Decoding the breast cancer microenvironment by spatial multi-omics: from architecture to clinical translation

Spatial multi-omics technologies are fundamentally shifting breast cancer research from static molecular inventories toward high-resolution spatial atlases of the tumor microenvironment (TME). By integrating spatial transcriptomics, spatial proteomics, and emerging metabolomic approaches, researchers can now map tumor cell heterogeneity, stromal cell subtypes, immune cell topography, and tertiary lymphoid structures (TLSs) within their native tissue context. These spatial insights have enabled the construction of prognostic models based on tumor-immune interface distances, improved prediction of chemotherapy and immunotherapy responses, and facilitated the design of niche‑specific combination therapies targeting, for example, fibroblasts‑rich or immunosuppressive regions. Despite these advances, major challenges remain, including multi‑modal data integration, batch effects, high costs, and the lack of standardized protocols for clinical implementation. Overcoming these bottlenecks is essential to establish "spatial pathology" as a routine tool in precision oncology, ultimately guiding more effective, personalized treatment strategies for breast cancer patients. Importantly, it should be noted that the clinical readiness of spatial features varies substantially: assessment of TLS on routine H&E sections is near-implementation, while high-dimensional spatial transcriptomic and proteomic signatures still require standardized protocols, prospective validation, and cost reduction before entering routine diagnostics.