37Integrative multi-omic characterization of the immune landscape in renal cell carcinoma

Abstract Background Renal Cell Carcinoma (RCC) has been characterized as being amongst the most immune infiltrated solid tumors with a highly heterogenous immune landscape. Within spatially organized cellular networks (CNs) of the tumor immune microenvironment (TIME), key immune cell-cell interactions (CCIs) impact immune cell function and organization ultimately impacting the patient’s overall response. Multiple studies have observed an association of tertiary lymphoid structures, a commonly observed spatial CN, with positive clinical outcomes in RCC, however additional CNs and the CCIs that control these networks need to be identified to better harness and potentially reprogram immune responses to improve patient outcomes. The recent explosion of interest in the heterogeneity of the immune landscape in RCC has led to numerous publications using the latest technologies in spatial transcriptomics, proteomics, and metabolomics. However, many of these studies use this data in isolation and therefore, may be hindered by the technological biases inherent in each method. Here, we have developed a novel approach to integrate spatial and single cell multi-omic data harnessing the strengths of each technology to better interrogate CNs that exist in the RCC TIME. Methods Fresh surgical samples were procured at the University Health Network (Toronto, Canada) through the REnal cancer MicroEnvironment DiscoverY (REMEDY) study. Bulk RNA sequencing (RNA-seq), single cell RNA sequencing (scRNA-seq), single cell suspension mass cytometry (SMC), whole transcriptome digital spatial profiling (DSP), and imaging mass cytometry (IMC) was performed on spatially concordant tumor regions across 54 patients. scRNA-seq enabled the identification of immune, stromal, and malignant high-resolution cell states, which informed a tailored antibody panel design for SMC and IMC and served as a reference framework for harmonized cell-type annotation across modalities. This enabled the integration of our transcriptomic and proteomic data to delineate RCC-specific CNs enriched for defined CCIs across unique biological pathways. Results Using this integrative approach, we identified seven high-resolution patient immunophenotypes. To evaluate their prognostic and predictive relevance, we derived representative gene signatures using a linear mixed model (Flash-MM) to interrogate publicly available bulk RNA-seq datasets, including TCGA, JAVELIN, and IMmotion151. This analysis revealed immunophenotype-specific associations with survival following surgery or systemic therapy in univariate models. To explore potential biological mechanisms associated with these divergent clinical outcomes, we incorporated spatial information into traditional CCI analyses and performed pathway analyses to define functional relationships. This revealed that patient subtypes with high lymphoid infiltration exhibit greater spatial heterogeneity, potentially reflecting the coexistence of multiple activated immune pathways. In contrast, patient subtypes with low immune infiltration were enriched in more developmental signaling pathways. In addition to our biological observations, we were also able to assess the technological differences between patient matched samples and compare the ability of each technology to capture inter-patient and intra-patient heterogeneity. Conclusions Collectively, this work identifies clinically distinct subgroups defined by CNs and details the interpatient cellular heterogeneity that exists in RCC, providing the foundation for future personalized therapeutic interventions against this disease.

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