Cancer cells exist in a highly intricate tumor microenvironment (TME) that significantly affects their biology and function. Immune cells are critical players in the TME that help determine the fate of cancer cells, either promoting or inhibiting carcinogenesis, progression, metastasis, and recurrence 1, 2. Recent immunotherapy strategies, such as immune checkpoint blockade, adoptive cell transfer, cytokines (e.g. interleukin-2), and cancer vaccines, have shown promising clinical efficacy by promoting the antitumor state of the immune system. However, many patients do not respond to these therapies, and some develop resistance or even immunotoxicity. Consequently, novel approaches are required to decipher the complexity of tumor immunity and thus enhance immunotherapy efficacy 3, 4, 5.

Recent breakthroughs in omics techniques have enabled high-throughput molecular profiling of tissues at the single-cell level but require tissue dissociation, leading to the loss of spatial organization. By preserving cell–environment interactions, spatial omics techniques can decipher the heterogeneity of the TME in various cancers 6, 7. One such technique, multiplex immunohistochemistry/immunofluorescence (mIHC/IF), facilitates simultaneous mapping of up to 100 markers in a single tissue sample, acting as a powerful discovery and validation tool in cancer immunology research 8, 9••. Similarly, spatial transcriptomics capture both the breadth and depth of biomarker sequence coverage with adequate resolution and spatial information [10]. Other spatial omics techniques, including epigenomics, metabolomics, and proteomics, are emerging to facilitate multilayer exploration of cell identity and state in situ. To fully leverage these techniques, uniform analysis pipelines, standardized image processing frameworks, and consistent application are essential. Furthermore, spatial omics development will also be accelerated by artificial intelligence (AI), improved image visualization and storage capacity, and effective validation of genetic and transcription findings at the protein level (Figure 1).

Here, we review the latest advances in omics technology and its data analysis within the field of spatial profiling of molecules around and beyond the central dogma to unravel the complexities of the TME and harness the full potential of cancer immunotherapy. We highlight platforms to showcase the insights these techniques promise and propose new developments in AI application, data sharing, and protein level validation to accelerate spatial omics advances.