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Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs

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Listed:
  • Henry Gerdes

    (Barts Cancer Institute, Queen Mary University of London, Charterhouse Square)

  • Pedro Casado

    (Barts Cancer Institute, Queen Mary University of London, Charterhouse Square)

  • Arran Dokal

    (Barts Cancer Institute, Queen Mary University of London, Charterhouse Square
    Kinomica Ltd, Alderley Park, Alderley Edge)

  • Maruan Hijazi

    (Barts Cancer Institute, Queen Mary University of London, Charterhouse Square)

  • Nosheen Akhtar

    (Barts Cancer Institute, Queen Mary University of London, Charterhouse Square
    National University of Medical Sciences)

  • Ruth Osuntola

    (Barts Cancer Institute, Queen Mary University of London, Charterhouse Square)

  • Vinothini Rajeeve

    (Barts Cancer Institute, Queen Mary University of London, Charterhouse Square)

  • Jude Fitzgibbon

    (Barts Cancer Institute, Queen Mary University of London, Charterhouse Square)

  • Jon Travers

    (Astra Zeneca Ltd, 1 Francis Crick Avenue, Cambridge Biomedical Campus)

  • David Britton

    (Barts Cancer Institute, Queen Mary University of London, Charterhouse Square
    Kinomica Ltd, Alderley Park, Alderley Edge)

  • Shirin Khorsandi

    (Kings College London)

  • Pedro R. Cutillas

    (Barts Cancer Institute, Queen Mary University of London, Charterhouse Square
    Barts Cancer Institute, Queen Mary University of London, Charterhouse Square
    The Alan Turing Institute, The British Library, 2QR)

Abstract

Artificial intelligence and machine learning (ML) promise to transform cancer therapies by accurately predicting the most appropriate therapies to treat individual patients. Here, we present an approach, named Drug Ranking Using ML (DRUML), which uses omics data to produce ordered lists of >400 drugs based on their anti-proliferative efficacy in cancer cells. To reduce noise and increase predictive robustness, instead of individual features, DRUML uses internally normalized distance metrics of drug response as features for ML model generation. DRUML is trained using in-house proteomics and phosphoproteomics data derived from 48 cell lines, and it is verified with data comprised of 53 cellular models from 12 independent laboratories. We show that DRUML predicts drug responses in independent verification datasets with low error (mean squared error

Suggested Citation

  • Henry Gerdes & Pedro Casado & Arran Dokal & Maruan Hijazi & Nosheen Akhtar & Ruth Osuntola & Vinothini Rajeeve & Jude Fitzgibbon & Jon Travers & David Britton & Shirin Khorsandi & Pedro R. Cutillas, 2021. "Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-22170-8
    DOI: 10.1038/s41467-021-22170-8
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    Cited by:

    1. David Ascheid & Magdalena Baumann & Jürgen Pinnecker & Mike Friedrich & Daniel Szi-Marton & Cornelia Medved & Maja Bundalo & Vanessa Ortmann & Asli Öztürk & Rajender Nandigama & Katherina Hemmen & Sül, 2024. "A vascularized breast cancer spheroid platform for the ranked evaluation of tumor microenvironment-targeted drugs by light sheet fluorescence microscopy," Nature Communications, Nature, vol. 15(1), pages 1-23, December.
    2. Smriti Chawla & Anja Rockstroh & Melanie Lehman & Ellca Ratther & Atishay Jain & Anuneet Anand & Apoorva Gupta & Namrata Bhattacharya & Sarita Poonia & Priyadarshini Rai & Nirjhar Das & Angshul Majumd, 2022. "Gene expression based inference of cancer drug sensitivity," Nature Communications, Nature, vol. 13(1), pages 1-15, December.

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