Computational identification of cross-reactive TCR epitopes with ARDitox.

BACKGROUND: Cellular immunotherapies, such as those utilizing T lymphocytes expressing native or engineered T cell receptors (TCRs), have demonstrated therapeutic efficacy. However, some engineered high-affinity TCRs have caused fatal off-target immunotoxicity due to targeting epitopes later found to be expressed by both tumor cells and healthy tissues. Unfortunately, TCRs can be cross-reactive to epitopes with highly distinct sequences, making prediction difficult, and the exquisite sequence specificity of TCRs means that safety studies in mice miss human-specific epitopes. METHODS: To address this issue, we developed ARDitox, a novel in silico method based on computational immunology and artificial intelligence (AI) for predicting and analyzing potential TCR off-target toxicities. We tested the performance of ARDitox on four TCRs reported to target tumor-associated antigens, two of which are known to cause clinical immunotoxicity (MAGEA3112-120 and MAGEA3168-176 epitopes), one of which has experimentally identified off-target antigens (AFP158-166 epitope), and the last one for which no cross-reactive epitopes are known (NY-ESO-1157-165). RESULTS: ARDitox confirmed the previously identified immunotoxic epitopes. We then expanded our analyses to a novel TCR targeting the tumor-associated antigen NLGN4X, frequently upregulated in gliomas. For this target, ARDitox identified a cross-reactive peptide that would not have been found using mouse models, highlighting the value of our computational approach. CONCLUSIONS: Our findings underscore the value of the ARDitox in silico method for the early and reliable identification of off-target epitopes for further preclinical evaluation. This platform strongly supports the development of safer TCR-mediated immunotherapies.