Adoptive immunotherapy with regulatory T cells (Tregs) is a promising treatment for allograft rejection and graft-versus-host disease (GVHD). Emerging data indicate that, compared with polyclonal Tregs, disease-relevant antigen-specific Tregs may have numerous advantages, such as a need for fewer cells and reduced risk of nonspecific immune suppression. Current methods to generate alloantigen-specific Tregs rely on expansion with allogeneic antigen-presenting cells, which requires access to donor and recipient cells and multiple MHC mismatches. The successful use of chimeric antigen receptors (CARs) for the generation of antigen-specific effector T cells suggests that a similar approach could be used to generate alloantigen-specific Tregs. Here, we have described the creation of an HLA-A2–specific CAR (A2-CAR) and its application in the generation of alloantigen-specific human Tregs. In vitro, A2-CAR–expressing Tregs maintained their expected phenotype and suppressive function before, during, and after A2-CAR–mediated stimulation. In mouse models, human A2-CAR–expressing Tregs were superior to Tregs expressing an irrelevant CAR at preventing xenogeneic GVHD caused by HLA-A2+ T cells. Together, our results demonstrate that use of CAR technology to generate potent, functional, and stable alloantigen-specific human Tregs markedly enhances their therapeutic potential in transplantation and sets the stage for using this approach for making antigen-specific Tregs for therapy of multiple diseases.
Katherine G. MacDonald, Romy E. Hoeppli, Qing Huang, Jana Gillies, Dan S. Luciani, Paul C. Orban, Raewyn Broady, Megan K. Levings
Usage data is cumulative from November 2018 through November 2019.
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.