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Cited by 23vaccines and immunoinformatics approachesT-cell and B-cell ImmunologyImmunotherapy and Immune Responses

Phage display enables machine learning discovery of cancer antigen–specific TCRs

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Croce, Giancarlo, Lani, Rachid, Tardivon, Delphine, Bobisse, Sara, de Tiani, Mariastella, Bragina, Maiia, Perez, Marta A. S., Michaux, Justine +12 more·2025·Science Advances
DOI 10.1126/sciadv.ads5589PMC PMC12225687

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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. 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-1 157–165 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.

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  • Phage display technologies allow phages to be engineered to express tumor-associated antigens or immunostimulatory peptides on their capsid surfaces.

    “Phage display technologies allow phages to be engineered to express tumor-associated antigens or immunostimulatory peptides on their capsid surfaces [15,76–78]”

    From Phage therapy in oncology: opportunities for cancer prevention and treatment (2026) · cites this paper as [77]

    The Results (page unavailable) describe expressing libraries of TCRs in phage and panning them with the NY-ESO-1 epitope. The cited paper therefore uses phage to display antigen-binding TCRs, not to display tumor-associated antigens or immunostimulatory peptides on phage capsids. (LLM, conf=0.86)

    Checked by a reasoning model — may be wrong.

    Experimental — accuracy not yet measured

  • By enhancing tumor antigen visibility and stimulating recruitment and activation of immune effector cells, phages can potentiate antitumor immune responses.

    “By enhancing tumor antigen visibility and stimulating the recruitment and activation of immune effector cells, phages can potentiate antitumor immune responses [15,77,78]”

    From Phage therapy in oncology: opportunities for cancer prevention and treatment (2026) · cites this paper as [77]

    The Discussion and Methods (pages unavailable) describe phages as a display and screening tool for finding cancer-antigen-specific TCRs. Searches for tumor-antigen visibility, immune-cell recruitment and activation, and phage-mediated antitumor responses found no passage saying that phages produce the claimed immune effects. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Croce, G., Lani, R., Tardivon, D., Bobisse, S., de Tiani, M., Bragina, M., Perez, M. A. S., Michaux, J., Pak, H. S., Michel, A., Gehret, T., Schmidt, J., Guillame, P., Bassani-Sternberg, M., Zoete, V., Harari, A., Rufer, N., Hebeisen, M., Dunn, S. M., & Gfeller, D.
Affiliations
  • SIB Swiss Institute of Bioinformatics (CH)
  • Centre Hospitalier Universitaire Vaudois (CH)
  • Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland.
  • Agora Cancer Research Centre, Lausanne, Switzerland.
Corresponding author
  • Dunn, Steven M.
  • Gfeller, David
ORCID
Venue
Science Advances
Publisher
American Association for the Advancement of Science (AAAS)
Published
2025-06-13
Volume
11
Issue
24
Pages
eads5589-eads5589
ISSN
2375-2548 (ISSN-L)
DOI
10.1126/sciadv.ads5589
PMC
PMC12225687
OpenAlex
W4411193874
Cited by
23
References
2
Citations by year
2026: 14 · 2025: 7 · 2024: 2
FWCI
6.28
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Science Advances (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • SERVAL (Université de Lausanne) (publishedVersion, cc-by)
  • IRIS (submittedVersion)
  • SERVAL (Université de Lausanne) (submittedVersion)
Funders
Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (192019, 205321, 193749)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
  • Life Sciences › Immunology and Microbiology › Immunology › T-cell and B-cell Immunology
  • Life Sciences › Immunology and Microbiology › Immunology › Immunotherapy and Immune Responses
Keywords
phage display, machine learning, cancer-testis antigens, NY-ESO-1, T cell receptor repertoire, epitope cross-reactivity
MeSH terms
Machine Learning, Antigens, Neoplasm, Humans, Membrane Proteins, Neoplasms, Receptors, Antigen, T-Cell, Epitopes, T-Lymphocyte, Peptide Library, Cell Surface Display Techniques

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