Decoding neutrophil spatial heterogeneity and clinical implications in breast cancer

Neutrophils are heterogeneous and critical components of the tumor microenvironment, yet their spatial heterogeneity and clinical implications remain poorly understood. We establish large-scale breast cancer cohorts integrating AI-powered computational pathology (n = 1,113) and single-cell spatial omics (n = 104) to investigate the clinical relevance and biological mechanisms underlying neutrophil spatial patterns. Clinical implications were greatest in triple-negative breast cancer (TNBC): neutrophil-tumor cell proximity associated with favorable prognosis, while neutrophil-immune cell proximity predicted superior immunotherapy response. Combining these patterns with established biomarkers improved TNBC stratification for prognosis and immunotherapy efficacy. Spatial omics and experimental validation demonstrated that tumor-proximal neutrophils enhanced MHC I expression on tumor cells via inflammatory crosstalk, promoting immune-mediated tumor clearance and improved prognosis. Neutrophils adjacent to immune cells exhibited an antigen-presenting phenotype, augmentable by targeting polyamine metabolism. Our study highlights the functional diversity and spatial heterogeneity of neutrophils and provides a translational strategy with routine computational pathology.