  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 to 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 ~20% lower predictive error on held out
data. When combined with a conditional randomization testing procedure, the
model discovers biomarkers of therapeutic response that recapitulate known
biology and suggest new avenues for investigation. All code for the paper is
publicly available at https://github.com/tansey/deep-dose-response.
