Automated network analysis identifies core pathways in glioblastoma Journal Article
| Authors: | Cerami, E.; Demir, E.; Schultz, N.; Taylor, B. S.; Sander, C. |
| Article 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. 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 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. © 2010 Cerami et al. |
| Keywords: | signal transduction; gene mutation; gene sequence; sequence analysis; genetics; mutation; brain tumor; brain neoplasms; genetic predisposition to disease; biological model; gtp-binding proteins; algorithms; phosphatidylinositol 3 kinase; protein p53; dna; algorithm; glioblastoma; 1-phosphatidylinositol 3-kinase; tumor suppressor protein p53; genomics; guanosine triphosphatase activating protein; gtpase-activating proteins; models, genetic; gene regulatory network; molecular interaction; computer program; software; tp53 protein, human; retinoblastoma protein; genetic predisposition; guanine nucleotide binding protein; nerve cell network; gene regulatory networks; centg1 protein, human |
| Journal Title: | PLoS ONE |
| Volume: | 5 |
| Issue: | 2 |
| ISSN: | 1932-6203 |
| Publisher: | Public Library of Science |
| Date Published: | 2010-02-12 |
| Start Page: | e8918 |
| Language: | English |
| DOI: | 10.1371/journal.pone.0008918 |
| PUBMED: | 20169195 |
| PROVIDER: | scopus |
| PMCID: | PMC2820542 |
| DOI/URL: | |
| Notes: | --- - "Cited By (since 1996): 13" - "Export Date: 20 April 2011" - "Art. No.: e8918" - "Source: Scopus" |
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