空间免疫评分系统预测肝细胞癌复发

Given the high recurrence rates of hepatocellular carcinoma (HCC) post-resection 1-3 , improved early identification of patients at high risk for post-resection recurrence would help to improve patient outcomes and prioritize healthcare resources 4-6 .

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 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.