Identification of an Immunotherapy-Associated CD1c

Purpose: Understanding which immune cell populations are linked to immunotherapy efficacy is essential for improving treatment stratification in breast cancer. This study aimed to identify immune-relevant cellular subsets from single-cell RNA sequencing (scRNA-seq) data and to develop a prognostic model based on genes characteristic of the relevant population. Methods: scRNA-seq data from breast cancer patients treated with immune checkpoint blockade were reanalyzed to identify cell subsets associated with therapeutic response. The existence of the key subset in tumor tissues was validated by multiplex immunohistochemistry. Marker genes from this subset were then used to construct a prognostic model through Cox regression and least absolute shrinkage and selection operator (LASSO) analysis. The model was further evaluated in two independent GEO cohorts, GSE177043 and GSE169246. Results: A tumor-infiltrating dendritic cell subset defined by CD1c and CLEC10A co-expression was enriched in tumors from responders. Based on the transcriptional signature of this population, we established a tumor-infiltrating dendritic cell-related prognostic model (TRPM). Patients with low TRPM scores had superior survival compared with those with high scores. In addition, lower TRPM scores were associated with a reduced TP53 mutation frequency, increased infiltration of anti-tumor immune cells, including M1 macrophages, plasma cells, CD8+ T cells, and CD4+ T cells, a more immune-active tumor microenvironment, greater predicted drug sensitivity, and improved benefit from immunotherapy. By contrast, higher TRPM scores were linked to a higher TP53 mutation rate, enrichment of M2 macrophages, an immunosuppressive phenotype, decreased drug sensitivity, and poorer predicted response to immunotherapy. In the external immunotherapy cohorts, the model showed good predictive performance, with AUC values of 0.73 in GSE177043 and 0.85 in GSE169246. Conclusion: TRPM may serve as a useful biomarker for stratifying breast cancer patients according to prognosis and likely treatment benefit. This model could support individualized therapeutic decision-making, although prospective clinical validation remains necessary.