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Published on: July 15, 2015
Dose-response modeling in high-throughput cancer drug screenings: an end-to-end approach
Wesley Tansey1, Kathy Li2, Haoran Zhang3
1Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, NewYork, NY, USA.
Abstract:
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.
Insights
This study introduces a new Bayesian model for predicting cancer drug response based on tumor molecular profiles. The model improves prediction accuracy and identifies key molecular markers for targeted cancer therapies.
Area of Science:
- Oncology
- Computational Biology
- Pharmacology
Background:
- Personalized cancer treatments leverage tumor molecular profiles for targeted therapies.
- High-throughput screening (HTS) is crucial for discovering novel therapeutics by testing compounds against various cancer cell lines.
- Identifying associations between specific molecular alterations and drug sensitivity is key for advancing precision oncology.
Purpose of the Study:
- To propose a hierarchical Bayesian model for analyzing cancer cell line drug response data from HTS experiments.
- To develop a robust method for fitting this model to real-world HTS data.
- To identify novel molecular markers predictive of therapeutic response in cancer.
Main Methods:
- Development of a hierarchical Bayesian statistical model to capture drug response patterns in cancer cell lines.
- Application of the model to high-throughput screening data, including a case study with Nutlin-3(a).
- Quantitative benchmarking against standard methods and integration with conditional randomization testing for marker discovery.
Main Results:
- The Bayesian model effectively captures complex associations between molecular features and drug sensitivity, exemplified by TP53 and MDM2 interactions with Nutlin-3(a).
- The model demonstrates superior performance, achieving approximately 20% lower predictive error compared to conventional approaches on unseen data.
- The integrated approach successfully identified known and novel biomarkers for therapeutic response, suggesting new research directions.
Conclusions:
- The proposed hierarchical Bayesian model offers a powerful tool for analyzing HTS data in cancer research.
- This approach enhances the discovery of targeted cancer therapies by accurately predicting drug response based on molecular profiles.
- The model facilitates the identification of predictive biomarkers, paving the way for more effective personalized cancer treatments.
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01:26Drug Discovery: Overview
01:03Dose-Response Relationship: Overview