Predicting Drug Response and Synergy Using a Deep Learning Model of Human Cancer Cells.

Autor:	Kuenzi BM; Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA., Park J; Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA., Fong SH; Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA; Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA., Sanchez KS; Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA., Lee J; Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA., Kreisberg JF; Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA., Ma J; Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA., Ideker T; Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA; Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA; Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA 92093, USA. Electronic address: tideker@ucsd.edu.
Jazyk:	angličtina
Zdroj:	Cancer cell [Cancer Cell] 2020 Nov 09; Vol. 38 (5), pp. 672-684.e6. Date of Electronic Publication: 2020 Oct 22.
DOI:	10.1016/j.ccell.2020.09.014
Abstrakt:	Most drugs entering clinical trials fail, often related to an incomplete understanding of the mechanisms governing drug response. Machine learning techniques hold immense promise for better drug response predictions, but most have not reached clinical practice due to their lack of interpretability and their focus on monotherapies. We address these challenges by developing DrugCell, an interpretable deep learning model of human cancer cells trained on the responses of 1,235 tumor cell lines to 684 drugs. Tumor genotypes induce states in cellular subsystems that are integrated with drug structure to predict response to therapy and, simultaneously, learn biological mechanisms underlying the drug response. DrugCell predictions are accurate in cell lines and also stratify clinical outcomes. Analysis of DrugCell mechanisms leads directly to the design of synergistic drug combinations, which we validate systematically by combinatorial CRISPR, drug-drug screening in vitro, and patient-derived xenografts. DrugCell provides a blueprint for constructing interpretable models for predictive medicine. Competing Interests: Declaration of Interests T.I. is a co-founder of Data4Cure, Inc., and has an equity interest. T.I. has an equity interest in Ideaya BioSciences, Inc. The terms of this arrangement have been reviewed and approved by the University of California San Diego in accordance with its conflict of interest policies. (Copyright © 2020 Elsevier Inc. All rights reserved.)
Databáze:	MEDLINE
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