Abstract Spatial transcriptomics (ST) and spatial proteomics (SP) have revolutionised our ability to map RNA and protein distributions within intact tissues, shedding new light on the dynamic interactions that drive physiological processes in healthy and diseased tissues. We discuss how the latest ST and SP technologies, large public data resources and advanced computational pipelines can be applied to study the tumor microenvironment (TME), focussing on the interactions within the TME. We also highlight how these developments have enabled the in‐depth spatial characterisation of tumors and their TME across the continuum of cancer progression, from initiation to metastasis. Despite these advances, major gaps persist in cross‐platform integration, data standardisation and computational scalability for high‐plex single‐cell datasets. The integration of artificial intelligence (AI) holds great promise for biological and translational applications but requires standardised workflows, cost‐effective pipelines, rigorous pre‐clinical and clinical validation, and improved interpretability of AI models. Additional cross‐disciplinary development of explainable, scalable tools for TME analysis of cellular interactions and disease progression will be essential to integrate spatial omics into daily precision cancer medicine.
Nguyen et al. (2026) studied this question.