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|Abstract:||The discovery of driver mutations is one of the key motivations for cancer genome sequencing. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancers across 38 tumour types, we describe DriverPower, a software package that uses mutational burden and functional impact evidence to identify driver mutations in coding and non-coding sites within cancer whole genomes. Using a total of 1373 genomic features derived from public sources, DriverPower’s background mutation model explains up to 93% of the regional variance in the mutation rate across multiple tumour types. By incorporating functional impact scores, we are able to further increase the accuracy of driver discovery. Testing across a collection of 2583 cancer genomes from the 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 Working 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.|
|Citation:||Shuai, Shimin, PCAWG Drivers and Functional Interpretation Working Group, Steven Gallinger, Lincoln Stein, and PCAWG Consortium. "Combined burden and functional impact tests for cancer driver discovery using DriverPower." Nature Communications 11, no. 1 (2020). doi:10.1038/s41467-019-13929-1|
|Type of Material:||Journal Article|
|Journal/Proceeding Title:||Nature Communications|
|Version:||Final published version. This is an open access article.|
|Notes:||Supplementary Information: https://www.nature.com/articles/s41467-019-13929-1#Sec33|
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