Multi-Drug Pharmacotyping improves Therapy Prediction in Pancreatic Cancer Organoids

Abstract Patient-Derived Organoids (PDOs) represent a promising technology for therapy prediction in pancreatic cancer, with the potential of enhancing treatment outcomes and allowing more effective, personalized treatment choices. However, classification approaches into sensitive and resistant models remain very variable and are based on single-agent testing only, neglecting synergistic effects of multi-drug combinations. Here, we established 13 PDOs and performed both combinatorial and single-agent drug testing. By comparing different clustering approaches of drug-response metrics and establishing a new classification approach based on pharmacokinetic modelling, we were able to evaluate which score best predicts the clinical response of patients. Our newly developed score considered the Area Under The Curve (AUC) of cell viability curves and reached a prediction accuracy of 85%. Our data supports previous findings for PDOs to constitute an effective platform for translational drug testing. Furthermore, our results suggest that the AUC is a more accurate drug-response metric than the half maximal inhibitory concentration (IC50), and that combinatorial drug testing yields a higher accuracy than single-agent testing. Based on our results, future PDO studies and PDO-based clinical trials should integrate combinatorial drug testing when performing pharmacotyping as it represents a more realistic translational approach towards drug testing and enhances the prediction accuracy of in-vitro drug testing. The methodology and outcomes presented in this study are of critical relevance for future PDO-based translational trials as they allow a new physiology-based approach towards multi-drug testing and classification of organoid response, which improves PDO prediction accuracy.