Ara Cho1, Jung Eun Shim1, Eiru Kim1, Fran Supek2,3,4, Ben Lehner2,3* and Insuk Lee1*
1Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, Korea. 2EMBL-CRG Systems Biology Unit, Centre for Genomic Regulation (CRG), 08003 Barcelona, Spain. 3Universitat Pompeu Fabra (UPF), 08003 Barcelona, Spain. 4Division of Electronics, Rudjer Boskovic Institute, 10000 Zagreb, Croatia.
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.
Keywords : Cancer gene prediction, Cancer somatic mutation, Cancer genomes, Mutation frequency, Functional gene network, Pathway-centric analysis