Spatial transcriptomic and proteomic approaches for profiling prognostic cell communities in solid tumors.
BACKGROUND A comprehensive understanding of the complexity and heterogeneity of the tumor microenvironment (TME) is critical for advancing cancer treatment. Recent advances in spatial omics technologies have opened new avenues for an in-depth exploration of the TME. By integrating high-resolution spatial information from omics, spatial omics enables the systematic characterization of the spatial distribution of various cell types within tissues and their interaction networks, providing a panoramic view of the TME. Emerging studies have highlighted that specific spatial structures within the TME are strongly associated with cancer prognosis and treatment responses. AIM OF REVIEW This review focuses on the patterns of spatial cell distributions or characteristic cell structures and their relationship with cancer prognosis, underscoring the pivotal role of spatial heterogeneity in tumors. It aims to provide theoretical foundations for precise medicine approaches in prognostic evaluation and therapy design. KEY SCIENTIFIC CONCEPTS OF REVIEW This review discusses how spatial transcriptomics and proteomics enable extraction of spatial metrics including cell density, proximity, and community-level organization. We further summarize the spatial distribution patterns of cells and key cell community structures, emphasizing the pivotal role of spatial heterogeneity in tumors. Furthermore, it highlights critical spatial features, including immune cell infiltration patterns and vascular niches, that are strongly linked to patient outcomes. Additionally, the review discusses the challenges and future prospects of applying spatial omics in clinical applications. As the field evolves, spatial omics holds transformative potential to revolutionize oncology research, enabling novel approaches for risk stratification and improving patient outcomes.