AN INTEGRATED PLATFORM FOR NOVEL PERSONALIZED LIVER CANCER THERAPEUTICS

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SUMMARY Liver cancer is the fourth leading cause of cancer related mortality worldwide. Currently, in addition to emerging immune checkpoint inhibitors, a small number of structurally related kinase inhibitors (KIs) are approved for advanced hepatocellular carcinoma (HCC), the most frequent form of liver cancer. While important, these drugs provide modest improvements in survival— typically months—and often at the cost of significant toxicity. The drugs, sorafenib, regorafenib, cabozantinib and lenvatinib are all multi-targeted KIs with poorly defined mechanisms of action. As such, these drugs are given to HCC patients without any consideration to a specific mutation within tumors. This presents a daunting challenge; without a clear target or mechanism, no clear path exists to guide the development of improved therapies for HCC. In this proposal, we combine chemical biology approaches to modify target preferences of clinically approved HCC KIs and epigenetic tool compounds, and we develop precision genetically-engineered mouse models and 3D tumor organoids in an integrated platform to identify new drug targets and therapeutics for HCC. Our preliminary data demonstrate that different genomic drivers establish unique epigenomic landscapes within tumor organoid lines, influencing the druggable space. Through chemical genetic screens, we have identified lead compounds that are either pan-active across all HCC genotypes tested and some which are selective to specific genetic backgrounds (e.g. WNTinib1 for WNT/β-Catenin driven tumors). By combining chemical, proteomic, and target engagement data for WNTinib1, we have identified an unique p38a/b to Ezh2 signalling axis as a key and selective dependency to antagonize the activity of mutant β-Catenin. The major hypothesis that we seek to test is that the unique epigenomic landscapes and dependencies on signaling pathways, which are established by different HCC cancer drivers, confer differential sensitivity to specific targets and small molecules. By taking advantage of driver-induced cancer mouse models, murine and human tumor organoids, and patient-derived xenografts (PDXs), we will be able to suggest stratification strategies and identify more effective tailored therapeutics for HCC. The long-term goal of this proposal is to identify and characterize at the mechanistic level, the signaling pathways and targets that enable (i) pan-activity across a variety of HCC sub-types, (ii) selective activity in the context of tumors driven by specific mutations, (iii) synergistic tumor inhibition in combination with immunotherapy approaches. Importantly, we have identified strong small molecule leads, including WNTinhib1, that display superior efficacy compared to standard-of-care KIs across several HCC models, including human samples. Key deliverables include new tools and leads for drug discovery derived from well-validated chemical starting points and mechanistic insights into patient stratification and therapeutics for HCC.