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DriverPower: Combined burden and functional impact tests for cancer driver discovery

By Shimin Shuai, Steven Gallinger, Lincoln Stein, on behalf of the PCAWG Drivers and Functional Interpretation Group and the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Network

Posted 06 Nov 2017
bioRxiv DOI: 10.1101/215244

We describe DriverPower, a software package that uses mutational burden and functional impact evidence to identify cancer driver mutations in coding and non-coding sites within cancer whole genomes. Using a total of 1,373 genomic features derived from public sources, DriverPower's background mutation model explains up to 93% of the regional variance in the mutation rate across a variety of tumour types. By incorporating functional impact scores, we are able to further increase the accuracy of driver discovery. Testing across a collection of 2,583 cancer genomes from the Pan-Cancer Analysis of Whole Genomes (PCAWG) project, DriverPower identifies 217 coding and 95 non-coding driver candidates. Comparing to six published methods used by the PCAWG Drivers and Functional Interpretation Group, DriverPower has the highest F1-score for both coding and non-coding driver discovery. This demonstrates that DriverPower is an effective framework for computational driver discovery.

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