HLA‐DR⁺ Tumor Cells Show an Association with a Distinct Immune Microenvironment and CD8⁺ T‐Cell Exhaustion in HBV‐Associated Hepatocellular Carcinoma

作者：Jenny C. Chang, Yan Guo, Wen‐Jing Yuan, Yu‐Man Chen, Bowei Liu, Wentao Li, Xunmeng Ding, Xudong Fu, Yu‐Han Lou, Zhuo‐Ran Chen, Xiaoying Luo, Songze Ding, Bingyong Zhang, Xiuling Li, Yong‐Zhi Hong, Shundong Cang, Dongxiao Li, Ling Lan · 发表于：Advanced Science · 年份：2025 · DOI：10.1002/advs.202502979 · 被引用次数：11 · 研究领域：Cancer Immunotherapy and Biomarkers、Immune cells in cancer、Immune Cell Function and Interaction

Abstract Hepatocellular carcinoma (HCC) is a leading cause of cancer‐related deaths worldwide, with hepatitis B virus (HBV) as a major driver. Despite the pivotal role of viral infections in shaping the tumor microenvironment (TME), the mechanistic differences among HBV‐, hepatitis C virus (HCV)‐, and non‐B non‐C (NBNC)‐associated HCC remain poorly understood. By integrating the largest publicly available single‐cell RNA sequencing (scRNA‐seq) dataset of HCC (160 samples from 124 patients) with multi‐scale protein‐level validation using multiplex immunofluorescence and tissue microarrays (198 HCC specimens), HLA‐DR⁺ tumor cells are identified as a distinctive feature of HBV + HCC. These tumor cells uniquely express MHC class II molecules, typically restricted to antigen‐presenting cells, and correlate with immune checkpoint activation and PD‐L1 expression, potentially contributing to an immunosuppressive microenvironment specific to HBV + HCC. Trajectory analysis revealed distinct CD8⁺ T‐cell differentiation pathways in HBV + HCC, characterized by enhanced exhaustion and stem‐like phenotypes. HLA‐DR⁺ tumor cells are associated with increased recruitment of CD8⁺ T cells and correlated with T‐cell exhaustion, potentially contributing to a suppressive TME. Clinically, high proportions of HLA‐DR⁺ tumor cells are linked to poor survival outcomes, especially when accompanied by elevated PD‐L1 expression, suggesting that HLA‐DR⁺ tumor cells may serve as a potential predictive biomar...