Abstract Hepatocellular carcinoma (HCC) management is challenging due to its heterogeneous tumor microenvironment and poor treatment responses. To develop personalized approaches, we conducted a clinical trial of transarterial chemoembolization (TACE) combined with immune checkpoint blockade (ICB) where we collected tumor specimens for comprehensive multi-omic profiling. Treatment response was associated with increased infiltration of anti-tumor T cells, driven by cGAS-STING activation within immune-suppressive epithelial cells. However, fibrotic processes impaired these responses in some patients. Based on this insight, we conducted an animal trial in an HCC model where we added an anti-fibrotic agent to ICB and TACE mimic. This improved efficacy compared to ICB and TACE mimic alone. To identify patients who would benefit from such treatment, we constructed a predictive model using data from a limited immunohistochemistry panel and a group sparse learning algorithm. Our findings provide a blueprint for crafting personalized therapies for HCC and other cancers.
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Westover et al. (2024) studied this question.
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