Cytokine-induced memory-like natural killer cells have enhanced function, proliferation, and in vivo expansion against ovarian cancer cells
Objective: Natural killer (NK) cells are lymphocytes well suited for adoptive immunotherapy. Attempts with adoptive NK cell immunotherapy against ovarian cancer have proven unsuccessful, with the main limitations including failure to expand and diminished effector function. We investigated if incubation of NK cells with interleukin (IL)-12, IL-15, and IL-18 for 16h could produce cytokine-induced memory-like (CIML) NK cells capable of enhanced function against ovarian cancer. Methods: NK cells were preactivated briefly with IL-12, IL-15, and IL-18, rested, then placed against ovarian cancer targets to assess phenotype and function via flow cytometry. Real-time NK-cell-mediated tumor-killing was evaluated. Using ascites cells and cell-free ascites fluid, NK cell proliferation and function within the immunosuppressive microenvironment was evaluated in vitro. Finally, CIML NK cells were injected intraperitoneal (IP) into an in vivo xenogeneic mouse model of ovarian cancer. Results: CIML NK cells demonstrate enhanced cytokine (IFN-γ) production and NK-cell-mediated killing of ovarian cancer. NK cells treated overnight with cytokines led to robust activation characterized by temporal shedding of CD16, induction of CD25, and enhanced proliferation. CIML NK cells proliferate more with enhanced effector function compared to controls in an immunosuppressive microenvironment. Finally, human CIML NK cells exhibited potent antitumor effects within a xenogeneic mouse model of ovarian cancer. Conclusions: CIML NK cells have enhanced functionality and persistence against ovarian cancer in vitro and in vivo, even when exposed to ascites fluid. These findings provide a strategy for NK cell-based immunotherapy to circumvent the immunosuppressive nature of ovarian cancer.
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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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