Predicting Antigen-Specificities of Orphan T Cell Receptors from Cancer Patients with TCRpcDist.

Autor: Perez MAS; Department of Oncology, Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, CH-1005, Switzerland.; Molecular Modeling Group, SIB Swiss Institute of Bioinformatics, University of Lausanne, Quartier UNIL-Sorge, Bâtiment Amphipole, Lausanne, CH-1015, Switzerland., Chiffelle J; Department of Oncology, Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, CH-1005, Switzerland.; Center for Cell Therapy, CHUV-Ludwig Institute, Lausanne, CH-1011, Switzerland., Bobisse S; Department of Oncology, Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, CH-1005, Switzerland.; Center for Cell Therapy, CHUV-Ludwig Institute, Lausanne, CH-1011, Switzerland., Mayol-Rullan F; Department of Oncology, Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, CH-1005, Switzerland.; Molecular Modeling Group, SIB Swiss Institute of Bioinformatics, University of Lausanne, Quartier UNIL-Sorge, Bâtiment Amphipole, Lausanne, CH-1015, Switzerland., Bugnon M; Department of Oncology, Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, CH-1005, Switzerland.; Molecular Modeling Group, SIB Swiss Institute of Bioinformatics, University of Lausanne, Quartier UNIL-Sorge, Bâtiment Amphipole, Lausanne, CH-1015, Switzerland., Bragina ME; Department of Oncology, Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, CH-1005, Switzerland.; Molecular Modeling Group, SIB Swiss Institute of Bioinformatics, University of Lausanne, Quartier UNIL-Sorge, Bâtiment Amphipole, Lausanne, CH-1015, Switzerland., Arnaud M; Department of Oncology, Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, CH-1005, Switzerland.; Center for Cell Therapy, CHUV-Ludwig Institute, Lausanne, CH-1011, Switzerland., Sauvage C; Department of Oncology, Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, CH-1005, Switzerland.; Center for Cell Therapy, CHUV-Ludwig Institute, Lausanne, CH-1011, Switzerland., Barras D; Department of Oncology, Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, CH-1005, Switzerland.; Center for Cell Therapy, CHUV-Ludwig Institute, Lausanne, CH-1011, Switzerland., Laniti DD; Department of Oncology, Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, CH-1005, Switzerland.; Center for Cell Therapy, CHUV-Ludwig Institute, Lausanne, CH-1011, Switzerland., Huber F; Department of Oncology, Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, CH-1005, Switzerland.; Center for Cell Therapy, CHUV-Ludwig Institute, Lausanne, CH-1011, Switzerland., Bassani-Sternberg M; Department of Oncology, Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, CH-1005, Switzerland.; Center for Cell Therapy, CHUV-Ludwig Institute, Lausanne, CH-1011, Switzerland., Coukos G; Department of Oncology, Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, CH-1005, Switzerland.; Center for Cell Therapy, CHUV-Ludwig Institute, Lausanne, CH-1011, Switzerland.; Department of Oncology, Immuno-Oncology Service, Lausanne University Hospital, Lausanne, CH-1011, Switzerland., Harari A; Department of Oncology, Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, CH-1005, Switzerland.; Center for Cell Therapy, CHUV-Ludwig Institute, Lausanne, CH-1011, Switzerland., Zoete V; Department of Oncology, Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, CH-1005, Switzerland.; Molecular Modeling Group, SIB Swiss Institute of Bioinformatics, University of Lausanne, Quartier UNIL-Sorge, Bâtiment Amphipole, Lausanne, CH-1015, Switzerland.
Jazyk: angličtina
Zdroj: Advanced science (Weinheim, Baden-Wurttemberg, Germany) [Adv Sci (Weinh)] 2024 Oct; Vol. 11 (40), pp. e2405949. Date of Electronic Publication: 2024 Aug 19.
DOI: 10.1002/advs.202405949
Abstrakt: Approaches to analyze and cluster T-cell receptor (TCR) repertoires to reflect antigen specificity are critical for the diagnosis and prognosis of immune-related diseases and the development of personalized therapies. Sequence-based approaches showed success but remain restrictive, especially when the amount of experimental data used for the training is scarce. Structure-based approaches which represent powerful alternatives, notably to optimize TCRs affinity toward specific epitopes, show limitations for large-scale predictions. To handle these challenges, TCRpcDist is presented, a 3D-based approach that calculates similarities between TCRs using a metric related to the physico-chemical properties of the loop residues predicted to interact with the epitope. By exploiting private and public datasets and comparing TCRpcDist with competing approaches, it is demonstrated that TCRpcDist can accurately identify groups of TCRs that are likely to bind the same epitopes. Importantly, the ability of TCRpcDist is experimentally validated to determine antigen specificities (neoantigens and tumor-associated antigens) of orphan tumor-infiltrating lymphocytes (TILs) in cancer patients. TCRpcDist is thus a promising approach to support TCR repertoire analysis and TCR deorphanization for individualized treatments including cancer immunotherapies.
(© 2024 The Author(s). Advanced Science published by Wiley‐VCH GmbH.)
Databáze: MEDLINE
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