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.
Description
SSA-ME Detection of cancer driver genes using mutual exclusivity by small subnetwork analysis
%0 Journal Article
%1 PulidoTamayo:2016:Sci-Rep:27808240
%A Pulido-Tamayo, S
%A Weytjens, B
%A De Maeyer, D
%A Marchal, K
%D 2016
%J Sci Rep
%K cancer-research drivers fulltext
%P 36257-36257
%R 10.1038/srep36257
%T SSA-ME Detection of cancer driver genes using mutual exclusivity by small subnetwork analysis
%U https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5093737/
%V 6
%X 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.
@article{PulidoTamayo:2016:Sci-Rep:27808240,
abstract = {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.},
added-at = {2017-05-20T23:40:06.000+0200},
author = {Pulido-Tamayo, S and Weytjens, B and De Maeyer, D and Marchal, K},
biburl = {https://www.bibsonomy.org/bibtex/24d3a44c4125ac434ddf18bef71a296d3/marcsaric},
description = {SSA-ME Detection of cancer driver genes using mutual exclusivity by small subnetwork analysis},
doi = {10.1038/srep36257},
interhash = {ca9556dfe71e084bdec9cf2e33b9ca46},
intrahash = {4d3a44c4125ac434ddf18bef71a296d3},
journal = {Sci Rep},
keywords = {cancer-research drivers fulltext},
month = nov,
pages = {36257-36257},
pmid = {27808240},
timestamp = {2017-05-20T23:40:06.000+0200},
title = {SSA-ME Detection of cancer driver genes using mutual exclusivity by small subnetwork analysis},
url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5093737/},
volume = 6,
year = 2016
}