Journal de protéomique et bioinformatique
Libre accès
ISSN: 0974-276X
ISSN: 0974-276X
Victor Murcia Pienkowski, Tamara Boschert, Piotr Skoczylas, Anna Sanecka-Duin, Maciej Jasinski, Bartlomiej Krol-Jozaga, Giovanni Mazzocco, Slawomir Stachura, Lukas Bunse, Jan Kaczmarczyk, Edward W. Green, Agnieszka Blum
Cellular immunotherapies, such as those utilizing T lymphocytes expressing native or engineered T-cell Receptors (TCRs), have already demonstrated therapeutic efficacy. However, some high-affinity TCRs have also proved to be fatal due to off-target immunotoxicity. This process occurs when the immune system acts against epitopes found on both tumor cells and healthy tissues. Moreover, some TCRs can be cross-reactive to epitopes with highly dissimilar sequences. 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 off-target toxicities. We tested the performance of ARDitox in silico on 4 different epitopes found in the literature where TCRs were used to target cancer-related antigens as well as on a set of TCR targeting a viral epitope. Two of them have a specific clinical outcome in which immunotoxicity was reported (MAGEA3112-120 and MAGEA3168-176 epitopes), one was tested using an X-scan approach (AFP158-166 epitope), and the last one with no cross-reactive epitopes identified in clinical trials (NY-ESO-1157-165 epitope). Overall, ARDitox has identified immunotoxic epitopes in line with the data available in the literature. In addition, we investigated a very promising TCR, which is still in development, against a peptide coded by the NLGN4X gene. For this epitope, we detected a cross-reactive peptide that otherwise would be difficult to detect in vitro. In conclusion, in silico approach is a powerful tool that accurately identifies off-target epitopes and should be considered in preclinical studies, as it can effectively complement the development of safer anti-cancer therapies.