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

Shimin Shuai; Federico Abascal; Samirkumar B. Amin; Gary D. Bader; Pratiti Bandopadhayay; Jonathan Barenboim; Rameen Beroukhim; Johanna Bertl; Keith A. Boroevich; Søren Brunak; Peter J. Campbell; Joana Carlevaro-Fita; Dimple Chakravarty; Calvin Wing Yiu Chan; Ken Chen; Jung Kyoon Choi; Jordi Deu-Pons; Priyanka Dhingra; Klev Diamanti; Lars Feuerbach; J. Lynn Fink; Nuno A. Fonseca; Joan Frigola; Carlo Gambacorti-Passerini; Dale W. Garsed; Mark Gerstein; Gad Getz; Qianyun Guo; Ivo G. Gut; David Haan; Mark P. Hamilton; Nicholas J. Haradhvala; Arif O. Harmanci; Mohamed Helmy; Carl Herrmann; Julian M. Hess; Asger Hobolth; Ermin Hodzic; Chen Hong; Henrik Hornshøj; Keren Isaev; Jose M.G. Izarzugaza; Rory Johnson; Todd A. Johnson; Malene Juul; Randi Istrup Juul; Andre Kahles; Abdullah Kahraman; Manolis Kellis; Ekta Khurana; PCAWG Consortium; PCAWG Drivers and Functional Interpretation Working Group; David Townend; Lincoln D. Stein

doi:10.1038/s41467-019-13929-1

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

Shimin Shuai^*, Federico Abascal, Samirkumar B. Amin, Gary D. Bader, Pratiti Bandopadhayay, Jonathan Barenboim, Rameen Beroukhim, Johanna Bertl, Keith A. Boroevich, Søren Brunak, Peter J. Campbell, Joana Carlevaro-Fita, Dimple Chakravarty, Calvin Wing Yiu Chan, Ken Chen, Jung Kyoon Choi, Jordi Deu-Pons, Priyanka Dhingra, Klev Diamanti, Lars FeuerbachJ. Lynn Fink, Nuno A. Fonseca, Joan Frigola, Carlo Gambacorti-Passerini, Dale W. Garsed, Mark Gerstein, Gad Getz, Qianyun Guo, Ivo G. Gut, David Haan, Mark P. Hamilton, Nicholas J. Haradhvala, Arif O. Harmanci, Mohamed Helmy, Carl Herrmann, Julian M. Hess, Asger Hobolth, Ermin Hodzic, Chen Hong, Henrik Hornshøj, Keren Isaev, Jose M.G. Izarzugaza, Rory Johnson, Todd A. Johnson, Malene Juul, Randi Istrup Juul, Andre Kahles, Abdullah Kahraman, Manolis Kellis, Ekta Khurana, PCAWG Consortium, PCAWG Drivers and Functional Interpretation Working Group, David Townend, Lincoln D. Stein^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

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.

Original language	English
Article number	734
Number of pages	12
Journal	Nature Communications
Volume	11
Issue number	1
DOIs	https://doi.org/10.1038/s41467-019-13929-1
Publication status	Published - 1 Dec 2020

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1 Erratum / corrigendum / retractions

Author Correction: Combined burden and functional impact tests for cancer driver discovery using DriverPower
Shuai, S., Abascal, F., Amin, S. B., Bader, G. D., Bandopadhayay, P., Barenboim, J., Beroukhim, R., Bertl, J., Boroevich, K. A., Brunak, S., Campbell, P. J., Carlevaro-Fita, J., Chakravarty, D., Chan, C. W. Y., Chen, K., Choi, J. K., Deu-Pons, J., Dhingra, P., Diamanti, K. & Feuerbach, L. & 34 others, Fink, J. L., Fonseca, N. A., Frigola, J., Gambacorti-Passerini, C., Garsed, D. W., Gerstein, M., Getz, G., Guo, Q., Gut, I. G., Haan, D., Hamilton, M. P., Haradhvala, N. J., Harmanci, A. O., Helmy, M., Herrmann, C., Hess, J. M., Hobolth, A., Hodzic, E., Hong, C., Hornshøj, H., Isaev, K., Izarzugaza, J. M. G., Johnson, R., Johnson, T. A., Juul, M., Juul, R. I., Kahles, A., Kahraman, A., Kellis, M., Khurana, E., PCAWG Consortium, PCAWG Drivers and Functional Interpretation Working Group, Townend, D. & Stein, L. D., 8 Dec 2022, In: Nature Communications. 13, 1, 1 p., 7571.
Research output: Contribution to journal › Erratum / corrigendum / retractions › Academic

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Shuai, S., Abascal, F., Amin, S. B., Bader, G. D., Bandopadhayay, P., Barenboim, J., Beroukhim, R., Bertl, J., Boroevich, K. A., Brunak, S., Campbell, P. J., Carlevaro-Fita, J., Chakravarty, D., Chan, C. W. Y., Chen, K., Choi, J. K., Deu-Pons, J., Dhingra, P., Diamanti, K., ... Stein, L. D. (2020). Combined burden and functional impact tests for cancer driver discovery using DriverPower. Nature Communications, 11(1), Article 734. https://doi.org/10.1038/s41467-019-13929-1

@article{69ae501f0c454dc6911a676cfda74051,

title = "Combined burden and functional impact tests for cancer driver discovery using DriverPower",

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{\textquoteright}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.",

author = "Shimin Shuai and Federico Abascal and Amin, {Samirkumar B.} and Bader, {Gary D.} and Pratiti Bandopadhayay and Jonathan Barenboim and Rameen Beroukhim and Johanna Bertl and Boroevich, {Keith A.} and S{\o}ren Brunak and Campbell, {Peter J.} and Joana Carlevaro-Fita and Dimple Chakravarty and Chan, {Calvin Wing Yiu} and Ken Chen and Choi, {Jung Kyoon} and Jordi Deu-Pons and Priyanka Dhingra and Klev Diamanti and Lars Feuerbach and Fink, {J. Lynn} and Fonseca, {Nuno A.} and Joan Frigola and Carlo Gambacorti-Passerini and Garsed, {Dale W.} and Mark Gerstein and Gad Getz and Qianyun Guo and Gut, {Ivo G.} and David Haan and Hamilton, {Mark P.} and Haradhvala, {Nicholas J.} and Harmanci, {Arif O.} and Mohamed Helmy and Carl Herrmann and Hess, {Julian M.} and Asger Hobolth and Ermin Hodzic and Chen Hong and Henrik Hornsh{\o}j and Keren Isaev and Izarzugaza, {Jose M.G.} and Rory Johnson and Johnson, {Todd A.} and Malene Juul and Juul, {Randi Istrup} and Andre Kahles and Abdullah Kahraman and Manolis Kellis and Ekta Khurana and {PCAWG Consortium} and {PCAWG Drivers and Functional Interpretation Working Group} and David Townend and Stein, {Lincoln D.}",

note = "Funding Information: This work was supported in part by the Government of Ontario. We acknowledge the contributions of the many clinical networks across ICGC and TCGA who provided samples and data to the PCAWG Consortium, and the contributions of the Technical Working Group and the Germline Working Group of the PCAWG Consortium for collation, realignment and harmonised variant calling of the cancer genomes used in this study. We thank the patients and their families for their participation in the individual ICGC and TCGA projects. Publisher Copyright: {\textcopyright} 2020, The Author(s).",

year = "2020",

month = dec,

day = "1",

doi = "10.1038/s41467-019-13929-1",

language = "English",

volume = "11",

journal = "Nature Communications",

issn = "2041-1723",

publisher = "Nature Publishing Group",

number = "1",

}

Shuai, S, Abascal, F, Amin, SB, Bader, GD, Bandopadhayay, P, Barenboim, J, Beroukhim, R, Bertl, J, Boroevich, KA, Brunak, S, Campbell, PJ, Carlevaro-Fita, J, Chakravarty, D, Chan, CWY, Chen, K, Choi, JK, Deu-Pons, J, Dhingra, P, Diamanti, K, Feuerbach, L, Fink, JL, Fonseca, NA, Frigola, J, Gambacorti-Passerini, C, Garsed, DW, Gerstein, M, Getz, G, Guo, Q, Gut, IG, Haan, D, Hamilton, MP, Haradhvala, NJ, Harmanci, AO, Helmy, M, Herrmann, C, Hess, JM, Hobolth, A, Hodzic, E, Hong, C, Hornshøj, H, Isaev, K, Izarzugaza, JMG, Johnson, R, Johnson, TA, Juul, M, Juul, RI, Kahles, A, Kahraman, A, Kellis, M, Khurana, E, PCAWG Consortium, PCAWG Drivers and Functional Interpretation Working Group, Townend, D & Stein, LD 2020, 'Combined burden and functional impact tests for cancer driver discovery using DriverPower', Nature Communications, vol. 11, no. 1, 734. https://doi.org/10.1038/s41467-019-13929-1

TY - JOUR

T1 - Combined burden and functional impact tests for cancer driver discovery using DriverPower

AU - Shuai, Shimin

AU - Abascal, Federico

AU - Amin, Samirkumar B.

AU - Bader, Gary D.

AU - Bandopadhayay, Pratiti

AU - Barenboim, Jonathan

AU - Beroukhim, Rameen

AU - Bertl, Johanna

AU - Boroevich, Keith A.

AU - Brunak, Søren

AU - Campbell, Peter J.

AU - Carlevaro-Fita, Joana

AU - Chakravarty, Dimple

AU - Chan, Calvin Wing Yiu

AU - Chen, Ken

AU - Choi, Jung Kyoon

AU - Deu-Pons, Jordi

AU - Dhingra, Priyanka

AU - Diamanti, Klev

AU - Feuerbach, Lars

AU - Fink, J. Lynn

AU - Fonseca, Nuno A.

AU - Frigola, Joan

AU - Gambacorti-Passerini, Carlo

AU - Garsed, Dale W.

AU - Gerstein, Mark

AU - Getz, Gad

AU - Guo, Qianyun

AU - Gut, Ivo G.

AU - Haan, David

AU - Hamilton, Mark P.

AU - Haradhvala, Nicholas J.

AU - Harmanci, Arif O.

AU - Helmy, Mohamed

AU - Herrmann, Carl

AU - Hess, Julian M.

AU - Hobolth, Asger

AU - Hodzic, Ermin

AU - Hong, Chen

AU - Hornshøj, Henrik

AU - Isaev, Keren

AU - Izarzugaza, Jose M.G.

AU - Johnson, Rory

AU - Johnson, Todd A.

AU - Juul, Malene

AU - Juul, Randi Istrup

AU - Kahles, Andre

AU - Kahraman, Abdullah

AU - Kellis, Manolis

AU - Khurana, Ekta

AU - PCAWG Consortium

AU - PCAWG Drivers and Functional Interpretation Working Group

AU - Townend, David

AU - Stein, Lincoln D.

N1 - Funding Information: This work was supported in part by the Government of Ontario. We acknowledge the contributions of the many clinical networks across ICGC and TCGA who provided samples and data to the PCAWG Consortium, and the contributions of the Technical Working Group and the Germline Working Group of the PCAWG Consortium for collation, realignment and harmonised variant calling of the cancer genomes used in this study. We thank the patients and their families for their participation in the individual ICGC and TCGA projects. Publisher Copyright: © 2020, The Author(s).

PY - 2020/12/1

Y1 - 2020/12/1

N2 - 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.

AB - 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.

U2 - 10.1038/s41467-019-13929-1

DO - 10.1038/s41467-019-13929-1

M3 - Article

SN - 2041-1723

VL - 11

JO - Nature Communications

JF - Nature Communications

IS - 1

M1 - 734

ER -

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

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

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