Integrative analysis identifies radiotherapy resistance-associated CAF subtypes shaping the tumor immune landscape in breast cancer.

BACKGROUND: Cancer-associated fibroblasts (CAFs) play a critical role in the tumor microenvironment (TME) of breast cancer (BC), influencing immune evasion and treatment resistance. This study aims to explore the heterogeneity of CAFs and their impact on prognosis and radiotherapy resistance. METHODS: Single-cell RNA sequencing was used to analyze CAF subsets in BC. Three major CAF subpopulations were identified, including inflammatory CAFs (iCAFs) and matrix-modulating CAFs (mmCAFs), which were enriched in Scissor⁺ cells associated with prognosis. A CAF-derived gene signature was constructed to stratify BC patients into transcriptome clusters with varying TME compositions. Survival analysis and multivariate regression were employed to assess the prognostic value of the gene signature and predict radiotherapy resistance. Validation was conducted using METABRIC and GEO cohorts. RESULTS: The iCAF and mmCAF subsets exhibited high stemness potential and facilitated immune rejection through interactions with epithelial and immune cells. The CAF gene signature identified high-risk clusters with increased stromal infiltration, suppressed CD8⁺ T-cell responses, and activation of immune-suppressive pathways, including VEGF. These high-risk clusters were significantly associated with radiotherapy resistance. A five-gene CAF risk model was developed, which robustly predicted radiotherapy resistance and poor prognosis in multiple cohorts. Validation confirmed the expression of risk genes, such as ENO1, which correlates with poor survival, TP53 mutation, and resistance to radiotherapy. CONCLUSIONS: CAFs play a pivotal role in TME remodeling, immune evasion, and treatment resistance in BC. The CAF-derived gene signature offers a clinically applicable tool for risk stratification, predicting prognosis and radiotherapy resistance. Targeting CAFs may provide new therapeutic opportunities for improving breast cancer treatment outcomes.