Combined burden and functional impact tests for cancer driver discovery using DriverPower
(2020) In Nature Communications 11. p.734-734- 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... (More)
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.
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- author
- Shuai, Shimin ; Gallinger, Steven and Stein, Lincoln D
- contributor
- Borg, Åke LU ; Ringnér, Markus LU and Staaf, Johan LU
- author collaboration
- organization
- publishing date
- 2020-02-05
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Algorithms, Genome, Human, Genomics/methods, Humans, MEF2 Transcription Factors/genetics, Mutation, Mutation Rate, Neoplasms/genetics, Peptide Elongation Factor 1/genetics, Receptors, G-Protein-Coupled/genetics, Software, Whole Genome Sequencing
- in
- Nature Communications
- volume
- 11
- pages
- 12 pages
- publisher
- Nature Publishing Group
- external identifiers
-
- scopus:85079072523
- pmid:32024818
- ISSN
- 2041-1723
- DOI
- 10.1038/s41467-019-13929-1
- language
- English
- LU publication?
- yes
- id
- a60212df-7616-4386-98d7-5cf5536ef5ac
- date added to LUP
- 2023-01-05 14:02:36
- date last changed
- 2024-09-17 03:20:26
@article{a60212df-7616-4386-98d7-5cf5536ef5ac, abstract = {{<p>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.</p>}}, author = {{Shuai, Shimin and Gallinger, Steven and Stein, Lincoln D}}, issn = {{2041-1723}}, keywords = {{Algorithms; Genome, Human; Genomics/methods; Humans; MEF2 Transcription Factors/genetics; Mutation; Mutation Rate; Neoplasms/genetics; Peptide Elongation Factor 1/genetics; Receptors, G-Protein-Coupled/genetics; Software; Whole Genome Sequencing}}, language = {{eng}}, month = {{02}}, pages = {{734--734}}, publisher = {{Nature Publishing Group}}, series = {{Nature Communications}}, title = {{Combined burden and functional impact tests for cancer driver discovery using DriverPower}}, url = {{http://dx.doi.org/10.1038/s41467-019-13929-1}}, doi = {{10.1038/s41467-019-13929-1}}, volume = {{11}}, year = {{2020}}, }