RT Journal Article
SR Electronic
T1 ModulOmics: Integrating Multi-Omics Data to Identify Cancer Driver Modules
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 288399
DO 10.1101/288399
A1 Dana Silverbush
A1 Simona Cristea
A1 Gali Yanovich
A1 Tamar Geiger
A1 Niko Beerenwinkel
A1 Roded Sharan
YR 2018
UL http://biorxiv.org/content/early/2018/03/24/288399.abstract
AB The identification of molecular pathways driving cancer progression is a fundamental unsolved problem in tumorigenesis, which can substantially further our understanding of cancer mechanisms and inform the development of targeted therapies. Most current approaches to address this problem use primarily somatic mutations, not fully exploiting additional layers of biological information. Here, we describe ModulOmics, a method to de novo identify cancer driver pathways, or modules, by integrating multiple data types (protein-protein interactions, mutual exclusivity of mutations or copy number alterations, transcriptional co-regulation, and RNA co-expression) into a single probabilistic model. To efficiently search the exponential space of candidate modules, ModulOmics employs a two-step optimization procedure that combines integer linear programming with stochastic search. Across several cancer types, ModulOmics identifies highly functionally connected modules enriched with cancer driver genes, outperforming state-of-the-art methods. For breast cancer subtypes, the inferred modules recapitulate known molecular mechanisms and suggest novel subtype-specific functionalities. These findings are supported by an independent patient cohort, as well as independent proteomic and phosphoproteomic datasets.