SSA-ME Detection of cancer driver genes using mutual exclusivity by small subnetwork analysis

Because of its clonal evolution a tumor rarely contains multiple genomic alterations in the same pathway as disrupting the pathway by one gene often is sufficient to confer the complete fitness advantage. As a result, many cancer driver genes display mutual exclusivity across tumors. However, search...

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Main Authors:	Pulido-Tamayo, Sergio, Weytjens, Bram, De Maeyer, Dries, Marchal, Kathleen
Format:	Online
Language:	English
Published:	Nature Publishing Group 2016
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5093737/

id	pubmed-5093737
recordtype	oai_dc
spelling	pubmed-50937372016-11-10 SSA-ME Detection of cancer driver genes using mutual exclusivity by small subnetwork analysis Pulido-Tamayo, Sergio Weytjens, Bram De Maeyer, Dries Marchal, Kathleen Article Because of its clonal evolution a tumor rarely contains multiple genomic alterations in the same pathway as disrupting the pathway by one gene often is sufficient to confer the complete fitness advantage. As a result, many cancer driver genes display mutual exclusivity across tumors. However, searching for mutually exclusive gene sets requires analyzing all possible combinations of genes, leading to a problem which is typically too computationally complex to be solved without a stringent a priori filtering, restricting the mutations included in the analysis. To overcome this problem, we present SSA-ME, a network-based method to detect cancer driver genes based on independently scoring small subnetworks for mutual exclusivity using a reinforced learning approach. Because of the algorithmic efficiency, no stringent upfront filtering is required. Analysis of TCGA cancer datasets illustrates the added value of SSA-ME: well-known recurrently mutated but also rarely mutated drivers are prioritized. We show that using mutual exclusivity to detect cancer driver genes is complementary to state-of-the-art approaches. This framework, in which a large number of small subnetworks are being analyzed in order to solve a computationally complex problem (SSA), can be generically applied to any problem in which local neighborhoods in a network hold useful information. Nature Publishing Group 2016-11-03 /pmc/articles/PMC5093737/ /pubmed/27808240 http://dx.doi.org/10.1038/srep36257 Text en Copyright © 2016, The Author(s) http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Pulido-Tamayo, Sergio Weytjens, Bram De Maeyer, Dries Marchal, Kathleen
spellingShingle	Pulido-Tamayo, Sergio Weytjens, Bram De Maeyer, Dries Marchal, Kathleen SSA-ME Detection of cancer driver genes using mutual exclusivity by small subnetwork analysis
author_facet	Pulido-Tamayo, Sergio Weytjens, Bram De Maeyer, Dries Marchal, Kathleen
author_sort	Pulido-Tamayo, Sergio
title	SSA-ME Detection of cancer driver genes using mutual exclusivity by small subnetwork analysis
title_short	SSA-ME Detection of cancer driver genes using mutual exclusivity by small subnetwork analysis
title_full	SSA-ME Detection of cancer driver genes using mutual exclusivity by small subnetwork analysis
title_fullStr	SSA-ME Detection of cancer driver genes using mutual exclusivity by small subnetwork analysis
title_full_unstemmed	SSA-ME Detection of cancer driver genes using mutual exclusivity by small subnetwork analysis
title_sort	ssa-me detection of cancer driver genes using mutual exclusivity by small subnetwork analysis
description	Because of its clonal evolution a tumor rarely contains multiple genomic alterations in the same pathway as disrupting the pathway by one gene often is sufficient to confer the complete fitness advantage. As a result, many cancer driver genes display mutual exclusivity across tumors. However, searching for mutually exclusive gene sets requires analyzing all possible combinations of genes, leading to a problem which is typically too computationally complex to be solved without a stringent a priori filtering, restricting the mutations included in the analysis. To overcome this problem, we present SSA-ME, a network-based method to detect cancer driver genes based on independently scoring small subnetworks for mutual exclusivity using a reinforced learning approach. Because of the algorithmic efficiency, no stringent upfront filtering is required. Analysis of TCGA cancer datasets illustrates the added value of SSA-ME: well-known recurrently mutated but also rarely mutated drivers are prioritized. We show that using mutual exclusivity to detect cancer driver genes is complementary to state-of-the-art approaches. This framework, in which a large number of small subnetworks are being analyzed in order to solve a computationally complex problem (SSA), can be generically applied to any problem in which local neighborhoods in a network hold useful information.
publisher	Nature Publishing Group
publishDate	2016
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5093737/
_version_	1613711267389767680

SSA-ME Detection of cancer driver genes using mutual exclusivity by small subnetwork analysis

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