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

The community has successfully used the co-expression extrapolation (COXEN) method in multiple studies to select genes for predicting the response of tumor cells to a specific drug treatment. Here, the authors enhanced 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. The authors applied the proposed method on benchmark in vitro drug screening datasets and compared 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 presented a statistically significantly improved prediction performance (adjusted p-value ≤ 0.05). The authors' results demonstrated the enhanced COXEN method can dramatically increase the power of gene expression data for predicting drug response.