- Main
Engineered IL13 variants direct specificity of IL13Rα2-targeted CAR T cell therapy
Published Web Location
https://doi.org/10.1073/pnas.2112006119Abstract
IL13Rα2 is an attractive target due to its overexpression in a variety of cancers and rare expression in healthy tissue, motivating expansion of interleukin 13 (IL13)-based chimeric antigen receptor (CAR) T cell therapy from glioblastoma into systemic malignancies. IL13Rα1, the other binding partner of IL13, is ubiquitously expressed in healthy tissue, raising concerns about the therapeutic window of systemic administration. IL13 mutants with diminished binding affinity to IL13Rα1 were previously generated by structure-guided protein engineering. In this study, two such variants, termed C4 and D7, are characterized for their ability to mediate IL13Rα2-specific response as binding domains for CAR T cells. Despite IL13Rα1 and IL13Rα2 sharing similar binding interfaces on IL13, mutations to IL13 that decrease binding affinity for IL13Rα1 did not drastically change binding affinity for IL13Rα2. Micromolar affinity to IL13Rα1 was sufficient to pacify IL13-mutein CAR T cells in the presence of IL13Rα1-overexpressing cells in vitro. Interestingly, effector activity of D7 CAR T cells, but not C4 CAR T cells, was demonstrated when cocultured with IL13Rα1/IL4Rα-coexpressing cancer cells. While low-affinity interactions with IL13Rα1 did not result in observable toxicities in mice, in vivo biodistribution studies demonstrated that C4 and D7 CAR T cells were better able to traffic away from IL13Rα1+ lung tissue than were wild-type (WT) CAR T cells. These results demonstrate the utility of structure-guided engineering of ligand-based binding domains with appropriate selectivity while validating IL13-mutein CARs with improved selectivity for application to systemic IL13Rα2-expressing malignancies.
Many UC-authored scholarly publications are freely available on this site because of the UC's open access policies. Let us know how this access is important for you.
Main Content
Enter the password to open this PDF file:
-
-
-
-
-
-
-
-
-
-
-
-
-
-