Fig. 4
From: Harnessing synthetic lethality to predict the response to cancer treatment
Drug-cSL-network predicts treatment outcomes in cancer patients. a The KM plot of predicted responders (blue) vs non-responders (red) to taxane-anthracycline chemotherapy64. We divided the patients into responders vs non-responders based on the median value of their cSL-scores. b The gene expression of the cSL partners in patients treated with erlotinib65,66, ordered according to their months-to-progression (on-top). As predicted, patients with many downregulated cSL partners progressed slower. c The responders (blue) to taxane-cisplatin therapy for ovarian cancer show significantly higher ISLE cSL-scores than the non-responders (red) (Wilcoxon rank sum P < 9.1E-3). The X-axis shows the different groups of partners studied, and Y-axis provides their cSL-scores in responders vs non-responders. d TCGA patients with a large number of downregulated drug-cSL partners in their tumors show better response based on RECIST criteria. X-axis shows six drugs that have considerable (>12 samples) drug response information in TCGA, and Y-axis represents the cSL-score (divided by total number of SL partners to guide visualization, mean and s.e.m) of their drug targets, where cancer types are controlled for (* marks the four drugs that are significantly predicted after multiple hypothesis corrections (FDR-corrected Wilcoxon rank sum P < 0.2, and epirubicin FDR < 0.23), drugs are listed in order of significance). Blue (red) bars denote the cSL-scores of the responders (non-responders), and the numbers marked in blue (red) below the figure indicate the number of responders (non-responders) for each drug. All the analyses were performed using the drug-cSL-network (presented in Supplementary Fig. 8) based on the drug-target mapping available listed in Supplementary Data 13
