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

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

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

In: Nature Communications, Vol. 11, No. 1, 734, 2020.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

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, Pedersen, JS, Sidiropoulos, N, Weischenfeldt, J, PCAWG Drivers and Functional Interpretation Working Group & PCAWG Consortium 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

APA

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., ... PCAWG Consortium (2020). Combined burden and functional impact tests for cancer driver discovery using DriverPower. Nature Communications, 11(1), [734]. https://doi.org/10.1038/s41467-019-13929-1

Vancouver

Shuai S, Abascal F, Amin SB, Bader GD, Bandopadhayay P, Barenboim J et al. Combined burden and functional impact tests for cancer driver discovery using DriverPower. Nature Communications. 2020;11(1). 734. https://doi.org/10.1038/s41467-019-13929-1

Author

Shuai, Shimin ; Abascal, Federico ; Amin, Samirkumar B. ; Bader, Gary D. ; Bandopadhayay, Pratiti ; Barenboim, Jonathan ; Beroukhim, Rameen ; Bertl, Johanna ; Boroevich, Keith A. ; Brunak, Søren ; Campbell, Peter J. ; Carlevaro-Fita, Joana ; Chakravarty, Dimple ; Chan, Calvin Wing Yiu ; Chen, Ken ; Choi, Jung Kyoon ; Deu-Pons, Jordi ; Dhingra, Priyanka ; Diamanti, Klev ; Feuerbach, Lars ; Fink, J. Lynn ; Fonseca, Nuno A. ; Frigola, Joan ; Gambacorti-Passerini, Carlo ; Garsed, Dale W. ; Gerstein, Mark ; Getz, Gad ; Guo, Qianyun ; Gut, Ivo G. ; Haan, David ; Hamilton, Mark P. ; Haradhvala, Nicholas J. ; Harmanci, Arif O. ; Helmy, Mohamed ; Herrmann, Carl ; Hess, Julian M. ; Hobolth, Asger ; Hodzic, Ermin ; Hong, Chen ; Hornshøj, Henrik ; Isaev, Keren ; Izarzugaza, Jose M.G. ; Johnson, Rory ; Johnson, Todd A. ; Juul, Malene ; Juul, Randi Istrup ; Kahles, Andre ; Pedersen, Jakob Skou ; Sidiropoulos, Nikos ; Weischenfeldt, Joachim ; PCAWG Drivers and Functional Interpretation Working Group ; PCAWG Consortium. / Combined burden and functional impact tests for cancer driver discovery using DriverPower. In: Nature Communications. 2020 ; Vol. 11, No. 1.

Bibtex

@article{23ec3f9ebfe94624934234ddbb7b8793,
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 Pedersen, {Jakob Skou} and Nikos Sidiropoulos and Joachim Weischenfeldt and {PCAWG Drivers and Functional Interpretation Working Group} and {PCAWG Consortium}",
year = "2020",
doi = "10.1038/s41467-019-13929-1",
language = "English",
volume = "11",
journal = "Nature Communications",
issn = "2041-1723",
publisher = "nature publishing group",
number = "1",

}

RIS

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 - Pedersen, Jakob Skou

AU - Sidiropoulos, Nikos

AU - Weischenfeldt, Joachim

AU - PCAWG Drivers and Functional Interpretation Working Group

AU - PCAWG Consortium

PY - 2020

Y1 - 2020

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 - Journal article

C2 - 32024818

AN - SCOPUS:85079072523

VL - 11

JO - Nature Communications

JF - Nature Communications

SN - 2041-1723

IS - 1

M1 - 734

ER -

ID: 236669353