BACKGROUND: Assays of multiple tumor samples frequently reveal recurrent genomic aberrations, including point mutations and copy-number alterations, that affect individual genes. Analyses that extend beyond single genes are often restricted to examining pathways, interactions and functional modules that are already known. METHODS: We present a method that identifies functional modules without any information other than patterns of recurrent and mutually exclusive aberrations (RME patterns) that arise due to positive selection for key cancer phenotypes. Our algorithm efficiently constructs and searches networks of potential interactions and identifies significant modules (RME modules) by using the algorithmic significance test. RESULTS: We apply the method to the TCGA collection of 145 glioblastoma samples, resulting in extension of known pathways and discovery of new functional modules. The method predicts a role for EP300 that was previously unknown in glioblastoma. We demonstrate the clinical relevance of these results by validating that expression of EP300 is prognostic, predicting survival independent of age at diagnosis and tumor grade. CONCLUSIONS: We have developed a sensitive, simple, and fast method for automatically detecting functional modules in tumors based solely on patterns of recurrent genomic aberration. Due to its ability to analyze very large amounts of diverse data, we expect it to be increasingly useful when applied to the many tumor panels scheduled to be assayed in the near future.
BACKGROUND: Assays of multiple tumor samples frequently reveal recurrent genomic aberrations, including point mutations and copy-number alterations, that affect individual genes. Analyses that extend beyond single genes are often restricted to examining pathways, interactions and functional modules that are already known. METHODS: We present a method that identifies functional modules without any information other than patterns of recurrent and mutually exclusive aberrations (RME patterns) that arise due to positive selection for key cancer phenotypes. Our algorithm efficiently constructs and searches networks of potential interactions and identifies significant modules (RME modules) by using the algorithmic significance test. RESULTS: We apply the method to the TCGA collection of 145 glioblastoma samples, resulting in extension of known pathways and discovery of new functional modules. The method predicts a role for EP300 that was previously unknown in glioblastoma. We demonstrate the clinical relevance of these results by validating that expression of EP300 is prognostic, predicting survival independent of age at diagnosis and tumor grade. CONCLUSIONS: We have developed a sensitive, simple, and fast method for automatically detecting functional modules in tumors based solely on patterns of recurrent genomic aberration. Due to its ability to analyze very large amounts of diverse data, we expect it to be increasingly useful when applied to the many tumor panels scheduled to be assayed in the near future.
Authors: Scott A Tomlins; Daniel R Rhodes; Sven Perner; Saravana M Dhanasekaran; Rohit Mehra; Xiao-Wei Sun; Sooryanarayana Varambally; Xuhong Cao; Joelle Tchinda; Rainer Kuefer; Charles Lee; James E Montie; Rajal B Shah; Kenneth J Pienta; Mark A Rubin; Arul M Chinnaiyan Journal: Science Date: 2005-10-28 Impact factor: 47.728
Authors: Lonneke A M Gravendeel; Mathilde C M Kouwenhoven; Olivier Gevaert; Johan J de Rooi; Andrew P Stubbs; J Elza Duijm; Anneleen Daemen; Fonnet E Bleeker; Linda B C Bralten; Nanne K Kloosterhof; Bart De Moor; Paul H C Eilers; Peter J van der Spek; Johan M Kros; Peter A E Sillevis Smitt; Martin J van den Bent; Pim J French Journal: Cancer Res Date: 2009-11-17 Impact factor: 12.701
Authors: Barry S Taylor; Jordi Barretina; Nicholas D Socci; Penelope Decarolis; Marc Ladanyi; Matthew Meyerson; Samuel Singer; Chris Sander Journal: PLoS One Date: 2008-09-11 Impact factor: 3.240
Authors: Giulio Caravagna; Alex Graudenzi; Daniele Ramazzotti; Rebeca Sanz-Pamplona; Luca De Sano; Giancarlo Mauri; Victor Moreno; Marco Antoniotti; Bud Mishra Journal: Proc Natl Acad Sci U S A Date: 2016-06-28 Impact factor: 11.205
Authors: Joris van de Haar; Sander Canisius; Michael K Yu; Emile E Voest; Lodewyk F A Wessels; Trey Ideker Journal: Cell Date: 2019-05-30 Impact factor: 41.582