1
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Bresser K, Nicolet BP, Jeko A, Wu W, Loayza-Puch F, Agami R, Heck AJR, Wolkers MC, Schumacher TN. Gene and protein sequence features augment HLA class I ligand predictions. Cell Rep 2024; 43:114325. [PMID: 38870014 DOI: 10.1016/j.celrep.2024.114325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 04/22/2024] [Accepted: 05/22/2024] [Indexed: 06/15/2024] Open
Abstract
The sensitivity of malignant tissues to T cell-based immunotherapies depends on the presence of targetable human leukocyte antigen (HLA) class I ligands. Peptide-intrinsic factors, such as HLA class I affinity and proteasomal processing, have been established as determinants of HLA ligand presentation. However, the role of gene and protein sequence features as determinants of epitope presentation has not been systematically evaluated. We perform HLA ligandome mass spectrometry to evaluate the contribution of 7,135 gene and protein sequence features to HLA sampling. This analysis reveals that a number of predicted modifiers of mRNA and protein abundance and turnover, including predicted mRNA methylation and protein ubiquitination sites, inform on the presence of HLA ligands. Importantly, integration of such "hard-coded" sequence features into a machine learning approach augments HLA ligand predictions to a comparable degree as experimental measures of gene expression. Our study highlights the value of gene and protein features for HLA ligand predictions.
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Affiliation(s)
- Kaspar Bresser
- Department of Molecular Oncology and Immunology, Netherlands Cancer Institute, Oncode Institute, Amsterdam, the Netherlands; Department of Hematology, Leiden University Medical Center, Leiden, the Netherlands
| | - Benoit P Nicolet
- Sanquin Blood Supply Foundation, Department of Research, T cell differentiation lab, Amsterdam, The Netherlands; Amsterdam UMC, University of Amsterdam, Landsteiner Laboratory, Amsterdam, The Netherlands; Oncode Institute, Utrecht, The Netherlands
| | - Anita Jeko
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, the Netherlands
| | - Wei Wu
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, the Netherlands
| | - Fabricio Loayza-Puch
- Translational Control and Metabolism, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Reuven Agami
- Division of Oncogenomics, Oncode Institute, The Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Albert J R Heck
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, the Netherlands
| | - Monika C Wolkers
- Sanquin Blood Supply Foundation, Department of Research, T cell differentiation lab, Amsterdam, The Netherlands; Amsterdam UMC, University of Amsterdam, Landsteiner Laboratory, Amsterdam, The Netherlands; Oncode Institute, Utrecht, The Netherlands
| | - Ton N Schumacher
- Department of Molecular Oncology and Immunology, Netherlands Cancer Institute, Oncode Institute, Amsterdam, the Netherlands; Department of Hematology, Leiden University Medical Center, Leiden, the Netherlands.
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2
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Barra C, Nilsson JB, Saksager A, Carri I, Deleuran S, Garcia Alvarez HM, Høie MH, Li Y, Clifford JN, Wan YTR, Moreta LS, Nielsen M. In Silico Tools for Predicting Novel Epitopes. Methods Mol Biol 2024; 2813:245-280. [PMID: 38888783 DOI: 10.1007/978-1-0716-3890-3_17] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/20/2024]
Abstract
Identifying antigens within a pathogen is a critical task to develop effective vaccines and diagnostic methods, as well as understanding the evolution and adaptation to host immune responses. Historically, antigenicity was studied with experiments that evaluate the immune response against selected fragments of pathogens. Using this approach, the scientific community has gathered abundant information regarding which pathogenic fragments are immunogenic. The systematic collection of this data has enabled unraveling many of the fundamental rules underlying the properties defining epitopes and immunogenicity, and has resulted in the creation of a large panel of immunologically relevant predictive (in silico) tools. The development and application of such tools have proven to accelerate the identification of novel epitopes within biomedical applications reducing experimental costs. This chapter introduces some basic concepts about MHC presentation, T cell and B cell epitopes, the experimental efforts to determine those, and focuses on state-of-the-art methods for epitope prediction, highlighting their strengths and limitations, and catering instructions for their rational use.
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Affiliation(s)
- Carolina Barra
- Section for Bioinformatics, Health Tech, Technical University of Denmark, Lyngby, Denmark.
| | | | - Astrid Saksager
- Section for Bioinformatics, Health Tech, Technical University of Denmark, Lyngby, Denmark
| | - Ibel Carri
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín (UNSAM) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), San Martín, Argentina
| | - Sebastian Deleuran
- Section for Bioinformatics, Health Tech, Technical University of Denmark, Lyngby, Denmark
| | - Heli M Garcia Alvarez
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín (UNSAM) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), San Martín, Argentina
| | - Magnus Haraldson Høie
- Section for Bioinformatics, Health Tech, Technical University of Denmark, Lyngby, Denmark
| | - Yuchen Li
- Section for Bioinformatics, Health Tech, Technical University of Denmark, Lyngby, Denmark
| | | | - Yat-Tsai Richie Wan
- Section for Bioinformatics, Health Tech, Technical University of Denmark, Lyngby, Denmark
| | - Lys Sanz Moreta
- Section for Bioinformatics, Health Tech, Technical University of Denmark, Lyngby, Denmark
| | - Morten Nielsen
- Section for Bioinformatics, Health Tech, Technical University of Denmark, Lyngby, Denmark
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín (UNSAM) - Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), San Martín, Argentina
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3
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Chaudhuri D, Majumder S, Datta J, Giri K. In silico designing of an epitope-based peptide vaccine cocktail against Nipah virus: an Indian population-based epidemiological study. Arch Microbiol 2023; 205:380. [PMID: 37955744 DOI: 10.1007/s00203-023-03717-3] [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: 09/20/2023] [Revised: 10/09/2023] [Accepted: 10/21/2023] [Indexed: 11/14/2023]
Abstract
Nipah virus, a zoonotic virus from the family Paramyxoviridae has led to significant loss of lives till date with the most recent outbreak in India reported in Kerala. The virus has a considerably high mortality rate along with lack of characteristic symptoms which results in the delay of the virus detection. No specific vaccine is available for the virus although monoclonal antibody treatment has been seen to be effective along with favipiravir. The high mortality and complications caused by the virus underscores the necessity to develop alternative modes of vaccination. One such method has been designed in this study using peptide cocktail consisting of the immunologically important epitopes for use as vaccine. The human leucocytic antigens that are used for the study were analyzed for their presence in various ethnic Indian populations. This study may serve as a new avenue for development of more efficient peptide cocktail vaccines in recent future based on the population genetics and ethnicity.
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Affiliation(s)
- Dwaipayan Chaudhuri
- Department of Life Sciences, Presidency University, 86/1 College Street, Kolkata, 700073, India
| | - Satyabrata Majumder
- Department of Life Sciences, Presidency University, 86/1 College Street, Kolkata, 700073, India
| | - Joyeeta Datta
- Department of Life Sciences, Presidency University, 86/1 College Street, Kolkata, 700073, India
| | - Kalyan Giri
- Department of Life Sciences, Presidency University, 86/1 College Street, Kolkata, 700073, India.
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4
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Schmalen A, Kammerl IE, Meiners S, Noessner E, Deeg CA, Hauck SM. A Lysine Residue at the C-Terminus of MHC Class I Ligands Correlates with Low C-Terminal Proteasomal Cleavage Probability. Biomolecules 2023; 13:1300. [PMID: 37759700 PMCID: PMC10527444 DOI: 10.3390/biom13091300] [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: 03/16/2023] [Revised: 08/10/2023] [Accepted: 08/23/2023] [Indexed: 09/29/2023] Open
Abstract
The majority of peptides presented by MHC class I result from proteasomal protein turnover. The specialized immunoproteasome, which is induced during inflammation, plays a major role in antigenic peptide generation. However, other cellular proteases can, either alone or together with the proteasome, contribute peptides to MHC class I loading non-canonically. We used an immunopeptidomics workflow combined with prediction software for proteasomal cleavage probabilities to analyze how inflammatory conditions affect the proteasomal processing of immune epitopes presented by A549 cells. The treatment of A549 cells with IFNγ enhanced the proteasomal cleavage probability of MHC class I ligands for both the constitutive proteasome and the immunoproteasome. Furthermore, IFNγ alters the contribution of the different HLA allotypes to the immunopeptidome. When we calculated the HLA allotype-specific proteasomal cleavage probabilities for MHC class I ligands, the peptides presented by HLA-A*30:01 showed characteristics hinting at a reduced C-terminal proteasomal cleavage probability independently of the type of proteasome. This was confirmed by HLA-A*30:01 ligands from the immune epitope database, which also showed this effect. Furthermore, two additional HLA allotypes, namely, HLA-A*03:01 and HLA-A*11:01, presented peptides with a markedly reduced C-terminal proteasomal cleavage probability. The peptides eluted from all three HLA allotypes shared a peptide binding motif with a C-terminal lysine residue, suggesting that this lysine residue impairs proteasome-dependent HLA ligand production and might, in turn, favor peptide generation by other cellular proteases.
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Affiliation(s)
- Adrian Schmalen
- Chair of Physiology, Department of Veterinary Sciences, LMU Munich, Martinsried, 82152 Planegg, Germany
- Core Facility—Metabolomics and Proteomics Core, Helmholtz Center Munich, German Research Center for Environmental Health (GmbH), 80939 Munich, Germany
| | - Ilona E. Kammerl
- Comprehensive Pneumology Center (CPC), University Hospital, Ludwig-Maximilians-University, Helmholtz Center Munich, Member of the German Center for Lung Research (DZL), 81377 Munich, Germany
| | - Silke Meiners
- Research Center Borstel, Leibniz Lung Center, Airway Research Center North (ARCN), Member of the German Center for Lung Research (DZL), 23845 Borstel, Germany
- Institute of Experimental Medicine, Christian-Albrechts University Kiel, 24118 Kiel, Germany
| | - Elfriede Noessner
- Immunoanalytics Research Group—Tissue Control of Immunocytes, Helmholtz Center Munich, 81377 Munich, Germany
| | - Cornelia A. Deeg
- Chair of Physiology, Department of Veterinary Sciences, LMU Munich, Martinsried, 82152 Planegg, Germany
| | - Stefanie M. Hauck
- Core Facility—Metabolomics and Proteomics Core, Helmholtz Center Munich, German Research Center for Environmental Health (GmbH), 80939 Munich, Germany
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5
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Tai W, Feng S, Chai B, Lu S, Zhao G, Chen D, Yu W, Ren L, Shi H, Lu J, Cai Z, Pang M, Tan X, Wang P, Lin J, Sun Q, Peng X, Cheng G. An mRNA-based T-cell-inducing antigen strengthens COVID-19 vaccine against SARS-CoV-2 variants. Nat Commun 2023; 14:2962. [PMID: 37221158 DOI: 10.1038/s41467-023-38751-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 05/12/2023] [Indexed: 05/25/2023] Open
Abstract
Herd immunity achieved through mass vaccination is an effective approach to prevent contagious diseases. Nonetheless, emerging SARS-CoV-2 variants with frequent mutations largely evaded humoral immunity induced by Spike-based COVID-19 vaccines. Herein, we develop a lipid nanoparticle (LNP)-formulated mRNA-based T-cell-inducing antigen, which targeted three SARS-CoV-2 proteome regions that enriched human HLA-I epitopes (HLA-EPs). Immunization of HLA-EPs induces potent cellular responses to prevent SARS-CoV-2 infection in humanized HLA-A*02:01/DR1 and HLA-A*11:01/DR1 transgenic mice. Of note, the sequences of HLA-EPs are highly conserved among SARS-CoV-2 variants of concern. In humanized HLA-transgenic mice and female rhesus macaques, dual immunization with the LNP-formulated mRNAs encoding HLA-EPs and the receptor-binding domain of the SARS-CoV-2 B.1.351 variant (RBDbeta) is more efficacious in preventing infection of SARS-CoV-2 Beta and Omicron BA.1 variants than single immunization of LNP-RBDbeta. This study demonstrates the necessity to strengthen the vaccine effectiveness by comprehensively stimulating both humoral and cellular responses, thereby offering insight for optimizing the design of COVID-19 vaccines.
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Affiliation(s)
- Wanbo Tai
- Institute of Infectious Diseases, Shenzhen Bay Laboratory, Shenzhen, 518132, China
- Tsinghua-Peking Joint Center for Life Sciences, School of Medicine, Tsinghua University, Beijing, 100084, China
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510182, China
| | - Shengyong Feng
- Tsinghua-Peking Joint Center for Life Sciences, School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Benjie Chai
- Tsinghua-Peking Joint Center for Life Sciences, School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Shuaiyao Lu
- National Kunming High-level Biosafety Primate Research Center, Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Kunming, 650118, China
| | - Guangyu Zhao
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Academy of Military Medical Sciences, Beijing, 100071, China
| | - Dong Chen
- Zhejiang Provincial Key Laboratory of Medical Genetics, Key Laboratory of Laboratory Medicine, Ministry of Education, School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, 325035, China
- Wenzhou Central Hospital, Wenzhou, 325000, China
| | - Wenhai Yu
- National Kunming High-level Biosafety Primate Research Center, Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Kunming, 650118, China
| | - Liting Ren
- Beijing Advanced Innovation Center for Structural Biology, Beijing Frontier Research Center for Biological Structure, MOE Key Laboratory of Bioorganic Phosphorus Chemistry & Chemical Biology, School of Pharmaceutical Sciences, Tsinghua University, Beijing, 100084, China
| | - Huicheng Shi
- Tsinghua-Peking Joint Center for Life Sciences, School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Jing Lu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Zhongshan Hospital, Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, 200438, China
| | - Zhuming Cai
- Institute of Infectious Diseases, Shenzhen Bay Laboratory, Shenzhen, 518132, China
- Tsinghua-Peking Joint Center for Life Sciences, School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Mujia Pang
- Institute of Infectious Diseases, Shenzhen Bay Laboratory, Shenzhen, 518132, China
| | - Xu Tan
- Beijing Advanced Innovation Center for Structural Biology, Beijing Frontier Research Center for Biological Structure, MOE Key Laboratory of Bioorganic Phosphorus Chemistry & Chemical Biology, School of Pharmaceutical Sciences, Tsinghua University, Beijing, 100084, China
| | - Penghua Wang
- Department of Immunology, School of Medicine, the University of Connecticut Health Center, Farmington, CT, 06030, USA
| | - Jinzhong Lin
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Zhongshan Hospital, Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, 200438, China.
| | - Qiangming Sun
- National Kunming High-level Biosafety Primate Research Center, Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Kunming, 650118, China.
| | - Xiaozhong Peng
- National Kunming High-level Biosafety Primate Research Center, Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Kunming, 650118, China.
| | - Gong Cheng
- Tsinghua-Peking Joint Center for Life Sciences, School of Medicine, Tsinghua University, Beijing, 100084, China.
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6
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Frank ML, Lu K, Erdogan C, Han Y, Hu J, Wang T, Heymach JV, Zhang J, Reuben A. T-Cell Receptor Repertoire Sequencing in the Era of Cancer Immunotherapy. Clin Cancer Res 2023; 29:994-1008. [PMID: 36413126 PMCID: PMC10011887 DOI: 10.1158/1078-0432.ccr-22-2469] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 10/07/2022] [Accepted: 11/14/2022] [Indexed: 11/23/2022]
Abstract
T cells are integral components of the adaptive immune system, and their responses are mediated by unique T-cell receptors (TCR) that recognize specific antigens from a variety of biological contexts. As a result, analyzing the T-cell repertoire offers a better understanding of immune responses and of diseases like cancer. Next-generation sequencing technologies have greatly enabled the high-throughput analysis of the TCR repertoire. On the basis of our extensive experience in the field from the past decade, we provide an overview of TCR sequencing, from the initial library preparation steps to sequencing and analysis methods and finally to functional validation techniques. With regards to data analysis, we detail important TCR repertoire metrics and present several computational tools for predicting antigen specificity. Finally, we highlight important applications of TCR sequencing and repertoire analysis to understanding tumor biology and developing cancer immunotherapies.
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Affiliation(s)
- Meredith L Frank
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.,The University of Texas MD Anderson Cancer Center UT Health Houston Graduate School of Biomedical Sciences, Houston, Texas
| | - Kaylene Lu
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.,The University of Texas MD Anderson Cancer Center UT Health Houston Graduate School of Biomedical Sciences, Houston, Texas.,Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Can Erdogan
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.,Rice University, Houston, Texas
| | - Yi Han
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Jian Hu
- The University of Texas MD Anderson Cancer Center UT Health Houston Graduate School of Biomedical Sciences, Houston, Texas.,Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Tao Wang
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, Texas.,Center for the Genetics of Host Defense, University of Texas Southwestern Medical Center, Dallas, Texas
| | - John V Heymach
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.,The University of Texas MD Anderson Cancer Center UT Health Houston Graduate School of Biomedical Sciences, Houston, Texas
| | - Jianjun Zhang
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.,The University of Texas MD Anderson Cancer Center UT Health Houston Graduate School of Biomedical Sciences, Houston, Texas.,Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Alexandre Reuben
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.,The University of Texas MD Anderson Cancer Center UT Health Houston Graduate School of Biomedical Sciences, Houston, Texas
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7
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Current Trends in Neoantigen-Based Cancer Vaccines. Pharmaceuticals (Basel) 2023; 16:ph16030392. [PMID: 36986491 PMCID: PMC10056833 DOI: 10.3390/ph16030392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 02/18/2023] [Accepted: 02/24/2023] [Indexed: 03/08/2023] Open
Abstract
Cancer immunotherapies are treatments that use drugs or cells to activate patients’ own immune systems against cancer cells. Among them, cancer vaccines have recently been rapidly developed. Based on tumor-specific antigens referred to as neoantigens, these vaccines can be in various forms such as messenger (m)RNA and synthetic peptides to activate cytotoxic T cells and act with or without dendritic cells. Growing evidence suggests that neoantigen-based cancer vaccines possess a very promising future, yet the processes of immune recognition and activation to relay identification of a neoantigen through the histocompatibility complex (MHC) and T-cell receptor (TCR) remain unclear. Here, we describe features of neoantigens and the biological process of validating neoantigens, along with a discussion of recent progress in the scientific development and clinical applications of neoantigen-based cancer vaccines.
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8
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The CD8+ and CD4+ T Cell Immunogen Atlas of Zika Virus Reveals E, NS1 and NS4 Proteins as the Vaccine Targets. Viruses 2022; 14:v14112332. [PMID: 36366430 PMCID: PMC9696057 DOI: 10.3390/v14112332] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 10/14/2022] [Accepted: 10/17/2022] [Indexed: 02/01/2023] Open
Abstract
Zika virus (ZIKV)-specific T cells are activated by different peptides derived from virus structural and nonstructural proteins, and contributed to the viral clearance or protective immunity. Herein, we have depicted the profile of CD8+ and CD4+ T cell immunogenicity of ZIKV proteins in C57BL/6 (H-2b) and BALB/c (H-2d) mice, and found that featured cellular immunity antigens were variant among different murine alleles. In H-2b mice, the proteins E, NS2, NS3 and NS5 are recognized as immunodominant antigens by CD8+ T cells, while NS4 is dominantly recognized by CD4+ T cells. In contrast, in H-2d mice, NS1 and NS4 are the dominant CD8+ T cell antigen and NS4 as the dominant CD4+ T cell antigen, respectively. Among the synthesized 364 overlapping polypeptides spanning the whole proteome of ZIKV, we mapped 91 and 39 polypeptides which can induce ZIKV-specific T cell responses in H-2b and H-2d mice, respectively. Through the identification of CD8+ T cell epitopes, we found that immunodominant regions E294-302 and NS42351-2360 are hotspots epitopes with a distinct immunodominance hierarchy present in H-2b and H-2d mice, respectively. Our data characterized an overall landscape of the immunogenic spectrum of the ZIKV polyprotein, and provide useful insight into the vaccine development.
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9
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Garanina E, Hamza S, Stott-Marshall RJ, Martynova E, Markelova M, Davidyuk Y, Shakirova V, Kaushal N, Baranwal M, Khaertynova IM, Rizvanov A, Foster TL, Khaiboullina S. Antibody and T Cell Immune Responses to SARS-CoV-2 Peptides in COVID-19 Convalescent Patients. Front Microbiol 2022; 13:842232. [PMID: 35509311 PMCID: PMC9058163 DOI: 10.3389/fmicb.2022.842232] [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: 12/23/2021] [Accepted: 01/26/2022] [Indexed: 11/13/2022] Open
Abstract
Identifying immunogenic targets of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is critical to advance diagnostic and disease control strategies. We analyzed humoral (ELISA) and T-cell (ELISpot) immune responses to spike (S) and nucleocapsid (N) SARS-CoV-2 proteins as well as to human endemic coronavirus (eCoV) peptides in serum from convalescent coronavirus disease 2019 (COVID-19) patients from Tatarstan, Russia. We identified multiple SARS-CoV-2 peptides that were reactive with serum antibodies and T cells from convalescent COVID-19. In addition, age and gender associated differences in the reactivity to S and N protein peptides were identified. Moreover, several SARS-CoV-2 peptides tested negatively correlated with disease severity and lung damage. Cross-reactivity to eCoV peptides was analyzed and found to be lower in COVID-19 compared to controls. In this study, we demonstrate the changing pattern of immunogenic peptide reactivity in COVID-19 serum based on age, gender and previous exposure to eCoVs. These data highlight how humoral immune responses and cytotoxic T cell responses to some of these peptides could contribute to SARS-CoV-2 pathogenesis.
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Affiliation(s)
- Ekaterina Garanina
- Intitute of Fundamental Medicine and Biology, Kazan Federal University, Kazan, Russia
| | - Shaimaa Hamza
- Intitute of Fundamental Medicine and Biology, Kazan Federal University, Kazan, Russia
| | - Robert J. Stott-Marshall
- Faculty of Medicine and Health Sciences, School of Veterinary Medicine and Science, University of Nottingham, Loughborough, United Kingdom
| | - Ekaterina Martynova
- Intitute of Fundamental Medicine and Biology, Kazan Federal University, Kazan, Russia
| | - Maria Markelova
- Intitute of Fundamental Medicine and Biology, Kazan Federal University, Kazan, Russia
| | - Yuriy Davidyuk
- Intitute of Fundamental Medicine and Biology, Kazan Federal University, Kazan, Russia
| | - Venera Shakirova
- Department of Infectious Diseases, Kazan State Medical University, Kazan, Russia
| | - Neha Kaushal
- Department of Biotechnology, Thapar Institute of Engineering and Technology, Patiala, India
| | - Manoj Baranwal
- Department of Biotechnology, Thapar Institute of Engineering and Technology, Patiala, India
| | | | - Albert Rizvanov
- Intitute of Fundamental Medicine and Biology, Kazan Federal University, Kazan, Russia
| | - Toshana L. Foster
- Faculty of Medicine and Health Sciences, School of Veterinary Medicine and Science, University of Nottingham, Loughborough, United Kingdom
| | - Svetlana Khaiboullina
- Intitute of Fundamental Medicine and Biology, Kazan Federal University, Kazan, Russia
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10
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Borden ES, Buetow KH, Wilson MA, Hastings KT. Cancer Neoantigens: Challenges and Future Directions for Prediction, Prioritization, and Validation. Front Oncol 2022; 12:836821. [PMID: 35311072 PMCID: PMC8929516 DOI: 10.3389/fonc.2022.836821] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 02/07/2022] [Indexed: 12/16/2022] Open
Abstract
Prioritization of immunogenic neoantigens is key to enhancing cancer immunotherapy through the development of personalized vaccines, adoptive T cell therapy, and the prediction of response to immune checkpoint inhibition. Neoantigens are tumor-specific proteins that allow the immune system to recognize and destroy a tumor. Cancer immunotherapies, such as personalized cancer vaccines, adoptive T cell therapy, and immune checkpoint inhibition, rely on an understanding of the patient-specific neoantigen profile in order to guide personalized therapeutic strategies. Genomic approaches to predicting and prioritizing immunogenic neoantigens are rapidly expanding, raising new opportunities to advance these tools and enhance their clinical relevance. Predicting neoantigens requires acquisition of high-quality samples and sequencing data, followed by variant calling and variant annotation. Subsequently, prioritizing which of these neoantigens may elicit a tumor-specific immune response requires application and integration of tools to predict the expression, processing, binding, and recognition potentials of the neoantigen. Finally, improvement of the computational tools is held in constant tension with the availability of datasets with validated immunogenic neoantigens. The goal of this review article is to summarize the current knowledge and limitations in neoantigen prediction, prioritization, and validation and propose future directions that will improve personalized cancer treatment.
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Affiliation(s)
- Elizabeth S Borden
- Department of Basic Medical Sciences, College of Medicine-Phoenix, University of Arizona, Phoenix, AZ, United States.,Department of Research and Internal Medicine (Dermatology), Phoenix Veterans Affairs Health Care System, Phoenix, AZ, United States
| | - Kenneth H Buetow
- School of Life Sciences, Arizona State University, Tempe, AZ, United States.,Center for Evolution and Medicine, Arizona State University, Tempe, AZ, United States
| | - Melissa A Wilson
- School of Life Sciences, Arizona State University, Tempe, AZ, United States.,Center for Evolution and Medicine, Arizona State University, Tempe, AZ, United States
| | - Karen Taraszka Hastings
- Department of Basic Medical Sciences, College of Medicine-Phoenix, University of Arizona, Phoenix, AZ, United States.,Department of Research and Internal Medicine (Dermatology), Phoenix Veterans Affairs Health Care System, Phoenix, AZ, United States
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11
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Verdon DJ, Jenkins MR. Identification and Targeting of Mutant Peptide Neoantigens in Cancer Immunotherapy. Cancers (Basel) 2021; 13:4245. [PMID: 34439399 PMCID: PMC8391927 DOI: 10.3390/cancers13164245] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 08/19/2021] [Accepted: 08/19/2021] [Indexed: 12/30/2022] Open
Abstract
In recent decades, adoptive cell transfer and checkpoint blockade therapies have revolutionized immunotherapeutic approaches to cancer treatment. Advances in whole exome/genome sequencing and bioinformatic detection of tumour-specific genetic variations and the amino acid sequence alterations they induce have revealed that T cell mediated anti-tumour immunity is substantially directed at mutated peptide sequences, and the identification and therapeutic targeting of patient-specific mutated peptide antigens now represents an exciting and rapidly progressing frontier of personalized medicine in the treatment of cancer. This review outlines the historical identification and validation of mutated peptide neoantigens as a target of the immune system, and the technical development of bioinformatic and experimental strategies for detecting, confirming and prioritizing both patient-specific or "private" and frequently occurring, shared "public" neoantigenic targets. Further, we examine the range of therapeutic modalities that have demonstrated preclinical and clinical anti-tumour efficacy through specifically targeting neoantigens, including adoptive T cell transfer, checkpoint blockade and neoantigen vaccination.
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Affiliation(s)
- Daniel J. Verdon
- Immunology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC 3052, Australia;
| | - Misty R. Jenkins
- Immunology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC 3052, Australia;
- Department of Medical Biology, The University of Melbourne, Parkville, VIC 3052, Australia
- La Trobe Institute of Molecular Science, La Trobe University, Bundoora, VIC 3086, Australia
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Dijkstra JM, Frenette AP, Dixon B. Most Japanese individuals are genetically predisposed to recognize an immunogenic protein fragment shared between COVID-19 and common cold coronaviruses. F1000Res 2021; 10:196. [PMID: 34026045 PMCID: PMC8108557 DOI: 10.12688/f1000research.51479.1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/25/2021] [Indexed: 12/15/2022] Open
Abstract
In the spring of 2020, we and others hypothesized that T cells in COVID-19 patients may recognize identical protein fragments shared between the coronaviruses of the common cold and COVID-19 and thereby confer cross-virus immune memory. Here, we look at this issue by screening studies that, since that time, have experimentally addressed COVID-19 associated T cell specificities. Currently, the identical T cell epitope shared between COVID-19 and common cold coronaviruses most convincingly identified as immunogenic is the CD8 + T cell epitope VYIGDPAQL if presented by the MHC class I allele HLA-A*24:02. The HLA-A*24:02 allele is found in the majority of Japanese individuals and several indigenous populations in Asia, Oceania, and the Americas. In combination with histories of common cold infections, HLA-A*24:02 may affect their protection from COVID-19.
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Affiliation(s)
- Johannes M. Dijkstra
- Institute for Comprehensive Medical Science, Fujita Health Universit, Toyoake-shi, 470-1192, Japan
| | - Aaron P. Frenette
- Department of Biology, University of Waterlo, Waterloo, ON, N2L 3G1, Canada
| | - Brian Dixon
- Department of Biology, University of Waterlo, Waterloo, ON, N2L 3G1, Canada
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Abstract
The 3rd edition of the computational methods for the immune system function workshop has been held in San Diego, CA, in conjunction with the IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2019) from November 18 to 21, 2019. The workshop has continued its growing tendency, with a total of 18 accepted papers that have been presented in a full day workshop. Among these, the best 10 papers have been selected and extended for presentation in this special issue. The covered topics range from computer-aided identification of T cell epitopes to the prediction of heart rate variability to prevent brain injuries, from In Silico modeling of Tuberculosis and generation of digital patients to machine learning applied to predict type-2 diabetes risk.
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Affiliation(s)
- Francesco Pappalardo
- Department of Drug and Health Sciences, University of Catania, V.le A. Doria 6, 95125 Catania, Italy
| | - Giulia Russo
- Department of Drug and Health Sciences, University of Catania, V.le A. Doria 6, 95125 Catania, Italy
| | - Pedro A. Reche
- Departamento de Immunología (Microbiología I), Universidad Complutense de Madrid, Facultad de Medicina, Plaza Ramón y Cajal, 28040 Madrid, Spain
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