{"entity": "publication", "iuid": "93738c6c7629469c8b824b68cd77ca38", "timestamp": "2026-08-20T21:35:19.542Z", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/93738c6c7629469c8b824b68cd77ca38.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/93738c6c7629469c8b824b68cd77ca38"}}, "title": "Combined burden and functional impact tests for cancer driver discovery using DriverPower.", "authors": [{"family": "Shuai", "given": "Shimin", "initials": "S", "orcid": "0000-0002-9527-8018", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/3896d0cb0c3b4d0fb8b34e39e654428c.json"}}, {"family": "PCAWG Drivers and Functional Interpretation Working Group", "given": "", "initials": ""}, {"family": "Gallinger", "given": "Steven", "initials": "S"}, {"family": "Stein", "given": "Lincoln D", "initials": "LD"}, {"family": "PCAWG Consortium", "given": "", "initials": ""}], "type": "journal article", "published": "2020-02-05", "journal": {"title": "Nat Commun", "issn": "2041-1723", "volume": "11", "issue": "1", "pages": "734", "issn-l": "2041-1723"}, "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.", "doi": "10.1038/s41467-019-13929-1", "pmid": "32024818", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC7002750"}, {"db": "pii", "key": "10.1038/s41467-019-13929-1"}], "notes": [], "created": "2026-08-20T08:50:52.614Z", "modified": "2026-08-20T08:50:52.677Z"}