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

Henry Gerdes, Pedro Casado, Arran Dokal, Maruan Hijazi, Nosheen Akhtar, Ruth Osuntola, Vinothini Rajeeve, Jude Fitzgibbon, Jon Travers, David Britton, Shirin Khorsandi and Pedro R. Cutillas ()
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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
Maruan Hijazi: Barts Cancer Institute, Queen Mary University of London, Charterhouse Square
Nosheen Akhtar: Barts Cancer Institute, Queen Mary University of London, Charterhouse Square
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
Shirin Khorsandi: Kings College London
Pedro R. Cutillas: Barts Cancer Institute, Queen Mary University of London, Charterhouse Square

Nature Communications, 2021, vol. 12, issue 1, 1-15

Abstract: 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

Date: 2021
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DOI: 10.1038/s41467-021-22170-8

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