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

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 d...

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Bibliographic Details
Published in:Nature communications 2021-03, Vol.12 (1), p.1850-15, Article 1850
Main Authors: Gerdes, Henry, Casado, Pedro, Dokal, Arran, Hijazi, Maruan, Akhtar, Nosheen, Osuntola, Ruth, Rajeeve, Vinothini, Fitzgibbon, Jude, Travers, Jon, Britton, David, Khorsandi, Shirin, Cutillas, Pedro R.
Format: Article
Language:English
Subjects:
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631/114/1305
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Anticancer properties
Antineoplastic Agents - therapeutic use
Antitumor agents
Artificial intelligence
Cancer
Cancer therapies
Cell Line, Tumor
Cell proliferation
Cell Proliferation - drug effects
Chemotherapy
Computational Biology - methods
Cytarabine
Cytarabine - therapeutic use
Drug Screening Assays, Antitumor - methods
Drugs
Hep G2 Cells
Humanities and Social Sciences
Humans
Learning algorithms
Leukemia
Leukemia - drug therapy
Leukemia - mortality
Machine Learning
multidisciplinary
Neoplasms - drug therapy
Noise prediction
Noise reduction
Patients
Prognosis
Proteomics
Proteomics - methods
Ranking
Science
Science (multidisciplinary)
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Summary: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 < 0.1 and mean Spearman’s rank 0.7). In addition, we demonstrate that DRUML predictions of cytarabine sensitivity in clinical leukemia samples are prognostic of patient survival (Log rank p  
ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-021-22170-8