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Personalized cancer treatments based on the molecular profile of a patient'stumor are an emerging and exciting class of treatments in oncology. As genomictumor profiling is becoming more common, targeted treatments to specificmolecular alterations are gaining traction. To discover new potentialtherapeutics that may apply to broad classes of tumors matching some molecularpattern, experimentalists and pharmacologists rely on high-throughput, in-vitroscreens of many compounds against many different cell lines. We propose ahierarchical Bayesian model of how cancer cell lines respond to drugs in theseexperiments and develop a method for fitting the model to real-worldhigh-throughput screening data. Through a case study, the model is shown tocapture nontrivial associations between molecular features and drug response,such as requiring both wild type TP53 and overexpression of MDM2 to besensitive to Nutlin-3(a). In quantitative benchmarks, the model outperforms astandard approach in biology, with ~20% lower predictive error on held outdata. When combined with a conditional randomization testing procedure, themodel discovers biomarkers of therapeutic response that recapitulate knownbiology and suggest new avenues for investigation. All code for the paper ispublicly available at https://github.com/tansey/deep-dose-response.

Dose-response modeling in high-throughput cancer drug screenings: An end-to-end approach