: Automated network analysis identifies core pathways in glioblastoma.
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.
  
  
  RESULTS
  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 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.
authors.affiliation: Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America. gbm-network@cbio.mskcc.org
authors.forename: Ethan
authors.initials: E
authors.lastname: Cerami
authors.forename: Emek
authors.initials: E
authors.lastname: Demir
authors.forename: Nikolaus
authors.initials: N
authors.lastname: Schultz
authors.forename: Barry S
authors.initials: BS
authors.lastname: Taylor
authors.forename: Chris
authors.initials: C
authors.lastname: Sander
mesh_terms: D000465:Algorithms; D001932:Brain Neoplasms / Q000235:genetics*; D019204:GTP-Binding Proteins / Q000235:genetics; D020690:GTPase-Activating Proteins / Q000235:genetics; D053263:Gene Regulatory Networks; D020022:Genetic Predisposition to Disease; D005909:Glioblastoma / Q000235:genetics*; D006801:Humans; D008957:Models, Genetic; D009154:Mutation; D019869:Phosphatidylinositol 3-Kinases / Q000235:genetics; D016160:Retinoblastoma Protein / Q000235:genetics; D015398:Signal Transduction / Q000235:genetics*; D012984:Software; D016159:Tumor Suppressor Protein p53 / Q000235:genetics
chemical_list: D020690:GTPase-Activating Proteins; D016160:Retinoblastoma Protein; C495901:TP53 protein, human; D016159:Tumor Suppressor Protein p53; D019869:Phosphatidylinositol 3-Kinases; C485997:AGAP2 protein, human; D019204:GTP-Binding Proteins