Dose-response modeling in high-throughput cancer drug screenings: A case study with recommendations for practitioners
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Personalized cancer treatments based on the molecular profile of a patient's tumor are becoming a standard of care in oncology. Experimentalists and pharmacologists rely on high-throughput, in vitro screenings of many compounds against many different cell lines to build models of drug response. These models help them discover new potential therapeutics that may apply to broad classes of tumors matching some molecular pattern. 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 data. Through a case study, the model is shown both quantitatively and qualitatively to capture nontrivial associations between molecular features and drug response. Finally, we draw five conclusions and recommendations that may benefit experimentalists, analysts, and clinicians working in the field of personalized medicine for cancer therapeutics.
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