Spatial immune scoring system predicts hepatocellular carcinoma recurrence

• 成果类型: Article
• 署名作者: Jia, Gengjie; He, Peiqi; Dai, Tianli; Goh, Denise; Wang, Jiabei; Sun, Mengyuan; Wee, Felicia; Li, Fuling; Lim, Jeffrey Chun Tatt; Hao, Shuxia; Liu, Yao; Lim, Tony Kiat Hon; Ngo, Nye-Thane; Tao, Qingping; Wang, Wei; Umar, Ahitsham; Nashan, Bjoern; Zhang, Yongchang; Ding, Chen; Yeong, Joe; Liu, Lianxin; Sun, Cheng
• 署名单位: Chinese Academy of Sciences; University of Science & Technology of China, CAS; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Chinese Academy of Agricultural Sciences; Agriculture Genomes Institute at Shenzhen, CAAS; Agency for Science Technology & Research (A*STAR); A*STAR - Institute of Molecular & Cell Biology (IMCB); National University of Singapore; Central South University; Fudan University; Fudan University; Singapore General Hospital; Agency for Science Technology & Research (A*STAR); A*STAR - Singapore Immunology Network (SIgN); National University of Singapore
• 刊物名称: Nature
• ISSN/ISSBN: 0028-3016
• DOI: 10.1038/s41586-025-08668-x
• 发表日期: 2025-04-24
• 关键词: postoperative tumor recurrence; killer-cell dysfunction; microvascular invasion; expression; resection

摘要

Given the high recurrence rates of hepatocellular carcinoma (HCC) post-resection(1, 2-3), improved early identification of patients at high risk for post-resection recurrence would help to improve patient outcomes and prioritize healthcare resources(4, 5-6). Here we observed a spatial and HCC recurrence-associated distribution of natural killer (NK) cells in the invasive front and tumour centre from 61 patients. Using extreme gradient boosting and inverse-variance weighting, we developed the tumour immune microenvironment spatial (TIMES) score based on the spatial expression patterns of five biomarkers (SPON2, ZFP36L2, ZFP36, VIM and HLA-DRB1) to predict HCC recurrence risk. The TIMES score (hazard ratio = 88.2, P < 0.001) outperformed current standard tools for patient risk stratification including the TNM and BCLC systems. We validated the model in 231 patients from five multicentred cohorts, achieving a real-world accuracy of 82.2% and specificity of 85.7%. The predictive power of these biomarkers emerged through the integration of their spatial distributions, rather than individual marker expression levels alone. In vivo models, including NK cell-specific Spon2-knockout mice, revealed that SPON2 enhances IFN gamma secretion and NK cell infiltration at the invasive front. Our study introduces TIMES, a publicly accessible tool for predicting HCC recurrence risk, offering insights into its potential to inform treatment decisions for early-stage HCC.