Investigation of tumor-associated macrophages (TAMs) and therapeutic resistance to immune checkpoint inhibitors (ICI) through single-cell analysis of renal cell carcinoma (RCC).

Abstract

4527 Background: RCC is characterized by a tumor microenvironment (TME) enriched in TAMs, which suppress antitumor immunity in RCC. We conducted a comprehensive dissection of the TME using pre- and post-ICI treatment samples to identify specific TAM populations associated with ICI treatment resistance in RCC. Methods: A total of 70 tumor samples (58 clear cell and12 non-clear cell) were collected from 63 patients with advanced RCC, including 9 patients who were untreated, 10 patients who received non-ICI-based systemic therapies, and 44 patients who received ICI-based therapies. We excluded 17 patients with stable disease from the 44 patients and analyzed 29 samples prior to (n = 15) or after exposure to (n = 14) ICI-based therapies (mono-ICI, n = 11; ICI + ICI, n = 11; ICI + VEGFi, n = 6; other, n = 1) from 27 patients. We performed single-cell RNA-sequencing (scRNA-seq; 10x Genomics) on all 70 samples and established a comprehensive transcriptomics atlas of the RCC TME. We utilized non-negative matrix factorization (NMF) to identify interpretable gene programs for TAMs, comparing responders (R) (n = 18; complete or partial response) with non-responders (NR) (n = 11; progressive disease) to ICI-based therapies according to the best response based on RECIST. P-values from Wilcoxon signed rank test are reported. Results: 443,337 high-quality viable cells were annotated to lymphoid, myeloid, tumor, endothelial, or fibroblast compartments, capturing the RCC TME landscape. Among TAMs, we discovered underlying gene programs through NMF analysis, including “antigen presentation”, “S100A8/9 inflammatory”, “stress response”, “C1Q/APOE/TREM2”, “CD163/MRC1”, “hypoxia”, “interferon-stimulated genes”, and “LILRB/SIGLEC10” programs. We identified that the LILRB/SIGLEC10-TAM subcluster was significantly increased in frequency in NR compared to R (p = 0.005). Notably, the significant increase in this program in NR was also observed in pre-treatment only samples (p = 0.014), suggesting a primary mechanism of resistance. This population was characterized by the expression of immune suppressive LILRB1/2/3 genes, together with significant upregulation of the macrophage checkpoints SIGLEC10 (a recently discovered “don’t eat me signal” receptor) and VISTA (an immune checkpoint) compared to other TAMs (p < 2.22E-16, for each). Conclusions: Our comprehensive dissection of the RCC TME reveals an association between TAM population with an immunosuppressive gene program and ICI resistance through analysis of a large scRNA-seq dataset. This study provides immunobiological insights into potential therapeutic targets for next-generation combination therapy with ICIs, offering a foundation for understanding treatment evolution in RCC.