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, 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.
Department of Information Technology, iGent Toren, Technologiepark 15, 9052 Gent, Belgium.|Department of Plant Biotechnology and Bioinformatics, UGent, Technologiepark 927, 9052 Gent, Belgium.|Bioinformatics Institute Ghent, Technologiepark 927, 9052 Gent, Belgium.|Dept. of Microbial and Molecular Systems, KU Leuven, Kasteelpark Arenberg 20, B-3001 Leuven, Belgium.|Grupo de Investigación en Ciencias Biológicas y Bioprocesos (Cibiop), Universidad EAFIT, Carrera 49 N° 7 Sur-50, Medellín, Colombia.
Sergio
S
Pulido-Tamayo
Department of Information Technology, iGent Toren, Technologiepark 15, 9052 Gent, Belgium.|Department of Plant Biotechnology and Bioinformatics, UGent, Technologiepark 927, 9052 Gent, Belgium.|Bioinformatics Institute Ghent, Technologiepark 927, 9052 Gent, Belgium.|Dept. of Microbial and Molecular Systems, KU Leuven, Kasteelpark Arenberg 20, B-3001 Leuven, Belgium.
Bram
B
Weytjens
Department of Information Technology, iGent Toren, Technologiepark 15, 9052 Gent, Belgium.|Department of Plant Biotechnology and Bioinformatics, UGent, Technologiepark 927, 9052 Gent, Belgium.|Bioinformatics Institute Ghent, Technologiepark 927, 9052 Gent, Belgium.|Dept. of Microbial and Molecular Systems, KU Leuven, Kasteelpark Arenberg 20, B-3001 Leuven, Belgium.
Dries
D
De Maeyer
Department of Information Technology, iGent Toren, Technologiepark 15, 9052 Gent, Belgium.|Department of Plant Biotechnology and Bioinformatics, UGent, Technologiepark 927, 9052 Gent, Belgium.|Bioinformatics Institute Ghent, Technologiepark 927, 9052 Gent, Belgium.|Department of Genetics, University of Pretoria, Hatfield Campus, Pretoria 0028, South Africa.
Kathleen
K
Marchal
D000465:Algorithms; D060965:Clonal Evolution / Q000235:genetics; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D015972:Gene Expression Regulation, Neoplastic; D053263:Gene Regulatory Networks; D020022:Genetic Predisposition to Disease / Q000235:genetics*; D006801:Humans; D009154:Mutation; D009369:Neoplasms / Q000235:genetics*; D011519:Proto-Oncogenes / Q000235:genetics*; D015398:Signal Transduction / Q000235:genetics