Investigation of T cell phenotypes associated with response or resistance to immune checkpoint inhibitors (ICI) through single-cell analysis of renal cell carcinoma (RCC).

Soki Kashima Yale Cancer Center, Yale School of Medicine, New Haven, CT info_outline Soki Kashima, Zhaochen Ye, Alia Meliki, Miya Hugaboom, Nicholas R. Schindler, Jess Graham, Vivien Moritz, Nourhan El Ahmar, Yasmin Nabil Laimon, John Canniff, Renee Maria Saliby, Gwo-Shu Mary Lee, Marc Machaalani, Maxine Sun, Wenxin Xu, Sabina Signoretti, Bradley Alexander McGregor, Rana R. McKay, Toni K. CHOUEIRI, David A. Braun

Authors Soki Kashima Yale Cancer Center, Yale School of Medicine, New Haven, CT info_outline Soki Kashima, Zhaochen Ye, Alia Meliki, Miya Hugaboom, Nicholas R. Schindler, Jess Graham, Vivien Moritz, Nourhan El Ahmar, Yasmin Nabil Laimon, John Canniff, Renee Maria Saliby, Gwo-Shu Mary Lee, Marc Machaalani, Maxine Sun, Wenxin Xu, Sabina Signoretti, Bradley Alexander McGregor, Rana R. McKay, Toni K. CHOUEIRI, David A. Braun Organizations Yale Cancer Center, Yale School of Medicine, New Haven, CT, Brigham and Women's Hospital, Boston, MA, Dana-Farber Cancer Institute, Boston, MA, The Lank Center for Genitourinary Oncology, Dana-Farber Cancer Institute, Boston, MA, Department of Pathology, Brigham and Women's Hospital, Boston, MA, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, University of California, San Diego Health, La Jolla, CA Abstract Disclosures Research Funding No funding sources reported Background: RCC is notable for a high CD8+ T cell infiltration despite its modest tumor mutational load. However, CD8+ T cell infiltration does not correlate with ICI response, highlighting the need to understand cellular composition and phenotype. We conducted a comprehensive dissection of the tumor microenvironment (TME) using pre- and post-ICI treatment samples to identify specific T-cell populations associated with ICI treatment efficacy in RCC. Methods: A total of 70 tumor samples (n = 59 clear cell; n = 11 non-clear cell) from 63 patients with RCC were collected before (n = 48) and/or after (n = 22) systemic therapies (VEGFi, n = 9; mono-ICI, n = 20; ICI + ICI, n = 17; ICI + VEGFi, n = 9; others, n = 15). This cohort contained 12 paired samples on pre and post from 5 patients, and 58 unpaired samples. Responders (R) were defined as complete and partial responses (n = 22), and non-responders (NR) as disease progression (n = 33) according to the best response based on RECIST. We performed single-cell RNA-sequence (scRNA-seq) on all samples and established a transcriptomics atlas in RCC. We utilized established gene expression signatures to interrogate cellular composition and functional states for samples from ICI-treated patients. We used non-negative matrix factorization (NMF) to identify gene programs, offering superior feature preservation and interpretability. Results: 443,337 high-quality viable cells were annotated to lymphoid, myeloid, tumor, endothelial, or fibroblast compartments, capturing the RCC TME landscape. Among CD8+ T cells, we observed significant heterogeneity, particularly in exhausted T cells (Tex) expressing PD-1 and TIM-3 . Tex in NR showed enrichment for tissue-residency and innate-like genes and gene programs, exemplified by significant upregulation of ZNF683 (p = 0.031) and ITGAE (p = 0.0041). In contrast, Tex in R exhibited a marked upregulation of heat shock protein genes, such as HSP1B (p < 2.22E-16) and DNAJB1 (p < 2.22E-16), highlighting a distinct genomic profile. Notably, through NMF analysis, Tex in R showed a significantly higher stress response program and terminal exhaustion program than in NR at baseline and after ICI treatment. Further analysis through gene signature scoring showed an association between Tex in R and enhanced IFN and chemokine activities, stress response, and terminal differentiation post-ICI. Conclusions: Our single-cell transcriptomic analysis uncovered the relationship between Tex with active stress responses and ICI efficacy, additional suggesting T cell revival with ICI-exposure. This study identifies the specific Tex characteristics associated with ICI responsiveness, highlighting scRNA-seq as a scientific strategy for deep correlative analysis in large patient cohorts, and emphasizing the need for further investigation into the unique intricacies of the RCC TME.