Abstract 2544: TGF-β receptors 1 and 2 are functional biomarkers that stratify risk of hepatocellular cancer (HCC). Artificial intelligence based validation at three centers

Background: Hepatocellular carcinoma (HCC) is the fastest rising cancer in the USA and is the fourth leading cause of cancer deaths globally. A critical unmet need to reduce high mortality associated with advanced HCC is the ability to identify high-risk individuals for cost-effective, targeted screening, early detection, and prevention strategies. Human genomics and animal models have revealed etiological patterns and multiple genes and signaling pathways such as TGF-β, WNT, VEGF to be associated with the initiation and progression of HCC. We used integrated functional approaches (combining bioinformatics analysis, in vivo mouse models, and in vitro biological and biochemical methods) and identified potential biomarkers for HCC. We examined and validated 10 functional biomarkers for HCC risk prediction. We further harnessed machine learning tools and Artificial Intelligence (AI)-based technology to validate manual analyses of Immunohistochemical (IHC) labeled slides across different centers. Methods: >280 liver tissue samples were collected from patients with HCC and cirrhosis. Immunohistochemistry was performed against 10 functional biomarkers (TGFBR1, TGFBR2, SPTBN1, HMGA2, RSPO3, ITIH4, EPCAM, PLK1, SIRT6, FANCD2). Artificial intelligence-based deep learning technology by AIFORIA was used for the unbiased, intensive, and rapid validation of IHC. Results: The most promising results were for TGFBR1 and TGFBR2, on >80 HCC and cirrhotic samples, IHC labeling revealed: • Both TGFBR1 (p