Topological imaging of single-cell CAF heterogeneity for predicting prognosis and adjuvant chemotherapy benefit in pancreatic cancer.
Abstract
Purpose
Cancer-associated fibroblasts (CAFs) are central drivers of PDAC progression and therapeutic resistance, yet their preoperative clinical utility remains unexplored. We aimed to translate single-cell CAF heterogeneity into a CT-based framework for preoperative risk stratification and adjuvant chemotherapy stratification in PDAC.
Materials And Methods
A total of 1452 PDAC patients were included across transcriptomic and imaging analyses. Single-cell RNA sequencing data from 24 patients were integrated with TCGA-PAAD bulk transcriptomics using the Scissor algorithm to identify prognosis-associated CAF subpopulations. A nine-gene CAPR score was validated across four independent transcriptomic cohorts (n = 594). A CT-based topological data analysis (TDA) classifier (ra-CAPR) was developed in an institutional cohort (n = 122), externally validated in the TCIA cohort (n = 50), and evaluated in an independent surgical cohort (n = 657), with performance benchmarked against conventional radiomics.
Results
Three adverse CAF subpopulations (ECM-remodelling myCAF, hypoxic CAF, and iCAF_chemokine) were identified, defining an immune-excluded, mutationally-burdened, and chemoresistant phenotype across validation cohorts. The TDA-based ra-CAPR classifier demonstrated superior cross-cohort robustness over conventional radiomics (AUC 0.774 vs. 0.715; 0.744 vs. 0.621), accompanied by markedly lower cross-cohort feature distributional shift (26% vs. 75%). In the surgical cohort, ra-CAPR independently predicted overall survival (HR 1.42, P = 0.005). Adjuvant chemotherapy improved OS in both ra-CAPR subgroups, with a larger absolute gain in ra-CAPR-low patients (29.5 vs 17.0 months).
Conclusion
By translating adverse CAF subpopulation signatures into a CT-based topological biomarker, ra-CAPR enables individualized preoperative risk stratification and may facilitate risk-adapted postoperative management.