Combined burden and functional impact tests for cancer driver discovery using DriverPower.

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PCAWG Drivers and Functional Interpretation Working Group 
Gallinger, Steven 
Stein, Lincoln D 
PCAWG Consortium 

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.


Funder: Province of Ontario

Algorithms, Genome, Human, Genomics, Humans, MEF2 Transcription Factors, Mutation, Mutation Rate, Neoplasms, Peptide Elongation Factor 1, Receptors, G-Protein-Coupled, Software, Whole Genome Sequencing
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Nat Commun
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Springer Science and Business Media LLC