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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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Huang J, Mao L, Lei Q, Guo AY. Bioinformatics tools and resources for cancer and application. Chin Med J (Engl) 2024:00029330-990000000-01162. [PMID: 39075637 DOI: 10.1097/cm9.0000000000003254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2024] [Indexed: 07/31/2024] Open
Abstract
ABSTRACT Tumor bioinformatics plays an important role in cancer research and precision medicine. The primary focus of traditional cancer research has been molecular and clinical studies of a number of fundamental pathways and genes. In recent years, driven by breakthroughs in high-throughput technologies, large-scale cancer omics data have accumulated rapidly. How to effectively utilize and share these data is particularly important. To address this crucial task, many computational tools and databases have been developed over the past few years. To help researchers quickly learn and understand the functions of these tools, in this review, we summarize publicly available bioinformatics tools and resources for pan-cancer multi-omics analysis, regulatory analysis of tumorigenesis, tumor treatment and prognosis, immune infiltration analysis, immune repertoire analysis, cancer driver gene and driver mutation analysis, and cancer single-cell analysis, which may further help researchers find more suitable tools for their research.
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Affiliation(s)
- Jin Huang
- Department of Thoracic Surgery, West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Lingzi Mao
- Hubei Bioinformatics & Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Qian Lei
- Department of Thoracic Surgery, West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - An-Yuan Guo
- Department of Thoracic Surgery, West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
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3
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Rendon-Marin S, Ruíz-Saenz J. Universal peptide-based potential vaccine design against canine distemper virus (CDV) using a vaccinomic approach. Sci Rep 2024; 14:16605. [PMID: 39026076 PMCID: PMC11258135 DOI: 10.1038/s41598-024-67781-5] [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: 12/19/2023] [Accepted: 07/16/2024] [Indexed: 07/20/2024] Open
Abstract
Canine distemper virus (CDV) affects many domestic and wild animals. Variations among CDV genome linages could lead to vaccination failure. To date, there are several vaccine alternatives, such as a modified live virus and a recombinant vaccine; however, most of these alternatives are based on the ancestral strain Onderstepoort, which has not been circulating for years. Vaccine failures and the need to update vaccines have been widely discussed, and the development of new vaccine candidates is necessary to reduce circulation and mortality. Current vaccination alternatives cannot be used in wildlife animals due to the lack of safety data for most of the species, in addition to the insufficient immune response against circulating strains worldwide in domestic species. Computational tools, including peptide-based therapies, have become essential for developing new-generation vaccines for diverse models. In this work, a peptide-based vaccine candidate with a peptide library derived from CDV H and F protein consensus sequences was constructed employing computational tools. The molecular docking and dynamics of the selected peptides with canine MHC-I and MHC-II and with TLR-2 and TLR-4 were evaluated. In silico safety was assayed through determination of antigenicity, allergenicity, toxicity potential, and homologous canine peptides. Additionally, in vitro safety was also evaluated through cytotoxicity in cell lines and canine peripheral blood mononuclear cells (cPBMCs) and through a hemolysis potential assay using canine red blood cells. A multiepitope CDV polypeptide was constructed, synthetized, and evaluated in silico and in vitro by employing the most promising peptides for comparison with single CDV immunogenic peptides. Our findings suggest that predicting immunogenic CDV peptides derived from most antigenic CDV proteins could aid in the development of new vaccine candidates, such as multiple single CDV peptides and multiepitope CDV polypeptides, that are safe in vitro and optimized in silico. In vivo studies are being conducted to validate potential vaccines that may be effective in preventing CDV infection in domestic and wild animals.
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Affiliation(s)
- Santiago Rendon-Marin
- Grupo de Investigación en Ciencias Animales - GRICA, Facultad de Medicina Veterinaria y Zootecnia, Universidad Cooperativa de Colombia, sede Bucaramanga, Bucaramanga, Colombia
- Grupo Infettare, Facultad de Medicina, Universidad Cooperativa de Colombia, Medellín, Colombia
| | - Julián Ruíz-Saenz
- Grupo de Investigación en Ciencias Animales - GRICA, Facultad de Medicina Veterinaria y Zootecnia, Universidad Cooperativa de Colombia, sede Bucaramanga, Bucaramanga, Colombia.
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4
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Yu X, Pan M, Ye J, Hathaway CA, Tworoger SS, Lea J, Li B. Quantifiable TCR repertoire changes in prediagnostic blood specimens among patients with high-grade ovarian cancer. Cell Rep Med 2024; 5:101612. [PMID: 38878776 DOI: 10.1016/j.xcrm.2024.101612] [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: 12/18/2023] [Revised: 04/16/2024] [Accepted: 05/20/2024] [Indexed: 06/25/2024]
Abstract
High-grade ovarian cancer (HGOC) is a major cause of death in women. Early detection of HGOC usually leads to a cure, yet it remains a clinical challenge with over 90% HGOCs diagnosed at advanced stages. This is mainly because conventional biomarkers are not sensitive enough to detect the microscopic yet metastatic early lesions. In this study, we sequence the blood T cell receptor (TCR) repertoires of 466 patients with ovarian cancer and controls and systematically investigate the immune repertoire signatures in HGOCs. We observe quantifiable changes of selected TCRs in HGOCs that are reproducible in multiple independent cohorts. Importantly, these changes are stronger during stage I. Using pre-diagnostic patient blood samples from the Nurses' Health Study, we confirm that HGOC signals can be detected in the blood TCR repertoire up to 4 years preceding conventional diagnosis. Our findings may provide the basis for future immune-based HGOC early detection criteria.
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Affiliation(s)
- Xuexin Yu
- Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Pathology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Mingyao Pan
- Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Pathology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jianfeng Ye
- Department of Neuroscience, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | | | - Shelley S Tworoger
- Knight Cancer Institute and Division of Oncological Sciences, Oregon Health & Science University, Portland, OR 97239, USA
| | - Jayanthi Lea
- Department of Gynecology, UT Southwestern Medical Center, Dallas, TX 75390, USA.
| | - Bo Li
- Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Pathology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
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5
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Yeh AC, Koyama M, Waltner OG, Minnie SA, Boiko JR, Shabaneh TB, Takahashi S, Zhang P, Ensbey KS, Schmidt CR, Legg SRW, Sekiguchi T, Nelson E, Bhise SS, Stevens AR, Goodpaster T, Chakka S, Furlan SN, Markey KA, Bleakley ME, Elson CO, Bradley PH, Hill GR. Microbiota dictate T cell clonal selection to augment graft-versus-host disease after stem cell transplantation. Immunity 2024; 57:1648-1664.e9. [PMID: 38876098 PMCID: PMC11236519 DOI: 10.1016/j.immuni.2024.05.018] [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: 06/26/2023] [Revised: 02/09/2024] [Accepted: 05/20/2024] [Indexed: 06/16/2024]
Abstract
Allogeneic T cell expansion is the primary determinant of graft-versus-host disease (GVHD), and current dogma dictates that this is driven by histocompatibility antigen disparities between donor and recipient. This paradigm represents a closed genetic system within which donor T cells interact with peptide-major histocompatibility complexes (MHCs), though clonal interrogation remains challenging due to the sparseness of the T cell repertoire. We developed a Bayesian model using donor and recipient T cell receptor (TCR) frequencies in murine stem cell transplant systems to define limited common expansion of T cell clones across genetically identical donor-recipient pairs. A subset of donor CD4+ T cell clonotypes differentially expanded in identical recipients and were microbiota dependent. Microbiota-specific T cells augmented GVHD lethality and could target microbial antigens presented by gastrointestinal epithelium during an alloreactive response. The microbiota serves as a source of cognate antigens that contribute to clonotypic T cell expansion and the induction of GVHD independent of donor-recipient genetics.
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MESH Headings
- Graft vs Host Disease/immunology
- Graft vs Host Disease/microbiology
- Animals
- Mice
- Mice, Inbred C57BL
- CD4-Positive T-Lymphocytes/immunology
- Receptors, Antigen, T-Cell/immunology
- Receptors, Antigen, T-Cell/genetics
- Receptors, Antigen, T-Cell/metabolism
- Microbiota/immunology
- Clonal Selection, Antigen-Mediated
- Transplantation, Homologous
- Bayes Theorem
- Stem Cell Transplantation/adverse effects
- Mice, Inbred BALB C
- Gastrointestinal Microbiome/immunology
- Hematopoietic Stem Cell Transplantation/adverse effects
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Affiliation(s)
- Albert C Yeh
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA, USA; Division of Medical Oncology, Department of Medicine, University of Washington, Seattle, WA, USA.
| | - Motoko Koyama
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Olivia G Waltner
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Simone A Minnie
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Julie R Boiko
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Tamer B Shabaneh
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Shuichiro Takahashi
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Ping Zhang
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Kathleen S Ensbey
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Christine R Schmidt
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Samuel R W Legg
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Tomoko Sekiguchi
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Ethan Nelson
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Shruti S Bhise
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Andrew R Stevens
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Tracy Goodpaster
- Experimental Histopathology Core, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Saranya Chakka
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Scott N Furlan
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Kate A Markey
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA, USA; Division of Medical Oncology, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Marie E Bleakley
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA, USA; Division of Hematology, Oncology, and Bone Marrow Transplantation, Department of Pediatrics, University of Washington, Seattle, WA, USA
| | - Charles O Elson
- Department of Medicine, The University of Alabama at Birmingham, Birmingham, AL, USA
| | - Philip H Bradley
- Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA; Department of Biochemistry and Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Geoffrey R Hill
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, WA, USA; Division of Medical Oncology, Department of Medicine, University of Washington, Seattle, WA, USA.
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6
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Jurczak S, Druchok M. Cancer Immunotherapies Ignited by a Thorough Machine Learning-Based Selection of Neoantigens. Adv Biol (Weinh) 2024:e2400114. [PMID: 38971967 DOI: 10.1002/adbi.202400114] [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: 02/27/2024] [Revised: 06/02/2024] [Indexed: 07/08/2024]
Abstract
Identification of neoantigens, derived from somatic DNA alterations, emerges as a promising strategy for cancer immunotherapies. However, not all somatic mutations result in immunogenicity, hence, efficient tools to predict the immunogenicity of neoepitopes are needed. A pipeline is presented that provides a comprehensive solution for the identification of neoepitopes based on genomic sequencing data. The pipeline consists of a data pre-processing step and three machine learning predictive steps. The pre-processing step analyzes genomic data for different types of alterations, produces a list of all possible antigens, and determines the human leukocyte antigen (HLA) type and T-cell receptor (TCR) repertoire. The first predictive step performs a classification into antigens and neoantigens, selecting neoantigens for further consideration. The next step predicts the strength of binding between neoantigens and available major histocompatibility complexes of class I (MHC-I). The third step is engaged to predict the likelihood of inducing an immune response. Neoepitopes satisfying all three predictive stages are assumed to be potent candidates to ensure immunogenicity. The predictive pipeline is used in two regimes: selecting neoantigens from patients' sequencing data and generating novel neoantigen candidates. Two different techniques - Monte Carlo and Reinforcement Learning - are implemented to facilitate the generative regime.
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Affiliation(s)
- Sebastian Jurczak
- SoftServe Inc., 11/13 Building B, Jaworska St., Wroclaw, 53-612, Poland
| | - Maksym Druchok
- SoftServe Inc., 2d Sadova St., Lviv, 79021, Ukraine
- Institute for Condensed Matter Physics, 1 Svientsitskii St., Lviv, 79011, Ukraine
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7
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Drost F, An Y, Bonafonte-Pardàs I, Dratva LM, Lindeboom RGH, Haniffa M, Teichmann SA, Theis F, Lotfollahi M, Schubert B. Multi-modal generative modeling for joint analysis of single-cell T cell receptor and gene expression data. Nat Commun 2024; 15:5577. [PMID: 38956082 PMCID: PMC11220149 DOI: 10.1038/s41467-024-49806-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 05/23/2024] [Indexed: 07/04/2024] Open
Abstract
Recent advances in single-cell immune profiling have enabled the simultaneous measurement of transcriptome and T cell receptor (TCR) sequences, offering great potential for studying immune responses at the cellular level. However, integrating these diverse modalities across datasets is challenging due to their unique data characteristics and technical variations. Here, to address this, we develop the multimodal generative model mvTCR to fuse modality-specific information across transcriptome and TCR into a shared representation. Our analysis demonstrates the added value of multimodal over unimodal approaches to capture antigen specificity. Notably, we use mvTCR to distinguish T cell subpopulations binding to SARS-CoV-2 antigens from bystander cells. Furthermore, when combined with reference mapping approaches, mvTCR can map newly generated datasets to extensive T cell references, facilitating knowledge transfer. In summary, we envision mvTCR to enable a scalable analysis of multimodal immune profiling data and advance our understanding of immune responses.
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Affiliation(s)
- Felix Drost
- Computational Health Center, Helmholtz Munich, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany
- School of Life Sciences Weihenstephan, Technical University of Munich, Alte Akademie 8, 85354, Freising, Germany
| | - Yang An
- Computational Health Center, Helmholtz Munich, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany
- School of Computation, Information and Technology, Technical University of Munich, Boltzmannstraße 3, 85748, Garching bei München, Germany
| | - Irene Bonafonte-Pardàs
- Computational Health Center, Helmholtz Munich, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany
| | - Lisa M Dratva
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Rik G H Lindeboom
- The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Muzlifah Haniffa
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, NE2 4HH, UK
| | - Sarah A Teichmann
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
- Department of Physics, Cavendish Laboratory, University of Cambridge, 19 JJ Thomson Avenue, Cambridge, UK
| | - Fabian Theis
- Computational Health Center, Helmholtz Munich, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany
- School of Life Sciences Weihenstephan, Technical University of Munich, Alte Akademie 8, 85354, Freising, Germany
- School of Computation, Information and Technology, Technical University of Munich, Boltzmannstraße 3, 85748, Garching bei München, Germany
| | - Mohammad Lotfollahi
- Computational Health Center, Helmholtz Munich, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany.
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK.
| | - Benjamin Schubert
- Computational Health Center, Helmholtz Munich, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany.
- School of Computation, Information and Technology, Technical University of Munich, Boltzmannstraße 3, 85748, Garching bei München, Germany.
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8
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Clark EA, Talatala ER, Ye W, Davis RJ, Collins SL, Hillel AT, Ramirez-Solano M, Sheng Q, Wanjalla CN, Mallal SA, Gelbard A. Similarity Network Analysis of the Adaptive Immune Response in the Proximal Airway. Laryngoscope 2024; 134:3245-3252. [PMID: 38450771 PMCID: PMC11182723 DOI: 10.1002/lary.31376] [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: 05/02/2023] [Revised: 01/23/2024] [Accepted: 02/12/2024] [Indexed: 03/08/2024]
Abstract
OBJECTIVES Recent immunologic study of the adaptive immune repertoire in the subglottic airway demonstrated high-frequency T cell clones that do not overlap between individuals. However, the anatomic distribution and antigenic target of the T cell repertoire in the proximal airway mucosa remain unresolved. METHODS Single-cell RNA sequencing of matched scar and unaffected mucosa from idiopathic subglottic stenosis patients (iSGS, n = 32) was performed and compared with airway mucosa from healthy controls (n = 10). T cell receptor (TCR) sequences were interrogated via similarity network analysis to explore antigenic targets using the published algorithm: Grouping of Lymphocyte Interactions by Paratope Hotspots (GLIPH2). RESULTS The mucosal T cell repertoire in healthy control airways consisted of highly expressed T cell clones conserved across anatomic subsites (trachea, bronchi, bronchioles, and lung). In iSGS, high-frequency clones were equally represented in both scar and adjacent non-scar tissue. Significant differences in repertoire structure between iSGS scar and unaffected mucosa was observed, driven by unique low-frequency clones. GLIPH2 results suggest low-frequency clones share targets between multiple iSGS patients. CONCLUSION Healthy airway mucosa has a highly conserved T cell repertoire across multiple anatomic subsites. Similarly, iSGS patients have highly expressed T cell clones present in both scar and unaffected mucosa. iSGS airway scar possesses an abundance of less highly expanded clones with predicted antigen targets shared between patients. Interrogation of these shared motifs suggests abundant adaptive immunity to viral targets in iSGS airway scar. These results provide insight into disease pathogenesis and illuminate new treatment strategies in iSGS. LEVEL OF EVIDENCE NA Laryngoscope, 134:3245-3252, 2024.
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Affiliation(s)
- Evan A. Clark
- Department of Otolaryngology-Head & Neck Surgery, Vanderbilt University Medical Center, Nashville, TN
| | - Edward R.R. Talatala
- Department of Otolaryngology-Head & Neck Surgery, Vanderbilt University Medical Center, Nashville, TN
| | - Wenda Ye
- Department of Otolaryngology-Head & Neck Surgery, Vanderbilt University Medical Center, Nashville, TN
| | - Ruth J. Davis
- Department of Otolaryngology-Head & Neck Surgery, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
| | - Samuel L. Collins
- Department of Otolaryngology-Head & Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Alexander T. Hillel
- Department of Otolaryngology-Head & Neck Surgery, Johns Hopkins University School of Medicine, Baltimore, MD
| | | | - Quanhu Sheng
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN
| | - Celestine N. Wanjalla
- Department of Medicine, Division of Infectious Disease, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Simon A. Mallal
- Department of Medicine, Division of Infectious Disease, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Alexander Gelbard
- Department of Otolaryngology-Head & Neck Surgery, Vanderbilt University Medical Center, Nashville, TN
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9
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Hao Q, Long Y, Yang Y, Deng Y, Ding Z, Yang L, Shu Y, Xu H. Development and Clinical Applications of Therapeutic Cancer Vaccines with Individualized and Shared Neoantigens. Vaccines (Basel) 2024; 12:717. [PMID: 39066355 PMCID: PMC11281709 DOI: 10.3390/vaccines12070717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2024] [Revised: 06/18/2024] [Accepted: 06/24/2024] [Indexed: 07/28/2024] Open
Abstract
Neoantigens, presented as peptides on the surfaces of cancer cells, have recently been proposed as optimal targets for immunotherapy in clinical practice. The promising outcomes of neoantigen-based cancer vaccines have inspired enthusiasm for their broader clinical applications. However, the individualized tumor-specific antigens (TSA) entail considerable costs and time due to the variable immunogenicity and response rates of these neoantigens-based vaccines, influenced by factors such as neoantigen response, vaccine types, and combination therapy. Given the crucial role of neoantigen efficacy, a number of bioinformatics algorithms and pipelines have been developed to improve the accuracy rate of prediction through considering a series of factors involving in HLA-peptide-TCR complex formation, including peptide presentation, HLA-peptide affinity, and TCR recognition. On the other hand, shared neoantigens, originating from driver mutations at hot mutation spots (e.g., KRASG12D), offer a promising and ideal target for the development of therapeutic cancer vaccines. A series of clinical practices have established the efficacy of these vaccines in patients with distinct HLA haplotypes. Moreover, increasing evidence demonstrated that a combination of tumor associated antigens (TAAs) and neoantigens can also improve the prognosis, thus expand the repertoire of shared neoantigens for cancer vaccines. In this review, we provide an overview of the complex process involved in identifying personalized neoantigens, their clinical applications, advances in vaccine technology, and explore the therapeutic potential of shared neoantigen strategies.
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Affiliation(s)
- Qing Hao
- State Key Laboratory of Biotherapy and Cancer Center, Department of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China; (Q.H.); (Y.L.); (Y.Y.); (Y.D.); (Z.D.); (L.Y.)
| | - Yuhang Long
- State Key Laboratory of Biotherapy and Cancer Center, Department of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China; (Q.H.); (Y.L.); (Y.Y.); (Y.D.); (Z.D.); (L.Y.)
| | - Yi Yang
- State Key Laboratory of Biotherapy and Cancer Center, Department of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China; (Q.H.); (Y.L.); (Y.Y.); (Y.D.); (Z.D.); (L.Y.)
| | - Yiqi Deng
- State Key Laboratory of Biotherapy and Cancer Center, Department of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China; (Q.H.); (Y.L.); (Y.Y.); (Y.D.); (Z.D.); (L.Y.)
- Colorectal Cancer Center, Department of General Surgery, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Zhenyu Ding
- State Key Laboratory of Biotherapy and Cancer Center, Department of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China; (Q.H.); (Y.L.); (Y.Y.); (Y.D.); (Z.D.); (L.Y.)
| | - Li Yang
- State Key Laboratory of Biotherapy and Cancer Center, Department of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China; (Q.H.); (Y.L.); (Y.Y.); (Y.D.); (Z.D.); (L.Y.)
| | - Yang Shu
- State Key Laboratory of Biotherapy and Cancer Center, Department of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China; (Q.H.); (Y.L.); (Y.Y.); (Y.D.); (Z.D.); (L.Y.)
- Gastric Cancer Center, Department of General Surgery, West China Hospital, Sichuan University, Chengdu 610041, China
- Institute of General Surgery, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Heng Xu
- State Key Laboratory of Biotherapy and Cancer Center, Department of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China; (Q.H.); (Y.L.); (Y.Y.); (Y.D.); (Z.D.); (L.Y.)
- Institute of General Surgery, West China Hospital, Sichuan University, Chengdu 610041, China
- Research Center of Clinical Laboratory Medicine, Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu 610041, China
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10
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Hu Y, Huang J, Wang S, Sun X, Wang X, Yu H. Deciphering Autoimmune Diseases: Unveiling the Diagnostic, Therapeutic, and Prognostic Potential of Immune Repertoire Sequencing. Inflammation 2024:10.1007/s10753-024-02079-2. [PMID: 38914737 DOI: 10.1007/s10753-024-02079-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 05/31/2024] [Accepted: 06/08/2024] [Indexed: 06/26/2024]
Abstract
Autoimmune diseases (AIDs) are immune system disorders where the body exhibits an immune response to its own antigens, causing damage to its own tissues and organs. The pathogenesis of AIDs is incompletely understood. However, recent advances in immune repertoire sequencing (IR-seq) technology have opened-up a new avenue to study the IR. These studies have revealed the prevalence in IR alterations, potentially inducing AIDs by disrupting immune tolerance and thereby contributing to our comprehension of AIDs. IR-seq harbors significant potential for the clinical diagnosis, personalized treatment, and prognosis of AIDs. This article reviews the application and progress of IR-seq in diseases, such as multiple sclerosis, systemic lupus erythematosus, rheumatoid arthritis, and type 1 diabetes, to enhance our understanding of the pathogenesis of AIDs and offer valuable references for the diagnosis and treatment of AIDs.
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Affiliation(s)
- Yuelin Hu
- Department of Immunology, Special Key Laboratory of Ocular Diseases of Guizhou Province, Zunyi Medical University, Zunyi, Guizhou, P.R. China
| | - Jialing Huang
- Department of Immunology, Special Key Laboratory of Ocular Diseases of Guizhou Province, Zunyi Medical University, Zunyi, Guizhou, P.R. China
| | - Shuqing Wang
- Department of Immunology, Special Key Laboratory of Ocular Diseases of Guizhou Province, Zunyi Medical University, Zunyi, Guizhou, P.R. China
| | - Xin Sun
- School of Basic Medical Sciences, Special Key Laboratory of Gene Detection and Therapy of Guizhou Province, Zunyi Medical University, Zunyi, Guizhou, P.R. China
| | - Xin Wang
- School of Basic Medical Sciences, Special Key Laboratory of Gene Detection and Therapy of Guizhou Province, Zunyi Medical University, Zunyi, Guizhou, P.R. China
| | - Hongsong Yu
- Department of Immunology, Special Key Laboratory of Ocular Diseases of Guizhou Province, Zunyi Medical University, Zunyi, Guizhou, P.R. China.
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11
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Dintwe OB, Ballweber Fleming L, Voillet V, McNevin J, Seese A, Naidoo A, Omarjee S, Bekker LG, Kublin JG, De Rosa SC, Newell EW, Fiore-Gartland A, Andersen-Nissen E, McElrath MJ. Adolescent BCG revaccination induces a phenotypic shift in CD4 + T cell responses to Mycobacterium tuberculosis. Nat Commun 2024; 15:5191. [PMID: 38890283 PMCID: PMC11189459 DOI: 10.1038/s41467-024-49050-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 05/15/2024] [Indexed: 06/20/2024] Open
Abstract
A recent clinical trial demonstrated that Bacille Calmette-Guérin (BCG) revaccination of adolescents reduced the risk of sustained infection with Mycobacterium tuberculosis (M.tb). In a companion phase 1b trial, HVTN 602/Aeras A-042, we characterize in-depth the cellular responses to BCG revaccination or to a H4:IC31 vaccine boost to identify T cell subsets that could be responsible for the protection observed. High-dimensional clustering analysis of cells profiled using a 26-color flow cytometric panel show marked increases in five effector memory CD4+ T cell subpopulations (TEM) after BCG revaccination, two of which are highly polyfunctional. CITE-Seq single-cell analysis shows that the activated subsets include an abundant cluster of Th1 cells with migratory potential. Additionally, a small cluster of Th17 TEM cells induced by BCG revaccination expresses high levels of CD103; these may represent recirculating tissue-resident memory cells that could provide pulmonary immune protection. Together, these results identify unique populations of CD4+ T cells with potential to be immune correlates of protection conferred by BCG revaccination.
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Affiliation(s)
- One B Dintwe
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Cape Town HVTN Immunology Laboratory, Hutchinson Centre Research Institute of South Africa, Cape Town, South Africa
| | | | - Valentin Voillet
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Cape Town HVTN Immunology Laboratory, Hutchinson Centre Research Institute of South Africa, Cape Town, South Africa
| | - John McNevin
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Aaron Seese
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Anneta Naidoo
- Cape Town HVTN Immunology Laboratory, Hutchinson Centre Research Institute of South Africa, Cape Town, South Africa
| | - Saleha Omarjee
- Cape Town HVTN Immunology Laboratory, Hutchinson Centre Research Institute of South Africa, Cape Town, South Africa
| | - Linda-Gail Bekker
- The Desmond Tutu HIV Centre, Institute of Infectious Diseases and Molecular Medicine, University of Cape Town, Cape Town, South Africa
| | - James G Kublin
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Stephen C De Rosa
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, WA, USA
| | - Evan W Newell
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Andrew Fiore-Gartland
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Erica Andersen-Nissen
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
- Cape Town HVTN Immunology Laboratory, Hutchinson Centre Research Institute of South Africa, Cape Town, South Africa.
| | - M Juliana McElrath
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
- Department of Medicine, University of Washington School of Medicine, Seattle, WA, USA.
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12
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Meynard-Piganeau B, Feinauer C, Weigt M, Walczak AM, Mora T. TULIP: A transformer-based unsupervised language model for interacting peptides and T cell receptors that generalizes to unseen epitopes. Proc Natl Acad Sci U S A 2024; 121:e2316401121. [PMID: 38838016 PMCID: PMC11181096 DOI: 10.1073/pnas.2316401121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 04/29/2024] [Indexed: 06/07/2024] Open
Abstract
The accurate prediction of binding between T cell receptors (TCR) and their cognate epitopes is key to understanding the adaptive immune response and developing immunotherapies. Current methods face two significant limitations: the shortage of comprehensive high-quality data and the bias introduced by the selection of the negative training data commonly used in the supervised learning approaches. We propose a method, Transformer-based Unsupervised Language model for Interacting Peptides and T cell receptors (TULIP), that addresses both limitations by leveraging incomplete data and unsupervised learning and using the transformer architecture of language models. Our model is flexible and integrates all possible data sources, regardless of their quality or completeness. We demonstrate the existence of a bias introduced by the sampling procedure used in previous supervised approaches, emphasizing the need for an unsupervised approach. TULIP recognizes the specific TCRs binding an epitope, performing well on unseen epitopes. Our model outperforms state-of-the-art models and offers a promising direction for the development of more accurate TCR epitope recognition models.
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Affiliation(s)
- Barthelemy Meynard-Piganeau
- Laboratory of Computational and Quantitative Biology, Institut de Biologie Paris Seine, CNRS, Sorbonne Université, Paris75005, France
- Department of Computing Sciences, Bocconi University, Milan20100, Italy
| | | | - Martin Weigt
- Laboratory of Computational and Quantitative Biology, Institut de Biologie Paris Seine, CNRS, Sorbonne Université, Paris75005, France
| | - Aleksandra M. Walczak
- Laboratoire de Physique de l’Ecole Normale Supérieure, Université Paris Sciences et Lettres, CNRS, Sorbonne Université, Université de Paris Cité, Paris75005, France
| | - Thierry Mora
- Laboratoire de Physique de l’Ecole Normale Supérieure, Université Paris Sciences et Lettres, CNRS, Sorbonne Université, Université de Paris Cité, Paris75005, France
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13
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Cai C, Keoshkerian E, Wing K, Samir J, Effenberger M, Schober K, Bull RA, Lloyd AR, Busch DH, Luciani F. Discovery of a monoclonal, high-affinity CD8 + T-cell clone following natural hepatitis C virus infection. Immunol Cell Biol 2024. [PMID: 38855806 DOI: 10.1111/imcb.12791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Revised: 05/20/2024] [Accepted: 05/22/2024] [Indexed: 06/11/2024]
Abstract
CD8+ T cells recognizing their cognate antigen are typically recruited as a polyclonal population consisting of multiple clonotypes with varying T-cell receptor (TCR) affinity to the target peptide-major histocompatibility complex (pMHC) complex. Advances in single-cell sequencing have increased accessibility toward identifying TCRs with matched antigens. Here we present the discovery of a monoclonal CD8+ T-cell population with specificity for a hepatitis C virus (HCV)-derived human leukocyte antigen (HLA) class I epitope (HLA-B*07:02 GPRLGVRAT) which was isolated directly ex vivo from an individual with an episode of acutely resolved HCV infection. This population was absent before infection and underwent expansion and stable maintenance for at least 2 years after infection as measured by HLA-multimer staining. Furthermore, the monoclonal clonotype was characterized by an unusually long dissociation time (half-life = 794 s and koff = 5.73 × 10-4) for its target antigen when compared with previously published results. A comparison with related populations of HCV-specific populations derived from the same individual and a second individual suggested that high-affinity TCR-pMHC interactions may be inherent to epitope identity and shape the phenotype of responses which has implications for rational TCR selection and design in the age of personalized immunotherapies.
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Affiliation(s)
- Curtis Cai
- School of Biomedical Sciences, Faculty of Health and Medicine, UNSW Sydney, Sydney, NSW, Australia
- The Kirby Institute, Faculty of Health and Medicine, UNSW Sydney, Sydney, NSW, Australia
| | - Elizabeth Keoshkerian
- The Kirby Institute, Faculty of Health and Medicine, UNSW Sydney, Sydney, NSW, Australia
| | - Kristof Wing
- School of Medicine and Health, Institute for Medical Microbiology, Immunology and Hygiene, Technical University of Munich, Munich, Germany
| | - Jerome Samir
- School of Biomedical Sciences, Faculty of Health and Medicine, UNSW Sydney, Sydney, NSW, Australia
| | - Manuel Effenberger
- School of Medicine and Health, Institute for Medical Microbiology, Immunology and Hygiene, Technical University of Munich, Munich, Germany
| | - Kilian Schober
- Mikrobiologisches Institut - Klinische Mikrobiologie, Immunologie und Hygiene Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Rowena A Bull
- School of Biomedical Sciences, Faculty of Health and Medicine, UNSW Sydney, Sydney, NSW, Australia
- The Kirby Institute, Faculty of Health and Medicine, UNSW Sydney, Sydney, NSW, Australia
| | - Andrew R Lloyd
- The Kirby Institute, Faculty of Health and Medicine, UNSW Sydney, Sydney, NSW, Australia
| | - Dirk H Busch
- School of Medicine and Health, Institute for Medical Microbiology, Immunology and Hygiene, Technical University of Munich, Munich, Germany
- German Center for Infection Research (Deutsches Zentrum für Infektionsforschung), Partner Site Munich, Munich, Germany
| | - Fabio Luciani
- School of Biomedical Sciences, Faculty of Health and Medicine, UNSW Sydney, Sydney, NSW, Australia
- The Kirby Institute, Faculty of Health and Medicine, UNSW Sydney, Sydney, NSW, Australia
- Cellular Genomics Future Institute, UNSW Sydney, Sydney, NSW, Australia
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York City, NY, USA
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14
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Yu Z, Jiang M, Lan X. HeteroTCR: A heterogeneous graph neural network-based method for predicting peptide-TCR interaction. Commun Biol 2024; 7:684. [PMID: 38834836 DOI: 10.1038/s42003-024-06380-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 05/23/2024] [Indexed: 06/06/2024] Open
Abstract
Identifying interactions between T-cell receptors (TCRs) and immunogenic peptides holds profound implications across diverse research domains and clinical scenarios. Unsupervised clustering models (UCMs) cannot predict peptide-TCR binding directly, while supervised predictive models (SPMs) often face challenges in identifying antigens previously unencountered by the immune system or possessing limited TCR binding repertoires. Therefore, we propose HeteroTCR, an SPM based on Heterogeneous Graph Neural Network (GNN), to accurately predict peptide-TCR binding probabilities. HeteroTCR captures within-type (TCR-TCR or peptide-peptide) similarity information and between-type (peptide-TCR) interaction insights for predictions on unseen peptides and TCRs, surpassing limitations of existing SPMs. Our evaluation shows HeteroTCR outperforms state-of-the-art models on independent datasets. Ablation studies and visual interpretation underscore the Heterogeneous GNN module's critical role in enhancing HeteroTCR's performance by capturing pivotal binding process features. We further demonstrate the robustness and reliability of HeteroTCR through validation using single-cell datasets, aligning with the expectation that pMHC-TCR complexes with higher predicted binding probabilities correspond to increased binding fractions.
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Affiliation(s)
- Zilan Yu
- School of Medicine, Tsinghua University, 100084, Beijing, China
- Centre for Life Sciences, Tsinghua University, 100084, Beijing, China
| | - Mengnan Jiang
- School of Medicine, Tsinghua University, 100084, Beijing, China
| | - Xun Lan
- School of Medicine, Tsinghua University, 100084, Beijing, China.
- Centre for Life Sciences, Tsinghua University, 100084, Beijing, China.
- Tsinghua-Peking Center for Life Sciences, MOE Key Laboratory of Tsinghua University, Beijing, China.
- MOE Key Laboratory of Bioinformatics, Tsinghua University, 100084, Beijing, China.
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15
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Li Y, Ji L, Zhang Y, Zhang J, Reuben A, Zeng H, Huang Q, Wei Q, Tan S, Xia X, Li W, Zhang J, Tian P. The combination of tumor mutational burden and T-cell receptor repertoire predicts the response to immunotherapy in patients with advanced non-small cell lung cancer. MedComm (Beijing) 2024; 5:e604. [PMID: 38840771 PMCID: PMC11151154 DOI: 10.1002/mco2.604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 03/27/2024] [Accepted: 05/01/2024] [Indexed: 06/07/2024] Open
Abstract
Tumor mutational burden (TMB) and T-cell receptor (TCR) might predict the response to immunotherapy in patients with non-small cell lung cancer (NSCLC). However, the predictive value of the combination of TMB and TCR was not clear. Targeted DNA and TCR sequencing were performed on tumor biopsy specimens. We combined TMB and TCR diversity into a TMB-and-TCR (TMR) score using logistic regression. In total, 38 patients with advanced NSCLC were divided into a discovery set (n = 17) and validation set (n = 21). A higher TMR score was associated with better response and longer progression-free survival to immunotherapy in both the discovery set and validation set. The performance of TMR score was confirmed in the two external validation cohorts of 225 NSCLC patients and 306 NSCLC patients. Tumors with higher TMR scores were more likely to combine with LRP1B gene mutation (p = 0.027) and top 1% CDR3 sequences (p = 0.001). Furthermore, LRP1B allele frequency was negatively correlated with the top 1% CDR3 sequences (r = -0.55, p = 0.033) and positively correlated with tumor shrinkage (r = 0.68, p = 0.007). The TMR score could serve as a potential predictive biomarker for the response to immunotherapy in advanced NSCLC.
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Affiliation(s)
- Yalun Li
- Department of Pulmonary and Critical Care MedicineState Key Laboratory of Respiratory Health and MultimorbidityWest China Hospital of Sichuan University, Precision Medicine Key Laboratory of Sichuan ProvinceChengduSichuanChina
- Lung Cancer Center/Lung Cancer InstituteWest China Hospital, Sichuan UniversityChengduSichuanChina
| | - Liyan Ji
- Geneplus‐Beijing InstituteBeijingChina
| | | | - Jiexin Zhang
- Departments of Bioinformatics and Computational BiologyUniversity of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Alexandre Reuben
- Department of Thoracic/Head and Neck Medical OncologyUniversity of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Hao Zeng
- Department of Pulmonary and Critical Care MedicineWest China Hospital, West China School of Medicine, Sichuan UniversityChengduSichuanChina
| | - Qin Huang
- Department of Pulmonary and Critical Care MedicineWest China Hospital, West China School of Medicine, Sichuan UniversityChengduSichuanChina
| | - Qi Wei
- Department of Pulmonary and Critical Care MedicineWest China Hospital, West China School of Medicine, Sichuan UniversityChengduSichuanChina
| | - Sihan Tan
- Department of Pulmonary and Critical Care MedicineWest China Hospital, West China School of Medicine, Sichuan UniversityChengduSichuanChina
| | | | - Weimin Li
- Department of Pulmonary and Critical Care MedicineState Key Laboratory of Respiratory Health and MultimorbidityWest China Hospital of Sichuan University, Precision Medicine Key Laboratory of Sichuan ProvinceChengduSichuanChina
| | - Jianjun Zhang
- Department of Thoracic/Head and Neck Medical OncologyUniversity of Texas MD Anderson Cancer CenterHoustonTexasUSA
- Department of Genomic MedicineUniversity of Texas MD Anderson Cancer CenterHoustonTexasUSA
- Lung Cancer Genomics ProgramUniversity of Texas MD Anderson Cancer CenterHoustonTexasUSA
- Lung Cancer Interception ProgramUniversity of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Panwen Tian
- Department of Pulmonary and Critical Care MedicineState Key Laboratory of Respiratory Health and MultimorbidityWest China Hospital of Sichuan University, Precision Medicine Key Laboratory of Sichuan ProvinceChengduSichuanChina
- Lung Cancer Center/Lung Cancer InstituteWest China Hospital, Sichuan UniversityChengduSichuanChina
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16
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Tiffeau-Mayer A. Unbiased estimation of sampling variance for Simpson's diversity index. Phys Rev E 2024; 109:064411. [PMID: 39020976 DOI: 10.1103/physreve.109.064411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 06/03/2024] [Indexed: 07/20/2024]
Abstract
Quantification of measurement uncertainty is crucial for robust scientific inference, yet accurate estimates of this uncertainty remain elusive for ecological measures of diversity. Here, we address this longstanding challenge by deriving a closed-form unbiased estimator for the sampling variance of Simpson's diversity index. In numerical tests the estimator consistently outperforms existing approaches, particularly for applications in which species richness exceeds sample size. We apply the estimator to quantify biodiversity loss in marine ecosystems and to demonstrate ligand-dependent contributions of T-cell-receptor chains to specificity, illustrating its versatility across fields. The novel estimator provides researchers with a reliable method for comparing diversity between samples, essential for quantifying biodiversity trends and making informed conservation decisions.
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17
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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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18
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Jiang F, Guo Y, Ma H, Na S, Zhong W, Han Y, Wang T, Huang J. GTE: a graph learning framework for prediction of T-cell receptors and epitopes binding specificity. Brief Bioinform 2024; 25:bbae343. [PMID: 39007599 PMCID: PMC11247411 DOI: 10.1093/bib/bbae343] [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: 01/29/2024] [Revised: 05/15/2024] [Accepted: 07/01/2024] [Indexed: 07/16/2024] Open
Abstract
The interaction between T-cell receptors (TCRs) and peptides (epitopes) presented by major histocompatibility complex molecules (MHC) is fundamental to the immune response. Accurate prediction of TCR-epitope interactions is crucial for advancing the understanding of various diseases and their prevention and treatment. Existing methods primarily rely on sequence-based approaches, overlooking the inherent topology structure of TCR-epitope interaction networks. In this study, we present $GTE$, a novel heterogeneous Graph neural network model based on inductive learning to capture the topological structure between TCRs and Epitopes. Furthermore, we address the challenge of constructing negative samples within the graph by proposing a dynamic edge update strategy, enhancing model learning with the nonbinding TCR-epitope pairs. Additionally, to overcome data imbalance, we adapt the Deep AUC Maximization strategy to the graph domain. Extensive experiments are conducted on four public datasets to demonstrate the superiority of exploring underlying topological structures in predicting TCR-epitope interactions, illustrating the benefits of delving into complex molecular networks. The implementation code and data are available at https://github.com/uta-smile/GTE.
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Affiliation(s)
- Feng Jiang
- Department of Computer Science and Engineering, University of Texas at Arlington, 701 S. Nedderman Drive, TX 76019, United States
| | - Yuzhi Guo
- Department of Computer Science and Engineering, University of Texas at Arlington, 701 S. Nedderman Drive, TX 76019, United States
| | - Hehuan Ma
- Department of Computer Science and Engineering, University of Texas at Arlington, 701 S. Nedderman Drive, TX 76019, United States
| | - Saiyang Na
- Department of Computer Science and Engineering, University of Texas at Arlington, 701 S. Nedderman Drive, TX 76019, United States
| | - Wenliang Zhong
- Department of Computer Science and Engineering, University of Texas at Arlington, 701 S. Nedderman Drive, TX 76019, United States
| | - Yi Han
- Public Health, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, TX 75390, United States
| | - Tao Wang
- Public Health, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, TX 75390, United States
| | - Junzhou Huang
- Department of Computer Science and Engineering, University of Texas at Arlington, 701 S. Nedderman Drive, TX 76019, United States
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19
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Glass DR, Mayer-Blackwell K, Ramchurren N, Parks KR, Duran GE, Wright AK, Bastidas Torres AN, Islas L, Kim YH, Fling SP, Khodadoust MS, Newell EW. Multi-omic profiling reveals the endogenous and neoplastic responses to immunotherapies in cutaneous T cell lymphoma. Cell Rep Med 2024; 5:101527. [PMID: 38670099 PMCID: PMC11148639 DOI: 10.1016/j.xcrm.2024.101527] [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: 09/14/2023] [Revised: 02/17/2024] [Accepted: 04/03/2024] [Indexed: 04/28/2024]
Abstract
Cutaneous T cell lymphomas (CTCLs) are skin cancers with poor survival rates and limited treatments. While immunotherapies have shown some efficacy, the immunological consequences of administering immune-activating agents to CTCL patients have not been systematically characterized. We apply a suite of high-dimensional technologies to investigate the local, cellular, and systemic responses in CTCL patients receiving either mono- or combination anti-PD-1 plus interferon-gamma (IFN-γ) therapy. Neoplastic T cells display no evidence of activation after immunotherapy. IFN-γ induces muted endogenous immunological responses, while anti-PD-1 elicits broader changes, including increased abundance of CLA+CD39+ T cells. We develop an unbiased multi-omic profiling approach enabling discovery of immune modules stratifying patients. We identify an enrichment of activated regulatory CLA+CD39+ T cells in non-responders and activated cytotoxic CLA+CD39+ T cells in leukemic patients. Our results provide insights into the effects of immunotherapy in CTCL patients and a generalizable framework for multi-omic analysis of clinical trials.
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Affiliation(s)
- David R Glass
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA.
| | - Koshlan Mayer-Blackwell
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Nirasha Ramchurren
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA; Cancer Immunotherapy Trials Network, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - K Rachael Parks
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - George E Duran
- Division of Oncology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Anna K Wright
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA; Cancer Immunotherapy Trials Network, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | | | - Laura Islas
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Youn H Kim
- Division of Oncology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Steven P Fling
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA; Cancer Immunotherapy Trials Network, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Michael S Khodadoust
- Division of Oncology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Evan W Newell
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA.
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20
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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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21
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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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22
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Jiang M, Yu Z, Lan X. VitTCR: A deep learning method for peptide recognition prediction. iScience 2024; 27:109770. [PMID: 38711451 PMCID: PMC11070698 DOI: 10.1016/j.isci.2024.109770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 01/21/2024] [Accepted: 04/15/2024] [Indexed: 05/08/2024] Open
Abstract
This study introduces VitTCR, a predictive model based on the vision transformer (ViT) architecture, aimed at identifying interactions between T cell receptors (TCRs) and peptides, crucial for developing cancer immunotherapies and vaccines. VitTCR converts TCR-peptide interactions into numerical AtchleyMaps using Atchley factors for prediction, achieving AUROC (0.6485) and AUPR (0.6295) values. Benchmark analysis indicates VitTCR's performance is comparable to other models, with further comparative studies suggested to understand its effectiveness in varied contexts. Additionally, integrating a positional bias weight matrix (PBWM), derived from amino acid contact probabilities in structurally resolved pMHC-TCR complexes, slightly improves VitTCR's accuracy. The model's predictions show weak yet statistically significant correlations with immunological factors like T cell clonal expansion and activation percentages, underscoring the biological relevance of VitTCR's predictive capabilities. VitTCR emerges as a valuable computational tool for predicting TCR-peptide interactions, offering insights for immunotherapy and vaccine development.
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Affiliation(s)
- Mengnan Jiang
- School of Medicine, Tsinghua University, Beijing 100084, China
| | - Zilan Yu
- School of Medicine, Tsinghua University, Beijing 100084, China
- Centre for Life Sciences, Tsinghua University, Beijing 100084, China
| | - Xun Lan
- School of Medicine, Tsinghua University, Beijing 100084, China
- Centre for Life Sciences, Tsinghua University, Beijing 100084, China
- Tsinghua-Peking Center for Life Sciences, MOE Key Laboratory of Tsinghua University, Beijing, China
- MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing 100084, China
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23
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Cross DL, Layton ED, Yu KK, Smith MT, Aguilar MS, Li S, Wilcox EC, Chapuis AG, Mayanja-Kizza H, Stein CM, Boom WH, Hawn TR, Bradley P, Newell EW, Seshadri C. MR1-restricted T cell clonotypes are associated with "resistance" to Mycobacterium tuberculosis infection. JCI Insight 2024; 9:e166505. [PMID: 38716731 PMCID: PMC11141901 DOI: 10.1172/jci.insight.166505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 03/27/2024] [Indexed: 05/14/2024] Open
Abstract
T cells are required for protective immunity against Mycobacterium tuberculosis. We recently described a cohort of Ugandan household contacts of tuberculosis cases who appear to "resist" M. tuberculosis infection (resisters; RSTRs) and showed that these individuals harbor IFN-γ-independent T cell responses to M. tuberculosis-specific peptide antigens. However, T cells also recognize nonprotein antigens via antigen-presenting systems that are independent of genetic background, known as donor-unrestricted T cells (DURTs). We used tetramer staining and flow cytometry to characterize the association between DURTs and "resistance" to M. tuberculosis infection. Peripheral blood frequencies of most DURT subsets were comparable between RSTRs and latently infected controls (LTBIs). However, we observed a 1.65-fold increase in frequency of MR1-restricted T (MR1T) cells among RSTRs in comparison with LTBIs. Single-cell RNA sequencing of 18,251 MR1T cells sorted from 8 donors revealed 5,150 clonotypes that expressed a common transcriptional program, the majority of which were private. Sequencing of the T cell receptor α/T cell receptor δ (TCRα/δ) repertoire revealed several DURT clonotypes were expanded among RSTRs, including 2 MR1T clonotypes that recognized mycobacteria-infected cells in a TCR-dependent manner. Overall, our data reveal unexpected donor-specific diversity in the TCR repertoire of human MR1T cells as well as associations between mycobacteria-reactive MR1T clonotypes and resistance to M. tuberculosis infection.
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Affiliation(s)
- Deborah L. Cross
- Department of Medicine, University of Washington School of Medicine, Seattle, Washington, USA
| | - Erik D. Layton
- Department of Medicine, University of Washington School of Medicine, Seattle, Washington, USA
| | - Krystle K.Q. Yu
- Department of Medicine, University of Washington School of Medicine, Seattle, Washington, USA
| | - Malisa T. Smith
- Department of Medicine, University of Washington School of Medicine, Seattle, Washington, USA
| | - Melissa S. Aguilar
- Department of Medicine, University of Washington School of Medicine, Seattle, Washington, USA
| | - Shamin Li
- Vaccine and Infectious Disease Division and
| | - Elise C. Wilcox
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
| | - Aude G. Chapuis
- Translational Science and Therapeutics Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
| | | | - Catherine M. Stein
- Department of Medicine and
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, USA
| | | | - Thomas R. Hawn
- Department of Medicine, University of Washington School of Medicine, Seattle, Washington, USA
| | - Philip Bradley
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
| | | | - Chetan Seshadri
- Department of Medicine, University of Washington School of Medicine, Seattle, Washington, USA
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24
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Gao Y, Dong K, Gao Y, Jin X, Yang J, Yan G, Liu Q. Unified cross-modality integration and analysis of T cell receptors and T cell transcriptomes by low-resource-aware representation learning. CELL GENOMICS 2024; 4:100553. [PMID: 38688285 PMCID: PMC11099349 DOI: 10.1016/j.xgen.2024.100553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 03/09/2024] [Accepted: 04/06/2024] [Indexed: 05/02/2024]
Abstract
Single-cell RNA sequencing (scRNA-seq) and T cell receptor sequencing (TCR-seq) are pivotal for investigating T cell heterogeneity. Integrating these modalities, which is expected to uncover profound insights in immunology that might otherwise go unnoticed with a single modality, faces computational challenges due to the low-resource characteristics of the multimodal data. Herein, we present UniTCR, a novel low-resource-aware multimodal representation learning framework designed for the unified cross-modality integration, enabling comprehensive T cell analysis. By designing a dual-modality contrastive learning module and a single-modality preservation module to effectively embed each modality into a common latent space, UniTCR demonstrates versatility in connecting TCR sequences with T cell transcriptomes across various tasks, including single-modality analysis, modality gap analysis, epitope-TCR binding prediction, and TCR profile cross-modality generation, in a low-resource-aware way. Extensive evaluations conducted on multiple scRNA-seq/TCR-seq paired datasets showed the superior performance of UniTCR, exhibiting the ability of exploring the complexity of immune system.
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Affiliation(s)
- Yicheng Gao
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Tongji Hospital, School of Medicine, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China; State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Kejing Dong
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Tongji Hospital, School of Medicine, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China; State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Yuli Gao
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Tongji Hospital, School of Medicine, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China; State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Xuan Jin
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Tongji Hospital, School of Medicine, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China; State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
| | - Jingya Yang
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai 201804, China
| | - Gang Yan
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai 201804, China.
| | - Qi Liu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Tongji Hospital, School of Medicine, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China; State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China; Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai 201804, China; Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou 311121, China.
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25
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Zdinak PM, Trivedi N, Grebinoski S, Torrey J, Martinez EZ, Martinez S, Hicks L, Ranjan R, Makani VKK, Roland MM, Kublo L, Arshad S, Anderson MS, Vignali DAA, Joglekar AV. De novo identification of CD4 + T cell epitopes. Nat Methods 2024; 21:846-856. [PMID: 38658646 PMCID: PMC11093748 DOI: 10.1038/s41592-024-02255-0] [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: 11/20/2022] [Accepted: 03/22/2024] [Indexed: 04/26/2024]
Abstract
CD4+ T cells recognize peptide antigens presented on class II major histocompatibility complex (MHC-II) molecules to carry out their function. The remarkable diversity of T cell receptor sequences and lack of antigen discovery approaches for MHC-II make profiling the specificities of CD4+ T cells challenging. We have expanded our platform of signaling and antigen-presenting bifunctional receptors to encode MHC-II molecules presenting covalently linked peptides (SABR-IIs) for CD4+ T cell antigen discovery. SABR-IIs can present epitopes to CD4+ T cells and induce signaling upon their recognition, allowing a readable output. Furthermore, the SABR-II design is modular in signaling and deployment to T cells and B cells. Here, we demonstrate that SABR-IIs libraries presenting endogenous and non-contiguous epitopes can be used for antigen discovery in the context of type 1 diabetes. SABR-II libraries provide a rapid, flexible, scalable and versatile approach for de novo identification of CD4+ T cell ligands from single-cell RNA sequencing data using experimental and computational approaches.
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Affiliation(s)
- Paul M Zdinak
- Department of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Center for Systems Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Program in Microbiology and Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Nishtha Trivedi
- Department of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Center for Systems Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Stephanie Grebinoski
- Department of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Program in Microbiology and Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Jessica Torrey
- Department of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Center for Systems Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Eduardo Zarate Martinez
- Department of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Center for Systems Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Microbiology and Immunology Diversity Scholars Program, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Salome Martinez
- Department of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Center for Systems Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Louise Hicks
- Department of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Center for Systems Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Rashi Ranjan
- Department of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Center for Systems Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Venkata Krishna Kanth Makani
- Department of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Center for Systems Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Mary Melissa Roland
- Department of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Center for Systems Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Lyubov Kublo
- Department of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Center for Systems Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Sanya Arshad
- Department of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Center for Systems Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Mark S Anderson
- Diabetes Center, University of California San Francisco, San Francisco, CA, USA
| | - Dario A A Vignali
- Department of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Tumor Microenvironment Center, UPMC Hillman Cancer Center, Pittsburgh, PA, USA
- Cancer Immunology and Immunotherapy Program, UPMC Hillman Cancer Center, Pittsburgh, PA, USA
| | - Alok V Joglekar
- Department of Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
- Center for Systems Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
- Cancer Immunology and Immunotherapy Program, UPMC Hillman Cancer Center, Pittsburgh, PA, USA.
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26
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McMaster B, Thorpe C, Ogg G, Deane CM, Koohy H. Can AlphaFold's breakthrough in protein structure help decode the fundamental principles of adaptive cellular immunity? Nat Methods 2024; 21:766-776. [PMID: 38654083 DOI: 10.1038/s41592-024-02240-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 03/08/2024] [Indexed: 04/25/2024]
Abstract
T cells are essential immune cells responsible for identifying and eliminating pathogens. Through interactions between their T-cell antigen receptors (TCRs) and antigens presented by major histocompatibility complex molecules (MHCs) or MHC-like molecules, T cells discriminate foreign and self peptides. Determining the fundamental principles that govern these interactions has important implications in numerous medical contexts. However, reconstructing a map between T cells and their antagonist antigens remains an open challenge for the field of immunology, and success of in silico reconstructions of this relationship has remained incremental. In this Perspective, we discuss the role that new state-of-the-art deep-learning models for predicting protein structure may play in resolving some of the unanswered questions the field faces linking TCR and peptide-MHC properties to T-cell specificity. We provide a comprehensive overview of structural databases and the evolution of predictive models, and highlight the breakthrough AlphaFold provided the field.
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Affiliation(s)
- Benjamin McMaster
- MRC Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Department of Statistics, University of Oxford, Oxford, UK
| | - Christopher Thorpe
- Open Targets, Wellcome Genome Campus, Hinxton, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, UK
| | - Graham Ogg
- MRC Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Chinese Academy of Medical Sciences Oxford Institute, University of Oxford, Oxford, UK
| | | | - Hashem Koohy
- MRC Translational Immune Discovery Unit, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK.
- Alan Turning Fellow in Health and Medicine, University of Oxford, Oxford, UK.
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27
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Kenison JE, Stevens NA, Quintana FJ. Therapeutic induction of antigen-specific immune tolerance. Nat Rev Immunol 2024; 24:338-357. [PMID: 38086932 PMCID: PMC11145724 DOI: 10.1038/s41577-023-00970-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/02/2023] [Indexed: 05/04/2024]
Abstract
The development of therapeutic approaches for the induction of robust, long-lasting and antigen-specific immune tolerance remains an important unmet clinical need for the management of autoimmunity, allergy, organ transplantation and gene therapy. Recent breakthroughs in our understanding of immune tolerance mechanisms have opened new research avenues and therapeutic opportunities in this area. Here, we review mechanisms of immune tolerance and novel methods for its therapeutic induction.
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Affiliation(s)
- Jessica E Kenison
- Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Nikolas A Stevens
- Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Francisco J Quintana
- Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- The Broad Institute of Harvard and MIT, Cambridge, MA, USA.
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28
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Amissah OB, Basnet R, Chen W, Habimana JDD, Baiden BE, Owusu OA, Saeed BJ, Li Z. Enhancing antitumor response by efficiently generating large-scale TCR-T cells targeting a single epitope across multiple cancer antigens. Cell Immunol 2024; 399-400:104827. [PMID: 38733699 DOI: 10.1016/j.cellimm.2024.104827] [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: 03/05/2024] [Revised: 04/26/2024] [Accepted: 05/07/2024] [Indexed: 05/13/2024]
Abstract
The need to contrive interventions to curb the rise in cancer incidence and mortality is critical for improving patients' prognoses. Adoptive cell therapy is challenged with quality large-scale production, heightening its production cost. Several cancer types have been associated with the expression of highly-immunogenic CTAG1 and CTAG2 antigens, which share common epitopes. Targeting two antigens on the same cancer could improve the antitumor response of TCR-T cells. In this study, we exploited an efficient way to generate large-fold quality TCR-T cells and also demonstrated that the common epitopes of CTAG1 and CTAG2 antigens provide an avenue for improved cancer-killing via dual-antigen-epitope targeting. Our study revealed that xeno/sera-free medium could expand TCR-T cells to over 500-fold, posing as a better replacement for FBS-supplemented media. Human AB serum was also shown to be a good alternative in the absence of xeno/sera-free media. Furthermore, TCR-T cells stimulated with beads-coated T-activator showed a better effector function than soluble T-activator stimulated TCR-T cells. Additionally, TCR-T cells that target multiple antigens in the same cancer yield better anticancer activity than those targeting a single antigen. This showed that targeting multiple antigens with a common epitope may enhance the antitumor response efficacy of T cell therapies.
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Affiliation(s)
- Obed Boadi Amissah
- CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China; University of Chinese Academy of Sciences, 19 Yuquan Road, Shijingshan District, Beijing 100049, China
| | - Rajesh Basnet
- CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China; University of Chinese Academy of Sciences, 19 Yuquan Road, Shijingshan District, Beijing 100049, China
| | - Wenfang Chen
- CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China; University of Chinese Academy of Sciences, 19 Yuquan Road, Shijingshan District, Beijing 100049, China
| | - Jean de Dieu Habimana
- CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China
| | - Belinda Edwina Baiden
- College of Health Sciences, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
| | - Osei Asibey Owusu
- Department of Clinical and Medical Sciences, University of Exeter, Exeter, UK
| | - Babangida Jabir Saeed
- CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China
| | - Zhiyuan Li
- CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China; University of Chinese Academy of Sciences, 19 Yuquan Road, Shijingshan District, Beijing 100049, China; GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, GIBH-CUHK Joint Research Laboratory on Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China; Department of Anatomy and Neurobiology, Xiangya School of Medicine, Central South University, Changsha 410013, China.
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29
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Irac SE, Soon MSF, Borcherding N, Tuong ZK. Single-cell immune repertoire analysis. Nat Methods 2024; 21:777-792. [PMID: 38637691 DOI: 10.1038/s41592-024-02243-4] [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: 12/05/2023] [Accepted: 03/12/2024] [Indexed: 04/20/2024]
Abstract
Single-cell T cell and B cell antigen receptor-sequencing data analysis can potentially perform in-depth assessments of adaptive immune cells that inform on understanding immune cell development to tracking clonal expansion in disease and therapy. However, it has been extremely challenging to analyze and interpret T cells and B cells and their adaptive immune receptor repertoires at the single-cell level due to not only the complexity of the data but also the underlying biology. In this Review, we delve into the computational breakthroughs that have transformed the analysis of single-cell T cell and B cell antigen receptor-sequencing data.
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Affiliation(s)
- Sergio E Irac
- Cancer Immunoregulation and Immunotherapy, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Megan Sioe Fei Soon
- Ian Frazer Centre for Children's Immunotherapy Research, Child Health Research Centre, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia
| | - Nicholas Borcherding
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA
- Omniscope, Palo Alto, CA, USA
| | - Zewen Kelvin Tuong
- Ian Frazer Centre for Children's Immunotherapy Research, Child Health Research Centre, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia.
- Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia.
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30
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Menon T, Illing PT, Chaurasia P, McQuilten HA, Shepherd C, Rowntree LC, Petersen J, Littler DR, Khuu G, Huang Z, Allen LF, Rockman S, Crowe J, Flanagan KL, Wakim LM, Nguyen THO, Mifsud NA, Rossjohn J, Purcell AW, van de Sandt CE, Kedzierska K. CD8 + T-cell responses towards conserved influenza B virus epitopes across anatomical sites and age. Nat Commun 2024; 15:3387. [PMID: 38684663 PMCID: PMC11059233 DOI: 10.1038/s41467-024-47576-y] [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: 09/05/2023] [Accepted: 04/03/2024] [Indexed: 05/02/2024] Open
Abstract
Influenza B viruses (IBVs) cause substantive morbidity and mortality, and yet immunity towards IBVs remains understudied. CD8+ T-cells provide broadly cross-reactive immunity and alleviate disease severity by recognizing conserved epitopes. Despite the IBV burden, only 18 IBV-specific T-cell epitopes restricted by 5 HLAs have been identified currently. A broader array of conserved IBV T-cell epitopes is needed to develop effective cross-reactive T-cell based IBV vaccines. Here we identify 9 highly conserved IBV CD8+ T-cell epitopes restricted to HLA-B*07:02, HLA-B*08:01 and HLA-B*35:01. Memory IBV-specific tetramer+CD8+ T-cells are present within blood and tissues. Frequencies of IBV-specific CD8+ T-cells decline with age, but maintain a central memory phenotype. HLA-B*07:02 and HLA-B*08:01-restricted NP30-38 epitope-specific T-cells have distinct T-cell receptor repertoires. We provide structural basis for the IBV HLA-B*07:02-restricted NS1196-206 (11-mer) and HLA-B*07:02-restricted NP30-38 epitope presentation. Our study increases the number of IBV CD8+ T-cell epitopes, and defines IBV-specific CD8+ T-cells at cellular and molecular levels, across tissues and age.
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Affiliation(s)
- Tejas Menon
- Department of Microbiology and Immunology, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Parkville, VIC, Australia
| | - Patricia T Illing
- Infection and Immunity Program & Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia
| | - Priyanka Chaurasia
- Infection and Immunity Program & Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia
| | - Hayley A McQuilten
- Department of Microbiology and Immunology, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Parkville, VIC, Australia
| | - Chloe Shepherd
- Infection and Immunity Program & Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia
| | - Louise C Rowntree
- Department of Microbiology and Immunology, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Parkville, VIC, Australia
| | - Jan Petersen
- Infection and Immunity Program & Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia
| | - Dene R Littler
- Infection and Immunity Program & Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia
| | - Grace Khuu
- Infection and Immunity Program & Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia
| | - Ziyi Huang
- Infection and Immunity Program & Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia
| | - Lilith F Allen
- Department of Microbiology and Immunology, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Parkville, VIC, Australia
| | - Steve Rockman
- Department of Microbiology and Immunology, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Parkville, VIC, Australia
- CSL Seqirus Ltd, Parkville, VIC, Australia
| | - Jane Crowe
- Deepdene Surgery, Deepdene, VIC, Australia
| | - Katie L Flanagan
- Tasmanian Vaccine Trial Centre, Launceston General Hospital, Launceston, TAS, Australia
- School of Health Sciences and School of Medicine, University of Tasmania, Launceston, TAS, Australia
- School of Health and Biomedical Science, RMIT University, Melbourne, VIC, Australia
| | - Linda M Wakim
- Department of Microbiology and Immunology, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Parkville, VIC, Australia
| | - Thi H O Nguyen
- Department of Microbiology and Immunology, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Parkville, VIC, Australia
| | - Nicole A Mifsud
- Infection and Immunity Program & Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia
| | - Jamie Rossjohn
- Infection and Immunity Program & Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia
- Institute of Infection and Immunity, Cardiff University School of Medicine, Cardiff, UK
| | - Anthony W Purcell
- Infection and Immunity Program & Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia
| | - Carolien E van de Sandt
- Department of Microbiology and Immunology, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Parkville, VIC, Australia
- Department of Hematopoiesis, Sanquin Research and Landsteiner Laboratory, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Katherine Kedzierska
- Department of Microbiology and Immunology, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Parkville, VIC, Australia.
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31
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Kidman J, Zemek RM, Sidhom JW, Correa D, Principe N, Sheikh F, Fear VS, Forbes CA, Chopra A, Boon L, Zaitouny A, de Jong E, Holt RA, Jones M, Millward MJ, Lassmann T, Forrest AR, Nowak AK, Watson M, Lake RA, Lesterhuis WJ, Chee J. Immune checkpoint therapy responders display early clonal expansion of tumor infiltrating lymphocytes. Oncoimmunology 2024; 13:2345859. [PMID: 38686178 PMCID: PMC11057660 DOI: 10.1080/2162402x.2024.2345859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 04/17/2024] [Indexed: 05/02/2024] Open
Abstract
Immune checkpoint therapy (ICT) causes durable tumour responses in a subgroup of patients, but it is not well known how T cell receptor beta (TCRβ) repertoire dynamics contribute to the therapeutic response. Using murine models that exclude variation in host genetics, environmental factors and tumour mutation burden, limiting variation between animals to naturally diverse TCRβ repertoires, we applied TCRseq, single cell RNAseq and flow cytometry to study TCRβ repertoire dynamics in ICT responders and non-responders. Increased oligoclonal expansion of TCRβ clonotypes was observed in responding tumours. Machine learning identified TCRβ CDR3 signatures unique to each tumour model, and signatures associated with ICT response at various timepoints before or during ICT. Clonally expanded CD8+ T cells in responding tumours post ICT displayed effector T cell gene signatures and phenotype. An early burst of clonal expansion during ICT is associated with response, and we report unique dynamics in TCRβ signatures associated with ICT response.
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MESH Headings
- Animals
- Immune Checkpoint Inhibitors/pharmacology
- Immune Checkpoint Inhibitors/therapeutic use
- Receptors, Antigen, T-Cell, alpha-beta/genetics
- Receptors, Antigen, T-Cell, alpha-beta/metabolism
- Mice
- Lymphocytes, Tumor-Infiltrating/immunology
- Lymphocytes, Tumor-Infiltrating/drug effects
- Lymphocytes, Tumor-Infiltrating/metabolism
- CD8-Positive T-Lymphocytes/immunology
- CD8-Positive T-Lymphocytes/drug effects
- CD8-Positive T-Lymphocytes/metabolism
- Humans
- Mice, Inbred C57BL
- Female
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Affiliation(s)
- Joel Kidman
- National Centre for Asbestos Related Diseases, Institute for Respiratory Health, University of Western Australia, Perth, Australia
| | | | | | - Debora Correa
- Complex Systems Group, Department of Mathematics and Statistics, University of Western Australia, Perth, Australia
| | - Nicola Principe
- National Centre for Asbestos Related Diseases, Institute for Respiratory Health, University of Western Australia, Perth, Australia
| | - Fezaan Sheikh
- National Centre for Asbestos Related Diseases, Institute for Respiratory Health, University of Western Australia, Perth, Australia
| | | | | | - Abha Chopra
- Medical Genomics Laboratories (IIID), Centre for Molecular Medicine and Innovative Therapeutics, Health Futures Institute, Murdoch University, Murdoch, Australia
| | | | - Ayham Zaitouny
- Complex Systems Group, Department of Mathematics and Statistics, University of Western Australia, Perth, Australia
- Department of Mathematical Sciences, United Arab Emirates University, Al Ain, United Arab Emirates
| | - Emma de Jong
- Telethon Kids Institute, Perth, Australia
- Medical School, University of Western Australia, Perth, Australia
| | | | - Matt Jones
- Harry Perkins Institute of Medical Research, QEII Medical Centre and Centre for Medical Research, The University of Western Australia, Perth, Australia
| | | | | | - Alistair R.R. Forrest
- Harry Perkins Institute of Medical Research, QEII Medical Centre and Centre for Medical Research, The University of Western Australia, Perth, Australia
| | - Anna K. Nowak
- National Centre for Asbestos Related Diseases, Institute for Respiratory Health, University of Western Australia, Perth, Australia
- Medical School, University of Western Australia, Perth, Australia
| | - Mark Watson
- Medical Genomics Laboratories (IIID), Centre for Molecular Medicine and Innovative Therapeutics, Health Futures Institute, Murdoch University, Murdoch, Australia
| | - Richard A. Lake
- National Centre for Asbestos Related Diseases, Institute for Respiratory Health, University of Western Australia, Perth, Australia
| | - W. Joost Lesterhuis
- National Centre for Asbestos Related Diseases, Institute for Respiratory Health, University of Western Australia, Perth, Australia
- Telethon Kids Institute, Perth, Australia
| | - Jonathan Chee
- National Centre for Asbestos Related Diseases, Institute for Respiratory Health, University of Western Australia, Perth, Australia
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32
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Chi H, Pepper M, Thomas PG. Principles and therapeutic applications of adaptive immunity. Cell 2024; 187:2052-2078. [PMID: 38670065 PMCID: PMC11177542 DOI: 10.1016/j.cell.2024.03.037] [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: 01/02/2024] [Revised: 03/01/2024] [Accepted: 03/25/2024] [Indexed: 04/28/2024]
Abstract
Adaptive immunity provides protection against infectious and malignant diseases. These effects are mediated by lymphocytes that sense and respond with targeted precision to perturbations induced by pathogens and tissue damage. Here, we review key principles underlying adaptive immunity orchestrated by distinct T cell and B cell populations and their extensions to disease therapies. We discuss the intracellular and intercellular processes shaping antigen specificity and recognition in immune activation and lymphocyte functions in mediating effector and memory responses. We also describe how lymphocytes balance protective immunity against autoimmunity and immunopathology, including during immune tolerance, response to chronic antigen stimulation, and adaptation to non-lymphoid tissues in coordinating tissue immunity and homeostasis. Finally, we discuss extracellular signals and cell-intrinsic programs underpinning adaptive immunity and conclude by summarizing key advances in vaccination and engineering adaptive immune responses for therapeutic interventions. A deeper understanding of these principles holds promise for uncovering new means to improve human health.
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Affiliation(s)
- Hongbo Chi
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN, USA.
| | - Marion Pepper
- Department of Immunology, University of Washington, Seattle, WA, USA.
| | - Paul G Thomas
- Department of Host-Microbe Interactions and Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN, USA.
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33
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Hoang MH, Skidmore ZL, Rindt H, Chu S, Fisk B, Foltz JA, Fronick C, Fulton R, Zhou M, Bivens NJ, Reinero CN, Fehniger TA, Griffith M, Bryan JN, Griffith OL. Single-cell T-cell receptor repertoire profiling in dogs. Commun Biol 2024; 7:484. [PMID: 38649520 PMCID: PMC11035579 DOI: 10.1038/s42003-024-06174-w] [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: 08/10/2021] [Accepted: 04/10/2024] [Indexed: 04/25/2024] Open
Abstract
Spontaneous cancers in companion dogs are robust models of human disease. Tracking tumor-specific immune responses in these models requires reagents to perform species-specific single cell T cell receptor sequencing (scTCRseq). scTCRseq and integration with scRNA data have not been demonstrated on companion dogs with cancer. Here, five healthy dogs, two dogs with T cell lymphoma and four dogs with melanoma are selected to demonstrate applicability of scTCRseq in a cancer immunotherapy setting. Single-cell suspensions of PBMCs or lymph node aspirates are profiled using scRNA and dog-specific scTCRseq primers. In total, 77,809 V(D)J-expressing cells are detected, with an average of 3498 (348 - 5,971) unique clonotypes identified per sample. In total, 29/34, 40/40, 22/22 and 9/9 known functional TRAV, TRAJ, TRBV and TRBJ gene segments are observed respectively. Pseudogene or otherwise defective gene segments are also detected supporting re-annotation of several as functional. Healthy dogs exhibit highly diverse repertoires, T cell lymphomas exhibit clonal repertoires, and vaccine-treated melanoma dogs are dominated by a small number of highly abundant clonotypes. scRNA libraries define large clusters of V(D)J-expressing CD8+ and CD4 + T cells. Dominant clonotypes observed in melanoma PBMCs are predominantly CD8 + T cells, with activated phenotypes, suggesting possible anti-tumor T cell populations.
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Affiliation(s)
- My H Hoang
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St Louis, MO, USA
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, USA
| | - Zachary L Skidmore
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St Louis, MO, USA
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, USA
| | - Hans Rindt
- Department of Veterinary Medicine and Surgery, University of Missouri, Columbia, MO, USA
| | - Shirley Chu
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St Louis, MO, USA
- Department of Veterinary Medicine and Surgery, University of Missouri, Columbia, MO, USA
| | - Bryan Fisk
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St Louis, MO, USA
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, USA
| | - Jennifer A Foltz
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St Louis, MO, USA
| | - Catrina Fronick
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, USA
| | - Robert Fulton
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, USA
| | - Mingyi Zhou
- Genomics Technology Core, University of Missouri, Columbia, MO, USA
| | - Nathan J Bivens
- Genomics Technology Core, University of Missouri, Columbia, MO, USA
| | - Carol N Reinero
- Department of Veterinary Medicine and Surgery, University of Missouri, Columbia, MO, USA
| | - Todd A Fehniger
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St Louis, MO, USA
- Siteman Cancer Center, Washington University School of Medicine, St Louis, MO, USA
| | - Malachi Griffith
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St Louis, MO, USA
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, USA
- Siteman Cancer Center, Washington University School of Medicine, St Louis, MO, USA
- Department of Genetics, Washington University School of Medicine, St Louis, MO, USA
| | - Jeffrey N Bryan
- Department of Veterinary Medicine and Surgery, University of Missouri, Columbia, MO, USA.
| | - Obi L Griffith
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St Louis, MO, USA.
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, USA.
- Siteman Cancer Center, Washington University School of Medicine, St Louis, MO, USA.
- Department of Genetics, Washington University School of Medicine, St Louis, MO, USA.
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34
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Marrer-Berger E, Nicastri A, Augustin A, Kramar V, Liao H, Hanisch LJ, Carpy A, Weinzierl T, Durr E, Schaub N, Nudischer R, Ortiz-Franyuti D, Breous-Nystrom E, Stucki J, Hobi N, Raggi G, Cabon L, Lezan E, Umaña P, Woodhouse I, Bujotzek A, Klein C, Ternette N. The physiological interactome of TCR-like antibody therapeutics in human tissues. Nat Commun 2024; 15:3271. [PMID: 38627373 PMCID: PMC11021511 DOI: 10.1038/s41467-024-47062-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 03/19/2024] [Indexed: 04/19/2024] Open
Abstract
Selective binding of TCR-like antibodies that target a single tumour-specific peptide antigen presented by human leukocyte antigens (HLA) is the absolute prerequisite for their therapeutic suitability and patient safety. To date, selectivity assessment has been limited to peptide library screening and predictive modeling. We developed an experimental platform to de novo identify interactomes of TCR-like antibodies directly in human tissues using mass spectrometry. As proof of concept, we confirm the target epitope of a MAGE-A4-specific TCR-like antibody. We further determine cross-reactive peptide sequences for ESK1, a TCR-like antibody with known off-target activity, in human liver tissue. We confirm off-target-induced T cell activation and ESK1-mediated liver spheroid killing. Off-target sequences feature an amino acid motif that allows a structural groove-coordination mimicking that of the target peptide, therefore allowing the interaction with the engager molecule. We conclude that our strategy offers an accurate, scalable route for evaluating the non-clinical safety profile of TCR-like antibody therapeutics prior to first-in-human clinical application.
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Affiliation(s)
- Estelle Marrer-Berger
- Roche Pharma Research & Early Development, Roche Innovation Center Basel, 4070, Basel, Switzerland
| | - Annalisa Nicastri
- The Jenner Institute, Old Road Campus Research Building, Oxford, OX37DQ, UK
- Centre for Immuno-Oncology, Old Road Campus Research Building, Oxford, OX37DQ, UK
| | - Angelique Augustin
- Roche Pharma Research & Early Development, Roche Innovation Center Basel, 4070, Basel, Switzerland
| | - Vesna Kramar
- Roche Innovation Center Zürich, 8952, Schlieren, Switzerland
| | - Hanqing Liao
- The Jenner Institute, Old Road Campus Research Building, Oxford, OX37DQ, UK
- Centre for Immuno-Oncology, Old Road Campus Research Building, Oxford, OX37DQ, UK
| | | | - Alejandro Carpy
- Roche Pharma Research & Early Development, Roche Innovation Center Munich, 82377, Penzberg, Germany
| | - Tina Weinzierl
- Roche Innovation Center Zürich, 8952, Schlieren, Switzerland
| | - Evelyne Durr
- Roche Pharma Research & Early Development, Roche Innovation Center Basel, 4070, Basel, Switzerland
| | - Nathalie Schaub
- Roche Pharma Research & Early Development, Roche Innovation Center Basel, 4070, Basel, Switzerland
| | - Ramona Nudischer
- Roche Pharma Research & Early Development, Roche Innovation Center Basel, 4070, Basel, Switzerland
| | - Daniela Ortiz-Franyuti
- Roche Pharma Research & Early Development, Roche Innovation Center Basel, 4070, Basel, Switzerland
| | - Ekaterina Breous-Nystrom
- Roche Pharma Research & Early Development, Roche Innovation Center Basel, 4070, Basel, Switzerland
| | - Janick Stucki
- Alveolix AG, Swiss Organs-on-Chip Innovation, 3010, Bern, Switzerland
| | - Nina Hobi
- Alveolix AG, Swiss Organs-on-Chip Innovation, 3010, Bern, Switzerland
| | - Giulia Raggi
- Alveolix AG, Swiss Organs-on-Chip Innovation, 3010, Bern, Switzerland
| | - Lauriane Cabon
- Roche Pharma Research & Early Development, Roche Innovation Center Basel, 4070, Basel, Switzerland
| | - Emmanuelle Lezan
- Roche Pharma Research & Early Development, Roche Innovation Center Basel, 4070, Basel, Switzerland
| | - Pablo Umaña
- Roche Innovation Center Zürich, 8952, Schlieren, Switzerland
| | - Isaac Woodhouse
- The Jenner Institute, Old Road Campus Research Building, Oxford, OX37DQ, UK
- Centre for Immuno-Oncology, Old Road Campus Research Building, Oxford, OX37DQ, UK
| | - Alexander Bujotzek
- Roche Pharma Research & Early Development, Roche Innovation Center Munich, 82377, Penzberg, Germany
| | - Christian Klein
- Roche Innovation Center Zürich, 8952, Schlieren, Switzerland.
| | - Nicola Ternette
- The Jenner Institute, Old Road Campus Research Building, Oxford, OX37DQ, UK.
- Centre for Immuno-Oncology, Old Road Campus Research Building, Oxford, OX37DQ, UK.
- Department of Pharmaceutical Sciences, University of Utrecht, 3584, CH, Utrecht, The Netherlands.
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35
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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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36
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Zaslavsky ME, Craig E, Michuda JK, Sehgal N, Ram-Mohan N, Lee JY, Nguyen KD, Hoh RA, Pham TD, Röltgen K, Lam B, Parsons ES, Macwana SR, DeJager W, Drapeau EM, Roskin KM, Cunningham-Rundles C, Moody MA, Haynes BF, Goldman JD, Heath JR, Nadeau KC, Pinsky BA, Blish CA, Hensley SE, Jensen K, Meyer E, Balboni I, Utz PJ, Merrill JT, Guthridge JM, James JA, Yang S, Tibshirani R, Kundaje A, Boyd SD. Disease diagnostics using machine learning of immune receptors. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2022.04.26.489314. [PMID: 35547855 PMCID: PMC9094102 DOI: 10.1101/2022.04.26.489314] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Clinical diagnosis typically incorporates physical examination, patient history, and various laboratory tests and imaging studies, but makes limited use of the human system's own record of antigen exposures encoded by receptors on B cells and T cells. We analyzed immune receptor datasets from 593 individuals to develop MAchine Learning for Immunological Diagnosis (Mal-ID) , an interpretive framework to screen for multiple illnesses simultaneously or precisely test for one condition. This approach detects specific infections, autoimmune disorders, vaccine responses, and disease severity differences. Human-interpretable features of the model recapitulate known immune responses to SARS-CoV-2, Influenza, and HIV, highlight antigen-specific receptors, and reveal distinct characteristics of Systemic Lupus Erythematosus and Type-1 Diabetes autoreactivity. This analysis framework has broad potential for scientific and clinical interpretation of human immune responses.
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37
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Shao W, Yao Y, Yang L, Li X, Ge T, Zheng Y, Zhu Q, Ge S, Gu X, Jia R, Song X, Zhuang A. Novel insights into TCR-T cell therapy in solid neoplasms: optimizing adoptive immunotherapy. Exp Hematol Oncol 2024; 13:37. [PMID: 38570883 PMCID: PMC10988985 DOI: 10.1186/s40164-024-00504-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 03/21/2024] [Indexed: 04/05/2024] Open
Abstract
Adoptive immunotherapy in the T cell landscape exhibits efficacy in cancer treatment. Over the past few decades, genetically modified T cells, particularly chimeric antigen receptor T cells, have enabled remarkable strides in the treatment of hematological malignancies. Besides, extensive exploration of multiple antigens for the treatment of solid tumors has led to clinical interest in the potential of T cells expressing the engineered T cell receptor (TCR). TCR-T cells possess the capacity to recognize intracellular antigen families and maintain the intrinsic properties of TCRs in terms of affinity to target epitopes and signal transduction. Recent research has provided critical insight into their capability and therapeutic targets for multiple refractory solid tumors, but also exposes some challenges for durable efficacy. In this review, we describe the screening and identification of available tumor antigens, and the acquisition and optimization of TCRs for TCR-T cell therapy. Furthermore, we summarize the complete flow from laboratory to clinical applications of TCR-T cells. Last, we emerge future prospects for improving therapeutic efficacy in cancer world with combination therapies or TCR-T derived products. In conclusion, this review depicts our current understanding of TCR-T cell therapy in solid neoplasms, and provides new perspectives for expanding its clinical applications and improving therapeutic efficacy.
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Affiliation(s)
- Weihuan Shao
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China
| | - Yiran Yao
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China
| | - Ludi Yang
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China
| | - Xiaoran Li
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China
| | - Tongxin Ge
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China
| | - Yue Zheng
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China
| | - Qiuyi Zhu
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China
| | - Shengfang Ge
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China
| | - Xiang Gu
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China
| | - Renbing Jia
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China.
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China.
| | - Xin Song
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China.
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China.
| | - Ai Zhuang
- Department of Ophthalmology, Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai Ninth People's Hospital, Shanghai, 200011, People's Republic of China.
- Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, 200011, People's Republic of China.
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38
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Pavlova AV, Zvyagin IV, Shugay M. Detecting T-cell clonal expansions and quantifying clone survival using deep profiling of immune repertoires. Front Immunol 2024; 15:1321603. [PMID: 38633256 PMCID: PMC11021634 DOI: 10.3389/fimmu.2024.1321603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Accepted: 03/12/2024] [Indexed: 04/19/2024] Open
Abstract
An individual's T-cell repertoire constantly changes under the influence of external and internal factors. Cells that do not receive a stimulatory signal die, while those that encounter and recognize a pathogen or receive a co-stimulatory signal divide, resulting in clonal expansions. T-cell clones can be traced by monitoring the presence of their unique T-cell receptor (TCR) sequence, which is assembled de novo through a process known as V(D)J rearrangement. Tracking T cells can provide valuable insights into the survival of cells after hematopoietic stem cell transplantation (HSCT) or cancer treatment response and can indicate the induction of protective immunity by vaccination. In this study, we report a bioinformatic method for quantifying the T-cell repertoire dynamics from TCR sequencing data. We demonstrate its utility by measuring the T-cell repertoire stability in healthy donors, by quantifying the effect of donor lymphocyte infusion (DLI), and by tracking the fate of the different T-cell subsets in HSCT patients and the expansion of pathogen-specific clones in vaccinated individuals.
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Affiliation(s)
- Anastasia V. Pavlova
- Institute of Translational Medicine, Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Pirogov Russian National Research Medical University, Moscow, Russia
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia
| | - Ivan V. Zvyagin
- Institute of Translational Medicine, Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Pirogov Russian National Research Medical University, Moscow, Russia
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia
- Dmitriy Rogachev National Center of Pediatric Hematology, Oncology and Immunology, Moscow, Russia
| | - Mikhail Shugay
- Institute of Translational Medicine, Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Pirogov Russian National Research Medical University, Moscow, Russia
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia
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Karnaukhov VK, Le Gac AL, Bilonda Mutala L, Darbois A, Perrin L, Legoux F, Walczak AM, Mora T, Lantz O. Innate-like T cell subset commitment in the murine thymus is independent of TCR characteristics and occurs during proliferation. Proc Natl Acad Sci U S A 2024; 121:e2311348121. [PMID: 38530897 PMCID: PMC10998581 DOI: 10.1073/pnas.2311348121] [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: 07/05/2023] [Accepted: 02/09/2024] [Indexed: 03/28/2024] Open
Abstract
How T-cell receptor (TCR) characteristics determine subset commitment during T-cell development is still unclear. Here, we addressed this question for innate-like T cells, mucosal-associated invariant T (MAIT) cells, and invariant natural killer T (iNKT) cells. MAIT and iNKT cells have similar developmental paths, leading in mice to two effector subsets, cytotoxic (MAIT1/iNKT1) and IL17-secreting (MAIT17/iNKT17). For iNKT1 vs iNKT17 fate choice, an instructive role for TCR affinity was proposed but recent data argue against this model. Herein, we examined TCR role in MAIT and iNKT subset commitment through scRNAseq and TCR repertoire analysis. In our dataset of thymic MAIT cells, we found pairs of T-cell clones with identical amino acid TCR sequences originating from distinct precursors, one of which committed to MAIT1 and the other to MAIT17 fates. Quantitative in silico simulations indicated that the number of such cases is best explained by lineage choice being independent of TCR characteristics. Comparison of TCR features of MAIT1 and MAIT17 clonotypes demonstrated that the subsets cannot be distinguished based on TCR sequence. To pinpoint the developmental stage associated with MAIT sublineage choice, we demonstrated that proliferation takes place both before and after MAIT fate commitment. Altogether, we propose a model of MAIT cell development in which noncommitted, intermediate-stage MAIT cells undergo a first round of proliferation, followed by TCR characteristics-independent commitment to MAIT1 or MAIT17 lineage, followed by an additional round of proliferation. Reanalyzing a published iNKT TCR dataset, we showed that this model is also relevant for iNKT cell development.
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Affiliation(s)
- Vadim K. Karnaukhov
- Institut Curie, Paris Sciences & Lettres University, Inserm U932, Immunity and Cancer, Paris75005, France
- Laboratoire de Physique de l’École Normale Supérieure, Paris Sciences & Lettres University, CNRS, Sorbonne Université and Université Paris Cité, Paris75005, France
| | - Anne-Laure Le Gac
- Institut Curie, Paris Sciences & Lettres University, Inserm U932, Immunity and Cancer, Paris75005, France
| | - Linda Bilonda Mutala
- Institut Curie, Paris Sciences & Lettres University, Inserm U932, Immunity and Cancer, Paris75005, France
| | - Aurélie Darbois
- Institut Curie, Paris Sciences & Lettres University, Inserm U932, Immunity and Cancer, Paris75005, France
| | - Laetitia Perrin
- Institut Curie, Paris Sciences & Lettres University, Inserm U932, Immunity and Cancer, Paris75005, France
| | - Francois Legoux
- Institut Curie, Paris Sciences & Lettres University, Inserm U932, Immunity and Cancer, Paris75005, France
- INSERM Equipe de Recherche Labellisée 1305, CNRSUMR6290, Université de Rennes, Institut de Génétique & Développement de Rennes35000, France
| | - Aleksandra M. Walczak
- Laboratoire de Physique de l’École Normale Supérieure, Paris Sciences & Lettres University, CNRS, Sorbonne Université and Université Paris Cité, Paris75005, France
| | - Thierry Mora
- Laboratoire de Physique de l’École Normale Supérieure, Paris Sciences & Lettres University, CNRS, Sorbonne Université and Université Paris Cité, Paris75005, France
| | - Olivier Lantz
- Institut Curie, Paris Sciences & Lettres University, Inserm U932, Immunity and Cancer, Paris75005, France
- Laboratoire d’Immunologie Clinique, Département de médecine diagnostique et théranostique, Institut Curie, Paris75005, France
- Centre d’Investigation Clinique en Biothérapie Gustave-Roussy Institut Curie (CIC-BT1428), Paris75005, France
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40
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Koo D, Mao Z, Dimatteo R, Noguchi M, Tsubamoto N, McLaughlin J, Tran W, Lee S, Cheng D, de Rutte J, Burton Sojo G, Witte ON, Di Carlo D. Defining T cell receptor repertoires using nanovial-based binding and functional screening. Proc Natl Acad Sci U S A 2024; 121:e2320442121. [PMID: 38536748 PMCID: PMC10998554 DOI: 10.1073/pnas.2320442121] [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: 11/20/2023] [Accepted: 02/27/2024] [Indexed: 04/08/2024] Open
Abstract
The ability to selectively bind to antigenic peptides and secrete effector molecules can define rare and low-affinity populations of cells with therapeutic potential in emerging T cell receptor (TCR) immunotherapies. We leverage cavity-containing hydrogel microparticles, called nanovials, each coated with peptide-major histocompatibility complex (pMHC) monomers to isolate antigen-reactive T cells. T cells are captured and activated by pMHCs inducing the secretion of effector molecules including IFN-γ and granzyme B that are accumulated on nanovials, allowing sorting based on both binding and function. The TCRs of sorted cells on nanovials are sequenced, recovering paired αβ-chains using microfluidic emulsion-based single-cell sequencing. By labeling nanovials having different pMHCs with unique oligonucleotide-barcodes and secretions with oligo-barcoded detection antibodies, we could accurately link TCR sequences to specific targets and rank each TCR based on the corresponding cell's secretion level. Using the technique, we identified an expanded repertoire of functional TCRs targeting viral antigens with high specificity and found rare TCRs with activity against cancer-specific splicing-enhanced epitopes.
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Affiliation(s)
- Doyeon Koo
- Department of Bioengineering, University of California, Los Angeles, CA90095
| | - Zhiyuan Mao
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, CA90095
| | - Robert Dimatteo
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA90095
| | - Miyako Noguchi
- Department of Microbiology, Immunology and Molecular Genetics, University of California, Los Angeles, CA90095
| | - Natalie Tsubamoto
- Department of Bioengineering, University of California, Los Angeles, CA90095
| | - Jami McLaughlin
- Department of Microbiology, Immunology and Molecular Genetics, University of California, Los Angeles, CA90095
| | - Wendy Tran
- Department of Microbiology, Immunology and Molecular Genetics, University of California, Los Angeles, CA90095
| | - Sohyung Lee
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA90095
| | - Donghui Cheng
- Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, Los Angeles, CA90095
| | - Joseph de Rutte
- Department of Bioengineering, University of California, Los Angeles, CA90095
- Partillion Bioscience, Pasadena, CA91107
| | - Giselle Burton Sojo
- Department of Microbiology, Immunology and Molecular Genetics, University of California, Los Angeles, CA90095
| | - Owen N. Witte
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, CA90095
- Department of Microbiology, Immunology and Molecular Genetics, University of California, Los Angeles, CA90095
- Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, Los Angeles, CA90095
- Molecular Biology Institute, University of California, Los Angeles, CA90095
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, CA90095
- Parker Institute for Cancer Immunotherapy, David Geffen School of Medicine, University of California, Los Angeles, CA90095
| | - Dino Di Carlo
- Department of Bioengineering, University of California, Los Angeles, CA90095
- Partillion Bioscience, Pasadena, CA91107
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, CA90095
- Department of Mechanical and Aerospace Engineering, University of California, Los Angeles, CA90095
- California NanoSystems Institute, Los Angeles, CA90095
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41
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Hao Q, Li R, Li H, Rui S, You L, Zhang L, Zhao Y, Li P, Li Y, Kong X, Chen H, Zou X, Liu F, Wang X, Zhou J, Zhang W, Huang L, Shu Y, Liu J, Sun R, Li C, Zhu J, Jiang Y, Wei T, Qian K, Bai B, Hu Y, Peng Y, Dai L, Caulin C, Xu H, Li Z, Park J, Luo H, Ying B. Dynamics of The Γδtcr Repertoires During The Dedifferentiation Process and Pilot Implications for Immunotherapy of Thyroid Cancer. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2306364. [PMID: 38286670 PMCID: PMC10987121 DOI: 10.1002/advs.202306364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 12/29/2023] [Indexed: 01/31/2024]
Abstract
γδ T cells are evolutionarily conserved T lymphocytes that manifest unique antitumor efficacy independent of tumor mutation burden (TMB) and conventional human leukocyte antigen (HLA) recognition. However, the dynamic changes in their T cell receptor (TCR) repertoire during cancer progression and treatment courses remain unclear. Here, a comprehensive characterization of γδTCR repertoires are performed in thyroid cancers with divergent differentiation states through cross-sectional studies. The findings revealed a significant correlation between the differentiation states and TCR repertoire diversity. Notably, highly expanded clones are prominently enriched in γδ T cell compartment of dedifferentiated patients. Moreover, by longitudinal investigations of the γδ T cell response to various antitumor therapies, it is found that the emergence and expansion of the Vδ2neg subset may be potentially associated with favorable clinical outcomes after post-radiotherapeutic immunotherapy. These findings are further validated at single-cell resolution in both advanced thyroid cancer patients and a murine model, underlining the importance of further investigations into the role of γδTCR in cancer immunity and therapeutic strategies.
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Affiliation(s)
- Qing Hao
- Department of Laboratory MedicineWest China Hospital, Sichuan UniversityChengduSichuan610041China
- State Key Laboratory of Biotherapy and Cancer Center, West China HospitalSichuan University and Collaborative Innovation CenterChengduSichuan610041China
| | - Ruicen Li
- Health Promotion CenterWest China Hospital, Sichuan UniversityChengduSichuan610041China
| | - Hancong Li
- Division of Thyroid Surgery, West China HospitalSichuan UniversityChengduSichuan610041China
- Laboratory of Thyroid and Parathyroid DiseaseFrontiers Science Center for Disease‐Related Molecular NetworkChengdu610041China
- Department of General Surgery, West China HospitalSichuan UniversityChengduSichuan610041China
| | - Shu Rui
- Division of Thyroid Surgery, West China HospitalSichuan UniversityChengduSichuan610041China
- Laboratory of Thyroid and Parathyroid DiseaseFrontiers Science Center for Disease‐Related Molecular NetworkChengdu610041China
- Department of General Surgery, West China HospitalSichuan UniversityChengduSichuan610041China
| | - Liting You
- Department of Laboratory MedicineWest China Hospital, Sichuan UniversityChengduSichuan610041China
| | - Lingyun Zhang
- School of Biomedical SciencesThe Chinese University of Hong KongHong Kong SAR999077China
| | - Yue Zhao
- Division of Thyroid Surgery, West China HospitalSichuan UniversityChengduSichuan610041China
- Laboratory of Thyroid and Parathyroid DiseaseFrontiers Science Center for Disease‐Related Molecular NetworkChengdu610041China
- Department of General Surgery, West China HospitalSichuan UniversityChengduSichuan610041China
| | - Peiheng Li
- Division of Thyroid Surgery, West China HospitalSichuan UniversityChengduSichuan610041China
- Laboratory of Thyroid and Parathyroid DiseaseFrontiers Science Center for Disease‐Related Molecular NetworkChengdu610041China
- Department of General Surgery, West China HospitalSichuan UniversityChengduSichuan610041China
| | - Yuanmin Li
- Division of Thyroid Surgery, West China HospitalSichuan UniversityChengduSichuan610041China
- Laboratory of Thyroid and Parathyroid DiseaseFrontiers Science Center for Disease‐Related Molecular NetworkChengdu610041China
- Key Laboratory of Transplant Engineering and Immunology, Frontiers Science Center for Disease Related Molecular Network, West China HospitalSichuan UniversityChengdu610041China
| | - Xinagyu Kong
- Division of Thyroid Surgery, West China HospitalSichuan UniversityChengduSichuan610041China
- Laboratory of Thyroid and Parathyroid DiseaseFrontiers Science Center for Disease‐Related Molecular NetworkChengdu610041China
- Department of General Surgery, West China HospitalSichuan UniversityChengduSichuan610041China
| | - Haining Chen
- Colorectal Cancer Center, West China HospitalSichuan UniversityChengduSichuan610041China
| | - Xiuhe Zou
- Division of Thyroid Surgery, West China HospitalSichuan UniversityChengduSichuan610041China
- Laboratory of Thyroid and Parathyroid DiseaseFrontiers Science Center for Disease‐Related Molecular NetworkChengdu610041China
- Department of General Surgery, West China HospitalSichuan UniversityChengduSichuan610041China
| | - Feng Liu
- Division of Thyroid Surgery, West China HospitalSichuan UniversityChengduSichuan610041China
- Laboratory of Thyroid and Parathyroid DiseaseFrontiers Science Center for Disease‐Related Molecular NetworkChengdu610041China
- Department of General Surgery, West China HospitalSichuan UniversityChengduSichuan610041China
| | - Xiaofei Wang
- Division of Thyroid Surgery, West China HospitalSichuan UniversityChengduSichuan610041China
- Laboratory of Thyroid and Parathyroid DiseaseFrontiers Science Center for Disease‐Related Molecular NetworkChengdu610041China
- Department of General Surgery, West China HospitalSichuan UniversityChengduSichuan610041China
| | - Juan Zhou
- Department of Laboratory MedicineWest China Hospital, Sichuan UniversityChengduSichuan610041China
| | - Weihan Zhang
- Gastric Cancer Center, West China HospitalSichuan UniversityChengduSichuan610041China
| | - Libing Huang
- Division of Gastrointestinal Surgery, State Key Laboratory of Biotherapy, West China HospitalSichuan UniversityChengduSichuan610041China
| | - Yang Shu
- Gastric Cancer Center, West China HospitalSichuan UniversityChengduSichuan610041China
| | - JiaYe Liu
- Division of Thyroid Surgery, West China HospitalSichuan UniversityChengduSichuan610041China
- Laboratory of Thyroid and Parathyroid DiseaseFrontiers Science Center for Disease‐Related Molecular NetworkChengdu610041China
- Department of General Surgery, West China HospitalSichuan UniversityChengduSichuan610041China
| | - Ronghao Sun
- Department of Head and Neck Surgery, Sichuan Cancer Hospital, Sichuan Cancer Institute, Sichuan Cancer Prevention and Treatment CenterCancer Hospital of University of Electronic Science and Technology School of MedicineChengdu610041China
| | - Chao Li
- Department of Head and Neck Surgery, Sichuan Cancer Hospital, Sichuan Cancer Institute, Sichuan Cancer Prevention and Treatment CenterCancer Hospital of University of Electronic Science and Technology School of MedicineChengdu610041China
| | - Jingqiang Zhu
- Division of Thyroid Surgery, West China HospitalSichuan UniversityChengduSichuan610041China
- Laboratory of Thyroid and Parathyroid DiseaseFrontiers Science Center for Disease‐Related Molecular NetworkChengdu610041China
- Department of General Surgery, West China HospitalSichuan UniversityChengduSichuan610041China
| | - Yong Jiang
- Division of Pathology, West China HospitalSichuan UniversityChengduSichuan610041China
| | - Tao Wei
- Division of Thyroid Surgery, West China HospitalSichuan UniversityChengduSichuan610041China
- Laboratory of Thyroid and Parathyroid DiseaseFrontiers Science Center for Disease‐Related Molecular NetworkChengdu610041China
- Department of General Surgery, West China HospitalSichuan UniversityChengduSichuan610041China
| | - Kun Qian
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Institute of Medical Robotics and Med‐X Research InstituteShanghai Jiao Tong UniversityShanghai200230China
| | - Bing Bai
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and TechnologyYunnan Key Laboratory of Primate Biomedical ResearchKunmingYunnan650500China
| | - Yiguo Hu
- State Key Laboratory of Biotherapy and Cancer Center, West China HospitalSichuan University and Collaborative Innovation CenterChengduSichuan610041China
- Division of Thyroid Surgery, West China HospitalSichuan UniversityChengduSichuan610041China
- Laboratory of Thyroid and Parathyroid DiseaseFrontiers Science Center for Disease‐Related Molecular NetworkChengdu610041China
| | - Yong Peng
- State Key Laboratory of Biotherapy and Cancer Center, West China HospitalSichuan University and Collaborative Innovation CenterChengduSichuan610041China
| | - Lunzhi Dai
- State Key Laboratory of Biotherapy and Cancer Center, West China HospitalSichuan University and Collaborative Innovation CenterChengduSichuan610041China
| | - Carlos Caulin
- Department of Otolaryngology – Head & Neck Surgery and University of Arizona Cancer CenterUniversity of ArizonaTucsonAZ85721USA
| | - Heng Xu
- Department of Laboratory MedicineWest China Hospital, Sichuan UniversityChengduSichuan610041China
- State Key Laboratory of Biotherapy and Cancer Center, West China HospitalSichuan University and Collaborative Innovation CenterChengduSichuan610041China
| | - Zhihui Li
- Division of Thyroid Surgery, West China HospitalSichuan UniversityChengduSichuan610041China
- Laboratory of Thyroid and Parathyroid DiseaseFrontiers Science Center for Disease‐Related Molecular NetworkChengdu610041China
- Department of General Surgery, West China HospitalSichuan UniversityChengduSichuan610041China
| | - Jihwan Park
- School of Life SciencesGwangju Institute of Science and Technology (GIST)Gwangju61005Republic of Korea
| | - Han Luo
- Department of Laboratory MedicineWest China Hospital, Sichuan UniversityChengduSichuan610041China
- Division of Thyroid Surgery, West China HospitalSichuan UniversityChengduSichuan610041China
- Laboratory of Thyroid and Parathyroid DiseaseFrontiers Science Center for Disease‐Related Molecular NetworkChengdu610041China
- Department of General Surgery, West China HospitalSichuan UniversityChengduSichuan610041China
- Sichuan Clinical Research Center for laboratory medicineChengduSichuan610041China
| | - Binwu Ying
- Department of Laboratory MedicineWest China Hospital, Sichuan UniversityChengduSichuan610041China
- Sichuan Clinical Research Center for laboratory medicineChengduSichuan610041China
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42
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Aoki H, Kitabatake M, Abe H, Xu P, Tsunoda M, Shichino S, Hara A, Ouji-Sageshima N, Motozono C, Ito T, Matsushima K, Ueha S. CD8 + T cell memory induced by successive SARS-CoV-2 mRNA vaccinations is characterized by shifts in clonal dominance. Cell Rep 2024; 43:113887. [PMID: 38458195 DOI: 10.1016/j.celrep.2024.113887] [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: 07/20/2023] [Revised: 12/27/2023] [Accepted: 02/14/2024] [Indexed: 03/10/2024] Open
Abstract
mRNA vaccines against the spike glycoprotein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) elicit strong T cell responses. However, a clonal-resolution analysis of T cell responses to mRNA vaccination has not been performed. Here, we temporally track the CD8+ T cell repertoire in individuals who received three shots of the BNT162b2 mRNA vaccine through longitudinal T cell receptor sequencing with peptide-human leukocyte antigen (HLA) tetramer analysis. We demonstrate a shift in T cell responses between the clonotypes with different kinetics: from early responders that expand rapidly after the first shot to main responders that greatly expand after the second shot. Although the main responders re-expand after the third shot, their clonal diversity is skewed, and newly elicited third responders partially replace them. Furthermore, this shift in clonal dominance occurs not only between, but also within, clonotypes specific for spike epitopes. Our study will be a valuable resource for understanding vaccine-induced T cell responses in general.
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Affiliation(s)
- Hiroyasu Aoki
- Division of Molecular Regulation of Inflammatory and Immune Diseases, Research Institute for Biomedical Sciences, Tokyo University of Science, Noda City, Chiba 2780022, Japan; Department of Preventive Medicine, Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo 1130033, Japan
| | - Masahiro Kitabatake
- Department of Immunology, Nara Medical University, Kashihara City, Nara 6348521, Japan
| | - Haruka Abe
- Division of Molecular Regulation of Inflammatory and Immune Diseases, Research Institute for Biomedical Sciences, Tokyo University of Science, Noda City, Chiba 2780022, Japan
| | - Peng Xu
- Division of Molecular Regulation of Inflammatory and Immune Diseases, Research Institute for Biomedical Sciences, Tokyo University of Science, Noda City, Chiba 2780022, Japan
| | - Mikiya Tsunoda
- Division of Molecular Regulation of Inflammatory and Immune Diseases, Research Institute for Biomedical Sciences, Tokyo University of Science, Noda City, Chiba 2780022, Japan
| | - Shigeyuki Shichino
- Division of Molecular Regulation of Inflammatory and Immune Diseases, Research Institute for Biomedical Sciences, Tokyo University of Science, Noda City, Chiba 2780022, Japan
| | - Atsushi Hara
- Department of Immunology, Nara Medical University, Kashihara City, Nara 6348521, Japan
| | - Noriko Ouji-Sageshima
- Department of Immunology, Nara Medical University, Kashihara City, Nara 6348521, Japan
| | - Chihiro Motozono
- Division of Infection and Immunity, Joint Research Center for Human Retrovirus Infection, Kumamoto University, Kumamoto City, Kumamoto 8600811, Japan
| | - Toshihiro Ito
- Department of Immunology, Nara Medical University, Kashihara City, Nara 6348521, Japan
| | - Kouji Matsushima
- Division of Molecular Regulation of Inflammatory and Immune Diseases, Research Institute for Biomedical Sciences, Tokyo University of Science, Noda City, Chiba 2780022, Japan
| | - Satoshi Ueha
- Division of Molecular Regulation of Inflammatory and Immune Diseases, Research Institute for Biomedical Sciences, Tokyo University of Science, Noda City, Chiba 2780022, Japan.
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43
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Foo IJH, Chua BY, Clemens EB, Chang SY, Jia X, McQuilten HA, Yap AHY, Cabug AF, Ashayeripanah M, McWilliam HEG, Villadangos JA, Evrard M, Mackay LK, Wakim LM, Fazakerley JK, Kedzierska K, Kedzierski L. Prior infection with unrelated neurotropic virus exacerbates influenza disease and impairs lung T cell responses. Nat Commun 2024; 15:2619. [PMID: 38521764 PMCID: PMC10960853 DOI: 10.1038/s41467-024-46822-7] [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: 07/28/2023] [Accepted: 03/07/2024] [Indexed: 03/25/2024] Open
Abstract
Immunity to infectious diseases is predominantly studied by measuring immune responses towards a single pathogen, although co-infections are common. In-depth mechanisms on how co-infections impact anti-viral immunity are lacking, but are highly relevant to treatment and prevention. We established a mouse model of co-infection with unrelated viruses, influenza A (IAV) and Semliki Forest virus (SFV), causing disease in different organ systems. SFV infection eight days before IAV infection results in prolonged IAV replication, elevated cytokine/chemokine levels and exacerbated lung pathology. This is associated with impaired lung IAV-specific CD8+ T cell responses, stemming from suboptimal CD8+ T cell activation and proliferation in draining lymph nodes, and dendritic cell paralysis. Prior SFV infection leads to increased blood brain barrier permeability and presence of IAV RNA in brain, associated with increased trafficking of IAV-specific CD8+ T cells and establishment of long-term tissue-resident memory. Relative to lung IAV-specific CD8+ T cells, brain memory IAV-specific CD8+ T cells have increased TCR repertoire diversity within immunodominant DbNP366+CD8+ and DbPA224+CD8+ responses, featuring suboptimal TCR clonotypes. Overall, our study demonstrates that infection with an unrelated neurotropic virus perturbs IAV-specific immune responses and exacerbates IAV disease. Our work provides key insights into therapy and vaccine regimens directed against unrelated pathogens.
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Affiliation(s)
- Isabelle Jia-Hui Foo
- Department of Microbiology and Immunology, The University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC 3000, Australia
- Department of Veterinary Biosciences, Faculty of Science, The University of Melbourne, Melbourne, VIC 3000, Australia
| | - Brendon Y Chua
- Department of Microbiology and Immunology, The University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC 3000, Australia
| | - E Bridie Clemens
- Department of Microbiology and Immunology, The University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC 3000, Australia
| | - So Young Chang
- Department of Microbiology and Immunology, The University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC 3000, Australia
| | - Xiaoxiao Jia
- Department of Microbiology and Immunology, The University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC 3000, Australia
| | - Hayley A McQuilten
- Department of Microbiology and Immunology, The University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC 3000, Australia
| | - Ashley Huey Yiing Yap
- Department of Microbiology and Immunology, The University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC 3000, Australia
| | - Aira F Cabug
- Department of Microbiology and Immunology, The University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC 3000, Australia
| | - Mitra Ashayeripanah
- Department of Microbiology and Immunology, The University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC 3000, Australia
| | - Hamish E G McWilliam
- Department of Microbiology and Immunology, The University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC 3000, Australia
| | - Jose A Villadangos
- Department of Microbiology and Immunology, The University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC 3000, Australia
- Department of Biochemistry and Pharmacology; Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Parkville, VIC 3010, Australia
| | - Maximilien Evrard
- Department of Microbiology and Immunology, The University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC 3000, Australia
| | - Laura K Mackay
- Department of Microbiology and Immunology, The University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC 3000, Australia
| | - Linda M Wakim
- Department of Microbiology and Immunology, The University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC 3000, Australia
| | - John K Fazakerley
- Department of Microbiology and Immunology, The University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC 3000, Australia
- Department of Veterinary Biosciences, Faculty of Science, The University of Melbourne, Melbourne, VIC 3000, Australia
| | - Katherine Kedzierska
- Department of Microbiology and Immunology, The University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC 3000, Australia.
| | - Lukasz Kedzierski
- Department of Microbiology and Immunology, The University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC 3000, Australia.
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Izosimova AV, Shabalkina AV, Myshkin MY, Shurganova EV, Myalik DS, Ryzhichenko EO, Samitova AF, Barsova EV, Shagina IA, Britanova OV, Yuzhakova DV, Sharonov GV. Local Enrichment with Convergence of Enriched T-Cell Clones Are Hallmarks of Effective Peptide Vaccination against B16 Melanoma. Vaccines (Basel) 2024; 12:345. [PMID: 38675728 DOI: 10.3390/vaccines12040345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 03/06/2024] [Accepted: 03/08/2024] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND Some peptide anticancer vaccines elicit a strong T-cell memory response but fail to suppress tumor growth. To gain insight into tumor resistance, we compared two peptide vaccines, p20 and p30, against B16 melanoma, with both exhibiting good in vitro T-cell responses but different tumor suppression abilities. METHODS We compared activation markers and repertoires of T-lymphocytes from tumor-draining (dLN) and non-draining (ndLN) lymph nodes for the two peptide vaccines. RESULTS We showed that the p30 vaccine had better tumor control as opposed to p20. p20 vaccine induced better in vitro T-cell responsiveness but failed to suppress tumor growth. Efficient antitumor vaccination is associated with a higher clonality of cytotoxic T-cells (CTLs) in dLNs compared with ndLNs and the convergence of most of the enriched clones. With the inefficient p20 vaccine, the most expanded and converged were clones of the bystander T-cells without an LN preference. CONCLUSIONS Here, we show that the clonality and convergence of the T-cell response are the hallmarks of efficient antitumor vaccination. The high individual and methodological dependencies of these parameters can be avoided by comparing dLNs and ndLNs.
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Affiliation(s)
- Anna Vyacheslavovna Izosimova
- Research Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, Nizhny Novgorod 603950, Russia
| | - Alexandra Valerievna Shabalkina
- Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow 117997, Russia
- Department of Genomics of Adaptive Immunity, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry RAS, Moscow 117997, Russia
| | - Mikhail Yurevich Myshkin
- Department of Genomics of Adaptive Immunity, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry RAS, Moscow 117997, Russia
| | - Elizaveta Viktorovna Shurganova
- Research Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, Nizhny Novgorod 603950, Russia
| | - Daria Sergeevna Myalik
- Research Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, Nizhny Novgorod 603950, Russia
- Pathoanatomical Department, Nizhny Novgorod Regional Clinical Cancer Hospital, Nizhny Novgorod 603126, Russia
| | | | - Alina Faritovna Samitova
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Pirogov Russian National Research Medical University, Moscow 117997, Russia
| | - Ekaterina Vladimirovna Barsova
- Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow 117997, Russia
- Department of Genomics of Adaptive Immunity, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry RAS, Moscow 117997, Russia
| | - Irina Aleksandrovna Shagina
- Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow 117997, Russia
- Department of Genomics of Adaptive Immunity, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry RAS, Moscow 117997, Russia
| | - Olga Vladimirovna Britanova
- Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow 117997, Russia
- Department of Genomics of Adaptive Immunity, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry RAS, Moscow 117997, Russia
| | - Diana Vladimirovna Yuzhakova
- Research Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, Nizhny Novgorod 603950, Russia
| | - George Vladimirovich Sharonov
- Research Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, Nizhny Novgorod 603950, Russia
- Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow 117997, Russia
- Department of Genomics of Adaptive Immunity, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry RAS, Moscow 117997, Russia
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45
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Roy P, Suthahar SSA, Makings J, Ley K. Identification of apolipoprotein B-reactive CDR3 motifs allows tracking of atherosclerosis-related memory CD4 +T cells in multiple donors. Front Immunol 2024; 15:1302031. [PMID: 38571941 PMCID: PMC10988780 DOI: 10.3389/fimmu.2024.1302031] [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: 09/25/2023] [Accepted: 02/02/2024] [Indexed: 04/05/2024] Open
Abstract
Introduction Atherosclerosis is a major pathological condition that underlies many cardiovascular diseases (CVDs). Its etiology involves breach of tolerance to self, leading to clonal expansion of autoreactive apolipoprotein B (APOB)-reactive CD4+T cells that correlates with clinical CVD. The T-cell receptor (TCR) sequences that mediate activation of APOB-specific CD4+T cells are unknown. Methods In a previous study, we had profiled the hypervariable complementarity determining region 3 (CDR3) of CD4+T cells that respond to six immunodominant APOB epitopes in most donors. Here, we comprehensively analyze this dataset of 149,065 APOB-reactive and 199,211 non-reactive control CDR3s from six human leukocyte antigen-typed donors. Results We identified 672 highly expanded (frequency threshold > 1.39E-03) clones that were significantly enriched in the APOB-reactive group as compared to the controls (log10 odds ratio ≥1, Fisher's test p < 0.01). Analysis of 114,755 naïve, 91,001 central memory (TCM) and 29,839 effector memory (TEM) CDR3 sequences from the same donors revealed that APOB+ clones can be traced to the complex repertoire of unenriched blood T cells. The fraction of APOB+ clones that overlapped with memory CDR3s ranged from 2.2% to 46% (average 16.4%). This was significantly higher than their overlap with the naïve pool, which ranged from 0.7% to 2% (average 1.36%). CDR3 motif analysis with the machine learning-based in-silico tool, GLIPHs (grouping of lymphocyte interactions by paratope hotspots), identified 532 APOB+ motifs. Analysis of naïve and memory CDR3 sequences with GLIPH revealed that ~40% (209 of 532) of these APOB+ motifs were enriched in the memory pool. Network analysis with Cytoscape revealed extensive sharing of the memory-affiliated APOB+ motifs across multiple donors. We identified six motifs that were present in TCM and TEM CDR3 sequences from >80% of the donors and were highly enriched in the APOB-reactive TCR repertoire. Discussion The identified APOB-reactive expanded CD4+T cell clones and conserved motifs can be used to annotate and track human atherosclerosis-related autoreactive CD4+T cells and measure their clonal expansion.
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Affiliation(s)
- Payel Roy
- Center for Autoimmunity and Inflammation, La Jolla Institute for Immunology, La Jolla, CA, United States
- Immunology Center of Georgia, Augusta University, Augusta, GA, United States
| | | | - Jeffrey Makings
- Center for Autoimmunity and Inflammation, La Jolla Institute for Immunology, La Jolla, CA, United States
| | - Klaus Ley
- Center for Autoimmunity and Inflammation, La Jolla Institute for Immunology, La Jolla, CA, United States
- Immunology Center of Georgia, Augusta University, Augusta, GA, United States
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Kirk AM, Crawford JC, Chou CH, Guy C, Pandey K, Kozlik T, Shah RK, Chung S, Nguyen P, Zhang X, Wang J, Bell M, Mettelman RC, Allen EK, Pogorelyy MV, Kim H, Minervina AA, Awad W, Bajracharya R, White T, Long D, Gordon B, Morrison M, Glazer ES, Murphy AJ, Jiang Y, Fitzpatrick EA, Yarchoan M, Sethupathy P, Croft NP, Purcell AW, Federico SM, Stewart E, Gottschalk S, Zamora AE, DeRenzo C, Strome SE, Thomas PG. DNAJB1-PRKACA fusion neoantigens elicit rare endogenous T cell responses that potentiate cell therapy for fibrolamellar carcinoma. Cell Rep Med 2024; 5:101469. [PMID: 38508137 PMCID: PMC10983114 DOI: 10.1016/j.xcrm.2024.101469] [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: 08/23/2023] [Revised: 11/29/2023] [Accepted: 02/20/2024] [Indexed: 03/22/2024]
Abstract
Fibrolamellar carcinoma (FLC) is a liver tumor with a high mortality burden and few treatment options. A promising therapeutic vulnerability in FLC is its driver mutation, a conserved DNAJB1-PRKACA gene fusion that could be an ideal target neoantigen for immunotherapy. In this study, we aim to define endogenous CD8 T cell responses to this fusion in FLC patients and evaluate fusion-specific T cell receptors (TCRs) for use in cellular immunotherapies. We observe that fusion-specific CD8 T cells are rare and that FLC patient TCR repertoires lack large clusters of related TCR sequences characteristic of potent antigen-specific responses, potentially explaining why endogenous immune responses are insufficient to clear FLC tumors. Nevertheless, we define two functional fusion-specific TCRs, one of which has strong anti-tumor activity in vivo. Together, our results provide insights into the fragmented nature of neoantigen-specific repertoires in humans and indicate routes for clinical development of successful immunotherapies for FLC.
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Affiliation(s)
- Allison M Kirk
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Jeremy Chase Crawford
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Ching-Heng Chou
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Cliff Guy
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Kirti Pandey
- Department of Biochemistry and Molecular Biology and Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Melbourne, VIC 3800, Australia
| | - Tanya Kozlik
- Department of Medicine, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Ravi K Shah
- Department of Medicine, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Shanzou Chung
- Department of Biochemistry and Molecular Biology and Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Melbourne, VIC 3800, Australia
| | - Phuong Nguyen
- Department of Bone Marrow Transplantation and Cellular Therapy, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Xiaoyu Zhang
- Department of Otorhinolaryngology-Head and Neck Surgery, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Jin Wang
- Department of Microbiology, Immunology, and Biochemistry, The University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Matthew Bell
- Department of Bone Marrow Transplantation and Cellular Therapy, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Robert C Mettelman
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - E Kaitlynn Allen
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Mikhail V Pogorelyy
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Hyunjin Kim
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Anastasia A Minervina
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Walid Awad
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Resha Bajracharya
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA; Department of Bone Marrow Transplantation and Cellular Therapy, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Toni White
- Department of Otorhinolaryngology-Head and Neck Surgery, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Donald Long
- Department of Biomedical Sciences, Cornell University, Ithaca, NY 14850, USA
| | - Brittney Gordon
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Michelle Morrison
- Center for Cancer Research, College of Medicine, The University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Evan S Glazer
- Center for Cancer Research, College of Medicine, The University of Tennessee Health Science Center, Memphis, TN 38163, USA; Department of Surgery, The University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Andrew J Murphy
- Department of Surgery, The University of Tennessee Health Science Center, Memphis, TN 38163, USA; Department of Surgery, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Yixing Jiang
- Department of Medical Oncology, Marlene and Stewart Greenebaum Cancer Center, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Elizabeth A Fitzpatrick
- Department of Microbiology, Immunology, and Biochemistry, The University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Mark Yarchoan
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA
| | - Praveen Sethupathy
- Department of Biomedical Sciences, Cornell University, Ithaca, NY 14850, USA
| | - Nathan P Croft
- Department of Biochemistry and Molecular Biology and Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Melbourne, VIC 3800, Australia
| | - Anthony W Purcell
- Department of Biochemistry and Molecular Biology and Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Melbourne, VIC 3800, Australia
| | - Sara M Federico
- Department of Oncology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Elizabeth Stewart
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA; Department of Oncology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Stephen Gottschalk
- Department of Bone Marrow Transplantation and Cellular Therapy, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Anthony E Zamora
- Department of Medicine, Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | - Christopher DeRenzo
- Department of Bone Marrow Transplantation and Cellular Therapy, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Scott E Strome
- College of Medicine, The University of Tennessee Health Science Center, Memphis, TN 38163, USA.
| | - Paul G Thomas
- Department of Immunology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA.
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Li X, Zhang Y, Guo S, Wu Z, Wang H, Huang Y, Wang Y, Qiu M, Lang J, Xiao Y, Zhu Y, Jin G, Hu L, Kong X. Global analysis of T-cell groups reveals immunological features and common antigen targets of digestive tract tumors. J Cancer Res Clin Oncol 2024; 150:129. [PMID: 38488909 PMCID: PMC10943170 DOI: 10.1007/s00432-024-05645-1] [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: 12/26/2023] [Accepted: 02/05/2024] [Indexed: 03/17/2024]
Abstract
BACKGROUND T cells are key players in the tumor immune microenvironment (TIME), as they can recognize and eliminate cancer cells that express neoantigens derived from somatic mutations. However, the diversity and specificity of T-cell receptors (TCRs) that recognize neoantigens are largely unknown, due to the high variability of TCR sequences among individuals. METHODS To address this challenge, we applied GLIPH2, a novel algorithm that groups TCRs based on their predicted antigen specificity and HLA restriction, to cluster the TCR repertoire of 1,702 patients with digestive tract cancer. The patients were divided into five groups based on whether they carried tumor-infiltrating or clonal-expanded TCRs and calculated their TCR diversity. The prognosis, tumor subtype, gene mutation, gene expression, and immune microenvironment of these groups were compared. Viral specificity inference and immunotherapy relevance analysis performed for the TCR groups. RESULTS This approach reduced the complexity of TCR sequences to 249 clonally expanded and 150 tumor-infiltrating TCR groups, which revealed distinct patterns of TRBV usage, HLA association, and TCR diversity. In gastric adenocarcinoma (STAD), patients with tumor-infiltrating TCRs (Patients-TI) had significantly worse prognosis than other patients (Patients-nonTI). Patients-TI had richer CD8+ T cells in the immune microenvironment, and their gene expression features were positively correlated with immunotherapy response. We also found that tumor-infiltrating TCR groups were associated with four distinct tumor subtypes, 26 common gene mutations, and 39 gene expression signatures. We discovered that tumor-infiltrating TCRs had cross-reactivity with viral antigens, indicating a possible link between viral infections and tumor immunity. CONCLUSION By applying GLIPH2 to TCR sequences from digestive tract tumors, we uncovered novel insights into the tumor immune landscape and identified potential candidates for shared TCRs and neoantigens.
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Affiliation(s)
- Xiaoxue Li
- Shanghai Institute of Nutrition and Health, CAS Key Laboratory of Tissue Microenvironment and Tumor, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Chinese Academy of Sciences (CAS), Beijing, China
| | - Yuchao Zhang
- Shanghai Institute of Nutrition and Health, CAS Key Laboratory of Tissue Microenvironment and Tumor, Chinese Academy of Sciences, Shanghai, China
| | - Shiwei Guo
- Department of Hepatobiliary Pancreatic Surgery, Changhai Hospital, Shanghai, China
| | - Zhenchuan Wu
- Anda Biology Medicine Development (Shenzhen) Co., Ltd., Shenzhen, China
| | - Hailong Wang
- Anda Biology Medicine Development (Shenzhen) Co., Ltd., Shenzhen, China
| | - Yi Huang
- Shanghai Institute of Nutrition and Health, CAS Key Laboratory of Tissue Microenvironment and Tumor, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Chinese Academy of Sciences (CAS), Beijing, China
| | - Yue Wang
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
| | - Mengni Qiu
- Shanghai Institute of Nutrition and Health, CAS Key Laboratory of Tissue Microenvironment and Tumor, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Chinese Academy of Sciences (CAS), Beijing, China
| | - Jingyu Lang
- Shanghai Institute of Nutrition and Health, CAS Key Laboratory of Tissue Microenvironment and Tumor, Chinese Academy of Sciences, Shanghai, China
| | - Yichuan Xiao
- Shanghai Institute of Nutrition and Health, CAS Key Laboratory of Tissue Microenvironment and Tumor, Chinese Academy of Sciences, Shanghai, China
| | - Yufei Zhu
- Shanghai Institute of Nutrition and Health, CAS Key Laboratory of Tissue Microenvironment and Tumor, Chinese Academy of Sciences, Shanghai, China
| | - Gang Jin
- Department of Hepatobiliary Pancreatic Surgery, Changhai Hospital, Shanghai, China.
| | - Landian Hu
- Anda Biology Medicine Development (Shenzhen) Co., Ltd., Shenzhen, China.
| | - Xiangyin Kong
- Shanghai Institute of Nutrition and Health, CAS Key Laboratory of Tissue Microenvironment and Tumor, Chinese Academy of Sciences, Shanghai, China.
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China.
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48
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Pothuri VS, Hogg GD, Conant L, Borcherding N, James CA, Mudd J, Williams G, Seo YD, Hawkins WG, Pillarisetty VG, DeNardo DG, Fields RC. Intratumoral T-cell receptor repertoire composition predicts overall survival in patients with pancreatic ductal adenocarcinoma. Oncoimmunology 2024; 13:2320411. [PMID: 38504847 PMCID: PMC10950267 DOI: 10.1080/2162402x.2024.2320411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 02/14/2024] [Indexed: 03/21/2024] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy that is refractory to immune checkpoint inhibitor therapy. However, intratumoral T-cell infiltration correlates with improved overall survival (OS). Herein, we characterized the diversity and antigen specificity of the PDAC T-cell receptor (TCR) repertoire to identify novel immune-relevant biomarkers. Demographic, clinical, and TCR-beta sequencing data were collated from 353 patients across three cohorts that underwent surgical resection for PDAC. TCR diversity was calculated using Shannon Wiener index, Inverse Simpson index, and "True entropy." Patients were clustered by shared repertoire specificity. TCRs predictive of OS were identified and their associated transcriptional states were characterized by single-cell RNAseq. In multivariate Cox regression models controlling for relevant covariates, high intratumoral TCR diversity predicted OS across multiple cohorts. Conversely, in peripheral blood, high abundance of T-cells, but not high diversity, predicted OS. Clustering patients based on TCR specificity revealed a subset of TCRs that predicts OS. Interestingly, these TCR sequences were more likely to encode CD8+ effector memory and CD4+ T-regulatory (Tregs) T-cells, all with the capacity to recognize beta islet-derived autoantigens. As opposed to T-cell abundance, intratumoral TCR diversity was predictive of OS in multiple PDAC cohorts, and a subset of TCRs enriched in high-diversity patients independently correlated with OS. These findings emphasize the importance of evaluating peripheral and intratumoral TCR repertoires as distinct and relevant biomarkers in PDAC.
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Affiliation(s)
- Vikram S. Pothuri
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA
| | - Graham D. Hogg
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Leah Conant
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA
| | - Nicholas Borcherding
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - C. Alston James
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA
| | - Jacqueline Mudd
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA
| | - Greg Williams
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA
| | - Yongwoo David Seo
- Department of Surgery, University of Washington School of Medicine, Seattle, WA, USA
- Department of Surgical Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - William G. Hawkins
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, MOUSA
| | - Venu G. Pillarisetty
- Department of Surgery, University of Washington School of Medicine, Seattle, WA, USA
- Fred Hutchinson Cancer Center, Seattle, WAUSA
| | - David G. DeNardo
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, MOUSA
| | - Ryan C. Fields
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, MOUSA
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49
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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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50
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Tayebi Z, Ali S, Murad T, Khan I, Patterson M. PseAAC2Vec protein encoding for TCR protein sequence classification. Comput Biol Med 2024; 170:107956. [PMID: 38217977 DOI: 10.1016/j.compbiomed.2024.107956] [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: 08/14/2023] [Revised: 12/07/2023] [Accepted: 01/01/2024] [Indexed: 01/15/2024]
Abstract
The classification and prediction of T-cell receptors (TCRs) protein sequences are of significant interest in understanding the immune system and developing personalized immunotherapies. In this study, we propose a novel approach using Pseudo Amino Acid Composition (PseAAC) protein encoding for accurate TCR protein sequence classification. The PseAAC2Vec encoding method captures the physicochemical properties of amino acids and their local sequence information, enabling the representation of protein sequences as fixed-length feature vectors. By incorporating physicochemical properties such as hydrophobicity, polarity, charge, molecular weight, and solvent accessibility, PseAAC2Vec provides a comprehensive and informative characterization of TCR protein sequences. To evaluate the effectiveness of the proposed PseAAC2Vec encoding approach, we assembled a large dataset of TCR protein sequences with annotated classes. We applied the PseAAC2Vec encoding scheme to each sequence and generated feature vectors based on a specified window size. Subsequently, we employed state-of-the-art machine learning algorithms, such as support vector machines (SVM) and random forests (RF), to classify the TCR protein sequences. Experimental results on the benchmark dataset demonstrated the superior performance of the PseAAC2Vec-based approach compared to existing methods. The PseAAC2Vec encoding effectively captures the discriminative patterns in TCR protein sequences, leading to improved classification accuracy and robustness. Furthermore, the encoding scheme showed promising results across different window sizes, indicating its adaptability to varying sequence contexts.
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Affiliation(s)
- Zahra Tayebi
- Department of Computer Science, Georgia State University, Atlanta, 30303, GA, USA.
| | - Sarwan Ali
- Department of Computer Science, Georgia State University, Atlanta, 30303, GA, USA.
| | - Taslim Murad
- Department of Computer Science, Georgia State University, Atlanta, 30303, GA, USA.
| | - Imdadullah Khan
- Department of Computer Science, Lahore University of Management Sciences, Lahore, Punjab, Pakistan.
| | - Murray Patterson
- Department of Computer Science, Georgia State University, Atlanta, 30303, GA, USA.
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