Predicting Anticancer Drug Responses Using a Dual-Layer Integrated Cell Line-Drug Network Model

The ability to predict the response of a cancer patient to a therapeutic agent is a major goal in modern oncology that should ultimately lead to personalized treatment. Existing approaches to predicting drug sensitivity rely primarily on profiling of cancer cell line panels that have been treated with different drugs and selecting genomic or functional genomic features to regress or classify the drug response. Here, we propose a dual-layer integrated cell line-drug network model, which uses both cell line similarity network (CSN) data and drug similarity network (DSN) data to predict the drug response of a given cell line using a weighted model. Using the Cancer Cell Line Encyclopedia (CCLE) and Cancer Genome Project (CGP) studies as benchmark datasets, our single-layer model with CSN or DSN and only a single parameter achieved a prediction performance comparable to the previously generated elastic net model. When using the dual-layer model integrating both CSN and DSN, our predicted response reached a 0.6 Pearson correlation coefficient with observed responses for most drugs, which is significantly better than the previous results using the elastic net model. We have also applied the dual-layer cell line-drug integrated network model to fill in the missing drug response values in the CGP dataset. Even though the dual-layer integrated cell line-drug network model does not specifically model mutation information, it correctly predicted that BRAF mutant cell lines would be more sensitive than BRAF wild-type cell lines to three MEK1/2 inhibitors tested.Author Summary: In this study, using the Cancer Cell Line Encyclopedia (CCLE) and Cancer Genome Project (CGP) studies as benchmark datasets, we explored the application of similarity information between cell lines and drugs in drug response prediction. We found that similar cell lines by gene expression profiles exhibit similar response to the same drug. Meanwhile, drugs with similar chemical structures also show similar inhibitory effects across different cell lines. Based on the above observations, we proposed a dual-layer network and local weighted model to predict drug response of a cell line using proximal information of the drug-cell line network. The only three parameters of our model are optimized by leave-one-out cross-validation for each drug. Two case studies of MAPK and ERK signal pathways on CCLE dataset proved that the predicted-to-observed correlations of our dual-layer network model is significantly better than the previous predictor using elastic net model. Interestingly, predictions based on drug similarity network (DSN) alone were much better than those based on cell line similarity network (CSN) alone for most drugs, implying that drug similarities are more informative for drug response prediction than cell line similarities. Our network model can be applied to predict the response of a new cell line to existing already tested drugs or to predict the response of an existing cell line to new drugs, thus potentially saving the cost in a drug-cell line screening.