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Citations to this article

Perturbation of NK cell peripheral homeostasis accelerates prostate carcinoma metastasis
Gang Liu, Shengjun Lu, Xuanjun Wang, Stephanie T. Page, Celestia S. Higano, Stephen R. Plymate, Norman M. Greenberg, Shaoli Sun, Zihai Li, Jennifer D. Wu
Gang Liu, Shengjun Lu, Xuanjun Wang, Stephanie T. Page, Celestia S. Higano, Stephen R. Plymate, Norman M. Greenberg, Shaoli Sun, Zihai Li, Jennifer D. Wu
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Research Article Oncology

Perturbation of NK cell peripheral homeostasis accelerates prostate carcinoma metastasis

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Abstract

The activating receptor NK cell group 2 member D (NKG2D) mediates antitumor immunity in experimental animal models. However, whether NKG2D ligands contribute to tumor suppression or progression clinically remains controversial. Here, we have described 2 novel lines of “humanized” bi-transgenic (bi-Tg) mice in which native human NKG2D ligand MHC class I polypeptide-related sequence B (MICB) or the engineered membrane-restricted MICB (MICB.A2) was expressed in the prostate of the transgenic adenocarcinoma of the mouse prostate (TRAMP) model of spontaneous carcinogenesis. Bi-Tg TRAMP/MICB mice exhibited a markedly increased incidence of progressed carcinomas and metastasis, whereas TRAMP/MICB.A2 mice enjoyed long-term tumor-free survival conferred by sustained NKG2D-mediated antitumor immunity. Mechanistically, we found that cancer progression in TRAMP/MICB mice was associated with loss of the peripheral NK cell pool owing to high serum levels of tumor-derived soluble MICB (sMICB). Prostate cancer patients also displayed reduction of peripheral NK cells and high sMIC levels. Our study has not only provided direct evidence in “humanized” mouse models that soluble and membrane-restricted NKG2D ligands pose opposite impacts on cancer progression, but also uncovered a mechanism of sMIC-induced impairment of NK cell antitumor immunity. Our findings suggest that the impact of soluble NKG2D ligands should be considered in NK cell–based cancer immunotherapy and that our unique mouse models should be valuable for therapy optimization.

Authors

Gang Liu, Shengjun Lu, Xuanjun Wang, Stephanie T. Page, Celestia S. Higano, Stephen R. Plymate, Norman M. Greenberg, Shaoli Sun, Zihai Li, Jennifer D. Wu

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