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

PCAWG Drivers and Functional Interpretation Working Group, PCAWG Consortium

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Article number734
JournalNature communications
Volume11
Issue number1
DOIs
StatePublished - Dec 1 2020

All Science Journal Classification (ASJC) codes

  • Chemistry(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Physics and Astronomy(all)

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    PCAWG Drivers and Functional Interpretation Working Group, & PCAWG Consortium (2020). Combined burden and functional impact tests for cancer driver discovery using DriverPower. Nature communications, 11(1), [734]. https://doi.org/10.1038/s41467-019-13929-1