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

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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
CHROMATIN ORGANIZATION, SOMATIC MUTATION, RECURRENT, EXPRESSION, VARIANTS, BINDING, MOF, HETEROGENEITY, PATHOGENICITY, ACETYLATION

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Citation

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MLA
Shuai, Shimin, et al. “Combined Burden and Functional Impact Tests for Cancer Driver Discovery Using DriverPower.” NATURE COMMUNICATIONS, vol. 11, no. 1, 2020.
APA
Shuai, S., Gallinger, S., Stein, L., PCAWG Drivers and Functional Interpretation Working Group, the, Marchal, K., & Pulido-Tamayo, S. (2020). Combined burden and functional impact tests for cancer driver discovery using DriverPower. NATURE COMMUNICATIONS, 11(1).
Chicago author-date
Shuai, Shimin, Steven Gallinger, Lincoln Stein, the PCAWG Drivers and Functional Interpretation Working Group, Kathleen Marchal, and Sergio Pulido-Tamayo. 2020. “Combined Burden and Functional Impact Tests for Cancer Driver Discovery Using DriverPower.” NATURE COMMUNICATIONS 11 (1).
Chicago author-date (all authors)
Shuai, Shimin, Steven Gallinger, Lincoln Stein, the PCAWG Drivers and Functional Interpretation Working Group, Kathleen Marchal, and Sergio Pulido-Tamayo. 2020. “Combined Burden and Functional Impact Tests for Cancer Driver Discovery Using DriverPower.” NATURE COMMUNICATIONS 11 (1).
Vancouver
1.
Shuai S, Gallinger S, Stein L, PCAWG Drivers and Functional Interpretation Working Group the, Marchal K, Pulido-Tamayo S. Combined burden and functional impact tests for cancer driver discovery using DriverPower. NATURE COMMUNICATIONS. 2020;11(1).
IEEE
[1]
S. Shuai, S. Gallinger, L. Stein, the PCAWG Drivers and Functional Interpretation Working Group, K. Marchal, and S. Pulido-Tamayo, “Combined burden and functional impact tests for cancer driver discovery using DriverPower,” NATURE COMMUNICATIONS, vol. 11, no. 1, 2020.
@article{8648614,
  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.},
  articleno    = {734},
  author       = {Shuai, Shimin and Gallinger, Steven and Stein, Lincoln and PCAWG Drivers and Functional Interpretation Working Group, the and Marchal, Kathleen and Pulido-Tamayo, Sergio},
  issn         = {2041-1723},
  journal      = {NATURE COMMUNICATIONS},
  keywords     = {CHROMATIN ORGANIZATION,SOMATIC MUTATION,RECURRENT,EXPRESSION,VARIANTS,BINDING,MOF,HETEROGENEITY,PATHOGENICITY,ACETYLATION},
  language     = {eng},
  number       = {1},
  pages        = {12},
  title        = {Combined burden and functional impact tests for cancer driver discovery using DriverPower},
  url          = {http://dx.doi.org/10.1038/s41467-019-13929-1},
  volume       = {11},
  year         = {2020},
}

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