A statistical framework for defining synergistic anticancer drug interactions

Synergistic drug combinations have the potential to delay drug resistance and improve clinical outcomes in patients with advanced cancers. However, current cell-based screens lack robust statistical assessment to identify significant synergistic interactions for downstream experimental or clinical validation. Leveraging a large-scale dataset that systematically evaluated more than 2,000 drug combinations across 125 pan-cancer cell lines, we established reference null distributions separately for various synergy metrics and cancer types. These data-driven reference distributions enable estimation of empirical p-values to assess the significance of observed drug combination effects, thereby standardizing synergy detection in future studies. The statistical evaluation confirmed key synergistic combinations and uncovered novel combination effects that met stringent statistical criteria, yet were overlooked in the original analyses. We revealed cell context-specific drug combination effects across tissue types and inherent differences in statistical behavior of the synergy metrics. To demonstrate the general applicability of our approach to smaller-scale studies, we applied it to evaluate the significance of combination effects in increasing smaller subsamples of an independent dataset. We provide a fast and statistically rigorous approach to detecting synergistic drug interactions in new combinatorial screens, thereby supporting more standardized drug combination discovery.