A regularized functional regression model enabling transcriptome-wide dosage-dependent association study of cancer drug response.

Evanthia Koukouli,Frank Dondelinger,Dennis Wang,Juhyun Park

doi:10.1371/journal.pcbi.1008066

Evanthia Koukouli, Frank Dondelinger + Show 2 more

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https://doi.org/10.1371/journal.pcbi.1008066

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Abstract

Cancer treatments can be highly toxic and frequently only a subset of the patient population will benefit from a given treatment. Tumour genetic makeup plays an important role in cancer drug sensitivity. We suspect that gene expression markers could be used as a decision aid for treatment selection or dosage tuning. Using in vitro cancer cell line dose-response and gene expression data from the Genomics of Drug Sensitivity in Cancer (GDSC) project, we build a dose-varying regression model. Unlike existing approaches, this allows us to estimate dosage-dependent associations with gene expression. We include the transcriptomic profiles as dose-invariant covariates into the regression model and assume that their effect varies smoothly over the dosage levels. A two-stage variable selection algorithm (variable screening followed by penalized regression) is used to identify genetic factors that are associated with drug response over the varying dosages. We evaluate the effectiveness of our method using simulation studies focusing on the choice of tuning parameters and cross-validation for predictive accuracy assessment. We further apply the model to data from five BRAF targeted compounds applied to different cancer cell lines under different dosage levels. We highlight the dosage-dependent dynamics of the associations between the selected genes and drug response, and we perform pathway enrichment analysis to show that the selected genes play an important role in pathways related to tumorigenesis and DNA damage response.

Highlights

Cancer is a heterogeneous disease, with individual tumours showing sometimes very different mutational and molecular profiles
We demonstrate its value using data from the Genomics of Drug Sensitivity in Cancer project to identify genes whose expression is associated with drug response
We considered a dose-varying coefficient model, along with a two-stage variable selection method in order to detect and evaluate drug-gene relationships, and applied this method to data extracted from the Genomics for Drug Sensitivity in Cancer (GDSC) project [7]

Summary

Introduction

Cancer is a heterogeneous disease, with individual tumours showing sometimes very different mutational and molecular profiles. The genetic makeup of a tumour influences how it reacts to a given anti-cancer drug. Due to lack of predictive markers of tumour response, often patients with very different tumour genetic makeup will receive the same therapy, resulting in high rates of treatment failure [1]. Large clinical trials in rapidly lethal diseases are expensive, complex and often lead to failure due to lack of efficacy at a given dosage [2]. One major issue for some cancer treatments, e.g. chemotherapies, are cytotoxic effects that result in collateral damage of the healthy host tissue [3]. Genetic factors can help fine-tune the dosage for individual patients, so that the minimal effective dosage can be delivered [4]

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