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

Author

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  • Michael P. Menden

    (Oncology, IMED Biotech Unit, AstraZeneca
    European Bioinformatics Institute, European Molecular Biology Laboratory
    Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health)

  • Dennis Wang

    (Oncology, IMED Biotech Unit, AstraZeneca
    University of Sheffield)

  • Mike J. Mason

    (Sage Bionetworks)

  • Bence Szalai

    (Semmelweis University
    Hungarian Academy of Sciences and Semmelweis University (MTA-SE)
    RWTH Aachen University, Faculty of Medicine, Joint Research Center for Computational Biomedicine)

  • Krishna C. Bulusu

    (Oncology, IMED Biotech Unit, AstraZeneca)

  • Yuanfang Guan

    (University of Michigan)

  • Thomas Yu

    (Sage Bionetworks)

  • Jaewoo Kang

    (Korea University)

  • Minji Jeon

    (Korea University)

  • Russ Wolfinger

    (SAS Institute, Inc)

  • Tin Nguyen

    (University of Nevada)

  • Mikhail Zaslavskiy

    (Owkin, Inc.)

  • In Sock Jang

    (Sage Bionetworks)

  • Zara Ghazoui

    (Oncology, IMED Biotech Unit, AstraZeneca)

  • Mehmet Eren Ahsen

    (Yorktown Heights)

  • Robert Vogel

    (Yorktown Heights)

  • Elias Chaibub Neto

    (Sage Bionetworks)

  • Thea Norman

    (Sage Bionetworks)

  • Eric K. Y. Tang

    (Oncology, IMED Biotech Unit, AstraZeneca)

  • Mathew J. Garnett

    (Wellcome Trust Sanger Institute)

  • Giovanni Y. Di Veroli

    (IMED Biotech Unit, AstraZeneca)

  • Stephen Fawell

    (AstraZeneca, R&D Boston)

  • Gustavo Stolovitzky

    (Yorktown Heights
    Icahn School of Medicine at Mount Sinai)

  • Justin Guinney

    (Sage Bionetworks)

  • Jonathan R. Dry

    (AstraZeneca, R&D Boston)

  • Julio Saez-Rodriguez

    (European Bioinformatics Institute, European Molecular Biology Laboratory
    RWTH Aachen University, Faculty of Medicine, Joint Research Center for Computational Biomedicine
    Heidelberg University, Faculty of Medicine, Institute for Computational Biomedicine, Bioquant)

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.

Suggested Citation

  • Michael P. Menden & Dennis Wang & Mike J. Mason & Bence Szalai & Krishna C. Bulusu & Yuanfang Guan & Thomas Yu & Jaewoo Kang & Minji Jeon & Russ Wolfinger & Tin Nguyen & Mikhail Zaslavskiy & In Sock J, 2019. "Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen," Nature Communications, Nature, vol. 10(1), pages 1-17, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-09799-2
    DOI: 10.1038/s41467-019-09799-2
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    Cited by:

    1. Boshu Ouyang & Caihua Shan & Shun Shen & Xinnan Dai & Qingwang Chen & Xiaomin Su & Yongbin Cao & Xifeng Qin & Ying He & Siyu Wang & Ruizhe Xu & Ruining Hu & Leming Shi & Tun Lu & Wuli Yang & Shaojun P, 2024. "AI-powered omics-based drug pair discovery for pyroptosis therapy targeting triple-negative breast cancer," Nature Communications, Nature, vol. 15(1), pages 1-20, December.
    2. Nishanth Ulhas Nair & Patricia Greninger & Xiaohu Zhang & Adam A. Friedman & Arnaud Amzallag & Eliane Cortez & Avinash Das Sahu & Joo Sang Lee & Anahita Dastur & Regina K. Egan & Ellen Murchie & Miche, 2023. "A landscape of response to drug combinations in non-small cell lung cancer," Nature Communications, Nature, vol. 14(1), pages 1-19, December.
    3. Jiaqi Li & Hongyan Xu & Richard A McIndoe, 2022. "A novel network based linear model for prioritization of synergistic drug combinations," PLOS ONE, Public Library of Science, vol. 17(4), pages 1-22, April.
    4. L. Mathur & B. Szalai & N. H. Du & R. Utharala & M. Ballinger & J. J. M. Landry & M. Ryckelynck & V. Benes & J. Saez-Rodriguez & C. A. Merten, 2022. "Combi-seq for multiplexed transcriptome-based profiling of drug combinations using deterministic barcoding in single-cell droplets," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    5. Hanrui Zhang & Julian Kreis & Sven-Eric Schelhorn & Heike Dahmen & Thomas Grombacher & Michael Zühlsdorf & Frank T. Zenke & Yuanfang Guan, 2023. "Mapping combinatorial drug effects to DNA damage response kinase inhibitors," Nature Communications, Nature, vol. 14(1), pages 1-8, December.
    6. Sean M. Gross & Farnaz Mohammadi & Crystal Sanchez-Aguila & Paulina J. Zhan & Tiera A. Liby & Mark A. Dane & Aaron S. Meyer & Laura M. Heiser, 2023. "Analysis and modeling of cancer drug responses using cell cycle phase-specific rate effects," Nature Communications, Nature, vol. 14(1), pages 1-12, December.

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