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Background Clear cell renal cell carcinoma (ccRCC) represents a biologically heterogeneous malignancy with diverse tumor and immune microenvironment interactions that influence disease progression and therapeutic response. Traditional profiling using a single technology obscures the real disease biology critical for understanding tumor-immune dynamics. Recent advances in spatial multi-omic technologies enable simultaneous mapping of transcriptomic and proteomic features within tissues and blood derived from individual patients. This study aims to comprehensively characterize the immune and oncological landscape of RCC using integrated multi-omic approaches to identify systemic response, microenvironmental niches, immune cell states and tumor heterogeneity that underpin immune evasion and therapeutic resistance.Methods We took an integrative approach of mapping localized and systemic immune cell responses in ccRCC patient samples from Stage 1 and Stage 3 patients ( figure 1). We employed CyTOF™ technology to profile peripheral blood mononuclear cells (PBMC) and tumor-derived cells (TDC) using a 50-plus-antibody panel. We obtained a matched FFPE tumor tissue section and performed spatial transcriptomics using the Xenium™ 5K assay and spatial proteomics using Imaging Mass Cytometry™ (IMC™) technology with a 43-marker immuno-oncology-focused antibody panel on the same tissue section. We combined the datasets for all modalities to explore tumor and immune-related signatures associated with specific stages of disease as well as correlation of immune cell phenotypes in systemic circulation compared with tumor tissue.Results Integrative analysis using CyTOF, Xenium and IMC technology characterized systemic and local immune environments and provided a comprehensive view of immune and tumor cell activities and interactions with an unprecedented level of detail. Immune cell composition identified by CyTOF technology demonstrated differences between PBMC and TDC samples in Stage 1 and 3 ccRCC samples. IMC mapped the spatial location of these cell phenotypes and their activation states in the tumor tissue. Through integrating spatial transcriptomics and proteomics, we further delved into deep phenotyping and categorized metabolic and signaling states of tumor cells and cytokine and transcription factor expression in immune cells.Conclusions This study demonstrates the power of spatial multi-omic profiling to unravel the complex immune and oncological landscape of ccRCC at unprecedented resolution. The identification of spatially defined immune cell states and specialized cellular niches provides mechanistic insights into tumor immune evasion and resistance. Our findings support the development of spatially informed biomarkers and targeted therapies to improve precision medicine approaches for ccRCC treatment.For Research Use Only. Not for use in diagnostic procedures.Abstract 107 Figure 1Workflow overview. Tumor tissue and whole blood from ccRCC patients. Tumor tissue was processed: (1) FFPE tumor was processed with the Xenium 5K assay and then stained with IMC antibody panels for Hyperion™ XTi acquisition; (2) tumor cells, PBMC were analyzed on a CyTOF XT