Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

AstraZeneca-Sanger Drug Combination DREAM Consortium

doi:10.1038/s41467-019-09799-2

Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

AstraZeneca-Sanger Drug Combination DREAM Consortium

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

157 Citations (Scopus)

Abstract

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.

Original language	English
Article number	2674
Journal	Nature communications
Volume	10
Issue number	1
DOIs	https://doi.org/10.1038/s41467-019-09799-2
Publication status	Published - 2019 Dec 1
Externally published	Yes

ASJC Scopus subject areas

Physics and Astronomy(all)
Chemistry(all)
Biochemistry, Genetics and Molecular Biology(all)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1038/s41467-019-09799-2

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@article{078ed3ce8e4641429a5210ad2eff39f0,

title = "Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen",

abstract = "The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca{\textquoteright}s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.",

author = "{AstraZeneca-Sanger Drug Combination DREAM Consortium} and Menden, {Michael P.} and Dennis Wang and Mason, {Mike J.} and Bence Szalai and Bulusu, {Krishna C.} and Yuanfang Guan and Thomas Yu and Jaewoo Kang and Minji Jeon and Russ Wolfinger and Tin Nguyen and Mikhail Zaslavskiy and Jordi Abante and Abecassis, {Barbara Schmitz} and Nanne Aben and Delasa Aghamirzaie and Tero Aittokallio and Akhtari, {Farida S.} and Bissan Al-lazikani and Tanvir Alam and Amin Allam and Chad Allen and {de Almeida}, {Mariana Pelicano} and Doaa Altarawy and Vinicius Alves and Alicia Amadoz and Benedict Anchang and Antolin, {Albert A.} and Ash, {Jeremy R.} and Aznar, {Victoria Romeo} and Wail Ba-alawi and Moeen Bagheri and Vladimir Bajic and Gordon Ball and Ballester, {Pedro J.} and Delora Baptista and Christopher Bare and Mathilde Bateson and Andreas Bender and Denis Bertrand and Bhagya Wijayawardena and Boroevich, {Keith A.} and Evert Bosdriesz and Salim Bougouffa and Gergana Bounova and Thomas Brouwer and Barbara Bryant and Manuel Calaza and Alberto Calderone and Motoki Shiga",

note = "Funding Information: We thank the Genomics of Drug Sensitivity in Cancer and COSMIC teams at the Wellcome Trust Sanger Institute for help with the preparation of the molecular data, Denes Turei for help with Omnipath, and Katjusa Koler for help with matching drug names across combination screens. We thank AstraZeneca for funding and provision of data to the DREAM Consortium to run the challenge, and funding from the European Union Horizon 2020 research (under grant agreement No 668858 PrECISE to J.S.R.), the Joint Research Center for Computational Biomedicine (which is partially funded by Bayer AG) to J.S.R., National Institute for Health Research (NIHR) Sheffield Biomedical Research Center, Premium Postdoctoral Fellowship Program of the Hungarian Academy of Sciences. M.G lab is supported by Wellcome Trust (102696 and 206194). Funding Information: Competing interests: K.C.B., Z.G., G.Y.D., E.K.Y.T., S.F., and J.R.D. are AstraZeneca employees. K.C.B., Z.G., E.K.Y.T., S.F., and J.R.D. are AstraZeneca shareholders. Y.G. receives personal compensation from Eli Lilly and Company, is a shareholder of Cleerly, Inc., and Ann Arbor Algorithms, Inc. M.G. receives research funding from AstraZeneca and has performed consultancy for Sanofi. The remaining authors declare no competing interests. Publisher Copyright: {\textcopyright} 2019, The Author(s).",

year = "2019",

month = dec,

day = "1",

doi = "10.1038/s41467-019-09799-2",

language = "English",

volume = "10",

journal = "Nature Communications",

issn = "2041-1723",

publisher = "Nature Publishing Group",

number = "1",

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T1 - Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

AU - AstraZeneca-Sanger Drug Combination DREAM Consortium

AU - Menden, Michael P.

AU - Wang, Dennis

AU - Mason, Mike J.

AU - Szalai, Bence

AU - Bulusu, Krishna C.

AU - Guan, Yuanfang

AU - Yu, Thomas

AU - Kang, Jaewoo

AU - Jeon, Minji

AU - Wolfinger, Russ

AU - Nguyen, Tin

AU - Zaslavskiy, Mikhail

AU - Abante, Jordi

AU - Abecassis, Barbara Schmitz

AU - Aben, Nanne

AU - Aghamirzaie, Delasa

AU - Aittokallio, Tero

AU - Akhtari, Farida S.

AU - Al-lazikani, Bissan

AU - Alam, Tanvir

AU - Allam, Amin

AU - Allen, Chad

AU - de Almeida, Mariana Pelicano

AU - Altarawy, Doaa

AU - Alves, Vinicius

AU - Amadoz, Alicia

AU - Anchang, Benedict

AU - Antolin, Albert A.

AU - Ash, Jeremy R.

AU - Aznar, Victoria Romeo

AU - Ba-alawi, Wail

AU - Bagheri, Moeen

AU - Bajic, Vladimir

AU - Ball, Gordon

AU - Ballester, Pedro J.

AU - Baptista, Delora

AU - Bare, Christopher

AU - Bateson, Mathilde

AU - Bender, Andreas

AU - Bertrand, Denis

AU - Wijayawardena, Bhagya

AU - Boroevich, Keith A.

AU - Bosdriesz, Evert

AU - Bougouffa, Salim

AU - Bounova, Gergana

AU - Brouwer, Thomas

AU - Bryant, Barbara

AU - Calaza, Manuel

AU - Calderone, Alberto

AU - Shiga, Motoki

N1 - Funding Information: We thank the Genomics of Drug Sensitivity in Cancer and COSMIC teams at the Wellcome Trust Sanger Institute for help with the preparation of the molecular data, Denes Turei for help with Omnipath, and Katjusa Koler for help with matching drug names across combination screens. We thank AstraZeneca for funding and provision of data to the DREAM Consortium to run the challenge, and funding from the European Union Horizon 2020 research (under grant agreement No 668858 PrECISE to J.S.R.), the Joint Research Center for Computational Biomedicine (which is partially funded by Bayer AG) to J.S.R., National Institute for Health Research (NIHR) Sheffield Biomedical Research Center, Premium Postdoctoral Fellowship Program of the Hungarian Academy of Sciences. M.G lab is supported by Wellcome Trust (102696 and 206194). Funding Information: Competing interests: K.C.B., Z.G., G.Y.D., E.K.Y.T., S.F., and J.R.D. are AstraZeneca employees. K.C.B., Z.G., E.K.Y.T., S.F., and J.R.D. are AstraZeneca shareholders. Y.G. receives personal compensation from Eli Lilly and Company, is a shareholder of Cleerly, Inc., and Ann Arbor Algorithms, Inc. M.G. receives research funding from AstraZeneca and has performed consultancy for Sanofi. The remaining authors declare no competing interests. Publisher Copyright: © 2019, The Author(s).

PY - 2019/12/1

Y1 - 2019/12/1

N2 - The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.

AB - The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.

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DO - 10.1038/s41467-019-09799-2

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SN - 2041-1723

VL - 10

JO - Nature Communications

JF - Nature Communications

IS - 1

M1 - 2674

ER -

Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

Abstract

ASJC Scopus subject areas

UN SDGs

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