Ó³»­´«Ã½

Breast cancer's intrinsic complexity stems from heterogeneous subtypes and the dynamic architecture of its tumour microenvironment (TME), which jointly shape disease progression and therapeutic response. Conventional bulk and single-cell assays average signals across cells and lose spatial context, obscuring cellular interactions, immune exclusion and stromal organisation that drive tumour behaviour and resistance. Spatial multiomics has emerged as a transformative approach, enabling spatially resolved profiling of gene expression, protein abundance, metabolic activity and chromatin state across intact tissues while capturing cell-cell interactions. In this breast cancer-focused literature review, we synthesise advances in spatial transcriptomic, proteomic, metabolomic and epigenomic technologies-including imaging- and sequencing-based modalities-with particular attention to FFPE-compatible assays. We also critically assess computational frameworks for data preprocessing, multimodal integration and spatial statistics, and highlight studies that reveal functional heterogeneity, including subclonal architecture, immune-evasive niches, metabolic gradients and spatial correlates of relapse and therapy resistance. Building on this evidence base, we map spatial multiomics onto four key clinical use-cases in breast oncology: predictive and prognostic biomarkers, spatial pharmacology, integration with surgical and diagnostic workflows, and mapping of residual disease and spatial risk. To our knowledge, this review is the first to integrate technological, computational and biological advances with data standards, interoperability and equity considerations into a pragmatic clinical roadmap centred on breast cancer, outlining how standardised spatial atlases that link spatial readouts to clinical endpoints can support precision medicine.