Abstract
Cancer results from the acquisition of somatic driver mutations. Several computational tools can predict driver genes from population-scale genomic data, but tools for analyzing personal cancer genomes are underdeveloped. Here we developed iCAGES, a novel statistical framework that infers driver variants by integrating contributions from coding, non-coding, and structural variants, identifies driver genes by combining genomic information and prior biological knowledge, then generates prioritized drug treatment. Analysis on The Cancer Genome Atlas (TCGA) data showed that iCAGES predicts whether patients respond to drug treatment (P = 0.006 by Fisher’s exact test) and long-term survival (P = 0.003 from Cox regression). iCAGES is available at http://icages.wglab.org.Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-016-0390-0) contains supplementary material, which is available to authorized users.
Highlights
Cancer carries somatic mutations acquired during the lifetime of an individual [1]
The first source measures the genomic potential of a gene being a personal cancer driver and the second source measures the prior knowledge of a gene being a driver for a specific cancer subtype, based on previous biological knowledge, through Phenolyzer predictions
We found that our radial support vector machine (SVM) model performed better than all other variant prioritization tools, including general missense mutation scoring tools and cancer-specific driver mutation detecting tools, in distinguishing drivers in general cases and in cancer driver genes
Summary
While the majority of these are “passengers”, which are mutated randomly and functionally neutral, a small proportion are “drivers”, which are causally implicated in oncogenesis [2]. When it comes to a patient, the challenge for his/her molecular diagnosis and treatment lies in rapid and accurate identification of these driver mutations from a large amount of background noise from passenger mutations [3, 4], which is important to devise appropriate targeted therapies. Next-generation sequencing technology has enabled researchers to rapidly identify somatic mutations from a patient by comparing the sequence from his/her tumors with that from blood or other non-cancerous tissues [5] These mutations have been well-classified, annotated, and visualized by endeavors such as IntOgen-mutations [6]. Several other computational tools were developed to help further pinpoint these cancer drivers using readily available personal cancer genomic
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