Enhanced Co-Expression Extrapolation (COXEN) Gene Selection Method for Building Anti-Cancer Drug Response Prediction Models

Abstract

The co-expression extrapolation (COXEN) method has been successfully used in multiple studies to select genes for predicting the response of tumor cells to a specific drug treatment. Here, we enhance the COXEN method to select genes that are predictive of the efficacies of multiple drugs for building general drug response prediction models that are not specific to a particular drug. The enhanced COXEN method first ranks the genes according to their prediction power for each individual drug and then takes a union of top predictive genes of all the drugs, among which the algorithm further selects genes whose co-expression patterns are well preserved between cancer cases for building prediction models. We apply the proposed method on benchmark in vitro drug screening datasets and compare the performance of prediction models built based on the genes selected by the enhanced COXEN method to that of models built on genes selected by the original COXEN method and randomly picked genes. Models built with the enhanced COXEN method always present a statistically significantly improved prediction performance (adjusted p-value ≤ 0.05). Our results demonstrate the enhanced COXEN method can dramatically increase the power of gene expression data for predicting drug response.

Keywords: co-expression extrapolation (COXEN); gene selection; general drug response prediction model; precision oncology.

Figure 1

Analysis flowchart of the COXEN framework. The original COXEN method and the enhanced COXEN method are different in the two boxes with dashed line border.

Figure 2

An illustration of generating the candidate gene pool by taking a union of top predictive genes for each drug.

Figure 3

Histograms of drug response AUC values in datasets. Mean and standard deviation (std) of AUC values are shown under each histogram.