SUMMARYThe Columbia Cancer Target Discovery and Development (CTD2) Center has developed PANACEA (PANcancer Analysis of Chemical Entity Activity), a collection of dose-response curves and perturbational profiles for 400 clinical oncology drugs in cell lines selected to optimally represent 19 cancer subtypes. This resource, developed to study tumor-specific drug mechanism of action, was instrumental in hosting a DREAM Challenge to assess computational models for de novo drug polypharmacology prediction. Dose-response and perturbational profiles for 32 kinase inhibitors were provided to 21 participating teams, who did not know the identity or nature of the compounds, and they were asked to predict high-affinity binding among ~1,300 possible protein targets. Best performing methods leveraged both gene expression profile similarity analysis, and deep-learning methodologies trained on individual datasets. This study lays the foundation for future integrative analyses of pharmacogenomic data, reconciliation of polypharmacology effects in different tumor contexts, and insights into network-based assessment of context-specific drug mechanism of action.

Combinatorial therapies have recently been proposed for improving anticancer treatment efficacy. The SynergyFinder R package is a software tool to analyse pre-clinical drug combination datasets. We report the major updates to the R package to improve the interpretation and annotation of drug combination screening results. Compared to the existing implementations, the novelty of the updated SynergyFinder R package consists of 1) extending to higher-order drug combination data analysis and the implementation of dimension reduction techniques for visualizing the synergy landscape for an unlimited number of drugs in a combination; 2) statistical analysis of drug combination synergy and sensitivity with confidence intervals and p-values; 3) incorporating a synergy barometer to harmonize multiple synergy scoring methods to provide a consensus metric of synergy; and 4) incorporating the evaluation of drug combination synergy and sensitivity simultaneously to provide an unbiased interpretation of the clinical potential. Furthermore, we enabled fast annotation for drugs and cell lines that are tested in an experiment, including their chemical information, targets and signalling network information. These annotations shall improve the interpretation of the mechanisms of action of drug combinations. To facilitate the use of the R package within the drug discovery community, we also provide a web server at www.synergyfinderplus.org that provides a user-friendly interface to enable a more flexible and versatile analysis of drug combination data.