Elimination of CD4lowHLA-G+ T cells overcomes castration-resistance in prostate cancer therapy
Androgen deprivation therapy (ADT) is a main treatment for prostate cancer (PCa) but the disease often recurs and becomes castration-resistant in nearly all patients. Recent data implicate the involvement of immune cells in the development of this castration-resistant prostate cancer (CRPC). In particular, T cells have been found to be expanded in both PCa patients and mouse models shortly after androgen deprivation. However, whether or which of the T cell subtypes play an important role during the development of CRPC is unknown. Here we identified a novel population of CD4lowHLA-G+ T cells that undergo significant expansion in PCa patients after ADT. In mouse PCa models, a similar CD4low T cell population expands during the early stages of CRPC onset. These cells are identified as IL-4-expressing TH17 cells, and are shown to be associated with CRPC onset in patients and essential for the development of CRPC in mouse models. Mechanistically, CD4lowHLA-G+ T cells drive androgen-independent growth of prostate cancer cells by modulating the activity and migration of CD11blowF4/80hi macrophages. Furthermore, following androgen deprivation, elevated PGE2-EP2 signaling inhibited the expression of CD4 in thymocytes, and subsequently induced the polarization of CD4low naïve T cells towards the IL-4-expressing TH17 phenotype via up-regulation of IL23R. Therapeutically, inactivating PGE2 signaling with celecoxib at a time when CD4lowHLA-G+ T cells appeared, but not immediately following androgen deprivation, dramatically suppressed the onset of CRPC. Collectively, our results indicate that an unusual population of CD4lowHLA-G+ T cells is essential for the development of CRPC and point to a new therapeutic avenue of combining ADT with PGE2 inhibition for the treatment of prostate cancer.
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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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