Hepatic CD4 T Cells Predict Hepatocellular Carcinoma Risk on Metabolic Dysfunction-Associated Steatohepatitis Patients.
Emilse Rodriguez, Peter Simon, Sabrina Dhooge, Marina Fernandez, Patricia Calafat, María Kurpis, Nicolás Nuñez, Jhon Prieto, Anna Saborowski, Arndt Vogel, José Daniel Debes, Domingo Cesar Balderramo, Andre Boonstra, Pablo Alberto Romagnoli
Abstract
Open AccessBACKGROUND & AIMS: Metabolic dysfunction-associated steatohepatitis (MASH) increasingly drives hepatocellular carcinoma (HCC) development. We characterized inflammatory infiltrates in liver biopsies from MASH patients who developed HCC versus controls to identify predictive immune signatures. METHOD: Formalin-fixed paraffin-embedded (FFPE) liver biopsies from MASH patients were categorized as pre-HCC MASH (n = 10) or control MASH (n = 13) by the ESCALON consortium. Standardized histological analysis and multiplexed immunohistochemistry were performed targeting CD4, CD8, PD1, PDL1, FoxP3, CXCR6, CD3, CD68, and CD20 using a PhenoImager Fusion scanner. Single-cell RNA-seq datasets characterized hepatic CD4 T cell heterogeneity. Clinical parameters measured included ALT, AST, GGT, alkaline phosphatase, platelets, and INR. RESULTS: Pre-HCC MASH showed inflammation extending from portal to periportal areas versus portal-only distribution in controls. Analysis of 291,908 cells revealed significantly higher CD4+ density (p = 0.0243) and CD4+PD1+ cells (p = 0.017) in pre-HCC patients, while CD8+ and regulatory T cell densities remained unchanged. Single-cell RNA-seq identified potential phenotypic shifts from Th1 cytotoxicity toward tissue-repair and Th17 CD4+ T cells in MASH livers. Combined immunological and clinical variables (sex, age, CD4+ T cell numbers, ALT, alkaline phosphatase and platelets) achieved excellent predictive performance (ROC-AUC = 0.944) for HCC development. CONCLUSIONS: Increase in liver CD4+ T cell infiltration characterizes MASH-to-HCC progression. These immune signatures combined with clinical parameters demonstrate remarkable predictive value for identifying high-risk MASH patients.