Drug response prediction by ensemble learning and drug-induced gene expression signatures

TL;DR

This paper presents an ensemble learning approach that combines multiple predictive models and novel gene expression signatures to improve drug response prediction in cancer cells, validated through in vitro experiments.

Contribution

It introduces a novel ensemble method integrating diverse models and gene expression signatures for enhanced drug response prediction accuracy.

Findings

Ensemble integration improves prediction performance.

Combining multiple data sources enhances accuracy.

Validated predictions with in vitro experiments.

Abstract

Chemotherapeutic response of cancer cells to a given compound is one of the most fundamental information one requires to design anti-cancer drugs. Recent advances in producing large drug screens against cancer cell lines provided an opportunity to apply machine learning methods for this purpose. In addition to cytotoxicity databases, considerable amount of drug-induced gene expression data has also become publicly available. Following this, several methods that exploit omics data were proposed to predict drug activity on cancer cells. However, due to the complexity of cancer drug mechanisms, none of the existing methods are perfect. One possible direction, therefore, is to combine the strengths of both the methods and the databases for improved performance. We demonstrate that integrating a large number of predictions by the proposed method improves the performance for this task. The predictors in the ensemble differ in several aspects such as the method itself, the number of tasks method considers (multi-task vs. single-task) and the subset of data considered (sub-sampling). We show that all these different aspects contribute to the success of the final ensemble. In addition, we attempt to use the drug screen data together with two novel signatures produced from the drug-induced gene expression profiles of cancer cell lines. Finally, we evaluate the method predictions by in vitro experiments in addition to the tests on data sets.The predictions of the methods, the signatures and the software are available from \url{http://mtan.etu.edu.tr/drug-response-prediction/}.