DriveWays: a method for identifying possibly overlapping driver pathways in cancer

Abstract

The majority of the previous methods for identifying cancer driver modules output nonoverlapping modules. This assumption is biologically inaccurate as genes can participate in multiple molecular pathways. This is particularly true for cancer-associated genes as many of them are network hubs connecting functionally distinct set of genes. It is important to provide combinatorial optimization problem definitions modeling this biological phenomenon and to suggest efficient algorithms for its solution. We provide a formal definition of the Overlapping Driver Module Identification in Cancer (ODMIC) problem. We show that the problem is NP-hard. We propose a seed-and-extend based heuristic named DriveWays that identifies overlapping cancer driver modules from the graph built from the IntAct PPI network. DriveWays incorporates mutual exclusivity, coverage, and the network connectivity information of the genes. We show that DriveWays outperforms the state-of-the-art methods in recovering well-known cancer driver genes performed on TCGA pan-cancer data. Additionally, DriveWay’s output modules show a stronger enrichment for the reference pathways in almost all cases. Overall, we show that enabling modules to overlap improves the recovery of functional pathways filtered with known cancer drivers, which essentially constitute the reference set of cancer-related pathways.

PersonaDrive: A Method for the Identification and Prioritization of Personalized Cancer Drivers

“…We note that similar overlap score definitions have been employed previously in measuring amount of overlaps among pairs of groups in studies on clustering biological networks (Nepusz et al, 2012;Baali et al, 2020).…”

“…Several related versions of the cancer driver identification problem have been proposed. In one version the goal is to come up with a set of driver modules where each module consists of genes expected to be important in the development of the cancer under study (Leiserson et al, 2015;Ciriello et al, 2012a;Vandin et al, 2012;Zhao et al, 2012;Leiserson et al, 2013;Babur et al, 2015;Kim et al, 2015;Ahmed et al, 2019;Baali et al, 2020). Another problem version sets the goal as that of ranking the altered genes with respect to their potentials for being cancer drivers.…”