THERAPEUTIC COMBINATION OPTIMIZATION FOR HEPATOCELLULAR CARCINOMA

Abstract

Hepatocellular carcinoma (HCC) is a difficult disease to treat due to high levels of heterogeneity and lack of druggable molecular drivers. While combinatorial therapy is an attractive approach for HCC treatment, identifying effective drug combinations remains a challenging task. Here, we paired rational drug combination design (using Quadratic Phenotypic Optimization Platform, QPOP) with patient-derived disease models such as patient-derived xenografts (PDXs) to achieve therapeutic optimization of more clinically relevant drug combinations against HCC. We first optimized the efficiency of QPOP by showing that a minimal resolution design with lesser number of experimental tests could achieve similar drug combination optimization capabilities. When QPOP was applied on HCC PDX-derived organoids, effective proteasome inhibitor-based drug combinations, including HCC subtype-specific combinations, were identified and subsequently validated in vitro and in vivo. Collectively, our work demonstrates the utility of rational drug combination optimization and highlights the relevance of proteasome inhibitor-based combinations for HCC treatment.