Presence of tertiary lymphoid structures and exhausted tissue-resident T cells determines clinical response to PD-1 blockade in renal cell carcinoma
Data files
Feb 20, 2025 version files 463.26 MB
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Data_S1.zip
463.26 MB
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README.md
1.62 KB
Abstract
Immune checkpoint inhibitors (ICI) targeting the PD-1 pathway have transformed treatment of advanced renal cell carcinoma (RCC), but mechanisms underlying therapeutic response remain largely unknown. Herein, we perform transcriptomic analysis on RCC biospecimens from 102 patients enrolled in a phase II clinical trial of frontline nivolumab (NCT03117309) to investigate determinants of response to anti-PD1 monotherapy. Through bulk analysis, we identify an enrichment of genes associated with tertiary lymphoid structures (TLS) in responding patients. Using single-cell transcriptomics and external cohort validation, we identify a population of tissue-resident (ZNF683+ SLAMF7+) exhausted CD8+ T cells enriched in patients with poor clinical outcomes. Integrating these findings, we find tumors with high TLS and low tissue-resident exhausted CD8+ T cells have superior clinical outcomes with nivolumab. Altogether, these analyses contribute to a growing understanding of how the tumor microenvironment drives ICI- resistance and propose possible therapeutic targets to rationally overcome resistance to anti-PD1 monotherapy.
https://doi.org/10.5061/dryad.dv41ns28r
Description of the data and file structure
This dataset contains single-cell RNA sequencing (scRNA-seq) data from 19 samples from 17 renal cell carcinoma (RCC) patients, including 2 sets of paired pre- and post-treatment (aPD-1) samples. Each dataset is provided as a zip file, corresponding to one patient. The data was processed using the 10x Genomics Chromium platform and is provided in the standard sparse matrix format.
Each zip file contains the following three files:
- barcodes.tsv – A list of cell barcodes.
- features.tsv – A list of gene identifiers.
- matrix.mtx – A sparse matrix storing gene expression counts.
File Naming Conventions
The files are named using the following format:
- 260-1XXX_BL_filtered_feature_bc_matrix.zip – Data from the baseline sequencing time point.
- 260-1XXX_PR_filtered_feature_bc_matrix.zip – Data from the progression sequencing time point.
where XXX is a unique patient identifier.
Files and variables
File: Data_S1.zip
Description: Lists all filtered single cell feature-barcode matrices.
Code/software
Where relevant, R v4.1 and packages within Bioconductor v3.13 were implemented. Filtered single cell feature-barcode matrices were loaded into R using DropletUtils and used to construct a SingleCellExperiment object (SingleCellExperiment v1.14.1).
