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D. Braun

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Open access Sep 2026

8 Single Cell Transcriptomic Investigation of Renal Cell Carcinoma (RCC) Reveals Tissue Resident Memory Exhausted CD8+ T Cell Signature Associated with Resistance to Immune Checkpoint Inhibition (ICI)

Abstract Background The current standard of care for advanced RCC is ICI-based combination therapies. However, most patients with advanced RCC develop disease progression despite ICI treatment, suggesting a lack of durable immune response. Although a lack of T cell infiltration or the presence of non-tumor-reactive “bystander” T cells are hypothesized mechanisms of ICI resistance across tumor types, therapeutic resistance in RCC may still occur in the presence of abundant infiltration of tumor-specific CD8+ T cells. We therefore investigated whether CD8+ T cell phenotype in the RCC tumor microenvironment (TME) impacts ICI response or resistance. Methods 70 tumor samples from 63 RCC patients were collected before (n = 48) or after (n = 22) therapies (VEGFi, n = 9; ICI monotherapy, n = 20; ICI combination, n = 26; others, n = 15). 11 samples were collected from patients without tumors. RCC variants included 59 clear cell and 11 non-clear cell samples. 18 were labeled as clinical benefit and 11 as no-clinical benefit. Single-cell RNA sequencing (10x Genomics) was performed on these samples to generate a transcriptome of the RCC TME. Graph-based clustering identified cell type populations, which were annotated with known lineage genes. Non-negative matrix factorization (NMF) identified gene programs within exhausted CD8+ T cells (Tex). Differential gene expression analysis determined the most differentially expressed genes between resident memory Tex and other cell populations. Results Within CD8+ T cells, Tex cells were identified through expression of TOX, PDCD1 (PD-1), and HAVCR2 (TIM-3). NMF generated 4 gene programs within Tex cells, expressing markers for immediate early genes (JUNB, FOS), exhaustion/activation (GZMK, CD74, LAG3), tissue residency (GZMH, ITGAE, IL7R), and stress response (HSPA1A, HSPA6). The tissue residency program was associated with resistance to ICI therapy (p = 0.05); this association was only found in samples with abundant tumor-specific CD8+ T cells. Differential expression between resident memory Tex (Tex-RM) and other cell types generated a signature of 10 markers that were most highly expressed in Tex-RM. Response and survival data of external bulk RNA-seq cohorts were analyzed. A signature score subtracting for Tex-RM signature was calculated (normalized to overall abundance of Tex cells by signature analysis), which was significantly higher in patients with progressive disease than those with complete/partial response (p = 0.0046), specifically for patients receiving ICI-based therapies. Additionally, survival analysis revealed that ICI-based patients with a higher (top 25%) signature score had significantly worse progression free survival (PFS; p = 0.0048) as well as overall survival (p = 0.0069) with ICI. For ICI-treated patients, the Tex-RM signature score was associated with worse PFS, with a hazard ratio of 2.1 (90% CI [1.3, 3.25]). There was no significant impact on patients receiving TKI monotherapy. Conclusions Through scRNA-seq analysis, we identify a tissue residency gene program in Tex cells associated with non-response to immunotherapy. A signature derived from this program was additionally shown to predict significantly worse response and outcomes for patients receiving ICI-based therapies within a group of bulk RNA-seq clinical trial cohorts. This study provides a framework for using scRNA-seq to identify mechanisms of ICI resistance in RCC and nominates resident memory exhausted CD8+ T cells as a targetable subset of cells to improve CD8+ T cell-mediated anti-tumor immunity. DOD CDMRP Funding yes

Rishabh Rout, S. Kashima, M. Hugaboom et al. · The Oncologist · 0 citations

Open access Sep 2026

51 Discovery of Exhausted CD8+ T-cell States Associated with Clinical Outcomes in Renal Cell Carcinoma

Abstract Background Therapeutic resistance to immune checkpoint inhibitors (ICIs) and antiangiogenic therapies remains a significant clinical challenge in advanced renal cell carcinoma (RCC). The prognostic value of CD8+ T-cell infiltration—a central player in cancer immunity—is often equivocal across RCC studies, supporting the need for a systematic characterization of response-associated T cell states. Here, we used a machine learning-based approach that integrated bulk RNA-sequencing (RNA-seq) data from multiple ICI-based clinical trials with an in-house single-cell RNA-sequencing (scRNA-seq) dataset to identify CD8+ T-cell states associated with progression-free survival (PFS), providing novel prognostic insights for advanced RCC. Methods We collected bulk RNA-seq data of baseline tumor samples and patient outcomes from four major clinical trials of advanced RCC with at least one treatment arm receiving ICI monotherapy or ICI combined with tyrosine kinase inhibitors (ICI+TKI): HCRN GU16-260, JAVELIN Renal 101, COSMIC-313, and CheckMate 9ER. An in-house scRNA-seq dataset of tumor-infiltrating CD8+ T cells from 70 advanced RCC patients was used as a cellular reference. Our analysis focused on the CD8+ exhausted T cells (Tex), which generally overlap with tumor-specific T-cell population. Using non-negative matrix factorization (NMF), we classified the Tex into four functional states. To link the scRNA-seq-derived T-cell states with the PFS and bulk RNA-seq data, we applied Scissor (Sun et al., 2022), a machine learning model that identifies phenotype-associated cell subsets through cell-sample correlations. For the bulk cohort meta-analysis, shared genes were normalized, scaled, and batch-corrected using ComBat (Johnson et al., 2007). Statistical significance was established via a permutation test of the PFS data, and calculating empirical p-values based on the concordance index (C-index). Results Our final bulk dataset comprised 1,527 RNA-seq tumor samples from four clinical trials of RCC, including 1,004 tumor samples from patients receiving ICI or ICI+TKI regimens. We first applied Scissor to each treatment arm of each trial individually, and found that worse PFS-associated Tex cells exhibited elevated expression of tissue-resident markers, such as ZNF683 and ITGAE, consistently across all ICI and ICI+TKI arms. These markers represent one specific NMF program defined in Tex, which we have termed the resident-memory-like exhausted state (Tex-rm). Notably, the Tex-rm population was enriched among worse PFS-associated cells in all ICI-based treatment arms (ratio of observed to expected [Ro/e] = 1.37-2.33) but not in any TKI monotherapy arms. Subsequently, we combined all ICI-based treatment arms and applied Scissor to the batch-corrected bulk dataset. This again identified Tex-rm as associated with worse PFS. Such a correlation was supported by a permutation test with p-value < 0.001. Conclusions Through machine learning-based integration of bulk and single-cell RNA-seq datasets, we found that baseline tumor infiltration by exhausted T cells with a resident memory phenotype (Tex-rm) is significantly associated with worse PFS in advanced RCC patients receiving ICI monotherapy or ICI+TKI combination therapy. This finding highlights the Tex-rm state as a potential predictive biomarker for ICI efficacy. DOD CDMRP Funding no

Zhao-Chen Ye, Soki Kashima, Rishabh Rout et al. · The Oncologist · 0 citations