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

Nat Commun. 2020 Feb 5;11(1):734. doi: 10.1038/s41467-019-13929-1.


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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Genome, Human
  • Genomics / methods*
  • Humans
  • MEF2 Transcription Factors / genetics
  • Mutation Rate
  • Mutation*
  • Neoplasms / genetics*
  • Peptide Elongation Factor 1 / genetics
  • Receptors, G-Protein-Coupled / genetics
  • Software*
  • Whole Genome Sequencing


  • ADGRG6 protein, human
  • EEF1A2 protein, human
  • MEF2 Transcription Factors
  • MEF2B protein, human
  • Peptide Elongation Factor 1
  • Receptors, G-Protein-Coupled