Metastatic uveal melanoma (UM) is a poor prognosis malignancy. Immunotherapy is commonly employed, despite the low activity, considering the lack of other effective systemic treatments. In this study, the prognostic and predictive role of soluble immune checkpoints and inflammatory cytokines/chemokines in 22 metastatic UM patients was evaluated. Baseline levels of these molecules were assessed, as well as their changes during anti-PD-1 therapy. The correlation between soluble immune checkpoints/cytokines/chemokines and survival was analyzed. A comparison between circulating immune profile of metastatic cutaneous melanoma (CM), for which immunotherapy is a mainstay of treatment, and UM during anti-PD-1 therapy was also performed. Three immune molecules resulted significantly higher in metastatic UM patients with survival <6 months versus patients with survival ≥6 months: IL-8, HVEM and IDO activity. Considering these three molecules, we obtained a baseline score able to predict patients' survival. The same three molecules, together with soluble(s) CD137, sGITR and sCD27, resulted significantly lower in patients with survival >30 months. We also observed an increase of sCD137, sCD28, sPD-1, sPD-L2 sLAG3, sCD80 and sTim3 during anti-PD-1 treatment, as well as IDO activity, IP-10 and CCL2. Several of these molecules were significantly higher in UM compared to CM patients during anti-PD-1 therapy. The analysis of circulating immune molecules allows to identify patients with poor prognosis despite immunotherapy and patients with long survival treated with an anti-PD-1 agent. The different serum concentration of these molecules during anti-PD-1 therapy between UM and CM reflects the different efficacy of immune checkpoint inhibitors.
Keywords:PD-1; Uveal melanoma; immunotherapy; soluble checkpoint.
Circulating immune profile can predict survival of metastatic uveal melanoma patients: results of an exploratory study
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基因水平:PCR Array、RT-PCR、PCR、单细胞测序
蛋白水平:MSD、Luminex、CBA、Elispot、Antibody Array、ELISA、Sengenics
细胞水平:细胞染色、细胞分选、细胞培养、细胞功能
组织水平:空间多组学、多重荧光免疫组化、免疫组化、免疫荧光
数据分析:流式数据分析、组化数据分析、多因子数据分析
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