A major challenge for distinguishing cancer-causing driver mutations from inconsequential passenger mutations is the long-tail of infrequently mutated genes in cancer genomes. Here, we present and evaluate a method for prioritizing cancer genes accounting not only for mutations in individual genes but also in their neighbors in functional networks, MUFFINN (MUtations For Functional Impact on Network Neighbors). This pathway-centric method shows high sensitivity compared with gene-centric analyses of mutation data. Notably, only a marginal decrease in performance is observed when using 10 % of TCGA patient samples, suggesting the method may potentiate cancer genome projects with small patient populations.

This research was partly supported by grants from the National Research Foundation of Korea (2012M3A9B4028641, 2012M3A9C7050151, 2015R1A2A1A15055859), Brain Korea 21 (BK21) PLUS program to I.L., Global PH.D Fellowship Program through the National Research Foundation of Korea (2011-0008548) to A.C., the European Research Council (Consolidator grant IR-DC, 616434), the Spanish Ministry of Economy and Competitiveness (BFU2011-26206 and SEV-2012-0208), the AXA Research Fund, and AGAUR to B.L., the FP7 FET grant MAESTRA (ICT-2013-612944) and Marie Curie Actions to F.S.