CELL INTELLIGENCE · 肿瘤细胞治疗研究

We analyzed 736 ccRCC cases (203 institutional and 533 TCGA) to identify histologically distinct activated cancer-associated fibroblasts (aCAFs; oval to mildly elongated immature fibroblasts occupying >5% of stroma) on H&E-stained slides. Clinicopathological correlations, immunohistochemical immune profiling, transcriptomic analyses, and machine learning-based survival modelling were performed; for the latter, 17 neoadjuvant-treated cases were excluded from the TCGA cohort, yielding an analytic cohort of 516 cases.

aCAFs were identified in 12.3% and 12.9% of institutional and TCGA cohorts, respectively, and were significantly associated with advanced tumor stage, higher histologic grade, sarcomatoid features, and poor disease-specific survival, remaining an independent prognostic factor on multivariate analysis. aCAF-positive tumors exhibited reduced tumor-infiltrating lymphocytes and CD4+ T-cell infiltration, enhanced TGF-β signaling, and molecular enrichment in FGFR2 and complement regulation pathways. In machine learning-based survival models, aCAFs ranked among the top five prognostic predictors, and their inclusion improved predictive accuracy with DSS AUC of 0.874 versus 0.858. In silico drug screening identified ponatinib and HG6-64-1 as candidate therapeutic agents for tumors with high fibroblast activation protein-α expression.

Morphologically defined aCAFs represent a histologically recognizable and clinically meaningful stromal component associated with immunosuppression and adverse prognosis in ccRCC, with potential utility for routine diagnostic application and therapeutic targeting.