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

AstraZeneca-Sanger Drug Combination DREAM Consortium

Research output: Contribution to JournalArticleAcademicpeer-review

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 languageEnglish
Article number2674
JournalNature Communications
Volume10
Issue number1
DOIs
Publication statusPublished - 1 Dec 2019

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Pharmacogenetics
Drug Combinations
drugs
cancer
predictions
Benchmarking
Neoplasms
Biomarkers
Phosphatidylinositol 3-Kinases
Drug Interactions
Pharmaceutical Preparations
inhibitors
Tumors
Cells
Cell Line
biomarkers
Therapeutics
cultured cells
therapy
tumors

Cite this

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

In: Nature Communications, Vol. 10, No. 1, 2674, 01.12.2019.

Research output: Contribution to JournalArticleAcademicpeer-review

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

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 - Calza, Stefano

AU - Capuzzi, Stephen

AU - Carbonell-Caballero, Jose

AU - Carlin, Daniel

AU - Carter, Hannah

AU - Castagnoli, Luisa

AU - Celebi, Remzi

AU - Cesareni, Gianni

AU - Chang, Hyeokyoon

AU - Chen, Guocai

AU - Chen, Haoran

AU - Chen, Huiyuan

AU - Cheng, Lijun

AU - Chernomoretz, Ariel

AU - Chicco, Davide

AU - Cho, Kwang Hyun

AU - Cho, Sunghwan

AU - Choi, Daeseon

AU - Choi, Jaejoon

AU - Choi, Kwanghun

AU - Choi, Minsoo

AU - Cock, Martine De

AU - Coker, Elizabeth

AU - Cortes-Ciriano, Isidro

AU - Cserzö, Miklós

AU - Cubuk, Cankut

AU - Curtis, Christina

AU - Daele, Dries Van

AU - Dang, Cuong C.

AU - Dijkstra, Tjeerd

AU - Dopazo, Joaquin

AU - Draghici, Sorin

AU - Drosou, Anastasios

AU - Dumontier, Michel

AU - Ehrhart, Friederike

AU - Eid, Fatma Elzahraa

AU - ElHefnawi, Mahmoud

AU - Elmarakeby, Haitham

AU - van Engelen, Bo

AU - Engin, Hatice Billur

AU - de Esch, Iwan

AU - Evelo, Chris

AU - Falcao, Andre O.

AU - Farag, Sherif

AU - Fernandez-Lozano, Carlos

AU - Fisch, Kathleen

AU - Flobak, Asmund

AU - Fornari, Chiara

AU - Foroushani, Amir B.K.

AU - Fotso, Donatien Chedom

AU - Fourches, Denis

AU - Friend, Stephen

AU - Frigessi, Arnoldo

AU - Gao, Feng

AU - Gao, Xiaoting

AU - Gerold, Jeffrey M.

AU - Gestraud, Pierre

AU - Ghosh, Samik

AU - Gillberg, Jussi

AU - Godoy-Lorite, Antonia

AU - Godynyuk, Lizzy

AU - Godzik, Adam

AU - Goldenberg, Anna

AU - Gomez-Cabrero, David

AU - Gonen, Mehmet

AU - de Graaf, Chris

AU - Gray, Harry

AU - Grechkin, Maxim

AU - Guimera, Roger

AU - Guney, Emre

AU - Haibe-Kains, Benjamin

AU - Han, Younghyun

AU - Hase, Takeshi

AU - He, Di

AU - He, Liye

AU - Heath, Lenwood S.

AU - Hellton, Kristoffer H.

AU - Helmer-Citterich, Manuela

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AU - Hill, Steven M.

AU - Hochreiter, Sepp

AU - Hong, Seungpyo

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AU - Hsueh, Ya Chih

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AU - Huang, Justin K.

AU - Huang, R. Stephanie

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AU - Hwang, Jinseub

AU - Hwang, Tae Hyun

AU - Hwang, Woochang

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AU - Ji, Jiadong

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AU - Kaski, Samuel

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AU - Muraru, Sebastian

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AU - Newton, Richard

AU - Ning, Zheng

AU - Niz, Carlos De

AU - Oliva, Baldo

AU - Olsen, Catharina

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AU - Panesar, Bhawan

AU - Papadopoulos, Stavros

AU - Park, Jaesub

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AU - Peluso, Daniele

AU - Pendyala, Sriram

AU - Peng, Jian

AU - Perfetto, Livia

AU - Pirro, Stefano

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AU - Politi, Regina

AU - Poon, Hoifung

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AU - Reid, John E.

AU - Reyal, Fabien

AU - Richardson, Sylvia

AU - Ricketts, Camir

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AU - Rodriguez-Gonzalvez, Carmen

AU - Roell, Kyle

AU - Rotroff, Daniel

AU - de Ruiter, Julian R.

AU - Rukawa, Ploy

AU - Sadacca, Benjamin

AU - Safikhani, Zhaleh

AU - Safitri, Fita

AU - Sales-Pardo, Marta

AU - Sauer, Sebastian

AU - Schlichting, Moritz

AU - Seoane, Jose A.

AU - Serra, Jordi

AU - Shang, Ming Mei

AU - Sharma, Alok

AU - Sharma, Hari

AU - Shen, Yang

AU - Shiga, Motoki

AU - Shin, Moonshik

AU - Shkedy, Ziv

AU - Shopsowitz, Kevin

AU - Sinai, Sam

AU - Skola, Dylan

AU - Smirnov, Petr

AU - Soerensen, Izel Fourie

AU - Soerensen, Peter

AU - Song, Je Hoon

AU - Song, Sang Ok

AU - Soufan, Othman

AU - Spitzmueller, Andreas

AU - Steipe, Boris

AU - Suphavilai, Chayaporn

AU - Tamayo, Sergio Pulido

AU - Tamborero, David

AU - Tang, Jing

AU - Tanoli, Zia ur Rehman

AU - Tarres-Deulofeu, Marc

AU - Tegner, Jesper

AU - Thommesen, Liv

AU - Tonekaboni, Seyed Ali Madani

AU - Tran, Hong

AU - Troyer, Ewoud De

AU - Truong, Amy

AU - Tsunoda, Tatsuhiko

AU - Turu, Gábor

AU - Tzeng, Guang Yo

AU - Verbeke, Lieven

AU - Videla, Santiago

AU - Vis, Daniel

AU - Voronkov, Andrey

AU - Votis, Konstantinos

AU - Wang, Ashley

AU - Wang, Hong Qiang Horace

AU - Wang, Po Wei

AU - Wang, Sheng

AU - Wang, Wei

AU - Wang, Xiaochen

AU - Wang, Xin

AU - Wennerberg, Krister

AU - Wernisch, Lorenz

AU - Wessels, Lodewyk

AU - van Westen, Gerard J.P.

AU - Westerman, Bart A.

AU - White, Simon Richard

AU - Willighagen, Egon

AU - Wurdinger, Tom

AU - Xie, Lei

AU - Xie, Shuilian

AU - Xu, Hua

AU - Yadav, Bhagwan

AU - Yau, Christopher

AU - Yeerna, Huwate

AU - Yin, Jia Wei

AU - Yu, Michael

AU - Yu, Min Hwan

AU - Yun, So Jeong

AU - Zakharov, Alexey

AU - Zamichos, Alexandros

AU - Zanin, Massimiliano

AU - Zeng, Li

AU - Zenil, Hector

AU - Zhang, Frederick

AU - Zhang, Pengyue

AU - Zhang, Wei

AU - Zhao, Hongyu

AU - Zhao, Lan

AU - Zheng, Wenjin

AU - Zoufir, Azedine

AU - Zucknick, Manuela

AU - Jang, In Sock

AU - Ghazoui, Zara

AU - Ahsen, Mehmet Eren

AU - Vogel, Robert

AU - Neto, Elias Chaibub

AU - Norman, Thea

AU - Tang, Eric K.Y.

AU - Garnett, Mathew J.

AU - Veroli, Giovanni Y.Di

AU - Fawell, Stephen

AU - Stolovitzky, Gustavo

AU - Guinney, Justin

AU - Dry, Jonathan R.

AU - Saez-Rodriguez, Julio

AU - AstraZeneca-Sanger Drug Combination DREAM Consortium

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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U2 - 10.1038/s41467-019-09799-2

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JO - Nature Communications

JF - Nature Communications

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