Abstract A009: Multicellular modules and tumor programs shape the tumor microenvironment and predict immune checkpoint inhibitor (ICI) response in clear cell renal cell carcinoma (ccRCC)

Abstract Introduction ccRCC exhibits diverse tumor microenvironments, and associations with ICI response have been demonstrated using bulk RNA sequencing and molecular subtype analyses. However, the factors that drive this microenvironmental heterogeneity remain unclear. This study aimed to analyze large-scale single-cell data to identify multicellular co-occurrence patterns within the tumor microenvironment and the tumor-intrinsic programs that shape these ecosystems. Methods We analyzed single-cell RNA-seq data from 58 ccRCC cases (224, 018 cells), including 6 untreated, 31 pre-ICI, 14 post-ICI, 3 pre-TKI, and 4 post-TKI (Kashima, ASCO 2024). We performed high-resolution clustering for each non-tumor cell lineage (CD8+ T, CD4+ T, regulatory T cells, myeloid cells, endothelial cells, and fibroblasts). Pairwise correlations of cluster frequencies across samples were computed to detect “modules” representing co-existing immune and stromal populations. These modules were validated using an independent single-cell RNA-seq dataset (HCRN GU16-260, N = 16; Hugaboom, Cancer Discov. 2025). Tumor cells (50, 346 cells) were separately analyzed using consensus NMF to identify shared gene programs. We compared the prevalence of modules in responders (R) (n = 10; complete or partial response) with non-responders (NR) (n = 8; progressive disease) to ICI therapies. Results We identified 78 fine clusters and 21 co-existence modules. These clusters and modules captured features not fully explained by bulk RNA-seq or small-scale single-cell data. Most modules consisted of multiple cell types rather than a single lineage, i. e. , a CXCL13+ CD4+ T cell–memory B cell module. Module structure was supported in the HCRN cohort (Mantel r = 0. 3, P 0. 001). Patients exhibited highly heterogeneous combinations of these modules. Several modules were associated with favorable ICI responses, including exhausted CD8+ T cells–MACRO+ macrophages (Module R1) and cDC–iCAF (Module R2). Conversely, modules such as Th17 CD4+ T cells–GNLY+ ILCs–pDCs (Module NR1) and GZMA+ CD4+ T cells–Trm CD8+ T cells–IFNγ+ ILCs (Module NR2) were associated with poor ICI response. Consensus NMF of tumor cells revealed six interpretable gene programs shared across samples. Notably, the tumor-intrinsic IL6 and VEGF programs were correlated with Module NR1, while Module NR2 was mainly associated with the MYC program. Modules R1 and R2 also demonstrated distinct associations with specific tumor gene programs. These findings suggest that patient-specific, multiple biological pathways may contribute to both resistance and responsiveness to ICI therapy. Conclusion Our large-scale single-cell analysis identifies reproducible multicellular modules within the ccRCC tumor microenvironment, several of which are positively or negatively associated with ICI response. The observed relationships between non-tumor cell modules and tumor-intrinsic gene programs indicate that tumor cell states and microenvironmental composition are interdependent, highlighting potential cellular crosstalk that may influence immunotherapy efficacy. Citation Format: Katsuhiro Ito, Soki Kashima, Rishabh Rout, Zhaochen Ye, Nicholas R. Schindler, David A. Braun. Multicellular modules and tumor programs shape the tumor microenvironment and predict immune checkpoint inhibitor (ICI) response in clear cell renal cell carcinoma (ccRCC) abstract. In: Proceedings of the AACR Special Conference in Cancer Research: Innovations in Kidney Cancer Research: From Molecular Insights to Therapeutic Breakthroughs; 2026 Mar 13-16; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2026;86 (5Suppl₂): Abstract nr A009.