Abstract 2061: Deep immune profiling by mass cytometry revealed an association between the state of immune system before treatment and response to checkpoint inhibitor therapy in clear cell renal cell carcinoma

Abstract

Abstract Checkpoint immunotherapy (CPI) and its combination with tyrosine kinase inhibitor (TKI) therapy are widely used in the treatment of many cancers, including clear cell renal cell carcinoma (ccRCC). Although recent advances in these therapies demonstrate effectiveness, a subset of patients are unresponsive. In this study, using deep immune profiling by mass cytometry (CyTOF), we analyzed peripheral blood mononuclear cell (PBMC) samples from 86 ccRCC patients pre- and post-treatment. The cohort included patients treated with first-line CPI (n = 43), TKI (n = 15) and combinatorial CPI+TKI (n = 28), with PBMC samples collected pre- (n = 41) and post- (n = 69) therapy (n = 24 patients total with paired samples). Samples were characterized by CyTOF analysis of 30 different cell surface markers. Using t-distributed stochastic neighbor embedding (tSNE) and Phenograph clustering, the presence of immune populations and subpopulations were characterized and associated with therapeutic response as well as longitudinally tracked. While most cell types did not differ between pre- and post-treatment states, a decrease in CD8 and CD4 PD1+ T cell subsets was observed in patients treated with CPI and CPI+TKI (p < 0.001). Also, increased CD8 effector memory T cells were observed in post-treatment samples in those treated with CPI or CPI+TKI (p < 0.001).Subsequent analysis of pre-treatment samples in the context of clinical outcome revealed specific features associated with favorable response to immunotherapy. Responders (R) in comparison with non-responders (NR) were characterized by an increased number of naive CD4 T cells (p = 0.03), CD8 T cells (p = 0.038), immature NK cells (p = 0.005) and lower percentage of granulocytes (p = 0.012). Using unsupervised hierarchical clustering, 8 patient groups were identified with a unique immune profile enriched by a certain PBMC population. Three groups (G1-G3), which included a high ratio of R, were enriched by naive T cells, immature NK cells and non-classical monocytes, respectively. The G4 group, enriched with B cells, consisted of R and NR in equal numbers. The G5-G8 groups, enriched with granulocytes, classical monocytes, mature NK-cells and effector CD8 T cells, respectively, were primarily comprised of NR. Patients with favorable pre-treatment immune profiles also belonged to favorable groups post-treatment, with a similar trend observed with unfavorable profiles. In conclusion, this analysis revealed a remarkable association between the initial immune status or “portrait” of the patient before therapy and subsequent therapeutic outcome as well as the development of therapeutic resistance. Citation Format: Svetlana Shishkova, Dmitry Tabakov, Evgeny Egorov, Akshaya Ramachandran, Yang Lyu, Krystle Nomie, Jessica Brown, Vladimir Zyrin, Alexander Zaytcev, Natalia Miheecheva, Ekaterina Postovalova, Alexander Bagaev, Ravshan Ataullakhanov, James Hsieh. Deep immune profiling by mass cytometry revealed an association between the state of immune system before treatment and response to checkpoint inhibitor therapy in clear cell renal cell carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2061.