1
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Sugiyama N, Terry FE, Gutierrez AH, Hirano T, Hoshi M, Mizuno Y, Martin W, Yasunaga S, Niiro H, Fujio K, De Groot AS. Individual and population-level variability in HLA-DR associated immunogenicity risk of biologics used for the treatment of rheumatoid arthritis. Front Immunol 2024; 15:1377911. [PMID: 38812524 PMCID: PMC11134572 DOI: 10.3389/fimmu.2024.1377911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Accepted: 04/24/2024] [Indexed: 05/31/2024] Open
Abstract
Hypothesis While conventional in silico immunogenicity risk assessments focus on measuring immunogenicity based on the potential of therapeutic proteins to be processed and presented by a global population-wide set of human leukocyte antigen (HLA) alleles to T cells, future refinements might adjust for HLA allele frequencies in different geographic regions or populations, as well for as individuals in those populations. Adjustment by HLA allele distribution may reveal risk patterns that are specific to population groups or individuals, which current methods that rely on global-population HLA prevalence may obscure. Key findings This analysis uses HLA frequency-weighted binding predictions to define immunogenicity risk for global and sub-global populations. A comparison of assessments tuned for North American/European versus Japanese/Asian populations suggests that the potential for anti-therapeutic responses (anti-therapeutic antibodies or ATA) for several commonly prescribed Rheumatoid Arthritis (RA) therapeutic biologics may differ, significantly, between the Caucasian and Japanese populations. This appears to align with reports of differing product-related immunogenicity that is observed in different populations. Relevance to clinical practice Further definition of population-level (regional) and individual patient-specific immunogenic risk profiles may enable prescription of the RA therapeutic with the highest probability of success to each patient, depending on their population of origin and/or their individual HLA background. Furthermore, HLA-specific immunogenicity outcomes data are limited, thus there is a need to expand HLA-association studies that examine the relationship between HLA haplotype and ATA in the clinic.
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Affiliation(s)
- Naonobu Sugiyama
- Rheumatology, Inflammation and Immunology Medical Affairs, Pfizer Japan Inc., Tokyo, Japan
| | | | | | - Toshitaka Hirano
- Rheumatology, Inflammation and Immunology Medical Affairs, Pfizer Japan Inc., Tokyo, Japan
| | - Masato Hoshi
- Rheumatology, Inflammation and Immunology Medical Affairs, Pfizer Japan Inc., Tokyo, Japan
| | - Yasushi Mizuno
- Rheumatology, Inflammation and Immunology Medical Affairs, Pfizer Japan Inc., Tokyo, Japan
| | | | - Shin’ichiro Yasunaga
- Department of Biochemistry, Faculty of Medicine, Fukuoka University, Fukuoka, Japan
| | - Hiroaki Niiro
- Department of Medical Education, Kyushu University Graduate School of Medical Sciences, Fukuoka, Japan
| | - Keishi Fujio
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
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2
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Weisbrod L, Capriotti L, Hofmann M, Spieler V, Dersch H, Voedisch B, Schmidt P, Knake S. FASTMAP-a flexible and scalable immunopeptidomics pipeline for HLA- and antigen-specific T-cell epitope mapping based on artificial antigen-presenting cells. Front Immunol 2024; 15:1386160. [PMID: 38779658 PMCID: PMC11109385 DOI: 10.3389/fimmu.2024.1386160] [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: 02/14/2024] [Accepted: 04/17/2024] [Indexed: 05/25/2024] Open
Abstract
The study of peptide repertoires presented by major histocompatibility complex (MHC) molecules and the identification of potential T-cell epitopes contribute to a multitude of immunopeptidome-based treatment approaches. Epitope mapping is essential for the development of promising epitope-based approaches in vaccination as well as for innovative therapeutics for autoimmune diseases, infectious diseases, and cancer. It also plays a critical role in the immunogenicity assessment of protein therapeutics with regard to safety and efficacy concerns. The main challenge emerges from the highly polymorphic nature of the human leukocyte antigen (HLA) molecules leading to the requirement of a peptide mapping strategy for a single HLA allele. As many autoimmune diseases are linked to at least one specific antigen, we established FASTMAP, an innovative strategy to transiently co-transfect a single HLA allele combined with a disease-specific antigen into a human cell line. This approach allows the specific identification of HLA-bound peptides using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Using FASTMAP, we found a comparable spectrum of endogenous peptides presented by the most frequently expressed HLA alleles in the world's population compared to what has been described in literature. To ensure a reliable peptide mapping workflow, we combined the HLA alleles with well-known human model antigens like coagulation factor VIII, acetylcholine receptor subunit alpha, protein structures of the SARS-CoV-2 virus, and myelin basic protein. Using these model antigens, we have been able to identify a broad range of peptides that are in line with already published and in silico predicted T-cell epitopes of the specific HLA/model antigen combination. The transient co-expression of a single affinity-tagged MHC molecule combined with a disease-specific antigen in a human cell line in our FASTMAP pipeline provides the opportunity to identify potential T-cell epitopes/endogenously processed MHC-bound peptides in a very cost-effective, fast, and customizable system with high-throughput potential.
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Affiliation(s)
- Luisa Weisbrod
- Recombinant Protein Discovery, CSL Innovation GmbH, Marburg, Germany
| | - Luigi Capriotti
- Analytical Biochemistry, Research and Development, CSL Behring AG, Bern, Switzerland
| | - Marco Hofmann
- Recombinant Protein Discovery, CSL Innovation GmbH, Marburg, Germany
| | - Valerie Spieler
- Recombinant Protein Discovery, CSL Innovation GmbH, Marburg, Germany
| | - Herbert Dersch
- Recombinant Protein Discovery, CSL Innovation GmbH, Marburg, Germany
| | - Bernd Voedisch
- Recombinant Protein Discovery, CSL Innovation GmbH, Marburg, Germany
| | - Peter Schmidt
- Protein Biochemistry, Bio21 Institute, CSL Limited, Parkville, VIC, Australia
| | - Susanne Knake
- Department of Neurology, Epilepsy Center Hessen, Philipps University Marburg, Marburg, Germany
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3
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Egholm Bruun Jensen E, Reynisson B, Barra C, Nielsen M. New light on the HLA-DR immunopeptidomic landscape. J Leukoc Biol 2024; 115:913-925. [PMID: 38214568 PMCID: PMC11057780 DOI: 10.1093/jleuko/qiae007] [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: 06/29/2022] [Revised: 12/18/2023] [Accepted: 12/27/2023] [Indexed: 01/13/2024] Open
Abstract
The set of peptides processed and presented by major histocompatibility complex class II molecules defines the immunopeptidome, and its characterization holds keys to understanding essential properties of the immune system. High-throughput mass spectrometry (MS) techniques enable interrogation of the diversity and complexity of the immunopeptidome at an unprecedented scale. Here, we analyzed a large set of MS immunopeptidomics data from 40 donors, 221 samples, covering 30 unique HLA-DR molecules. We identified likely co-immunoprecipitated HLA-DR irrelevant contaminants using state-of-the-art prediction methods and unveiled novel light on the properties of HLA antigen processing and presentation. The ligandome (HLA binders) was enriched in 15-mer peptides, and the contaminome (nonbinders) in longer peptides. Classification of singletons and nested sets showed that the first were enriched in contaminants. Investigating the source protein location of ligands revealed that only contaminants shared a positional bias. Regarding subcellular localization, nested peptides were found to be predominantly of endolysosomal origin, whereas singletons shared an equal distribution between the cytosolic and endolysosomal origin. According to antigen-processing signatures, no significant differences were observed between the cytosolic and endolysosomal ligands. Further, the sensitivity of MS immunopeptidomics was investigated by analyzing overlap and saturation between biological MS replicas, concluding that at least 5 replicas are needed to identify 80% of the immunopeptidome. Moreover, the overlap in immunopeptidome between donors was found to be very low both in terms of peptides and source proteins, the latter indicating a critical HLA bias in the antigen sampling in the HLA antigen presentation. Finally, the complementarity between MS and in silico approaches for comprehensively sampling the immunopeptidome was demonstrated.
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Affiliation(s)
| | - Birkir Reynisson
- Department of Health Technology, Building 204, Technical University of Denmark, DK-2800 Lyngby, Denmark
| | - Carolina Barra
- Department of Health Technology, Building 204, Technical University of Denmark, DK-2800 Lyngby, Denmark
| | - Morten Nielsen
- Department of Health Technology, Building 204, Technical University of Denmark, DK-2800 Lyngby, Denmark
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, B 1650 HMP, Buenos Aires, Argentina
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4
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Ma W, Zhang J, Yao H. NeoMUST: an accurate and efficient multi-task learning model for neoantigen presentation. Life Sci Alliance 2024; 7:e202302255. [PMID: 38290755 PMCID: PMC10828515 DOI: 10.26508/lsa.202302255] [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: 07/06/2023] [Revised: 01/19/2024] [Accepted: 01/22/2024] [Indexed: 02/01/2024] Open
Abstract
Accurate identification of neoantigens is important for advancing cancer immunotherapies. This study introduces Neoantigen MUlti-taSk Tower (NeoMUST), a model employing multi-task learning to effectively capture task-specific information across related tasks. Our results show that NeoMUST rivals existing algorithms in predicting the presentation of neoantigens via MHC-I molecules, while demonstrating a significantly shorter training time for enhanced computational efficiency. The use of multi-task learning enables NeoMUST to leverage shared knowledge and task dependencies, leading to improved performance metrics and a significant reduction in the training time. NeoMUST, implemented in Python, is freely accessible at the GitHub repository. Our model will facilitate neoantigen prediction and empower the development of effective cancer immunotherapeutic approaches.
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Affiliation(s)
- Wang Ma
- Fresh Wind Biotechnologies Inc. (Tianjin), Tianjin, China
| | - Jiawei Zhang
- Fresh Wind Biotechnologies Inc. (Tianjin), Tianjin, China
| | - Hui Yao
- Fresh Wind Biotechnologies USA Inc., Houston, TX, USA
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5
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Li Y, Wu X, Fang D, Luo Y. Informing immunotherapy with multi-omics driven machine learning. NPJ Digit Med 2024; 7:67. [PMID: 38486092 PMCID: PMC10940614 DOI: 10.1038/s41746-024-01043-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Accepted: 02/14/2024] [Indexed: 03/18/2024] Open
Abstract
Progress in sequencing technologies and clinical experiments has revolutionized immunotherapy on solid and hematologic malignancies. However, the benefits of immunotherapy are limited to specific patient subsets, posing challenges for broader application. To improve its effectiveness, identifying biomarkers that can predict patient response is crucial. Machine learning (ML) play a pivotal role in harnessing multi-omic cancer datasets and unlocking new insights into immunotherapy. This review provides an overview of cutting-edge ML models applied in omics data for immunotherapy analysis, including immunotherapy response prediction and immunotherapy-relevant tumor microenvironment identification. We elucidate how ML leverages diverse data types to identify significant biomarkers, enhance our understanding of immunotherapy mechanisms, and optimize decision-making process. Additionally, we discuss current limitations and challenges of ML in this rapidly evolving field. Finally, we outline future directions aimed at overcoming these barriers and improving the efficiency of ML in immunotherapy research.
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Affiliation(s)
- Yawei Li
- Department of Preventive Medicine, Northwestern University, Feinberg School of Medicine, Chicago, IL, 60611, USA
- Center for Collaborative AI in Healthcare, Northwestern University, Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Xin Wu
- Department of Medicine, University of Illinois at Chicago, Chicago, IL, 60612, USA
| | - Deyu Fang
- Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, Feinberg School of Medicine, Chicago, IL, 60611, USA.
- Center for Collaborative AI in Healthcare, Northwestern University, Feinberg School of Medicine, Chicago, IL, 60611, USA.
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6
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Yang Y, Wei Z, Cia G, Song X, Pucci F, Rooman M, Xue F, Hou Q. MHCII-peptide presentation: an assessment of the state-of-the-art prediction methods. Front Immunol 2024; 15:1293706. [PMID: 38646540 PMCID: PMC11027168 DOI: 10.3389/fimmu.2024.1293706] [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: 09/13/2023] [Accepted: 02/19/2024] [Indexed: 04/23/2024] Open
Abstract
Major histocompatibility complex Class II (MHCII) proteins initiate and regulate immune responses by presentation of antigenic peptides to CD4+ T-cells and self-restriction. The interactions between MHCII and peptides determine the specificity of the immune response and are crucial in immunotherapy and cancer vaccine design. With the ever-increasing amount of MHCII-peptide binding data available, many computational approaches have been developed for MHCII-peptide interaction prediction over the last decade. There is thus an urgent need to provide an up-to-date overview and assessment of these newly developed computational methods. To benchmark the prediction performance of these methods, we constructed an independent dataset containing binding and non-binding peptides to 20 human MHCII protein allotypes from the Immune Epitope Database, covering DP, DR and DQ alleles. After collecting 11 known predictors up to January 2022, we evaluated those available through a webserver or standalone packages on this independent dataset. The benchmarking results show that MixMHC2pred and NetMHCIIpan-4.1 achieve the best performance among all predictors. In general, newly developed methods perform better than older ones due to the rapid expansion of data on which they are trained and the development of deep learning algorithms. Our manuscript not only draws a full picture of the state-of-art of MHCII-peptide binding prediction, but also guides researchers in the choice among the different predictors. More importantly, it will inspire biomedical researchers in both academia and industry for the future developments in this field.
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Affiliation(s)
- Yaqing Yang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- National Institute of Health Data Science of China, Shandong University, Jinan, China
| | - Zhonghui Wei
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- National Institute of Health Data Science of China, Shandong University, Jinan, China
| | - Gabriel Cia
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, Brussels, Belgium
- Interuniversity Institute of Bioinformatics in Brussels, Brussels, Belgium
| | - Xixi Song
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- National Institute of Health Data Science of China, Shandong University, Jinan, China
| | - Fabrizio Pucci
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, Brussels, Belgium
- Interuniversity Institute of Bioinformatics in Brussels, Brussels, Belgium
| | - Marianne Rooman
- Computational Biology and Bioinformatics, Université Libre de Bruxelles, Brussels, Belgium
- Interuniversity Institute of Bioinformatics in Brussels, Brussels, Belgium
| | - Fuzhong Xue
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- National Institute of Health Data Science of China, Shandong University, Jinan, China
| | - Qingzhen Hou
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- National Institute of Health Data Science of China, Shandong University, Jinan, China
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7
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Fasoulis R, Rigo MM, Antunes DA, Paliouras G, Kavraki LE. Transfer learning improves pMHC kinetic stability and immunogenicity predictions. IMMUNOINFORMATICS (AMSTERDAM, NETHERLANDS) 2024; 13:100030. [PMID: 38577265 PMCID: PMC10994007 DOI: 10.1016/j.immuno.2023.100030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/06/2024]
Abstract
The cellular immune response comprises several processes, with the most notable ones being the binding of the peptide to the Major Histocompability Complex (MHC), the peptide-MHC (pMHC) presentation to the surface of the cell, and the recognition of the pMHC by the T-Cell Receptor. Identifying the most potent peptide targets for MHC binding, presentation and T-cell recognition is vital for developing peptide-based vaccines and T-cell-based immunotherapies. Data-driven tools that predict each of these steps have been developed, and the availability of mass spectrometry (MS) datasets has facilitated the development of accurate Machine Learning (ML) methods for class-I pMHC binding prediction. However, the accuracy of ML-based tools for pMHC kinetic stability prediction and peptide immunogenicity prediction is uncertain, as stability and immunogenicity datasets are not abundant. Here, we use transfer learning techniques to improve stability and immunogenicity predictions, by taking advantage of a large number of binding affinity and MS datasets. The resulting models, TLStab and TLImm, exhibit comparable or better performance than state-of-the-art approaches on different stability and immunogenicity test sets respectively. Our approach demonstrates the promise of learning from the task of peptide binding to improve predictions on downstream tasks. The source code of TLStab and TLImm is publicly available at https://github.com/KavrakiLab/TL-MHC.
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Affiliation(s)
- Romanos Fasoulis
- Department of Computer Science, Rice University, 6100 Main St, Houston, 77005, TX, United States
| | - Mauricio Menegatti Rigo
- Department of Computer Science, Rice University, 6100 Main St, Houston, 77005, TX, United States
| | - Dinler Amaral Antunes
- Department of Biology and Biochemistry, University of Houston, 4800 Calhoun Rd, Houston, 77004, TX, United States
| | - Georgios Paliouras
- Institute of Informatics and Telecommunications, NCSR Demokritos, Patr. Gregoriou E and 27 Neapoleos St, Athens, 15341, Greece
| | - Lydia E. Kavraki
- Department of Computer Science, Rice University, 6100 Main St, Houston, 77005, TX, United States
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8
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Harris CT, Cohen S. Reducing Immunogenicity by Design: Approaches to Minimize Immunogenicity of Monoclonal Antibodies. BioDrugs 2024; 38:205-226. [PMID: 38261155 PMCID: PMC10912315 DOI: 10.1007/s40259-023-00641-2] [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] [Accepted: 12/13/2023] [Indexed: 01/24/2024]
Abstract
Monoclonal antibodies (mAbs) have transformed therapeutic strategies for various diseases. Their high specificity to target antigens makes them ideal therapeutic agents for certain diseases. However, a challenge to their application in clinical practice is their potential risk to induce unwanted immune response, termed immunogenicity. This challenge drives the continued efforts to deimmunize these protein therapeutics while maintaining their pharmacokinetic properties and therapeutic efficacy. Because mAbs hold a central position in therapeutic strategies against an array of diseases, the importance of conducting comprehensive immunogenicity risk assessment during the drug development process cannot be overstated. Such assessment necessitates the employment of in silico, in vitro, and in vivo strategies to evaluate the immunogenicity risk of mAbs. Understanding the intricacies of the mechanisms that drive mAb immunogenicity is crucial to improving their therapeutic efficacy and safety and developing the most effective strategies to determine and mitigate their immunogenic risk. This review highlights recent advances in immunogenicity prediction strategies, with a focus on protein engineering strategies used throughout development to reduce immunogenicity.
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Affiliation(s)
- Chantal T Harris
- Department of BioAnalytical Sciences, Genentech Inc., South San Francisco, CA, 94080-4990, USA
| | - Sivan Cohen
- Department of BioAnalytical Sciences, Genentech Inc., South San Francisco, CA, 94080-4990, USA.
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9
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Mikhaylov V, Brambley CA, Keller GLJ, Arbuiso AG, Weiss LI, Baker BM, Levine AJ. Accurate modeling of peptide-MHC structures with AlphaFold. Structure 2024; 32:228-241.e4. [PMID: 38113889 PMCID: PMC10872456 DOI: 10.1016/j.str.2023.11.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 08/17/2023] [Accepted: 11/22/2023] [Indexed: 12/21/2023]
Abstract
Major histocompatibility complex (MHC) proteins present peptides on the cell surface for T cell surveillance. Reliable in silico prediction of which peptides would be presented and which T cell receptors would recognize them is an important problem in structural immunology. Here, we introduce an AlphaFold-based pipeline for predicting the three-dimensional structures of peptide-MHC complexes for class I and class II MHC molecules. Our method demonstrates high accuracy, outperforming existing tools in class I modeling accuracy and class II peptide register prediction. We validate its performance and utility with new experimental data on a recently described cancer neoantigen/wild-type peptide pair and explore applications toward improving peptide-MHC binding prediction.
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Affiliation(s)
- Victor Mikhaylov
- The Simons Center for Systems Biology, Institute for Advanced Study, 1 Einstein Drive, Princeton, NJ 08540, USA.
| | - Chad A Brambley
- Department of Chemistry and Biochemistry and the Harper Cancer Research Institute, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Grant L J Keller
- Department of Chemistry and Biochemistry and the Harper Cancer Research Institute, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Alyssa G Arbuiso
- Department of Chemistry and Biochemistry and the Harper Cancer Research Institute, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Laura I Weiss
- Department of Chemistry and Biochemistry and the Harper Cancer Research Institute, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Brian M Baker
- Department of Chemistry and Biochemistry and the Harper Cancer Research Institute, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Arnold J Levine
- The Simons Center for Systems Biology, Institute for Advanced Study, 1 Einstein Drive, Princeton, NJ 08540, USA
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10
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Weingarten-Gabbay S, Chen DY, Sarkizova S, Taylor HB, Gentili M, Hernandez GM, Pearlman LR, Bauer MR, Rice CM, Clauser KR, Hacohen N, Carr SA, Abelin JG, Saeed M, Sabeti PC. The HLA-II immunopeptidome of SARS-CoV-2. Cell Rep 2024; 43:113596. [PMID: 38117652 PMCID: PMC10860710 DOI: 10.1016/j.celrep.2023.113596] [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: 05/15/2023] [Revised: 11/08/2023] [Accepted: 12/01/2023] [Indexed: 12/22/2023] Open
Abstract
Targeted synthetic vaccines have the potential to transform our response to viral outbreaks, yet the design of these vaccines requires a comprehensive knowledge of viral immunogens. Here, we report severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) peptides that are naturally processed and loaded onto human leukocyte antigen-II (HLA-II) complexes in infected cells. We identify over 500 unique viral peptides from canonical proteins as well as from overlapping internal open reading frames. Most HLA-II peptides colocalize with known CD4+ T cell epitopes in coronavirus disease 2019 patients, including 2 reported immunodominant regions in the SARS-CoV-2 membrane protein. Overall, our analyses show that HLA-I and HLA-II pathways target distinct viral proteins, with the structural proteins accounting for most of the HLA-II peptidome and nonstructural and noncanonical proteins accounting for the majority of the HLA-I peptidome. These findings highlight the need for a vaccine design that incorporates multiple viral elements harboring CD4+ and CD8+ T cell epitopes to maximize vaccine effectiveness.
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Affiliation(s)
- Shira Weingarten-Gabbay
- Broad Institute of MIT and Harvard University, Cambridge, MA, USA; Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA; Laboratory of Virology and Infectious Disease, The Rockefeller University, New York, NY, USA.
| | - Da-Yuan Chen
- Department of Biochemistry & Cell Biology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA; National Emerging Infectious Diseases Laboratories, Boston University, Boston, MA, USA
| | | | - Hannah B Taylor
- Broad Institute of MIT and Harvard University, Cambridge, MA, USA
| | - Matteo Gentili
- Broad Institute of MIT and Harvard University, Cambridge, MA, USA
| | | | - Leah R Pearlman
- Broad Institute of MIT and Harvard University, Cambridge, MA, USA
| | - Matthew R Bauer
- Harvard Program in Biological and Biomedical Sciences, Division of Medical Sciences, Harvard University Medical School, Boston, MA, USA
| | - Charles M Rice
- Laboratory of Virology and Infectious Disease, The Rockefeller University, New York, NY, USA
| | - Karl R Clauser
- Broad Institute of MIT and Harvard University, Cambridge, MA, USA
| | - Nir Hacohen
- Broad Institute of MIT and Harvard University, Cambridge, MA, USA; Department of Medicine, Massachusetts General Hospital, Boston, MA, USA; Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
| | - Steven A Carr
- Broad Institute of MIT and Harvard University, Cambridge, MA, USA
| | | | - Mohsan Saeed
- Department of Biochemistry & Cell Biology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA; National Emerging Infectious Diseases Laboratories, Boston University, Boston, MA, USA
| | - Pardis C Sabeti
- Broad Institute of MIT and Harvard University, Cambridge, MA, USA; Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA; Massachusetts Consortium on Pathogen Readiness, Boston, MA, USA; Department of Immunology and Infectious Disease, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Howard Hughes Medical Institute, Chevy Chase, MD, USA
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11
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Bravi B. Development and use of machine learning algorithms in vaccine target selection. NPJ Vaccines 2024; 9:15. [PMID: 38242890 PMCID: PMC10798987 DOI: 10.1038/s41541-023-00795-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 12/07/2023] [Indexed: 01/21/2024] Open
Abstract
Computer-aided discovery of vaccine targets has become a cornerstone of rational vaccine design. In this article, I discuss how Machine Learning (ML) can inform and guide key computational steps in rational vaccine design concerned with the identification of B and T cell epitopes and correlates of protection. I provide examples of ML models, as well as types of data and predictions for which they are built. I argue that interpretable ML has the potential to improve the identification of immunogens also as a tool for scientific discovery, by helping elucidate the molecular processes underlying vaccine-induced immune responses. I outline the limitations and challenges in terms of data availability and method development that need to be addressed to bridge the gap between advances in ML predictions and their translational application to vaccine design.
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Affiliation(s)
- Barbara Bravi
- Department of Mathematics, Imperial College London, London, SW7 2AZ, UK.
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12
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Wallace RP, Refvik KC, Antane JT, Brünggel K, Tremain AC, Raczy MR, Alpar AT, Nguyen M, Solanki A, Slezak AJ, Watkins EA, Lauterbach AL, Cao S, Wilson DS, Hubbell JA. Synthetically mannosylated antigens induce antigen-specific humoral tolerance and reduce anti-drug antibody responses to immunogenic biologics. Cell Rep Med 2024; 5:101345. [PMID: 38128533 PMCID: PMC10829756 DOI: 10.1016/j.xcrm.2023.101345] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 09/21/2023] [Accepted: 11/27/2023] [Indexed: 12/23/2023]
Abstract
Immunogenic biologics trigger an anti-drug antibody (ADA) response in patients that reduces efficacy and increases adverse reactions. Our laboratory has shown that targeting protein antigen to the liver microenvironment can reduce antigen-specific T cell responses; herein, we present a strategy to increase delivery of otherwise immunogenic biologics to the liver via conjugation to a synthetic mannose polymer, p(Man). This delivery leads to reduced antigen-specific T follicular helper cell and B cell responses resulting in diminished ADA production, which is maintained throughout subsequent administrations of the native biologic. We find that p(Man)-antigen treatment impairs the ADA response against recombinant uricase, a highly immunogenic biologic, without a dependence on hapten immunodominance or control by T regulatory cells. We identify increased T cell receptor signaling and increased apoptosis and exhaustion in T cells as effects of p(Man)-antigen treatment via transcriptomic analyses. This modular platform may enhance tolerance to biologics, enabling long-term solutions for an ever-increasing healthcare problem.
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Affiliation(s)
- Rachel P Wallace
- Pritzker School for Molecular Engineering, University of Chicago, Chicago, IL 60637, USA
| | - Kirsten C Refvik
- Pritzker School for Molecular Engineering, University of Chicago, Chicago, IL 60637, USA
| | - Jennifer T Antane
- Pritzker School for Molecular Engineering, University of Chicago, Chicago, IL 60637, USA
| | - Kym Brünggel
- Pritzker School for Molecular Engineering, University of Chicago, Chicago, IL 60637, USA
| | - Andrew C Tremain
- Committee on Immunology, University of Chicago, Chicago, IL 60637, USA
| | - Michal R Raczy
- Pritzker School for Molecular Engineering, University of Chicago, Chicago, IL 60637, USA
| | - Aaron T Alpar
- Pritzker School for Molecular Engineering, University of Chicago, Chicago, IL 60637, USA
| | - Mindy Nguyen
- Animal Resources Center, University of Chicago, Chicago, IL 60637, USA
| | - Ani Solanki
- Animal Resources Center, University of Chicago, Chicago, IL 60637, USA
| | - Anna J Slezak
- Pritzker School for Molecular Engineering, University of Chicago, Chicago, IL 60637, USA
| | - Elyse A Watkins
- Pritzker School for Molecular Engineering, University of Chicago, Chicago, IL 60637, USA
| | - Abigail L Lauterbach
- Pritzker School for Molecular Engineering, University of Chicago, Chicago, IL 60637, USA
| | - Shijie Cao
- Pritzker School for Molecular Engineering, University of Chicago, Chicago, IL 60637, USA
| | - D Scott Wilson
- Pritzker School for Molecular Engineering, University of Chicago, Chicago, IL 60637, USA; Biomedical Engineering Department, Johns Hopkins University, Baltimore, MD 21211, USA.
| | - Jeffrey A Hubbell
- Pritzker School for Molecular Engineering, University of Chicago, Chicago, IL 60637, USA; Committee on Immunology, University of Chicago, Chicago, IL 60637, USA; Committee on Cancer Biology, University of Chicago, Chicago, IL 60637, USA.
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13
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Wongklaew P, Sriswasdi S, Chuangsuwanich E. MHCSeqNet2-improved peptide-class I MHC binding prediction for alleles with low data. Bioinformatics 2024; 40:btad780. [PMID: 38152987 PMCID: PMC10783953 DOI: 10.1093/bioinformatics/btad780] [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: 05/25/2023] [Revised: 12/14/2023] [Accepted: 12/27/2023] [Indexed: 12/29/2023] Open
Abstract
MOTIVATION The binding of a peptide antigen to a Class I major histocompatibility complex (MHC) protein is part of a key process that lets the immune system recognize an infected cell or a cancer cell. This mechanism enabled the development of peptide-based vaccines that can activate the patient's immune response to treat cancers. Hence, the ability of accurately predict peptide-MHC binding is an essential component for prioritizing the best peptides for each patient. However, peptide-MHC binding experimental data for many MHC alleles are still lacking, which limited the accuracy of existing prediction models. RESULTS In this study, we presented an improved version of MHCSeqNet that utilized sub-word-level peptide features, a 3D structure embedding for MHC alleles, and an expanded training dataset to achieve better generalizability on MHC alleles with small amounts of data. Visualization of MHC allele embeddings confirms that the model was able to group alleles with similar binding specificity, including those with no peptide ligand in the training dataset. Furthermore, an external evaluation suggests that MHCSeqNet2 can improve the prioritization of T cell epitopes for MHC alleles with small amount of training data. AVAILABILITY AND IMPLEMENTATION The source code and installation instruction for MHCSeqNet2 are available at https://github.com/cmb-chula/MHCSeqNet2.
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Affiliation(s)
- Patiphan Wongklaew
- Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand
| | - Sira Sriswasdi
- Center of Excellence in Computational Molecular Biology, Division of Research Affairs, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand
- Center for Artificial Intelligence in Medicine, Division of Research Affairs, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand
| | - Ekapol Chuangsuwanich
- Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand
- Center of Excellence in Computational Molecular Biology, Division of Research Affairs, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand
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14
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Fan T, Zhang M, Yang J, Zhu Z, Cao W, Dong C. Therapeutic cancer vaccines: advancements, challenges, and prospects. Signal Transduct Target Ther 2023; 8:450. [PMID: 38086815 PMCID: PMC10716479 DOI: 10.1038/s41392-023-01674-3] [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: 05/14/2023] [Revised: 09/08/2023] [Accepted: 09/19/2023] [Indexed: 12/18/2023] Open
Abstract
With the development and regulatory approval of immune checkpoint inhibitors and adoptive cell therapies, cancer immunotherapy has undergone a profound transformation over the past decades. Recently, therapeutic cancer vaccines have shown promise by eliciting de novo T cell responses targeting tumor antigens, including tumor-associated antigens and tumor-specific antigens. The objective was to amplify and diversify the intrinsic repertoire of tumor-specific T cells. However, the complete realization of these capabilities remains an ongoing pursuit. Therefore, we provide an overview of the current landscape of cancer vaccines in this review. The range of antigen selection, antigen delivery systems development the strategic nuances underlying effective antigen presentation have pioneered cancer vaccine design. Furthermore, this review addresses the current status of clinical trials and discusses their strategies, focusing on tumor-specific immunogenicity and anti-tumor efficacy assessment. However, current clinical attempts toward developing cancer vaccines have not yielded breakthrough clinical outcomes due to significant challenges, including tumor immune microenvironment suppression, optimal candidate identification, immune response evaluation, and vaccine manufacturing acceleration. Therefore, the field is poised to overcome hurdles and improve patient outcomes in the future by acknowledging these clinical complexities and persistently striving to surmount inherent constraints.
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Affiliation(s)
- Ting Fan
- Department of Oncology, East Hospital Affiliated to Tongji University, Tongji University School of Medicine, Shanghai, China
| | - Mingna Zhang
- Postgraduate Training Base, Shanghai East Hospital, Jinzhou Medical University, Shanghai, 200120, China
| | - Jingxian Yang
- Department of Oncology, East Hospital Affiliated to Tongji University, Tongji University School of Medicine, Shanghai, China
| | - Zhounan Zhu
- Department of Oncology, East Hospital Affiliated to Tongji University, Tongji University School of Medicine, Shanghai, China
| | - Wanlu Cao
- Department of Oncology, East Hospital Affiliated to Tongji University, Tongji University School of Medicine, Shanghai, China.
| | - Chunyan Dong
- Department of Oncology, East Hospital Affiliated to Tongji University, Tongji University School of Medicine, Shanghai, China.
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15
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Nilsson JB, Kaabinejadian S, Yari H, Kester MG, van Balen P, Hildebrand WH, Nielsen M. Accurate prediction of HLA class II antigen presentation across all loci using tailored data acquisition and refined machine learning. SCIENCE ADVANCES 2023; 9:eadj6367. [PMID: 38000035 PMCID: PMC10672173 DOI: 10.1126/sciadv.adj6367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 10/25/2023] [Indexed: 11/26/2023]
Abstract
Accurate prediction of antigen presentation by human leukocyte antigen (HLA) class II molecules is crucial for rational development of immunotherapies and vaccines targeting CD4+ T cell activation. So far, most prediction methods for HLA class II antigen presentation have focused on HLA-DR because of limited availability of immunopeptidomics data for HLA-DQ and HLA-DP while not taking into account alternative peptide binding modes. We present an update to the NetMHCIIpan prediction method, which closes the performance gap between all three HLA class II loci. We accomplish this by first integrating large immunopeptidomics datasets describing the HLA class II specificity space across all loci using a refined machine learning framework that accommodates inverted peptide binders. Next, we apply targeted immunopeptidomics assays to generate data that covers additional HLA-DP specificities. The final method, NetMHCIIpan-4.3, achieves high accuracy and molecular coverage across all HLA class II allotypes.
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Affiliation(s)
- Jonas B. Nilsson
- Department of Health Technology, Technical University of Denmark, DK-2800 Lyngby, Denmark
| | - Saghar Kaabinejadian
- Pure MHC LLC, Oklahoma City, OK, USA
- Department of Microbiology and Immunology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Hooman Yari
- Department of Microbiology and Immunology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Michel G. D. Kester
- Department of Hematology, Leiden University Medical Center, Leiden, Netherlands
| | - Peter van Balen
- Department of Hematology, Leiden University Medical Center, Leiden, Netherlands
| | - William H. Hildebrand
- Department of Microbiology and Immunology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Morten Nielsen
- Department of Health Technology, Technical University of Denmark, DK-2800 Lyngby, Denmark
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16
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Federico L, Malone B, Tennøe S, Chaban V, Osen JR, Gainullin M, Smorodina E, Kared H, Akbar R, Greiff V, Stratford R, Clancy T, Munthe LA. Experimental validation of immunogenic SARS-CoV-2 T cell epitopes identified by artificial intelligence. Front Immunol 2023; 14:1265044. [PMID: 38045681 PMCID: PMC10691274 DOI: 10.3389/fimmu.2023.1265044] [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: 07/21/2023] [Accepted: 11/01/2023] [Indexed: 12/05/2023] Open
Abstract
During the COVID-19 pandemic we utilized an AI-driven T cell epitope prediction tool, the NEC Immune Profiler (NIP) to scrutinize and predict regions of T cell immunogenicity (hotspots) from the entire SARS-CoV-2 viral proteome. These immunogenic regions offer potential for the development of universally protective T cell vaccine candidates. Here, we validated and characterized T cell responses to a set of minimal epitopes from these AI-identified universal hotspots. Utilizing a flow cytometry-based T cell activation-induced marker (AIM) assay, we identified 59 validated screening hits, of which 56% (33 peptides) have not been previously reported. Notably, we found that most of these novel epitopes were derived from the non-spike regions of SARS-CoV-2 (Orf1ab, Orf3a, and E). In addition, ex vivo stimulation with NIP-predicted peptides from the spike protein elicited CD8+ T cell response in PBMC isolated from most vaccinated donors. Our data confirm the predictive accuracy of AI platforms modelling bona fide immunogenicity and provide a novel framework for the evaluation of vaccine-induced T cell responses.
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Affiliation(s)
- Lorenzo Federico
- Department of Immunology, Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for B cell Malignancies, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | | | | | - Viktoriia Chaban
- Department of Immunology, Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for B cell Malignancies, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Julie Røkke Osen
- Department of Immunology, Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for B cell Malignancies, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Murat Gainullin
- Department of Immunology, Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for B cell Malignancies, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Eva Smorodina
- Department of Immunology, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Oslo University Hospital, Oslo, Norway
| | - Hassen Kared
- Department of Immunology, Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for B cell Malignancies, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Rahmad Akbar
- Department of Immunology, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Oslo University Hospital, Oslo, Norway
| | - Victor Greiff
- Department of Immunology, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Oslo University Hospital, Oslo, Norway
| | | | | | - Ludvig Andre Munthe
- Department of Immunology, Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for B cell Malignancies, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
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17
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Müller M, Huber F, Arnaud M, Kraemer AI, Altimiras ER, Michaux J, Taillandier-Coindard M, Chiffelle J, Murgues B, Gehret T, Auger A, Stevenson BJ, Coukos G, Harari A, Bassani-Sternberg M. Machine learning methods and harmonized datasets improve immunogenic neoantigen prediction. Immunity 2023; 56:2650-2663.e6. [PMID: 37816353 DOI: 10.1016/j.immuni.2023.09.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 06/26/2023] [Accepted: 09/05/2023] [Indexed: 10/12/2023]
Abstract
The accurate selection of neoantigens that bind to class I human leukocyte antigen (HLA) and are recognized by autologous T cells is a crucial step in many cancer immunotherapy pipelines. We reprocessed whole-exome sequencing and RNA sequencing (RNA-seq) data from 120 cancer patients from two external large-scale neoantigen immunogenicity screening assays combined with an in-house dataset of 11 patients and identified 46,017 somatic single-nucleotide variant mutations and 1,781,445 neo-peptides, of which 212 mutations and 178 neo-peptides were immunogenic. Beyond features commonly used for neoantigen prioritization, factors such as the location of neo-peptides within protein HLA presentation hotspots, binding promiscuity, and the role of the mutated gene in oncogenicity were predictive for immunogenicity. The classifiers accurately predicted neoantigen immunogenicity across datasets and improved their ranking by up to 30%. Besides insights into machine learning methods for neoantigen ranking, we have provided homogenized datasets valuable for developing and benchmarking companion algorithms for neoantigen-based immunotherapies.
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Affiliation(s)
- Markus Müller
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center Bugnon 25A, 1005 Lausanne, Switzerland; Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland; Agora Cancer Research Centre, 1011 Lausanne, Switzerland; SIB Swiss Institute of Bioinformatics, Quartier Sorge, Bâtiment Amphipôle, 1015 Lausanne, Switzerland.
| | - Florian Huber
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center Bugnon 25A, 1005 Lausanne, Switzerland; Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland; Agora Cancer Research Centre, 1011 Lausanne, Switzerland
| | - Marion Arnaud
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center Bugnon 25A, 1005 Lausanne, Switzerland; Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland; Agora Cancer Research Centre, 1011 Lausanne, Switzerland
| | - Anne I Kraemer
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center Bugnon 25A, 1005 Lausanne, Switzerland; Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland; Agora Cancer Research Centre, 1011 Lausanne, Switzerland
| | - Emma Ricart Altimiras
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center Bugnon 25A, 1005 Lausanne, Switzerland; Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland; Agora Cancer Research Centre, 1011 Lausanne, Switzerland
| | - Justine Michaux
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center Bugnon 25A, 1005 Lausanne, Switzerland; Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland; Agora Cancer Research Centre, 1011 Lausanne, Switzerland
| | - Marie Taillandier-Coindard
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center Bugnon 25A, 1005 Lausanne, Switzerland; Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland; Agora Cancer Research Centre, 1011 Lausanne, Switzerland
| | - Johanna Chiffelle
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center Bugnon 25A, 1005 Lausanne, Switzerland; Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland; Agora Cancer Research Centre, 1011 Lausanne, Switzerland
| | - Baptiste Murgues
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center Bugnon 25A, 1005 Lausanne, Switzerland; Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland; Agora Cancer Research Centre, 1011 Lausanne, Switzerland
| | - Talita Gehret
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center Bugnon 25A, 1005 Lausanne, Switzerland; Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland; Agora Cancer Research Centre, 1011 Lausanne, Switzerland
| | - Aymeric Auger
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center Bugnon 25A, 1005 Lausanne, Switzerland; Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland; Agora Cancer Research Centre, 1011 Lausanne, Switzerland
| | - Brian J Stevenson
- Agora Cancer Research Centre, 1011 Lausanne, Switzerland; SIB Swiss Institute of Bioinformatics, Quartier Sorge, Bâtiment Amphipôle, 1015 Lausanne, Switzerland
| | - George Coukos
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center Bugnon 25A, 1005 Lausanne, Switzerland; Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland; Agora Cancer Research Centre, 1011 Lausanne, Switzerland; Center of Experimental Therapeutics, Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland
| | - Alexandre Harari
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center Bugnon 25A, 1005 Lausanne, Switzerland; Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland; Agora Cancer Research Centre, 1011 Lausanne, Switzerland; Center of Experimental Therapeutics, Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland
| | - Michal Bassani-Sternberg
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center Bugnon 25A, 1005 Lausanne, Switzerland; Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland; Agora Cancer Research Centre, 1011 Lausanne, Switzerland; Center of Experimental Therapeutics, Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland.
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18
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Meng W, Schreiber RD, Lichti CF. Recent advances in immunopeptidomic-based tumor neoantigen discovery. Adv Immunol 2023; 160:1-36. [PMID: 38042584 DOI: 10.1016/bs.ai.2023.10.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2023]
Abstract
The role of aberrantly expressed proteins in tumors in driving immune-mediated control of cancer has been well documented for more than five decades. Today, we know that both aberrantly expressed normal proteins as well as mutant proteins (neoantigens) can function as tumor antigens in both humans and mice. Next-generation sequencing (NGS) and high-resolution mass spectrometry (MS) technologies have made significant advances since the early 2010s, enabling detection of rare but clinically relevant neoantigens recognized by T cells. MS profiling of tumor-specific immunopeptidomes remains the most direct method to identify mutant peptides bound to cellular MHC. However, the need for use of large numbers of cells or significant amounts of tumor tissue to achieve neoantigen detection has historically limited the application of MS. Newer, more sensitive MS technologies have recently demonstrated the capacities to detect neoantigens from fewer cells. Here, we highlight recent advancements in immunopeptidomics-based characterization of tumor-specific neoantigens. Various tumor antigen categories and neoantigen identification approaches are also discussed. Furthermore, we summarize recent reports that achieved successful tumor neoantigen detection by MS using a variety of starting materials, MS acquisition modes, and novel ion mobility devices.
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Affiliation(s)
- Wei Meng
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO, United States; The Andrew M. and Jane M. Bursky Center for Human Immunology and Immunotherapy Programs, Washington University School of Medicine, Saint Louis, MO, United States
| | - Robert D Schreiber
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO, United States; The Andrew M. and Jane M. Bursky Center for Human Immunology and Immunotherapy Programs, Washington University School of Medicine, Saint Louis, MO, United States.
| | - Cheryl F Lichti
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, MO, United States; The Andrew M. and Jane M. Bursky Center for Human Immunology and Immunotherapy Programs, Washington University School of Medicine, Saint Louis, MO, United States.
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19
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Boughter CT, Meier-Schellersheim M. An integrated approach to the characterization of immune repertoires using AIMS: An Automated Immune Molecule Separator. PLoS Comput Biol 2023; 19:e1011577. [PMID: 37862356 PMCID: PMC10619816 DOI: 10.1371/journal.pcbi.1011577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 11/01/2023] [Accepted: 10/06/2023] [Indexed: 10/22/2023] Open
Abstract
The adaptive immune system employs an array of receptors designed to respond with high specificity to pathogens or molecular aberrations faced by the host organism. Binding of these receptors to molecular fragments-collectively referred to as antigens-initiates immune responses. These antigenic targets are recognized in their native state on the surfaces of pathogens by antibodies, whereas T cell receptors (TCR) recognize processed antigens as short peptides, presented on major histocompatibility complex (MHC) molecules. Recent research has led to a wealth of immune repertoire data that are key to interrogating the nature of these molecular interactions. However, existing tools for the analysis of these large datasets typically focus on molecular sets of a single type, forcing researchers to separately analyze strongly coupled sequences of interacting molecules. Here, we introduce a software package for the integrated analysis of immune repertoire data, capable of identifying distinct biophysical differences in isolated TCR, MHC, peptide, antibody, and antigen sequence data. This integrated analytical approach allows for direct comparisons across immune repertoire subsets and provides a starting point for the identification of key interaction hotspots in complementary receptor-antigen pairs. The software (AIMS-Automated Immune Molecule Separator) is freely available as an open access package in GUI or command-line form.
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Affiliation(s)
- Christopher T. Boughter
- Computational Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Martin Meier-Schellersheim
- Computational Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, United States of America
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20
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Hartman K, Steiner G, Siegel M, Looney CM, Hickling TP, Bray-French K, Springer S, Marban-Doran C, Ducret A. Expanding the MAPPs Assay to Accommodate MHC-II Pan Receptors for Improved Predictability of Potential T Cell Epitopes. BIOLOGY 2023; 12:1265. [PMID: 37759665 PMCID: PMC10525474 DOI: 10.3390/biology12091265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Revised: 09/08/2023] [Accepted: 09/11/2023] [Indexed: 09/29/2023]
Abstract
A critical step in the immunogenicity cascade is attributed to human leukocyte antigen (HLA) II presentation triggering T cell immune responses. The liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based major histocompatibility complex (MHC) II-associated peptide proteomics (MAPPs) assay is implemented during preclinical risk assessments to identify biotherapeutic-derived T cell epitopes. Although studies indicate that HLA-DP and HLA-DQ alleles are linked to immunogenicity, most MAPPs studies are restricted to using HLA-DR as the dominant HLA II genotype due to the lack of well-characterized immunoprecipitating antibodies. Here, we address this issue by testing various commercially available clones of MHC-II pan (CR3/43, WR18, and Tü39), HLA-DP (B7/21), and HLA-DQ (SPV-L3 and 1a3) antibodies in the MAPPs assay, and characterizing identified peptides according to binding specificity. Our results reveal that HLA II receptor-precipitating reagents with similar reported specificities differ based on clonality and that MHC-II pan antibodies do not entirely exhibit pan-specific tendencies. Since no individual antibody clone is able to recover the complete HLA II peptide repertoire, we recommend a mixed strategy of clones L243, WR18, and SPV-L3 in a single immunoprecipitation step for more robust compound-specific peptide detection. Ultimately, our optimized MAPPs strategy improves the predictability and additional identification of T cell epitopes in immunogenicity risk assessments.
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Affiliation(s)
- Katharina Hartman
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Grenzacherstrasse 124, 4070 Basel, Switzerland (C.M.L.)
| | - Guido Steiner
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Grenzacherstrasse 124, 4070 Basel, Switzerland (C.M.L.)
| | - Michel Siegel
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Grenzacherstrasse 124, 4070 Basel, Switzerland (C.M.L.)
| | - Cary M. Looney
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Grenzacherstrasse 124, 4070 Basel, Switzerland (C.M.L.)
| | - Timothy P. Hickling
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Grenzacherstrasse 124, 4070 Basel, Switzerland (C.M.L.)
| | - Katharine Bray-French
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Grenzacherstrasse 124, 4070 Basel, Switzerland (C.M.L.)
| | - Sebastian Springer
- School of Science, Department of Biochemistry and Cell Biology, Constructor University, Campus Ring 1, 28759 Bremen, Germany
| | - Céline Marban-Doran
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Grenzacherstrasse 124, 4070 Basel, Switzerland (C.M.L.)
| | - Axel Ducret
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Grenzacherstrasse 124, 4070 Basel, Switzerland (C.M.L.)
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21
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Pu T, Peddle A, Zhu J, Tejpar S, Verbandt S. Neoantigen identification: Technological advances and challenges. Methods Cell Biol 2023; 183:265-302. [PMID: 38548414 DOI: 10.1016/bs.mcb.2023.06.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/02/2024]
Abstract
Neoantigens have emerged as promising targets for cutting-edge immunotherapies, such as cancer vaccines and adoptive cell therapy. These neoantigens are unique to tumors and arise exclusively from somatic mutations or non-genomic aberrations in tumor proteins. They encompass a wide range of alterations, including genomic mutations, post-transcriptomic variants, and viral oncoproteins. With the advancements in technology, the identification of immunogenic neoantigens has seen rapid progress, raising new opportunities for enhancing their clinical significance. Prediction of neoantigens necessitates the acquisition of high-quality samples and sequencing data, followed by mutation calling. Subsequently, the pipeline involves integrating various tools that can predict the expression, processing, binding, and recognition potential of neoantigens. However, the continuous improvement of computational tools is constrained by the availability of datasets which contain validated immunogenic neoantigens. This review article aims to provide a comprehensive summary of the current knowledge as well as limitations in neoantigen prediction and validation. Additionally, it delves into the origin and biological role of neoantigens, offering a deeper understanding of their significance in the field of cancer immunotherapy. This article thus seeks to contribute to the ongoing efforts to harness neoantigens as powerful weapons in the fight against cancer.
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Affiliation(s)
- Ting Pu
- Digestive Oncology Unit, KULeuven, Leuven, Belgium
| | | | - Jingjing Zhu
- de Duve Institute, Université catholique de Louvain, Brussels, Belgium
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22
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Wallace RP, Refvik KC, Antane JT, Brünggel K, Tremain AC, Raczy MR, Alpar AT, Nguyen M, Solanki A, Slezak AJ, Watkins EA, Lauterbach AL, Cao S, Wilson DS, Hubbell JA. Synthetically mannosylated antigens induce antigen-specific humoral tolerance and reduce anti-drug antibody responses to immunogenic biologics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.07.534593. [PMID: 37066302 PMCID: PMC10104138 DOI: 10.1101/2023.04.07.534593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
Immunogenic biologics trigger an anti-drug antibody (ADA) response in patients, which reduces efficacy and increases adverse reactions. Our laboratory has previously shown that targeting protein antigen to the liver microenvironment can reduce antigen-specific T cell responses; herein, we present a strategy to increase delivery of otherwise immunogenic biologics to the liver via conjugation to a synthetic mannose polymer (p(Man)). This delivery leads to reduced antigen-specific T follicular helper cell and B cell responses resulting in diminished ADA production, which is maintained throughout subsequent administrations of the native biologic. We found that p(Man)-antigen treatment impairs the ADA response against recombinant uricase, a highly immunogenic biologic, without a dependence on hapten immunodominance or control by Tregs. We identify increased TCR signaling and increased apoptosis and exhaustion in T cells as effects of p(Man)-antigen treatment via transcriptomic analyses. This modular platform may enhance tolerance to biologics, enabling long-term solutions for an ever-increasing healthcare problem.
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Prensner JR, Abelin JG, Kok LW, Clauser KR, Mudge JM, Ruiz-Orera J, Bassani-Sternberg M, Moritz RL, Deutsch EW, van Heesch S. What Can Ribo-Seq, Immunopeptidomics, and Proteomics Tell Us About the Noncanonical Proteome? Mol Cell Proteomics 2023; 22:100631. [PMID: 37572790 PMCID: PMC10506109 DOI: 10.1016/j.mcpro.2023.100631] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Revised: 07/21/2023] [Accepted: 08/08/2023] [Indexed: 08/14/2023] Open
Abstract
Ribosome profiling (Ribo-Seq) has proven transformative for our understanding of the human genome and proteome by illuminating thousands of noncanonical sites of ribosome translation outside the currently annotated coding sequences (CDSs). A conservative estimate suggests that at least 7000 noncanonical ORFs are translated, which, at first glance, has the potential to expand the number of human protein CDSs by 30%, from ∼19,500 annotated CDSs to over 26,000 annotated CDSs. Yet, additional scrutiny of these ORFs has raised numerous questions about what fraction of them truly produce a protein product and what fraction of those can be understood as proteins according to conventional understanding of the term. Adding further complication is the fact that published estimates of noncanonical ORFs vary widely by around 30-fold, from several thousand to several hundred thousand. The summation of this research has left the genomics and proteomics communities both excited by the prospect of new coding regions in the human genome but searching for guidance on how to proceed. Here, we discuss the current state of noncanonical ORF research, databases, and interpretation, focusing on how to assess whether a given ORF can be said to be "protein coding."
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Affiliation(s)
- John R Prensner
- Division of Pediatric Hematology/Oncology, Department of Pediatrics, University of Michigan Medical School, Ann Arbor, Michigan, USA; Department of Biological Chemistry, University of Michigan Medical School, Ann Arbor, Michigan, USA.
| | | | - Leron W Kok
- Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands
| | - Karl R Clauser
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Jonathan M Mudge
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, UK
| | - Jorge Ruiz-Orera
- Cardiovascular and Metabolic Sciences, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | - Michal Bassani-Sternberg
- Ludwig Institute for Cancer Research, Agora Center Bugnon 25A, University of Lausanne, Lausanne, Switzerland; Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland; Agora Cancer Research Centre, Lausanne, Switzerland
| | - Robert L Moritz
- Institute for Systems Biology (ISB), Seattle, Washington, USA
| | - Eric W Deutsch
- Institute for Systems Biology (ISB), Seattle, Washington, USA
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Li J, Xiao Z, Wang D, Jia L, Nie S, Zeng X, Hu W. The screening, identification, design and clinical application of tumor-specific neoantigens for TCR-T cells. Mol Cancer 2023; 22:141. [PMID: 37649123 PMCID: PMC10466891 DOI: 10.1186/s12943-023-01844-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 08/16/2023] [Indexed: 09/01/2023] Open
Abstract
Recent advances in neoantigen research have accelerated the development of tumor immunotherapies, including adoptive cell therapies (ACTs), cancer vaccines and antibody-based therapies, particularly for solid tumors. With the development of next-generation sequencing and bioinformatics technology, the rapid identification and prediction of tumor-specific antigens (TSAs) has become possible. Compared with tumor-associated antigens (TAAs), highly immunogenic TSAs provide new targets for personalized tumor immunotherapy and can be used as prospective indicators for predicting tumor patient survival, prognosis, and immune checkpoint blockade response. Here, the identification and characterization of neoantigens and the clinical application of neoantigen-based TCR-T immunotherapy strategies are summarized, and the current status, inherent challenges, and clinical translational potential of these strategies are discussed.
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Affiliation(s)
- Jiangping Li
- Division of Thoracic Tumor Multimodality Treatment, Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, People's Republic of China.
| | - Zhiwen Xiao
- Department of Otolaryngology Head and Neck Surgery, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510655, People's Republic of China
| | - Donghui Wang
- Department of Radiation Oncology, The Third Affiliated Hospital Sun Yat-Sen University, Guangzhou, 510630, People's Republic of China
| | - Lei Jia
- International Health Medicine Innovation Center, Shenzhen University, Shenzhen, 518060, People's Republic of China
| | - Shihong Nie
- Department of Radiation Oncology, West China Hospital, Sichuan University, Cancer Center, Chengdu, 610041, People's Republic of China
| | - Xingda Zeng
- Department of Parasitology of Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Wei Hu
- Division of Vascular Surgery, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, People's Republic of China
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25
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Tian J, Ma J. The Value of Microbes in Cancer Neoantigen Immunotherapy. Pharmaceutics 2023; 15:2138. [PMID: 37631352 PMCID: PMC10459105 DOI: 10.3390/pharmaceutics15082138] [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: 07/17/2023] [Revised: 08/06/2023] [Accepted: 08/11/2023] [Indexed: 08/27/2023] Open
Abstract
Tumor neoantigens are widely used in cancer immunotherapy, and a growing body of research suggests that microbes play an important role in these neoantigen-based immunotherapeutic processes. The human body and its surrounding environment are filled with a large number of microbes that are in long-term interaction with the organism. The microbiota can modulate our immune system, help activate neoantigen-reactive T cells, and play a great role in the process of targeting tumor neoantigens for therapy. Recent studies have revealed the interconnection between microbes and neoantigens, which can cross-react with each other through molecular mimicry, providing theoretical guidance for more relevant studies. The current applications of microbes in immunotherapy against tumor neoantigens are mainly focused on cancer vaccine development and immunotherapy with immune checkpoint inhibitors. This article summarizes the related fields and suggests the importance of microbes in immunotherapy against neoantigens.
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Affiliation(s)
- Junrui Tian
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer Metabolism, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410013, China;
- Cancer Research Institute and School of Basic Medical Science, Central South University, Changsha 410078, China
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Hunan Key Laboratory of Nonresolving Inflammation and Cancer, Changsha 410078, China
| | - Jian Ma
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer Metabolism, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410013, China;
- Cancer Research Institute and School of Basic Medical Science, Central South University, Changsha 410078, China
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Hunan Key Laboratory of Nonresolving Inflammation and Cancer, Changsha 410078, China
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26
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Macy AM, Herrmann LM, Adams AC, Hastings KT. Major histocompatibility complex class II in the tumor microenvironment: functions of nonprofessional antigen-presenting cells. Curr Opin Immunol 2023; 83:102330. [PMID: 37130456 PMCID: PMC10524529 DOI: 10.1016/j.coi.2023.102330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 03/30/2023] [Accepted: 04/02/2023] [Indexed: 05/04/2023]
Abstract
Major histocompatibility complex class-II-restricted presentation by nonprofessional antigen-presenting cells in the tumor microenvironment can regulate antitumor T-cell responses. In murine models, tumor cell-specific MHC class II expression decreases in vivo tumor growth, dependent on T cells. Tumor cell-specific MHC class II expression is associated with improved survival and response to immune checkpoint inhibitors in human cancers. Antigen-presenting cancer-associated fibroblasts (apCAF) present MHC class-II-restricted antigens and activate CD4 T cells. The role of MHC class II on apCAFs depends on the cell of origin. MHC class II on tumoral lymphatic endothelial cells leads to expansion of regulatory T cells and increased in vivo tumor growth.
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Affiliation(s)
- Anne M Macy
- University of Arizona College of Medicine Phoenix, 425 N. 5th St., Phoenix, AZ 85004, USA; Phoenix Veterans Affairs Health Care System, 650 E. Indian School Rd., Phoenix, AZ 85023, USA
| | - Lauren M Herrmann
- University of Arizona College of Medicine Phoenix, 425 N. 5th St., Phoenix, AZ 85004, USA; Phoenix Veterans Affairs Health Care System, 650 E. Indian School Rd., Phoenix, AZ 85023, USA
| | - Anngela C Adams
- University of Arizona College of Medicine Phoenix, 425 N. 5th St., Phoenix, AZ 85004, USA; Phoenix Veterans Affairs Health Care System, 650 E. Indian School Rd., Phoenix, AZ 85023, USA
| | - K Taraszka Hastings
- University of Arizona College of Medicine Phoenix, 425 N. 5th St., Phoenix, AZ 85004, USA; Phoenix Veterans Affairs Health Care System, 650 E. Indian School Rd., Phoenix, AZ 85023, USA; University of Arizona Cancer Center, University of Arizona, 1515 N. Campbell Ave., Tucson, AZ 85724, USA.
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27
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Zhu D, Zhou M, Zhang H, Gong L, Hu J, Luo H, Zhou X. Network analysis identifies a gene biomarker panel for sepsis-induced acute respiratory distress syndrome. BMC Med Genomics 2023; 16:165. [PMID: 37443002 PMCID: PMC10339646 DOI: 10.1186/s12920-023-01595-8] [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: 10/26/2022] [Accepted: 06/28/2023] [Indexed: 07/15/2023] Open
Abstract
BACKGROUND Acute respiratory distress syndrome (ARDS) is characterized by non-cardiogenic pulmonary edema caused by inflammation, which can lead to serious respiratory complications. Due to the high mortality of ARDS caused by sepsis, biological markers that enable early diagnosis are urgently needed for clinical treatment. METHODS In the present study, we used the public microarray data of whole blood from patients with sepsis-induced ARDS, patients with sepsis-alone and healthy controls to perform an integrated analysis based on differential expressed genes (DEGs) and co-expression network to identify the key genes and pathways related to the development of sepsis into ARDS that may be key targets for diagnosis and treatment. RESULTS Compared with controls, we identified 180 DEGs in the sepsis-alone group and 152 DEGs in the sepsis-induced ARDS group. About 70% of these genes were unique to the two groups. Functional analysis of DEGs showed that neutrophil-mediated inflammation and mitochondrial dysfunction are the main features of ARDS induced by sepsis. Gene network analysis identified key modules and screened out key regulatory genes related to ARDS. The key genes and their upstream regulators comprised a gene panel, including EOMES, LTF, CSF1R, HLA-DRA, IRF8 and MPEG1. Compared with the healthy controls, the panel had an area under the curve (AUC) of 0.900 and 0.914 for sepsis-alone group and sepsis-induced ARDS group, respectively. The AUC was 0.746 between the sepsis-alone group and sepsis-induced ARDS group. Moreover, the panel of another independent blood transcriptional expression profile dataset showed the AUC was 0.769 in diagnosing sepsis-alone group and sepsis-induced ARDS group. CONCLUSIONS Taken together, our method contributes to the diagnosis of sepsis and sepsis-induced ARDS. The biological pathway involved in this gene biomarker panel may also be a critical target in combating ARDS caused by sepsis.
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Affiliation(s)
- Duan Zhu
- Department of Respiratory and Critical Care Medicine, the First Affiliated Hospital of Army Medical University (Southwest Hospital), No.30 Gaotanyan Main Street, Chongqing, 400038, China
| | - Mi Zhou
- Department of Biochemistry and Molecular Biology, Army Medical University, Chongqing, China
| | - Houli Zhang
- Department of Respiratory and Critical Care Medicine, the First Affiliated Hospital of Army Medical University (Southwest Hospital), No.30 Gaotanyan Main Street, Chongqing, 400038, China
| | - Liang Gong
- Department of Respiratory and Critical Care Medicine, the First Affiliated Hospital of Army Medical University (Southwest Hospital), No.30 Gaotanyan Main Street, Chongqing, 400038, China
| | - Jianlin Hu
- Department of Respiratory and Critical Care Medicine, the First Affiliated Hospital of Army Medical University (Southwest Hospital), No.30 Gaotanyan Main Street, Chongqing, 400038, China
| | - Hu Luo
- Department of Respiratory and Critical Care Medicine, the First Affiliated Hospital of Army Medical University (Southwest Hospital), No.30 Gaotanyan Main Street, Chongqing, 400038, China.
| | - Xiangdong Zhou
- Department of Respiratory and Critical Care Medicine, the First Affiliated Hospital of Army Medical University (Southwest Hospital), No.30 Gaotanyan Main Street, Chongqing, 400038, China.
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28
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Stražar M, Park J, Abelin JG, Taylor HB, Pedersen TK, Plichta DR, Brown EM, Eraslan B, Hung YM, Ortiz K, Clauser KR, Carr SA, Xavier RJ, Graham DB. HLA-II immunopeptidome profiling and deep learning reveal features of antigenicity to inform antigen discovery. Immunity 2023; 56:1681-1698.e13. [PMID: 37301199 PMCID: PMC10519123 DOI: 10.1016/j.immuni.2023.05.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 02/08/2023] [Accepted: 05/11/2023] [Indexed: 06/12/2023]
Abstract
CD4+ T cell responses are exquisitely antigen specific and directed toward peptide epitopes displayed by human leukocyte antigen class II (HLA-II) on antigen-presenting cells. Underrepresentation of diverse alleles in ligand databases and an incomplete understanding of factors affecting antigen presentation in vivo have limited progress in defining principles of peptide immunogenicity. Here, we employed monoallelic immunopeptidomics to identify 358,024 HLA-II binders, with a particular focus on HLA-DQ and HLA-DP. We uncovered peptide-binding patterns across a spectrum of binding affinities and enrichment of structural antigen features. These aspects underpinned the development of context-aware predictor of T cell antigens (CAPTAn), a deep learning model that predicts peptide antigens based on their affinity to HLA-II and full sequence of their source proteins. CAPTAn was instrumental in discovering prevalent T cell epitopes from bacteria in the human microbiome and a pan-variant epitope from SARS-CoV-2. Together CAPTAn and associated datasets present a resource for antigen discovery and the unraveling genetic associations of HLA alleles with immunopathologies.
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Affiliation(s)
- Martin Stražar
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Jihye Park
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | | | - Hannah B Taylor
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Thomas K Pedersen
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Technical University of Denmark, Kongens Lyngby, Denmark
| | | | - Eric M Brown
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Computational and Integrative Biology, Department of Molecular Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Basak Eraslan
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Yuan-Mao Hung
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Computational and Integrative Biology, Department of Molecular Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Kayla Ortiz
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Karl R Clauser
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Steven A Carr
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Ramnik J Xavier
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Computational and Integrative Biology, Department of Molecular Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
| | - Daniel B Graham
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Center for Computational and Integrative Biology, Department of Molecular Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA; Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
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29
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Stutzmann C, Peng J, Wu Z, Savoie C, Sirois I, Thibault P, Wheeler AR, Caron E. Unlocking the potential of microfluidics in mass spectrometry-based immunopeptidomics for tumor antigen discovery. CELL REPORTS METHODS 2023; 3:100511. [PMID: 37426761 PMCID: PMC10326451 DOI: 10.1016/j.crmeth.2023.100511] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
The identification of tumor-specific antigens (TSAs) is critical for developing effective cancer immunotherapies. Mass spectrometry (MS)-based immunopeptidomics has emerged as a powerful tool for identifying TSAs as physical molecules. However, current immunopeptidomics platforms face challenges in measuring low-abundance TSAs in a precise, sensitive, and reproducible manner from small needle-tissue biopsies (<1 mg). Inspired by recent advances in single-cell proteomics, microfluidics technology offers a promising solution to these limitations by providing improved isolation of human leukocyte antigen (HLA)-associated peptides with higher sensitivity. In this context, we highlight the challenges in sample preparation and the rationale for developing microfluidics technology in immunopeptidomics. Additionally, we provide an overview of promising microfluidic methods, including microchip pillar arrays, valved-based systems, droplet microfluidics, and digital microfluidics, and discuss the latest research on their application in MS-based immunopeptidomics and single-cell proteomics.
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Affiliation(s)
| | - Jiaxi Peng
- Department of Chemistry, University of Toronto, Toronto, ON, Canada
| | - Zhaoguan Wu
- CHU Sainte Justine Research Center, Montreal, QC, Canada
| | | | | | - Pierre Thibault
- Institute for Research in Immunology and Cancer, University of Montreal, Montreal, QC, Canada
- Department of Chemistry, University of Montreal, Montreal, QC, Canada
| | - Aaron R. Wheeler
- Department of Chemistry, University of Toronto, Toronto, ON, Canada
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada
| | - Etienne Caron
- CHU Sainte Justine Research Center, Montreal, QC, Canada
- Department of Pathology and Cellular Biology, University of Montreal, Montreal, QC, Canada
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30
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Weingarten-Gabbay S, Chen DY, Sarkizova S, Taylor HB, Gentili M, Pearlman LR, Bauer MR, Rice CM, Clauser KR, Hacohen N, Carr SA, Abelin JG, Saeed M, Sabeti PC. The HLA-II immunopeptidome of SARS-CoV-2. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.26.542482. [PMID: 37398281 PMCID: PMC10312465 DOI: 10.1101/2023.05.26.542482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Targeted synthetic vaccines have the potential to transform our response to viral outbreaks; yet the design of these vaccines requires a comprehensive knowledge of viral immunogens, including T-cell epitopes. Having previously mapped the SARS-CoV-2 HLA-I landscape, here we report viral peptides that are naturally processed and loaded onto HLA-II complexes in infected cells. We identified over 500 unique viral peptides from canonical proteins, as well as from overlapping internal open reading frames (ORFs), revealing, for the first time, the contribution of internal ORFs to the HLA-II peptide repertoire. Most HLA-II peptides co-localized with the known CD4+ T cell epitopes in COVID-19 patients. We also observed that two reported immunodominant regions in the SARS-CoV-2 membrane protein are formed at the level of HLA-II presentation. Overall, our analyses show that HLA-I and HLA-II pathways target distinct viral proteins, with the structural proteins accounting for most of the HLA-II peptidome and non-structural and non-canonical proteins accounting for the majority of the HLA-I peptidome. These findings highlight the need for a vaccine design that incorporates multiple viral elements harboring CD4+ and CD8+ T cell epitopes to maximize the vaccine effectiveness.
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31
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Huff AL, Zaidi N. Vaccine boosts T cells that target pancreatic tumours. Nature 2023; 618:37-38. [PMID: 37165219 PMCID: PMC10548444 DOI: 10.1038/d41586-023-01526-8] [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] [Indexed: 05/12/2023]
Abstract
Efforts to tackle pancreatic cancer by harnessing immune cells have had limited success. A clinical trial reports promising results from testing a personalized approach to boosting immune responses to such tumours. See p.144
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32
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Arieta CM, Xie YJ, Rothenberg DA, Diao H, Harjanto D, Meda S, Marquart K, Koenitzer B, Sciuto TE, Lobo A, Zuiani A, Krumm SA, Cadima Couto CI, Hein S, Heinen AP, Ziegenhals T, Liu-Lupo Y, Vogel AB, Srouji JR, Fesser S, Thanki K, Walzer K, Addona TA, Türeci Ö, Şahin U, Gaynor RB, Poran A. The T-cell-directed vaccine BNT162b4 encoding conserved non-spike antigens protects animals from severe SARS-CoV-2 infection. Cell 2023; 186:2392-2409.e21. [PMID: 37164012 PMCID: PMC10099181 DOI: 10.1016/j.cell.2023.04.007] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 03/12/2023] [Accepted: 04/05/2023] [Indexed: 05/12/2023]
Abstract
T cell responses play an important role in protection against beta-coronavirus infections, including SARS-CoV-2, where they associate with decreased COVID-19 disease severity and duration. To enhance T cell immunity across epitopes infrequently altered in SARS-CoV-2 variants, we designed BNT162b4, an mRNA vaccine component that is intended to be combined with BNT162b2, the spike-protein-encoding vaccine. BNT162b4 encodes variant-conserved, immunogenic segments of the SARS-CoV-2 nucleocapsid, membrane, and ORF1ab proteins, targeting diverse HLA alleles. BNT162b4 elicits polyfunctional CD4+ and CD8+ T cell responses to diverse epitopes in animal models, alone or when co-administered with BNT162b2 while preserving spike-specific immunity. Importantly, we demonstrate that BNT162b4 protects hamsters from severe disease and reduces viral titers following challenge with viral variants. These data suggest that a combination of BNT162b2 and BNT162b4 could reduce COVID-19 disease severity and duration caused by circulating or future variants. BNT162b4 is currently being clinically evaluated in combination with the BA.4/BA.5 Omicron-updated bivalent BNT162b2 (NCT05541861).
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Affiliation(s)
| | - Yushu Joy Xie
- BioNTech US, 40 Erie Street, Cambridge, MA 02139, USA
| | | | - Huitian Diao
- BioNTech US, 40 Erie Street, Cambridge, MA 02139, USA
| | - Dewi Harjanto
- BioNTech US, 40 Erie Street, Cambridge, MA 02139, USA
| | - Shirisha Meda
- BioNTech US, 40 Erie Street, Cambridge, MA 02139, USA
| | | | | | | | | | - Adam Zuiani
- BioNTech US, 40 Erie Street, Cambridge, MA 02139, USA
| | | | | | | | | | | | | | | | - John R Srouji
- BioNTech US, 40 Erie Street, Cambridge, MA 02139, USA
| | | | | | | | | | - Özlem Türeci
- BioNTech SE, An der Goldgrube 12, 55131 Mainz, Germany; HI-TRON - Helmholtz Institute for Translational Oncology Mainz by DKFZ, Obere Zahlbacherstr. 63, 55131 Mainz, Germany
| | - Uğur Şahin
- BioNTech SE, An der Goldgrube 12, 55131 Mainz, Germany; TRON gGmbH - Translational Oncology at the University Medical Center of the Johannes Gutenberg University, Freiligrathstraße 12, 55131 Mainz, Germany
| | | | - Asaf Poran
- BioNTech US, 40 Erie Street, Cambridge, MA 02139, USA.
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33
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Prensner JR, Abelin JG, Kok LW, Clauser KR, Mudge JM, Ruiz-Orera J, Bassani-Sternberg M, Deutsch EW, van Heesch S. What can Ribo-seq and proteomics tell us about the non-canonical proteome? BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.16.541049. [PMID: 37292611 PMCID: PMC10245706 DOI: 10.1101/2023.05.16.541049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Ribosome profiling (Ribo-seq) has proven transformative for our understanding of the human genome and proteome by illuminating thousands of non-canonical sites of ribosome translation outside of the currently annotated coding sequences (CDSs). A conservative estimate suggests that at least 7,000 non-canonical open reading frames (ORFs) are translated, which, at first glance, has the potential to expand the number of human protein-coding sequences by 30%, from ∼19,500 annotated CDSs to over 26,000. Yet, additional scrutiny of these ORFs has raised numerous questions about what fraction of them truly produce a protein product and what fraction of those can be understood as proteins according to conventional understanding of the term. Adding further complication is the fact that published estimates of non-canonical ORFs vary widely by around 30-fold, from several thousand to several hundred thousand. The summation of this research has left the genomics and proteomics communities both excited by the prospect of new coding regions in the human genome, but searching for guidance on how to proceed. Here, we discuss the current state of non-canonical ORF research, databases, and interpretation, focusing on how to assess whether a given ORF can be said to be "protein-coding". In brief The human genome encodes thousands of non-canonical open reading frames (ORFs) in addition to protein-coding genes. As a nascent field, many questions remain regarding non-canonical ORFs. How many exist? Do they encode proteins? What level of evidence is needed for their verification? Central to these debates has been the advent of ribosome profiling (Ribo-seq) as a method to discern genome-wide ribosome occupancy, and immunopeptidomics as a method to detect peptides that are processed and presented by MHC molecules and not observed in traditional proteomics experiments. This article provides a synthesis of the current state of non-canonical ORF research and proposes standards for their future investigation and reporting. Highlights Combined use of Ribo-seq and proteomics-based methods enables optimal confidence in detecting non-canonical ORFs and their protein products.Ribo-seq can provide more sensitive detection of non-canonical ORFs, but data quality and analytical pipelines will impact results.Non-canonical ORF catalogs are diverse and span both high-stringency and low-stringency ORF nominations.A framework for standardized non-canonical ORF evidence will advance the research field.
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Affiliation(s)
- John R. Prensner
- Department of Pediatrics, Division of Pediatric Hematology/Oncology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | | | - Leron W. Kok
- Princess Máxima Center for Pediatric Oncology, Heidelberglaan 25, 3584 CS, Utrecht, the Netherlands
| | - Karl R. Clauser
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Jonathan M. Mudge
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Jorge Ruiz-Orera
- Cardiovascular and Metabolic Sciences, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125 Berlin, Germany
| | - Michal Bassani-Sternberg
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center Bugnon 25A, 1005 Lausanne, Switzerland
- Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland
- Agora Cancer Research Centre, 1011 Lausanne, Switzerland
| | - Eric W. Deutsch
- Institute for Systems Biology (ISB), Seattle, Washington 98109, USA
| | - Sebastiaan van Heesch
- Princess Máxima Center for Pediatric Oncology, Heidelberglaan 25, 3584 CS, Utrecht, the Netherlands
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34
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Admon A. The biogenesis of the immunopeptidome. Semin Immunol 2023; 67:101766. [PMID: 37141766 DOI: 10.1016/j.smim.2023.101766] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 04/26/2023] [Accepted: 04/26/2023] [Indexed: 05/06/2023]
Abstract
The immunopeptidome is the repertoire of peptides bound and presented by the MHC class I, class II, and non-classical molecules. The peptides are produced by the degradation of most cellular proteins, and in some cases, peptides are produced from extracellular proteins taken up by the cells. This review attempts to first describe some of its known and well-accepted concepts, and next, raise some questions about a few of the established dogmas in this field: The production of novel peptides by splicing is questioned, suggesting here that spliced peptides are extremely rare, if existent at all. The degree of the contribution to the immunopeptidome by degradation of cellular protein by the proteasome is doubted, therefore this review attempts to explain why it is likely that this contribution to the immunopeptidome is possibly overstated. The contribution of defective ribosome products (DRiPs) and non-canonical peptides to the immunopeptidome is noted and methods are suggested to quantify them. In addition, the common misconception that the MHC class II peptidome is mostly derived from extracellular proteins is noted, and corrected. It is stressed that the confirmation of sequence assignments of non-canonical and spliced peptides should rely on targeted mass spectrometry using spiking-in of heavy isotope-labeled peptides. Finally, the new methodologies and modern instrumentation currently available for high throughput kinetics and quantitative immunopeptidomics are described. These advanced methods open up new possibilities for utilizing the big data generated and taking a fresh look at the established dogmas and reevaluating them critically.
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Affiliation(s)
- Arie Admon
- Faculty of Biology, Technion-Israel Institute of Technology, Israel.
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35
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Phulphagar KM, Ctortecka C, Jacome ASV, Klaeger S, Verzani EK, Hernandez GM, Udeshi ND, Clauser KR, Abelin JG, Carr SA. Sensitive, high-throughput HLA-I and HLA-II immunopeptidomics using parallel accumulation-serial fragmentation mass spectrometry. Mol Cell Proteomics 2023:100563. [PMID: 37142057 DOI: 10.1016/j.mcpro.2023.100563] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 04/21/2023] [Accepted: 04/24/2023] [Indexed: 05/06/2023] Open
Abstract
Comprehensive, in-depth identification of the human leukocyte antigen HLA-I and HLA-II tumor immunopeptidome can inform the development of cancer immunotherapies. Mass spectrometry (MS) is powerful technology for direct identification of HLA peptides from patient derived tumor samples or cell lines. However, achieving sufficient coverage to detect rare, clinically relevant antigens requires highly sensitive MS-based acquisition methods and large amounts of sample. While immunopeptidome depth can be increased by off-line fractionation prior to MS, its use is impractical when analyzing limited amounts of primary tissue biopsies. To address this challenge, we developed and applied a high throughput, sensitive, single-shot MS-based immunopeptidomics workflow that leverages trapped ion mobility time-of-flight mass spectrometry on the Bruker timsTOF SCP. We demonstrate >2-fold improved coverage of HLA immunopeptidomes relative to prior methods with up to 15,000 distinct HLA-I and HLA-II peptides from 4e7 cells. Our optimized single-shot MS acquisition method on the timsTOF SCP maintains high coverage, eliminates the need for off-line fractionation and reduces input requirements to as few as 1e6 A375 cells for > 800 distinct HLA-I peptides. This depth is sufficient to identify HLA-I peptides derived from cancer-testis antigen, and non-canonical proteins. We also apply our optimized single-shot SCP acquisition methods to tumor derived samples, enabling sensitive, high throughput and reproducible immunopeptidome profiling with detection of clinically relevant peptides from less than 4e7 cells or 15 mg wet weight tissue.
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36
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Oreper D, Klaeger S, Jhunjhunwala S, Delamarre L. The peptide woods are lovely, dark and deep: Hunting for novel cancer antigens. Semin Immunol 2023; 67:101758. [PMID: 37027981 DOI: 10.1016/j.smim.2023.101758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Revised: 03/22/2023] [Accepted: 03/22/2023] [Indexed: 04/08/2023]
Abstract
Harnessing the patient's immune system to control a tumor is a proven avenue for cancer therapy. T cell therapies as well as therapeutic vaccines, which target specific antigens of interest, are being explored as treatments in conjunction with immune checkpoint blockade. For these therapies, selecting the best suited antigens is crucial. Most of the focus has thus far been on neoantigens that arise from tumor-specific somatic mutations. Although there is clear evidence that T-cell responses against mutated neoantigens are protective, the large majority of these mutations are not immunogenic. In addition, most somatic mutations are unique to each individual patient and their targeting requires the development of individualized approaches. Therefore, novel antigen types are needed to broaden the scope of such treatments. We review high throughput approaches for discovering novel tumor antigens and some of the key challenges associated with their detection, and discuss considerations when selecting tumor antigens to target in the clinic.
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Affiliation(s)
- Daniel Oreper
- Genentech, 1 DNA way, South San Francisco, 94080 CA, USA.
| | - Susan Klaeger
- Genentech, 1 DNA way, South San Francisco, 94080 CA, USA.
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37
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Abelin JG, Bergstrom EJ, Rivera KD, Taylor HB, Klaeger S, Xu C, Verzani EK, Jackson White C, Woldemichael HB, Virshup M, Olive ME, Maynard M, Vartany SA, Allen JD, Phulphagar K, Harry Kane M, Rachimi S, Mani DR, Gillette MA, Satpathy S, Clauser KR, Udeshi ND, Carr SA. Workflow enabling deepscale immunopeptidome, proteome, ubiquitylome, phosphoproteome, and acetylome analyses of sample-limited tissues. Nat Commun 2023; 14:1851. [PMID: 37012232 PMCID: PMC10070353 DOI: 10.1038/s41467-023-37547-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 03/20/2023] [Indexed: 04/05/2023] Open
Abstract
Serial multi-omic analysis of proteome, phosphoproteome, and acetylome provides insights into changes in protein expression, cell signaling, cross-talk and epigenetic pathways involved in disease pathology and treatment. However, ubiquitylome and HLA peptidome data collection used to understand protein degradation and antigen presentation have not together been serialized, and instead require separate samples for parallel processing using distinct protocols. Here we present MONTE, a highly sensitive multi-omic native tissue enrichment workflow, that enables serial, deep-scale analysis of HLA-I and HLA-II immunopeptidome, ubiquitylome, proteome, phosphoproteome, and acetylome from the same tissue sample. We demonstrate that the depth of coverage and quantitative precision of each 'ome is not compromised by serialization, and the addition of HLA immunopeptidomics enables the identification of peptides derived from cancer/testis antigens and patient specific neoantigens. We evaluate the technical feasibility of the MONTE workflow using a small cohort of patient lung adenocarcinoma tumors.
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Affiliation(s)
- Jennifer G Abelin
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA.
| | - Erik J Bergstrom
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - Keith D Rivera
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - Hannah B Taylor
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - Susan Klaeger
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - Charles Xu
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - Eva K Verzani
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - C Jackson White
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - Hilina B Woldemichael
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - Maya Virshup
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - Meagan E Olive
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - Myranda Maynard
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - Stephanie A Vartany
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - Joseph D Allen
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - Kshiti Phulphagar
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - M Harry Kane
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - Suzanna Rachimi
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - D R Mani
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - Michael A Gillette
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
- Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Shankha Satpathy
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - Karl R Clauser
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA
| | - Namrata D Udeshi
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA.
| | - Steven A Carr
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, 02142, USA.
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38
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Racle J, Guillaume P, Schmidt J, Michaux J, Larabi A, Lau K, Perez MAS, Croce G, Genolet R, Coukos G, Zoete V, Pojer F, Bassani-Sternberg M, Harari A, Gfeller D. Machine learning predictions of MHC-II specificities reveal alternative binding mode of class II epitopes. Immunity 2023:S1074-7613(23)00129-2. [PMID: 37023751 DOI: 10.1016/j.immuni.2023.03.009] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 11/09/2022] [Accepted: 03/15/2023] [Indexed: 04/08/2023]
Abstract
CD4+ T cells orchestrate the adaptive immune response against pathogens and cancer by recognizing epitopes presented on class II major histocompatibility complex (MHC-II) molecules. The high polymorphism of MHC-II genes represents an important hurdle toward accurate prediction and identification of CD4+ T cell epitopes. Here we collected and curated a dataset of 627,013 unique MHC-II ligands identified by mass spectrometry. This enabled us to precisely determine the binding motifs of 88 MHC-II alleles across humans, mice, cattle, and chickens. Analysis of these binding specificities combined with X-ray crystallography refined our understanding of the molecular determinants of MHC-II motifs and revealed a widespread reverse-binding mode in HLA-DP ligands. We then developed a machine-learning framework to accurately predict binding specificities and ligands of any MHC-II allele. This tool improves and expands predictions of CD4+ T cell epitopes and enables us to discover viral and bacterial epitopes following the aforementioned reverse-binding mode.
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Affiliation(s)
- Julien Racle
- 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.
| | - Philippe Guillaume
- 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
| | - 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
| | - Justine Michaux
- 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; Center of Experimental Therapeutics, Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Amédé Larabi
- School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Kelvin Lau
- School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Marta A S Perez
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland; Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland; Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
| | - 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
| | - Raphaël Genolet
- 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
| | - George Coukos
- 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
| | - Vincent Zoete
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland; Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland; Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
| | - Florence Pojer
- School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Michal Bassani-Sternberg
- 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; Center of Experimental Therapeutics, Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), 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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39
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ElAbd H, Bacher P, Tholey A, Lenz TL, Franke A. Challenges and opportunities in analyzing and modeling peptide presentation by HLA-II proteins. Front Immunol 2023; 14:1107266. [PMID: 37063883 PMCID: PMC10090296 DOI: 10.3389/fimmu.2023.1107266] [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: 11/24/2022] [Accepted: 03/13/2023] [Indexed: 03/31/2023] Open
Abstract
The human leukocyte antigen (HLA) proteins are an indispensable component of adaptive immunity because of their role in presenting self and foreign peptides to T cells. Further, many complex diseases are associated with genetic variation in the HLA region, implying an important role for specific HLA-presented peptides in the etiology of these diseases. Identifying the specific set of peptides presented by an individual’s HLA proteins in vivo, as a whole being referred to as the immunopeptidome, has therefore gathered increasing attention for different reasons. For example, identifying neoepitopes for cancer immunotherapy, vaccine development against infectious pathogens, or elucidating the role of HLA in autoimmunity. Despite the tremendous progress made during the last decade in these areas, several questions remain unanswered. In this perspective, we highlight five remaining key challenges in the analysis of peptide presentation and T cell immunogenicity and discuss potential solutions to these problems. We believe that addressing these questions would not only improve our understanding of disease etiology but will also have a direct translational impact in terms of engineering better vaccines and in developing more potent immunotherapies.
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Affiliation(s)
- Hesham ElAbd
- Institute of Clinical Molecular Biology, University of Kiel, Kiel, Germany
| | - Petra Bacher
- Institute of Clinical Molecular Biology, University of Kiel, Kiel, Germany
- Institute of Immunology, University of Kiel, Kiel, Germany
| | - Andreas Tholey
- Proteomics & Bioanalytics, Institute for Experimental Medicine, University of Kiel, Kiel, Germany
| | - Tobias L. Lenz
- Research Unit for Evolutionary Immunogenomics, Department of Biology, University of Hamburg, Hamburg, Germany
| | - Andre Franke
- Institute of Clinical Molecular Biology, University of Kiel, Kiel, Germany
- *Correspondence: Andre Franke,
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40
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Ibtehaz N, Sourav SMSH, Bayzid MS, Rahman MS. Align-gram: Rethinking the Skip-gram Model for Protein Sequence Analysis. Protein J 2023; 42:135-146. [PMID: 36977849 DOI: 10.1007/s10930-023-10096-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/13/2023] [Indexed: 03/29/2023]
Abstract
The inception of next generations sequencing technologies have exponentially increased the volume of biological sequence data. Protein sequences, being quoted as the 'language of life', has been analyzed for a multitude of applications and inferences. Owing to the rapid development of deep learning, in recent years there have been a number of breakthroughs in the domain of Natural Language Processing. Since these methods are capable of performing different tasks when trained with a sufficient amount of data, off-the-shelf models are used to perform various biological applications. In this study, we investigated the applicability of the popular Skip-gram model for protein sequence analysis and made an attempt to incorporate some biological insights into it. We propose a novel k-mer embedding scheme, Align-gram, which is capable of mapping the similar k-mers close to each other in a vector space. Furthermore, we experiment with other sequence-based protein representations and observe that the embeddings derived from Align-gram aids modeling and training deep learning models better. Our experiments with a simple baseline LSTM model and a much complex CNN model of DeepGoPlus shows the potential of Align-gram in performing different types of deep learning applications for protein sequence analysis.
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41
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CD4 + T cells in cancer. NATURE CANCER 2023; 4:317-329. [PMID: 36894637 DOI: 10.1038/s43018-023-00521-2] [Citation(s) in RCA: 62] [Impact Index Per Article: 62.0] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 01/20/2023] [Indexed: 03/11/2023]
Abstract
Cancer immunology and immunotherapy are driving forces of research and development in oncology, mostly focusing on CD8+ T cells and the tumor microenvironment. Recent progress highlights the importance of CD4+ T cells, corresponding to the long-known fact that CD4+ T cells are central players and coordinators of innate and antigen-specific immune responses. Moreover, they have now been recognized as anti-tumor effector cells in their own right. Here we review the current status of CD4+ T cells in cancer, which hold great promise for improving knowledge and therapies in cancer.
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42
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Mikhaylov V, Levine AJ. Accurate modeling of peptide-MHC structures with AlphaFold. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.06.531396. [PMID: 36945436 PMCID: PMC10028922 DOI: 10.1101/2023.03.06.531396] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
Abstract
Major histocompatibility complex (MHC) proteins present peptides on the cell surface for T-cell surveillance. Reliable in silico prediction of which peptides would be presented and which T-cell receptors would recognize them is an important problem in structural immunology. Here, we introduce an AlphaFold-based pipeline for predicting the three-dimensional structures of peptide-MHC complexes for class I and class II MHC molecules. Our method demonstrates high accuracy, outperforming existing tools in class I modeling precision and class II peptide register prediction. We explore applications of this method towards improving peptide-MHC binding prediction.
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Affiliation(s)
- Victor Mikhaylov
- Institute for Advanced Study, 1 Einstein Dr., Princeton, NJ 08540
| | - Arnold J Levine
- Institute for Advanced Study, 1 Einstein Dr., Princeton, NJ 08540
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43
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Li T, Li Y, Zhu X, He Y, Wu Y, Ying T, Xie Z. Artificial intelligence in cancer immunotherapy: Applications in neoantigen recognition, antibody design and immunotherapy response prediction. Semin Cancer Biol 2023; 91:50-69. [PMID: 36870459 DOI: 10.1016/j.semcancer.2023.02.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 02/13/2023] [Accepted: 02/28/2023] [Indexed: 03/06/2023]
Abstract
Cancer immunotherapy is a method of controlling and eliminating tumors by reactivating the body's cancer-immunity cycle and restoring its antitumor immune response. The increased availability of data, combined with advancements in high-performance computing and innovative artificial intelligence (AI) technology, has resulted in a rise in the use of AI in oncology research. State-of-the-art AI models for functional classification and prediction in immunotherapy research are increasingly used to support laboratory-based experiments. This review offers a glimpse of the current AI applications in immunotherapy, including neoantigen recognition, antibody design, and prediction of immunotherapy response. Advancing in this direction will result in more robust predictive models for developing better targets, drugs, and treatments, and these advancements will eventually make their way into the clinical setting, pushing AI forward in the field of precision oncology.
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Affiliation(s)
- Tong Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Yupeng Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Xiaoyi Zhu
- MOE/NHC Key Laboratory of Medical Molecular Virology, Shanghai Institute of Infectious Disease and Biosecurity, School of Basic Medical Sciences, Shanghai Medical College, Fudan University, Shanghai, China; Shanghai Engineering Research Center for Synthetic Immunology, Shanghai, China
| | - Yao He
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Yanling Wu
- MOE/NHC Key Laboratory of Medical Molecular Virology, Shanghai Institute of Infectious Disease and Biosecurity, School of Basic Medical Sciences, Shanghai Medical College, Fudan University, Shanghai, China; Shanghai Engineering Research Center for Synthetic Immunology, Shanghai, China
| | - Tianlei Ying
- MOE/NHC Key Laboratory of Medical Molecular Virology, Shanghai Institute of Infectious Disease and Biosecurity, School of Basic Medical Sciences, Shanghai Medical College, Fudan University, Shanghai, China; Shanghai Engineering Research Center for Synthetic Immunology, Shanghai, China.
| | - Zhi Xie
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China; Center for Precision Medicine, Sun Yat-sen University, Guangzhou, China.
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44
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Shapiro IE, Bassani-Sternberg M. The impact of immunopeptidomics: From basic research to clinical implementation. Semin Immunol 2023; 66:101727. [PMID: 36764021 DOI: 10.1016/j.smim.2023.101727] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 01/30/2023] [Accepted: 02/01/2023] [Indexed: 02/10/2023]
Abstract
The immunopeptidome is the set of peptides presented by the major histocompatibility complex (MHC) molecules, in humans also known as the human leukocyte antigen (HLA), on the surface of cells that mediate T-cell immunosurveillance. The immunopeptidome is a sampling of the cellular proteome and hence it contains information about the health state of cells. The peptide repertoire is influenced by intra- and extra-cellular perturbations - such as in the case of drug exposure, infection, or oncogenic transformation. Immunopeptidomics is the bioanalytical method by which the presented peptides are extracted from biological samples and analyzed by high-performance liquid chromatography coupled to tandem mass spectrometry (MS), resulting in a deep qualitative and quantitative snapshot of the immunopeptidome. In this review, we discuss published immunopeptidomics studies from recent years, grouped into three main domains: i) basic, ii) pre-clinical and iii) clinical research and applications. We review selected fundamental immunopeptidomics studies on the antigen processing and presentation machinery, on HLA restriction and studies that advanced our understanding of various diseases, and how exploration of the antigenic landscape allowed immune targeting at the pre-clinical stage, paving the way to pioneering exploratory clinical trials where immunopeptidomics is directly implemented in the conception of innovative treatments for cancer patients.
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Affiliation(s)
- Ilja E Shapiro
- Ludwig Institute for Cancer Research, University of Lausanne, 1005 Lausanne, Switzerland; Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), 1005 Lausanne, Switzerland; Agora Cancer Research Centre, 1011 Lausanne, Switzerland
| | - Michal Bassani-Sternberg
- Ludwig Institute for Cancer Research, University of Lausanne, 1005 Lausanne, Switzerland; Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), 1005 Lausanne, Switzerland; Agora Cancer Research Centre, 1011 Lausanne, Switzerland; Center of Experimental Therapeutics, Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), 1005 Lausanne, Switzerland.
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45
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Huisman BD, Balivada PA, Birnbaum ME. Yeast display platform with expression of linear peptide epitopes for high-throughput assessment of peptide-MHC-II binding. J Biol Chem 2023; 299:102913. [PMID: 36649909 PMCID: PMC9971316 DOI: 10.1016/j.jbc.2023.102913] [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: 08/10/2022] [Revised: 01/06/2023] [Accepted: 01/09/2023] [Indexed: 01/15/2023] Open
Abstract
Yeast display can serve as a powerful tool to assess the binding of peptides to the major histocompatibility complex (pMHC) and pMHC-T-cell receptor binding. However, this approach is often limited by the need to optimize MHC proteins for yeast surface expression, which can be laborious and may not yield productive results. Here we present a second-generation yeast display platform for class II MHC molecules (MHC-II), which decouples MHC-II expression from yeast-expressed peptides, referred to as "peptide display." Peptide display obviates the need for yeast-specific MHC optimizations and increases the scale of MHC-II alleles available for use in yeast display screens. Because MHC identity is separated from the peptide library, a further benefit of this platform is the ability to assess a single library of peptides against any MHC-II. We demonstrate the utility of the peptide display platform across MHC-II proteins, screening HLA-DR, HLA-DP, and HLA-DQ alleles. We further explore parameters of selections, including reagent dependencies, MHC avidity, and use of competitor peptides. In summary, this approach presents an advance in the throughput and accessibility of screening peptide-MHC-II binding.
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Affiliation(s)
- Brooke D Huisman
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, USA; Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, USA
| | - Pallavi A Balivada
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, USA; Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, USA
| | - Michael E Birnbaum
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, USA; Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, USA; Ragon Institute of MGH, MIT and Harvard, Cambridge, USA.
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46
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Contemplating immunopeptidomes to better predict them. Semin Immunol 2023; 66:101708. [PMID: 36621290 DOI: 10.1016/j.smim.2022.101708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 12/16/2022] [Accepted: 12/20/2022] [Indexed: 01/09/2023]
Abstract
The identification of T-cell epitopes is key for a complete molecular understanding of immune recognition mechanisms in infectious diseases, autoimmunity and cancer. T-cell epitopes further provide targets for personalized vaccines and T-cell therapy, with several therapeutic applications in cancer immunotherapy and elsewhere. T-cell epitopes consist of short peptides displayed on Major Histocompatibility Complex (MHC) molecules. The recent advances in mass spectrometry (MS) based technologies to profile the ensemble of peptides displayed on MHC molecules - the so-called immunopeptidome - had a major impact on our understanding of antigen presentation and MHC ligands. On the one hand, these techniques enabled researchers to directly identify hundreds of thousands of peptides presented on MHC molecules, including some that elicited T-cell recognition. On the other hand, the data collected in these experiments revealed fundamental properties of antigen presentation pathways and significantly improved our ability to predict naturally presented MHC ligands and T-cell epitopes across the wide spectrum of MHC alleles found in human and other organisms. Here we review recent computational developments to analyze experimentally determined immunopeptidomes and harness these data to improve our understanding of antigen presentation and MHC binding specificities, as well as our ability to predict MHC ligands. We further discuss the strengths and limitations of the latest approaches to move beyond predictions of antigen presentation and tackle the challenges of predicting TCR recognition and immunogenicity.
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47
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Proof-of-Concept Analysis of B Cell Receptor Repertoire in COVID-19 Patients Undergoing ECMO by Single-Cell V(D)J and Gene Expression Sequencing. Curr Issues Mol Biol 2023; 45:1471-1482. [PMID: 36826040 PMCID: PMC9955795 DOI: 10.3390/cimb45020095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 02/06/2023] [Accepted: 02/08/2023] [Indexed: 02/12/2023] Open
Abstract
SARS-CoV-2, which causes COVID-19, has altered human activities all over the world and has become a global hazard to public health. Despite considerable advancements in pandemic containment techniques, in which vaccination played a key role, COVID-19 remains a global threat, particularly for frail patients and unvaccinated individuals, who may be more susceptible to developing ARDS. Several studies reported that patients with COVID-19-related ARDS who were treated with ECMO had a similar survival rate to those with COVID-19-unrelated ARDS. In order to shed light on the potential mechanisms underlying the COVID-19 infection, we conducted this proof-of-concept study using single-cell V(D)J and gene expression sequencing of B cells to examine the dynamic changes in the transcriptomic BCR repertoire present in patients with COVID-19 at various stages. We compared a recovered and a deceased COVID-19 patient supported by ECMO with one COVID-19-recovered patient who did not receive ECMO treatment and one healthy subject who had never been infected previously. Our analysis revealed a downregulation of FXYD, HLA-DRB1, and RPS20 in memory B cells; MTATP8 and HLA-DQA1 in naïve cells; RPS4Y1 in activated B cells; and IGHV3-73 in plasma cells in COVID-19 patients. We further described an increased ratio of IgA + IgG to IgD + IgM, suggestive of an intensive memory antibody response, in the COVID ECMO D patient. Finally, we assessed a V(D)J rearrangement of heavy chain IgHV3, IGHJ4, and IGHD3/IGHD2 families in COVID-19 patients regardless of the severity of the disease.
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48
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Brightman SE, Naradikian MS, Thota RR, Becker A, Montero L, Bahmanof M, Premlal ALR, Greenbaum JA, Peters B, Cohen EE, Miller AM, Schoenberger SP. Tumor cells fail to present MHC-II-restricted epitopes derived from oncogenes to CD4+ T cells. JCI Insight 2023; 8:165570. [PMID: 36512410 PMCID: PMC9977289 DOI: 10.1172/jci.insight.165570] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 12/07/2022] [Indexed: 12/15/2022] Open
Abstract
CD4+ T cells play a critical role in antitumor immunity via recognition of peptide antigens presented on MHC class II (MHC-II). Although some solid cancers can be induced to express MHC-II, the extent to which this enables direct recognition by tumor-specific CD4+ T cells is unclear. We isolated and characterized T cell antigen receptors (TCRs) from naturally primed CD4+ T cells specific for 2 oncoproteins, HPV-16 E6 and the activating KRASG12V mutation, from patients with head and neck squamous cell carcinoma and pancreatic ductal adenocarcinoma, respectively, and determined their ability to recognize autologous or human leukocyte antigen-matched antigen-expressing tumor cells. We found in both cases that the TCRs were capable of recognizing peptide-loaded target cells expressing the relevant MHC-II or B cell antigen-presenting cells (APCs) when the antigens were endogenously expressed and directed to the endosomal pathway but failed to recognize tumor cells expressing the source protein even after induction of surface MHC-II expression by IFN-γ or transduction with CIITA. These results suggest that priming and functional recognition of both a nuclear (E6) and a membrane-associated (KRAS) oncoprotein are predominantly confined to crosspresenting APCs rather than via direct recognition of tumor cells induced to express MHC-II.
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Affiliation(s)
- Spencer E. Brightman
- Division of Developmental Immunology, La Jolla Institute for Immunology, La Jolla, California, USA.,Biomedical Sciences Program, School of Medicine, UCSD, La Jolla, California, USA
| | - Martin S. Naradikian
- Division of Developmental Immunology, La Jolla Institute for Immunology, La Jolla, California, USA.,Division of Hematology and Oncology, UCSD Moores Cancer Center, La Jolla, California, USA.,Novartis, San Diego, California, USA
| | - Rukman R. Thota
- Division of Developmental Immunology, La Jolla Institute for Immunology, La Jolla, California, USA
| | - Angelica Becker
- Division of Developmental Immunology, La Jolla Institute for Immunology, La Jolla, California, USA.,IconOVir Bio, San Diego, California, USA
| | - Leslie Montero
- Division of Developmental Immunology, La Jolla Institute for Immunology, La Jolla, California, USA
| | - Milad Bahmanof
- Division of Developmental Immunology, La Jolla Institute for Immunology, La Jolla, California, USA
| | | | | | - Bjoern Peters
- Division of Vaccine Discovery, La Jolla Institute for Immunology, La Jolla, California, USA.,Department of Medicine, UCSD, La Jolla, California, USA
| | - Ezra E.W. Cohen
- Division of Hematology and Oncology, UCSD Moores Cancer Center, La Jolla, California, USA
| | - Aaron M. Miller
- Division of Developmental Immunology, La Jolla Institute for Immunology, La Jolla, California, USA.,Division of Hematology and Oncology, UCSD Moores Cancer Center, La Jolla, California, USA
| | - Stephen P. Schoenberger
- Division of Developmental Immunology, La Jolla Institute for Immunology, La Jolla, California, USA
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49
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Vyasamneni R, Kohler V, Karki B, Mahimkar G, Esaulova E, McGee J, Kallin D, Sheen JH, Harjanto D, Kirsch M, Poran A, Dong J, Srinivasan L, Gaynor RB, Bushway ME, Srouji JR. A universal MHCII technology platform to characterize antigen-specific CD4 + T cells. CELL REPORTS METHODS 2023; 3:100388. [PMID: 36814840 PMCID: PMC9939426 DOI: 10.1016/j.crmeth.2022.100388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 11/08/2022] [Accepted: 12/20/2022] [Indexed: 01/15/2023]
Abstract
CD4+ T cells are critical to the immune system and perform multiple functions; therefore, their identification and characterization are crucial to better understanding the immune system in both health and disease states. However, current methods rarely preserve their ex vivo phenotype, thus limiting our understanding of their in vivo functions. Here we introduce a flexible, rapid, and robust platform for ex vivo CD4+ T cell identification. By combining MHCII allele purification, allele-independent peptide loading, and multiplexed flow cytometry technologies, we can enable high-throughput personalized CD4+ T cell identification, immunophenotyping, and sorting. Using this platform in combination with single-cell sorting and multimodal analyses, we identified and characterized antigen-specific CD4+ T cells relevant to COVID-19 and cancer neoantigen immunotherapy. Overall, our platform can be used to detect and characterize CD4+ T cells across multiple diseases, with potential to guide CD4+ T cell epitope design for any disease-specific immunization strategy.
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Affiliation(s)
| | | | - Binisha Karki
- BioNTech US, Inc., 40 Erie Street, Cambridge, MA 02139, USA
| | - Gauri Mahimkar
- BioNTech US, Inc., 40 Erie Street, Cambridge, MA 02139, USA
| | | | - Jonathan McGee
- BioNTech US, Inc., 40 Erie Street, Cambridge, MA 02139, USA
| | - Daniel Kallin
- BioNTech US, Inc., 40 Erie Street, Cambridge, MA 02139, USA
| | | | - Dewi Harjanto
- BioNTech US, Inc., 40 Erie Street, Cambridge, MA 02139, USA
| | - Miles Kirsch
- BioNTech US, Inc., 40 Erie Street, Cambridge, MA 02139, USA
| | - Asaf Poran
- BioNTech US, Inc., 40 Erie Street, Cambridge, MA 02139, USA
| | - Jesse Dong
- BioNTech US, Inc., 40 Erie Street, Cambridge, MA 02139, USA
| | | | | | | | - John R. Srouji
- BioNTech US, Inc., 40 Erie Street, Cambridge, MA 02139, USA
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50
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Cai Y, Chen R, Gao S, Li W, Liu Y, Su G, Song M, Jiang M, Jiang C, Zhang X. Artificial intelligence applied in neoantigen identification facilitates personalized cancer immunotherapy. Front Oncol 2023; 12:1054231. [PMID: 36698417 PMCID: PMC9868469 DOI: 10.3389/fonc.2022.1054231] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 12/16/2022] [Indexed: 01/10/2023] Open
Abstract
The field of cancer neoantigen investigation has developed swiftly in the past decade. Predicting novel and true neoantigens derived from large multi-omics data became difficult but critical challenges. The rise of Artificial Intelligence (AI) or Machine Learning (ML) in biomedicine application has brought benefits to strengthen the current computational pipeline for neoantigen prediction. ML algorithms offer powerful tools to recognize the multidimensional nature of the omics data and therefore extract the key neoantigen features enabling a successful discovery of new neoantigens. The present review aims to outline the significant technology progress of machine learning approaches, especially the newly deep learning tools and pipelines, that were recently applied in neoantigen prediction. In this review article, we summarize the current state-of-the-art tools developed to predict neoantigens. The standard workflow includes calling genetic variants in paired tumor and blood samples, and rating the binding affinity between mutated peptide, MHC (I and II) and T cell receptor (TCR), followed by characterizing the immunogenicity of tumor epitopes. More specifically, we highlight the outstanding feature extraction tools and multi-layer neural network architectures in typical ML models. It is noted that more integrated neoantigen-predicting pipelines are constructed with hybrid or combined ML algorithms instead of conventional machine learning models. In addition, the trends and challenges in further optimizing and integrating the existing pipelines are discussed.
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Affiliation(s)
- Yu Cai
- School of Medicine, Northwest University, Xi’an, Shaanxi, China
| | - Rui Chen
- School of Medicine, Northwest University, Xi’an, Shaanxi, China
| | - Shenghan Gao
- School of Medicine, Northwest University, Xi’an, Shaanxi, China
| | - Wenqing Li
- School of Medicine, Northwest University, Xi’an, Shaanxi, China
| | - Yuru Liu
- School of Medicine, Northwest University, Xi’an, Shaanxi, China
| | - Guodong Su
- School of Medicine, Northwest University, Xi’an, Shaanxi, China
| | - Mingming Song
- School of Medicine, Northwest University, Xi’an, Shaanxi, China
| | - Mengju Jiang
- School of Medicine, Northwest University, Xi’an, Shaanxi, China
| | - Chao Jiang
- Department of Neurology, The Second Affiliated Hospital of Xi’an Medical University, Xi’an, Shaanxi, China,*Correspondence: Chao Jiang, ; Xi Zhang,
| | - Xi Zhang
- School of Medicine, Northwest University, Xi’an, Shaanxi, China,*Correspondence: Chao Jiang, ; Xi Zhang,
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