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Yadav S, Vora DS, Sundar D, Dhanjal JK. TCR-ESM: Employing protein language embeddings to predict TCR-peptide-MHC binding. Comput Struct Biotechnol J 2024; 23:165-173. [PMID: 38146434 PMCID: PMC10749252 DOI: 10.1016/j.csbj.2023.11.037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Revised: 11/19/2023] [Accepted: 11/20/2023] [Indexed: 12/27/2023] Open
Abstract
Cognate target identification for T-cell receptors (TCRs) is a significant barrier in T-cell therapy development, which may be overcome by accurately predicting TCR interaction with peptide-bound major histocompatibility complex (pMHC). In this study, we have employed peptide embeddings learned from a large protein language model- Evolutionary Scale Modeling (ESM), to predict TCR-pMHC binding. The TCR-ESM model presented outperforms existing predictors. The complementarity-determining region 3 (CDR3) of the hypervariable TCR is located at the center of the paratope and plays a crucial role in peptide recognition. TCR-ESM trained on paired TCR data with both CDR3α and CDR3β chain information performs significantly better than those trained on data with only CDR3β, suggesting that both TCR chains contribute to specificity, the relative importance however depends on the specific peptide-MHC targeted. The study illuminates the importance of MHC information in TCR-peptide binding which remained inconclusive so far and was thought dependent on the dataset characteristics. TCR-ESM outperforms existing approaches on external datasets, suggesting generalizability. Overall, the potential of deep learning for predicting TCR-pMHC interactions and improving the understanding of factors driving TCR specificity are highlighted. The prediction model is available at http://tcresm.dhanjal-lab.iiitd.edu.in/ as an online tool.
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Affiliation(s)
- Shashank Yadav
- Department of Biomedical Engineering, University of Arizona, Tucson 85721, AZ, USA
| | - Dhvani Sandip Vora
- Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, New Delhi 110016, India
- Department of Computational Biology, Indraprastha Institute of Information Technology, Delhi, New Delhi 110020, India
| | - Durai Sundar
- Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, New Delhi 110016, India
| | - Jaspreet Kaur Dhanjal
- Department of Computational Biology, Indraprastha Institute of Information Technology, Delhi, New Delhi 110020, India
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2
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Lin V, Cheung M, Gowthaman R, Eisenberg M, Baker BM, Pierce BG. TCR3d 2.0: expanding the T cell receptor structure database with new structures, tools and interactions. Nucleic Acids Res 2024:gkae840. [PMID: 39329260 DOI: 10.1093/nar/gkae840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2024] [Revised: 09/07/2024] [Accepted: 09/13/2024] [Indexed: 09/28/2024] Open
Abstract
Recognition of antigens by T cell receptors (TCRs) is a key component of adaptive immunity. Understanding the structures of these TCR interactions provides major insights into immune protection and diseases, and enables design of therapeutics, vaccines and predictive modeling algorithms. Previously, we released TCR3d, a database and resource for structures of TCRs and their recognition. Due to the growth of available structures and categories of complexes, the content of TCR3d has expanded substantially in the past 5 years. This expansion includes new tables dedicated to TCR mimic antibody complex structures, TCR-CD3 complexes and annotated Class I and II peptide-MHC complexes. Additionally, tools are available for users to calculate docking geometries for input TCR and TCR mimic complex structures. The core tables of TCR-peptide-MHC complexes have grown by 50%, and include binding affinity data for experimentally determined structures. These major content and feature updates enhance TCR3d as a resource for immunology, therapeutics and structural biology research, and enable advanced approaches for predictive TCR modeling and design. TCR3d is available at: https://tcr3d.ibbr.umd.edu.
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Affiliation(s)
- Valerie Lin
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD 20742, USA
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD 20850, USA
| | - Melyssa Cheung
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD 20850, USA
- Department of Chemistry and Biochemistry, University of Maryland, College Park, MD 20742, USA
| | - Ragul Gowthaman
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD 20742, USA
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD 20850, USA
| | - Maya Eisenberg
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD 20742, USA
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD 20850, USA
| | - Brian M Baker
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN 46556, USA
- Harper Cancer Research Institute, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Brian G Pierce
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD 20742, USA
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD 20850, USA
- University of Maryland Marlene and Stewart Greenebaum Comprehensive Cancer Center, Baltimore, MD 21201, USA
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3
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Raybould MIJ, Greenshields-Watson A, Agarwal P, Aguilar-Sanjuan B, Olsen TH, Turnbull OM, Quast NP, Deane CM. The Observed T Cell Receptor Space database enables paired-chain repertoire mining, coherence analysis, and language modeling. Cell Rep 2024; 43:114704. [PMID: 39216000 DOI: 10.1016/j.celrep.2024.114704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2024] [Revised: 08/05/2024] [Accepted: 08/15/2024] [Indexed: 09/04/2024] Open
Abstract
T cell activation is governed through T cell receptors (TCRs), heterodimers of two sequence-variable chains (often an α and β chain) that synergistically recognize antigen fragments presented on cell surfaces. Despite this, there only exist repositories dedicated to collecting single-chain, not paired-chain, TCR sequence data. We addressed this gap by creating the Observed TCR Space (OTS) database, a source of consistently processed and annotated, full-length, paired-chain TCR sequences. Currently, OTS contains 5.35 million redundant (1.63 million non-redundant), predominantly human sequences from across 50 studies and at least 75 individuals. Using OTS, we identify pairing biases, public TCRs, and distinct chain coherence patterns relative to antibodies. We also release a paired-chain TCR language model, providing paired embedding representations and a method for residue in-filling conditional on the partner chain. OTS will be updated as a central community resource and is freely downloadable and available as a web application.
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Affiliation(s)
- Matthew I J Raybould
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, 24-29 St Giles', OX1 3LB Oxford, UK.
| | - Alexander Greenshields-Watson
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, 24-29 St Giles', OX1 3LB Oxford, UK
| | - Parth Agarwal
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, 24-29 St Giles', OX1 3LB Oxford, UK
| | - Broncio Aguilar-Sanjuan
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, 24-29 St Giles', OX1 3LB Oxford, UK
| | - Tobias H Olsen
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, 24-29 St Giles', OX1 3LB Oxford, UK
| | - Oliver M Turnbull
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, 24-29 St Giles', OX1 3LB Oxford, UK
| | - Nele P Quast
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, 24-29 St Giles', OX1 3LB Oxford, UK
| | - Charlotte M Deane
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, 24-29 St Giles', OX1 3LB Oxford, UK.
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4
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Kim J, Lee BJ, Moon S, Lee H, Lee J, Kim BS, Jung K, Seo H, Chung Y. Strategies to Overcome Hurdles in Cancer Immunotherapy. Biomater Res 2024; 28:0080. [PMID: 39301248 PMCID: PMC11411167 DOI: 10.34133/bmr.0080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Revised: 08/07/2024] [Accepted: 08/23/2024] [Indexed: 09/22/2024] Open
Abstract
Despite marked advancements in cancer immunotherapy over the past few decades, there remains an urgent need to develop more effective treatments in humans. This review explores strategies to overcome hurdles in cancer immunotherapy, leveraging innovative technologies including multi-specific antibodies, chimeric antigen receptor (CAR) T cells, myeloid cells, cancer-associated fibroblasts, artificial intelligence (AI)-predicted neoantigens, autologous vaccines, and mRNA vaccines. These approaches aim to address the diverse facets and interactions of tumors' immune evasion mechanisms. Specifically, multi-specific antibodies and CAR T cells enhance interactions with tumor cells, bolstering immune responses to facilitate tumor infiltration and destruction. Modulation of myeloid cells and cancer-associated fibroblasts targets the tumor's immunosuppressive microenvironment, enhancing immunotherapy efficacy. AI-predicted neoantigens swiftly and accurately identify antigen targets, which can facilitate the development of personalized anticancer vaccines. Additionally, autologous and mRNA vaccines activate individuals' immune systems, fostering sustained immune responses against cancer neoantigens as therapeutic vaccines. Collectively, these strategies are expected to enhance efficacy of cancer immunotherapy, opening new horizons in anticancer treatment.
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Affiliation(s)
- Jihyun Kim
- Research Institute for Pharmaceutical Sciences, College of Pharmacy, College of Pharmacy,Seoul National University, Seoul 08826, Republic of Korea
| | - Byung Joon Lee
- Interdisciplinary Program for Bioengineering, Seoul National University, Seoul 08826, Republic of Korea
| | - Sehoon Moon
- Research Institute for Pharmaceutical Sciences, College of Pharmacy, College of Pharmacy,Seoul National University, Seoul 08826, Republic of Korea
| | - Hojeong Lee
- Department of Anatomy and Cell Biology, Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
| | - Juyong Lee
- Research Institute for Pharmaceutical Sciences, College of Pharmacy, College of Pharmacy,Seoul National University, Seoul 08826, Republic of Korea
- Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul 08826, Republic of Korea
- Arontier Co., Seoul 06735, Republic of Korea
| | - Byung-Soo Kim
- Interdisciplinary Program for Bioengineering, Seoul National University, Seoul 08826, Republic of Korea
- School of Chemical and Biological Engineering, Seoul National University, Seoul 08826, Republic of Korea
- Institute of Chemical Processes, Institute of Engineering Research, and BioMAX, Seoul National University, Seoul 08826, Republic of Korea
| | - Keehoon Jung
- Department of Anatomy and Cell Biology, Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
| | - Hyungseok Seo
- Research Institute for Pharmaceutical Sciences, College of Pharmacy, College of Pharmacy,Seoul National University, Seoul 08826, Republic of Korea
| | - Yeonseok Chung
- Research Institute for Pharmaceutical Sciences, College of Pharmacy, College of Pharmacy,Seoul National University, Seoul 08826, Republic of Korea
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5
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Drost F, Dorigatti E, Straub A, Hilgendorf P, Wagner KI, Heyer K, López Montes M, Bischl B, Busch DH, Schober K, Schubert B. Predicting T cell receptor functionality against mutant epitopes. CELL GENOMICS 2024; 4:100634. [PMID: 39151427 DOI: 10.1016/j.xgen.2024.100634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 04/22/2024] [Accepted: 07/22/2024] [Indexed: 08/19/2024]
Abstract
Cancer cells and pathogens can evade T cell receptors (TCRs) via mutations in immunogenic epitopes. TCR cross-reactivity (i.e., recognition of multiple epitopes with sequence similarities) can counteract such escape but may cause severe side effects in cell-based immunotherapies through targeting self-antigens. To predict the effect of epitope point mutations on T cell functionality, we here present the random forest-based model Predicting T Cell Epitope-Specific Activation against Mutant Versions (P-TEAM). P-TEAM was trained and tested on three datasets with TCR responses to single-amino-acid mutations of the model epitope SIINFEKL, the tumor neo-epitope VPSVWRSSL, and the human cytomegalovirus antigen NLVPMVATV, totaling 9,690 unique TCR-epitope interactions. P-TEAM was able to accurately classify T cell reactivities and quantitatively predict T cell functionalities for unobserved single-point mutations and unseen TCRs. Overall, P-TEAM provides an effective computational tool to study T cell responses against mutated epitopes.
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Affiliation(s)
- Felix Drost
- Institute of Computational Biology, Helmholtz Center Munich, 85764 Neuherberg, Germany; School of Life Sciences Weihenstephan, Technical University of Munich, 85354 Freising, Germany
| | - Emilio Dorigatti
- Institute of Computational Biology, Helmholtz Center Munich, 85764 Neuherberg, Germany; Department of Statistics, Ludwig Maximilian Universität, 80539 Munich, Germany; Munich Center for Machine Learning (MCML), Ludwig Maximilian Universität, 80538 Munich, Germany
| | - Adrian Straub
- Institute for Medical Microbiology, Immunology, and Hygiene, Technical University of Munich, 81675 Munich, Germany
| | - Philipp Hilgendorf
- Institute for Medical Microbiology, Immunology, and Hygiene, Technical University of Munich, 81675 Munich, Germany; Mikrobiologisches Institut-Klinische Mikrobiologie, Immunologie, und Hygiene, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany
| | - Karolin I Wagner
- Institute for Medical Microbiology, Immunology, and Hygiene, Technical University of Munich, 81675 Munich, Germany
| | - Kersten Heyer
- Institute for Medical Microbiology, Immunology, and Hygiene, Technical University of Munich, 81675 Munich, Germany
| | - Marta López Montes
- Institute for Medical Microbiology, Immunology, and Hygiene, Technical University of Munich, 81675 Munich, Germany
| | - Bernd Bischl
- Department of Statistics, Ludwig Maximilian Universität, 80539 Munich, Germany; Munich Center for Machine Learning (MCML), Ludwig Maximilian Universität, 80538 Munich, Germany
| | - Dirk H Busch
- Institute for Medical Microbiology, Immunology, and Hygiene, Technical University of Munich, 81675 Munich, Germany; German Center for Infection Research, Deutschen Zentrum für Infektionsforschung (DZIF), Partner Site Munich, 81675 Munich, Germany
| | - Kilian Schober
- Institute for Medical Microbiology, Immunology, and Hygiene, Technical University of Munich, 81675 Munich, Germany; Mikrobiologisches Institut-Klinische Mikrobiologie, Immunologie, und Hygiene, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany; Medical Immunology Campus Erlangen, Friedrich-Alexander-Universität (FAU) Erlangen-Nürnberg, 91054 Erlangen, Germany
| | - Benjamin Schubert
- Institute of Computational Biology, Helmholtz Center Munich, 85764 Neuherberg, Germany; School of Computation, Information, and Technology, Technical University of Munich, 85748 Garching bei München, Germany.
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6
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Mittl K, Hayashi F, Dandekar R, Schubert RD, Gerdts J, Oshiro L, Loudermilk R, Greenfield A, Augusto DG, Ramesh A, Tran E, Koshal K, Kizer K, Dreux J, Cagalingan A, Schustek F, Flood L, Moore T, Kirkemo LL, Cooper T, Harms M, Gomez R, Sibener L, Cree BAC, Hauser SL, Hollenbach JA, Gee M, Wilson MR, Zamvil SS, Sabatino JJ. Antigen-specificity of clonally-enriched CD8+ T cells in multiple sclerosis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.09.07.611010. [PMID: 39282370 PMCID: PMC11398516 DOI: 10.1101/2024.09.07.611010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 09/19/2024]
Abstract
CD8+ T cells are the dominant lymphocyte population in multiple sclerosis (MS) lesions where they are highly clonally expanded. The clonal identity, function, and antigen specificity of CD8+ T cells in MS are not well understood. Here we report a comprehensive single-cell RNA-seq and T cell receptor (TCR)-seq analysis of the cerebrospinal fluid (CSF) and blood from a cohort of treatment-naïve MS patients and control participants. A small subset of highly expanded and activated CSF-enriched CD8+ T cells were abundant in people with MS and displayed high cytotoxicity and tissue-homing transcriptional profiles. Using a combination of unbiased and targeted antigen discovery approaches, several MS-derived CD8+ T cell clonotypes recognizing Epstein-Barr virus (EBV) antigens and novel mimotopes were identified. These findings shed insight into the functions of CD8+ T cells in MS and may serve as potential disease biomarkers and therapeutic targets.
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Affiliation(s)
- Kristen Mittl
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Fumie Hayashi
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Ravi Dandekar
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Ryan D Schubert
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Josiah Gerdts
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Lindsay Oshiro
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Rita Loudermilk
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Ariele Greenfield
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Danillo G Augusto
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
- Department of Biological Sciences, The University of North Carolina at Charlotte, NC, USA
| | - Akshaya Ramesh
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Edwina Tran
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Kaniskha Koshal
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Kerry Kizer
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | | | | | | | | | | | | | - Tiffany Cooper
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Meagan Harms
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Refujia Gomez
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | | | - Bruce A C Cree
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Stephen L Hauser
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Jill A Hollenbach
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA USA
| | | | - Michael R Wilson
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Scott S Zamvil
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
| | - Joseph J Sabatino
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, USA
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7
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Kim HY, Kim S, Park WY, Kim D. TSpred: a robust prediction framework for TCR-epitope interactions using paired chain TCR sequence data. Bioinformatics 2024; 40:btae472. [PMID: 39052940 PMCID: PMC11297499 DOI: 10.1093/bioinformatics/btae472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 06/11/2024] [Accepted: 07/25/2024] [Indexed: 07/27/2024] Open
Abstract
MOTIVATION Prediction of T-cell receptor (TCR)-epitope interactions is important for many applications in biomedical research, such as cancer immunotherapy and vaccine design. The prediction of TCR-epitope interactions remains challenging especially for novel epitopes, due to the scarcity of available data. RESULTS We propose TSpred, a new deep learning approach for the pan-specific prediction of TCR binding specificity based on paired chain TCR data. We develop a robust model that generalizes well to unseen epitopes by combining the predictive power of CNN and the attention mechanism. In particular, we design a reciprocal attention mechanism which focuses on extracting the patterns underlying TCR-epitope interactions. Upon a comprehensive evaluation of our model, we find that TSpred achieves state-of-the-art performances in both seen and unseen epitope specificity prediction tasks. Also, compared to other predictors, TSpred is more robust to bias related to peptide imbalance in the dataset. In addition, the reciprocal attention component of our model allows for model interpretability by capturing structurally important binding regions. Results indicate that TSpred is a robust and reliable method for the task of TCR-epitope binding prediction. AVAILABILITY AND IMPLEMENTATION Source code is available at https://github.com/ha01994/TSpred.
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Affiliation(s)
- Ha Young Kim
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, South Korea
| | | | - Woong-Yang Park
- GENINUS Inc., Seoul 05836, South Korea
- Samsung Genome Institute, Samsung Medical Center, Seoul 06351, South Korea
- Department of Molecular Cell Biology, Sungkyunkwan University School of Medicine, Suwon 16419, South Korea
| | - Dongsup Kim
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, South Korea
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8
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Pertseva M, Follonier O, Scarcella D, Reddy ST. TCR clustering by contrastive learning on antigen specificity. Brief Bioinform 2024; 25:bbae375. [PMID: 39129361 PMCID: PMC11317525 DOI: 10.1093/bib/bbae375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Revised: 07/09/2024] [Accepted: 07/25/2024] [Indexed: 08/13/2024] Open
Abstract
Effective clustering of T-cell receptor (TCR) sequences could be used to predict their antigen-specificities. TCRs with highly dissimilar sequences can bind to the same antigen, thus making their clustering into a common antigen group a central challenge. Here, we develop TouCAN, a method that relies on contrastive learning and pretrained protein language models to perform TCR sequence clustering and antigen-specificity predictions. Following training, TouCAN demonstrates the ability to cluster highly dissimilar TCRs into common antigen groups. Additionally, TouCAN demonstrates TCR clustering performance and antigen-specificity predictions comparable to other leading methods in the field.
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Affiliation(s)
- Margarita Pertseva
- Department of Biosystems Science and Engineering, ETH Zurich, Schanzenstrasse 44, 4056 Basel, Switzerland
- Life Science Zurich Graduate School, ETH Zurich and University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
| | - Oceane Follonier
- Department of Biosystems Science and Engineering, ETH Zurich, Schanzenstrasse 44, 4056 Basel, Switzerland
| | - Daniele Scarcella
- Department of Biosystems Science and Engineering, ETH Zurich, Schanzenstrasse 44, 4056 Basel, Switzerland
| | - Sai T Reddy
- Department of Biosystems Science and Engineering, ETH Zurich, Schanzenstrasse 44, 4056 Basel, Switzerland
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9
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Ham B, Kim SY, Kim YA, Han D, Park T, Cha S, Jung S, Kim JH, Park G, Gong G, Lee HJ, Shin J. Persistence and enrichment of dominant T cell clonotypes in expanded tumor-infiltrating lymphocytes of breast cancer. Br J Cancer 2024; 131:196-204. [PMID: 38750113 PMCID: PMC11231331 DOI: 10.1038/s41416-024-02707-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 04/25/2024] [Accepted: 04/29/2024] [Indexed: 07/10/2024] Open
Abstract
BACKGROUND Adoptive cell therapy using tumor-infiltrating lymphocytes (TILs) has shown promising results in cancer treatment, including breast cancer. However, clonal dynamics and clinical significance of TIL expansion ex vivo remain poorly understood. METHODS We investigated T cell receptor (TCR) repertoire changes in expanded TILs from 19 patients with breast cancer. We compared TCR repertoire of TILs at different stages of expansion, including initial (2W TILs) and rapid expansion (REP TILs), and their overlap with formalin fixed paraffin embedded (FFPE) and peripheral blood. Additionally, we examined differences in TCR repertoire between CD4+ and CD8+ REP TILs. RESULTS In descending order of proportion, average of 60% of the top 10% clonotypes of FFPE was retained in 2W TIL (60% in TRB, 64.7% in TRA). Among the overlapped clonotypes between 2W TILs and REP TILs, 69.9% was placed in top 30% of 2W TIL. The proportion of clonotypes in 2W TIL and REP TIL showed a significant positive correlation. CD4+ and CD8+ T cells show similar results in diversity and CDR3 length. CONCLUSIONS Our study traces the changes in TILs repertoire from FFPE to 2W TIL and REP TIL and confirmed that clonotypes with high frequencies in TILs have a high likelihood of maintaining their priority throughout culture process.
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Affiliation(s)
- Baknoon Ham
- NeogenTC Corp., Seoul, 05505, Republic of Korea
| | - Su Yeon Kim
- University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea
| | | | - DoYeon Han
- Department of Medical Science, AMIST, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea
| | - Taehyun Park
- Department of Medical Science, AMIST, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea
| | - Sumin Cha
- Department of Medical Science, AMIST, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea
| | - SungWook Jung
- Department of Medical Science, AMIST, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea
| | - Jong Hyeok Kim
- Department of Medical Science, AMIST, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea
| | - Gisung Park
- Department of Medical Science, AMIST, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea
| | - Gyungyub Gong
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea.
| | - Hee Jin Lee
- NeogenTC Corp., Seoul, 05505, Republic of Korea.
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea.
| | - Junyoung Shin
- Department of Pathology, National Cancer Center, Ilsan, 10408, Republic of Korea.
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10
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Karnaukhov VK, Shcherbinin DS, Chugunov AO, Chudakov DM, Efremov RG, Zvyagin IV, Shugay M. Structure-based prediction of T cell receptor recognition of unseen epitopes using TCRen. NATURE COMPUTATIONAL SCIENCE 2024; 4:510-521. [PMID: 38987378 DOI: 10.1038/s43588-024-00653-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Accepted: 06/04/2024] [Indexed: 07/12/2024]
Abstract
T cell receptor (TCR) recognition of foreign peptides presented by major histocompatibility complex protein is a major event in triggering the adaptive immune response to pathogens or cancer. The prediction of TCR-peptide interactions has great importance for therapy of cancer as well as infectious and autoimmune diseases but remains a major challenge, particularly for novel (unseen) peptide epitopes. Here we present TCRen, a structure-based method for ranking candidate unseen epitopes for a given TCR. The first stage of the TCRen pipeline is modeling of the TCR-peptide-major histocompatibility complex structure. Then a TCR-peptide residue contact map is extracted from this structure and used to rank all candidate epitopes on the basis of an interaction score with the target TCR. Scoring is performed using an energy potential derived from the statistics of TCR-peptide contact preferences in existing crystal structures. We show that TCRen has high performance in discriminating cognate versus unrelated peptides and can facilitate the identification of cancer neoepitopes recognized by tumor-infiltrating lymphocytes.
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MESH Headings
- Receptors, Antigen, T-Cell/immunology
- Receptors, Antigen, T-Cell/chemistry
- Receptors, Antigen, T-Cell/metabolism
- Humans
- Peptides/immunology
- Peptides/chemistry
- Epitopes/immunology
- Epitopes/chemistry
- Models, Molecular
- Neoplasms/immunology
- Epitopes, T-Lymphocyte/immunology
- Epitopes, T-Lymphocyte/chemistry
- Major Histocompatibility Complex/immunology
- Protein Conformation
- Lymphocytes, Tumor-Infiltrating/immunology
- Lymphocytes, Tumor-Infiltrating/metabolism
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Affiliation(s)
- Vadim K Karnaukhov
- Center of Life Sciences, Skolkovo Institute of Science and Technology, Moscow, Russia.
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia.
| | - Dmitrii S Shcherbinin
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia
- Institute of Translational Medicine, Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Pirogov Russian National Research Medical University, Moscow, Russia
| | - Anton O Chugunov
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia
- Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny, Russia
| | - Dmitriy M Chudakov
- Center of Life Sciences, Skolkovo Institute of Science and Technology, Moscow, Russia.
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia.
- Institute of Translational Medicine, Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Pirogov Russian National Research Medical University, Moscow, Russia.
- Central European Institute of Technology, Brno, Czech Republic.
| | - Roman G Efremov
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia
- Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, Dolgoprudny, Russia
- Higher School of Economics, Moscow, Russia
| | - Ivan V Zvyagin
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia
- Institute of Translational Medicine, Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Pirogov Russian National Research Medical University, Moscow, Russia
| | - Mikhail Shugay
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia.
- Institute of Translational Medicine, Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Pirogov Russian National Research Medical University, Moscow, Russia.
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11
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Tian G, Song S, Zhi Y, Qiu W, Chen Y, Sun X, Huang H, Yu Y, Jiao W, Li M, Lv G. Alloreactive T cells temporarily increased in the peripheral blood of patients before liver allograft rejection. Liver Transpl 2024:01445473-990000000-00407. [PMID: 38900031 DOI: 10.1097/lvt.0000000000000425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Accepted: 06/03/2024] [Indexed: 06/21/2024]
Abstract
T cells are key mediators of alloresponse during liver transplantation (LTx). However, the dynamics of donor-reactive T-cell clones in peripheral blood during a clinical T-cell-mediated rejection (TCMR) episode remain unknown. Here, we collected serial peripheral blood mononuclear cell samples spanning from pre-LTx to 1 year after LTx and available biopsies during the TCMR episodes from 26 rejecting patients, and serial peripheral blood mononuclear cell samples were collected from 96 nonrejectors. Immunophenotypic and repertoire analyses were integrated on T cells from rejectors, and they were longitudinally compared to nonrejected patients. Donor-reactive T-cell clone was identified and tracked by cross-matching with the mappable donor-reactive T-cell receptor repertoire of each donor-recipient pair in 9 rejectors and 5 nonrejectors. Before transplantation, the naive T-cell percentage and T-cell receptor repertoire diversity of rejectors was comparable to that of healthy control, but it was reduced in nonrejectors. After transplantation, the naïve T-cell percentages decreased, and T-cell receptor repertoires were skewed in rejectors; the phenomenon was not observed in nonrejectors. Alloreactive clones increased in proportion in the peripheral blood of rejectors before TCMR for weeks. The increase was accompanied by the naïve T-cell decline and memory T-cell increase and acquired an activated phenotype. Intragraft alloreactive clone tracking in pre-LTx and post-LTx peripheral blood mononuclear cell samples revealed that the pretransplant naïve T cells were significant contributors to the donor-reactive clones, and they temporarily increased in proportion and subsequently reduced in blood at the beginning of TCMR. Together, our findings offer an insight into the dynamic and origin of alloreactive T cells in clinical LTx TCMR cases and may facilitate disease prediction and management.
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Affiliation(s)
- Guangyao Tian
- Department of Hepatobiliary and Pancreatic Surgery, General Surgery Center, The First Hospital of Jilin University, Changchun, Jilin, China
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12
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Meynard-Piganeau B, Feinauer C, Weigt M, Walczak AM, Mora T. TULIP: A transformer-based unsupervised language model for interacting peptides and T cell receptors that generalizes to unseen epitopes. Proc Natl Acad Sci U S A 2024; 121:e2316401121. [PMID: 38838016 PMCID: PMC11181096 DOI: 10.1073/pnas.2316401121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 04/29/2024] [Indexed: 06/07/2024] Open
Abstract
The accurate prediction of binding between T cell receptors (TCR) and their cognate epitopes is key to understanding the adaptive immune response and developing immunotherapies. Current methods face two significant limitations: the shortage of comprehensive high-quality data and the bias introduced by the selection of the negative training data commonly used in the supervised learning approaches. We propose a method, Transformer-based Unsupervised Language model for Interacting Peptides and T cell receptors (TULIP), that addresses both limitations by leveraging incomplete data and unsupervised learning and using the transformer architecture of language models. Our model is flexible and integrates all possible data sources, regardless of their quality or completeness. We demonstrate the existence of a bias introduced by the sampling procedure used in previous supervised approaches, emphasizing the need for an unsupervised approach. TULIP recognizes the specific TCRs binding an epitope, performing well on unseen epitopes. Our model outperforms state-of-the-art models and offers a promising direction for the development of more accurate TCR epitope recognition models.
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Affiliation(s)
- Barthelemy Meynard-Piganeau
- Laboratory of Computational and Quantitative Biology, Institut de Biologie Paris Seine, CNRS, Sorbonne Université, Paris75005, France
- Department of Computing Sciences, Bocconi University, Milan20100, Italy
| | | | - Martin Weigt
- Laboratory of Computational and Quantitative Biology, Institut de Biologie Paris Seine, CNRS, Sorbonne Université, Paris75005, France
| | - Aleksandra M. Walczak
- Laboratoire de Physique de l’Ecole Normale Supérieure, Université Paris Sciences et Lettres, CNRS, Sorbonne Université, Université de Paris Cité, Paris75005, France
| | - Thierry Mora
- Laboratoire de Physique de l’Ecole Normale Supérieure, Université Paris Sciences et Lettres, CNRS, Sorbonne Université, Université de Paris Cité, Paris75005, France
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13
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Yu Z, Jiang M, Lan X. HeteroTCR: A heterogeneous graph neural network-based method for predicting peptide-TCR interaction. Commun Biol 2024; 7:684. [PMID: 38834836 DOI: 10.1038/s42003-024-06380-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 05/23/2024] [Indexed: 06/06/2024] Open
Abstract
Identifying interactions between T-cell receptors (TCRs) and immunogenic peptides holds profound implications across diverse research domains and clinical scenarios. Unsupervised clustering models (UCMs) cannot predict peptide-TCR binding directly, while supervised predictive models (SPMs) often face challenges in identifying antigens previously unencountered by the immune system or possessing limited TCR binding repertoires. Therefore, we propose HeteroTCR, an SPM based on Heterogeneous Graph Neural Network (GNN), to accurately predict peptide-TCR binding probabilities. HeteroTCR captures within-type (TCR-TCR or peptide-peptide) similarity information and between-type (peptide-TCR) interaction insights for predictions on unseen peptides and TCRs, surpassing limitations of existing SPMs. Our evaluation shows HeteroTCR outperforms state-of-the-art models on independent datasets. Ablation studies and visual interpretation underscore the Heterogeneous GNN module's critical role in enhancing HeteroTCR's performance by capturing pivotal binding process features. We further demonstrate the robustness and reliability of HeteroTCR through validation using single-cell datasets, aligning with the expectation that pMHC-TCR complexes with higher predicted binding probabilities correspond to increased binding fractions.
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Affiliation(s)
- Zilan Yu
- School of Medicine, Tsinghua University, 100084, Beijing, China
- Centre for Life Sciences, Tsinghua University, 100084, Beijing, China
| | - Mengnan Jiang
- School of Medicine, Tsinghua University, 100084, Beijing, China
| | - Xun Lan
- School of Medicine, Tsinghua University, 100084, Beijing, China.
- Centre for Life Sciences, Tsinghua University, 100084, Beijing, China.
- Tsinghua-Peking Center for Life Sciences, MOE Key Laboratory of Tsinghua University, Beijing, China.
- MOE Key Laboratory of Bioinformatics, Tsinghua University, 100084, Beijing, China.
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14
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Machaca V, Goyzueta V, Cruz MG, Sejje E, Pilco LM, López J, Túpac Y. Transformers meets neoantigen detection: a systematic literature review. J Integr Bioinform 2024; 21:jib-2023-0043. [PMID: 38960869 PMCID: PMC11377031 DOI: 10.1515/jib-2023-0043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 03/20/2024] [Indexed: 07/05/2024] Open
Abstract
Cancer immunology offers a new alternative to traditional cancer treatments, such as radiotherapy and chemotherapy. One notable alternative is the development of personalized vaccines based on cancer neoantigens. Moreover, Transformers are considered a revolutionary development in artificial intelligence with a significant impact on natural language processing (NLP) tasks and have been utilized in proteomics studies in recent years. In this context, we conducted a systematic literature review to investigate how Transformers are applied in each stage of the neoantigen detection process. Additionally, we mapped current pipelines and examined the results of clinical trials involving cancer vaccines.
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Affiliation(s)
| | | | | | - Erika Sejje
- Universidad Nacional de San Agustín, Arequipa, Perú
| | | | | | - Yván Túpac
- 187038 Universidad Católica San Pablo , Arequipa, Perú
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15
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Delmonte OM, Oguz C, Dobbs K, Myint-Hpu K, Palterer B, Abers MS, Draper D, Truong M, Kaplan IM, Gittelman RM, Zhang Y, Rosen LB, Snow AL, Dalgard CL, Burbelo PD, Imberti L, Sottini A, Quiros-Roldan E, Castelli F, Rossi C, Brugnoni D, Biondi A, Bettini LR, D'Angio M, Bonfanti P, Anderson MV, Saracino A, Chironna M, Di Stefano M, Fiore JR, Santantonio T, Castagnoli R, Marseglia GL, Magliocco M, Bosticardo M, Pala F, Shaw E, Matthews H, Weber SE, Xirasagar S, Barnett J, Oler AJ, Dimitrova D, Bergerson JRE, McDermott DH, Rao VK, Murphy PM, Holland SM, Lisco A, Su HC, Lionakis MS, Cohen JI, Freeman AF, Snyder TM, Lack J, Notarangelo LD. Perturbations of the T-cell receptor repertoire in response to SARS-CoV-2 in immunocompetent and immunocompromised individuals. J Allergy Clin Immunol 2024; 153:1655-1667. [PMID: 38154666 PMCID: PMC11162338 DOI: 10.1016/j.jaci.2023.12.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 12/13/2023] [Accepted: 12/19/2023] [Indexed: 12/30/2023]
Abstract
BACKGROUND Functional T-cell responses are essential for virus clearance and long-term protection after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, whereas certain clinical factors, such as older age and immunocompromise, are associated with worse outcome. OBJECTIVE We sought to study the breadth and magnitude of T-cell responses in patients with coronavirus disease 2019 (COVID-19) and in individuals with inborn errors of immunity (IEIs) who had received COVID-19 mRNA vaccine. METHODS Using high-throughput sequencing and bioinformatics tools to characterize the T-cell receptor β repertoire signatures in 540 individuals after SARS-CoV-2 infection, 31 IEI recipients of COVID-19 mRNA vaccine, and healthy controls, we quantified HLA class I- and class II-restricted SARS-CoV-2-specific responses and also identified several HLA allele-clonotype motif associations in patients with COVID-19, including a subcohort of anti-type 1 interferon (IFN-1)-positive patients. RESULTS Our analysis revealed that elderly patients with COVID-19 with critical disease manifested lower SARS-CoV-2 T-cell clonotype diversity as well as T-cell responses with reduced magnitude, whereas the SARS-CoV-2-specific clonotypes targeted a broad range of HLA class I- and class II-restricted epitopes across the viral proteome. The presence of anti-IFN-I antibodies was associated with certain HLA alleles. Finally, COVID-19 mRNA immunization induced an increase in the breadth of SARS-CoV-2-specific clonotypes in patients with IEIs, including those who had failed to seroconvert. CONCLUSIONS Elderly individuals have impaired capacity to develop broad and sustained T-cell responses after SARS-CoV-2 infection. Genetic factors may play a role in the production of anti-IFN-1 antibodies. COVID-19 mRNA vaccines are effective in inducing T-cell responses in patients with IEIs.
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Affiliation(s)
- Ottavia M Delmonte
- Laboratory of Clinical Immunology and Microbiology National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md.
| | - Cihan Oguz
- Integrated Data Sciences Section, Research Technology Branch, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - Kerry Dobbs
- Laboratory of Clinical Immunology and Microbiology National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - Katherine Myint-Hpu
- Laboratory of Clinical Immunology and Microbiology National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - Boaz Palterer
- Laboratory of Clinical Immunology and Microbiology National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - Michael S Abers
- Laboratory of Clinical Immunology and Microbiology National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - Deborah Draper
- Laboratory of Clinical Immunology and Microbiology National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - Meng Truong
- Laboratory of Clinical Immunology and Microbiology National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | | | | | - Yu Zhang
- Laboratory of Clinical Immunology and Microbiology National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - Lindsey B Rosen
- Laboratory of Clinical Immunology and Microbiology National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - Andrew L Snow
- Laboratory of Clinical Immunology and Microbiology National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md; Department of Pharmacology & Molecular Therapeutics, Uniformed Services University of the Health Sciences, Bethesda, Md
| | - Clifton L Dalgard
- Department of Anatomy, Physiology & Genetics, Uniformed Services University of the Health Sciences, Bethesda, Md; The American Genome Center, Uniformed Services University of the Health Sciences, Bethesda, Md
| | - Peter D Burbelo
- Adeno-Associated Virus Biology Section, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, Md
| | - Luisa Imberti
- Section of Microbiology, University of Brescia, ASST Spedali Civili, Brescia, Italy
| | - Alessandra Sottini
- Section of Microbiology, University of Brescia, ASST Spedali Civili, Brescia, Italy
| | - Eugenia Quiros-Roldan
- Department of Infectious and Tropical Diseases, University of Brescia, ASST Spedali Civili, Brescia, Italy
| | - Francesco Castelli
- Department of Infectious and Tropical Diseases, University of Brescia, ASST Spedali Civili, Brescia, Italy
| | - Camillo Rossi
- Direzione Sanitaria, ASST Spedali Civili, Brescia, Italy
| | - Duilio Brugnoni
- Laboratorio Analisi Chimico-Cliniche, ASST Spedali Civili, Brescia, Italy
| | - Andrea Biondi
- Pediatric Department and Centro Tettamanti-European Reference Network on Paediatric Cancer, European Reference Network on Haematological Diseases, and European Reference Network on Hereditary Metabolic Disorders, University of Milano-Bicocca-Fondazione MBBM, Monza, Italy
| | - Laura Rachele Bettini
- Pediatric Department and Centro Tettamanti-European Reference Network on Paediatric Cancer, European Reference Network on Haematological Diseases, and European Reference Network on Hereditary Metabolic Disorders, University of Milano-Bicocca-Fondazione MBBM, Monza, Italy
| | - Mariella D'Angio
- Pediatric Department and Centro Tettamanti-European Reference Network on Paediatric Cancer, European Reference Network on Haematological Diseases, and European Reference Network on Hereditary Metabolic Disorders, University of Milano-Bicocca-Fondazione MBBM, Monza, Italy
| | - Paolo Bonfanti
- Department of Infectious Diseases, San Gerardo Hospital, University of Milano-Bicocca, Monza, Italy
| | - Megan V Anderson
- Laboratory of Immunoregulation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - Annalisa Saracino
- Clinic of Infectious Diseases, Azienda Ospedaliero-Universitaria Consorziale Policlinico di Bari, University of Bari, Bari, Italy
| | - Maria Chironna
- Hygiene Section, Department of Interdisciplinary Medicine, University of Bari Aldo Moro, Bari, Italy
| | - Mariantonietta Di Stefano
- Department of Medical and Surgical Sciences, Section of Infectious Diseases, University of Foggia, Foggia, Italy
| | - Jose Ramon Fiore
- Department of Medical and Surgical Sciences, Section of Infectious Diseases, University of Foggia, Foggia, Italy
| | - Teresa Santantonio
- Department of Medical and Surgical Sciences, Section of Infectious Diseases, University of Foggia, Foggia, Italy
| | - Riccardo Castagnoli
- Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy; Pediatric Clinic, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Gian Luigi Marseglia
- Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy; Pediatric Clinic, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Mary Magliocco
- Molecular Development of the Immune System Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - Marita Bosticardo
- Laboratory of Clinical Immunology and Microbiology National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - Francesca Pala
- Laboratory of Clinical Immunology and Microbiology National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - Elana Shaw
- Laboratory of Clinical Immunology and Microbiology National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - Helen Matthews
- Molecular Development of the Immune System Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - Sarah E Weber
- Molecular Development of the Immune System Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - Sandhya Xirasagar
- Bioinformatics and Computational Biosciences Branch, Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - Jason Barnett
- Bioinformatics and Computational Biosciences Branch, Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - Andrew J Oler
- Bioinformatics and Computational Biosciences Branch, Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - Dimana Dimitrova
- Center for Immuno-Oncology, National Cancer Institute, National Institutes of Health, Bethesda, Md
| | - Jenna R E Bergerson
- Laboratory of Clinical Immunology and Microbiology National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - David H McDermott
- Laboratory of Molecular Immunology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - V Koneti Rao
- Laboratory of Clinical Immunology and Microbiology National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - Philip M Murphy
- Laboratory of Molecular Immunology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - Steven M Holland
- Laboratory of Clinical Immunology and Microbiology National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - Andrea Lisco
- Laboratory of Immunoregulation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - Helen C Su
- Laboratory of Clinical Immunology and Microbiology National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - Michail S Lionakis
- Laboratory of Clinical Immunology and Microbiology National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - Jeffrey I Cohen
- Laboratory of Infectious Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - Alexandra F Freeman
- Laboratory of Clinical Immunology and Microbiology National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | | | - Justin Lack
- Integrated Data Sciences Section, Research Technology Branch, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - Luigi D Notarangelo
- Laboratory of Clinical Immunology and Microbiology National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md.
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16
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Bulashevska A, Nacsa Z, Lang F, Braun M, Machyna M, Diken M, Childs L, König R. Artificial intelligence and neoantigens: paving the path for precision cancer immunotherapy. Front Immunol 2024; 15:1394003. [PMID: 38868767 PMCID: PMC11167095 DOI: 10.3389/fimmu.2024.1394003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 05/13/2024] [Indexed: 06/14/2024] Open
Abstract
Cancer immunotherapy has witnessed rapid advancement in recent years, with a particular focus on neoantigens as promising targets for personalized treatments. The convergence of immunogenomics, bioinformatics, and artificial intelligence (AI) has propelled the development of innovative neoantigen discovery tools and pipelines. These tools have revolutionized our ability to identify tumor-specific antigens, providing the foundation for precision cancer immunotherapy. AI-driven algorithms can process extensive amounts of data, identify patterns, and make predictions that were once challenging to achieve. However, the integration of AI comes with its own set of challenges, leaving space for further research. With particular focus on the computational approaches, in this article we have explored the current landscape of neoantigen prediction, the fundamental concepts behind, the challenges and their potential solutions providing a comprehensive overview of this rapidly evolving field.
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Affiliation(s)
- Alla Bulashevska
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Zsófia Nacsa
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Franziska Lang
- TRON - Translational Oncology at the University Medical Center of the Johannes Gutenberg University gGmbH, Mainz, Germany
| | - Markus Braun
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Martin Machyna
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Mustafa Diken
- TRON - Translational Oncology at the University Medical Center of the Johannes Gutenberg University gGmbH, Mainz, Germany
| | - Liam Childs
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Renate König
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
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17
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Leary AY, Scott D, Gupta NT, Waite JC, Skokos D, Atwal GS, Hawkins PG. Designing meaningful continuous representations of T cell receptor sequences with deep generative models. Nat Commun 2024; 15:4271. [PMID: 38769289 PMCID: PMC11106309 DOI: 10.1038/s41467-024-48198-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Accepted: 04/24/2024] [Indexed: 05/22/2024] Open
Abstract
T Cell Receptor (TCR) antigen binding underlies a key mechanism of the adaptive immune response yet the vast diversity of TCRs and the complexity of protein interactions limits our ability to build useful low dimensional representations of TCRs. To address the current limitations in TCR analysis we develop a capacity-controlled disentangling variational autoencoder trained using a dataset of approximately 100 million TCR sequences, that we name TCR-VALID. We design TCR-VALID such that the model representations are low-dimensional, continuous, disentangled, and sufficiently informative to provide high-quality TCR sequence de novo generation. We thoroughly quantify these properties of the representations, providing a framework for future protein representation learning in low dimensions. The continuity of TCR-VALID representations allows fast and accurate TCR clustering and is benchmarked against other state-of-the-art TCR clustering tools and pre-trained language models.
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Affiliation(s)
- Allen Y Leary
- Regeneron Pharmaceuticals Inc., 777 Old Saw Mill River Road, Tarrytown, NY, 10591, USA.
| | - Darius Scott
- Regeneron Pharmaceuticals Inc., 777 Old Saw Mill River Road, Tarrytown, NY, 10591, USA
| | - Namita T Gupta
- Regeneron Pharmaceuticals Inc., 777 Old Saw Mill River Road, Tarrytown, NY, 10591, USA
| | - Janelle C Waite
- Regeneron Pharmaceuticals Inc., 777 Old Saw Mill River Road, Tarrytown, NY, 10591, USA
| | - Dimitris Skokos
- Regeneron Pharmaceuticals Inc., 777 Old Saw Mill River Road, Tarrytown, NY, 10591, USA
| | - Gurinder S Atwal
- Regeneron Pharmaceuticals Inc., 777 Old Saw Mill River Road, Tarrytown, NY, 10591, USA
| | - Peter G Hawkins
- Regeneron Pharmaceuticals Inc., 777 Old Saw Mill River Road, Tarrytown, NY, 10591, USA.
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18
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Mehta P, Sanz-Magallón Duque de Estrada B, Denneny EK, Foster K, Turner CT, Mayer A, Milighetti M, Platé M, Worlock KB, Yoshida M, Brown JS, Nikolić MZ, Chain BM, Noursadeghi M, Chambers RC, Porter JC, Tomlinson GS. Single-cell analysis of bronchoalveolar cells in inflammatory and fibrotic post-COVID lung disease. Front Immunol 2024; 15:1372658. [PMID: 38827740 PMCID: PMC11140060 DOI: 10.3389/fimmu.2024.1372658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Accepted: 05/07/2024] [Indexed: 06/04/2024] Open
Abstract
Background Persistent radiological lung abnormalities are evident in many survivors of acute coronavirus disease 2019 (COVID-19). Consolidation and ground glass opacities are interpreted to indicate subacute inflammation whereas reticulation is thought to reflect fibrosis. We sought to identify differences at molecular and cellular level, in the local immunopathology of post-COVID inflammation and fibrosis. Methods We compared single-cell transcriptomic profiles and T cell receptor (TCR) repertoires of bronchoalveolar cells obtained from convalescent individuals with each radiological pattern, targeting lung segments affected by the predominant abnormality. Results CD4 central memory T cells and CD8 effector memory T cells were significantly more abundant in those with inflammatory radiology. Clustering of similar TCRs from multiple donors was a striking feature of both phenotypes, consistent with tissue localised antigen-specific immune responses. There was no enrichment for known SARS-CoV-2-reactive TCRs, raising the possibility of T cell-mediated immunopathology driven by failure in immune self-tolerance. Conclusions Post-COVID radiological inflammation and fibrosis show evidence of shared antigen-specific T cell responses, suggesting a role for therapies targeting T cells in limiting post-COVID lung damage.
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Affiliation(s)
- Puja Mehta
- UCL Respiratory, University College London, London, United Kingdom
| | | | - Emma K. Denneny
- UCL Respiratory, University College London, London, United Kingdom
| | - Kane Foster
- UCL Cancer Institute, University College London, London, United Kingdom
| | - Carolin T. Turner
- Division of Infection and Immunity, University College London, London, United Kingdom
| | - Andreas Mayer
- Division of Infection and Immunity, University College London, London, United Kingdom
| | - Martina Milighetti
- Division of Infection and Immunity, University College London, London, United Kingdom
| | - Manuela Platé
- UCL Respiratory, University College London, London, United Kingdom
| | | | - Masahiro Yoshida
- UCL Respiratory, University College London, London, United Kingdom
| | - Jeremy S. Brown
- UCL Respiratory, University College London, London, United Kingdom
| | - Marko Z. Nikolić
- UCL Respiratory, University College London, London, United Kingdom
| | - Benjamin M. Chain
- Division of Infection and Immunity, University College London, London, United Kingdom
| | - Mahdad Noursadeghi
- Division of Infection and Immunity, University College London, London, United Kingdom
| | | | - Joanna C. Porter
- UCL Respiratory, University College London, London, United Kingdom
| | - Gillian S. Tomlinson
- Division of Infection and Immunity, University College London, London, United Kingdom
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19
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Jiang M, Yu Z, Lan X. VitTCR: A deep learning method for peptide recognition prediction. iScience 2024; 27:109770. [PMID: 38711451 PMCID: PMC11070698 DOI: 10.1016/j.isci.2024.109770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 01/21/2024] [Accepted: 04/15/2024] [Indexed: 05/08/2024] Open
Abstract
This study introduces VitTCR, a predictive model based on the vision transformer (ViT) architecture, aimed at identifying interactions between T cell receptors (TCRs) and peptides, crucial for developing cancer immunotherapies and vaccines. VitTCR converts TCR-peptide interactions into numerical AtchleyMaps using Atchley factors for prediction, achieving AUROC (0.6485) and AUPR (0.6295) values. Benchmark analysis indicates VitTCR's performance is comparable to other models, with further comparative studies suggested to understand its effectiveness in varied contexts. Additionally, integrating a positional bias weight matrix (PBWM), derived from amino acid contact probabilities in structurally resolved pMHC-TCR complexes, slightly improves VitTCR's accuracy. The model's predictions show weak yet statistically significant correlations with immunological factors like T cell clonal expansion and activation percentages, underscoring the biological relevance of VitTCR's predictive capabilities. VitTCR emerges as a valuable computational tool for predicting TCR-peptide interactions, offering insights for immunotherapy and vaccine development.
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Affiliation(s)
- Mengnan Jiang
- School of Medicine, Tsinghua University, Beijing 100084, China
| | - Zilan Yu
- School of Medicine, Tsinghua University, Beijing 100084, China
- Centre for Life Sciences, Tsinghua University, Beijing 100084, China
| | - Xun Lan
- School of Medicine, Tsinghua University, Beijing 100084, China
- Centre for Life Sciences, Tsinghua University, Beijing 100084, China
- Tsinghua-Peking Center for Life Sciences, MOE Key Laboratory of Tsinghua University, Beijing, China
- MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing 100084, China
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20
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Gupta T, Antanaviciute A, Hyun-Jung Lee C, Ottakandathil Babu R, Aulicino A, Christoforidou Z, Siejka-Zielinska P, O'Brien-Ball C, Chen H, Fawkner-Corbett D, Geros AS, Bridges E, McGregor C, Cianci N, Fryer E, Alham NK, Jagielowicz M, Santos AM, Fellermeyer M, Davis SJ, Parikh K, Cheung V, Al-Hillawi L, Sasson S, Slevin S, Brain O, Fernandes RA, Koohy H, Simmons A. Tracking in situ checkpoint inhibitor-bound target T cells in patients with checkpoint-induced colitis. Cancer Cell 2024; 42:797-814.e15. [PMID: 38744246 DOI: 10.1016/j.ccell.2024.04.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 02/09/2024] [Accepted: 04/17/2024] [Indexed: 05/16/2024]
Abstract
The success of checkpoint inhibitors (CPIs) for cancer has been tempered by immune-related adverse effects including colitis. CPI-induced colitis is hallmarked by expansion of resident mucosal IFNγ cytotoxic CD8+ T cells, but how these arise is unclear. Here, we track CPI-bound T cells in intestinal tissue using multimodal single-cell and subcellular spatial transcriptomics (ST). Target occupancy was increased in inflamed tissue, with drug-bound T cells located in distinct microdomains distinguished by specific intercellular signaling and transcriptional gradients. CPI-bound cells were largely CD4+ T cells, including enrichment in CPI-bound peripheral helper, follicular helper, and regulatory T cells. IFNγ CD8+ T cells emerged from both tissue-resident memory (TRM) and peripheral populations, displayed more restricted target occupancy profiles, and co-localized with damaged epithelial microdomains lacking effective regulatory cues. Our multimodal analysis identifies causal pathways and constitutes a resource to inform novel preventive strategies.
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Affiliation(s)
- Tarun Gupta
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK; Translational Gastroenterology Unit, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Agne Antanaviciute
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK; MRC WIMM Centre For Computational Biology, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK.
| | - Chloe Hyun-Jung Lee
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK; MRC WIMM Centre For Computational Biology, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
| | - Rosana Ottakandathil Babu
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK; MRC WIMM Centre For Computational Biology, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
| | - Anna Aulicino
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
| | - Zoe Christoforidou
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
| | - Paulina Siejka-Zielinska
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
| | - Caitlin O'Brien-Ball
- Chinese Academy of Medical Sciences (CAMS) Oxford Institute (COI), University of Oxford, Oxford OX3 7BN, UK
| | - Hannah Chen
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
| | - David Fawkner-Corbett
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK; Academic Paediatric Surgery Unit (APSU), Nuffield Department of Surgical Sciences, University of Oxford, Oxford OX3 9DU, UK
| | - Ana Sousa Geros
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
| | - Esther Bridges
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
| | - Colleen McGregor
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK; Translational Gastroenterology Unit, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Nicole Cianci
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK; Translational Gastroenterology Unit, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Eve Fryer
- Pathology, Department of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
| | - Nasullah Khalid Alham
- Nuffield Department of Surgical Sciences and Oxford NIHR Biomedical Research Centre (BRC), University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Marta Jagielowicz
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
| | - Ana Mafalda Santos
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK; Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DU, UK
| | - Martin Fellermeyer
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK; Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DU, UK
| | - Simon J Davis
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK; Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DU, UK
| | - Kaushal Parikh
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK
| | - Vincent Cheung
- Translational Gastroenterology Unit, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Lulia Al-Hillawi
- Translational Gastroenterology Unit, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Sarah Sasson
- Translational Gastroenterology Unit, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Stephanie Slevin
- Translational Gastroenterology Unit, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Oliver Brain
- Translational Gastroenterology Unit, John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Ricardo A Fernandes
- Chinese Academy of Medical Sciences (CAMS) Oxford Institute (COI), University of Oxford, Oxford OX3 7BN, UK
| | - Hashem Koohy
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK; MRC WIMM Centre For Computational Biology, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK.
| | - Alison Simmons
- Medical Research Council (MRC) Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), Radcliffe Department of Medicine, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK; Translational Gastroenterology Unit, John Radcliffe Hospital, Oxford OX3 9DU, UK.
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21
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Gao Y, Dong K, Gao Y, Jin X, Yang J, Yan G, Liu Q. Unified cross-modality integration and analysis of T cell receptors and T cell transcriptomes by low-resource-aware representation learning. CELL GENOMICS 2024; 4:100553. [PMID: 38688285 PMCID: PMC11099349 DOI: 10.1016/j.xgen.2024.100553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 03/09/2024] [Accepted: 04/06/2024] [Indexed: 05/02/2024]
Abstract
Single-cell RNA sequencing (scRNA-seq) and T cell receptor sequencing (TCR-seq) are pivotal for investigating T cell heterogeneity. Integrating these modalities, which is expected to uncover profound insights in immunology that might otherwise go unnoticed with a single modality, faces computational challenges due to the low-resource characteristics of the multimodal data. Herein, we present UniTCR, a novel low-resource-aware multimodal representation learning framework designed for the unified cross-modality integration, enabling comprehensive T cell analysis. By designing a dual-modality contrastive learning module and a single-modality preservation module to effectively embed each modality into a common latent space, UniTCR demonstrates versatility in connecting TCR sequences with T cell transcriptomes across various tasks, including single-modality analysis, modality gap analysis, epitope-TCR binding prediction, and TCR profile cross-modality generation, in a low-resource-aware way. Extensive evaluations conducted on multiple scRNA-seq/TCR-seq paired datasets showed the superior performance of UniTCR, exhibiting the ability of exploring the complexity of immune system.
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Affiliation(s)
- Yicheng Gao
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Tongji Hospital, School of Medicine, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China; State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Kejing Dong
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Tongji Hospital, School of Medicine, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China; State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Yuli Gao
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Tongji Hospital, School of Medicine, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China; State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Xuan Jin
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Tongji Hospital, School of Medicine, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China; State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Jingya Yang
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai 201804, China
| | - Gang Yan
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai 201804, China.
| | - Qi Liu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Tongji Hospital, School of Medicine, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China; State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China; Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai 201804, China; Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou 311121, China.
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22
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Croce G, Bobisse S, Moreno DL, Schmidt J, Guillame P, Harari A, Gfeller D. Deep learning predictions of TCR-epitope interactions reveal epitope-specific chains in dual alpha T cells. Nat Commun 2024; 15:3211. [PMID: 38615042 PMCID: PMC11016097 DOI: 10.1038/s41467-024-47461-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 04/03/2024] [Indexed: 04/15/2024] Open
Abstract
T cells have the ability to eliminate infected and cancer cells and play an essential role in cancer immunotherapy. T cell activation is elicited by the binding of the T cell receptor (TCR) to epitopes displayed on MHC molecules, and the TCR specificity is determined by the sequence of its α and β chains. Here, we collect and curate a dataset of 17,715 αβTCRs interacting with dozens of class I and class II epitopes. We use this curated data to develop MixTCRpred, an epitope-specific TCR-epitope interaction predictor. MixTCRpred accurately predicts TCRs recognizing several viral and cancer epitopes. MixTCRpred further provides a useful quality control tool for multiplexed single-cell TCR sequencing assays of epitope-specific T cells and pinpoints a substantial fraction of putative contaminants in public databases. Analysis of epitope-specific dual α T cells demonstrates that MixTCRpred can identify α chains mediating epitope recognition. Applying MixTCRpred to TCR repertoires from COVID-19 patients reveals enrichment of clonotypes predicted to bind an immunodominant SARS-CoV-2 epitope. Overall, MixTCRpred provides a robust tool to predict TCRs interacting with specific epitopes and interpret TCR-sequencing data from both bulk and epitope-specific T cells.
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Affiliation(s)
- Giancarlo Croce
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
- Agora Cancer Research Centre, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
| | - Sara Bobisse
- Agora Cancer Research Centre, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University Hospital of Lausanne, Lausanne, Switzerland
| | - Dana Léa Moreno
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
- Agora Cancer Research Centre, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
| | - Julien Schmidt
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University Hospital of Lausanne, Lausanne, Switzerland
| | - Philippe Guillame
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University Hospital of Lausanne, Lausanne, Switzerland
| | - Alexandre Harari
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Agora Cancer Research Centre, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University Hospital of Lausanne, Lausanne, Switzerland
| | - David Gfeller
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland.
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland.
- Agora Cancer Research Centre, Lausanne, Switzerland.
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland.
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23
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Shao W, Yao Y, Yang L, Li X, Ge T, Zheng Y, Zhu Q, Ge S, Gu X, Jia R, Song X, Zhuang A. Novel insights into TCR-T cell therapy in solid neoplasms: optimizing adoptive immunotherapy. Exp Hematol Oncol 2024; 13:37. [PMID: 38570883 PMCID: PMC10988985 DOI: 10.1186/s40164-024-00504-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 03/21/2024] [Indexed: 04/05/2024] Open
Abstract
Adoptive immunotherapy in the T cell landscape exhibits efficacy in cancer treatment. Over the past few decades, genetically modified T cells, particularly chimeric antigen receptor T cells, have enabled remarkable strides in the treatment of hematological malignancies. Besides, extensive exploration of multiple antigens for the treatment of solid tumors has led to clinical interest in the potential of T cells expressing the engineered T cell receptor (TCR). TCR-T cells possess the capacity to recognize intracellular antigen families and maintain the intrinsic properties of TCRs in terms of affinity to target epitopes and signal transduction. Recent research has provided critical insight into their capability and therapeutic targets for multiple refractory solid tumors, but also exposes some challenges for durable efficacy. In this review, we describe the screening and identification of available tumor antigens, and the acquisition and optimization of TCRs for TCR-T cell therapy. Furthermore, we summarize the complete flow from laboratory to clinical applications of TCR-T cells. Last, we emerge future prospects for improving therapeutic efficacy in cancer world with combination therapies or TCR-T derived products. In conclusion, this review depicts our current understanding of TCR-T cell therapy in solid neoplasms, and provides new perspectives for expanding its clinical applications and improving therapeutic efficacy.
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Affiliation(s)
- Weihuan Shao
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China
| | - Yiran Yao
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China
| | - Ludi Yang
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China
| | - Xiaoran Li
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China
| | - Tongxin Ge
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China
| | - Yue Zheng
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China
| | - Qiuyi Zhu
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China
| | - Shengfang Ge
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China
| | - Xiang Gu
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China
| | - Renbing Jia
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China.
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China.
| | - Xin Song
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China.
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China.
| | - Ai Zhuang
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China.
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China.
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24
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He S, Liu SQ, Teng XY, He JY, Liu Y, Gao JH, Wu Y, Hu W, Dong ZJ, Bei JX, Xu JH. Comparative single-cell RNA sequencing analysis of immune response to inactivated vaccine and natural SARS-CoV-2 infection. J Med Virol 2024; 96:e29577. [PMID: 38572977 DOI: 10.1002/jmv.29577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 03/02/2024] [Accepted: 03/22/2024] [Indexed: 04/05/2024]
Abstract
Uncovering the immune response to an inactivated SARS-CoV-2 vaccine (In-Vac) and natural infection is crucial for comprehending COVID-19 immunology. Here we conducted an integrated analysis of single-cell RNA sequencing (scRNA-seq) data from serial peripheral blood mononuclear cell (PBMC) samples derived from 12 individuals receiving In-Vac compared with those from COVID-19 patients. Our study reveals that In-Vac induces subtle immunological changes in PBMC, including cell proportions and transcriptomes, compared with profound changes for natural infection. In-Vac modestly upregulates IFN-α but downregulates NF-κB pathways, while natural infection triggers hyperactive IFN-α and NF-κB pathways. Both In-Vac and natural infection alter T/B cell receptor repertoires, but COVID-19 has more significant change in preferential VJ gene, indicating a vigorous immune response. Our study reveals distinct patterns of cellular communications, including a selective activation of IL-15RA/IL-15 receptor pathway after In-Vac boost, suggesting its potential role in enhancing In-Vac-induced immunity. Collectively, our study illuminates multifaceted immune responses to In-Vac and natural infection, providing insights for optimizing SARS-CoV-2 vaccine efficacy.
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Affiliation(s)
- Shuai He
- Medical Laboratory Center, Shunde Hospital of Guangzhou University of Chinese Medicine, Foshan, China
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Shu-Qiang Liu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Xiang-Yun Teng
- Medical Laboratory Center, Maoming Hospital of Guangzhou University of Chinese Medicine, Maoming, China
| | - Jin-Yong He
- Medical Laboratory Center, Shunde Hospital of Guangzhou University of Chinese Medicine, Foshan, China
| | - Yang Liu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Jia-Hui Gao
- Medical Laboratory Center, Shunde Hospital of Guangzhou University of Chinese Medicine, Foshan, China
| | - Yue Wu
- Medical Laboratory Center, Shunde Hospital of Guangzhou University of Chinese Medicine, Foshan, China
| | - Wei Hu
- Medical Laboratory Center, Shunde Hospital of Guangzhou University of Chinese Medicine, Foshan, China
| | - Zhong-Jun Dong
- School of Medicine and Institute for Immunology, Tsinghua University, Beijing, China
| | - Jin-Xin Bei
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Jian-Hua Xu
- Medical Laboratory Center, Shunde Hospital of Guangzhou University of Chinese Medicine, Foshan, China
- Medical Laboratory Center, Maoming Hospital of Guangzhou University of Chinese Medicine, Maoming, China
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25
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Ye X, Shih DJH, Ku Z, Hong J, Barrett DF, Rupp RE, Zhang N, Fu TM, Zheng WJ, An Z. Transcriptional signature of durable effector T cells elicited by a replication defective HCMV vaccine. NPJ Vaccines 2024; 9:70. [PMID: 38561339 PMCID: PMC10984989 DOI: 10.1038/s41541-024-00860-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 02/26/2024] [Indexed: 04/04/2024] Open
Abstract
Human cytomegalovirus (HCMV) is a leading infectious cause of birth defects and the most common opportunistic infection that causes life-threatening diseases post-transplantation; however, an effective vaccine remains elusive. V160 is a live-attenuated replication defective HCMV vaccine that showed a 42.4% efficacy against primary HCMV infection among seronegative women in a phase 2b clinical trial. Here, we integrated the multicolor flow cytometry, longitudinal T cell receptor (TCR) sequencing, and single-cell RNA/TCR sequencing approaches to characterize the magnitude, phenotype, and functional quality of human T cell responses to V160. We demonstrated that V160 de novo induces IE-1 and pp65 specific durable polyfunctional effector CD8 T cells that are comparable to those induced by natural HCMV infection. We identified a variety of V160-responsive T cell clones which exhibit distinctive "transient" and "durable" expansion kinetics, and revealed a transcriptional signature that marks durable CD8 T cells post-vaccination. Our study enhances the understanding of human T-cell immune responses to V160 vaccination.
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Affiliation(s)
- Xiaohua Ye
- Texas Therapeutics Institute, Brown Foundation Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, TX, USA
- Center for Infectious Disease Research, School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
| | - David J H Shih
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA
- School of Biomedical Sciences, The University of Hong Kong, Pokfulam, Hong Kong SAR
| | - Zhiqiang Ku
- Texas Therapeutics Institute, Brown Foundation Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Junping Hong
- Texas Therapeutics Institute, Brown Foundation Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Diane F Barrett
- Sealy Institute for Vaccine Sciences, University of Texas Medical Branch, Galveston, TX, USA
| | - Richard E Rupp
- Sealy Institute for Vaccine Sciences, University of Texas Medical Branch, Galveston, TX, USA
| | - Ningyan Zhang
- Texas Therapeutics Institute, Brown Foundation Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Tong-Ming Fu
- Texas Therapeutics Institute, Brown Foundation Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - W Jim Zheng
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA.
| | - Zhiqiang An
- Texas Therapeutics Institute, Brown Foundation Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, TX, USA.
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26
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Izosimova AV, Shabalkina AV, Myshkin MY, Shurganova EV, Myalik DS, Ryzhichenko EO, Samitova AF, Barsova EV, Shagina IA, Britanova OV, Yuzhakova DV, Sharonov GV. Local Enrichment with Convergence of Enriched T-Cell Clones Are Hallmarks of Effective Peptide Vaccination against B16 Melanoma. Vaccines (Basel) 2024; 12:345. [PMID: 38675728 DOI: 10.3390/vaccines12040345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 03/06/2024] [Accepted: 03/08/2024] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND Some peptide anticancer vaccines elicit a strong T-cell memory response but fail to suppress tumor growth. To gain insight into tumor resistance, we compared two peptide vaccines, p20 and p30, against B16 melanoma, with both exhibiting good in vitro T-cell responses but different tumor suppression abilities. METHODS We compared activation markers and repertoires of T-lymphocytes from tumor-draining (dLN) and non-draining (ndLN) lymph nodes for the two peptide vaccines. RESULTS We showed that the p30 vaccine had better tumor control as opposed to p20. p20 vaccine induced better in vitro T-cell responsiveness but failed to suppress tumor growth. Efficient antitumor vaccination is associated with a higher clonality of cytotoxic T-cells (CTLs) in dLNs compared with ndLNs and the convergence of most of the enriched clones. With the inefficient p20 vaccine, the most expanded and converged were clones of the bystander T-cells without an LN preference. CONCLUSIONS Here, we show that the clonality and convergence of the T-cell response are the hallmarks of efficient antitumor vaccination. The high individual and methodological dependencies of these parameters can be avoided by comparing dLNs and ndLNs.
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Affiliation(s)
- Anna Vyacheslavovna Izosimova
- Research Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, Nizhny Novgorod 603950, Russia
| | - Alexandra Valerievna Shabalkina
- Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow 117997, Russia
- Department of Genomics of Adaptive Immunity, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry RAS, Moscow 117997, Russia
| | - Mikhail Yurevich Myshkin
- Department of Genomics of Adaptive Immunity, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry RAS, Moscow 117997, Russia
| | - Elizaveta Viktorovna Shurganova
- Research Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, Nizhny Novgorod 603950, Russia
| | - Daria Sergeevna Myalik
- Research Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, Nizhny Novgorod 603950, Russia
- Pathoanatomical Department, Nizhny Novgorod Regional Clinical Cancer Hospital, Nizhny Novgorod 603126, Russia
| | | | - Alina Faritovna Samitova
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Pirogov Russian National Research Medical University, Moscow 117997, Russia
| | - Ekaterina Vladimirovna Barsova
- Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow 117997, Russia
- Department of Genomics of Adaptive Immunity, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry RAS, Moscow 117997, Russia
| | - Irina Aleksandrovna Shagina
- Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow 117997, Russia
- Department of Genomics of Adaptive Immunity, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry RAS, Moscow 117997, Russia
| | - Olga Vladimirovna Britanova
- Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow 117997, Russia
- Department of Genomics of Adaptive Immunity, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry RAS, Moscow 117997, Russia
| | - Diana Vladimirovna Yuzhakova
- Research Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, Nizhny Novgorod 603950, Russia
| | - George Vladimirovich Sharonov
- Research Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, Nizhny Novgorod 603950, Russia
- Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow 117997, Russia
- Department of Genomics of Adaptive Immunity, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry RAS, Moscow 117997, Russia
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27
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Jensen MF, Nielsen M. Enhancing TCR specificity predictions by combined pan- and peptide-specific training, loss-scaling, and sequence similarity integration. eLife 2024; 12:RP93934. [PMID: 38437160 PMCID: PMC10942633 DOI: 10.7554/elife.93934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2024] Open
Abstract
Predicting the interaction between Major Histocompatibility Complex (MHC) class I-presented peptides and T-cell receptors (TCR) holds significant implications for vaccine development, cancer treatment, and autoimmune disease therapies. However, limited paired-chain TCR data, skewed towards well-studied epitopes, hampers the development of pan-specific machine-learning (ML) models. Leveraging a larger peptide-TCR dataset, we explore various alterations to the ML architectures and training strategies to address data imbalance. This leads to an overall improved performance, particularly for peptides with scant TCR data. However, challenges persist for unseen peptides, especially those distant from training examples. We demonstrate that such ML models can be used to detect potential outliers, which when removed from training, leads to augmented performance. Integrating pan-specific and peptide-specific models alongside with similarity-based predictions, further improves the overall performance, especially when a low false positive rate is desirable. In the context of the IMMREP22 benchmark, this modeling framework attained state-of-the-art performance. Moreover, combining these strategies results in acceptable predictive accuracy for peptides characterized with as little as 15 positive TCRs. This observation places great promise on rapidly expanding the peptide covering of the current models for predicting TCR specificity. The NetTCR 2.2 model incorporating these advances is available on GitHub (https://github.com/mnielLab/NetTCR-2.2) and as a web server at https://services.healthtech.dtu.dk/services/NetTCR-2.2/.
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Affiliation(s)
- Mathias Fynbo Jensen
- Department of Health Technology, Section for Bioinformatics, Technical University of DenmarkLyngbyDenmark
| | - Morten Nielsen
- Department of Health Technology, Section for Bioinformatics, Technical University of DenmarkLyngbyDenmark
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28
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Hudson D, Lubbock A, Basham M, Koohy H. A comparison of clustering models for inference of T cell receptor antigen specificity. IMMUNOINFORMATICS (AMSTERDAM, NETHERLANDS) 2024; 13:None. [PMID: 38525047 PMCID: PMC10955519 DOI: 10.1016/j.immuno.2024.100033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 01/18/2024] [Accepted: 01/23/2024] [Indexed: 03/26/2024]
Abstract
The vast potential sequence diversity of TCRs and their ligands has presented an historic barrier to computational prediction of TCR epitope specificity, a holy grail of quantitative immunology. One common approach is to cluster sequences together, on the assumption that similar receptors bind similar epitopes. Here, we provide the first independent evaluation of widely used clustering algorithms for TCR specificity inference, observing some variability in predictive performance between models, and marked differences in scalability. Despite these differences, we find that different algorithms produce clusters with high degrees of similarity for receptors recognising the same epitope. Our analysis strengthens the case for use of clustering models to identify signals of common specificity from large repertoires, whilst highlighting scope for improvement of complex models over simple comparators.
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Affiliation(s)
- Dan Hudson
- MRC Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
- The Rosalind Franklin Institute, Didcot, UK
| | | | | | - Hashem Koohy
- MRC Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
- Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
- Alan Turning Fellow in Health and Medicine, UK
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29
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Hafler D, Lu B, Lucca L, Lewis W, Wang J, Nogeuira C, Heer S, Axisa PP, Buitrago-Pocasangre N, Pham G, Kojima M, Wei W, Aizenbud L, Bacchiocchi A, Zhang L, Walewski J, Chiang V, Olino K, Clune J, Halaban R, Kluger Y, Coyle A, Kisielow J, Obermair FJ, Kluger H. Circulating Tumor Reactive KIR+CD8+ T cells Suppress Anti-Tumor Immunity in Patients with Melanoma. RESEARCH SQUARE 2024:rs.3.rs-3956671. [PMID: 38464315 PMCID: PMC10925449 DOI: 10.21203/rs.3.rs-3956671/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Effective anti-tumor immunity is largely driven by cytotoxic CD8+ T cells that can specifically recognize tumor antigens. However, the factors which ultimately dictate successful tumor rejection remain poorly understood. Here we identify a subpopulation of CD8+ T cells which are tumor antigen-specific in patients with melanoma but resemble KIR+CD8+ T cells with a regulatory function (Tregs). These tumor antigen-specific KIR+CD8+ T cells are detectable in both the tumor and the blood, and higher levels of this population are associated with worse overall survival. Our findings therefore suggest that KIR+CD8+ Tregs are tumor antigen-specific but uniquely suppress anti-tumor immunity in patients with melanoma.
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30
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Maurer K, Park CY, Mani S, Borji M, Penter L, Jin Y, Zhang JY, Shin C, Brenner JR, Southard J, Krishna S, Lu W, Lyu H, Abbondanza D, Mangum C, Olsen LR, Neuberg DS, Bachireddy P, Farhi SL, Li S, Livak KJ, Ritz J, Soiffer RJ, Wu CJ, Azizi E. Coordinated Immune Cell Networks in the Bone Marrow Microenvironment Define the Graft versus Leukemia Response with Adoptive Cellular Therapy. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.09.579677. [PMID: 38405900 PMCID: PMC10888840 DOI: 10.1101/2024.02.09.579677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Understanding how intra-tumoral immune populations coordinate to generate anti-tumor responses following therapy can guide precise treatment prioritization. We performed systematic dissection of an established adoptive cellular therapy, donor lymphocyte infusion (DLI), by analyzing 348,905 single-cell transcriptomes from 74 longitudinal bone-marrow samples of 25 patients with relapsed myeloid leukemia; a subset was evaluated by protein-based spatial analysis. In acute myelogenous leukemia (AML) responders, diverse immune cell types within the bone-marrow microenvironment (BME) were predicted to interact with a clonally expanded population of ZNF683 + GZMB + CD8+ cytotoxic T lymphocytes (CTLs) which demonstrated in vitro specificity for autologous leukemia. This population, originating predominantly from the DLI product, expanded concurrently with NK and B cells. AML nonresponder BME revealed a paucity of crosstalk and elevated TIGIT expression in CD8+ CTLs. Our study highlights recipient BME differences as a key determinant of effective anti-leukemia response and opens new opportunities to modulate cell-based leukemia-directed therapy.
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31
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Ravishankar S, Towlerton AM, Mooka P, Kafeero J, Coffey DG, Aicher LD, Mubiru KR, Okoche L, Atwinirembabazi P, Okonye J, Phipps WT, Warren EH. The signature of a T-cell response to KSHV persists across space and time in individuals with epidemic and endemic KS from Uganda. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.06.579223. [PMID: 38370623 PMCID: PMC10871354 DOI: 10.1101/2024.02.06.579223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Inadequate T-cell control of Kaposi sarcoma-associated herpesvirus (KSHV) infection predisposes to development of Kaposi sarcoma (KS), but little is known about the T-cell response to KSHV. Postulating that KS tumors contain abundant KSHV-specific T-cells, we performed transcriptional profiling and T-cell receptor (TCR) repertoire analysis of tumor biopsies from 144 Ugandan adults with KS. We show that CD8+ T-cells and M2-polarized macrophages dominate the tumor micro-environment (TME). The TCR repertoire of KS tumor infiltrating lymphocytes (TIL) is shared across non-contiguous tumors and persists across time. Clusters of T-cells with predicted shared specificity for uncharacterized antigens, potentially encoded by KSHV, comprise ~25% of KS TIL, and are shared across tumors from different time points and individuals. Single-cell RNA-sequencing of blood identifies a non-proliferating effector memory phenotype and captured the TCRs in 14,698 putative KSHV-specific T-cells. These results suggest that a polyspecific KSHV-specific T-cell response inhibited by M2 macrophages exists within the KS TME, and provide a foundation for studies to define its specificity at a large scale.
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Affiliation(s)
- Shashidhar Ravishankar
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
| | - Andrea M.H. Towlerton
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
- Hutchinson Centre Research Institute – Uganda, Kampala, Uganda
| | - Peter Mooka
- Hutchinson Centre Research Institute – Uganda, Kampala, Uganda
| | - James Kafeero
- Hutchinson Centre Research Institute – Uganda, Kampala, Uganda
| | - David G. Coffey
- Division of Myeloma, Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL, United States
| | - Lauri D. Aicher
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
| | | | - Lazarus Okoche
- Hutchinson Centre Research Institute – Uganda, Kampala, Uganda
| | | | - Joseph Okonye
- Hutchinson Centre Research Institute – Uganda, Kampala, Uganda
| | - Warren T. Phipps
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
- Department of Medicine, University of Washington, Seattle, WA, United States
| | - Edus H. Warren
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, United States
- Department of Medicine, University of Washington, Seattle, WA, United States
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, United States
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32
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Banerjee A, Pattinson DJ, Wincek CL, Bunk P, Chapin SR, Navlakha S, Meyer HV. BATMAN: Improved T cell receptor cross-reactivity prediction benchmarked on a comprehensive mutational scan database. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.22.576714. [PMID: 38370810 PMCID: PMC10871174 DOI: 10.1101/2024.01.22.576714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Predicting T cell receptor (TCR) activation is challenging due to the lack of both unbiased benchmarking datasets and computational methods that are sensitive to small mutations to a peptide. To address these challenges, we curated a comprehensive database encompassing complete single amino acid mutational assays of 10,750 TCR-peptide pairs, centered around 14 immunogenic peptides against 66 TCRs. We then present an interpretable Bayesian model, called BATMAN, that can predict the set of peptides that activates a TCR. When validated on our database, BATMAN outperforms existing methods by 20% and reveals important biochemical predictors of TCR-peptide interactions.
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Affiliation(s)
- Amitava Banerjee
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - David J Pattinson
- Department of Pathobiological Sciences, School of Veterinary Medicine, University of Wisconsin-Madison, Madison, WI 53711, USA
| | - Cornelia L. Wincek
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
- Heidelberg University, 69117 Heidelberg, Germany
| | - Paul Bunk
- School of Biological Sciences, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Sarah R. Chapin
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Saket Navlakha
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Hannah V. Meyer
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
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33
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Rocco JM, Boswell KL, Laidlaw E, Epling B, Anderson M, Serebryannyy L, Narpala S, O'Connell S, Kalish H, Kelly S, Porche S, Oguz C, McDermott A, Manion M, Koup RA, Lisco A, Sereti I. Immune responses to SARS-CoV-2 mRNA vaccination in people with idiopathic CD4 lymphopenia. J Allergy Clin Immunol 2024; 153:503-512. [PMID: 38344971 PMCID: PMC10861932 DOI: 10.1016/j.jaci.2023.10.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 10/02/2023] [Accepted: 10/10/2023] [Indexed: 02/15/2024]
Abstract
BACKGROUND The immunogenicity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mRNA vaccines is variable in individuals with different inborn errors of immunity or acquired immune deficiencies and is yet unknown in people with idiopathic CD4 lymphopenia (ICL). OBJECTIVE We sought to determine the immunogenicity of mRNA vaccines in patients with ICL with a broad range of CD4 T-cell counts. METHODS Samples were collected from 25 patients with ICL and 23 age- and sex-matched healthy volunteers (HVs) after their second or third SARS-CoV-2 mRNA vaccine dose. Anti-spike and anti-receptor binding domain antibodies were measured. T-cell receptor sequencing and stimulation assays were performed to quantify SARS-CoV-2-specific T-cell responses. RESULTS The median age of ICL participants was 51 years, and their median CD4 count was 150 cells/μL; 11 participants had CD4 counts ≤100 cells/μL. Anti-spike IgG antibody levels were greater in HVs than in patients with ICL after 2 and 3 doses of mRNA vaccine. There was no detectable significant difference, however, in anti-S IgG between HVs and participants with ICL and CD4 counts >100 cells/μL. The depth of spike-specific T-cell responses by T-cell receptor sequencing was lower in individuals with ICL. Activation-induced markers and cytokine production of spike-specific CD4 T cells in participants with ICL did not differ significantly compared with HVs after 2 or 3 vaccine doses. CONCLUSIONS Patients with ICL and CD4 counts >100 cells/μL can mount vigorous humoral and cellular immune responses to SARS-CoV-2 vaccination; however, patients with more severe CD4 lymphopenia have blunted vaccine-induced immunity and may require additional vaccine doses and other risk mitigation strategies.
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Affiliation(s)
- Joseph M Rocco
- Laboratory of Immunoregulation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - Kristin L Boswell
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - Elizabeth Laidlaw
- Laboratory of Immunoregulation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - Brian Epling
- Laboratory of Immunoregulation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - Megan Anderson
- Laboratory of Immunoregulation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - Leonid Serebryannyy
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - Sandeep Narpala
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - Sarah O'Connell
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - Heather Kalish
- Trans-NIH Shared Resource on Biomedical Engineering and Physical Science, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, Md
| | - Sophie Kelly
- Trans-NIH Shared Resource on Biomedical Engineering and Physical Science, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, Md
| | - Sarah Porche
- Trans-NIH Shared Resource on Biomedical Engineering and Physical Science, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, Md
| | - Cihan Oguz
- Integrated Data Sciences Section, Research Technologies Branch, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - Adrian McDermott
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - Maura Manion
- Laboratory of Immunoregulation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - Richard A Koup
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - Andrea Lisco
- Laboratory of Immunoregulation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md
| | - Irini Sereti
- Laboratory of Immunoregulation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Md.
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34
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Zhang J, Wang Y, Huang Y, Tan X, Xu J, Yan Q, Tan J, Zhang Y, Zhang J, Ma Q, Zhu H, Ye J, Zhu Z, Lan W. Characterization of T cell receptor repertoire in penile cancer. Cancer Immunol Immunother 2024; 73:24. [PMID: 38280010 PMCID: PMC10822009 DOI: 10.1007/s00262-023-03615-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 12/04/2023] [Indexed: 01/29/2024]
Abstract
Tumor-infiltrating lymphocytes (TILs) play a key role in regulating the host immune response and shaping tumor microenvironment. It has been previously shown that T cell infiltration in penile tumors was associated with clinical outcomes. However, few studies have reported the T cell receptor (TCR) repertoire in patients with penile cancer. In the present study, we evaluated the TCR repertoires in tumor and adjacent normal tissues from 22 patients with penile squamous cell carcinoma (PSCC). Analysis of the T cell receptor beta-variable (TRBV) and joining (TRBJ) genes usage and analysis of complementarity determining region 3 (CDR3) length distribution did not show significant differences between tumor and matched normal tissues. Moreover, analysis of the median Jaccard index indicated a limited overlap of TCR repertoire between these groups. Compared with normal tissues, a significantly lower diversity and higher clonality of TCR repertoire was observed in tumor samples, which was associated with clinical characteristics. Further analysis of transcriptional profiles demonstrated that tumor samples with high clonality showed increased expression of genes associated with CD8 + T cells. In addition, we analyzed the TCR repertoire of CD4 + T cells and CD8 + T cells isolated from tumor tissues. We identified that expanded clonotypes were predominantly in the CD8 + T cell compartment, which presented with an exhausted phenotype. Overall, we comprehensively compared TCR repertoire between penile tumor and normal tissues and demonstrated the presence of distinct T cell immune microenvironments in patients with PSCC.
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Affiliation(s)
- Junying Zhang
- Chongqing Key Laboratory of High Active Traditional Chinese Drug Delivery System, Chongqing Engineering Research Center of Pharmaceutical Sciences, Chongqing Medical and Pharmaceutical College, Chongqing, 401331, People's Republic of China
- College of Pharmacy, Chongqing Medical University, Chongqing, 400016, People's Republic of China
| | - Yapeng Wang
- Department of Urology, Daping Hospital, Army Medical University, Chongqing, 400042, People's Republic of China
| | - Yiqiang Huang
- Department of Urology, Daping Hospital, Army Medical University, Chongqing, 400042, People's Republic of China
| | - Xintao Tan
- Department of Urology, Daping Hospital, Army Medical University, Chongqing, 400042, People's Republic of China
| | - Jing Xu
- Department of Urology, Daping Hospital, Army Medical University, Chongqing, 400042, People's Republic of China
| | - Qian Yan
- Department of Urology, Daping Hospital, Army Medical University, Chongqing, 400042, People's Republic of China
| | - Jiao Tan
- School of Pharmacy, Chongqing Medical and Pharmaceutical College, Chongqing, 401331, People's Republic of China
| | - Yao Zhang
- Department of Urology, Daping Hospital, Army Medical University, Chongqing, 400042, People's Republic of China
| | - Jun Zhang
- Department of Urology, Daping Hospital, Army Medical University, Chongqing, 400042, People's Republic of China
| | - Qiang Ma
- Department of Urology, Daping Hospital, Army Medical University, Chongqing, 400042, People's Republic of China
| | - Hailin Zhu
- Department of Urology, Daping Hospital, Army Medical University, Chongqing, 400042, People's Republic of China
| | - Jin Ye
- Urinary Nephropathy Center, The Thirteenth People's Hospital of Chongqing, Chongqing, 400053, People's Republic of China.
| | - Zhaojing Zhu
- Chongqing Key Laboratory of High Active Traditional Chinese Drug Delivery System, Chongqing Engineering Research Center of Pharmaceutical Sciences, Chongqing Medical and Pharmaceutical College, Chongqing, 401331, People's Republic of China.
| | - Weihua Lan
- Department of Urology, Daping Hospital, Army Medical University, Chongqing, 400042, People's Republic of China.
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Li X, You J, Hong L, Liu W, Guo P, Hao X. Neoantigen cancer vaccines: a new star on the horizon. Cancer Biol Med 2023; 21:j.issn.2095-3941.2023.0395. [PMID: 38164734 PMCID: PMC11033713 DOI: 10.20892/j.issn.2095-3941.2023.0395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 11/22/2023] [Indexed: 01/03/2024] Open
Abstract
Immunotherapy represents a promising strategy for cancer treatment that utilizes immune cells or drugs to activate the patient's own immune system and eliminate cancer cells. One of the most exciting advances within this field is the targeting of neoantigens, which are peptides derived from non-synonymous somatic mutations that are found exclusively within cancer cells and absent in normal cells. Although neoantigen-based therapeutic vaccines have not received approval for standard cancer treatment, early clinical trials have yielded encouraging outcomes as standalone monotherapy or when combined with checkpoint inhibitors. Progress made in high-throughput sequencing and bioinformatics have greatly facilitated the precise and efficient identification of neoantigens. Consequently, personalized neoantigen-based vaccines tailored to each patient have been developed that are capable of eliciting a robust and long-lasting immune response which effectively eliminates tumors and prevents recurrences. This review provides a concise overview consolidating the latest clinical advances in neoantigen-based therapeutic vaccines, and also discusses challenges and future perspectives for this innovative approach, particularly emphasizing the potential of neoantigen-based therapeutic vaccines to enhance clinical efficacy against advanced solid tumors.
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Affiliation(s)
- Xiaoling Li
- Cell Biotechnology Laboratory, Tianjin Cancer Hospital Airport Hospital, Tianjin 300308, China
- National Clinical Research Center for Cancer, Tianjin 300060, China
- Haihe Laboratory of Synthetic Biology, Tianjin 300090, China
| | - Jian You
- Department of Thoracic Oncology, Tianjin Cancer Hospital Airport Hospital, Tianjin 300308, China
- Department of Thoracic Oncology Surgery, Tianjin Medical University Cancer Institute & Hospital, Tianjin 300060, China
| | - Liping Hong
- Cell Biotechnology Laboratory, Tianjin Cancer Hospital Airport Hospital, Tianjin 300308, China
- National Clinical Research Center for Cancer, Tianjin 300060, China
- Haihe Laboratory of Synthetic Biology, Tianjin 300090, China
| | - Weijiang Liu
- Cell Biotechnology Laboratory, Tianjin Cancer Hospital Airport Hospital, Tianjin 300308, China
- National Clinical Research Center for Cancer, Tianjin 300060, China
- Haihe Laboratory of Synthetic Biology, Tianjin 300090, China
| | - Peng Guo
- Cell Biotechnology Laboratory, Tianjin Cancer Hospital Airport Hospital, Tianjin 300308, China
- National Clinical Research Center for Cancer, Tianjin 300060, China
- Haihe Laboratory of Synthetic Biology, Tianjin 300090, China
| | - Xishan Hao
- Cell Biotechnology Laboratory, Tianjin Cancer Hospital Airport Hospital, Tianjin 300308, China
- National Clinical Research Center for Cancer, Tianjin 300060, China
- Haihe Laboratory of Synthetic Biology, Tianjin 300090, China
- Tianjin Medical University Cancer Institute & Hospital, Tianjin 300060, China
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36
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Sidiropoulos DN, Ho WJ, Jaffee EM, Kagohara LT, Fertig EJ. Systems immunology spanning tumors, lymph nodes, and periphery. CELL REPORTS METHODS 2023; 3:100670. [PMID: 38086385 PMCID: PMC10753389 DOI: 10.1016/j.crmeth.2023.100670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Revised: 10/20/2023] [Accepted: 11/17/2023] [Indexed: 12/21/2023]
Abstract
The immune system defines a complex network of tissues and cell types that orchestrate responses across the body in a dynamic manner. The local and systemic interactions between immune and cancer cells contribute to disease progression. Lymphocytes are activated in lymph nodes, traffic through the periphery, and impact cancer progression through their interactions with tumor cells. As a result, therapeutic response and resistance are mediated across tissues, and a comprehensive understanding of lymphocyte dynamics requires a systems-level approach. In this review, we highlight experimental and computational methods that can leverage the study of leukocyte trafficking through an immunomics lens and reveal how adaptive immunity shapes cancer.
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Affiliation(s)
- Dimitrios N Sidiropoulos
- Johns Hopkins University School of Medicine, Baltimore, MD, USA; Johns Hopkins Convergence Institute, Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD, USA; Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins Medicine, Baltimore, MD, USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins Medicine, Baltimore, MD, USA
| | - Won Jin Ho
- Johns Hopkins Convergence Institute, Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD, USA; Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins Medicine, Baltimore, MD, USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins Medicine, Baltimore, MD, USA
| | - Elizabeth M Jaffee
- Johns Hopkins Convergence Institute, Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD, USA; Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins Medicine, Baltimore, MD, USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins Medicine, Baltimore, MD, USA
| | - Luciane T Kagohara
- Johns Hopkins Convergence Institute, Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD, USA; Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins Medicine, Baltimore, MD, USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins Medicine, Baltimore, MD, USA.
| | - Elana J Fertig
- Johns Hopkins Convergence Institute, Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD, USA; Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins Medicine, Baltimore, MD, USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins Medicine, Baltimore, MD, USA; Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
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37
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Zhao M, Xu SX, Yang Y, Yuan M. GGNpTCR: A Generative Graph Structure Neural Network for Predicting Immunogenic Peptides for T-cell Immune Response. J Chem Inf Model 2023; 63:7557-7567. [PMID: 37990917 DOI: 10.1021/acs.jcim.3c01293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2023]
Abstract
Identifying the interactions between T-cell receptor (TCRs) and human antigens is a crucial step in developing new vaccines, diagnostics, and immunotherapy. Current methods primarily focus on learning binding patterns from known TCR binding repertoires by using sequence information alone without considering the binding specificity of new antigens or exogenous peptides that have not appeared in the training set. Furthermore, the spatial structure of antigens plays a critical role in immune studies and immunotherapy, which should be addressed properly in the identification of interacting TCR-antigen pairs. In this study, we introduced a novel deep learning framework based on generative graph structures, GGNpTCR, for predicting interactions between TCR and peptides from sequence information. Results of real data analysis indicate that our model achieved excellent prediction for new antigens unseen in the training data set, making significant improvements compared to existing methods. We also applied the model to a large COVID-19 data set with no antigens in the training data set, and the improvement was also significant. Furthermore, through incorporation of additional supervised mechanisms, GGNpTCR demonstrated the ability to precisely forecast the locations of peptide-TCR interactions within 3D configurations. This enhancement substantially improved the model's interpretability. In summary, based on the performance on multiple data sets, GGNpTCR has made significant progress in terms of performance, universality, and interpretability.
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Affiliation(s)
- Minghua Zhao
- Department of Statistics and Finance, University of Science and Technology of China, Hefei 230026, China
| | - Steven X Xu
- Genmab US, Inc., Princeton, New Jersey 08540, United States
| | - Yaning Yang
- Department of Statistics and Finance, University of Science and Technology of China, Hefei 230026, China
| | - Min Yuan
- School of Public Health Administration, Anhui Medical University, Hefei 230032, China
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38
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Korpela D, Jokinen E, Dumitrescu A, Huuhtanen J, Mustjoki S, Lähdesmäki H. EPIC-TRACE: predicting TCR binding to unseen epitopes using attention and contextualized embeddings. Bioinformatics 2023; 39:btad743. [PMID: 38070156 PMCID: PMC10963061 DOI: 10.1093/bioinformatics/btad743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 11/20/2023] [Accepted: 12/07/2023] [Indexed: 12/21/2023] Open
Abstract
MOTIVATION T cells play an essential role in adaptive immune system to fight pathogens and cancer but may also give rise to autoimmune diseases. The recognition of a peptide-MHC (pMHC) complex by a T cell receptor (TCR) is required to elicit an immune response. Many machine learning models have been developed to predict the binding, but generalizing predictions to pMHCs outside the training data remains challenging. RESULTS We have developed a new machine learning model that utilizes information about the TCR from both α and β chains, epitope sequence, and MHC. Our method uses ProtBERT embeddings for the amino acid sequences of both chains and the epitope, as well as convolution and multi-head attention architectures. We show the importance of each input feature as well as the benefit of including epitopes with only a few TCRs to the training data. We evaluate our model on existing databases and show that it compares favorably against other state-of-the-art models. AVAILABILITY AND IMPLEMENTATION https://github.com/DaniTheOrange/EPIC-TRACE.
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Affiliation(s)
- Dani Korpela
- Department of Computer Science, Aalto University, 02150 Espoo, Finland
| | - Emmi Jokinen
- Department of Computer Science, Aalto University, 02150 Espoo, Finland
- Translational Immunology Research Program, Department of Clinical Chemistry and Hematology, University of Helsinki, 00290 Helsinki, Finland
- Hematology Research Unit Helsinki, Helsinki University Hospital Comprehensive Cancer Center, 00290 Helsinki, Finland
| | | | - Jani Huuhtanen
- Translational Immunology Research Program, Department of Clinical Chemistry and Hematology, University of Helsinki, 00290 Helsinki, Finland
- Hematology Research Unit Helsinki, Helsinki University Hospital Comprehensive Cancer Center, 00290 Helsinki, Finland
| | - Satu Mustjoki
- Translational Immunology Research Program, Department of Clinical Chemistry and Hematology, University of Helsinki, 00290 Helsinki, Finland
- Hematology Research Unit Helsinki, Helsinki University Hospital Comprehensive Cancer Center, 00290 Helsinki, Finland
- iCAN Digital Precision Cancer Medicine Flagship, Helsinki, Finland
| | - Harri Lähdesmäki
- Department of Computer Science, Aalto University, 02150 Espoo, Finland
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Louie RHY, Cai C, Samir J, Singh M, Deveson IW, Ferguson JM, Amos TG, McGuire HM, Gowrishankar K, Adikari T, Balderas R, Bonomi M, Ruella M, Bishop D, Gottlieb D, Blyth E, Micklethwaite K, Luciani F. CAR + and CAR - T cells share a differentiation trajectory into an NK-like subset after CD19 CAR T cell infusion in patients with B cell malignancies. Nat Commun 2023; 14:7767. [PMID: 38012187 PMCID: PMC10682404 DOI: 10.1038/s41467-023-43656-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 11/15/2023] [Indexed: 11/29/2023] Open
Abstract
Chimeric antigen receptor (CAR) T cell therapy is effective in treating B cell malignancies, but factors influencing the persistence of functional CAR+ T cells, such as product composition, patients' lymphodepletion, and immune reconstitution, are not well understood. To shed light on this issue, here we conduct a single-cell multi-omics analysis of transcriptional, clonal, and phenotypic profiles from pre- to 1-month post-infusion of CAR+ and CAR- T cells from patients from a CARTELL study (ACTRN12617001579381) who received a donor-derived 4-1BB CAR product targeting CD19. Following infusion, CAR+ T cells and CAR- T cells shows similar differentiation profiles with clonally expanded populations across heterogeneous phenotypes, demonstrating clonal lineages and phenotypic plasticity. We validate these findings in 31 patients with large B cell lymphoma treated with CD19 CAR T therapy. For these patients, we identify using longitudinal mass-cytometry data an association between NK-like subsets and clinical outcomes at 6 months with both CAR+ and CAR- T cells. These results suggest that non-CAR-derived signals can provide information about patients' immune recovery and be used as correlate of clinically relevant parameters.
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Affiliation(s)
- Raymond Hall Yip Louie
- School of Computer Science and Engineering, UNSW Sydney, Sydney, NSW, Australia
- Kirby Institute for Infection and Immunity, UNSW Sydney, Sydney, NSW, Australia
- School of Medical Sciences, UNSW Sydney, Sydney, NSW, Australia
| | - Curtis Cai
- Kirby Institute for Infection and Immunity, UNSW Sydney, Sydney, NSW, Australia
- School of Medical Sciences, UNSW Sydney, Sydney, NSW, Australia
| | - Jerome Samir
- Kirby Institute for Infection and Immunity, UNSW Sydney, Sydney, NSW, Australia
- School of Medical Sciences, UNSW Sydney, Sydney, NSW, Australia
| | - Mandeep Singh
- Garvan Institute for Medical Research, Sydney, NSW, Australia
| | - Ira W Deveson
- Garvan Institute for Medical Research, Sydney, NSW, Australia
| | | | - Timothy G Amos
- Garvan Institute for Medical Research, Sydney, NSW, Australia
| | - Helen Marie McGuire
- Ramaciotti Facility for Human Systems Biology, The University of Sydney, Sydney, NSW, Australia
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia
- Infection, Immunity and Inflammation Theme, School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW, Australia
| | - Kavitha Gowrishankar
- Blood Transplant and Cell Therapies Program, Department of Haematology, Westmead Hospital, Sydney, NSW, Australia
- Westmead Institute for Medical Research, Sydney, NSW, Australia
| | - Thiruni Adikari
- Kirby Institute for Infection and Immunity, UNSW Sydney, Sydney, NSW, Australia
- School of Medical Sciences, UNSW Sydney, Sydney, NSW, Australia
| | | | - Martina Bonomi
- Kirby Institute for Infection and Immunity, UNSW Sydney, Sydney, NSW, Australia
- Department of Physics, University of Bologna, Bologna, Italy
| | - Marco Ruella
- Division of Hematology and Oncology, University of Pennsylvania, Philadelphia, PA, USA
| | - David Bishop
- Blood Transplant and Cell Therapies Program, Department of Haematology, Westmead Hospital, Sydney, NSW, Australia
- Westmead Institute for Medical Research, Sydney, NSW, Australia
- Sydney Medical School, The University of Sydney, Sydney, NSW, Australia
| | - David Gottlieb
- Blood Transplant and Cell Therapies Program, Department of Haematology, Westmead Hospital, Sydney, NSW, Australia
- Westmead Institute for Medical Research, Sydney, NSW, Australia
- Sydney Medical School, The University of Sydney, Sydney, NSW, Australia
| | - Emily Blyth
- Blood Transplant and Cell Therapies Program, Department of Haematology, Westmead Hospital, Sydney, NSW, Australia
- Westmead Institute for Medical Research, Sydney, NSW, Australia
- Sydney Medical School, The University of Sydney, Sydney, NSW, Australia
| | - Kenneth Micklethwaite
- Blood Transplant and Cell Therapies Program, Department of Haematology, Westmead Hospital, Sydney, NSW, Australia
- Westmead Institute for Medical Research, Sydney, NSW, Australia
- Sydney Medical School, The University of Sydney, Sydney, NSW, Australia
- NSW Health Pathology Blood Transplant and Cell Therapies Laboratory - ICPMR Westmead, Sydney, NSW, Australia
| | - Fabio Luciani
- Kirby Institute for Infection and Immunity, UNSW Sydney, Sydney, NSW, Australia.
- School of Medical Sciences, UNSW Sydney, Sydney, NSW, Australia.
- Garvan Institute for Medical Research, Sydney, NSW, Australia.
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40
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Zhang J, Ma W, Yao H. Accurate TCR-pMHC interaction prediction using a BERT-based transfer learning method. Brief Bioinform 2023; 25:bbad436. [PMID: 38040492 PMCID: PMC10783865 DOI: 10.1093/bib/bbad436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 10/05/2023] [Accepted: 11/06/2023] [Indexed: 12/03/2023] Open
Abstract
Accurate prediction of TCR-pMHC binding is important for the development of cancer immunotherapies, especially TCR-based agents. Existing algorithms often experience diminished performance when dealing with unseen epitopes, primarily due to the complexity in TCR-pMHC recognition patterns and the scarcity of available data for training. We have developed a novel deep learning model, 'TCR Antigen Binding Recognition' based on BERT, named as TABR-BERT. Leveraging BERT's potent representation learning capabilities, TABR-BERT effectively captures essential information regarding TCR-pMHC interactions from TCR sequences, antigen epitope sequences and epitope-MHC binding. By transferring this knowledge to predict TCR-pMHC recognition, TABR-BERT demonstrated better results in benchmark tests than existing methods, particularly for unseen epitopes.
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Affiliation(s)
- Jiawei Zhang
- Fresh Wind Biotechnologies Inc. (Tianjin), Tianjin, China
| | - Wang Ma
- Fresh Wind Biotechnologies Inc. (Tianjin), Tianjin, China
| | - Hui Yao
- Fresh Wind Biotechnologies USA Inc., Houston, TX, USA
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41
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Kim T, Lim H, Jun S, Park J, Lee D, Lee JH, Lee JY, Bang D. Globally shared TCR repertoires within the tumor-infiltrating lymphocytes of patients with metastatic gynecologic cancer. Sci Rep 2023; 13:20485. [PMID: 37993659 PMCID: PMC10665396 DOI: 10.1038/s41598-023-47740-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Accepted: 11/17/2023] [Indexed: 11/24/2023] Open
Abstract
Gynecologic cancer, including ovarian cancer and endometrial cancer, is characterized by morphological and molecular heterogeneity. Germline and somatic testing are available for patients to screen for pathogenic variants in genes such as BRCA1/2. Tissue expression levels of immunogenomic markers such as PD-L1 are also being used in clinical research. The basic therapeutic approach to gynecologic cancer combines surgery with chemotherapy. Immunotherapy, while not yet a mainstream treatment for gynecologic cancers, is advancing, with Dostarlimab recently receiving approval as a treatment for endometrial cancer. The goal remains to harness stimulated immune cells in the bloodstream to eradicate multiple metastases, a feat currently deemed challenging in a typical clinical setting. For the discovery of novel immunotherapy-based tumor targets, tumor-infiltrating lymphocytes (TILs) give a key insight on tumor-related immune activities by providing T cell receptor (TCR) sequences. Understanding the TCR repertoires of TILs in metastatic tissues and the circulation is important from an immunotherapy standpoint, as a subset of T cells in the blood have the potential to help kill tumor cells. To explore the relationship between distant tissue biopsy regions and blood circulation, we investigated the TCR beta chain (TCRβ) in bulk tumor and matched blood samples from 39 patients with gynecologic cancer. We found that the TCR clones of TILs at different tumor sites were globally shared within patients and had high overlap with the TCR clones in peripheral blood.
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Affiliation(s)
- Taehoon Kim
- Department of Chemistry, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Hyeonseob Lim
- Department of Chemistry, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Soyeong Jun
- Department of Chemistry, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Junsik Park
- Department of Obstetrics and Gynecology, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Dongin Lee
- Department of Chemistry, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
| | - Ji Hyun Lee
- Department of Clinical Pharmacology and Therapeutics, College of Medicine, Kyung Hee University, 26 Kyungheedae-ro, Dongdaemun-gu, Seoul, 02447, Korea
- Department of Biomedical Science and Technology, Kyung Hee Medical Science Research Institute, Kyung Hee University, 26 Kyungheedae-ro, Dongdaemun-gu, Seoul, 02447, Korea
| | - Jung-Yun Lee
- Department of Obstetrics and Gynecology, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea.
| | - Duhee Bang
- Department of Chemistry, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea.
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42
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Sponaugle A, Weideman AMK, Ranek J, Atassi G, Kuruc J, Adimora AA, Archin NM, Gay C, Kuritzkes DR, Margolis DM, Vincent BG, Stanley N, Hudgens MG, Eron JJ, Goonetilleke N. Dominant CD4 + T cell receptors remain stable throughout antiretroviral therapy-mediated immune restoration in people with HIV. Cell Rep Med 2023; 4:101268. [PMID: 37949070 PMCID: PMC10694675 DOI: 10.1016/j.xcrm.2023.101268] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 06/05/2023] [Accepted: 10/10/2023] [Indexed: 11/12/2023]
Abstract
In people with HIV (PWH), the post-antiretroviral therapy (ART) window is critical for immune restoration and HIV reservoir stabilization. We employ deep immune profiling and T cell receptor (TCR) sequencing and examine proliferation to assess how ART impacts T cell homeostasis. In PWH on long-term ART, lymphocyte frequencies and phenotypes are mostly stable. By contrast, broad phenotypic changes in natural killer (NK) cells, γδ T cells, B cells, and CD4+ and CD8+ T cells are observed in the post-ART window. Whereas CD8+ T cells mostly restore, memory CD4+ T subsets and cytolytic NK cells show incomplete restoration 1.4 years post ART. Surprisingly, the hierarchies and frequencies of dominant CD4 TCR clonotypes (0.1%-11% of all CD4+ T cells) remain stable post ART, suggesting that clonal homeostasis can be independent of homeostatic processes regulating CD4+ T cell absolute number, phenotypes, and function. The slow restoration of host immunity post ART also has implications for the design of ART interruption studies.
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Affiliation(s)
- Alexis Sponaugle
- Department of Microbiology & Immunology, UNC Chapel Hill, Chapel Hill, NC, USA
| | - Ann Marie K Weideman
- Department of Biostatistics, UNC Chapel Hill, Chapel Hill, NC, USA; Center for AIDS Research, School of Medicine, UNC Chapel Hill, Chapel Hill, NC, USA
| | - Jolene Ranek
- Computational Medicine Program, UNC Chapel Hill, Chapel Hill, NC, USA; Curriculum in Bioinformatics and Computational Biology, UNC Chapel Hill, Chapel Hill, NC, USA
| | - Gatphan Atassi
- Lineberger Comprehensive Cancer Center, UNC Chapel Hill, Chapel Hill, NC, USA
| | - JoAnn Kuruc
- Department of Medicine, UNC Chapel Hill, Chapel Hill, NC, USA
| | - Adaora A Adimora
- Center for AIDS Research, School of Medicine, UNC Chapel Hill, Chapel Hill, NC, USA; Department of Medicine, UNC Chapel Hill, Chapel Hill, NC, USA; Department of Epidemiology, Gillings School of Global Public Health, UNC Chapel Hill, Chapel Hill, NC, USA
| | - Nancie M Archin
- Department of Medicine, UNC Chapel Hill, Chapel Hill, NC, USA
| | - Cynthia Gay
- Department of Medicine, UNC Chapel Hill, Chapel Hill, NC, USA
| | - Daniel R Kuritzkes
- Division of Infectious Diseases, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - David M Margolis
- Department of Microbiology & Immunology, UNC Chapel Hill, Chapel Hill, NC, USA; Department of Medicine, UNC Chapel Hill, Chapel Hill, NC, USA
| | - Benjamin G Vincent
- Department of Microbiology & Immunology, UNC Chapel Hill, Chapel Hill, NC, USA; Department of Medicine, UNC Chapel Hill, Chapel Hill, NC, USA; Curriculum in Bioinformatics and Computational Biology, UNC Chapel Hill, Chapel Hill, NC, USA
| | - Natalie Stanley
- Computational Medicine Program, UNC Chapel Hill, Chapel Hill, NC, USA; Department of Computer Science, UNC Chapel Hill, Chapel Hill, NC, USA
| | - Michael G Hudgens
- Department of Biostatistics, UNC Chapel Hill, Chapel Hill, NC, USA; Center for AIDS Research, School of Medicine, UNC Chapel Hill, Chapel Hill, NC, USA
| | - Joseph J Eron
- Department of Medicine, UNC Chapel Hill, Chapel Hill, NC, USA
| | - Nilu Goonetilleke
- Department of Microbiology & Immunology, UNC Chapel Hill, Chapel Hill, NC, USA; Department of Medicine, UNC Chapel Hill, Chapel Hill, NC, USA.
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43
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Meng Z, Rodriguez Ehrenfried A, Tan CL, Steffens LK, Kehm H, Zens S, Lauenstein C, Paul A, Schwab M, Förster JD, Salek M, Riemer AB, Wu H, Eckert C, Leonhardt CS, Strobel O, Volkmar M, Poschke I, Offringa R. Transcriptome-based identification of tumor-reactive and bystander CD8 + T cell receptor clonotypes in human pancreatic cancer. Sci Transl Med 2023; 15:eadh9562. [PMID: 37967201 DOI: 10.1126/scitranslmed.adh9562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 10/16/2023] [Indexed: 11/17/2023]
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is generally refractory to immune checkpoint blockade, although patients with genetically unstable tumors can show modest therapeutic benefit. We previously demonstrated the presence of tumor-reactive CD8+ T cells in PDAC samples. Here, we charted the tumor-infiltrating T cell repertoire in PDAC by combining single-cell transcriptomics with functional testing of T cell receptors (TCRs) for reactivity against autologous tumor cells. On the basis of a comprehensive dataset including 93 tumor-reactive and 65 bystander TCR clonotypes, we delineated a gene signature that effectively distinguishes between these T cell subsets in PDAC, as well as in other tumor indications. This revealed a high frequency of tumor-reactive TCR clonotypes in three genetically unstable samples. In contrast, the T cell repertoire in six genetically stable PDAC tumors was largely dominated by bystander T cells. Nevertheless, multiple tumor-reactive TCRs were successfully identified in each of these samples, thereby providing a perspective for personalized immunotherapy in this treatment-resistant indication.
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Affiliation(s)
- Zibo Meng
- Department of General, Visceral and Transplantation Surgery, University Hospital Heidelberg, 69120 Heidelberg, Germany
- Division of Molecular Oncology of Gastrointestinal Tumors, German Cancer Research Center, 69120 Heidelberg, Germany
- Sino-German Laboratory of Personalized Medicine for Pancreatic Cancer, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 430022 Wuhan, China
| | - Aaron Rodriguez Ehrenfried
- Division of Molecular Oncology of Gastrointestinal Tumors, German Cancer Research Center, 69120 Heidelberg, Germany
- Helmholtz-Institute for Translational Oncology by DKFZ (HI-TRON), 55131 Mainz, Germany
- Faculty of Biosciences, Heidelberg University, 69120 Heidelberg, Germany
| | - Chin Leng Tan
- Division of Molecular Oncology of Gastrointestinal Tumors, German Cancer Research Center, 69120 Heidelberg, Germany
- Faculty of Biosciences, Heidelberg University, 69120 Heidelberg, Germany
- Clinical Cooperation Unit Neuroimmunology and Brain Tumor Immunology, German Cancer Research Center, 69120 Heidelberg, Germany
| | - Laura K Steffens
- Division of Molecular Oncology of Gastrointestinal Tumors, German Cancer Research Center, 69120 Heidelberg, Germany
- Faculty of Biosciences, Heidelberg University, 69120 Heidelberg, Germany
| | - Hannes Kehm
- Division of Molecular Oncology of Gastrointestinal Tumors, German Cancer Research Center, 69120 Heidelberg, Germany
- Faculty of Biosciences, Heidelberg University, 69120 Heidelberg, Germany
| | - Stefan Zens
- Division of Molecular Oncology of Gastrointestinal Tumors, German Cancer Research Center, 69120 Heidelberg, Germany
- Faculty of Biosciences, Heidelberg University, 69120 Heidelberg, Germany
| | - Claudia Lauenstein
- Division of Molecular Oncology of Gastrointestinal Tumors, German Cancer Research Center, 69120 Heidelberg, Germany
| | - Alina Paul
- Division of Molecular Oncology of Gastrointestinal Tumors, German Cancer Research Center, 69120 Heidelberg, Germany
- Faculty of Biosciences, Heidelberg University, 69120 Heidelberg, Germany
| | - Marius Schwab
- Department of General, Visceral and Transplantation Surgery, University Hospital Heidelberg, 69120 Heidelberg, Germany
- Division of Molecular Oncology of Gastrointestinal Tumors, German Cancer Research Center, 69120 Heidelberg, Germany
| | - Jonas D Förster
- Faculty of Biosciences, Heidelberg University, 69120 Heidelberg, Germany
- Division of Immunotherapy & Immunoprevention, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
- Molecular Vaccine Design, German Center for Infection Research (DZIF), partner site Heidelberg, 69120 Heidelberg, Germany
| | - Mogjiborahman Salek
- Division of Immunotherapy & Immunoprevention, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
- Molecular Vaccine Design, German Center for Infection Research (DZIF), partner site Heidelberg, 69120 Heidelberg, Germany
| | - Angelika B Riemer
- Division of Immunotherapy & Immunoprevention, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
- Molecular Vaccine Design, German Center for Infection Research (DZIF), partner site Heidelberg, 69120 Heidelberg, Germany
| | - Heshui Wu
- Sino-German Laboratory of Personalized Medicine for Pancreatic Cancer, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 430022 Wuhan, China
- Department of Pancreatic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 430022 Wuhan, China
| | - Christoph Eckert
- Pathology Institute, University Hospital Heidelberg, 69120 Heidelberg, Germany
| | - Carl-Stephan Leonhardt
- Department of General, Visceral and Transplantation Surgery, University Hospital Heidelberg, 69120 Heidelberg, Germany
| | - Oliver Strobel
- Department of General, Visceral and Transplantation Surgery, University Hospital Heidelberg, 69120 Heidelberg, Germany
| | - Michael Volkmar
- Department of General, Visceral and Transplantation Surgery, University Hospital Heidelberg, 69120 Heidelberg, Germany
- Division of Molecular Oncology of Gastrointestinal Tumors, German Cancer Research Center, 69120 Heidelberg, Germany
- Helmholtz-Institute for Translational Oncology by DKFZ (HI-TRON), 55131 Mainz, Germany
| | - Isabel Poschke
- Department of General, Visceral and Transplantation Surgery, University Hospital Heidelberg, 69120 Heidelberg, Germany
- Division of Molecular Oncology of Gastrointestinal Tumors, German Cancer Research Center, 69120 Heidelberg, Germany
- Clinical Cooperation Unit Neuroimmunology and Brain Tumor Immunology, German Cancer Research Center, 69120 Heidelberg, Germany
- Immune Monitoring Unit, National Center for Tumor Diseases (NCT), 69120 Heidelberg, Germany
| | - Rienk Offringa
- Department of General, Visceral and Transplantation Surgery, University Hospital Heidelberg, 69120 Heidelberg, Germany
- Division of Molecular Oncology of Gastrointestinal Tumors, German Cancer Research Center, 69120 Heidelberg, Germany
- Sino-German Laboratory of Personalized Medicine for Pancreatic Cancer, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 430022 Wuhan, China
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44
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Seitz V, Gennermann K, Elezkurtaj S, Groth D, Schaper S, Dröge A, Lachmann N, Berg E, Lenze D, Kühl AA, Husemann C, Kleo K, Horst D, Lennerz V, Hennig S, Hummel M, Schumann M. Specific T-cell receptor beta-rearrangements of gluten-triggered CD8 + T-cells are enriched in celiac disease patients' duodenal mucosa. Clin Immunol 2023; 256:109795. [PMID: 37769786 DOI: 10.1016/j.clim.2023.109795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 09/12/2023] [Accepted: 09/25/2023] [Indexed: 10/02/2023]
Abstract
Celiac disease (CeD) is an autoimmune disorder affecting the small intestine with gluten as disease trigger. Infections including Influenza A, increase the CeD risk. While gluten-specific CD4+ T-cells, recognizing HLA-DQ2/DQ8 presented gluten-peptides, initiate and sustain the celiac immune response, CD8+ α/β intraepithelial T-cells elicit mucosal damage. Here, we subjected TCRs from a cohort of 56 CeD patients and 22 controls to an analysis employing 749 published CeD-related TCRβ-rearrangements derived from gluten-specific CD4+ T-cells and gluten-triggered peripheral blood CD8+ T-cells. We show, that in addition to TCRs from gluten-specific CD4+ T-cells, TCRs of gluten-triggered CD8+ T-cells are significantly enriched in CeD duodenal tissue samples. TCRβ-rearrangements of gluten-triggered CD8+ T-cells were even more expanded in patients than TCRs from gluten-specific CD4+ T-cells (p < 0.0002) and highest in refractory CeD. Sequence alignments with TCR-antigen databases suggest that a subgroup of these most likely indirectly gluten-triggered TCRs recognize microbial, viral, and autoantigens.
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Affiliation(s)
- V Seitz
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany; HS Diagnomics GmbH, Berlin, Germany
| | | | - S Elezkurtaj
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - D Groth
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
| | | | - A Dröge
- HS Diagnomics GmbH, Berlin, Germany
| | - N Lachmann
- Centre for Tumor Medicine, Histocompatibility & Immunogenetics Laboratory, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - E Berg
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - D Lenze
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - A A Kühl
- iPATH.Berlin - Core Unit of the Charité Universitätsmedizin Berlin, corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - C Husemann
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany; German Cancer Consortium (DKTK), Partner Site Berlin, Berlin, Germany; German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - K Kleo
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - D Horst
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | | | - S Hennig
- HS Diagnomics GmbH, Berlin, Germany
| | - M Hummel
- Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany; German Cancer Consortium (DKTK), Partner Site Berlin, Berlin, Germany; German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - M Schumann
- Medizinische Klinik m. S. Gastroenterologie, Infektiologie und Rheumatologie, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
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45
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Boughter CT, Meier-Schellersheim M. An integrated approach to the characterization of immune repertoires using AIMS: An Automated Immune Molecule Separator. PLoS Comput Biol 2023; 19:e1011577. [PMID: 37862356 PMCID: PMC10619816 DOI: 10.1371/journal.pcbi.1011577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 11/01/2023] [Accepted: 10/06/2023] [Indexed: 10/22/2023] Open
Abstract
The adaptive immune system employs an array of receptors designed to respond with high specificity to pathogens or molecular aberrations faced by the host organism. Binding of these receptors to molecular fragments-collectively referred to as antigens-initiates immune responses. These antigenic targets are recognized in their native state on the surfaces of pathogens by antibodies, whereas T cell receptors (TCR) recognize processed antigens as short peptides, presented on major histocompatibility complex (MHC) molecules. Recent research has led to a wealth of immune repertoire data that are key to interrogating the nature of these molecular interactions. However, existing tools for the analysis of these large datasets typically focus on molecular sets of a single type, forcing researchers to separately analyze strongly coupled sequences of interacting molecules. Here, we introduce a software package for the integrated analysis of immune repertoire data, capable of identifying distinct biophysical differences in isolated TCR, MHC, peptide, antibody, and antigen sequence data. This integrated analytical approach allows for direct comparisons across immune repertoire subsets and provides a starting point for the identification of key interaction hotspots in complementary receptor-antigen pairs. The software (AIMS-Automated Immune Molecule Separator) is freely available as an open access package in GUI or command-line form.
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Affiliation(s)
- Christopher T. Boughter
- Computational Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Martin Meier-Schellersheim
- Computational Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, United States of America
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46
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Kacsoh DB, Diaz MJ, Gozlan EC, Sahoo A, Song JJ, Yeagley M, Chobrutskiy A, Chobrutskiy BI, Blanck G. Blood-based T cell receptor anti-viral CDR3s are associated with worse overall survival for neuroblastoma. J Cancer Res Clin Oncol 2023; 149:12047-12056. [PMID: 37421457 DOI: 10.1007/s00432-023-05059-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 06/28/2023] [Indexed: 07/10/2023]
Abstract
With the advent of large collections of adaptive immune receptor recombination reads representing cancer, there is the opportunity to further investigate the adaptive immune response to viruses in the cancer setting. This is a particularly important goal due to longstanding but still not well-resolved questions about viral etiologies in cancer and viral infections as comorbidities. In this report, we assessed the T cell receptor complementarity determining region-3 (CDR3) amino acid (AA) sequences, for blood-sourced TCRs from neuroblastoma (NBL) cases, for exact AA sequence matches to previously identified anti-viral TCR CDR3 AA sequences. Results indicated the presence of anti-viral TCR CDR3 AA sequences in the NBL blood samples highly significantly correlated with worse overall survival. Furthermore, the TCR CDR3 AA sequences demonstrating chemical complementarity to many cytomegalovirus antigens represented cases with a worse outcome, including cases where such CDR3s were obtained from tumor samples. Overall, these results indicate a significant need for, and provide a novel strategy for assessing viral infection complications in NBL patients.
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Affiliation(s)
- Dorottya B Kacsoh
- College of Medicine, University of Central Florida, Orlando, FL, 32827, USA
| | - Michael J Diaz
- Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, 12901 Bruce B. Downs Bd. MDC7, Tampa, FL, 33612, USA
| | - Etienne C Gozlan
- Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, 12901 Bruce B. Downs Bd. MDC7, Tampa, FL, 33612, USA
| | - Arpan Sahoo
- Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, 12901 Bruce B. Downs Bd. MDC7, Tampa, FL, 33612, USA
| | - Joanna J Song
- Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, 12901 Bruce B. Downs Bd. MDC7, Tampa, FL, 33612, USA
| | - Michelle Yeagley
- University of Pittsburgh Medical Center, Pittsburgh, PA, 15261, USA
| | - Andrea Chobrutskiy
- Department of Pediatrics, Oregon Health and Science University, Portland, OR, 97239, USA
| | - Boris I Chobrutskiy
- Department of Internal Medicine, Oregon Health and Science University Hospital, Portland, OR, 97239, USA
| | - George Blanck
- Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, 12901 Bruce B. Downs Bd. MDC7, Tampa, FL, 33612, USA.
- Department of Immunology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA.
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47
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Montemurro A, Povlsen HR, Jessen LE, Nielsen M. Benchmarking data-driven filtering for denoising of TCRpMHC single-cell data. Sci Rep 2023; 13:16147. [PMID: 37752190 PMCID: PMC10522655 DOI: 10.1038/s41598-023-43048-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 09/18/2023] [Indexed: 09/28/2023] Open
Abstract
Pairing of the T cell receptor (TCR) with its cognate peptide-MHC (pMHC) is a cornerstone in T cell-mediated immunity. Recently, single-cell sequencing coupled with DNA-barcoded MHC multimer staining has enabled high-throughput studies of T cell specificities. However, the immense variability of TCR-pMHC interactions combined with the relatively low signal-to-noise ratio in the data generated using current technologies are complicating these studies. Several approaches have been proposed for denoising single-cell TCR-pMHC specificity data. Here, we present a benchmark evaluating two such denoising methods, ICON and ITRAP. We applied and evaluated the methods on publicly available immune profiling data provided by 10x Genomics. We find that both methods identified approximately 75% of the raw data as noise. We analyzed both internal metrics developed for the purpose and performance on independent data using machine learning methods trained on the raw and denoised 10x data. We find an increased signal-to-noise ratio comparing the denoised to the raw data for both methods, and demonstrate an overall superior performance of the ITRAP method in terms of both data consistency and performance. In conclusion, this study demonstrates that Improving the data quality from high throughput studies of TCRpMHC-specificity by denoising is paramount in increasing our understanding of T cell-mediated immunity.
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Affiliation(s)
- Alessandro Montemurro
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, DTU, 2800, Kgs. Lyngby, Denmark
| | - Helle Rus Povlsen
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, DTU, 2800, Kgs. Lyngby, Denmark
| | - Leon Eyrich Jessen
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, DTU, 2800, Kgs. Lyngby, Denmark
| | - Morten Nielsen
- Department of Health Technology, Section for Bioinformatics, Technical University of Denmark, DTU, 2800, Kgs. Lyngby, Denmark.
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48
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Flumens D, Gielis S, Bartholomeus E, Campillo-Davo D, van der Heijden S, Versteven M, De Reu H, Smits E, Ogunjimi B, Laukens K, Meysman P, Lion E. Training of epitope-TCR prediction models with healthy donor-derived cancer-specific T cells. Methods Cell Biol 2023; 183:143-160. [PMID: 38548410 DOI: 10.1016/bs.mcb.2023.08.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/02/2024]
Abstract
Discovery of epitope-specific T-cell receptors (TCRs) for cancer therapies is a time consuming and expensive procedure that usually requires a large amount of patient cells. To maximize information from and minimize the need of precious samples in cancer research, prediction models have been developed to identify in silico epitope-specific TCRs. In this chapter, we provide a step-by-step protocol to train a prediction model using the user-friendly TCRex webtool for the nearly universal tumor-associated antigen Wilms' tumor 1 (WT1)-specific TCR repertoire. WT1 is a self-antigen overexpressed in numerous solid and hematological malignancies with a high clinical relevance. Training of computational models starts from a list of known epitope-specific TCRs which is often not available for new cancer epitopes. Therefore, we describe a workflow to assemble a training data set consisting of TCR sequences obtained from WT137-45-reactive CD8 T cell clones expanded and sorted from healthy donor peripheral blood mononuclear cells.
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Affiliation(s)
- Donovan Flumens
- Laboratory of Experimental Hematology (LEH), Vaccine & Infectious Disease Institute (VAXINFECTIO), Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
| | - Sofie Gielis
- Adrem Data Lab, Department of Computer Science, University of Antwerp, Antwerp, Belgium; Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), University of Antwerp, Antwerp, Belgium; Biomedical Informatics Research Network Antwerp (Biomina), University of Antwerp, Antwerp, Belgium
| | - Esther Bartholomeus
- Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), University of Antwerp, Antwerp, Belgium; Centre for Health Economics Research & Modeling Infectious Diseases (CHERMID), VAXINFECTIO, University of Antwerp, Antwerp, Belgium; Antwerp Center for Translational Immunology and Virology (ACTIV), Vaccine and Infectious Disease Institute (VAXINFECTIO), University of Antwerp, Antwerp, Belgium
| | - Diana Campillo-Davo
- Laboratory of Experimental Hematology (LEH), Vaccine & Infectious Disease Institute (VAXINFECTIO), Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
| | - Sanne van der Heijden
- Laboratory of Experimental Hematology (LEH), Vaccine & Infectious Disease Institute (VAXINFECTIO), Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium; Center for Oncological Research (CORE), Integrated Personalized & Precision Oncology Network (IPPON), University of Antwerp, Antwerp, Belgium
| | - Maarten Versteven
- Laboratory of Experimental Hematology (LEH), Vaccine & Infectious Disease Institute (VAXINFECTIO), Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
| | - Hans De Reu
- Laboratory of Experimental Hematology (LEH), Vaccine & Infectious Disease Institute (VAXINFECTIO), Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
| | - Evelien Smits
- Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), University of Antwerp, Antwerp, Belgium; Centre for Health Economics Research & Modeling Infectious Diseases (CHERMID), VAXINFECTIO, University of Antwerp, Antwerp, Belgium
| | - Benson Ogunjimi
- Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), University of Antwerp, Antwerp, Belgium; Centre for Health Economics Research & Modeling Infectious Diseases (CHERMID), VAXINFECTIO, University of Antwerp, Antwerp, Belgium; Antwerp Center for Translational Immunology and Virology (ACTIV), Vaccine and Infectious Disease Institute (VAXINFECTIO), University of Antwerp, Antwerp, Belgium
| | - Kris Laukens
- Adrem Data Lab, Department of Computer Science, University of Antwerp, Antwerp, Belgium; Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), University of Antwerp, Antwerp, Belgium; Biomedical Informatics Research Network Antwerp (Biomina), University of Antwerp, Antwerp, Belgium
| | - Pieter Meysman
- Adrem Data Lab, Department of Computer Science, University of Antwerp, Antwerp, Belgium; Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), University of Antwerp, Antwerp, Belgium; Biomedical Informatics Research Network Antwerp (Biomina), University of Antwerp, Antwerp, Belgium
| | - Eva Lion
- Laboratory of Experimental Hematology (LEH), Vaccine & Infectious Disease Institute (VAXINFECTIO), Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium; Antwerp Unit for Data Analysis and Computation in Immunology and Sequencing (AUDACIS), University of Antwerp, Antwerp, Belgium; Center for Cell Therapy & Regenerative Medicine (CCRG), Antwerp University Hospital, Edegem, Belgium.
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49
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Bravi B, Di Gioacchino A, Fernandez-de-Cossio-Diaz J, Walczak AM, Mora T, Cocco S, Monasson R. A transfer-learning approach to predict antigen immunogenicity and T-cell receptor specificity. eLife 2023; 12:e85126. [PMID: 37681658 PMCID: PMC10522340 DOI: 10.7554/elife.85126] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 09/07/2023] [Indexed: 09/09/2023] Open
Abstract
Antigen immunogenicity and the specificity of binding of T-cell receptors to antigens are key properties underlying effective immune responses. Here we propose diffRBM, an approach based on transfer learning and Restricted Boltzmann Machines, to build sequence-based predictive models of these properties. DiffRBM is designed to learn the distinctive patterns in amino-acid composition that, on the one hand, underlie the antigen's probability of triggering a response, and on the other hand the T-cell receptor's ability to bind to a given antigen. We show that the patterns learnt by diffRBM allow us to predict putative contact sites of the antigen-receptor complex. We also discriminate immunogenic and non-immunogenic antigens, antigen-specific and generic receptors, reaching performances that compare favorably to existing sequence-based predictors of antigen immunogenicity and T-cell receptor specificity.
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Affiliation(s)
- Barbara Bravi
- Department of Mathematics, Imperial College LondonLondonUnited Kingdom
- Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université Paris-CitéParisFrance
| | - Andrea Di Gioacchino
- Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université Paris-CitéParisFrance
| | - Jorge Fernandez-de-Cossio-Diaz
- Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université Paris-CitéParisFrance
| | - Aleksandra M Walczak
- Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université Paris-CitéParisFrance
| | - Thierry Mora
- Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université Paris-CitéParisFrance
| | - Simona Cocco
- Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université Paris-CitéParisFrance
| | - Rémi Monasson
- Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université Paris-CitéParisFrance
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50
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Massey J, Artuz C, Dyer Z, Jackson K, Khoo M, Visweswaran M, Withers B, Moore J, Ma D, Sutton I. Diversification and expansion of the EBV-reactive cytotoxic T lymphocyte repertoire following autologous haematopoietic stem cell transplant for multiple sclerosis. Clin Immunol 2023; 254:109709. [PMID: 37495004 DOI: 10.1016/j.clim.2023.109709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 07/07/2023] [Accepted: 07/23/2023] [Indexed: 07/28/2023]
Abstract
Both genetic susceptibility and environmental exposures are thought to be involved in multiple sclerosis (MS) pathogenesis. Of all viruses potentially relevant to MS aetiology, Epstein-Barr virus (EBV) is the best-studied. EBV is a B cell lymphotropic virus which is able to evade the immune system by establishing latent infection in memory B cells, and EBV reactivation is restricted by CD8 cytotoxic T cell (CTL) responses in immune competent individuals. Autologous haematopoietic stem cell transplantation (AHSCT) is considered to be the most effective therapy in the treatment of relapsing MS even though chemotherapy-induced lymphopenia can associate with the re-emergence of latent viruses. Despite the increasing interest in EBV and MS pathogenesis the relationship between AHSCT, EBV and viral immunity in people with MS has not been investigated to date. This study analysed immune responses to EBV in a well characterised cohort of 13 individuals with MS by utilising pre-AHSCT, and 6-, 12- and 24-month post AHSCT bio-banked peripheral blood mononuclear cells and plasma samples. It is demonstrated that the infused stem cell product contains latently EBV-infected memory B cells, and that EBV viremia occurs in the immune-compromised recipient post-transplant. High throughput TCR analysis detected expansion and diversification of the CD8 CTL responses reactive with EBV lytic and latent antigens from 6 to 24 months following AHSCT. Increased levels of latent EBV infection found within the B cell pool following treatment, as measured by EBV genomic detection, did not associate with disease relapse. This is the first study of EBV immunity following application of AHSCT in the treatment of MS and not only raises important questions about the role of EBV infection in MS pathogenesis, but is of clinical importance given the expanding clinical trials of adoptive EBV-specific CTLs in MS.
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Affiliation(s)
- Jennifer Massey
- Department of Neurology, St Vincent's Hospital, Darlinghurst, NSW 2010, Australia; Blood Stem Cell and Cancer Research Group, St Vincent's Centre for Applied Medical Research, Darlinghurst, NSW 2010, Australia; School of Clinical Medicine, St Vincent's Healthcare Clinical Campus, Faculty of Medicine and Health, UNSW, Sydney, Australia.
| | - Crisbel Artuz
- Blood Stem Cell and Cancer Research Group, St Vincent's Centre for Applied Medical Research, Darlinghurst, NSW 2010, Australia; School of Clinical Medicine, St Vincent's Healthcare Clinical Campus, Faculty of Medicine and Health, UNSW, Sydney, Australia
| | - Zoe Dyer
- Blood Stem Cell and Cancer Research Group, St Vincent's Centre for Applied Medical Research, Darlinghurst, NSW 2010, Australia
| | - Katherine Jackson
- Garvan Institute of Medical Research, Darlinghurst, NSW 2010, Australia
| | - Melissa Khoo
- Blood Stem Cell and Cancer Research Group, St Vincent's Centre for Applied Medical Research, Darlinghurst, NSW 2010, Australia; School of Clinical Medicine, St Vincent's Healthcare Clinical Campus, Faculty of Medicine and Health, UNSW, Sydney, Australia
| | - Malini Visweswaran
- Blood Stem Cell and Cancer Research Group, St Vincent's Centre for Applied Medical Research, Darlinghurst, NSW 2010, Australia; School of Clinical Medicine, St Vincent's Healthcare Clinical Campus, Faculty of Medicine and Health, UNSW, Sydney, Australia
| | - Barbara Withers
- Blood Stem Cell and Cancer Research Group, St Vincent's Centre for Applied Medical Research, Darlinghurst, NSW 2010, Australia; School of Clinical Medicine, St Vincent's Healthcare Clinical Campus, Faculty of Medicine and Health, UNSW, Sydney, Australia; Department of Haematology, St Vincent's Hospital; Darlinghurst, NSW 2010, Australia
| | - John Moore
- Blood Stem Cell and Cancer Research Group, St Vincent's Centre for Applied Medical Research, Darlinghurst, NSW 2010, Australia; School of Clinical Medicine, St Vincent's Healthcare Clinical Campus, Faculty of Medicine and Health, UNSW, Sydney, Australia; Department of Haematology, St Vincent's Hospital; Darlinghurst, NSW 2010, Australia
| | - David Ma
- Blood Stem Cell and Cancer Research Group, St Vincent's Centre for Applied Medical Research, Darlinghurst, NSW 2010, Australia; School of Clinical Medicine, St Vincent's Healthcare Clinical Campus, Faculty of Medicine and Health, UNSW, Sydney, Australia; Department of Haematology, St Vincent's Hospital; Darlinghurst, NSW 2010, Australia
| | - Ian Sutton
- School of Clinical Medicine, St Vincent's Healthcare Clinical Campus, Faculty of Medicine and Health, UNSW, Sydney, Australia; Department of Neurology, St Vincent's Clinic; Darlinghurst, NSW 2010, Australia
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