Mutual exclusivity analysis identifies oncogenic network modules.

Details

Serval ID
serval:BIB_84A4C8EC9C76
Type
Article: article from journal or magazin.
Collection
Publications
Title
Mutual exclusivity analysis identifies oncogenic network modules.
Journal
Genome research
Author(s)
Ciriello G., Cerami E., Sander C., Schultz N.
ISSN
1549-5469 (Electronic)
ISSN-L
1088-9051
Publication state
Published
Issued date
02/2012
Peer-reviewed
Oui
Volume
22
Number
2
Pages
398-406
Language
english
Notes
Publication types: Journal Article ; Research Support, N.I.H., Extramural
Publication Status: ppublish
Abstract
Although individual tumors of the same clinical type have surprisingly diverse genomic alterations, these events tend to occur in a limited number of pathways, and alterations that affect the same pathway tend to not co-occur in the same patient. While pathway analysis has been a powerful tool in cancer genomics, our knowledge of oncogenic pathway modules is incomplete. To systematically identify such modules, we have developed a novel method, Mutual Exclusivity Modules in cancer (MEMo). The method uses correlation analysis and statistical tests to identify network modules by three criteria: (1) Member genes are recurrently altered across a set of tumor samples; (2) member genes are known to or are likely to participate in the same biological process; and (3) alteration events within the modules are mutually exclusive. Applied to data from the Cancer Genome Atlas (TCGA), the method identifies the principal known altered modules in glioblastoma (GBM) and highlights the striking mutual exclusivity of genomic alterations in the PI(3)K, p53, and Rb pathways. In serous ovarian cancer, we make the novel observation that inactivation of BRCA1 and BRCA2 is mutually exclusive of amplification of CCNE1 and inactivation of RB1, suggesting distinct alternative causes of genomic instability in this cancer type; and, we identify RBBP8 as a candidate oncogene involved in Rb-mediated cell cycle control. When applied to any cancer genomics data set, the algorithm can nominate oncogenic alterations that have a particularly strong selective effect and may also be useful in the design of therapeutic combinations in cases where mutual exclusivity reflects synthetic lethality.
Keywords
Algorithms, Female, Gene Expression Regulation, Neoplastic, Gene Regulatory Networks, Glioblastoma/genetics, Humans, Internet, Male, Models, Genetic, Neoplasms/genetics, Ovarian Neoplasms/genetics, Signal Transduction, Software
Pubmed
Web of science
Create date
06/07/2018 11:02
Last modification date
21/08/2019 5:34
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