60 Dissecting the Cellular and Epigenetic Landscape of Clear Cell Renal Cell Carcinoma Through Paired snRNA/ATAC-Seq Analysis

Abstract Background Clear cell renal cell carcinoma (ccRCC) stands as one of the most prevalent and aggressive forms of renal malignancies, accounting for a substantial portion of kidney cancer-related mortality worldwide. The heterogeneity within ccRCC poses a significant challenge to understanding its molecular mechanisms and devising effective therapeutic strategies. 1 Previous studies utilizing single nucleus RNA sequencing (snRNA-seq) have revealed transcriptional contributions to cell-type specificity in both mature human and mouse kidneys. Moreover, recent advancements have expanded this approach to single-cell profiling of chromatin accessibility. Single-nuclei assay for transposase-accessible chromatin using sequencing (snATAC-seq) has enabled the measurement of chromatin accessibility in thousands of individual cells, providing insights into the dynamic process that drives nephron development and differentiation. Integration and analysis of multimodal single-cell datasets, such as snRNA-seq and snATAC-seq, allows for the prediction of cell-type-specific cis-regulatory DNA interactions and transcription factor activity, complementing the transcriptional information obtained by snRNA-seq.2 3 1 Yang, Wang, and Yang, “Treatment Strategies for Clear Cell Renal Cell Carcinoma.” 2 Muto et al., “Single Cell Transcriptional and Chromatin Accessibility Profiling Redefine Cellular Heterogeneity in the Adult Human Kidney.” 3 Monteagudo-Sánchez, Noordermeer, and Greenberg, “The Impact of DNA Methylation on CTCF-Mediated 3D Genome Organization.” Methods This study leveraged paired snRNA/ATAC-seq in tumor and normal-adjacent samples from the Renal Tumor Biobank at Dartmouth (n= 63) to explore the ccRCC landscape at single-cell resolution using 10x multiome technology. The snRNA-seq dataset was processed with Seurat v5.0 to remove low-quality nuclei. Doublets were removed with DoubletFinder v2.02, and ambient DNA was removed using decontX from celda. Normalization was conducted via SCTransform, and missing data was imputed with ALRA. Clustering was performed by constructing a KNN graph and running the Louvain algorithm. Cell type assignments were predicted using Azimuth with the human kidney reference dataset. The snATAC-seq data was processed with ArchR v1.0.1, which performed dimensionality reduction and clustering. Cluster identities defined in the Seurat workflow were cross-annotated to the ATAT-seq data, and open peaks were identified within these cell-type clusters using MACS3 v3.0.1. Identification of marker peaks, motif enrichment, and motif enrichment deviations were inferred utilizing ArchR’s getmarkerFeatures, peakAnnoEnrichment, and addMotifAnnotations functions, respectively. Results Motif enrichment analysis revealed canonical cancer-associated transcription factor (TF) dysregulation in tumor cells (nephron loop-like), including enrichment of HNF1B, SMARCC1, FOSL1, and JUND. Interestingly, HNF and SMARCC were also enriched in the lymphoid compartment, indicating possible crosstalk between these two cell types. Previous work has investigated the involvement of the HNF1 in ccRCC and in chromophobe RCC for prognosis,and we plan on investigating the relationship of these enrichments to tumor grade, stage and outcome.1 Additional TF enrichments included BCL families and ELF within the myeloid compartment, and vascularization related TF families in endothelial cell types including enrichment of various ETS TFs. Investigation of cell-cell interactions between immune and endothelial cell populations as well as between tumor and normal adjacent tissues will be performed to further disentangle the role differentially accessable chromatin plays in ccRCC. 1 Buchner et al., “Downregulation of HNF-1B in Renal Cell Carcinoma Is Associated With Tumor Progression and Poor Prognosis.” Conclusions Our multi-omic approach has several advantages. First, it enables the characterization of cellular heterogeneity within the tumor microenvironment, identifying cell populations and their contributions to disease progression. Secondly, by integrating transcriptomic and epigenomic data, we gain insights into the dynamic interplay between gene expression and chromatin accessibility, uncovering potential therapeutic vulnerabilities and biomarkers for personalized treatment strategies. DOD CDMRP Funding: yes