MaxMIF: A New Method for Identifying Cancer Driver Genes through Effective Data Integration. Issue 9 (23rd July 2018) - Hou, Yingnan; Gao, Bo; Li, Guojun; Su, Zhengchang
Abstract: Identification of a few cancer driver mutation genes from a much larger number of passenger mutation genes in cancer samples remains a highly challenging task. Here, a novel method for distinguishing the driver genes from the passenger genes by effective integration of somatic mutation data and molecular interaction data using a maximal mutational impact function (MaxMIF) is presented. When evaluated on six somatic mutation datasets of Pan‐Cancer and 19 datasets of different cancer types from TCGA, MaxMIF almost always significantly outperforms all the existing state‐of‐the‐art methods in terms of predictive accuracy, sensitivity, and specificity. It recovers about 30% more known cancer genes in 500 top‐ranked candidate genes than the best among the other tools evaluated. MaxMIF is also highly robust to data perturbation. Intriguingly, MaxMIF is able to identify potential cancer driver genes, with strong experimental data support. Therefore, MaxMIF can be very useful for identifying or prioritizing cancer driver genes in the increasing number of available cancer genomic data. Abstract : A new method and a tool for identifying potential cancer driver genes, based on combination of mutation data and protein–protein interaction data by a maximal mutational impact function, is presented. The tool is designed to be effective and user‐friendly with high predictive accuracy, sensitivity, and specificity, and is able to concentrate most likely candidate genes into the top‐ranked candidate list.