Phage display enables machine learning discovery of cancer antigen–specific TCRs
T cells targeting epitopes in infectious diseases or cancer play a central role in spontaneous and therapy-induced immune responses. 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 a TCR targeting the cancer-testis antigen NY-ESO-1<sub>157-165</sub> epitope, we built large phage display libraries of TCRs with randomized complementary determining region 3 of the β chain. The TCR libraries were panned against NY-ESO-1, which enabled us to collect thousands of epitope-specific TCR sequences. Leveraging these data, we trained a machine learning TCR-epitope interaction predictor and identified several epitope-specific TCRs from TCR repertoires. Cellular assays revealed that the predicted TCRs displayed activity toward NY-ESO-1 and no detectable cross-reactivity. Our work demonstrates how display technologies combined with TCR-epitope interaction predictors can effectively leverage large TCR repertoires for TCR discovery.
Authors
- Mariastella de Tiani
- Maiia E. Bragina
- David Gfeller (ORCID: https://orcid.org/0000-0002-3952-0930)
- Steven M. Dunn (ORCID: https://orcid.org/0000-0002-7402-9581)
- Justine Michaux (ORCID: https://orcid.org/0009-0006-3128-2436)
- Julien Schmidt (ORCID: https://orcid.org/0000-0003-3034-6857)
- Alexandre Harari (ORCID: https://orcid.org/0000-0002-1055-2090)
- Nathalie Rufer (ORCID: https://orcid.org/0000-0002-9871-6271)
- Vincent Zoete (ORCID: https://orcid.org/0000-0002-2336-6537)
- Giancarlo Croce (ORCID: https://orcid.org/0000-0003-0944-1884)
- Michaël Hebeisen (ORCID: https://orcid.org/0000-0003-3416-1112)
- Sara Bobisse (ORCID: https://orcid.org/0000-0002-3502-3224)
- Michal Bassani‐Sternberg (ORCID: https://orcid.org/0000-0002-1934-954X)
- Philippe Guillame
- Talita Gehret
- Delphine Tardivon
- Alexandra Michel (ORCID: https://orcid.org/0000-0002-0092-1921)
- Rachid Lani
- Hui Song Pak
- Marta A. S. Perez (ORCID: https://orcid.org/0000-0002-3963-5965)
Institutions
- SIB Swiss Institute of Bioinformatics (CH)
- Centre Hospitalier Universitaire Vaudois (CH)
- Ludwig Cancer Research (CH)
- Swiss Cancer Center Léman (CH)
- University of Lausanne (CH)
Publication Details
- Journal
- Science Advances
- Published
- 2025-06-11
- DOI
- https://doi.org/10.1126/sciadv.ads5589
- Citations
- 11
- Primary Topic
- Monoclonal and Polyclonal Antibodies Research
- Type
- article
- Field-Weighted Citation Impact
- 14.96