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

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Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen. / AstraZeneca-Sanger Drug Combination DREAM Consortium ; Kaski, Samuel; Marttinen, Pekka.

In: Nature Communications, Vol. 10, No. 1, 2674, 17.06.2019, p. 1-17.

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@article{2c3f63c0c16a47f6a82ae01b3cf9bfc2,
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’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.",
keywords = "ANDROGEN RECEPTOR, BREAST-CANCER, GENE, CELL, INHIBITION, RESISTANCE, PATHWAY, MUTATIONS, LANDSCAPE, RESOURCE",
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 Mehmet Gonen and Samuel Kaski and Khan, {Suleiman Ali} and Hiroshi Mamitsuka and Pekka Marttinen and Xin Wang and Alexey Zakharov",
note = "| openaire: EC/H2020/668858/EU//PrECISE",
year = "2019",
month = "6",
day = "17",
doi = "10.1038/s41467-019-09799-2",
language = "English",
volume = "10",
pages = "1--17",
journal = "Nature Communications",
issn = "2041-1723",
publisher = "Nature Publishing Group",
number = "1",

}

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TY - JOUR

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 - Gonen, Mehmet

AU - Kaski, Samuel

AU - Khan, Suleiman Ali

AU - Mamitsuka, Hiroshi

AU - Marttinen, Pekka

AU - Wang, Xin

AU - Zakharov, Alexey

N1 - | openaire: EC/H2020/668858/EU//PrECISE

PY - 2019/6/17

Y1 - 2019/6/17

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.

KW - ANDROGEN RECEPTOR

KW - BREAST-CANCER

KW - GENE

KW - CELL

KW - INHIBITION

KW - RESISTANCE

KW - PATHWAY

KW - MUTATIONS

KW - LANDSCAPE

KW - RESOURCE

UR - http://www.scopus.com/inward/record.url?scp=85067453487&partnerID=8YFLogxK

U2 - 10.1038/s41467-019-09799-2

DO - 10.1038/s41467-019-09799-2

M3 - Article

VL - 10

SP - 1

EP - 17

JO - Nature Communications

JF - Nature Communications

SN - 2041-1723

IS - 1

M1 - 2674

ER -

ID: 35123737