A major challenge in cancer genomics is to identify cancer driver genes and modules. Most existing methods to identify cancer driver modules (iCDM) identify groups of genes whose somatic mutational patterns exhibit either mutual exclusivity or high coverage of patient samples, without considering other biological information from multiomics data sets. Here we integrate mutual exclusivity, coverage, and protein-protein interaction information to construct an edge-weighted network, and present a graph clustering approach based on symmetric non-negative matrix factorization to iCDM. iCDM was tested on pan-cancer data and the results were compared with those from several advanced computational methods. Our approach outperformed other methods in recovering known cancer driver modules, and the identified driver modules showed high accuracy in classifying normal and tumor samples.
