Abstract
BACKGROUND: Metastatic renal cell carcinoma (mRCC) still lacks tissue-based biomarkers that predict benefit from first-line immune checkpoint inhibitor (ICI) regimens. High-resolution spatial transcriptomics can dissect the tumor microenvironment (TME) and reveal prognostic niches invisible to bulk assays, offering a path toward precision immunotherapy. METHODS: Formalin-fixed, paraffin-embedded samples from 12 therapy-naïve patients with mRCC treated with ICI-based combinations were profiled with 10x Genomics Visium and NanoString COSMx. Six patients contributed matched metastatic lesions. Cell-type deconvolution used robust cell type decomposition with a public single-cell RNA-seq reference. Clinical outcomes (mean follow-up 16.75 months) were correlated with spatial features. Validation employed Phase 2/3 JAVELIN Renal 101 as well as Checkmate 010/025 transcriptomic data. RESULTS: Spatial transcriptomics uncovered pronounced divergences between primary tumors and metastases in cellular composition and microarchitectural organization. Canonical RCC molecular subtypes displayed marked intrapatient and interpatient spatial heterogeneity, underscoring limitations of bulk classifications. Five recurrent immune niches emerged across samples. One niche-rich in macrophages and CD8(+) T cells and defined by chemokine signaling-was closely associated with objective response in metastatic lesions yet absent or infrequent in matched primaries. A gene signature reflecting this niche stratified responders within the immuno-oncology (IO) -treated arms of JAVELIN 101 and CheckMate 010/025 trials. On the other hand, signatures derived from angiogenic tumor cell-rich niches defined responders within the comparator arms (tyrosine kinase inhibitor/everolimus) in these trials. CONCLUSION: Metastasis-specific immune niches, rather than primary-tumor characteristics, appear decisive for ICI efficacy in mRCC. Spatial transcriptomic profiling can identify these clinically relevant microenvironments and generate transferable gene signatures, supporting biomarker-driven patient selection and trial design. Integrating spatial TME analysis into future studies may accelerate personalized immunotherapy strategies for mRCC.