Fig. 2.

ISLE-based prediction of in vitro and in vivo drug response. a Prediction of in vitro drug response using drug-cSL-network in the CCLE⁵⁸ collections. The ROC curve compares the prediction performance of ISLE, ncSL, and DAISY (Methods). b Predicting in vivo drug response using drug-cSL-network. Mouse xenograft samples marked as responders (blue) show higher cSL-scores compared to the samples marked as non-responders (red). The X-axis shows seven drugs where sufficient drug response data are available and the Y-axis depicts the cSL-score (divided by the total number of SL partners to guide visualization, mean and s.e.m). (* marks the five drugs that are significantly predicted after multiple hypothesis correction (FDR-corrected Wilcoxon rank sum P < 0.2), and drugs are listed in order of significance). c Benchmarking ISLE-based drug response prediction versus the DREAM7 challenge. The figure shows prediction accuracy (evaluated using a variant of concordance index (Methods); Y-axis) of ISLE (red) and top five approaches (gray) both for the drug response to single agent (left columns) and drug combinations (right columns). d Predicting drug synergy. The AUC of ROC curves displaying the SL-based prediction accuracy of synergistic drug combination screens of a recent DREAM challenge, and a large collection of mouse xenograft models⁵⁹. Results are shown for ISLE cSL interactions (red) and compared with the DAISY SL-network (yellow), ncSL network (green), and randomly permuted networks (gray)