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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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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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3
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Licht P, Dominelli N, Kleemann J, Pastore S, Müller ES, Haist M, Hartmann KS, Stege H, Bros M, Meissner M, Grabbe S, Heermann R, Mailänder V. The skin microbiome stratifies patients with cutaneous T cell lymphoma and determines event-free survival. NPJ Biofilms Microbiomes 2024; 10:74. [PMID: 39198450 PMCID: PMC11358159 DOI: 10.1038/s41522-024-00542-4] [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: 11/27/2023] [Accepted: 07/31/2024] [Indexed: 09/01/2024] Open
Abstract
Mycosis fungoides (MF) is the most common entity of Cutaneous T cell lymphomas (CTCL) and is characterized by the presence of clonal malignant T cells in the skin. The role of the skin microbiome for MF development and progression are currently poorly understood. Using shotgun metagenomic profiling, real-time qPCR, and T cell receptor sequencing, we compared lesional and nonlesional skin of 20 MF patients with early and advanced MF. Additionally, we isolated Staphylococcus aureus and other bacteria from MF skin for functional profiling and to study the S. aureus virulence factor spa. We identified a subgroup of MF patients with substantial dysbiosis on MF lesions and concomitant outgrowth of S. aureus on plaque-staged lesions, while the other MF patients had a balanced microbiome on lesional skin. Dysbiosis and S. aureus outgrowth were accompanied by ectopic levels of cutaneous antimicrobial peptides (AMPs), including adaptation of the plaque-derived S. aureus strain. Furthermore, the plaque-derived S. aureus strain showed a reduced susceptibility towards antibiotics and an upregulation of the virulence factor spa, which may activate the NF-κB pathway. Remarkably, patients with dysbiosis on MF lesions had a restricted T cell receptor repertoire and significantly lower event-free survival. Our study highlights the potential for microbiome-modulating treatments targeting S. aureus to prevent MF progression.
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Affiliation(s)
- Philipp Licht
- University Medical Centre Mainz, Department of Dermatology, Mainz, Germany.
| | - Nazzareno Dominelli
- Johannes Gutenberg-University, Institute of Molecular Physiology (imP), Biocenter II, Microbiology and Biotechnology, Mainz, Germany
| | - Johannes Kleemann
- University Hospital Frankfurt, Department of Dermatology, Venerology and Allergology, Frankfurt am Main, Germany
| | - Stefan Pastore
- University Medical Centre Mainz, Institute of Human Genetics, Mainz, Germany
- Johannes Gutenberg-University, Institute of Pharmaceutical and Biomedical Sciences, Mainz, Germany
| | - Elena-Sophia Müller
- Johannes Gutenberg-University, Institute of Molecular Physiology (imP), Biocenter II, Microbiology and Biotechnology, Mainz, Germany
| | - Maximilian Haist
- University Medical Centre Mainz, Department of Dermatology, Mainz, Germany
| | | | - Henner Stege
- University Medical Centre Mainz, Department of Dermatology, Mainz, Germany
| | - Matthias Bros
- University Medical Centre Mainz, Department of Dermatology, Mainz, Germany
| | - Markus Meissner
- University Hospital Frankfurt, Department of Dermatology, Venerology and Allergology, Frankfurt am Main, Germany
| | - Stephan Grabbe
- University Medical Centre Mainz, Department of Dermatology, Mainz, Germany
| | - Ralf Heermann
- Johannes Gutenberg-University, Institute of Molecular Physiology (imP), Biocenter II, Microbiology and Biotechnology, Mainz, Germany
| | - Volker Mailänder
- University Medical Centre Mainz, Department of Dermatology, Mainz, Germany.
- Max Planck Institute for Polymer Research, Mainz, Germany.
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4
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Ehrlich R, Glynn E, Singh M, Ghersi D. Computational Methods for Predicting Key Interactions in T Cell-Mediated Adaptive Immunity. Annu Rev Biomed Data Sci 2024; 7:295-316. [PMID: 38748864 DOI: 10.1146/annurev-biodatasci-102423-122741] [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: 08/25/2024]
Abstract
The adaptive immune system recognizes pathogen- and cancer-specific features and is endowed with memory, enabling it to respond quickly and efficiently to repeated encounters with the same antigens. T cells play a central role in the adaptive immune system by directly targeting intracellular pathogens and helping to activate B cells to secrete antibodies. Several fundamental protein interactions-including those between major histocompatibility complex (MHC) proteins and antigen-derived peptides as well as between T cell receptors and peptide-MHC complexes-underlie the ability of T cells to recognize antigens with great precision. Computational approaches to predict these interactions are increasingly being used for medically relevant applications, including vaccine design and prediction of patient response to cancer immunotherapies. We provide computational researchers with an accessible introduction to the adaptive immune system, review computational approaches to predict the key protein interactions underlying T cell-mediated adaptive immunity, and highlight remaining challenges.
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Affiliation(s)
- Ryan Ehrlich
- School of Interdisciplinary Informatics, University of Nebraska, Omaha, Nebraska, USA;
| | - Eric Glynn
- Lewis-Sigler Institute, Princeton University, Princeton, New Jersey, USA
| | - Mona Singh
- Department of Computer Science, Princeton University, Princeton, New Jersey, USA;
- Lewis-Sigler Institute, Princeton University, Princeton, New Jersey, USA
| | - Dario Ghersi
- School of Interdisciplinary Informatics, University of Nebraska, Omaha, Nebraska, USA;
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5
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Zhang M, Cheng Q, Wei Z, Xu J, Wu S, Xu N, Zhao C, Yu L, Feng W. BertTCR: a Bert-based deep learning framework for predicting cancer-related immune status based on T cell receptor repertoire. Brief Bioinform 2024; 25:bbae420. [PMID: 39177262 PMCID: PMC11342255 DOI: 10.1093/bib/bbae420] [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: 05/28/2024] [Revised: 07/24/2024] [Accepted: 08/08/2024] [Indexed: 08/24/2024] Open
Abstract
The T cell receptor (TCR) repertoire is pivotal to the human immune system, and understanding its nuances can significantly enhance our ability to forecast cancer-related immune responses. However, existing methods often overlook the intra- and inter-sequence interactions of T cell receptors (TCRs), limiting the development of sequence-based cancer-related immune status predictions. To address this challenge, we propose BertTCR, an innovative deep learning framework designed to predict cancer-related immune status using TCRs. BertTCR combines a pre-trained protein large language model with deep learning architectures, enabling it to extract deeper contextual information from TCRs. Compared to three state-of-the-art sequence-based methods, BertTCR improves the AUC on an external validation set for thyroid cancer detection by 21 percentage points. Additionally, this model was trained on over 2000 publicly available TCR libraries covering 17 types of cancer and healthy samples, and it has been validated on multiple public external datasets for its ability to distinguish cancer patients from healthy individuals. Furthermore, BertTCR can accurately classify various cancer types and healthy individuals. Overall, BertTCR is the advancing method for cancer-related immune status forecasting based on TCRs, offering promising potential for a wide range of immune status prediction tasks.
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Affiliation(s)
- Min Zhang
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, No. 145 Nantong Street, Nangang District, Harbin, 150001, China
| | - Qi Cheng
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, No. 145 Nantong Street, Nangang District, Harbin, 150001, China
| | - Zhenyu Wei
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, No. 145 Nantong Street, Nangang District, Harbin, 150001, China
| | - Jiayu Xu
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, No. 145 Nantong Street, Nangang District, Harbin, 150001, China
| | - Shiwei Wu
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, No. 145 Nantong Street, Nangang District, Harbin, 150001, China
| | - Nan Xu
- Institute of Biomedical Engineering and Technology, Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, No. 500 Dongchuan Road, Shanghai, 200241, China
- Shanghai Unicar-Therapy Bio-medicine Technology Co., Ltd, No. 1525 Minqiang Road, Shanghai, 201612, China
| | - Chengkui Zhao
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, No. 145 Nantong Street, Nangang District, Harbin, 150001, China
- Shanghai Unicar-Therapy Bio-medicine Technology Co., Ltd, No. 1525 Minqiang Road, Shanghai, 201612, China
| | - Lei Yu
- Institute of Biomedical Engineering and Technology, Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, No. 500 Dongchuan Road, Shanghai, 200241, China
- Shanghai Unicar-Therapy Bio-medicine Technology Co., Ltd, No. 1525 Minqiang Road, Shanghai, 201612, China
| | - Weixing Feng
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, No. 145 Nantong Street, Nangang District, Harbin, 150001, China
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6
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Song B, Wang K, Na S, Yao J, Fattah FJ, von Itzstein MS, Yang DM, Liu J, Xue Y, Liang C, Guo Y, Raman I, Zhu C, Dowell JE, Homsi J, Rashdan S, Yang S, Gwin ME, Hsiehchen D, Gloria-McCutchen Y, Raj P, Bai X, Wang J, Conejo-Garcia J, Xie Y, Gerber DE, Huang J, Wang T. Cmai: Predicting Antigen-Antibody Interactions from Massive Sequencing Data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.27.601035. [PMID: 39005456 PMCID: PMC11244862 DOI: 10.1101/2024.06.27.601035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
The interaction between antigens and antibodies (B cell receptors, BCRs) is the key step underlying the function of the humoral immune system in various biological contexts. The capability to profile the landscape of antigen-binding affinity of a vast number of BCRs will provide a powerful tool to reveal novel insights at unprecedented levels and will yield powerful tools for translational development. However, current experimental approaches for profiling antibody-antigen interactions are costly and time-consuming, and can only achieve low-to-mid throughput. On the other hand, bioinformatics tools in the field of antibody informatics mostly focus on optimization of antibodies given known binding antigens, which is a very different research question and of limited scope. In this work, we developed an innovative Artificial Intelligence tool, Cmai, to address the prediction of the binding between antibodies and antigens that can be scaled to high-throughput sequencing data. Cmai achieved an AUROC of 0.91 in our validation cohort. We devised a biomarker metric based on the output from Cmai applied to high-throughput BCR sequencing data. We found that, during immune-related adverse events (irAEs) caused by immune-checkpoint inhibitor (ICI) treatment, the humoral immunity is preferentially responsive to intracellular antigens from the organs affected by the irAEs. In contrast, extracellular antigens on malignant tumor cells are inducing B cell infiltrations, and the infiltrating B cells have a greater tendency to co-localize with tumor cells expressing these antigens. We further found that the abundance of tumor antigen-targeting antibodies is predictive of ICI treatment response. Overall, Cmai and our biomarker approach filled in a gap that is not addressed by current antibody optimization works nor works such as AlphaFold3 that predict the structures of complexes of proteins that are known to bind.
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7
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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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8
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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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9
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Rollins ZA, Curtis MB, George SC, Faller R. A Computational Strategy for the Rapid Identification and Ranking of Patient-Specific T Cell Receptors Bound to Neoantigens. Macromol Rapid Commun 2024:e2400225. [PMID: 38839076 DOI: 10.1002/marc.202400225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Revised: 06/02/2024] [Indexed: 06/07/2024]
Abstract
T cell receptor (TCR) recognition of a peptide-major histocompatibility complex (pMHC) is crucial for adaptive immune response. The identification of therapeutically relevant TCR-pMHC protein pairs is a bottleneck in the implementation of TCR-based immunotherapies. The ability to computationally design TCRs to target a specific pMHC requires automated integration of next-generation sequencing, protein-protein structure prediction, molecular dynamics, and TCR ranking. A pipeline to evaluate patient-specific, sequence-based TCRs to a target pMHC is presented. Using the three most frequently expressed TCRs from 16 colorectal cancer patients, the protein-protein structure of the TCRs to the target CEA peptide-MHC is predicted using Modeller and ColabFold. TCR-pMHC structures are compared using automated equilibration and successive analysis. ColabFold generated configurations require an ≈2.5× reduction in equilibration time of TCR-pMHC structures compared to Modeller. The structural differences between Modeller and ColabFold are demonstrated by root mean square deviation (≈0.20 nm) between clusters of equilibrated configurations, which impact the number of hydrogen bonds and Lennard-Jones contacts between the TCR and pMHC. TCR ranking criteria that may prioritize TCRs for evaluation of in vitro immunogenicity are identified, and this ranking is validated by comparing to state-of-the-art machine learning-based methods trained to predict the probability of TCR-pMHC binding.
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Affiliation(s)
- Zachary A Rollins
- Department of Chemical Engineering, University of California, Davis, 1 Shields Ave, Bainer Hall, Davis, CA, 95616, USA
| | - Matthew B Curtis
- Department of Biomedical Engineering, University of California, Davis, 451 E. Health Sciences Dr., GBSF 2303, Davis, CA, 95616, USA
| | - Steven C George
- Department of Biomedical Engineering, University of California, Davis, 451 E. Health Sciences Dr., GBSF 2303, Davis, CA, 95616, USA
| | - Roland Faller
- Department of Chemical Engineering, University of California, Davis, 1 Shields Ave, Bainer Hall, Davis, CA, 95616, USA
- Department of Chemical Engineering, Texas Tech University, Lubbock, TX, 79409, USA
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10
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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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11
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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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12
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Wang A, Lin X, Chau KN, Onuchic JN, Levine H, George JT. RACER-m leverages structural features for sparse T cell specificity prediction. SCIENCE ADVANCES 2024; 10:eadl0161. [PMID: 38748791 PMCID: PMC11095454 DOI: 10.1126/sciadv.adl0161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 04/10/2024] [Indexed: 05/19/2024]
Abstract
Reliable prediction of T cell specificity against antigenic signatures is a formidable task, complicated by the immense diversity of T cell receptor and antigen sequence space and the resulting limited availability of training sets for inferential models. Recent modeling efforts have demonstrated the advantage of incorporating structural information to overcome the need for extensive training sequence data, yet disentangling the heterogeneous TCR-antigen interface to accurately predict MHC-allele-restricted TCR-peptide interactions has remained challenging. Here, we present RACER-m, a coarse-grained structural model leveraging key biophysical information from the diversity of publicly available TCR-antigen crystal structures. Explicit inclusion of structural content substantially reduces the required number of training examples and maintains reliable predictions of TCR-recognition specificity and sensitivity across diverse biological contexts. Our model capably identifies biophysically meaningful point-mutant peptides that affect binding affinity, distinguishing its ability in predicting TCR specificity of point-mutants from alternative sequence-based methods. Its application is broadly applicable to studies involving both closely related and structurally diverse TCR-peptide pairs.
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Affiliation(s)
- Ailun Wang
- Center for Theoretical Biological Physics, Northeastern University, Boston, MA, USA
- Department of Physics, Northeastern University, Boston, MA, USA
| | - Xingcheng Lin
- Department of Physics, North Carolina State University, Raleigh, NC, USA
- Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA
| | - Kevin Ng Chau
- Center for Theoretical Biological Physics, Northeastern University, Boston, MA, USA
- Department of Physics, Northeastern University, Boston, MA, USA
| | - José N. Onuchic
- Departments of Physics and Astronomy, Chemistry, and Biosciences, Rice University, Houston, TX, USA
- Center for Theoretical Biological Physics, Rice University, Houston, TX, USA
| | - Herbert Levine
- Center for Theoretical Biological Physics, Northeastern University, Boston, MA, USA
- Department of Physics, Northeastern University, Boston, MA, USA
- Department of Bioengineering, Northeastern University, Boston, MA, USA
| | - Jason T. George
- Center for Theoretical Biological Physics, Rice University, Houston, TX, USA
- Department of Biomedical Engineering, Texas A&M University, Houston, TX, USA
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13
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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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14
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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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15
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Lee Y, Kim SW, Lee E, Shin HY, Kim M, Lee CY, Park BJ, Kim HE, Yang YH, Choi J, Ju S, Park J, Kim N, Choi J, Lee JG, Kwon S, Chung J. Stereotypic T cell receptor clonotypes in the thymus and peripheral blood of Myasthenia gravis patients. Heliyon 2024; 10:e26663. [PMID: 38420468 PMCID: PMC10901099 DOI: 10.1016/j.heliyon.2024.e26663] [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: 10/13/2023] [Revised: 02/16/2024] [Accepted: 02/16/2024] [Indexed: 03/02/2024] Open
Abstract
Myasthenia Gravis (MG) patients with anti-acetylcholine receptor (AChR) antibodies frequently show hyperplastic thymi with ectopic germinal centers, where autoreactive B cells proliferate with the aid of T cells. In this study, thymus and peripheral blood (PB) samples were collected from ten AChR antibody-positive MG patients. T cell receptor (TCR) repertoires were analyzed using next-generation sequencing (NGS), and compared with that of an age and sex matched control group generated from a public database. Certain V genes and VJ gene recombination pairs were significantly upregulated in the TCR chains of αβ-T cells in the PB of MG patients compared to the control group. Furthermore, the TCR chains found in the thymi of MG patients had a weighted distribution to longer CDR3 lengths when compared to the PB of MG patients, and the TCR beta chains (TRB) in the MG group's PB showed increased clonality encoded by one upregulated V gene. When TRB sequences were sub-divided into groups based on their CDR3 lengths, certain groups showed decreased clonality in the MG group's PB compared to the control group's PB. Finally, we demonstrated that stereotypic MG patient-specific TCR clonotypes co-exist in both the PB and thymi at a much higher frequency than that of the clonotypes confined to the PB. These results strongly suggest the existence of a biased T cell-mediated immune response in MG patients, as observed in other autoimmune diseases.
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Affiliation(s)
- Yonghee Lee
- Department of Electrical and Computer Engineering, Seoul National University, Seoul 08826, Republic of Korea
| | - Seung Woo Kim
- Department of Neurology, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Eunjae Lee
- Department of Biomedical Science, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
- Department of Biochemistry and Molecular Biology, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
| | - Ha Young Shin
- Department of Neurology, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - MinGi Kim
- Department of Neurology, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Chang Young Lee
- Department of Thoracic and Cardiovascular Surgery, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Byung Jo Park
- Department of Thoracic and Cardiovascular Surgery, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Ha Eun Kim
- Department of Thoracic and Cardiovascular Surgery, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Young Ho Yang
- Department of Thoracic and Cardiovascular Surgery, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Jinny Choi
- Cancer Research Institute, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
| | - Soyeon Ju
- Department of Biomedical Science, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
- Department of Biochemistry and Molecular Biology, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
| | - Jungheum Park
- Department of Biomedical Science, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
- Department of Biochemistry and Molecular Biology, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
| | - Namphil Kim
- Department of Electrical and Computer Engineering, Seoul National University, Seoul 08826, Republic of Korea
| | - Jaewon Choi
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul 08826, Republic of Korea
- Integrated Major in Innovative Medical Science, Seoul National University, Seoul 08826, Republic of Korea
| | - Jin Gu Lee
- Department of Thoracic and Cardiovascular Surgery, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Sunghoon Kwon
- Department of Electrical and Computer Engineering, Seoul National University, Seoul 08826, Republic of Korea
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul 08826, Republic of Korea
- Bio-MAX Institute, Seoul National University, Seoul 08826, Republic of Korea
| | - Junho Chung
- Department of Biomedical Science, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
- Department of Biochemistry and Molecular Biology, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
- Cancer Research Institute, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
- Interdisciplinary Program in Cancer Biology Major, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
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16
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Bravi B. Development and use of machine learning algorithms in vaccine target selection. NPJ Vaccines 2024; 9:15. [PMID: 38242890 PMCID: PMC10798987 DOI: 10.1038/s41541-023-00795-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 12/07/2023] [Indexed: 01/21/2024] Open
Abstract
Computer-aided discovery of vaccine targets has become a cornerstone of rational vaccine design. In this article, I discuss how Machine Learning (ML) can inform and guide key computational steps in rational vaccine design concerned with the identification of B and T cell epitopes and correlates of protection. I provide examples of ML models, as well as types of data and predictions for which they are built. I argue that interpretable ML has the potential to improve the identification of immunogens also as a tool for scientific discovery, by helping elucidate the molecular processes underlying vaccine-induced immune responses. I outline the limitations and challenges in terms of data availability and method development that need to be addressed to bridge the gap between advances in ML predictions and their translational application to vaccine design.
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Affiliation(s)
- Barbara Bravi
- Department of Mathematics, Imperial College London, London, SW7 2AZ, UK.
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17
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Barra C, Nilsson JB, Saksager A, Carri I, Deleuran S, Garcia Alvarez HM, Høie MH, Li Y, Clifford JN, Wan YTR, Moreta LS, Nielsen M. In Silico Tools for Predicting Novel Epitopes. Methods Mol Biol 2024; 2813:245-280. [PMID: 38888783 DOI: 10.1007/978-1-0716-3890-3_17] [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: 06/20/2024]
Abstract
Identifying antigens within a pathogen is a critical task to develop effective vaccines and diagnostic methods, as well as understanding the evolution and adaptation to host immune responses. Historically, antigenicity was studied with experiments that evaluate the immune response against selected fragments of pathogens. Using this approach, the scientific community has gathered abundant information regarding which pathogenic fragments are immunogenic. The systematic collection of this data has enabled unraveling many of the fundamental rules underlying the properties defining epitopes and immunogenicity, and has resulted in the creation of a large panel of immunologically relevant predictive (in silico) tools. The development and application of such tools have proven to accelerate the identification of novel epitopes within biomedical applications reducing experimental costs. This chapter introduces some basic concepts about MHC presentation, T cell and B cell epitopes, the experimental efforts to determine those, and focuses on state-of-the-art methods for epitope prediction, highlighting their strengths and limitations, and catering instructions for their rational use.
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Affiliation(s)
- Carolina Barra
- Section for Bioinformatics, Health Tech, Technical University of Denmark, Lyngby, Denmark.
| | | | - Astrid Saksager
- Section for Bioinformatics, Health Tech, Technical University of Denmark, Lyngby, Denmark
| | - Ibel Carri
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín (UNSAM) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), San Martín, Argentina
| | - Sebastian Deleuran
- Section for Bioinformatics, Health Tech, Technical University of Denmark, Lyngby, Denmark
| | - Heli M Garcia Alvarez
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín (UNSAM) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), San Martín, Argentina
| | - Magnus Haraldson Høie
- Section for Bioinformatics, Health Tech, Technical University of Denmark, Lyngby, Denmark
| | - Yuchen Li
- Section for Bioinformatics, Health Tech, Technical University of Denmark, Lyngby, Denmark
| | | | - Yat-Tsai Richie Wan
- Section for Bioinformatics, Health Tech, Technical University of Denmark, Lyngby, Denmark
| | - Lys Sanz Moreta
- Section for Bioinformatics, Health Tech, Technical University of Denmark, Lyngby, Denmark
| | - Morten Nielsen
- Section for Bioinformatics, Health Tech, Technical University of Denmark, Lyngby, Denmark
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín (UNSAM) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), San Martín, Argentina
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18
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Koyama K, Hashimoto K, Nagao C, Mizuguchi K. Attention network for predicting T-cell receptor-peptide binding can associate attention with interpretable protein structural properties. FRONTIERS IN BIOINFORMATICS 2023; 3:1274599. [PMID: 38170146 PMCID: PMC10759225 DOI: 10.3389/fbinf.2023.1274599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 11/27/2023] [Indexed: 01/05/2024] Open
Abstract
Understanding how a T-cell receptor (TCR) recognizes its specific ligand peptide is crucial for gaining an insight into biological functions and disease mechanisms. Despite its importance, experimentally determining TCR-peptide-major histocompatibility complex (TCR-pMHC) interactions is expensive and time-consuming. To address this challenge, computational methods have been proposed, but they are typically evaluated by internal retrospective validation only, and few researchers have incorporated and tested an attention layer from language models into structural information. Therefore, in this study, we developed a machine learning model based on a modified version of Transformer, a source-target attention neural network, to predict the TCR-pMHC interaction solely from the amino acid sequences of the TCR complementarity-determining region (CDR) 3 and the peptide. This model achieved competitive performance on a benchmark dataset of the TCR-pMHC interaction, as well as on a truly new external dataset. Additionally, by analyzing the results of binding predictions, we associated the neural network weights with protein structural properties. By classifying the residues into large- and small-attention groups, we identified statistically significant properties associated with the largely attended residues such as hydrogen bonds within CDR3. The dataset that we created and the ability of our model to provide an interpretable prediction of TCR-peptide binding should increase our knowledge about molecular recognition and pave the way for designing new therapeutics.
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Affiliation(s)
- Kyohei Koyama
- Laboratory for Computational Biology, Institute for Protein Research, Osaka University, Osaka, Japan
- National Institutes of Biomedical Innovation, Health and Nutrition, Osaka, Japan
- Graduate School of Frontier Biosciences, Osaka University, Osaka, Japan
| | - Kosuke Hashimoto
- Laboratory for Computational Biology, Institute for Protein Research, Osaka University, Osaka, Japan
| | - Chioko Nagao
- Laboratory for Computational Biology, Institute for Protein Research, Osaka University, Osaka, Japan
| | - Kenji Mizuguchi
- Laboratory for Computational Biology, Institute for Protein Research, Osaka University, Osaka, Japan
- National Institutes of Biomedical Innovation, Health and Nutrition, Osaka, Japan
- Graduate School of Frontier Biosciences, Osaka University, Osaka, Japan
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19
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Han Y, Yang Y, Tian Y, Fattah FJ, von Itzstein MS, Hu Y, Zhang M, Kang X, Yang DM, Liu J, Xue Y, Liang C, Raman I, Zhu C, Xiao O, Dowell JE, Homsi J, Rashdan S, Yang S, Gwin ME, Hsiehchen D, Gloria-McCutchen Y, Pan K, Wu F, Gibbons D, Wang X, Yee C, Huang J, Reuben A, Cheng C, Zhang J, Gerber DE, Wang T. pan-MHC and cross-Species Prediction of T Cell Receptor-Antigen Binding. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.01.569599. [PMID: 38105939 PMCID: PMC10723300 DOI: 10.1101/2023.12.01.569599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Profiling the binding of T cell receptors (TCRs) of T cells to antigenic peptides presented by MHC proteins is one of the most important unsolved problems in modern immunology. Experimental methods to probe TCR-antigen interactions are slow, labor-intensive, costly, and yield moderate throughput. To address this problem, we developed pMTnet-omni, an Artificial Intelligence (AI) system based on hybrid protein sequence and structure information, to predict the pairing of TCRs of αβ T cells with peptide-MHC complexes (pMHCs). pMTnet-omni is capable of handling peptides presented by both class I and II pMHCs, and capable of handling both human and mouse TCR-pMHC pairs, through information sharing enabled this hybrid design. pMTnet-omni achieves a high overall Area Under the Curve of Receiver Operator Characteristics (AUROC) of 0.888, which surpasses competing tools by a large margin. We showed that pMTnet-omni can distinguish binding affinity of TCRs with similar sequences. Across a range of datasets from various biological contexts, pMTnet-omni characterized the longitudinal evolution and spatial heterogeneity of TCR-pMHC interactions and their functional impact. We successfully developed a biomarker based on pMTnet-omni for predicting immune-related adverse events of immune checkpoint inhibitor (ICI) treatment in a cohort of 57 ICI-treated patients. pMTnet-omni represents a major advance towards developing a clinically usable AI system for TCR-pMHC pairing prediction that can aid the design and implementation of TCR-based immunotherapeutics.
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20
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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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21
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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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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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23
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Agrez M, Chandler C, Thurecht KJ, Fletcher NL, Liu F, Subramaniam G, Howard CB, Blyth B, Parker S, Turner D, Rzepecka J, Knox G, Nika A, Hall AM, Gooding H, Gallagher L. An immunomodulating peptide with potential to suppress tumour growth and autoimmunity. Sci Rep 2023; 13:19741. [PMID: 37957274 PMCID: PMC10643673 DOI: 10.1038/s41598-023-47229-y] [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: 07/06/2023] [Accepted: 11/10/2023] [Indexed: 11/15/2023] Open
Abstract
Cancers and autoimmune diseases commonly co-exist and immune checkpoint inhibitor therapy (ICI) exacerbates autoimmune pathologies. We recently described a lipidic peptide, designated IK14004, that promotes expansion of immunosuppressive T regulatory (Treg) cells and uncouples interleukin-2 from interferon-gamma production while activating CD8+ T cells. Herein, we report IK14004-mediated inhibition of Lewis lung cancer (LLC) growth and re-invigoration of splenocyte-derived exhausted CD4+ T cells. In human immune cells from healthy donors, IK14004 modulates expression of the T cell receptor α/β subunits, induces Type I IFN expression, stimulates natural killer (NK) cells to express NKG2D/NKp44 receptors and enhances K562 cytotoxicity. In both T and NK cells, IK14004 alters the IL-12 receptor β1/β2 chain ratio to favour IL-12p70 binding. Taken together, this novel peptide offers an opportunity to gain further insight into the complexity of ICI immunotherapy so that autoimmune responses may be minimised without promoting tumour evasion from the immune system.
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Affiliation(s)
- Michael Agrez
- InterK Peptide Therapeutics Limited, New South Wales, Australia.
- Australian Institute for Bioengineering and Nanotechnology and the ARC Training Centre for Innovation in Biomedical Imaging Technologies, University of Queensland, Brisbane, Australia.
| | | | - Kristofer J Thurecht
- Centre for Advanced Imaging, University of Queensland, Brisbane, Australia
- Australian Institute for Bioengineering and Nanotechnology and the ARC Training Centre for Innovation in Biomedical Imaging Technologies, University of Queensland, Brisbane, Australia
| | - Nicholas L Fletcher
- Centre for Advanced Imaging, University of Queensland, Brisbane, Australia
- Australian Institute for Bioengineering and Nanotechnology and the ARC Training Centre for Innovation in Biomedical Imaging Technologies, University of Queensland, Brisbane, Australia
| | - Feifei Liu
- Centre for Advanced Imaging, University of Queensland, Brisbane, Australia
- Australian Institute for Bioengineering and Nanotechnology and the ARC Training Centre for Innovation in Biomedical Imaging Technologies, University of Queensland, Brisbane, Australia
| | - Gayathri Subramaniam
- Centre for Advanced Imaging, University of Queensland, Brisbane, Australia
- Australian Institute for Bioengineering and Nanotechnology and the ARC Training Centre for Innovation in Biomedical Imaging Technologies, University of Queensland, Brisbane, Australia
| | - Christopher B Howard
- Centre for Advanced Imaging, University of Queensland, Brisbane, Australia
- Australian Institute for Bioengineering and Nanotechnology and the ARC Training Centre for Innovation in Biomedical Imaging Technologies, University of Queensland, Brisbane, Australia
| | - Benjamin Blyth
- Department of Oncology,, Peter MacCallum Cancer Centre and Sir Peter MacCallum, University of Melbourne, Melbourne, Australia
| | - Stephen Parker
- InterK Peptide Therapeutics Limited, New South Wales, Australia
| | | | | | - Gavin Knox
- Concept Life Sciences, Edinburgh, Scotland
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24
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Liu S, Bradley P, Sun W. Neural network models for sequence-based TCR and HLA association prediction. PLoS Comput Biol 2023; 19:e1011664. [PMID: 37983288 PMCID: PMC10695368 DOI: 10.1371/journal.pcbi.1011664] [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: 06/21/2023] [Revised: 12/04/2023] [Accepted: 11/06/2023] [Indexed: 11/22/2023] Open
Abstract
T cells rely on their T cell receptors (TCRs) to discern foreign antigens presented by human leukocyte antigen (HLA) proteins. The TCRs of an individual contain a record of this individual's past immune activities, such as immune response to infections or vaccines. Mining the TCR data may recover useful information or biomarkers for immune related diseases or conditions. Some TCRs are observed only in the individuals with certain HLA alleles, and thus characterizing TCRs requires a thorough understanding of TCR-HLA associations. The extensive diversity of HLA alleles and the rareness of some HLA alleles present a formidable challenge for this task. Existing methods either treat HLA as a categorical variable or represent an HLA by its alphanumeric name, and have limited ability to generalize to the HLAs that are not seen in the training process. To address this challenge, we propose a neural network-based method named Deep learning Prediction of TCR-HLA association (DePTH) to predict TCR-HLA associations based on their amino acid sequences. We demonstrate that DePTH is capable of making reasonable predictions for TCR-HLA associations, even when neither the HLA nor the TCR have been included in the training dataset. Furthermore, we establish that DePTH can be used to quantify the functional similarities among HLA alleles, and that these HLA similarities are associated with the survival outcomes of cancer patients who received immune checkpoint blockade treatments.
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Affiliation(s)
- Si Liu
- Biostatistics Program, Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America
| | - Philip Bradley
- Herbold Computational Biology Program, Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America
- Institute for Protein Design. University of Washington, Seattle, Washington, United States of America
| | - Wei Sun
- Biostatistics Program, Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America
- Department of Biostatistics, University of Washington, Seattle, Washington, United States of America
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, United States of America
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25
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Linsley P, Nakayama M, Balmas E, Chen J, Pour F, Bansal S, Serti E, Speake C, Pugliese A, Cerosaletti K. Self-reactive germline-like TCR alpha chains shared between blood and pancreas. RESEARCH SQUARE 2023:rs.3.rs-3446917. [PMID: 37886513 PMCID: PMC10602137 DOI: 10.21203/rs.3.rs-3446917/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Abstract
Human islet antigen reactive CD4 + memory T cells (IAR T cells) from peripheral blood have been studied extensively for their role in the pathogenesis of autoimmune type 1 diabetes (T1D). However, IAR T cells are rare, and it remains poorly understood how they affect T1D progression in the pancreas. Using single cell RNA-sequencing coupled with a multiplexed activation induced marker (AIM) enrichment assay, we identified paired TCR alpha/beta (TRA/TRB) T cell receptors (TCRs) in IAR T cells from the blood of healthy, at-risk, new onset, and established T1D donors. Using TCR sequences as barcodes, we measured infiltration of IAR T cells from blood into pancreas of organ donors with and without T1D. We detected extensive TCR sharing between IAR T cells from peripheral blood and pancreatic infiltrating T cells (PIT), with perfectly matched or single mismatched TRA junctions and J gene regions, comprising ~ 34% of unique IAR TCRs. PIT-matching IAR T cells had public TRA chains that showed increased use of germline-encoded residues in epitope engagement and a propensity for cross-reactivity. The link with T cells in the pancreas implicates autoreactive IAR T cells with shared TRA junctions and increased levels in blood with the prediabetic and new onset phases of T1D progression.
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26
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Pospiech M, Tamizharasan M, Wei YC, Kumar AMS, Lou M, Milstein J, Alachkar H. Features of the TCR repertoire associate with patients' clinical and molecular characteristics in acute myeloid leukemia. Front Immunol 2023; 14:1236514. [PMID: 37928542 PMCID: PMC10620936 DOI: 10.3389/fimmu.2023.1236514] [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: 06/07/2023] [Accepted: 09/07/2023] [Indexed: 11/07/2023] Open
Abstract
Background Allogeneic hematopoietic stem cell transplant remains the most effective strategy for patients with high-risk acute myeloid leukemia (AML). Leukemia-specific neoantigens presented by the major histocompatibility complexes (MHCs) are recognized by the T cell receptors (TCR) triggering the graft-versus-leukemia effect. A unique TCR signature is generated by a complex V(D)J rearrangement process to form TCR capable of binding to the peptide-MHC. The generated TCR repertoire undergoes dynamic changes with disease progression and treatment. Method Here we applied two different computational tools (TRUST4 and MIXCR) to extract the TCR sequences from RNA-seq data from The Cancer Genome Atlas (TCGA) and examine the association between features of the TCR repertoire in adult patients with AML and their clinical and molecular characteristics. Results We found that only ~30% of identified TCR CDR3s were shared by the two computational tools. Yet, patterns of TCR associations with patients' clinical and molecular characteristics based on data obtained from either tool were similar. The numbers of unique TCR clones were highly correlated with patients' white blood cell counts, bone marrow blast percentage, and peripheral blood blast percentage. Multivariable regressions of TCRA and TCRB median normalized number of unique clones with mutational status of AML patients using TRUST4 showed significant association of TCRA or TCRB with WT1 mutations, WBC count, %BM blast, and sex (adjusted in TCRB model). We observed a correlation between TCRA/B number of unique clones and the expression of T cells inhibitory signal genes (TIGIT, LAG3, CTLA-4) and foxp3, but not IL2RA, CD69 and TNFRSF9 suggestive of exhausted T cell phenotypes in AML. Conclusion Benchmarking of computational tools is needed to increase the accuracy of the identified clones. The utilization of RNA-seq data enables identification of highly abundant TCRs and correlating these clones with patients' clinical and molecular characteristics. This study further supports the value of high-resolution TCR-Seq analyses to characterize the TCR repertoire in patients.
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Affiliation(s)
- Mateusz Pospiech
- Department of Clinical Pharmacy, School of Pharmacy, University of Southern California, Los Angeles, CA, United States
| | - Mukund Tamizharasan
- Department of Clinical Pharmacy, School of Pharmacy, University of Southern California, Los Angeles, CA, United States
- Department of Computer Science, University of Southern California, Los Angeles, CA, United States
| | - Yu-Chun Wei
- Department of Clinical Pharmacy, School of Pharmacy, University of Southern California, Los Angeles, CA, United States
| | - Advaith Maya Sanjeev Kumar
- Department of Clinical Pharmacy, School of Pharmacy, University of Southern California, Los Angeles, CA, United States
- Department of Computer Science, University of Southern California, Los Angeles, CA, United States
| | - Mimi Lou
- Department of Clinical Pharmacy, School of Pharmacy, University of Southern California, Los Angeles, CA, United States
| | - Joshua Milstein
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Houda Alachkar
- Department of Clinical Pharmacy, School of Pharmacy, University of Southern California, Los Angeles, CA, United States
- Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, United States
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27
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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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28
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Ghoreyshi ZS, George JT. Quantitative approaches for decoding the specificity of the human T cell repertoire. Front Immunol 2023; 14:1228873. [PMID: 37781387 PMCID: PMC10539903 DOI: 10.3389/fimmu.2023.1228873] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 08/17/2023] [Indexed: 10/03/2023] Open
Abstract
T cell receptor (TCR)-peptide-major histocompatibility complex (pMHC) interactions play a vital role in initiating immune responses against pathogens, and the specificity of TCRpMHC interactions is crucial for developing optimized therapeutic strategies. The advent of high-throughput immunological and structural evaluation of TCR and pMHC has provided an abundance of data for computational approaches that aim to predict favorable TCR-pMHC interactions. Current models are constructed using information on protein sequence, structures, or a combination of both, and utilize a variety of statistical learning-based approaches for identifying the rules governing specificity. This review examines the current theoretical, computational, and deep learning approaches for identifying TCR-pMHC recognition pairs, placing emphasis on each method's mathematical approach, predictive performance, and limitations.
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Affiliation(s)
- Zahra S. Ghoreyshi
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, United States
| | - Jason T. George
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, United States
- Engineering Medicine Program, Texas A&M University, Houston, TX, United States
- Center for Theoretical Biological Physics, Rice University, Houston, TX, United States
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29
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Kusuda M, Nakasone H, Yoshimura K, Okada Y, Tamaki M, Matsuoka A, Ishikawa T, Meno T, Nakamura Y, Kawamura M, Takeshita J, Kawamura S, Yoshino N, Misaki Y, Gomyo A, Tanihara A, Kimura SI, Kako S, Kanda Y. Gene expression and TCR amino acid sequences selected by HLA-A02:01-restricted CTLs specific to HTLV-1 in ATL patients. Br J Haematol 2023; 202:578-588. [PMID: 37317804 DOI: 10.1111/bjh.18918] [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: 03/29/2023] [Revised: 05/28/2023] [Accepted: 05/31/2023] [Indexed: 06/16/2023]
Abstract
Adult T-cell leukaemia/lymphoma (ATL) is an aggressive malignancy of peripheral T cells caused by human T-cell lymphotropic virus type-1 (HTLV-1). Tax is the most important regulatory protein for HTLV-1. We aimed to reveal a unique amino acid sequence (AA) of complementarity-determining region 3 (CDR3) of the T-cell receptor (TCR)β and TCRα chains of HLA-A*02:01-restricted Tax11-19 -specific cytotoxic T cells (Tax-CTLs). The gene expression profiles (GEP) of Tax-CTLs were assessed by the next-generation sequence (NGS) method with SMARTer technology. Tax-CTLs seemed to be oligoclonal, and their gene compositions were skewed. The unique motifs of 'DSWGK' in TCRα and 'LAG' in TCRβ at CDR3 were observed in almost all patients. Tax-CTL clones harbouring the 'LAG' motif with BV28 had a higher binding score than those without either of them, besides a higher binding score associated with longer survival. Tax-CTLs established from a single cell showed killing activities against Tax-peptide-pulsed HLA-A2+ T2 cell lines. GEP of Tax-CTLs revealed that genes associated with immune response activity were well preserved in long-term survivors with stable status. These methods and results can help us better understand immunity against ATL, and should contribute to future studies on the clinical application of adoptive T-cell therapies.
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Affiliation(s)
- Machiko Kusuda
- Division of Hematology, Jichi Medical University Saitama Medical Center, Saitama, Japan
| | - Hideki Nakasone
- Division of Hematology, Jichi Medical University Saitama Medical Center, Saitama, Japan
- Division of Stem Cell Regulation, Center for Molecular Medicine, Jichi Medicial University, Shimotsuke, Japan
| | - Kazuki Yoshimura
- Division of Hematology, Jichi Medical University Saitama Medical Center, Saitama, Japan
| | - Yosuke Okada
- Division of Hematology, Jichi Medical University Saitama Medical Center, Saitama, Japan
| | - Masaharu Tamaki
- Division of Hematology, Jichi Medical University Saitama Medical Center, Saitama, Japan
| | - Akari Matsuoka
- Division of Hematology, Jichi Medical University Saitama Medical Center, Saitama, Japan
| | - Takuto Ishikawa
- Division of Hematology, Jichi Medical University Saitama Medical Center, Saitama, Japan
| | - Tomohiro Meno
- Division of Hematology, Jichi Medical University Saitama Medical Center, Saitama, Japan
| | - Yuhei Nakamura
- Division of Hematology, Jichi Medical University Saitama Medical Center, Saitama, Japan
| | - Masakatsu Kawamura
- Division of Hematology, Jichi Medical University Saitama Medical Center, Saitama, Japan
| | - Junko Takeshita
- Division of Hematology, Jichi Medical University Saitama Medical Center, Saitama, Japan
| | - Shunto Kawamura
- Division of Hematology, Jichi Medical University Saitama Medical Center, Saitama, Japan
| | - Nozomu Yoshino
- Division of Hematology, Jichi Medical University Saitama Medical Center, Saitama, Japan
| | - Yukiko Misaki
- Division of Hematology, Jichi Medical University Saitama Medical Center, Saitama, Japan
| | - Ayumi Gomyo
- Division of Hematology, Jichi Medical University Saitama Medical Center, Saitama, Japan
| | - Aki Tanihara
- Division of Hematology, Jichi Medical University Saitama Medical Center, Saitama, Japan
| | - Shun-Ichi Kimura
- Division of Hematology, Jichi Medical University Saitama Medical Center, Saitama, Japan
| | - Shinichi Kako
- Division of Hematology, Jichi Medical University Saitama Medical Center, Saitama, Japan
| | - Yoshinobu Kanda
- Division of Hematology, Jichi Medical University Saitama Medical Center, Saitama, Japan
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30
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Hudson D, Fernandes RA, Basham M, Ogg G, Koohy H. Can we predict T cell specificity with digital biology and machine learning? Nat Rev Immunol 2023; 23:511-521. [PMID: 36755161 PMCID: PMC9908307 DOI: 10.1038/s41577-023-00835-3] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/07/2022] [Indexed: 02/10/2023]
Abstract
Recent advances in machine learning and experimental biology have offered breakthrough solutions to problems such as protein structure prediction that were long thought to be intractable. However, despite the pivotal role of the T cell receptor (TCR) in orchestrating cellular immunity in health and disease, computational reconstruction of a reliable map from a TCR to its cognate antigens remains a holy grail of systems immunology. Current data sets are limited to a negligible fraction of the universe of possible TCR-ligand pairs, and performance of state-of-the-art predictive models wanes when applied beyond these known binders. In this Perspective article, we make the case for renewed and coordinated interdisciplinary effort to tackle the problem of predicting TCR-antigen specificity. We set out the general requirements of predictive models of antigen binding, highlight critical challenges and discuss how recent advances in digital biology such as single-cell technology and machine learning may provide possible solutions. Finally, we describe how predicting TCR specificity might contribute to our understanding of the broader puzzle of antigen immunogenicity.
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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
| | - Ricardo A Fernandes
- Chinese Academy of Medical Sciences Oxford Institute, University of Oxford, Oxford, UK
| | | | - Graham Ogg
- MRC Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
- Chinese Academy of Medical Sciences Oxford Institute, University of Oxford, Oxford, 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.
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31
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Wolmarans HJ, Barker VR, Chobrutskiy A, Chobrutskiy BI, Huda TI, Blanck G. Exploiting big data survival information to unify risk-stratification related, adaptive immune receptor parameters for multiple myeloma. Genes Immun 2023; 24:194-199. [PMID: 37443300 DOI: 10.1038/s41435-023-00212-z] [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: 03/14/2023] [Revised: 06/27/2023] [Accepted: 06/29/2023] [Indexed: 07/15/2023]
Abstract
With the improvement of treatment options, multiple myeloma related life expectancy has been prolonged, but the disease remains largely incurable. Immunotherapy is a growing field that shows promise in advancements for treatment, and recent work has demonstrated an opportunity to use immune receptor, complementarity determining region-3 (CDR3)-candidate antigen chemical complementarity scores to identify survival distinctions among subgroups of patients. Here, we have applied the complementarity scoring algorithm to identify multiple myeloma related, CDR3-cancer testis antigen (CTA) relationships associated with survival distinctions. Furthermore, we have overlapped these immune receptor features with a previous study that showed a dramatic survival distinction based on T-cell receptor, V- and J-gene segment usage, HLA allele combinations, whereby 100% of the patients in certain combination groups had no mortality related to multiple myeloma, during the study period. This overlap evaluation was consistent with the idea that there are likely considerable constraints on productive TRB-antigen-HLA combinations but more flexibility, and unpredictability, for the TRA-antigen-HLA combinations. Also, the approaches in this reported indicated the potential importance of the CTA, IGSF11, as a multiple myeloma antigen, an antigen previously, independently considered as a vaccine candidate in other settings.
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Affiliation(s)
- Hope J Wolmarans
- Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL, 33612, USA
| | - Vayda R Barker
- Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL, 33612, USA
| | - Andrea Chobrutskiy
- Department of Pediatrics, Oregon Health and Science University Hospital, Portland, OR, 97239, USA
| | - Boris I Chobrutskiy
- Department of Internal Medicine, Oregon Health and Science University Hospital, Portland, OR, 97239, USA
| | - Taha I Huda
- Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL, 33612, USA
| | - George Blanck
- Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL, 33612, USA.
- Department of Immunology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, 33612, USA.
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32
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Heumos L, Schaar AC, Lance C, Litinetskaya A, Drost F, Zappia L, Lücken MD, Strobl DC, Henao J, Curion F, Schiller HB, Theis FJ. Best practices for single-cell analysis across modalities. Nat Rev Genet 2023; 24:550-572. [PMID: 37002403 PMCID: PMC10066026 DOI: 10.1038/s41576-023-00586-w] [Citation(s) in RCA: 168] [Impact Index Per Article: 168.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/14/2023] [Indexed: 04/03/2023]
Abstract
Recent advances in single-cell technologies have enabled high-throughput molecular profiling of cells across modalities and locations. Single-cell transcriptomics data can now be complemented by chromatin accessibility, surface protein expression, adaptive immune receptor repertoire profiling and spatial information. The increasing availability of single-cell data across modalities has motivated the development of novel computational methods to help analysts derive biological insights. As the field grows, it becomes increasingly difficult to navigate the vast landscape of tools and analysis steps. Here, we summarize independent benchmarking studies of unimodal and multimodal single-cell analysis across modalities to suggest comprehensive best-practice workflows for the most common analysis steps. Where independent benchmarks are not available, we review and contrast popular methods. Our article serves as an entry point for novices in the field of single-cell (multi-)omic analysis and guides advanced users to the most recent best practices.
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Affiliation(s)
- Lukas Heumos
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Institute of Lung Health and Immunity and Comprehensive Pneumology Center, Helmholtz Munich; Member of the German Center for Lung Research (DZL), Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - Anna C Schaar
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
- Munich Center for Machine Learning, Technical University of Munich, Garching, Germany
| | - Christopher Lance
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Paediatrics, Dr von Hauner Children's Hospital, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Anastasia Litinetskaya
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Felix Drost
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - Luke Zappia
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Malte D Lücken
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Institute of Lung Health and Immunity, Helmholtz Munich, Munich, Germany
| | - Daniel C Strobl
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
- Institute of Clinical Chemistry and Pathobiochemistry, School of Medicine, Technical University of Munich, Munich, Germany
- TranslaTUM, Center for Translational Cancer Research, Technical University of Munich, Munich, Germany
| | - Juan Henao
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
| | - Fabiola Curion
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Herbert B Schiller
- Institute of Lung Health and Immunity and Comprehensive Pneumology Center, Helmholtz Munich; Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Fabian J Theis
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany.
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany.
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany.
- Munich Center for Machine Learning, Technical University of Munich, Garching, Germany.
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Bujak J, Kłęk S, Balawejder M, Kociniak A, Wilkus K, Szatanek R, Orzeszko Z, Welanyk J, Torbicz G, Jęckowski M, Kucharczyk T, Wohadlo Ł, Borys M, Stadnik H, Wysocki M, Kayser M, Słomka ME, Kosmowska A, Horbacka K, Gach T, Markowska B, Kowalczyk T, Karoń J, Karczewski M, Szura M, Sanecka-Duin A, Blum A. Creating an Innovative Artificial Intelligence-Based Technology (TCRact) for Designing and Optimizing T Cell Receptors for Use in Cancer Immunotherapies: Protocol for an Observational Trial. JMIR Res Protoc 2023; 12:e45872. [PMID: 37440307 PMCID: PMC10375398 DOI: 10.2196/45872] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 06/01/2023] [Accepted: 06/02/2023] [Indexed: 07/14/2023] Open
Abstract
BACKGROUND Cancer continues to be the leading cause of mortality in high-income countries, necessitating the development of more precise and effective treatment modalities. Immunotherapy, specifically adoptive cell transfer of T cell receptor (TCR)-engineered T cells (TCR-T therapy), has shown promise in engaging the immune system for cancer treatment. One of the biggest challenges in the development of TCR-T therapies is the proper prediction of the pairing between TCRs and peptide-human leukocyte antigen (pHLAs). Modern computational immunology, using artificial intelligence (AI)-based platforms, provides the means to optimize the speed and accuracy of TCR screening and discovery. OBJECTIVE This study proposes an observational clinical trial protocol to collect patient samples and generate a database of pHLA:TCR sequences to aid the development of an AI-based platform for efficient selection of specific TCRs. METHODS The multicenter observational study, involving 8 participating hospitals, aims to enroll patients diagnosed with stage II, III, or IV colorectal cancer adenocarcinoma. RESULTS Patient recruitment has recently been completed, with 100 participants enrolled. Primary tumor tissue and peripheral blood samples have been obtained, and peripheral blood mononuclear cells have been isolated and cryopreserved. Nucleic acid extraction (DNA and RNA) has been performed in 86 cases. Additionally, 57 samples underwent whole exome sequencing to determine the presence of somatic mutations and RNA sequencing for gene expression profiling. CONCLUSIONS The results of this study may have a significant impact on the treatment of patients with colorectal cancer. The comprehensive database of pHLA:TCR sequences generated through this observational clinical trial will facilitate the development of the AI-based platform for TCR selection. The results obtained thus far demonstrate successful patient recruitment and sample collection, laying the foundation for further analysis and the development of an innovative tool to expedite and enhance TCR selection for precision cancer treatments. TRIAL REGISTRATION ClinicalTrials.gov NCT04994093; https://clinicaltrials.gov/ct2/show/NCT04994093. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/45872.
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Affiliation(s)
- Joanna Bujak
- Ardigen SA, Cracow, Poland
- Department of Physics and Biophysics, Institute of Biology, Warsaw University of Life Sciences, Warszawa, Poland
| | - Stanisław Kłęk
- Surgical Oncology Clinic, Maria Sklodowska-Curie National Research Institute of Oncology, Cracow, Poland
| | | | | | | | | | - Zofia Orzeszko
- Department of General and Oncological Surgery, Brothers Hospitallers Hospital, Cracow, Poland
| | - Joanna Welanyk
- Surgical Oncology Clinic, Maria Sklodowska-Curie National Research Institute of Oncology, Cracow, Poland
| | - Grzegorz Torbicz
- Department of General Surgery and Surgical Oncology, Ludwik Rydygier Memorial Hospital, Cracow, Poland
| | - Mateusz Jęckowski
- Colon Cancer Unit, Department of Oncological Surgery, Voivodeship Multi-Specialist Center for Oncology and Traumatology, Lodz, Poland
| | - Tomasz Kucharczyk
- Holy Cross Cancer Center Clinic of Clinical Oncology, Cracow, Poland
| | - Łukasz Wohadlo
- Department of General Surgery, Andrzej Frycz Modrzewski Krakow University, Cracow, Poland
| | - Maciej Borys
- Department of General Surgery and Surgical Oncology, Ludwik Rydygier Memorial Hospital, Cracow, Poland
| | - Honorata Stadnik
- Department of General and Transplant Surgery, Poznan University of Medical Sciences, University Hospital, Poznan, Poland
| | - Michał Wysocki
- Department of General Surgery and Surgical Oncology, Ludwik Rydygier Memorial Hospital, Cracow, Poland
| | - Magdalena Kayser
- General and Colorectal Surgery Department, J Struś Multispecialist Municipal Hospital, Poznan, Poland
| | - Marta Ewa Słomka
- Colon Cancer Unit, Department of Oncological Surgery, Voivodeship Multi-Specialist Center for Oncology and Traumatology, Lodz, Poland
| | - Anna Kosmowska
- General and Colorectal Surgery Department, J Struś Multispecialist Municipal Hospital, Poznan, Poland
| | - Karolina Horbacka
- General and Colorectal Surgery Department, J Struś Multispecialist Municipal Hospital, Poznan, Poland
| | - Tomasz Gach
- Surgical Clinic Institute of Physiotherapy, Faculty of Health Sciences, Jagiellonian University Medical College, Cracow, Poland
| | - Beata Markowska
- Surgical Clinic Institute of Physiotherapy, Faculty of Health Sciences, Jagiellonian University Medical College, Cracow, Poland
| | - Tomasz Kowalczyk
- Department of General Surgery, Andrzej Frycz Modrzewski Krakow University, Cracow, Poland
| | - Jacek Karoń
- General and Colorectal Surgery Department, J Struś Multispecialist Municipal Hospital, Poznan, Poland
| | - Marek Karczewski
- Department of General and Transplant Surgery, Poznan University of Medical Sciences, University Hospital, Poznan, Poland
| | - Mirosław Szura
- Surgical Clinic Institute of Physiotherapy, Faculty of Health Sciences, Jagiellonian University Medical College, Cracow, Poland
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Gouttefangeas C, Klein R, Maia A. The good and the bad of T cell cross-reactivity: challenges and opportunities for novel therapeutics in autoimmunity and cancer. Front Immunol 2023; 14:1212546. [PMID: 37409132 PMCID: PMC10319254 DOI: 10.3389/fimmu.2023.1212546] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 05/24/2023] [Indexed: 07/07/2023] Open
Abstract
T cells are main actors of the immune system with an essential role in protection against pathogens and cancer. The molecular key event involved in this absolutely central task is the interaction of membrane-bound specific T cell receptors with peptide-MHC complexes which initiates T cell priming, activation and recall, and thus controls a range of downstream functions. While textbooks teach us that the repertoire of mature T cells is highly diverse, it is clear that this diversity cannot possibly cover all potential foreign peptides that might be encountered during life. TCR cross-reactivity, i.e. the ability of a single TCR to recognise different peptides, offers the best solution to this biological challenge. Reports have shown that indeed, TCR cross-reactivity is surprisingly high. Hence, the T cell dilemma is the following: be as specific as possible to target foreign danger and spare self, while being able to react to a large spectrum of body-threatening situations. This has major consequences for both autoimmune diseases and cancer, and significant implications for the development of T cell-based therapies. In this review, we will present essential experimental evidence of T cell cross-reactivity, implications for two opposite immune conditions, i.e. autoimmunity vs cancer, and how this can be differently exploited for immunotherapy approaches. Finally, we will discuss the tools available for predicting cross-reactivity and how improvements in this field might boost translational approaches.
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Affiliation(s)
- Cécile Gouttefangeas
- Nencki Institute of Experimental Biology of the Polish Academy of Sciences, Warsaw, Poland
- Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, Tübingen, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ) partner site Tübingen, Tübingen, Germany
| | - Reinhild Klein
- Department of Hematology, Oncology, Clinical Immunology and Rheumatology, University Hospital Tübingen, Tübingen, Germany
| | - Ana Maia
- Nencki Institute of Experimental Biology of the Polish Academy of Sciences, Warsaw, Poland
- Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, Tübingen, Germany
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Pakasticali N, Chobrutskiy A, Patel DN, Hsiang M, Zaman S, Cios KJ, Blanck G, Chobrutskiy BI. Chemical Complementarity of Breast Cancer Resident, T-Cell Receptor CDR3 Domains and the Cancer Antigen, ARMC3, is Associated With Higher Levels of Survival and Granzyme Expression. Cancer Inform 2023; 22:11769351231177269. [PMID: 37313373 PMCID: PMC10259117 DOI: 10.1177/11769351231177269] [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: 01/04/2023] [Accepted: 05/03/2023] [Indexed: 06/15/2023] Open
Abstract
Introduction One of the most pressing goals for cancer immunotherapy at this time is the identification of actionable antigens. Methods This study relies on the following considerations and approaches to identify potential breast cancer antigens: (i) the significant role of the adaptive immune receptor, complementarity determining region-3 (CDR3) in antigen binding, and the existence cancer testis antigens (CTAs); (ii) chemical attractiveness; and (iii) informing the relevance of the integration of items (i) and (ii) with patient outcome and tumor gene expression data. Results We have assessed CTAs for associations with survival, based on their chemical complementarity with tumor resident T-cell receptor (TCR), CDR3s. Also, we have established gene expression correlations with the high TCR CDR3-CTA chemical complementarities, for Granzyme B, and other immune biomarkers. Conclusions Overall, for several independent TCR CDR3 breast cancer datasets, the CTA, ARMC3, stood out as a completely novel, candidate antigen based on multiple algorithms with highly consistent approaches. This conclusion was facilitated by use of the recently constructed Adaptive Match web tool.
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Affiliation(s)
- Nagehan Pakasticali
- Department of Pathology, Morsani College of Medicine, University of South Florida, Tampa, FL, USA
| | - Andrea Chobrutskiy
- Department of Pediatrics, Oregon Health and Science University Hospital, Portland, OR, USA
| | - Dhruv N. Patel
- Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL, USA
| | - Monica Hsiang
- Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL, USA
| | - Saif Zaman
- Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL, USA
| | - Konrad J. Cios
- Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL, USA
| | - George Blanck
- Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL, USA
- Department of Immunology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Boris I. Chobrutskiy
- Department of Internal Medicine, Oregon Health and Science University Hospital, Portland, OR, USA
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Chen S, Huang C, Liao G, Sun H, Xie Y, Liao C, Wang J, He M, Hu H, Dai Z, Ren X, Zeng X, Lin Z, Zhang GP, Xie W, Shen S, Li S, Peng S, Kuang DM, Zhao Q, Duda DG, Kuang M. Distinct single-cell immune ecosystems distinguish true and de novo HBV-related hepatocellular carcinoma recurrences. Gut 2023; 72:1196-1210. [PMID: 36596711 DOI: 10.1136/gutjnl-2022-328428] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 11/24/2022] [Indexed: 01/05/2023]
Abstract
OBJECTIVE Revealing the single-cell immune ecosystems in true versus de novo hepatocellular carcinoma (HCC) recurrences could help the optimal development of immunotherapies. DESIGN We performed 5'and VDJ single-cell RNA-sequencing on 34 samples from 20 recurrent HCC patients. Bulk RNA-sequencing, flow cytometry, multiplexed immunofluorescence, and in vitro functional analyses were performed on samples from two validation cohorts. RESULTS Analyses of mutational profiles and evolutionary trajectories in paired primary and recurrent HCC samples using whole-exome sequencing identified de novo versus true recurrences, some of which occurred before clinical diagnosis. The tumour immune microenvironment (TIME) of truly recurrent HCCs was characterised by an increased abundance in KLRB1+CD8+ T cells with memory phenotype and low cytotoxicity. In contrast, we found an enrichment in cytotoxic and exhausted CD8+ T cells in the TIME of de novo recurrent HCCs. Transcriptomic and interaction analyses showed elevated GDF15 expression on HCC cells in proximity to dendritic cells, which may have dampened antigen presentation and inhibited antitumour immunity in truly recurrent lesions. In contrast, myeloid cells' cross talk with T cells-mediated T cell exhaustion and immunosuppression in the TIME of de novo recurrent HCCs. Consistent with these findings, a phase 2 trial of neoadjuvant anti-PD-1 immunotherapy showed more responses in de novo recurrent HCC patients. CONCLUSION True and de novo HCC recurrences occur early, have distinct TIME and may require different immunotherapy strategies. Our study provides a source for genomic diagnosis and immune profiling for guiding immunotherapy based on the type of HCC recurrence and the specific TIME.
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Affiliation(s)
- Shuling Chen
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China
- Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China
- Precision Medicine Institute, the First Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Cheng Huang
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China
| | - Guanrui Liao
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China
| | - Huichuan Sun
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China
| | - Yubin Xie
- Precision Medicine Institute, the First Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Changyi Liao
- Department of Oncology, Cancer Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China
| | - Jianping Wang
- Department of Gastroenterology and Hepatology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China
| | - Minghui He
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China
| | - Huanjing Hu
- Precision Medicine Institute, the First Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Zihao Dai
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China
- Precision Medicine Institute, the First Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Xiaoxue Ren
- Department of Oncology, Cancer Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China
| | - Xuezhen Zeng
- Precision Medicine Institute, the First Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Zhilong Lin
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China
| | - Guo-Pei Zhang
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China
| | - Wenxuan Xie
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China
| | - Shunli Shen
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China
| | - Shaoqiang Li
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China
| | - Sui Peng
- Precision Medicine Institute, the First Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China
- Department of Gastroenterology and Hepatology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China
- Clinical Trials Unit, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China
| | - Dong-Ming Kuang
- State Key Laboratory of Oncology in South China, Cancer Center, MOE Key Laboratory of Gene Function and Regulation, School of Life Sciences, Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China
| | - Qiang Zhao
- Organ Transplant Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China
- Guangdong Provincial Key Laboratory of Organ Donation and Transplant Immunology, Guangzhou, Guangdong, People's Republic of China
| | - Dan G Duda
- Steele Laboratories for Tumor Biology, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Ming Kuang
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China
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Liu S, Bradley P, Sun W. Neural Network Models for Sequence-Based TCR and HLA Association Prediction. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.25.542327. [PMID: 37293077 PMCID: PMC10245990 DOI: 10.1101/2023.05.25.542327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
T cells rely on their T cell receptors (TCRs) to recognize foreign antigens presented by human leukocyte antigen (HLA) proteins. TCRs contain a record of an individual's past immune activities, and some TCRs are observed only in individuals with certain HLA alleles. As a result, characterising TCRs requires a thorough understanding of TCR-HLA associations. To this end, we propose a neural network method named Deep learning Prediction of TCR-HLA association (DePTH) to predict TCR-HLA associations based on their amino acid sequences. We show that the DePTH can be used to quantify the functional similarities of HLA alleles, and that these HLA similarities are associated with the survival outcomes of cancer patients who received immune checkpoint blockade treatment.
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Affiliation(s)
- Si Liu
- Public Health Science Division, Fred Hutchinson Cancer Center, Seattle, USA
| | - Philip Bradley
- Public Health Science Division, Fred Hutchinson Cancer Center, Seattle, USA
- Herbold Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, USA
- Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle, USA
| | - Wei Sun
- Public Health Science Division, Fred Hutchinson Cancer Center, Seattle, USA
- Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle, USA
- Department of Biostatistics, University of Washington, Seattle, USA
- Department of Biostatistics, University of North Carolina, Chapel Hill, USA
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Gao Y, Bergman I. Anti-tumor memory CD4 and CD8 T-cells quantified by bulk T-cell receptor (TCR) clonal analysis. Front Immunol 2023; 14:1137054. [PMID: 37033929 PMCID: PMC10076582 DOI: 10.3389/fimmu.2023.1137054] [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: 01/03/2023] [Accepted: 03/13/2023] [Indexed: 04/11/2023] Open
Abstract
Simple, reliable methods to detect anti-tumor memory T-cells are necessary to develop a clinical tumor vaccination program. A mouse model of curative viral onco-immunotherapy found that peritoneal tumor challenge following cure identified an oligoclonal anti-tumor memory CD4 and CD8 T-cell response. Clonotypes differed among the challenged animals but were congruent in blood, spleen and peritoneal cells (PC) of the same animal. Adoptive transfer demonstrated that the high-frequency responding T-cells were tumor specific. Tetramer analysis confirmed that clonotype frequency determined by T-cell receptor (TCR)- chain (TRB) analysis closely approximated cell clone frequency. The mean frequency of resting anti-tumor memory CD4 T-cells in unchallenged spleen was 0.028% and of memory CD8 T-cells was 0.11% which was not high enough to distinguish them from background. Stimulation produced a mean ~10-fold increase in splenic and 100-fold increase in peritoneal anti-tumor memory T-cell clonotypes. This methodology can be developed to use blood and tissue sampling to rapidly quantify the effectiveness of a tumor vaccine or any vaccine generating therapeutic T-cells.
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Affiliation(s)
- Yanhua Gao
- Department of Pediatrics, Children's Hospital of Pittsburgh, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States
| | - Ira Bergman
- Department of Pediatrics, Children's Hospital of Pittsburgh, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States
- Department of Neurology, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States
- Department of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States
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39
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Jiang Y, Li SC. Deep autoregressive generative models capture the intrinsics embedded in T-cell receptor repertoires. Brief Bioinform 2023; 24:7031156. [PMID: 36752378 DOI: 10.1093/bib/bbad038] [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: 09/14/2022] [Revised: 01/07/2023] [Accepted: 01/18/2023] [Indexed: 02/09/2023] Open
Abstract
T-cell receptors (TCRs) play an essential role in the adaptive immune system. Probabilistic models for TCR repertoires can help decipher the underlying complex sequence patterns and provide novel insights into understanding the adaptive immune system. In this work, we develop TCRpeg, a deep autoregressive generative model to unravel the sequence patterns of TCR repertoires. TCRpeg largely outperforms state-of-the-art methods in estimating the probability distribution of a TCR repertoire, boosting the average accuracy from 0.672 to 0.906 measured by the Pearson correlation coefficient. Furthermore, with promising performance in probability inference, TCRpeg improves on a range of TCR-related tasks: profiling TCR repertoire probabilistically, classifying antigen-specific TCRs, validating previously discovered TCR motifs, generating novel TCRs and augmenting TCR data. Our results and analysis highlight the flexibility and capacity of TCRpeg to extract TCR sequence information, providing a novel approach for deciphering complex immunogenomic repertoires.
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Affiliation(s)
- Yuepeng Jiang
- Department of Computer science, City University of Hong Kong, Kowloon Tong, Hong Kong
| | - Shuai Cheng Li
- Department of Computer science, City University of Hong Kong, Kowloon Tong, Hong Kong
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40
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Smirnov AS, Rudik AV, Filimonov DA, Lagunin AA. TCR-Pred: A new web-application for prediction of epitope and MHC specificity for CDR3 TCR sequences using molecular fragment descriptors. Immunology 2023. [PMID: 36929656 DOI: 10.1111/imm.13641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 03/06/2023] [Indexed: 03/18/2023] Open
Abstract
The search for the relationships between CDR3 TCR sequences and epitopes or MHC types is a challenging task in modern immunology. We propose a new approach to develop the classification models of structure-activity relationships (SAR) using molecular fragment descriptors MNA (Multilevel Neighbourhoods of Atoms) to represent CDR3 TCR sequences and the naïve Bayes classifier algorithm. We have created the freely available TCR-Pred web application (http://way2drug.com/TCR-pred/) to predict the interactions between α chain CDR3 TCR sequences and 116 epitopes or 25 MHC types, as well as the interactions between β chain CDR3 TCR sequences and 202 epitopes or 28 MHC types. The TCR-Pred web application is based on the data (more 250 000 unique CDR3 TCR sequences) from VDJdb, McPAS-TCR, and IEDB databases and the proposed approach. The average AUC values of the prediction accuracy calculated using a 20-fold cross-validation procedure varies from 0.857 to 0.884. The created web application may be useful in studies related with T-cell profiling based on CDR3 TCR sequences.
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Affiliation(s)
- Anton S Smirnov
- Department of Bioinformatics, Pirogov Russian National Research Medical University, Moscow, Russia
| | - Anastasia V Rudik
- Laboratory of Structure-Function Based Drug Design, Institute of Biomedical Chemistry, Moscow, Russia
| | - Dmitry A Filimonov
- Laboratory of Structure-Function Based Drug Design, Institute of Biomedical Chemistry, Moscow, Russia
| | - Alexey A Lagunin
- Department of Bioinformatics, Pirogov Russian National Research Medical University, Moscow, Russia.,Laboratory of Structure-Function Based Drug Design, Institute of Biomedical Chemistry, Moscow, Russia
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41
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Frank ML, Lu K, Erdogan C, Han Y, Hu J, Wang T, Heymach JV, Zhang J, Reuben A. T-Cell Receptor Repertoire Sequencing in the Era of Cancer Immunotherapy. Clin Cancer Res 2023; 29:994-1008. [PMID: 36413126 PMCID: PMC10011887 DOI: 10.1158/1078-0432.ccr-22-2469] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 10/07/2022] [Accepted: 11/14/2022] [Indexed: 11/23/2022]
Abstract
T cells are integral components of the adaptive immune system, and their responses are mediated by unique T-cell receptors (TCR) that recognize specific antigens from a variety of biological contexts. As a result, analyzing the T-cell repertoire offers a better understanding of immune responses and of diseases like cancer. Next-generation sequencing technologies have greatly enabled the high-throughput analysis of the TCR repertoire. On the basis of our extensive experience in the field from the past decade, we provide an overview of TCR sequencing, from the initial library preparation steps to sequencing and analysis methods and finally to functional validation techniques. With regards to data analysis, we detail important TCR repertoire metrics and present several computational tools for predicting antigen specificity. Finally, we highlight important applications of TCR sequencing and repertoire analysis to understanding tumor biology and developing cancer immunotherapies.
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Affiliation(s)
- Meredith L. Frank
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
- The University of Texas MD Anderson Cancer Center UT Health Houston Graduate School of Biomedical Sciences, Houston, Texas
| | - Kaylene Lu
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
- The University of Texas MD Anderson Cancer Center UT Health Houston Graduate School of Biomedical Sciences, Houston, Texas
- Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Can Erdogan
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
- Rice University, Houston, Texas
| | - Yi Han
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Jian Hu
- The University of Texas MD Anderson Cancer Center UT Health Houston Graduate School of Biomedical Sciences, Houston, Texas
- Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Tao Wang
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, Texas
- Center for the Genetics of Host Defense, University of Texas Southwestern Medical Center, Dallas, Texas
| | - John V. Heymach
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
- The University of Texas MD Anderson Cancer Center UT Health Houston Graduate School of Biomedical Sciences, Houston, Texas
| | - Jianjun Zhang
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
- The University of Texas MD Anderson Cancer Center UT Health Houston Graduate School of Biomedical Sciences, Houston, Texas
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Alexandre Reuben
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
- The University of Texas MD Anderson Cancer Center UT Health Houston Graduate School of Biomedical Sciences, Houston, Texas
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42
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Gao Y, Gao Y, Fan Y, Zhu C, Wei Z, Zhou C, Chuai G, Chen Q, Zhang H, Liu Q. Pan-Peptide Meta Learning for T-cell receptor–antigen binding recognition. NAT MACH INTELL 2023. [DOI: 10.1038/s42256-023-00619-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2023]
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43
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Akerman O, Isakov H, Levi R, Psevkin V, Louzoun Y. Counting is almost all you need. Front Immunol 2023; 13:1031011. [PMID: 36741395 PMCID: PMC9896581 DOI: 10.3389/fimmu.2022.1031011] [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: 08/29/2022] [Accepted: 12/27/2022] [Indexed: 01/21/2023] Open
Abstract
The immune memory repertoire encodes the history of present and past infections and immunological attributes of the individual. As such, multiple methods were proposed to use T-cell receptor (TCR) repertoires to detect disease history. We here show that the counting method outperforms two leading algorithms. We then show that the counting can be further improved using a novel attention model to weigh the different TCRs. The attention model is based on the projection of TCRs using a Variational AutoEncoder (VAE). Both counting and attention algorithms predict better than current leading algorithms whether the host had CMV and its HLA alleles. As an intermediate solution between the complex attention model and the very simple counting model, we propose a new Graph Convolutional Network approach that obtains the accuracy of the attention model and the simplicity of the counting model. The code for the models used in the paper is provided at: https://github.com/louzounlab/CountingIsAlmostAllYouNeed.
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Affiliation(s)
- Ofek Akerman
- Department of Mathematics, Bar-Ilan University, Ramat Gan, Israel
- Department of Computer Science, Bar-Ilan University, Ramat Gan, Israel
| | - Haim Isakov
- Department of Mathematics, Bar-Ilan University, Ramat Gan, Israel
| | - Reut Levi
- Department of Mathematics, Bar-Ilan University, Ramat Gan, Israel
| | - Vladimir Psevkin
- Department of Mathematics, Bar-Ilan University, Ramat Gan, Israel
| | - Yoram Louzoun
- Department of Mathematics, Bar-Ilan University, Ramat Gan, Israel
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44
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Lybaert L, Lefever S, Fant B, Smits E, De Geest B, Breckpot K, Dirix L, Feldman SA, van Criekinge W, Thielemans K, van der Burg SH, Ott PA, Bogaert C. Challenges in neoantigen-directed therapeutics. Cancer Cell 2023; 41:15-40. [PMID: 36368320 DOI: 10.1016/j.ccell.2022.10.013] [Citation(s) in RCA: 31] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 08/19/2022] [Accepted: 10/11/2022] [Indexed: 11/11/2022]
Abstract
A fundamental prerequisite for the efficacy of cancer immunotherapy is the presence of functional, antigen-specific T cells within the tumor. Neoantigen-directed therapy is a promising strategy that aims at targeting the host's immune response against tumor-specific antigens, thereby eradicating cancer cells. Initial forays have been made in clinical environments utilizing vaccines and adoptive cell therapy; however, many challenges lie ahead. We provide an in-depth overview of the current state of the field with an emphasis on in silico neoantigen discovery and the clinical aspects that need to be addressed to unlock the full potential of this therapy.
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Affiliation(s)
| | | | | | - Evelien Smits
- Center for Oncological Research, University of Antwerp, 2610 Wilrijk, Belgium
| | - Bruno De Geest
- Department of Pharmaceutics, Ghent University, 9000 Ghent, Belgium
| | - Karine Breckpot
- Laboratory of Molecular and Cellular Therapy, Department of Biomedical Sciences, Vrije Universiteit Brussel, Brussels, Belgium
| | - Luc Dirix
- Translational Cancer Research Unit, Center for Oncological Research, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
| | - Steven A Feldman
- Center for Cancer Cell Therapy, Stanford University School of Medicine, Stanford, CA, USA
| | - Wim van Criekinge
- Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, Belgium
| | - Kris Thielemans
- Laboratory of Molecular and Cellular Therapy, Department of Biomedical Sciences, Vrije Universiteit Brussel, Brussels, Belgium
| | - Sjoerd H van der Burg
- Medical Oncology, Oncode Institute, Leiden University Medical Center, Leiden, the Netherlands
| | - Patrick A Ott
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
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45
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Hudock TR, Barker VR, Manley BJ, Chobrutskiy A, Chobrutskiy BI, Diaz MJ, Song JJ, Blanck G. TRB CDR3-cancer testis antigen chemical complementarity scoring for identifying productive immune responses in renal cell carcinoma. Cancer Biomark 2023; 38:103-110. [PMID: 37545223 DOI: 10.3233/cbm-230047] [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: 08/08/2023]
Abstract
BACKGROUND Immunogenomics approaches to the characterization of renal cell carcinoma (RCC) have helped to better our understanding of the features of RCC immune dysfunction. However, much is still unknown with regard to specific immune interactions and their impact in the tumor microenvironment. OBJECTIVE This study applied chemical complementarity scoring for the TRB complementarity determining region-3 (CDR3) amino acid sequences and cancer testis antigens (CTAs) to determine whether such complementarity correlated with survival and the expression of immune marker genes. METHODS TRB recombination reads from RCC tumor samples from RNAseq files obtained from two separate databases, Moffitt Cancer Center and The Cancer Genome Atlas (TCGA), were evaluated. Chemical complementarity scores (CSs) were calculated for TRB CDR3-CTA pairs and survival assessments based on those CSs were performed. RESULTS Moffitt Cancer Center and TCGA cases representing the upper 50th percentile of chemical CSs for TRB CDR3 amino acid sequences and the CTA POTEA were found to be associated with a better overall survival (OS) Also, greater tumor RNA expression of multiple immune signature genes, including granzyme A, granzyme B, and interferon-gamma were correlated with the higher chemical CSs. CONCLUSIONS These results indicate that TRB CDR3-CTA chemical complementarity scoring may be useful in distinguishing RCC cases with a productive, anti-tumor immune response from cases where basic immune parameter assessments are inconsistent with a productive immune response.
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Affiliation(s)
- Tabitha R Hudock
- Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL, USA
| | - Vayda R Barker
- Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL, USA
| | - Brandon J Manley
- Department of Genitourinary Oncology, Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Andrea Chobrutskiy
- Department of Pediatrics, Oregon Health and Science University Hospital, Portland, OR, USA
| | - Boris I Chobrutskiy
- Department of Internal Medicine, Oregon Health and Science University Hospital, Portland, OR, USA
| | - Michael J Diaz
- Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL, USA
| | - Joanna J Song
- Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL, USA
| | - George Blanck
- Department of Molecular Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL, USA
- Department of Immunology, Moffitt Cancer Center and Research Institute, Tampa, FL, USA
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46
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Grazioli F, Machart P, Mösch A, Li K, Castorina LV, Pfeifer N, Min MR. Attentive Variational Information Bottleneck for TCR-peptide interaction prediction. Bioinformatics 2022; 39:6960920. [PMID: 36571499 PMCID: PMC9825246 DOI: 10.1093/bioinformatics/btac820] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 11/18/2022] [Accepted: 12/23/2022] [Indexed: 12/27/2022] Open
Abstract
MOTIVATION We present a multi-sequence generalization of Variational Information Bottleneck and call the resulting model Attentive Variational Information Bottleneck (AVIB). Our AVIB model leverages multi-head self-attention to implicitly approximate a posterior distribution over latent encodings conditioned on multiple input sequences. We apply AVIB to a fundamental immuno-oncology problem: predicting the interactions between T-cell receptors (TCRs) and peptides. RESULTS Experimental results on various datasets show that AVIB significantly outperforms state-of-the-art methods for TCR-peptide interaction prediction. Additionally, we show that the latent posterior distribution learned by AVIB is particularly effective for the unsupervised detection of out-of-distribution amino acid sequences. AVAILABILITY AND IMPLEMENTATION The code and the data used for this study are publicly available at: https://github.com/nec-research/vibtcr. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Pierre Machart
- Biomedical AI Group, NEC Laboratories Europe, Heidelberg 69115, Germany
| | - Anja Mösch
- Biomedical AI Group, NEC Laboratories Europe, Heidelberg 69115, Germany
| | - Kai Li
- Machine Learning Department, NEC Laboratories America, Princeton, NJ 08540, USA
| | | | - Nico Pfeifer
- Methods in Medical Informatics, Department of Computer Science, University of Tübingen, Tübingen 72076, Germany
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47
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Jokinen E, Dumitrescu A, Huuhtanen J, Gligorijević V, Mustjoki S, Bonneau R, Heinonen M, Lähdesmäki H. TCRconv: predicting recognition between T cell receptors and epitopes using contextualized motifs. Bioinformatics 2022; 39:6881078. [PMID: 36477794 PMCID: PMC9825763 DOI: 10.1093/bioinformatics/btac788] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 11/01/2022] [Accepted: 12/06/2022] [Indexed: 12/12/2022] Open
Abstract
MOTIVATION T cells use T cell receptors (TCRs) to recognize small parts of antigens, called epitopes, presented by major histocompatibility complexes. Once an epitope is recognized, an immune response is initiated and T cell activation and proliferation by clonal expansion begin. Clonal populations of T cells with identical TCRs can remain in the body for years, thus forming immunological memory and potentially mappable immunological signatures, which could have implications in clinical applications including infectious diseases, autoimmunity and tumor immunology. RESULTS We introduce TCRconv, a deep learning model for predicting recognition between TCRs and epitopes. TCRconv uses a deep protein language model and convolutions to extract contextualized motifs and provides state-of-the-art TCR-epitope prediction accuracy. Using TCR repertoires from COVID-19 patients, we demonstrate that TCRconv can provide insight into T cell dynamics and phenotypes during the disease. AVAILABILITY AND IMPLEMENTATION TCRconv is available at https://github.com/emmijokinen/tcrconv. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Alexandru Dumitrescu
- Department of Computer Science, Aalto University, Espoo 02150, Finland,Helsinki Institute of Life Science, University of Helsinki, Helsinki 00014, Finland
| | - Jani Huuhtanen
- Department of Clinical Chemistry and Hematology, Translational Immunology Research Program, University of Helsinki, Helsinki 00290, Finland,Hematology Research Unit Helsinki, Helsinki University Hospital Comprehensive Cancer Center, Helsinki 00290, Finland
| | - Vladimir Gligorijević
- Center for Computational Biology (CCB), Flatiron Institute, Simons Foundation, New York, NY 10010, USA,Prescient Design, Genentech, New York, NY, USA
| | - Satu Mustjoki
- Department of Clinical Chemistry and Hematology, Translational Immunology Research Program, University of Helsinki, Helsinki 00290, Finland,Hematology Research Unit Helsinki, Helsinki University Hospital Comprehensive Cancer Center, Helsinki 00290, Finland,iCAN Digital Precision Cancer Medicine Flagship, Helsinki, Finland
| | - Richard Bonneau
- Center for Computational Biology (CCB), Flatiron Institute, Simons Foundation, New York, NY 10010, USA,Prescient Design, Genentech, New York, NY, USA,Center for Data Science, New York University, New York, NY 10011, USA,Department of Computer Science, New York University, Courant Institute of Mathematical Sciences, New York, NY 10012, USA
| | - Markus Heinonen
- Department of Computer Science, Aalto University, Espoo 02150, Finland
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48
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Montemurro A, Jessen LE, Nielsen M. NetTCR-2.1: Lessons and guidance on how to develop models for TCR specificity predictions. Front Immunol 2022; 13:1055151. [PMID: 36561755 PMCID: PMC9763291 DOI: 10.3389/fimmu.2022.1055151] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 11/22/2022] [Indexed: 12/12/2022] Open
Abstract
T cell receptors (TCR) define the specificity of T cells and are responsible for their interaction with peptide antigen targets presented in complex with major histocompatibility complex (MHC) molecules. Understanding the rules underlying this interaction hence forms the foundation for our understanding of basic adaptive immunology. Over the last decade, efforts have been dedicated to developing assays for high throughput identification of peptide-specific TCRs. Based on such data, several computational methods have been proposed for predicting the TCR-pMHC interaction. The general conclusion from these studies is that the prediction of TCR interactions with MHC-peptide complexes remains highly challenging. Several reasons form the basis for this including scarcity and quality of data, and ill-defined modeling objectives imposed by the high redundancy of the available data. In this work, we propose a framework for dealing with this redundancy, allowing us to address essential questions related to the modeling of TCR specificity including the use of peptide- versus pan-specific models, how to best define negative data, and the performance impact of integrating of CDR1 and 2 loops. Further, we illustrate how and why it is strongly recommended to include simple similarity-based modeling approaches when validating an improved predictive power of machine learning models, and that such validation should include a performance evaluation as a function of "distance" to the training data, to quantify the potential for generalization of the proposed model. The conclusion of the work is that, given current data, TCR specificity is best modeled using peptide-specific approaches, integrating information from all 6 CDR loops, and with negative data constructed from a combination of true and mislabeled negatives. Comparing such machine learning models to similarity-based approaches demonstrated an increased performance gain of the former as the "distance" to the training data was increased; thus demonstrating an improved generalization ability of the machine learning-based approaches. We believe these results demonstrate that the outlined modeling framework and proposed evaluation strategy form a solid basis for investigating the modeling of TCR specificities and that adhering to such a framework will allow for faster progress within the field. The final devolved model, NetTCR-2.1, is available at https://services.healthtech.dtu.dk/service.php?NetTCR-2.1.
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Affiliation(s)
- Alessandro Montemurro
- 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,Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina,*Correspondence: Morten Nielsen,
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49
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Grazioli F, Mösch A, Machart P, Li K, Alqassem I, O’Donnell TJ, Min MR. On TCR binding predictors failing to generalize to unseen peptides. Front Immunol 2022; 13:1014256. [PMID: 36341448 PMCID: PMC9634250 DOI: 10.3389/fimmu.2022.1014256] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 10/05/2022] [Indexed: 11/18/2022] Open
Abstract
Several recent studies investigate TCR-peptide/-pMHC binding prediction using machine learning or deep learning approaches. Many of these methods achieve impressive results on test sets, which include peptide sequences that are also included in the training set. In this work, we investigate how state-of-the-art deep learning models for TCR-peptide/-pMHC binding prediction generalize to unseen peptides. We create a dataset including positive samples from IEDB, VDJdb, McPAS-TCR, and the MIRA set, as well as negative samples from both randomization and 10X Genomics assays. We name this collection of samples TChard. We propose the hard split, a simple heuristic for training/test split, which ensures that test samples exclusively present peptides that do not belong to the training set. We investigate the effect of different training/test splitting techniques on the models’ test performance, as well as the effect of training and testing the models using mismatched negative samples generated randomly, in addition to the negative samples derived from assays. Our results show that modern deep learning methods fail to generalize to unseen peptides. We provide an explanation why this happens and verify our hypothesis on the TChard dataset. We then conclude that robust prediction of TCR recognition is still far for being solved.
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Affiliation(s)
- Filippo Grazioli
- Biomedical AI Group, NEC Laboratories Europe, Heidelberg, Germany
- *Correspondence: Filippo Grazioli, ; Martin Renqiang Min,
| | - Anja Mösch
- Biomedical AI Group, NEC Laboratories Europe, Heidelberg, Germany
| | - Pierre Machart
- Biomedical AI Group, NEC Laboratories Europe, Heidelberg, Germany
| | - Kai Li
- Machine Learning Department, NEC Laboratories America, Princeton, NJ, United States
| | - Israa Alqassem
- Biomedical AI Group, NEC Laboratories Europe, Heidelberg, Germany
| | - Timothy J. O’Donnell
- Division of Hematology and Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Martin Renqiang Min
- Machine Learning Department, NEC Laboratories America, Princeton, NJ, United States
- *Correspondence: Filippo Grazioli, ; Martin Renqiang Min,
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50
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Abstract
The human liver is a complex organ made up of multiple specialized cell types that carry out key physiological functions. An incomplete understanding of liver biology limits our ability to develop therapeutics to prevent chronic liver diseases, liver cancers, and death as a result of organ failure. Recently, single-cell modalities have expanded our understanding of the cellular phenotypic heterogeneity and intercellular cross-talk in liver health and disease. This review summarizes these findings and looks forward to highlighting new avenues for the application of single-cell genomics to unravel unknown pathogenic pathways and disease mechanisms for the development of new therapeutics targeting liver pathology. As these technologies mature, their integration into clinical data analysis will aid in patient stratification and in developing treatment plans for patients suffering from liver disease.
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Affiliation(s)
- Jawairia Atif
- Ajmera Transplant Centre, Schwartz Reisman Liver Research Centre, Toronto General Research Institute, University Health Network, Toronto, Ontario, Canada
- Department of Immunology, University of Toronto, Medical Sciences Building, Toronto, Ontario, Canada
| | - Cornelia Thoeni
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
| | - Gary D. Bader
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Ian D. McGilvray
- Ajmera Transplant Centre, Schwartz Reisman Liver Research Centre, Toronto General Research Institute, University Health Network, Toronto, Ontario, Canada
| | - Sonya A. MacParland
- Ajmera Transplant Centre, Schwartz Reisman Liver Research Centre, Toronto General Research Institute, University Health Network, Toronto, Ontario, Canada
- Department of Immunology, University of Toronto, Medical Sciences Building, Toronto, Ontario, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
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