DGPathinter: a novel model for identifying driver genes via knowledge-driven matrix factorization with prior knowledge from interactome and pathways

Abstract

Cataloging mutated driver genes that confer a selective growth advantage for tumor cells from sporadic passenger mutations is a critical problem in cancer genomic research. Previous studies have reported that some driver genes are not highly frequently mutated and cannot be tested as statistically significant, which complicates the identification of driver genes. To address this issue, some existing approaches incorporate prior knowledge from an interactome to detect driver genes which may be dysregulated by interaction network context. However, altered operations of many pathways in cancer progression have been frequently observed, and prior knowledge from pathways is not exploited in the driver gene identification task. In this paper, we introduce a driver gene prioritization method called driver gene identification through pathway and interactome information (DGPathinter), which is based on knowledge-based matrix factorization model with prior knowledge from both interactome and pathways incorporated. When DGPathinter is applied on somatic mutation datasets of three types of cancers and evaluated by known driver genes, the prioritizing performances of DGPathinter are better than the existing interactome driven methods. The top ranked genes detected by DGPathinter are also significantly enriched for known driver genes. Moreover, most of the top ranked scored pathways given by DGPathinter are also cancer progression-associated pathways. These results suggest that DGPathinter is a useful tool to identify potential driver genes.