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A Bayesian model for drug response estimation and biomarker testing using Gaussian processes

Activity: External academic engagementInvited talk


Large-scale drug response assays on cancer cell lines have in recent years produced a wealth of data that benefits cancer treatment, drug development and biomarker discovery. We present a novel Bayesian approach for modelling dose-response curves using Gaussian processes (GPs). Our model has several advantages over the traditional sigmoid curve: 1) it is non-parametric and allows us to fit a variety of responses; 2) it allows for a hierarchical Bayesian setup with information sharing across different curves; and 3) we automatically obtain a measure of the uncertainty of our curve fits from the variance of the Gaussian process. We extend the model with a Bayesian biomarker testing framework that allows us to test for a difference in the proportion of responsive curves in mutated versus wild type cell lines. We test the model on cell line drug response data from the Cancer Genome Project, and demonstrate that the Gaussian process model shows greater robustness to outliers and to unusual response patterns. The Bayesian testing model successfully identifies known biomarkers, and is able to leverage information about the complete dose-response curve, rather than relying on summary measures.


Title10th International Conference of the ERCIM WG on Computational and Methodological Statistics
Abbreviated titleCMStatistics 2017
CountryUnited Kingdom