Integrated spatial transcriptomic and proteomic profiling reveals tumor-immune niches driving heterogeneity and resistance in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is a biologically heterogeneous malignancy shaped by complex tumor immune microenvironment interactions. Single-modality profiling often fails to capture this complexity. Spatial transcriptomics enables high-resolution mapping of gene expression, but proteins mediate functional states, signaling and therapeutic targets. Sequential integration of Imaging Mass Cytometry™ (IMC™) technology after spatial transcriptomics on the same tissue section adds a critical layer by quantifying protein expression and post-translational markers while preserving spatial context. Unlike fluorescence-based multiplexing, IMC technology avoids spectral overlap and autofluorescence, making it ideal for post-transcriptomic analysis. This multi-omic approach enables direct correlation of transcriptomic signatures with protein-level functional states, uncovering mechanisms of immune evasion and therapeutic resistance that remain hidden using transcriptomics alone. FFPE tumor sections from Stage 3 ccRCC patient were analyzed using spatial transcriptomics (Xenium 5K assay) followed by hematoxylin and eosin (H&E) staining and IMC with a 43-marker immuno-oncology panel. Data was coregistered for single-cell resolution integration of RNA, H&E and protein expression. Integrated analysis provided unprecedented resolution of the ccRCC microenvironment: Functional subtyping identified metabolically active tumor cells (high LDHA, GLUT-1), TIM-3+ tumor populations with angiogenic potential, and spatially organized immune niches such as tertiary lymphoid structures (TLS) containing B cells, activated T cells and macrophages. Combined profiling revealed spatial proximity and interaction of immune cells (CD4, CD8, CD20, CD38, TCF1, CD11c), stromal components (alphaSMA, vimentin, fibronectin), and vascular features (CD34, PLVAP) associated with tissue remodeling and dysfunctionality. Unsupervised clustering of RNA and protein data revealed robust immune activation in TLS and differentiated tumor cell states with metastatic potential. IMC analysis after spatial transcriptomics enables pathologist-in-the-loop evaluation, advancing diagnosis from morphology-based to molecularly informed and spatially precise interpretation of tumor heterogeneity and disease progression. Spatial multi-omic profiling delivers unprecedented resolution of tumor and immune landscapes in ccRCC, supporting development of spatially informed biomarkers and targeted therapies. Clinically, this approach could potentially refine patient stratification, identify resistance-associated niches, and guide combination strategies targeting both tumor and immune compartments. For Research Use Only. Not for use in diagnostic procedures.