CD73 Dysregulates Monocyte Anti-Tumor Activity in Multiple Myeloma
Purpose: Multiple myeloma (MM) is characterized by immune cell dysfunction in the tumor microenvironment (TME). We aimed at evaluating the effect of CD73, an overexpressed factor in some tumors, on anti-tumor immune function in the TME of MM. Patients and methods: We analyzed the expression of CD73 in T-, B-, and natural killer (NK)-lymphocytes and monocytes in bone marrow (BM), peripheral blood (PB) from MM patients and healthy controls, and residual CD138+ cells using flow cytometry. The anti-tumor activity of these monocytes was confirmed by co-culture with RPMI-8226 cells treated with a CD73 inhibitor. We determined the interleukin (IL)-2, IL-4, IL-6, IL-10, tumor necrosis factor (TNF)-α, and interferon (IFN)-γ levels using a cytometric bead array. Monocyte phagocytosis in cell culture sediment was then observed and measured. Results: CD73 was highly expressed in T-, B-, and NK-lymphocytes and monocytes from the BM and PB isolated from patients with MM. Compared with healthy controls, MM samples exhibited significantly higher CD73 expression and TNF-α, IFN-γ, IL-10 levels in monocytes. Inhibiting CD73 in BM immune cells from MM samples significantly increased the secretion of IL-2, TNF-α, and IFN-γ, as well as the killing ability of immune cells. However, monocyte phagocytosis was seldom observed. Inhibiting CD73 in MM monocytes significantly increased the secretion of IL-2, TNF-α, and IFN-γ in monocytes and improved monocyte killing and phagocytosis. Conclusion: Monocytes from MM exhibited weakened anti-tumor effects, and CD73 was involved in forming an immunosuppressive microenvironment. Inhibiting CD73 partly restored the anti-tumor activity of monocytes, a potential strategy for the treatment of MM.Keywords: CD73; anti-tumor activity; immune cells; monocytes; multiple myeloma; phagocytosis.
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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
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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