e14073 Background: Accurate prediction of tumor response to a drug treatment is of paramount importance for precision oncology. The co-expression extrapolation (COXEN) gene selection approach has been successfully used in multiple studies to select genes for predicting the response of tumor cells to a specific drug. Here, we enhance the original COXEN approach to select genes that are predictive of the efficacies of multiple drugs simultaneously for building general drug response prediction model. Methods: We implemented two methods to select predictive genes. The first method 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. The second method uses a linear regression model to evaluate the prediction power of a gene for all drugs while the drugs are one-hot encoded in the regression model. Among the predictive genes, we further select genes by evaluating the preservation of co-expression patterns between cancer cases with drug response data available and cancer cases for which drug response needs to be predicted, because the preservation of co-expression patterns indicates the similarity of genomic regulations between cancer cases. Results: To test the enhanced COXEN method, we used a lightGBM regression model to predict drug response based on the selected genes on two benchmark in vitro drug screening datasets. The table below compares the performance of prediction models built based on 200 genes selected by the enhanced COXEN method to that of models built on 200 genes randomly picked from the LINCS gene set, which includes 976 “landmark” genes well-representing cellular transcriptomic changes identified in the Library of Integrated Network-Based Cellular Signatures (LINCS) project. The enhanced COXEN approach selects genes better than random LINCS genes as demonstrated by the increased average coefficient of determination (R2) for predicting the area under the dose response curve through cross-validation. Pair-wise t-test indicates the improvement is statistically significant (p-value ≤ 0.05) on both datasets. Conclusions: Our result demonstrates the benefit of using an enhanced COXEN approach to select genes for building general drug response prediction model. [Table: see text]