Dose–response modeling in high-throughput cancer drug screenings: an end-to-end approach. (7th January 2021)

• Record Type: Journal Article
• Authors: Tansey, Wesley; Li, Kathy; Zhang, Haoran; Linderman, Scott W; Rabadan, Raul; Blei, David M; Wiggins, Chris H
• Is Part Of: Biostatistics. Volume 23:Number 2(2022)
• Page Start: 643
• Page End: 665
• Publication Date: 2021-01-07
• Subjects: Deep learning -- Dose–response modeling -- Drug discovery -- Empirical Bayes -- High-throughput screening -- Personalized medicine; Medical statistics -- Periodicals; Biometry -- Periodicals; Health risk assessment -- Periodicals; Medicine -- Research -- Statistical methods -- Periodicals; 610.727
• DOI: 10.1093/biostatistics/kxaa047
• Languages: English
• ISSNs: 1465-4644

Abstract

Summary: Personalized cancer treatments based on the molecular profile of a patient's tumor are an emerging and exciting class of treatments in oncology. As genomic tumor profiling is becoming more common, targeted treatments for specific molecular alterations are gaining traction. To discover new potential therapeutics that may apply to broad classes of tumors matching some molecular pattern, experimentalists and pharmacologists rely on high-throughput, in vitro screens of many compounds against many different cell lines. We propose a hierarchical Bayesian model of how cancer cell lines respond to drugs in these experiments and develop a method for fitting the model to real-world high-throughput screening data. Through a case study, the model is shown to capture nontrivial associations between molecular features and drug response, such as requiring both wild type TP53 and overexpression of MDM2 to be sensitive to Nutlin-3(a). In quantitative benchmarks, the model outperforms a standard approach in biology, with $\approx20\%$ lower predictive error on held out data. When combined with a conditional randomization testing procedure, the model discovers markers of therapeutic response that recapitulate known biology and suggest new avenues for investigation. All code for the article is publicly available at https://github.com/tansey/deep-dose-response .