Phage display profiling of CDR3β loops enables machine learning predictions of NY-ESO-1 specific TCRs

Croce, G.; Lani, R.; Tardivon, D.; Bobisse, S.; de Tiani, M.; Bragina, M.; Perez, M. A.; Schmidt, J.; Guillame, P.; Zoete, V.; Harari, A.; Rufer, N.; Hebeisen, M.; Dunn, S.; Gfeller, D.

doi:10.1101/2024.06.27.600973

bioRxiv · 10.1101/2024.06.27.600973

Phage display profiling of CDR3β loops enables machine learning predictions of NY-ESO-1 specific TCRs

Croce, G.·Lani, R.·Tardivon, D.·Bobisse, S.·de Tiani, M.·Bragina, M.·Perez, M. A.·Schmidt, J.·Guillame, P.·Zoete, V.·Harari, A.·Rufer, N.·Hebeisen, M.·Dunn, S.·Gfeller, D.

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Abstract

T cells targeting epitopes in infectious diseases or cancer play a central role in spontaneous and therapy-induced immune responses. T-cell epitope recognition is mediated by the binding of the T-Cell Receptor (TCR) and TCRs recognizing clinically relevant epitopes are promising for T-cell based therapies. Starting from one of the few known TCRs targeting the cancer-testis antigen NY-ESO-1157-165 epitope, we built large phage display libraries of TCRs with randomized Complementary Determining Region 3 of the {beta} chain. The TCR libraries were panned against the NY-ESO-1 epitope, which enabled us to collect thousands of epitope-specific TCR sequences. We then trained a machine learning TCR-epitope interaction predictor with this data and could identify several epitope-specific TCRs directly from TCR repertoires. Cellular binding and functional assays revealed that the predicted TCRs displayed activity towards the NY-ESO-1 epitope and no detectable cross-reactivity with self-peptides. Overall, our work demonstrates how display technologies combined with machine learning models of TCR-epitope recognition can effectively leverage large TCR repertoires for TCR discovery.

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Croce, G., Lani, R., Tardivon, D., Bobisse, S., de Tiani, M., Bragina, M., Perez, M. A., Schmidt, J., Guillame, P., Zoete, V., Harari, A., Rufer, N., Hebeisen, M., Dunn, S., Gfeller, D.. 2024-07-01. Phage display profiling of CDR3β loops enables machine learning predictions of NY-ESO-1 specific TCRs. https://doi.org/10.1101/2024.06.27.600973

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