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

Shuai, Shimin ; Gallinger, Steven and Stein, Lincoln D (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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type
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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
2024-04-15 12:15:07
@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}},
}