Triple-negative breast cancer (TNBC) represents the most aggressive breast cancer subtype, with its highly heterogeneous tumor microenvironment posing substantial challenges for precision diagnosis and therapy. To address this, we aim to construct a novel prognostic framework based on tumor-immune interactions. Through integrative analysis of single-cell RNA sequencing data from 30 TNBC samples (106,132 cells), we identify key tumor expression metaprograms and uncover their interaction with an immunosuppressive dendritic-cell subset, a process associated with the NECTIN1–NECTIN4 axis. Leveraging these interactions, we developed and validated two immunological prognostic models using multi-cohort transcriptomic data, including the stress response tumor cell and pDC_CLEC4C prognostic model (SPSM) and the immune response tumor cell and pDC_CLEC4C prognostic model (IPSM). These models effectively stratified TNBC patients into distinct risk groups, with the low-risk group characterized by an immunologically active microenvironment and elevated expression of immune checkpoint genes, suggesting a potential responsiveness to immunotherapy. Furthermore, we identified several potential therapeutic agents, including imatinib and bortezomib. Collectively, our dual-model framework provides a tool for risk stratification, offers translational insights for precision treatment, and presents new directions for understanding TNBC heterogeneity and therapeutic development.

Breast cancer progression is closely linked to immune cells within the tumor microenvironment (TME), but the immune landscape differences across HER2-positive (HER2+), triple-negative (TNBC), and ER-positive (ER+) subtypes have yet to be systematically characterized. In this study, we characterized immune landscape differences across HER2-positive (HER2+), triple-negative (TNBC), and ER-positive (ER+) breast cancers using single-cell RNA sequencing data, focusing on T cells, macrophages, and B cells. Our analysis revealed that cytotoxic CD8 + T cells were more abundant in TNBC but showed signs of dysfunction. Macrophage subsets, including an NDUFB8 + population, exhibited divergent functional orientations across subtypes. B cells in TNBC exhibited functional alterations, characterized by a decreased proportion of plasmablasts and an increased proportion of memory B cells. Single-cell regulatory network inference and clustering (SCENIC) analysis predicted a set of subtype-specific candidate transcription factors (TFs), including E2F8 across all subtypes and ATF7 in HER2 + tumors, whose activity correlated with clinical outcomes. Finally, mapping multicellular communication networks revealed both conserved and subtype-specific signaling pathways. Together, these results present a detailed transcriptional landscape of immune heterogeneity in breast cancer and contribute novel insights that may inform the design of subtype-specific immunotherapies.