Sunday, June 1, 2025
6/1/2025
Discovery of chemoresistant tumor subclones in pediatric liver cancers - PROJECT SUMMARY The overall survival rate for children with high-risk liver cancer, including metastatic or relapse/refractory hepatoblastoma (HB), hepatocellular carcinoma (HCC), and HB with HCC features (HBC) is below 50%. The best odds for survival for patients with these cancers is complete surgical resection with negative margins, but this is not achievable at diagnosis for most patients. Instead, high-risk patients are treated with combinations of chemotherapy and surgery, where chemotherapy is used to reduce tumor burdens before surgery and to eliminate residual cancer cells after surgery. The low survival rates for high-risk liver cancers are associated with chemoresistance and improving these survival rates requires alternative therapies to target chemoresistant cancer cells in combination with chemotherapy. We propose a comprehensive approach to characterize chemoresistant cancer cells in children with high-risk liver cancers, design technologies to detect these cancers at diagnosis, discover and establish therapies to target these chemoresistant cancer cells, and use patient-derived models to test personalized treatment combinations before prescribing them to high-risk patients. We propose to proceed with three aims. First, we will establish chemoresistant models and molecularly and clinically characterize them. Second, we will use molecular data to enable the identification of chemoresistant cancers at diagnosis utilizing prospectively collected specimens from a major clinical trial. We will use personalized models to predict treatment response in prospectively enrolled cancer patients and determine if chemoresistance can be detected at diagnosis. Finally, we will identify and investigate targeted combination therapies in patient-derived models. Conceptual innovations in our proposal include building a pediatric liver cancer atlas of molecularly and clinically characterized cell types that could be used to predict patient responses to therapy at diagnosis, as well as mapping out the drivers of chemoresistance in pediatric liver cancer. The study will evaluate the largest cohort of patient-derived models for pediatric liver cancers and the utility of these models as pre-clinical tools to develop and evaluate therapies for children with high-risk liver cancers. If successful, this study will help efforts to transform the diagnosis and treatment of children with high-risk liver cancers leading to dramatically improved outcomes.
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