Abstract
BACKGROUND: Tumor-associated macrophages (TAMs) are pivotal components of the breast cancer (BC) tumor microenvironment (TME), significantly influencing tumor progression and response to therapy. However, the heterogeneity and specific roles of TAM subpopulations in BC remain inadequately understood. METHODS: We performed an integrated analysis of single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (RNA-seq) data from BC patients to comprehensively characterize TAM heterogeneity. Utilizing the MetaTiME computational framework and consensus clustering, we identified distinct TAM subtypes and assessed their associations with clinical outcomes and treatment responses. A machine learning-based predictive model was developed to evaluate the prognostic significance of TAM-related gene expression profiles. RESULTS: Our analysis revealed three distinct TAM subgroups. Notably, we identified a novel macrophage subtype, M_Macrophage-SPP1-C1Q, characterized by high expression of SPP1 and C1QA, representing an intermediate differentiation state with unique proliferative and oncogenic properties. High infiltration of M_Macrophage-SPP1-C1Q was significantly associated with poor overall survival (OS) and chemotherapy resistance in BC patients. We developed a Random Forest (RF)-based predictive model, Macro.RF, which accurately stratified patients based on survival outcomes and chemotherapy responses, independent of established prognostic parameters. CONCLUSION: This study uncovers a previously unrecognized TAM subtype that drives poor prognosis in BC. The identification of M_Macrophage-SPP1-C1Q enhances our understanding of TAM heterogeneity within the TME and offers a novel prognostic biomarker. The Macro.RF model provides a robust tool for predicting clinical outcomes and guiding personalized treatment strategies in BC patients.