%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 }