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
Abstract Background Clear cell renal cell carcinoma (ccRCC) is characterized by a highly heterogeneous tumor immune microenvironment. However, how tumor-intrinsic programs shape immune cell states remains incompletely understood. We aimed to identify multicellular programs (MCPs) linking tumor and immune compartments and to define tumor-derived signals that modulate immune phenotypes. Methods We applied DIALOGUE to identify MCPs across tumor and non-tumor compartments, including CD8+ T cells, CD4+ T cells, regulatory T cells, myeloid cells, endothelial cells, and fibroblasts. A total of 189,999 cells from 26 patients with sufficient tumor cell representation (>100 cells) were analyzed from our previously reported cohort (Kashima, ASCO 2024). MCP scores were derived from DIALOGUE and summarized at the sample level by averaging single-cell MCP scores. Cell–cell interactions were evaluated using CellChat and NicheNet. Spatial relationships were assessed using G4X spatial transcriptomic data (Yochum, AACR Kidney 2026). Spatial niches were defined using Seurat v5 based on k-nearest neighbor composition, and annotated as stromal, sarcomatoid, or clear cell according to cell-type composition and gene expression features. Results We identified two major MCPs, including tumor–myeloid (MCP1) and tumor–CD4+ T cell (MCP2) programs. The tumor component of MCP1 was enriched for mesenchymal and extracellular matrix (ECM)-related genes, including structural components and remodeling factors (COL5A2, MMP2, MRC2), as well as integrin-associated adhesion and cytoskeletal genes (ITGA3, ITGB1, VCL). Expression of HMGA2 further supported a dedifferentiated, EMT-like state. Clinically, a high MCP1 score was associated with sarcomatoid features (p = 0.04). In the myeloid compartment, MCP1 was characterized by immunoregulatory and tissue-remodeling markers (MRC1, MARCO, VSIG4, MS4A4A), chemokines (CCL13, CCL18, CCL23), and complement-related genes (C2, CFH, CR1), consistent with an M2-like macrophage phenotype. Cell–cell interaction analysis revealed increased communication between MCP1-high tumor cells and MARCO+ and CXCL9+ macrophage populations. Mechanistically, ECM-related signaling pathways, particularly collagen and laminin interactions, were predicted to activate CD44-mediated signaling in macrophages. These findings suggest an ECM–CD44 axis linking tumor mesenchymal programs to M2-like polarization. Spatial transcriptomic analysis further demonstrated preferential accumulation of macrophages in stromal-rich regions. Quantitative comparison across stromal, sarcomatoid, and clear cell regions showed increased macrophage infiltration in stromal and sarcomatoid areas compared to clear cell regions (3900 and 1920 vs 830 cells/mm², respectively). Differential expression analysis of macrophages across these regions showed that macrophages in sarcomatoid areas upregulated immunoregulatory and M2-associated markers, including CD163, MRC1, CSF1R, and TGFB1. CD44 expression was also significantly increased, supporting activation of ECM–CD44-associated signaling. Response to immune checkpoint inhibitors (ICIs) was evaluable in 13 patients. Although MCPs were not significantly associated with response, patients with higher myeloid MCP1 score (> mean), but not tumoral MCP1 score, showed a numerically higher response rate compared to those with low MCP1 score (4/4 vs 5/9 patients). Of note, all 4 responders in the myeloid MCP1-high group received combination therapy with CTLA-4 and PD-1 blockade. This suggests myeloid MCP1 may be associated with response to combination ICI therapy. Conclusions Our study identifies a tumor–immune MCP in which mesenchymal and ECM remodeling programs in tumor cells are associated with M2-like macrophage polarization. These findings highlight an ECM–CD44-associated signaling axis as a potential mechanism by which tumor cells shape an immunosuppressive microenvironment in ccRCC.
Katsuhiro Ito, S. Kashima, Rishabh Rout et al.· The Oncologist· 0 citations
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