Drug sensitivity prediction with normal inverse Gaussian shrinkage informed by external data

In precision medicine, a common problem is drug sensitivity prediction from cancer tissue cell lines. These types of problems entail modelling multivariate drug responses on high‐dimensional molecular feature sets in typically >1000 cell lines. The dimensions of the problem require specialised mo...

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Bibliographic Details
Published in:Biometrical journal 2021-02, Vol.63 (2), p.289-304
Main Authors: Münch, Magnus M., van de Wiel, Mark A., Richardson, Sylvia, Leday, Gwenaël G. R.
Format: Article
Language:English
Subjects:
Bayesian analysis
Biotechnology
Cancer
drug sensitivity
empirical Bayes
Genomics of Drug Sensitivity in Cancer (GDSC)
Novel Bayesian Developments in Clinical Trials
Precision medicine
Research Paper
Sensitivity
Shrinkage
Tumor cell lines
variational Bayes
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Summary:In precision medicine, a common problem is drug sensitivity prediction from cancer tissue cell lines. These types of problems entail modelling multivariate drug responses on high‐dimensional molecular feature sets in typically >1000 cell lines. The dimensions of the problem require specialised models and estimation methods. In addition, external information on both the drugs and the features is often available. We propose to model the drug responses through a linear regression with shrinkage enforced through a normal inverse Gaussian prior. We let the prior depend on the external information, and estimate the model and external information dependence in an empirical‐variational Bayes framework. We demonstrate the usefulness of this model in both a simulated setting and in the publicly available Genomics of Drug Sensitivity in Cancer data.
ISSN:0323-3847
1521-4036
DOI:10.1002/bimj.201900371