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.
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
Usage data is cumulative from April 2019 through April 2020.
Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.
Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.