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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: 16] [Impact Index Per Article: 16.0] [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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Pastor Y, Ghazzaui N, Hammoudi A, Centlivre M, Cardinaud S, Levy Y. Refining the DC-targeting vaccination for preventing emerging infectious diseases. Front Immunol 2022; 13:949779. [PMID: 36016929 PMCID: PMC9396646 DOI: 10.3389/fimmu.2022.949779] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Accepted: 07/14/2022] [Indexed: 11/26/2022] Open
Abstract
The development of safe, long-term, effective vaccines is still a challenge for many infectious diseases. Thus, the search of new vaccine strategies and production platforms that allow rapidly and effectively responding against emerging or reemerging pathogens has become a priority in the last years. Targeting the antigens directly to dendritic cells (DCs) has emerged as a new approach to enhance the immune response after vaccination. This strategy is based on the fusion of the antigens of choice to monoclonal antibodies directed against specific DC surface receptors such as CD40. Since time is essential, in silico approaches are of high interest to select the most immunogenic and conserved epitopes to improve the T- and B-cells responses. The purpose of this review is to present the advances in DC vaccination, with special focus on DC targeting vaccines and epitope mapping strategies and provide a new framework for improving vaccine responses against infectious diseases.
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Affiliation(s)
- Yadira Pastor
- Vaccine Research Institute, Université Paris-Est Créteil, Institut Mondor de Recherche Biomédicale, Inserm U955, Team 16, Créteil, France
| | - Nour Ghazzaui
- Vaccine Research Institute, Université Paris-Est Créteil, Institut Mondor de Recherche Biomédicale, Inserm U955, Team 16, Créteil, France
| | - Adele Hammoudi
- Vaccine Research Institute, Université Paris-Est Créteil, Institut Mondor de Recherche Biomédicale, Inserm U955, Team 16, Créteil, France
| | - Mireille Centlivre
- Vaccine Research Institute, Université Paris-Est Créteil, Institut Mondor de Recherche Biomédicale, Inserm U955, Team 16, Créteil, France
| | - Sylvain Cardinaud
- Vaccine Research Institute, Université Paris-Est Créteil, Institut Mondor de Recherche Biomédicale, Inserm U955, Team 16, Créteil, France
| | - Yves Levy
- Vaccine Research Institute, Université Paris-Est Créteil, Institut Mondor de Recherche Biomédicale, Inserm U955, Team 16, Créteil, France
- Assistance Publique-Hôpitaux de Paris, Groupe Henri-Mondor Albert-Chenevier, Service Immunologie Clinique, Créteil, France
- *Correspondence: Yves Levy,
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Mohammadzadeh R, Soleimanpour S, Pishdadian A, Farsiani H. Designing and development of epitope-based vaccines against Helicobacter pylori. Crit Rev Microbiol 2021; 48:489-512. [PMID: 34559599 DOI: 10.1080/1040841x.2021.1979934] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Helicobacter pylori infection is the principal cause of serious diseases (e.g. gastric cancer and peptic ulcers). Antibiotic therapy is an inadequate strategy in H. pylori eradication because of which vaccination is an inevitable approach. Despite the presence of countless vaccine candidates, current vaccines in clinical trials have performed with poor efficacy which makes vaccination extremely challenging. Remarkable advancements in immunology and pathogenic biology have provided an appropriate opportunity to develop various epitope-based vaccines. The fusion of proper antigens involved in different aspects of H. pylori colonization and pathogenesis as well as peptide linkers and built-in adjuvants results in producing epitope-based vaccines with excellent therapeutic efficacy and negligible adverse effects. Difficulties of the in vitro culture of H. pylori, high genetic variation, and unfavourable immune responses against feeble epitopes in the complete antigen are major drawbacks of current vaccine strategies that epitope-based vaccines may overcome. Besides decreasing the biohazard risk, designing precise formulations, saving time and cost, and induction of maximum immunity with minimum adverse effects are the advantages of epitope-based vaccines. The present article is a comprehensive review of strategies for designing and developing epitope-based vaccines to provide insights into the innovative vaccination against H. pylori.
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Affiliation(s)
- Roghayeh Mohammadzadeh
- Antimicrobial Resistance Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.,Department of Microbiology and Virology, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Saman Soleimanpour
- Antimicrobial Resistance Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.,Reference Tuberculosis Laboratory, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Abbas Pishdadian
- Department of Immunology, School of Medicine, Zabol University of Medical Sciences, Zabol, Iran
| | - Hadi Farsiani
- Antimicrobial Resistance Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.,Department of Microbiology and Virology, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
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Parvizpour S, Pourseif MM, Razmara J, Rafi MA, Omidi Y. Epitope-based vaccine design: a comprehensive overview of bioinformatics approaches. Drug Discov Today 2020; 25:1034-1042. [DOI: 10.1016/j.drudis.2020.03.006] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2019] [Revised: 01/12/2020] [Accepted: 03/06/2020] [Indexed: 12/26/2022]
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Mösch A, Raffegerst S, Weis M, Schendel DJ, Frishman D. Machine Learning for Cancer Immunotherapies Based on Epitope Recognition by T Cell Receptors. Front Genet 2019; 10:1141. [PMID: 31798635 PMCID: PMC6878726 DOI: 10.3389/fgene.2019.01141] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Accepted: 10/21/2019] [Indexed: 12/30/2022] Open
Abstract
In the last years, immunotherapies have shown tremendous success as treatments for multiple types of cancer. However, there are still many obstacles to overcome in order to increase response rates and identify effective therapies for every individual patient. Since there are many possibilities to boost a patient's immune response against a tumor and not all can be covered, this review is focused on T cell receptor-mediated therapies. CD8+ T cells can detect and destroy malignant cells by binding to peptides presented on cell surfaces by MHC (major histocompatibility complex) class I molecules. CD4+ T cells can also mediate powerful immune responses but their peptide recognition by MHC class II molecules is more complex, which is why the attention has been focused on CD8+ T cells. Therapies based on the power of T cells can, on the one hand, enhance T cell recognition by introducing TCRs that preferentially direct T cells to tumor sites (so called TCR-T therapy) or through vaccination to induce T cells in vivo. On the other hand, T cell activity can be improved by immune checkpoint inhibition or other means that help create a microenvironment favorable for cytotoxic T cell activity. The manifold ways in which the immune system and cancer interact with each other require not only the use of large omics datasets from gene, to transcript, to protein, and to peptide but also make the application of machine learning methods inevitable. Currently, discovering and selecting suitable TCRs is a very costly and work intensive in vitro process. To facilitate this process and to additionally allow for highly personalized therapies that can simultaneously target multiple patient-specific antigens, especially neoepitopes, breakthrough computational methods for predicting antigen presentation and TCR binding are urgently required. Particularly, potential cross-reactivity is a major consideration since off-target toxicity can pose a major threat to patient safety. The current speed at which not only datasets grow and are made available to the public, but also at which new machine learning methods evolve, is assuring that computational approaches will be able to help to solve problems that immunotherapies are still facing.
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Affiliation(s)
- Anja Mösch
- Department of Bioinformatics, Wissenschaftszentrum Weihenstephan, Technische Universität München, Freising, Germany
- Medigene Immunotherapies GmbH, a subsidiary of Medigene AG, Planegg, Germany
| | - Silke Raffegerst
- Medigene Immunotherapies GmbH, a subsidiary of Medigene AG, Planegg, Germany
| | - Manon Weis
- Medigene Immunotherapies GmbH, a subsidiary of Medigene AG, Planegg, Germany
| | - Dolores J. Schendel
- Medigene Immunotherapies GmbH, a subsidiary of Medigene AG, Planegg, Germany
| | - Dmitrij Frishman
- Department of Bioinformatics, Wissenschaftszentrum Weihenstephan, Technische Universität München, Freising, Germany
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Abstract
Given the many cell types and molecular components of the human immune system, along with vast variations across individuals, how should we go about developing causal and predictive explanations of immunity? A central strategy in human studies is to leverage natural variation to find relationships among variables, including DNA variants, epigenetic states, immune phenotypes, clinical descriptors, and others. Here, we focus on how natural variation is used to find patterns, infer principles, and develop predictive models for two areas: (a) immune cell activation-how single-cell profiling boosts our ability to discover immune cell types and states-and (b) antigen presentation and recognition-how models can be generated to predict presentation of antigens on MHC molecules and their detection by T cell receptors. These are two examples of a shift in how we find the drivers and targets of immunity, especially in the human system in the context of health and disease.
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Affiliation(s)
- Alexandra-Chloé Villani
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA.,Center for Immunology and Inflammatory Diseases, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts 02129, USA.,Harvard Medical School, Boston, Massachusetts 02115, USA;
| | - Siranush Sarkizova
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA.,Harvard Medical School, Boston, Massachusetts 02115, USA; .,Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts 02142, USA
| | - Nir Hacohen
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA.,Harvard Medical School, Boston, Massachusetts 02115, USA; .,Center for Cancer Research, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
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Cross-modality deep learning-based prediction of TAP binding and naturally processed peptide. Immunogenetics 2018; 70:419-428. [PMID: 29492592 DOI: 10.1007/s00251-018-1054-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2017] [Accepted: 02/06/2018] [Indexed: 12/23/2022]
Abstract
Epitopes presented on MHC class I molecules pass multiple processing stages before their presentation on MHC molecules, the main ones being proteasomal cleavage and TAP binding. Transporter associated with antigen processing (TAP) binding is a necessary stage for most, but not all, MHC-I-binding peptides. The molecular determinants of TAP-binding peptides can be experimentally estimated from binding experiments and from the properties of peptides inducing a CD8 T cell response. We here propose novel optimization formalisms to combine binding and activation experimental results to produce a classifier for TAP binding using dual-output kernel and deep learning approaches. The application of these algorithms to the human and murine TAP binding leads to predictors that are much more precise than current state of the art methods. Moreover, the computed score is highly correlated with the observed binding energy. The new predictors show that TAP binding may be much more selective than previously assumed in humans and mice and sensitive to the properties of most positions of the peptides. Beyond the improved precision for TAP binding, we propose that the same approach holds in most molecular binding problems, where functional and binding measures are simultaneously available, and can be used to significantly improve the precision of binding prediction algorithms in general and immune system molecules specifically.
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Dhanda SK, Usmani SS, Agrawal P, Nagpal G, Gautam A, Raghava GPS. Novel in silico tools for designing peptide-based subunit vaccines and immunotherapeutics. Brief Bioinform 2017; 18:467-478. [PMID: 27016393 DOI: 10.1093/bib/bbw025] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2015] [Indexed: 12/19/2022] Open
Abstract
The conventional approach for designing vaccine against a particular disease involves stimulation of the immune system using the whole pathogen responsible for the disease. In the post-genomic era, a major challenge is to identify antigenic regions or epitopes that can stimulate different arms of the immune system. In the past two decades, numerous methods and databases have been developed for designing vaccine or immunotherapy against various pathogen-causing diseases. This review describes various computational resources important for designing subunit vaccines or epitope-based immunotherapy. First, different immunological databases are described that maintain epitopes, antigens and vaccine targets. This is followed by in silico tools used for predicting linear and conformational B-cell epitopes required for activating humoral immunity. Finally, information on T-cell epitope prediction methods is provided that includes indirect methods like prediction of Major Histocompatibility Complex and transporter-associated protein binders. Different studies for validating the predicted epitopes are also examined critically. This review enlists novel in silico resources and tools available for predicting humoral and cell-mediated immune potential. These predicted epitopes could be used for designing epitope-based vaccines or immunotherapy as they may activate the adaptive immunity. Authors emphasized the need to develop tools for the prediction of adjuvants to activate innate and adaptive immune system simultaneously. In addition, attention has also been given to novel prediction methods to predict general therapeutic properties of peptides like half-life, cytotoxicity and immune toxicity.
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Capietto AH, Jhunjhunwala S, Delamarre L. Characterizing neoantigens for personalized cancer immunotherapy. Curr Opin Immunol 2017; 46:58-65. [PMID: 28478383 DOI: 10.1016/j.coi.2017.04.007] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2017] [Accepted: 04/17/2017] [Indexed: 12/18/2022]
Abstract
Somatic mutations can generate neoantigens that are presented on MHC molecules and drive effective T cells responses against cancer. Mutation load in cancer patients predicts response to immune checkpoint blockade therapy. Additionally, vaccination targeting neoantigens controls established tumor growth in preclinical models. These recent findings led to a renewed interest in the field of cancer vaccines and the development of antigen-targeted cancer immunotherapies. However, targeting neoantigens is challenging, as most mutations are unique to each cancer patient. In addition, only a small fraction of the mutations are immunogenic and therefore their accurate prediction is critical. In this review, we discuss the properties of neoantigens that influence their immunogenicity, along with questions that remain to be addressed in order to improve prediction algorithms.
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Prediction of IL4 inducing peptides. Clin Dev Immunol 2013; 2013:263952. [PMID: 24489573 PMCID: PMC3893860 DOI: 10.1155/2013/263952] [Citation(s) in RCA: 195] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2013] [Revised: 10/08/2013] [Accepted: 11/13/2013] [Indexed: 12/04/2022]
Abstract
The secretion of Interleukin-4 (IL4) is the characteristic of T-helper 2 responses. IL4 is a cytokine produced by CD4+ T cells in response to helminthes and other extracellular parasites. It has a critical role in guiding antibody class switching, hematopoiesis and inflammation, and the development of appropriate effector T-cell responses. In this study, it is the first time an attempt has been made to understand whether it is possible to predict IL4 inducing peptides. The data set used in this study comprises 904 experimentally validated IL4 inducing and 742 noninducing MHC class II binders. Our analysis revealed that certain types of residues are preferred at certain positions in IL4 inducing peptides. It was also observed that IL4 inducing and noninducing epitopes differ in compositional and motif pattern. Based on our analysis we developed classification models where the hybrid method of amino acid pairs and motif information performed the best with maximum accuracy of 75.76% and MCC of 0.51. These results indicate that it is possible to predict IL4 inducing peptides with reasonable precession. These models would be useful in designing the peptides that may induce desired Th2 response.
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Dhanda SK, Vir P, Raghava GPS. Designing of interferon-gamma inducing MHC class-II binders. Biol Direct 2013; 8:30. [PMID: 24304645 PMCID: PMC4235049 DOI: 10.1186/1745-6150-8-30] [Citation(s) in RCA: 434] [Impact Index Per Article: 39.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2013] [Accepted: 11/25/2013] [Indexed: 02/03/2023] Open
Abstract
Background The generation of interferon-gamma (IFN-γ) by MHC class II activated CD4+ T helper cells play a substantial contribution in the control of infections such as caused by Mycobacterium tuberculosis. In the past, numerous methods have been developed for predicting MHC class II binders that can activate T-helper cells. Best of author’s knowledge, no method has been developed so far that can predict the type of cytokine will be secreted by these MHC Class II binders or T-helper epitopes. In this study, an attempt has been made to predict the IFN-γ inducing peptides. The main dataset used in this study contains 3705 IFN-γ inducing and 6728 non-IFN-γ inducing MHC class II binders. Another dataset called IFNgOnly contains 4483 IFN-γ inducing epitopes and 2160 epitopes that induce other cytokine except IFN-γ. In addition we have alternate dataset that contains IFN-γ inducing and equal number of random peptides. Results It was observed that the peptide length, positional conservation of residues and amino acid composition affects IFN-γ inducing capabilities of these peptides. We identified the motifs in IFN-γ inducing binders/peptides using MERCI software. Our analysis indicates that IFN-γ inducing and non-inducing peptides can be discriminated using above features. We developed models for predicting IFN-γ inducing peptides using various approaches like machine learning technique, motifs-based search, and hybrid approach. Our best model based on the hybrid approach achieved maximum prediction accuracy of 82.10% with MCC of 0.62 on main dataset. We also developed hybrid model on IFNgOnly dataset and achieved maximum accuracy of 81.39% with 0.57 MCC. Conclusion Based on this study, we have developed a webserver for predicting i) IFN-γ inducing peptides, ii) virtual screening of peptide libraries and iii) identification of IFN-γ inducing regions in antigen (http://crdd.osdd.net/raghava/ifnepitope/). Reviewers This article was reviewed by Prof Kurt Blaser, Prof Laurence Eisenlohr and Dr Manabu Sugai.
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Affiliation(s)
- Sandeep Kumar Dhanda
- Bioinformatics Centre, CSIR-Institute of Microbial Technology, Sector 39A, Chandigarh 160036, India.
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Characterization of the binding profile of peptide to transporter associated with antigen processing (TAP) using Gaussian process regression. Comput Biol Med 2011; 41:865-70. [DOI: 10.1016/j.compbiomed.2011.07.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2011] [Revised: 07/10/2011] [Accepted: 07/18/2011] [Indexed: 11/22/2022]
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