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

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.

journal_name

Sci Rep

journal_title

Scientific reports

authors

Pulido-Tamayo S,Weytjens B,De Maeyer D,Marchal K

doi

10.1038/srep36257

subject

Has Abstract

pub_date

2016-11-03 00:00:00

pages

36257

issn

2045-2322

pii

srep36257

journal_volume

6

pub_type

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