Combined burden and functional impact tests for cancer driver discovery using DriverPower
Open Access
- 5 February 2020
- journal article
- research article
- Published by Springer Science and Business Media LLC in Nature Communications
- Vol. 11 (1), 1-12
- https://doi.org/10.1038/s41467-019-13929-1
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.Keywords
Funding Information
- Province of Ontario
This publication has 70 references indexed in Scilit:
- Pan-cancer analysis of whole genomesNature, 2020
- Role of non-coding sequence variants in cancerNature Reviews Genetics, 2016
- Somatic mutation in cancer and normal cellsScience, 2015
- Integrative analysis of 111 reference human epigenomesNature, 2015
- An oncogenic super-enhancer formed through somatic mutation of a noncoding intergenic elementScience, 2014
- Mutational heterogeneity in cancer and the search for new cancer-associated genesNature, 2013
- Cancer Genome LandscapesScience, 2013
- Lessons from the Cancer GenomeCell, 2013
- Highly Recurrent TERT Promoter Mutations in Human MelanomaScience, 2013
- The cancer genomeNature, 2009