1
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Yao P, Gao M, Hu W, Wang J, Wang Y, Wang Q, Ji J. Proteogenomic analysis identifies neoantigens and bacterial peptides as immunotherapy targets in colorectal cancer. Pharmacol Res 2024; 204:107209. [PMID: 38740147 DOI: 10.1016/j.phrs.2024.107209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Revised: 05/08/2024] [Accepted: 05/08/2024] [Indexed: 05/16/2024]
Abstract
Considerable progress has recently been made in cancer immunotherapy, including immune checkpoint blockade, cancer vaccine, and adoptive T cell methods. The lack of effective targets is a major cause of the low immunotherapy response rate in colorectal cancer (CRC). Here, we used a proteogenomic strategy comprising immunopeptidomics, whole exome sequencing, and 16 S ribosomal DNA sequencing analyses of 8 patients with CRC to identify neoantigens and bacterial peptides that can serve as antitumor targets. This study directly identified several personalized neoantigens and bacterial immunopeptides. Immunoassays showed that all neoantigens and 5 of 8 bacterial immunopeptides could be recognized by autologous T cells. Additionally, T cell receptor (TCR) αβ sequencing revealed the TCR repertoire of epitope-reactive CD8+ T cells. Functional studies showed that T cell receptor-T (TCR-T) could be activated by epitope pulsed lymphoblastoid cells. Overall, this study comprehensively profiled the CRC immunopeptidome, revealing several neoantigens and bacterial peptides with potential to serve as immunotherapy targets in CRC.
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Affiliation(s)
- Pengju Yao
- State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing, China
| | - Mingjie Gao
- State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing, China
| | - Weiyi Hu
- State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing, China
| | - Jiahao Wang
- State Key Laboratory of Natural and Biomimetic Drugs, Institute of Molecular Medicine, Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, China
| | - Yuhao Wang
- State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing, China
| | - Qingsong Wang
- State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing, China
| | - Jianguo Ji
- State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing, China.
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2
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Ragone C, Cavalluzzo B, Mauriello A, Tagliamonte M, Buonaguro L. Lack of shared neoantigens in prevalent mutations in cancer. J Transl Med 2024; 22:344. [PMID: 38600547 PMCID: PMC11005154 DOI: 10.1186/s12967-024-05110-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Accepted: 03/19/2024] [Indexed: 04/12/2024] Open
Abstract
Tumors are mostly characterized by genetic instability, as result of mutations in surveillance mechanisms, such as DNA damage checkpoint, DNA repair machinery and mitotic checkpoint. Defect in one or more of these mechanisms causes additive accumulation of mutations. Some of these mutations are drivers of transformation and are positively selected during the evolution of the cancer, giving a growth advantage on the cancer cells. If such mutations would result in mutated neoantigens, these could be actionable targets for cancer vaccines and/or adoptive cell therapies. However, the results of the present analysis show, for the first time, that the most prevalent mutations identified in human cancers do not express mutated neoantigens. The hypothesis is that this is the result of the selection operated by the immune system in the very early stages of tumor development. At that stage, the tumor cells characterized by mutations giving rise to highly antigenic non-self-mutated neoantigens would be efficiently targeted and eliminated. Consequently, the outgrowing tumor cells cannot be controlled by the immune system, with an ultimate growth advantage to form large tumors embedded in an immunosuppressive tumor microenvironment (TME). The outcome of such a negative selection operated by the immune system is that the development of off-the-shelf vaccines, based on shared mutated neoantigens, does not seem to be at hand. This finding represents the first demonstration of the key role of the immune system on shaping the tumor antigen presentation and the implication in the development of antitumor immunological strategies.
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Affiliation(s)
- Concetta Ragone
- Lab of Innovative Immunological Models Unit, Istituto Nazionale Tumori, IRCCS - "Fondazione Pascale", Via Mariano Semmola, 52, 80131, Naples, Italy
| | - Beatrice Cavalluzzo
- Lab of Innovative Immunological Models Unit, Istituto Nazionale Tumori, IRCCS - "Fondazione Pascale", Via Mariano Semmola, 52, 80131, Naples, Italy
| | - Angela Mauriello
- Lab of Innovative Immunological Models Unit, Istituto Nazionale Tumori, IRCCS - "Fondazione Pascale", Via Mariano Semmola, 52, 80131, Naples, Italy
| | - Maria Tagliamonte
- Lab of Innovative Immunological Models Unit, Istituto Nazionale Tumori, IRCCS - "Fondazione Pascale", Via Mariano Semmola, 52, 80131, Naples, Italy.
| | - Luigi Buonaguro
- Lab of Innovative Immunological Models Unit, Istituto Nazionale Tumori, IRCCS - "Fondazione Pascale", Via Mariano Semmola, 52, 80131, Naples, Italy.
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3
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Garzia I, Nocchi L, Avalle L, Troise F, Leoni G, Seclì L, Antonucci L, Cotugno G, Allocca S, Romano G, Conti L, Caiazza C, Mallardo M, Poli V, Scarselli E, D'Alise AM. Tumor Burden Dictates the Neoantigen Features Required to Generate an Effective Cancer Vaccine. Cancer Immunol Res 2024; 12:440-452. [PMID: 38331413 PMCID: PMC10985473 DOI: 10.1158/2326-6066.cir-23-0609] [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: 07/26/2023] [Revised: 11/24/2023] [Accepted: 02/06/2024] [Indexed: 02/10/2024]
Abstract
Tumor neoantigens (nAg) represent a promising target for cancer immunotherapy. The identification of nAgs that can generate T-cell responses and have therapeutic activity has been challenging. Here, we sought to unravel the features of nAgs required to induce tumor rejection. We selected clinically validated Great Ape-derived adenoviral vectors (GAd) as a nAg delivery system for differing numbers and combinations of nAgs. We assessed their immunogenicity and efficacy in murine models of low to high disease burden, comparing multi-epitope versus mono-epitope vaccines. We demonstrated that the breadth of immune response is critical for vaccine efficacy and having multiple immunogenic nAgs encoded in a single vaccine improves efficacy. The contribution of each single neoantigen was examined, leading to the identification of 2 nAgs able to induce CD8+ T cell-mediated tumor rejection. They were both active as individual nAgs in a setting of prophylactic vaccination, although to different extents. However, the efficacy of these single nAgs was lost in a setting of therapeutic vaccination in tumor-bearing mice. The presence of CD4+ T-cell help restored the efficacy for only the most expressed of the two nAgs, demonstrating a key role for CD4+ T cells in sustaining CD8+ T-cell responses and the necessity of an efficient recognition of the targeted epitopes on cancer cells by CD8+ T cells for an effective antitumor response. This study provides insight into understanding the determinants of nAgs relevant for effective treatment and highlights features that could contribute to more effective antitumor vaccines. See related Spotlight by Slingluff Jr, p. 382.
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Affiliation(s)
| | | | - Lidia Avalle
- Department of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy
| | | | | | | | | | | | | | | | - Laura Conti
- Department of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy
| | - Carmen Caiazza
- Department of Molecular Medicine and Medical Biotechnology, University of Naples Federico II, Naples, Italy
| | - Massimo Mallardo
- Department of Molecular Medicine and Medical Biotechnology, University of Naples Federico II, Naples, Italy
| | - Valeria Poli
- Department of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy
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4
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Zhang W, Sun S, Zhu W, Meng D, Hu W, Yang S, Gao M, Yao P, Wang Y, Wang Q, Ji J. Birinapant Reshapes the Tumor Immunopeptidome and Enhances Antigen Presentation. Int J Mol Sci 2024; 25:3660. [PMID: 38612472 PMCID: PMC11011986 DOI: 10.3390/ijms25073660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 03/18/2024] [Accepted: 03/20/2024] [Indexed: 04/14/2024] Open
Abstract
Birinapant, an antagonist of the inhibitor of apoptosis proteins, upregulates MHCs in tumor cells and displays a better tumoricidal effect when used in combination with immune checkpoint inhibitors, indicating that Birinapant may affect the antigen presentation pathway; however, the mechanism remains elusive. Based on high-resolution mass spectrometry and in vitro and in vivo models, we adopted integrated genomics, proteomics, and immunopeptidomics strategies to study the mechanism underlying the regulation of tumor immunity by Birinapant from the perspective of antigen presentation. Firstly, in HT29 and MCF7 cells, Birinapant increased the number and abundance of immunopeptides and source proteins. Secondly, a greater number of cancer/testis antigen peptides with increased abundance and more neoantigens were identified following Birinapant treatment. Moreover, we demonstrate the existence and immunogenicity of a neoantigen derived from insertion/deletion mutation. Thirdly, in HT29 cell-derived xenograft models, Birinapant administration also reshaped the immunopeptidome, and the tumor exhibited better immunogenicity. These data suggest that Birinapant can reshape the tumor immunopeptidome with respect to quality and quantity, which improves the presentation of CTA peptides and neoantigens, thus enhancing the immunogenicity of tumor cells. Such changes may be vital to the effectiveness of combination therapy, which can be further transferred to the clinic or aid in the development of new immunotherapeutic strategies to improve the anti-tumor immune response.
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Affiliation(s)
- Weiyan Zhang
- State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing 100871, China; (W.Z.)
| | - Shenghuan Sun
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA 94143, USA;
| | - Wenyuan Zhu
- State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing 100871, China; (W.Z.)
| | - Delan Meng
- State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing 100871, China; (W.Z.)
| | - Weiyi Hu
- State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing 100871, China; (W.Z.)
| | - Siqi Yang
- State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing 100871, China; (W.Z.)
| | - Mingjie Gao
- State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing 100871, China; (W.Z.)
| | - Pengju Yao
- State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing 100871, China; (W.Z.)
| | - Yuhao Wang
- State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing 100871, China; (W.Z.)
| | - Qingsong Wang
- State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing 100871, China; (W.Z.)
| | - Jianguo Ji
- State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing 100871, China; (W.Z.)
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5
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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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6
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Wan YTR, Koşaloğlu‐Yalçın Z, Peters B, Nielsen M. A large-scale study of peptide features defining immunogenicity of cancer neo-epitopes. NAR Cancer 2024; 6:zcae002. [PMID: 38288446 PMCID: PMC10823584 DOI: 10.1093/narcan/zcae002] [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: 09/17/2023] [Revised: 01/03/2024] [Accepted: 01/12/2024] [Indexed: 01/31/2024] Open
Abstract
Accurate prediction of immunogenicity for neo-epitopes arising from a cancer associated mutation is a crucial step in many bioinformatics pipelines that predict outcome of checkpoint blockade treatments or that aim to design personalised cancer immunotherapies and vaccines. In this study, we performed a comprehensive analysis of peptide features relevant for prediction of immunogenicity using the Cancer Epitope Database and Analysis Resource (CEDAR), a curated database of cancer epitopes with experimentally validated immunogenicity annotations from peer-reviewed publications. The developed model, ICERFIRE (ICore-based Ensemble Random Forest for neo-epitope Immunogenicity pREdiction), extracts the predicted ICORE from the full neo-epitope as input, i.e. the nested peptide with the highest predicted major histocompatibility complex (MHC) binding potential combined with its predicted likelihood of antigen presentation (%Rank). Key additional features integrated into the model include assessment of the BLOSUM mutation score of the neo-epitope, and antigen expression levels of the wild-type counterpart which is often reflecting a neo-epitope's abundance. We demonstrate improved and robust performance of ICERFIRE over existing immunogenicity and epitope prediction models, both in cross-validation and on external validation datasets.
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Affiliation(s)
- Yat-tsai Richie Wan
- Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, DK 28002, Denmark
| | - Zeynep Koşaloğlu‐Yalçın
- Center for Infectious Disease and Vaccine Research, La Jolla Institute of Immunology, La Jolla, CA 92037, USA
| | - Bjoern Peters
- Center for Infectious Disease and Vaccine Research, La Jolla Institute of Immunology, La Jolla, CA 92037, USA
| | - Morten Nielsen
- Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, DK 28002, Denmark
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7
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Katsikis PD, Ishii KJ, Schliehe C. Challenges in developing personalized neoantigen cancer vaccines. Nat Rev Immunol 2024; 24:213-227. [PMID: 37783860 DOI: 10.1038/s41577-023-00937-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/17/2023] [Indexed: 10/04/2023]
Abstract
The recent success of cancer immunotherapies has highlighted the benefit of harnessing the immune system for cancer treatment. Vaccines have a long history of promoting immunity to pathogens and, consequently, vaccines targeting cancer neoantigens have been championed as a tool to direct and amplify immune responses against tumours while sparing healthy tissue. In recent years, extensive preclinical research and more than one hundred clinical trials have tested different strategies of neoantigen discovery and vaccine formulations. However, despite the enthusiasm for neoantigen vaccines, proof of unequivocal efficacy has remained beyond reach for the majority of clinical trials. In this Review, we focus on the key obstacles pertaining to vaccine design and tumour environment that remain to be overcome in order to unleash the true potential of neoantigen vaccines in cancer therapy.
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Affiliation(s)
- Peter D Katsikis
- Department of Immunology, Erasmus University Medical Center, Rotterdam, Netherlands.
| | - Ken J Ishii
- Division of Vaccine Science, Department of Microbiology and Immunology, The Institute of Medical Science, The University of Tokyo (IMSUT), Tokyo, Japan
- International Vaccine Design Center (vDesC), The Institute of Medical Science, The University of Tokyo (IMSUT), Tokyo, Japan
| | - Christopher Schliehe
- Department of Immunology, Erasmus University Medical Center, Rotterdam, Netherlands
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8
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Srivastava PK. Cancer neoepitopes viewed through negative selection and peripheral tolerance: a new path to cancer vaccines. J Clin Invest 2024; 134:e176740. [PMID: 38426497 PMCID: PMC10904052 DOI: 10.1172/jci176740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/02/2024] Open
Abstract
A proportion of somatic mutations in tumors create neoepitopes that can prime T cell responses that target the MHC I-neoepitope complexes on tumor cells, mediating tumor control or rejection. Despite the compelling centrality of neoepitopes to cancer immunity, we know remarkably little about what constitutes a neoepitope that can mediate tumor control in vivo and what distinguishes such a neoepitope from the vast majority of similar candidate neoepitopes that are inefficacious in vivo. Studies in mice as well as clinical trials have begun to reveal the unexpected paradoxes in this area. Because cancer neoepitopes straddle that ambiguous ground between self and non-self, some rules that are fundamental to immunology of frankly non-self antigens, such as viral or model antigens, do not appear to apply to neoepitopes. Because neoepitopes are so similar to self-epitopes, with only small changes that render them non-self, immune response to them is regulated at least partially the way immune response to self is regulated. Therefore, neoepitopes are viewed and understood here through the clarifying lens of negative thymic selection. Here, the emergent questions in the biology and clinical applications of neoepitopes are discussed critically and a mechanistic and testable framework that explains the complexity and translational potential of these wonderful antigens is proposed.
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9
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Miller AM, Koşaloğlu-Yalçın Z, Westernberg L, Montero L, Bahmanof M, Frentzen A, Lanka M, Logandha Ramamoorthy Premlal A, Seumois G, Greenbaum J, Brightman SE, Soria Zavala K, Thota RR, Naradikian MS, Makani SS, Lippman SM, Sette A, Cohen EEW, Peters B, Schoenberger SP. A functional identification platform reveals frequent, spontaneous neoantigen-specific T cell responses in patients with cancer. Sci Transl Med 2024; 16:eabj9905. [PMID: 38416845 DOI: 10.1126/scitranslmed.abj9905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 01/29/2024] [Indexed: 03/01/2024]
Abstract
The clinical impact of tumor-specific neoantigens as both immunotherapeutic targets and biomarkers has been impeded by the lack of efficient methods for their identification and validation from routine samples. We have developed a platform that combines bioinformatic analysis of tumor exomes and transcriptional data with functional testing of autologous peripheral blood mononuclear cells (PBMCs) to simultaneously identify and validate neoantigens recognized by naturally primed CD4+ and CD8+ T cell responses across a range of tumor types and mutational burdens. The method features a human leukocyte antigen (HLA)-agnostic bioinformatic algorithm that prioritizes mutations recognized by patient PBMCs at a greater than 40% positive predictive value followed by a short-term in vitro functional assay, which allows interrogation of 50 to 75 expressed mutations from a single 50-ml blood sample. Neoantigens validated by this method include both driver and passenger mutations, and this method identified neoantigens that would not have been otherwise detected using an in silico prediction approach. These findings reveal an efficient approach to systematically validate clinically actionable neoantigens and the T cell receptors that recognize them and demonstrate that patients across a variety of human cancers have a diverse repertoire of neoantigen-specific T cells.
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Affiliation(s)
- Aaron M Miller
- Center for Cancer Immunotherapy, La Jolla Institute for Immunology, 9420 Athena Circle, La Jolla, CA 92037, USA
- Division of Hematology and Oncology, UCSD Moores Cancer Center, 3855 Health Sciences Drive, La Jolla, CA 92093, USA
| | - Zeynep Koşaloğlu-Yalçın
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, 9420 Athena Circle, La Jolla, CA 92037, USA
| | - Luise Westernberg
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, 9420 Athena Circle, La Jolla, CA 92037, USA
| | - Leslie Montero
- Center for Cancer Immunotherapy, La Jolla Institute for Immunology, 9420 Athena Circle, La Jolla, CA 92037, USA
| | - Milad Bahmanof
- Center for Cancer Immunotherapy, La Jolla Institute for Immunology, 9420 Athena Circle, La Jolla, CA 92037, USA
| | - Angela Frentzen
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, 9420 Athena Circle, La Jolla, CA 92037, USA
| | - Manasa Lanka
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, 9420 Athena Circle, La Jolla, CA 92037, USA
| | | | - Gregory Seumois
- Center for Cancer Immunotherapy, La Jolla Institute for Immunology, 9420 Athena Circle, La Jolla, CA 92037, USA
| | - Jason Greenbaum
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, 9420 Athena Circle, La Jolla, CA 92037, USA
| | - Spencer E Brightman
- Center for Cancer Immunotherapy, La Jolla Institute for Immunology, 9420 Athena Circle, La Jolla, CA 92037, USA
| | - Karla Soria Zavala
- Center for Cancer Immunotherapy, La Jolla Institute for Immunology, 9420 Athena Circle, La Jolla, CA 92037, USA
| | - Rukman R Thota
- Center for Cancer Immunotherapy, La Jolla Institute for Immunology, 9420 Athena Circle, La Jolla, CA 92037, USA
| | - Martin S Naradikian
- Center for Cancer Immunotherapy, La Jolla Institute for Immunology, 9420 Athena Circle, La Jolla, CA 92037, USA
| | - Samir S Makani
- Division of Hematology and Oncology, UCSD Moores Cancer Center, 3855 Health Sciences Drive, La Jolla, CA 92093, USA
| | - Scott M Lippman
- Division of Hematology and Oncology, UCSD Moores Cancer Center, 3855 Health Sciences Drive, La Jolla, CA 92093, USA
| | - Alessandro Sette
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, 9420 Athena Circle, La Jolla, CA 92037, USA
| | - Ezra E W Cohen
- Division of Hematology and Oncology, UCSD Moores Cancer Center, 3855 Health Sciences Drive, La Jolla, CA 92093, USA
| | - Bjoern Peters
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, 9420 Athena Circle, La Jolla, CA 92037, USA
- Department of Medicine, University of California, San Diego (UCSD), La Jolla, CA 92037, USA
| | - Stephen P Schoenberger
- Center for Cancer Immunotherapy, La Jolla Institute for Immunology, 9420 Athena Circle, La Jolla, CA 92037, USA
- Division of Hematology and Oncology, UCSD Moores Cancer Center, 3855 Health Sciences Drive, La Jolla, CA 92093, USA
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10
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Shahjahan, Dey JK, Dey SK. Translational bioinformatics approach to combat cardiovascular disease and cancers. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2024; 139:221-261. [PMID: 38448136 DOI: 10.1016/bs.apcsb.2023.11.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
Bioinformatics is an interconnected subject of science dealing with diverse fields including biology, chemistry, physics, statistics, mathematics, and computer science as the key fields to answer complicated physiological problems. Key intention of bioinformatics is to store, analyze, organize, and retrieve essential information about genome, proteome, transcriptome, metabolome, as well as organisms to investigate the biological system along with its dynamics, if any. The outcome of bioinformatics depends on the type, quantity, and quality of the raw data provided and the algorithm employed to analyze the same. Despite several approved medicines available, cardiovascular disorders (CVDs) and cancers comprises of the two leading causes of human deaths. Understanding the unknown facts of both these non-communicable disorders is inevitable to discover new pathways, find new drug targets, and eventually newer drugs to combat them successfully. Since, all these goals involve complex investigation and handling of various types of macro- and small- molecules of the human body, bioinformatics plays a key role in such processes. Results from such investigation has direct human application and thus we call this filed as translational bioinformatics. Current book chapter thus deals with diverse scope and applications of this translational bioinformatics to find cure, diagnosis, and understanding the mechanisms of CVDs and cancers. Developing complex yet small or long algorithms to address such problems is very common in translational bioinformatics. Structure-based drug discovery or AI-guided invention of novel antibodies that too with super-high accuracy, speed, and involvement of considerably low amount of investment are some of the astonishing features of the translational bioinformatics and its applications in the fields of CVDs and cancers.
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Affiliation(s)
- Shahjahan
- Laboratory for Structural Biology of Membrane Proteins, Dr. B.R. Ambedkar Center for Biomedical Research, University of Delhi, Delhi, India
| | - Joy Kumar Dey
- Central Council for Research in Homoeopathy, Ministry of Ayush, Govt. of India, New Delhi, Delhi, India
| | - Sanjay Kumar Dey
- Laboratory for Structural Biology of Membrane Proteins, Dr. B.R. Ambedkar Center for Biomedical Research, University of Delhi, Delhi, India.
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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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Lang F, Sorn P, Schrörs B, Weber D, Kramer S, Sahin U, Löwer M. Multiple instance learning to predict immune checkpoint blockade efficacy using neoantigen candidates. iScience 2023; 26:108014. [PMID: 37965155 PMCID: PMC10641489 DOI: 10.1016/j.isci.2023.108014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 10/28/2022] [Accepted: 09/18/2023] [Indexed: 11/16/2023] Open
Abstract
Previous studies showed that the neoantigen candidate load is an imperfect predictor of immune checkpoint blockade (ICB) efficacy. Further studies provided evidence that the response to ICB is also affected by the qualitative properties of a few or even single candidates, limiting the predictive power based on candidate quantity alone. Here, we predict ICB efficacy based on neoantigen candidates and their neoantigen features in the context of the mutation type, using Multiple-Instance Learning via Embedded Instance Selection (MILES). Multiple instance learning is a type of supervised machine learning that classifies labeled bags that are formed by a set of unlabeled instances. MILES performed better compared with neoantigen candidate load alone for low-abundant fusion genes in renal cell carcinoma. Our findings suggest that MILES is an appropriate method to predict the efficacy of ICB therapy based on neoantigen candidates without requiring direct T cell response information.
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Affiliation(s)
- Franziska Lang
- TRON - Translational Oncology at the University Medical Center of the Johannes Gutenberg University gGmbH, 55131 Mainz, Germany
| | - Patrick Sorn
- TRON - Translational Oncology at the University Medical Center of the Johannes Gutenberg University gGmbH, 55131 Mainz, Germany
| | - Barbara Schrörs
- TRON - Translational Oncology at the University Medical Center of the Johannes Gutenberg University gGmbH, 55131 Mainz, Germany
| | - David Weber
- TRON - Translational Oncology at the University Medical Center of the Johannes Gutenberg University gGmbH, 55131 Mainz, Germany
| | - Stefan Kramer
- Institute of Computer Science, Johannes Gutenberg University, 55128 Mainz, Germany
| | - Ugur Sahin
- BioNTech SE, 55131 Mainz, Germany
- University Medical Center of the Johannes Gutenberg University, 55131 Mainz, Germany
| | - Martin Löwer
- TRON - Translational Oncology at the University Medical Center of the Johannes Gutenberg University gGmbH, 55131 Mainz, Germany
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13
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Bravi B, Di Gioacchino A, Fernandez-de-Cossio-Diaz J, Walczak AM, Mora T, Cocco S, Monasson R. A transfer-learning approach to predict antigen immunogenicity and T-cell receptor specificity. eLife 2023; 12:e85126. [PMID: 37681658 PMCID: PMC10522340 DOI: 10.7554/elife.85126] [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: 11/23/2022] [Accepted: 09/07/2023] [Indexed: 09/09/2023] Open
Abstract
Antigen immunogenicity and the specificity of binding of T-cell receptors to antigens are key properties underlying effective immune responses. Here we propose diffRBM, an approach based on transfer learning and Restricted Boltzmann Machines, to build sequence-based predictive models of these properties. DiffRBM is designed to learn the distinctive patterns in amino-acid composition that, on the one hand, underlie the antigen's probability of triggering a response, and on the other hand the T-cell receptor's ability to bind to a given antigen. We show that the patterns learnt by diffRBM allow us to predict putative contact sites of the antigen-receptor complex. We also discriminate immunogenic and non-immunogenic antigens, antigen-specific and generic receptors, reaching performances that compare favorably to existing sequence-based predictors of antigen immunogenicity and T-cell receptor specificity.
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Affiliation(s)
- Barbara Bravi
- Department of Mathematics, Imperial College LondonLondonUnited Kingdom
- Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université Paris-CitéParisFrance
| | - Andrea Di Gioacchino
- Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université Paris-CitéParisFrance
| | - Jorge Fernandez-de-Cossio-Diaz
- Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université Paris-CitéParisFrance
| | - Aleksandra M Walczak
- Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université Paris-CitéParisFrance
| | - Thierry Mora
- Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université Paris-CitéParisFrance
| | - Simona Cocco
- Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université Paris-CitéParisFrance
| | - Rémi Monasson
- Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université Paris-CitéParisFrance
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14
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Schmidt J, Chiffelle J, Perez MAS, Magnin M, Bobisse S, Arnaud M, Genolet R, Cesbron J, Barras D, Navarro Rodrigo B, Benedetti F, Michel A, Queiroz L, Baumgaertner P, Guillaume P, Hebeisen M, Michielin O, Nguyen-Ngoc T, Huber F, Irving M, Tissot-Renaud S, Stevenson BJ, Rusakiewicz S, Dangaj Laniti D, Bassani-Sternberg M, Rufer N, Gfeller D, Kandalaft LE, Speiser DE, Zoete V, Coukos G, Harari A. Neoantigen-specific CD8 T cells with high structural avidity preferentially reside in and eliminate tumors. Nat Commun 2023; 14:3188. [PMID: 37280206 DOI: 10.1038/s41467-023-38946-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 05/23/2023] [Indexed: 06/08/2023] Open
Abstract
The success of cancer immunotherapy depends in part on the strength of antigen recognition by T cells. Here, we characterize the T cell receptor (TCR) functional (antigen sensitivity) and structural (monomeric pMHC-TCR off-rates) avidities of 371 CD8 T cell clones specific for neoantigens, tumor-associated antigens (TAAs) or viral antigens isolated from tumors or blood of patients and healthy donors. T cells from tumors exhibit stronger functional and structural avidity than their blood counterparts. Relative to TAA, neoantigen-specific T cells are of higher structural avidity and, consistently, are preferentially detected in tumors. Effective tumor infiltration in mice models is associated with high structural avidity and CXCR3 expression. Based on TCR biophysicochemical properties, we derive and apply an in silico model predicting TCR structural avidity and validate the enrichment in high avidity T cells in patients' tumors. These observations indicate a direct relationship between neoantigen recognition, T cell functionality and tumor infiltration. These results delineate a rational approach to identify potent T cells for personalized cancer immunotherapy.
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Affiliation(s)
- Julien Schmidt
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
- Center of Experimental Therapeutics, Department of Oncology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Johanna Chiffelle
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - Marta A S Perez
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Morgane Magnin
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - Sara Bobisse
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - Marion Arnaud
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - Raphael Genolet
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - Julien Cesbron
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - David Barras
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - Blanca Navarro Rodrigo
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - Fabrizio Benedetti
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - Alexandra Michel
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - Lise Queiroz
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - Petra Baumgaertner
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center of Experimental Therapeutics, Department of Oncology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Philippe Guillaume
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
- Center of Experimental Therapeutics, Department of Oncology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Michael Hebeisen
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
| | - Olivier Michielin
- Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - Tu Nguyen-Ngoc
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
| | - Florian Huber
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - Melita Irving
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
| | - Stéphanie Tissot-Renaud
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center of Experimental Therapeutics, Department of Oncology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Brian J Stevenson
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Sylvie Rusakiewicz
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center of Experimental Therapeutics, Department of Oncology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Denarda Dangaj Laniti
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - Michal Bassani-Sternberg
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - Nathalie Rufer
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
| | - David Gfeller
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Lana E Kandalaft
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
- Center of Experimental Therapeutics, Department of Oncology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Daniel E Speiser
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
| | - Vincent Zoete
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - George Coukos
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
- Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland
| | - Alexandre Harari
- Ludwig Institute for Cancer Research, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland.
- Center for Cell Therapy, Department of Oncology, Lausanne University Hospital, Lausanne, Switzerland.
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15
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Viborg N, Pavlidis MA, Barrio-Calvo M, Friis S, Trolle T, Sørensen AB, Thygesen CB, Kofoed SV, Kleine-Kohlbrecher D, Hadrup SR, Rønø B. DNA based neoepitope vaccination induces tumor control in syngeneic mouse models. NPJ Vaccines 2023; 8:77. [PMID: 37244905 DOI: 10.1038/s41541-023-00671-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 05/10/2023] [Indexed: 05/29/2023] Open
Abstract
Recent findings have positioned tumor mutation-derived neoepitopes as attractive targets for cancer immunotherapy. Cancer vaccines that deliver neoepitopes via various vaccine formulations have demonstrated promising preliminary results in patients and animal models. In the presented work, we assessed the ability of plasmid DNA to confer neoepitope immunogenicity and anti-tumor effect in two murine syngeneic cancer models. We demonstrated that neoepitope DNA vaccination led to anti-tumor immunity in the CT26 and B16F10 tumor models, with the long-lasting presence of neoepitope-specific T-cell responses in blood, spleen, and tumors after immunization. We further observed that engagement of both the CD4+ and CD8+ T cell compartments was essential to hamper tumor growth. Additionally, combination therapy with immune checkpoint inhibition provided an additive effect, superior to either monotherapy. DNA vaccination offers a versatile platform that allows the encoding of multiple neoepitopes in a single formulation and is thus a feasible strategy for personalized immunotherapy via neoepitope vaccination.
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Affiliation(s)
- Nadia Viborg
- Evaxion Biotech, Hørsholm, Denmark
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | | | | | | | | | | | | | | | | | - Sine Reker Hadrup
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
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16
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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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17
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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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18
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Cioffi R, Galli F, Rabaiotti E, Candiani M, Pella F, Candotti G, Bocciolone L, De Marzi P, Mangili G, Bergamini A. Experimental drugs for fallopian cancer: promising agents in the clinical trials and key stumbling blocks for researchers. Expert Opin Investig Drugs 2022; 31:1339-1357. [PMID: 36537209 DOI: 10.1080/13543784.2022.2160313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
INTRODUCTION Fallopian tube carcinoma (FC) as a single entity is a rare disease. Although its diagnosis is increasing thanks to the widespread use of prophylactic salpingectomy, there are no clinical trials exclusively designed for FC. AREAS COVERED This review aims at identifying the most promising trials and future therapeutic pathways in the setting of FC. EXPERT OPINION Hot topics in FC treatment include the consequences of using PARP inhibitors (PARPi) as first-line therapy, ways to overcome platinum resistance, and the role of immunotherapy. Patient selection is a key point for future development of target therapies. Next-generation sequencing (NGS) is one of the most investigated technologies both for drug discovery and identification of reverse mutations, involved in resistance to PARPi and platinum. New, promising molecular targets are emerging. Notwithstanding the disappointing outcomes when used by itself, immunotherapy in FC treatment could still have a role in combination with other agents, exploiting synergistic effects at the molecular level. The development of cancer vaccines is currently hampered by the high variability of tumor neoantigens in FC. Genomic profiling could be a solution, allowing the synthesis of individualized vaccines.
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Affiliation(s)
- Raffaella Cioffi
- Obstetrics and Gynecology Unit, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
| | - Federica Galli
- Obstetrics and Gynecology Unit, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
| | - Emanuela Rabaiotti
- Obstetrics and Gynecology Unit, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
| | - Massimo Candiani
- Obstetrics and Gynecology Unit, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
| | - Francesca Pella
- Obstetrics and Gynecology Unit, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
| | - Giorgio Candotti
- Obstetrics and Gynecology Unit, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
| | - Luca Bocciolone
- Obstetrics and Gynecology Unit, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
| | - Patrizia De Marzi
- Obstetrics and Gynecology Unit, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
| | - Giorgia Mangili
- Obstetrics and Gynecology Unit, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
| | - Alice Bergamini
- Obstetrics and Gynecology Unit, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
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19
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Hong J, Guo G, Wu S, Lin S, Zhou Z, Chen S, Ye C, Li J, Lin W, Ye Y. Altered MUC1 epitope-specific CTLs: A potential target for immunotherapy of pancreatic cancer. J Leukoc Biol 2022; 112:1577-1590. [PMID: 36222123 DOI: 10.1002/jlb.5ma0922-749r] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 08/26/2022] [Indexed: 01/04/2023] Open
Abstract
The efficacy of conventional treatments for pancreatic cancer remains unsatisfactory, and immunotherapy is an emerging option for adjuvant treatment of this highly deadly disorder. The tumor-associated antigen (TAA) MUC1 is expressed in a variety of human cancers and is overexpressed in more than 90% of pancreatic cancer, which makes it an attractive target for cancer immunotherapy. As a self-protein, MUC1 shows a low immunogenicity because of immune tolerance, and the most effective approach to breaking immune tolerance is alteration of the antigen structure. In this study, the altered MUC11068-1076Y1 epitope (YLQRDISEM) by modification of amino acid residues in sequences presented a higher immunogenicity and elicited more CTLs relative to the wild-type (WT) MUC11068-1076 epitope (ELQRDISEM). In addition, the altered MUC11068-1076Y1 epitope was found to cross-recognize pancreatic cancer cells expressing WT MUC1 peptides in an HLA-A0201-restricted manner and trigger stronger immune responses against pancreatic cancer via the perforin/granzyme apoptosis pathway. As a potential HLA-A0201-restricted CTL epitope, the altered MUC11068-1076Y1 epitope is considered as a promising target for immunotherapy of pancreatic cancer. Alteration of epitope residues may be feasible to solve the problem of the low immunogenicity of TAA and break immune tolerance to induce immune responses against human cancers.
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Affiliation(s)
- Jingwen Hong
- School of Basic Medical Sciences, Fujian Medical University, 1 Xue Yuan Road, University Town, Fuzhou, Fujian, 350122, China.,Laboratory of Immuno-Oncology, Fujian Medical University Cancer Hospital & Fujian Cancer Hospital, No. 420, Fuma Road, Jinan District, Fuzhou, Fujian, 350014, China
| | - Guoxiang Guo
- School of Basic Medical Sciences, Fujian Medical University, 1 Xue Yuan Road, University Town, Fuzhou, Fujian, 350122, China.,Laboratory of Immuno-Oncology, Fujian Medical University Cancer Hospital & Fujian Cancer Hospital, No. 420, Fuma Road, Jinan District, Fuzhou, Fujian, 350014, China
| | - Suxin Wu
- School of Basic Medical Sciences, Fujian Medical University, 1 Xue Yuan Road, University Town, Fuzhou, Fujian, 350122, China.,Laboratory of Immuno-Oncology, Fujian Medical University Cancer Hospital & Fujian Cancer Hospital, No. 420, Fuma Road, Jinan District, Fuzhou, Fujian, 350014, China
| | - Shengzhe Lin
- Department of Hepatobiliary Surgery and Fujian Institute of Hepatobiliary Surgery, Fujian Medical University Union Hospital, NO. 29, Xinquan Road, Fuzhou, Fujian 350001, China
| | - Zhifeng Zhou
- School of Basic Medical Sciences, Fujian Medical University, 1 Xue Yuan Road, University Town, Fuzhou, Fujian, 350122, China.,Laboratory of Immuno-Oncology, Fujian Medical University Cancer Hospital & Fujian Cancer Hospital, No. 420, Fuma Road, Jinan District, Fuzhou, Fujian, 350014, China.,Fujian Key Laboratory of Translational Cancer Medicine, No. 420, Fuma Road, Jinan District, Fuzhou City, Fujian 350014, China
| | - Shuping Chen
- Laboratory of Immuno-Oncology, Fujian Medical University Cancer Hospital & Fujian Cancer Hospital, No. 420, Fuma Road, Jinan District, Fuzhou, Fujian, 350014, China.,Fujian Key Laboratory of Translational Cancer Medicine, No. 420, Fuma Road, Jinan District, Fuzhou City, Fujian 350014, China
| | - Chunmei Ye
- School of Basic Medical Sciences, Fujian Medical University, 1 Xue Yuan Road, University Town, Fuzhou, Fujian, 350122, China
| | - Jieyu Li
- School of Basic Medical Sciences, Fujian Medical University, 1 Xue Yuan Road, University Town, Fuzhou, Fujian, 350122, China.,Laboratory of Immuno-Oncology, Fujian Medical University Cancer Hospital & Fujian Cancer Hospital, No. 420, Fuma Road, Jinan District, Fuzhou, Fujian, 350014, China.,Fujian Key Laboratory of Translational Cancer Medicine, No. 420, Fuma Road, Jinan District, Fuzhou City, Fujian 350014, China
| | - Wansong Lin
- School of Basic Medical Sciences, Fujian Medical University, 1 Xue Yuan Road, University Town, Fuzhou, Fujian, 350122, China.,Laboratory of Immuno-Oncology, Fujian Medical University Cancer Hospital & Fujian Cancer Hospital, No. 420, Fuma Road, Jinan District, Fuzhou, Fujian, 350014, China.,Fujian Key Laboratory of Translational Cancer Medicine, No. 420, Fuma Road, Jinan District, Fuzhou City, Fujian 350014, China
| | - Yunbin Ye
- School of Basic Medical Sciences, Fujian Medical University, 1 Xue Yuan Road, University Town, Fuzhou, Fujian, 350122, China.,Laboratory of Immuno-Oncology, Fujian Medical University Cancer Hospital & Fujian Cancer Hospital, No. 420, Fuma Road, Jinan District, Fuzhou, Fujian, 350014, China.,Fujian Key Laboratory of Translational Cancer Medicine, No. 420, Fuma Road, Jinan District, Fuzhou City, Fujian 350014, China
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20
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Sources of Cancer Neoantigens beyond Single-Nucleotide Variants. Int J Mol Sci 2022; 23:ijms231710131. [PMID: 36077528 PMCID: PMC9455963 DOI: 10.3390/ijms231710131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 09/01/2022] [Accepted: 09/02/2022] [Indexed: 11/17/2022] Open
Abstract
The success of checkpoint blockade therapy against cancer has unequivocally shown that cancer cells can be effectively recognized by the immune system and eliminated. However, the identity of the cancer antigens that elicit protective immunity remains to be fully explored. Over the last decade, most of the focus has been on somatic mutations derived from non-synonymous single-nucleotide variants (SNVs) and small insertion/deletion mutations (indels) that accumulate during cancer progression. Mutated peptides can be presented on MHC molecules and give rise to novel antigens or neoantigens, which have been shown to induce potent anti-tumor immune responses. A limitation with SNV-neoantigens is that they are patient-specific and their accurate prediction is critical for the development of effective immunotherapies. In addition, cancer types with low mutation burden may not display sufficient high-quality [SNV/small indels] neoantigens to alone stimulate effective T cell responses. Accumulating evidence suggests the existence of alternative sources of cancer neoantigens, such as gene fusions, alternative splicing variants, post-translational modifications, and transposable elements, which may be attractive novel targets for immunotherapy. In this review, we describe the recent technological advances in the identification of these novel sources of neoantigens, the experimental evidence for their presentation on MHC molecules and their immunogenicity, as well as the current clinical development stage of immunotherapy targeting these neoantigens.
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21
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Weber D, Ibn-Salem J, Sorn P, Suchan M, Holtsträter C, Lahrmann U, Vogler I, Schmoldt K, Lang F, Schrörs B, Löwer M, Sahin U. Accurate detection of tumor-specific gene fusions reveals strongly immunogenic personal neo-antigens. Nat Biotechnol 2022; 40:1276-1284. [PMID: 35379963 PMCID: PMC7613288 DOI: 10.1038/s41587-022-01247-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 02/02/2022] [Indexed: 02/03/2023]
Abstract
Cancer-associated gene fusions are a potential source for highly immunogenic neoantigens, but the lack of computational tools for accurate, sensitive identification of personal gene fusions has limited their targeting in personalized cancer immunotherapy. Here we present EasyFuse, a machine learning computational pipeline for detecting cancer-specific gene fusions in transcriptome data obtained from human cancer samples. EasyFuse predicts personal gene fusions with high precision and sensitivity, outperforming previously described tools. By testing immunogenicity with autologous blood lymphocytes from patients with cancer, we detected pre-established CD4+ and CD8+ T cell responses for 10 of 21 (48%) and for 1 of 30 (3%) identified gene fusions, respectively. The high frequency of T cell responses detected in patients with cancer supports the relevance of individual gene fusions as neoantigens that might be targeted in personalized immunotherapies, especially for tumors with low mutation burden.
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Affiliation(s)
- D Weber
- TRON − Translational Oncology at the University Medical Center of Johannes Gutenberg University Mainz gGmbH, Mainz, Germany
| | - J Ibn-Salem
- TRON − Translational Oncology at the University Medical Center of Johannes Gutenberg University Mainz gGmbH, Mainz, Germany
| | - P Sorn
- TRON − Translational Oncology at the University Medical Center of Johannes Gutenberg University Mainz gGmbH, Mainz, Germany
| | - M Suchan
- TRON − Translational Oncology at the University Medical Center of Johannes Gutenberg University Mainz gGmbH, Mainz, Germany
| | - C Holtsträter
- TRON − Translational Oncology at the University Medical Center of Johannes Gutenberg University Mainz gGmbH, Mainz, Germany
| | | | | | | | - F Lang
- TRON − Translational Oncology at the University Medical Center of Johannes Gutenberg University Mainz gGmbH, Mainz, Germany
| | - B Schrörs
- TRON − Translational Oncology at the University Medical Center of Johannes Gutenberg University Mainz gGmbH, Mainz, Germany
| | - M Löwer
- TRON − Translational Oncology at the University Medical Center of Johannes Gutenberg University Mainz gGmbH, Mainz, Germany
| | - U Sahin
- TRON − Translational Oncology at the University Medical Center of Johannes Gutenberg University Mainz gGmbH, Mainz, Germany,BioNTech SE, Mainz, Germany,Johannes Gutenberg University Mainz, Mainz, Germany,corresponding author:
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22
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Buckley PR, Lee CH, Ma R, Woodhouse I, Woo J, Tsvetkov VO, Shcherbinin DS, Antanaviciute A, Shughay M, Rei M, Simmons A, Koohy H. Evaluating performance of existing computational models in predicting CD8+ T cell pathogenic epitopes and cancer neoantigens. Brief Bioinform 2022; 23:6573960. [PMID: 35471658 PMCID: PMC9116217 DOI: 10.1093/bib/bbac141] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 03/09/2022] [Accepted: 03/26/2022] [Indexed: 12/16/2022] Open
Abstract
T cell recognition of a cognate peptide-major histocompatibility complex (pMHC) presented on the surface of infected or malignant cells is of the utmost importance for mediating robust and long-term immune responses. Accurate predictions of cognate pMHC targets for T cell receptors would greatly facilitate identification of vaccine targets for both pathogenic diseases and personalized cancer immunotherapies. Predicting immunogenic peptides therefore has been at the center of intensive research for the past decades but has proven challenging. Although numerous models have been proposed, performance of these models has not been systematically evaluated and their success rate in predicting epitopes in the context of human pathology has not been measured and compared. In this study, we evaluated the performance of several publicly available models, in identifying immunogenic CD8+ T cell targets in the context of pathogens and cancers. We found that for predicting immunogenic peptides from an emerging virus such as severe acute respiratory syndrome coronavirus 2, none of the models perform substantially better than random or offer considerable improvement beyond HLA ligand prediction. We also observed suboptimal performance for predicting cancer neoantigens. Through investigation of potential factors associated with ill performance of models, we highlight several data- and model-associated issues. In particular, we observed that cross-HLA variation in the distribution of immunogenic and non-immunogenic peptides in the training data of the models seems to substantially confound the predictions. We additionally compared key parameters associated with immunogenicity between pathogenic peptides and cancer neoantigens and observed evidence for differences in the thresholds of binding affinity and stability, which suggested the need to modulate different features in identifying immunogenic pathogen versus cancer peptides. Overall, we demonstrate that accurate and reliable predictions of immunogenic CD8+ T cell targets remain unsolved; thus, we hope our work will guide users and model developers regarding potential pitfalls and unsettled questions in existing immunogenicity predictors.
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Affiliation(s)
- Paul R Buckley
- MRC Human Immunology Unit, Medical Research Council (MRC) Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom,MRC WIMM Centre for Computational Biology, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
| | - Chloe H Lee
- MRC Human Immunology Unit, Medical Research Council (MRC) Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom,MRC WIMM Centre for Computational Biology, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
| | - Ruichong Ma
- MRC Human Immunology Unit, Medical Research Council (MRC) Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom,Department of Neurosurgery, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom,Nuffield Department of Surgical Sciences, University of Oxford, Oxford, United Kingdom
| | - Isaac Woodhouse
- Centre for Immuno-Oncology, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Jeongmin Woo
- MRC Human Immunology Unit, Medical Research Council (MRC) Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom,MRC WIMM Centre for Computational Biology, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
| | | | - Dmitrii S Shcherbinin
- Department of Genomics of Adaptive Immunity, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, 117997, Russia,Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow, 117997, Russia
| | - Agne Antanaviciute
- MRC Human Immunology Unit, Medical Research Council (MRC) Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom,MRC WIMM Centre for Computational Biology, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
| | - Mikhail Shughay
- Department of Genomics of Adaptive Immunity, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, 117997, Russia,Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow, 117997, Russia
| | - Margarida Rei
- The Ludwig Institute for Cancer Research, Old Road Campus Research Building, University of Oxford, Oxford, United Kingdom
| | - Alison Simmons
- MRC Human Immunology Unit, Medical Research Council (MRC) Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
| | - Hashem Koohy
- MRC Human Immunology Unit, Medical Research Council (MRC) Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom,MRC WIMM Centre for Computational Biology, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom,Alan Turning Fellow, University of Oxford, Oxford, United Kingdom,Corresponding author: Hashem Koohy, Associate Professor of Systems immunology, Alan Turing Fellow, Group Head, MRC Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DS, UK. Tel: 44(0)1865222430; E-mail:
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23
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Neoantigen-specific CD8 T cell responses in the peripheral blood following PD-L1 blockade might predict therapy outcome in metastatic urothelial carcinoma. Nat Commun 2022; 13:1935. [PMID: 35410325 PMCID: PMC9001725 DOI: 10.1038/s41467-022-29342-0] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 03/07/2022] [Indexed: 12/14/2022] Open
Abstract
CD8+ T cell reactivity towards tumor mutation-derived neoantigens is widely believed to facilitate the antitumor immunity induced by immune checkpoint blockade (ICB). Here we show that broadening in the number of neoantigen-reactive CD8+ T cell (NART) populations between pre-treatment to 3-weeks post-treatment distinguishes patients with controlled disease compared to patients with progressive disease in metastatic urothelial carcinoma (mUC) treated with PD-L1-blockade. The longitudinal analysis of peripheral CD8+ T cell recognition of patient-specific neopeptide libraries consisting of DNA barcode-labelled pMHC multimers in a cohort of 24 patients from the clinical trial NCT02108652 also shows that peripheral NARTs derived from patients with disease control are characterised by a PD1+ Ki67+ effector phenotype and increased CD39 levels compared to bystander bulk- and virus-antigen reactive CD8+ T cells. The study provides insights into NART characteristics following ICB and suggests that early-stage NART expansion and activation are associated with response to ICB in patients with mUC. Immune checkpoint blockade therapy is successful in a high proportion of cancer patients, but others remain unresponsive. Authors here show that therapeutic success might be predictable in metastatic bladder cancer by longitudinal analysis of the early neoantigen-specific CD8 T cell response in peripheral blood.
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24
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Nielsen M, Ternette N, Barra C. The interdependence of machine learning and LC-MS approaches for an unbiased understanding of the cellular immunopeptidome. Expert Rev Proteomics 2022; 19:77-88. [PMID: 35390265 DOI: 10.1080/14789450.2022.2064278] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION The comprehensive collection of peptides presented by Major Histocompatibility Complex (MHC) molecules on the cell surface is collectively known as the immunopeptidome. The analysis and interpretation of such data sets holds great promise for furthering our understanding of basic immunology and adaptive immune activation and regulation, and for direct rational discovery of T cell antigens and the design of T-cell based therapeutics and vaccines. These applications are however challenged by the complex nature of immunopeptidome data. AREAS COVERED Here, we describe the benefits and shortcomings of applying liquid chromatography-tandem mass spectrometry (MS) to obtain large scale immunopeptidome data sets and illustrate how the accurate analysis and optimal interpretation of such data is reliant on the availability of refined and highly optimized machine learning approaches. EXPERT OPINION Further we demonstrate how the accuracy of immunoinformatics prediction methods within the field of MHC antigen presentation has benefited greatly from the availability of MS-immunopeptidomics data, and exemplify how optimal antigen discovery is best performed in a synergistic combination of MS experiments and such in silico models trained on large scale immunopeptidomics data.
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Affiliation(s)
- Morten Nielsen
- Department of Health technology, Technical University of Denmark, DK-2800 Lyngby, Denmark
| | - Nicola Ternette
- Centre for Cellular and Molecular Physiology, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK
| | - Carolina Barra
- Department of Health technology, Technical University of Denmark, DK-2800 Lyngby, Denmark
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25
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Lang F, Schrörs B, Löwer M, Türeci Ö, Sahin U. Identification of neoantigens for individualized therapeutic cancer vaccines. Nat Rev Drug Discov 2022; 21:261-282. [PMID: 35105974 PMCID: PMC7612664 DOI: 10.1038/s41573-021-00387-y] [Citation(s) in RCA: 150] [Impact Index Per Article: 75.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/13/2021] [Indexed: 02/07/2023]
Abstract
Somatic mutations in cancer cells can generate tumour-specific neoepitopes, which are recognized by autologous T cells in the host. As neoepitopes are not subject to central immune tolerance and are not expressed in healthy tissues, they are attractive targets for therapeutic cancer vaccines. Because the vast majority of cancer mutations are unique to the individual patient, harnessing the full potential of this rich source of targets requires individualized treatment approaches. Many computational algorithms and machine-learning tools have been developed to identify mutations in sequence data, to prioritize those that are more likely to be recognized by T cells and to design tailored vaccines for every patient. In this Review, we fill the gaps between the understanding of basic mechanisms of T cell recognition of neoantigens and the computational approaches for discovery of somatic mutations and neoantigen prediction for cancer immunotherapy. We present a new classification of neoantigens, distinguishing between guarding, restrained and ignored neoantigens, based on how they confer proficient antitumour immunity in a given clinical context. Such context-based differentiation will contribute to a framework that connects neoantigen biology to the clinical setting and medical peculiarities of cancer, and will enable future neoantigen-based therapies to provide greater clinical benefit.
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Affiliation(s)
- Franziska Lang
- TRON Translational Oncology, Mainz, Germany
- Faculty of Biology, Johannes Gutenberg University Mainz, Mainz, Germany
| | | | | | | | - Ugur Sahin
- BioNTech, Mainz, Germany.
- University Medical Center, Johannes Gutenberg University, Mainz, Germany.
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26
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Combined assessment of MHC binding and antigen abundance improves T cell epitope predictions. iScience 2022; 25:103850. [PMID: 35128348 PMCID: PMC8806398 DOI: 10.1016/j.isci.2022.103850] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 10/19/2021] [Accepted: 01/26/2022] [Indexed: 01/16/2023] Open
Abstract
Many steps of the MHC class I antigen processing pathway can be predicted using computational methods. Here we show that epitope predictions can be further improved by considering abundance levels of peptides' source proteins. We utilized biophysical principles and existing MHC binding prediction tools in concert with abundance estimates of source proteins to derive a function that estimates the likelihood of a peptide to be an MHC class I ligand. We found that this combination improved predictions for both naturally eluted ligands and cancer neoantigen epitopes. We compared the use of different measures of antigen abundance, including mRNA expression by RNA-Seq, gene translation by Ribo-Seq, and protein abundance by proteomics on a dataset of SARS-CoV-2 epitopes. Epitope predictions were improved above binding predictions alone in all cases and gave the highest performance when using proteomic data. Our results highlight the value of incorporating antigen abundance levels to improve epitope predictions. HLA ligands originate from highly expressed transcripts Antigen abundance and HLA binding are independent predictors of ligands and epitopes Utilizing RNA-Seq, Ribo-Seq, or proteomic data improves epitope predictions Cancer-type-matched TCGA RNA-Seq data can be used to estimate gene expression in patient
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27
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Neoantigen Cancer Vaccines: Generation, Optimization, and Therapeutic Targeting Strategies. Vaccines (Basel) 2022; 10:vaccines10020196. [PMID: 35214655 PMCID: PMC8877108 DOI: 10.3390/vaccines10020196] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 01/21/2022] [Accepted: 01/23/2022] [Indexed: 12/30/2022] Open
Abstract
Alternatives to conventional cancer treatments are highly sought after for high-risk malignancies that have a poor response to established treatment modalities. With research advancing rapidly in the past decade, neoantigen-based immunotherapeutic approaches represent an effective and highly tolerable therapeutic option. Neoantigens are tumor-specific antigens that are not expressed in normal cells and possess significant immunogenic potential. Several recent studies have described the conceptual framework and methodologies to generate neoantigen-based vaccines as well as the formulation of appropriate clinical trials to advance this approach for patient care. This review aims to describe some of the key studies in the recent literature in this rapidly evolving field and summarize the current advances in neoantigen identification and selection, vaccine generation and delivery, and the optimization of neoantigen-based therapeutic strategies, including the early data from pivotal clinical studies.
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28
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Roesler AS, Anderson KS. Beyond Sequencing: Prioritizing and Delivering Neoantigens for Cancer Vaccines. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2410:649-670. [PMID: 34914074 DOI: 10.1007/978-1-0716-1884-4_35] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Neoantigens are tumor-specific proteins and peptides that can be highly immunogenic. Immune-mediated tumor rejection is strongly associated with cytotoxic responses to neoantigen-derived peptides in noncovalent association with self-HLA molecules. Neoantigen-based therapies, such as adoptive T cell transfer, have shown the potential to induce remission of treatment-resistant metastatic disease in select patients. Cancer vaccines are similarly designed to elicit or amplify antigen-specific T cell populations and stimulate directed antitumor immunity, but the selection and prioritization of the neoantigens remains a challenge. Bioinformatic algorithms can predict tumor neoantigens from somatic mutations, insertion-deletions, and other aberrant peptide products, but this often leads to hundreds of potential neoepitopes, all unique for that tumor. Selecting neoantigens for cancer vaccines is complicated by the technical challenges of neoepitope discovery, the diversity of HLA molecules, and intratumoral heterogeneity of passenger mutations leading to immune escape. Despite strong preclinical evidence, few neoantigen cancer vaccines tested in vivo have generated epitope-specific T cell populations, suggesting suboptimal immune system activation. In this chapter, we review factors affecting the prioritization and delivery of candidate neoantigens in the design of therapeutic and preventive cancer vaccines and consider synergism with standard chemotherapies.
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Affiliation(s)
- Alexander S Roesler
- School of Medicine, Duke University, Durham, NC, USA
- Mayo Clinic, Scottsdale, AZ, USA
| | - Karen S Anderson
- Mayo Clinic, Scottsdale, AZ, USA.
- Center for Personalized Diagnostics, Biodesign Institute, Arizona State University, Tempe, AZ, USA.
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29
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Kristensen NP, Heeke C, Tvingsholm SA, Borch A, Draghi A, Crowther MD, Carri I, Munk KK, Holm JS, Bjerregaard AM, Bentzen AK, Marquard AM, Szallasi Z, McGranahan N, Andersen R, Nielsen M, Jönsson GB, Donia M, Svane IM, Hadrup SR. Neoantigen-reactive CD8+ T cells affect clinical outcome of adoptive transfer with tumor-infiltrating lymphocytes in melanoma. J Clin Invest 2021; 132:150535. [PMID: 34813506 PMCID: PMC8759789 DOI: 10.1172/jci150535] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 11/18/2021] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Neoantigen-driven recognition and T cell-mediated killing contribute to tumor clearance following adoptive cell therapy (ACT) with Tumor-Infiltrating Lymphocytes (TILs). Yet, how diversity, frequency, and persistence of expanded neoepitope-specific CD8+ T cells derived from TIL infusion products affect patient outcome is not fully determined. METHODS Using barcoded pMHC multimers, we provide a comprehensive mapping of CD8+ T cells recognizing neoepitopes in TIL infusion products and blood samples from 26 metastatic mela-noma patients who received ACT. RESULTS We identified 106 neoepitopes within TIL infusion products corresponding to 1.8% of all predicted neoepitopes. We observed neoepitope-specific recognition to be virtually devoid in TIL infusion products given to patients with progressive disease outcome. Moreover, we found that the frequency of neoepitope-specific CD8+ T cells in TIL infusion products correlated with in-creased survival, and that detection of engrafted CD8+ T cells in post-treatment (i.e. originating from the TIL infusion product) were unique to responders of TIL-ACT. Finally, we found that a transcriptional signature for lymphocyte activity within the tumor microenvironment was associated with a higher frequency of neoepitope-specific CD8+ T cells in the infusion product. CONCLUSIONS These data support previous case studies of neoepitope-specific CD8+ T cells in melanoma, and indicate that successful TIL-ACT is associated with an expansion of neoepitope-specific CD8+ T cells. FUNDING NEYE Foundation; European Research Council; Lundbeck Foundation Fellowship; Carlsberg Foundation.
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Affiliation(s)
- Nikolaj Pagh Kristensen
- Department of Health Technology, Technical University of Denmark (DTU), Kgs. Lyngby, Denmark
| | - Christina Heeke
- Department of Health Technology, Technical University of Denmark (DTU), Kgs. Lyngby, Denmark
| | - Siri A Tvingsholm
- Department of Health Technology, Technical University of Denmark (DTU), Kgs. Lyngby, Denmark
| | - Annie Borch
- Department of Health Technology, Technical University of Denmark (DTU), Kgs. Lyngby, Denmark
| | - Arianna Draghi
- Department of Oncology, Copenhagen University Hospital, Herlev, Denmark
| | | | - Ibel Carri
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina
| | - Kamilla K Munk
- Department of Health Technology, Technical University of Denmark (DTU), Kgs. Lyngby, Denmark
| | - Jeppe Sejerø Holm
- Department of Health Technology, Technical University of Denmark (DTU), Kgs. Lyngby, Denmark
| | - Anne-Mette Bjerregaard
- Department of Health Technology, Technical University of Denmark (DTU), Kgs. Lyngby, Denmark
| | - Amalie Kai Bentzen
- Department of Health Technology, Technical University of Denmark (DTU), Kgs. Lyngby, Denmark
| | - Andrea M Marquard
- Department of Health Technology, Technical University of Denmark (DTU), Kgs. Lyngby, Denmark
| | - Zoltan Szallasi
- Danish Cancer Society Research Center, Danish Cancer Society, Copenhagen, Denmark
| | | | - Rikke Andersen
- Department of Oncology, Copenhagen University Hospital, Herlev, Denmark
| | - Morten Nielsen
- Section for Bioinformatics, Department of Health Technology, Technical University of Denmark (DTU), Kgs. Lyngby, Denmark
| | - Göran B Jönsson
- Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Marco Donia
- Department of Oncology, Copenhagen University Hospital, Herlev, Denmark
| | - Inge Marie Svane
- Department of Oncology, Copenhagen University Hospital, Herlev, Denmark
| | - Sine Reker Hadrup
- Department of Health Technology, Technical University of Denmark (DTU), Kgs. Lyngby, Denmark
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30
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Schaap-Johansen AL, Vujović M, Borch A, Hadrup SR, Marcatili P. T Cell Epitope Prediction and Its Application to Immunotherapy. Front Immunol 2021; 12:712488. [PMID: 34603286 PMCID: PMC8479193 DOI: 10.3389/fimmu.2021.712488] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 07/12/2021] [Indexed: 12/13/2022] Open
Abstract
T cells play a crucial role in controlling and driving the immune response with their ability to discriminate peptides derived from healthy as well as pathogenic proteins. In this review, we focus on the currently available computational tools for epitope prediction, with a particular focus on tools aimed at identifying neoepitopes, i.e. cancer-specific peptides and their potential for use in immunotherapy for cancer treatment. This review will cover how these tools work, what kind of data they use, as well as pros and cons in their respective applications.
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Affiliation(s)
| | - Milena Vujović
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Annie Borch
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Sine Reker Hadrup
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Paolo Marcatili
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
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31
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Chiang CLL, Rovelli R, Sarivalasis A, Kandalaft LE. Integrating Cancer Vaccines in the Standard-of-Care of Ovarian Cancer: Translating Preclinical Models to Human. Cancers (Basel) 2021; 13:cancers13184553. [PMID: 34572778 PMCID: PMC8469371 DOI: 10.3390/cancers13184553] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 09/01/2021] [Accepted: 09/06/2021] [Indexed: 12/22/2022] Open
Abstract
Simple Summary The overall survival of ovarian cancer (OC) remains poor for most patients. Despite incorporation of novel therapeutic agents such as bevacizumab and PARP inhibitors to OC standard-of-care, efficacy is only observed in a subset of patients. Cancer vaccination has demonstrated effectiveness in OC patients and could be considered for potential incorporation into OC standard-of-care. This review provides an overview of the different types of cancer vaccination strategies and discusses the use of murine OC tumor models to evaluate combinatorial regimens comprising cancer vaccines and OC standard-of-care. Abstract As the majority of ovarian cancer (OC) patients are diagnosed with metastatic disease, less than 40% will survive past 5 years after diagnosis. OC is characterized by a succession of remissions and recurrences. The most promising time point for immunotherapeutic interventions in OC is following debulking surgery. Accumulating evidence shows that T cells are important in OC; thus, cancer vaccines capable of eliciting antitumor T cells will be effective in OC treatment. In this review, we discuss different cancer vaccines and propose strategies for their incorporation into the OC standard-of-care regimens. Using the murine ID8 ovarian tumor model, we provide evidence that a cancer vaccine can be effectively combined with OC standard-of-care to achieve greater overall efficacy. We demonstrate several important similarities between the ID8 model and OC patients, in terms of response to immunotherapies, and the ID8 model can be an important tool for evaluating combinatorial regimens and clinical trial designs in OC. Other emerging models, including patient-derived xenograft and genetically engineered mouse models, are continuing to improve and can be useful for evaluating cancer vaccination therapies in the near future. Here, we provide a comprehensive review of the completed and current clinical trials evaluating cancer vaccines in OC.
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Affiliation(s)
- Cheryl Lai-Lai Chiang
- Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), University of Lausanne, CH-1011 Lausanne, Switzerland; (R.R.); (A.S.)
- Ludwig Institute for Cancer Research, University of Lausanne, CH-1066 Lausanne, Switzerland
- Correspondence: (C.L.-L.C.); (L.E.K.)
| | - Raphaël Rovelli
- Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), University of Lausanne, CH-1011 Lausanne, Switzerland; (R.R.); (A.S.)
- Ludwig Institute for Cancer Research, University of Lausanne, CH-1066 Lausanne, Switzerland
| | - Apostolos Sarivalasis
- Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), University of Lausanne, CH-1011 Lausanne, Switzerland; (R.R.); (A.S.)
| | - Lana E. Kandalaft
- Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), University of Lausanne, CH-1011 Lausanne, Switzerland; (R.R.); (A.S.)
- Ludwig Institute for Cancer Research, University of Lausanne, CH-1066 Lausanne, Switzerland
- Center of Experimental Therapeutics, Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), CH-1011 Lausanne, Switzerland
- Correspondence: (C.L.-L.C.); (L.E.K.)
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32
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Bravi B, Balachandran VP, Greenbaum BD, Walczak AM, Mora T, Monasson R, Cocco S. Probing T-cell response by sequence-based probabilistic modeling. PLoS Comput Biol 2021; 17:e1009297. [PMID: 34473697 PMCID: PMC8476001 DOI: 10.1371/journal.pcbi.1009297] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Revised: 09/27/2021] [Accepted: 07/22/2021] [Indexed: 11/26/2022] Open
Abstract
With the increasing ability to use high-throughput next-generation sequencing to quantify the diversity of the human T cell receptor (TCR) repertoire, the ability to use TCR sequences to infer antigen-specificity could greatly aid potential diagnostics and therapeutics. Here, we use a machine-learning approach known as Restricted Boltzmann Machine to develop a sequence-based inference approach to identify antigen-specific TCRs. Our approach combines probabilistic models of TCR sequences with clone abundance information to extract TCR sequence motifs central to an antigen-specific response. We use this model to identify patient personalized TCR motifs that respond to individual tumor and infectious disease antigens, and to accurately discriminate specific from non-specific responses. Furthermore, the hidden structure of the model results in an interpretable representation space where TCRs responding to the same antigen cluster, correctly discriminating the response of TCR to different viral epitopes. The model can be used to identify condition specific responding TCRs. We focus on the examples of TCRs reactive to candidate neoantigens and selected epitopes in experiments of stimulated TCR clone expansion.
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Affiliation(s)
- Barbara Bravi
- Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université de Paris, Paris, France
| | - Vinod P. Balachandran
- Immuno-Oncology Service, Human Oncology and Pathogenesis Program, Hepatopancreatobiliary Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York State, United States of America
| | - Benjamin D. Greenbaum
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York State, United States of America
| | - Aleksandra M. Walczak
- Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université de Paris, Paris, France
| | - Thierry Mora
- Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université de Paris, Paris, France
| | - Rémi Monasson
- Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université de Paris, Paris, France
| | - Simona Cocco
- Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université de Paris, Paris, France
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33
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Manczinger M, Koncz B, Balogh GM, Papp BT, Asztalos L, Kemény L, Papp B, Pál C. Negative trade-off between neoantigen repertoire breadth and the specificity of HLA-I molecules shapes antitumor immunity. NATURE CANCER 2021; 2:950-961. [PMID: 35121862 DOI: 10.1038/s43018-021-00226-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Accepted: 05/19/2021] [Indexed: 02/06/2023]
Abstract
Human leukocyte antigen class I (HLA-I) genes shape our immune response against pathogens and cancer. Certain HLA-I variants can bind a wider range of peptides than others, a feature that could be favorable against a range of viral diseases. However, the implications of this phenomenon on cancer immune response are unknown. Here we quantified peptide repertoire breadth (or promiscuity) of a representative set of HLA-I alleles and found that patients with cancer who were carrying HLA-I alleles with high peptide-binding promiscuity have significantly worse prognosis after immune checkpoint inhibition. This can be explained by a reduced capacity of the immune system to discriminate tumor neopeptides from self-peptides when patients carry highly promiscuous HLA-I variants, shifting the regulation of tumor-infiltrating T cells from activation to tolerance. In summary, HLA-I peptide-binding specificity shapes neopeptide immunogenicity and the self-immunopeptidome repertoire in an antagonistic manner, and could underlie a negative trade-off between antitumor immunity and genetic susceptibility to viral infections.
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Affiliation(s)
- Máté Manczinger
- Biological Research Centre, Institute of Biochemistry, Synthetic and Systems Biology Unit, Eötvös Loránd Research Network (ELKH), Szeged, Hungary. .,Department of Dermatology and Allergology, University of Szeged, Szeged, Hungary. .,MTA-SZTE Dermatological Research Group, Eötvös Loránd Research Network (ELKH), University of Szeged, Szeged, Hungary. .,HCEMM-USZ Skin Research Group, Szeged, Hungary.
| | - Balázs Koncz
- Department of Dermatology and Allergology, University of Szeged, Szeged, Hungary
| | - Gergő Mihály Balogh
- Department of Dermatology and Allergology, University of Szeged, Szeged, Hungary
| | - Benjamin Tamás Papp
- Department of Dermatology and Allergology, University of Szeged, Szeged, Hungary.,Szeged Scientist Academy, Szeged, Hungary
| | - Leó Asztalos
- Department of Dermatology and Allergology, University of Szeged, Szeged, Hungary.,Szeged Scientist Academy, Szeged, Hungary
| | - Lajos Kemény
- Department of Dermatology and Allergology, University of Szeged, Szeged, Hungary.,MTA-SZTE Dermatological Research Group, Eötvös Loránd Research Network (ELKH), University of Szeged, Szeged, Hungary.,HCEMM-USZ Skin Research Group, Szeged, Hungary
| | - Balázs Papp
- Biological Research Centre, Institute of Biochemistry, Synthetic and Systems Biology Unit, Eötvös Loránd Research Network (ELKH), Szeged, Hungary.,HCEMM-BRC Metabolic Systems Biology Lab, Szeged, Hungary
| | - Csaba Pál
- Biological Research Centre, Institute of Biochemistry, Synthetic and Systems Biology Unit, Eötvös Loránd Research Network (ELKH), Szeged, Hungary.
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Koşaloğlu-Yalçın Z, Blazeska N, Carter H, Nielsen M, Cohen E, Kufe D, Conejo-Garcia J, Robbins P, Schoenberger SP, Peters B, Sette A. The Cancer Epitope Database and Analysis Resource: A Blueprint for the Establishment of a New Bioinformatics Resource for Use by the Cancer Immunology Community. Front Immunol 2021; 12:735609. [PMID: 34504503 PMCID: PMC8421848 DOI: 10.3389/fimmu.2021.735609] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 08/09/2021] [Indexed: 12/17/2022] Open
Abstract
Recent years have witnessed a dramatic rise in interest towards cancer epitopes in general and particularly neoepitopes, antigens that are encoded by somatic mutations that arise as a consequence of tumorigenesis. There is also an interest in the specific T cell and B cell receptors recognizing these epitopes, as they have therapeutic applications. They can also aid in basic studies to infer the specificity of T cells or B cells characterized in bulk and single-cell sequencing data. The resurgence of interest in T cell and B cell epitopes emphasizes the need to catalog all cancer epitope-related data linked to the biological, immunological, and clinical contexts, and most importantly, making this information freely available to the scientific community in a user-friendly format. In parallel, there is also a need to develop resources for epitope prediction and analysis tools that provide researchers access to predictive strategies and provide objective evaluations of their performance. For example, such tools should enable researchers to identify epitopes that can be effectively used for immunotherapy or in defining biomarkers to predict the outcome of checkpoint blockade therapies. We present here a detailed vision, blueprint, and work plan for the development of a new resource, the Cancer Epitope Database and Analysis Resource (CEDAR). CEDAR will provide a freely accessible, comprehensive collection of cancer epitope and receptor data curated from the literature and provide easily accessible epitope and T cell/B cell target prediction and analysis tools. The curated cancer epitope data will provide a transparent benchmark dataset that can be used to assess how well prediction tools perform and to develop new prediction tools relevant to the cancer research community.
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MESH Headings
- Antigens, Neoplasm/genetics
- Antigens, Neoplasm/immunology
- Computational Biology
- Databases, Genetic
- Epitopes, B-Lymphocyte
- Epitopes, T-Lymphocyte
- Humans
- Immunotherapy
- Mutation
- Neoplasms/genetics
- Neoplasms/immunology
- Neoplasms/therapy
- Receptors, Antigen, B-Cell/genetics
- Receptors, Antigen, B-Cell/immunology
- Receptors, Antigen, T-Cell/genetics
- Receptors, Antigen, T-Cell/immunology
- Tumor Microenvironment
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Affiliation(s)
- Zeynep Koşaloğlu-Yalçın
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, United States
| | - Nina Blazeska
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, United States
| | - Hannah Carter
- Department of Medicine, University of California San Diego, La Jolla, CA, United States
- Moore’s Cancer Center, University of California San Diego, La Jolla, CA, United States
| | - Morten Nielsen
- Department of Bio and Health Informatics, Technical University of Denmark, Lyngby, Denmark
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, San Martín, Argentina
| | - Ezra Cohen
- Moore’s Cancer Center, University of California San Diego, La Jolla, CA, United States
| | - Donald Kufe
- Dana Farber Cancer Institute, Harvard Medical School, Boston, MA, United States
| | - Jose Conejo-Garcia
- Department of Gynecologic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States
- Department of Immunology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States
| | - Paul Robbins
- National Cancer Institute, National Institutes of Health, Bethesda, MD, United States
| | - Stephen P. Schoenberger
- Laboratory of Cellular Immunology, La Jolla Institute for Immunology, La Jolla, CA, United States
| | - Bjoern Peters
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, United States
- Department of Medicine, University of California San Diego, La Jolla, CA, United States
| | - Alessandro Sette
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, United States
- Department of Medicine, University of California San Diego, La Jolla, CA, United States
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35
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Mestrinho LA, Santos RR. Translational oncotargets for immunotherapy: From pet dogs to humans. Adv Drug Deliv Rev 2021; 172:296-313. [PMID: 33705879 DOI: 10.1016/j.addr.2021.02.020] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 02/10/2021] [Accepted: 02/27/2021] [Indexed: 12/21/2022]
Abstract
Preclinical studies in rodent models have been a pivotal role in human clinical research, but many of them fail in the translational process. Spontaneous tumors in pet dogs have the potential to bridge the gap between preclinical models and human clinical trials. Their natural occurrence in an immunocompetent system overcome the limitations of preclinical rodent models. Due to its reasonable cellular, molecular, and genetic homology to humans, the pet dog represents a valuable model to accelerate the translation of preclinical studies to clinical trials in humans, actually with benefits for both species. Moreover, their unique genetic features of breeding and breed-related mutations have contributed to assess and optimize therapeutics in individuals with different genetic backgrounds. This review aims to outline four main immunotherapy approaches - cancer vaccines, adaptive T-cell transfer, antibodies, and cytokines -, under research in veterinary medicine and how they can serve the clinical application crosstalk with humans.
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36
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Yu Y, Zhang J, Ni L, Zhu Y, Yu H, Teng Y, Lin L, Xue Z, Xue X, Shen X, Song H, Su X, Sun W, Cai Z. Neoantigen-reactive T cells exhibit effective anti-tumor activity against colorectal cancer. Hum Vaccin Immunother 2021; 18:1-11. [PMID: 33689574 PMCID: PMC8920255 DOI: 10.1080/21645515.2021.1891814] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Neoantigens play a crucial role in cancer immunotherapy. However, the effectiveness and safety of neoantigen-based immunotherapies in patients with colorectal cancer (CRC), particularly in the Chinese population, have not been well studied. This study explored the feasibility and effectiveness of neoantigens in the treatment of CRC. Whole-exome sequencing (WES) and transcriptome sequencing were used to identify somatic mutations, RNA expression, and human leukocyte antigen (HLA) alleles. Neoantigen candidates were predicted, and immunogenicity was assessed. The neoantigens TSHZ3-L523P, RARA-R83H, TP53-R248W, EYA2-V333I, and NRAS-G12D from Patient 4 (PW4); TASP1-P161L, RAP1GAP-S215R, MOSPD1-V63I, and NAV2-D1973N from Patient 10 (PW10); and HAVCR2-F39V, SEC11A-R11L, SMPDL3B-T452M, LRFN3-R118Q, and ULK1-S248L from Patient 11 (HLA-A0201+PW11) induced a heightened neoantigen-reactive T cell (NRT) response as compared with the controls in peripheral blood lymphocytes (PBLs) isolated from patients with CRC. In addition, we identified neoantigen-containing peptides SEC11A-R11L and ULK1-S248L from HLA-A0201+PW11, which more effectively elicited specific CTL responses than the corresponding native peptides in PBLs isolated from HLA-A0201+PW11 as well as in HLA-A2.1/Kb transgenic mice. Importantly, adoptive transfer of NRTs induced by vaccination with two mutant peptides could effectively inhibit tumor growth in tumor-bearing mouse models. These data indicate that neoantigen-containing peptides with high immunogenicity represent promising candidates for peptide-mediated personalized therapy. Abbreviations: CRC: colorectal cancer; DCs: dendritic cells; ELISPOT: enzyme-linked immunosorbent spot; E:T: effector:target; HLA: human leukocyte antigen; MHC: major histocompatibility complex; Mut: mutant type; NGS: next-generation sequencing; NRTs: neoantigen-reactive T cells; PBMCs: peripheral blood mononuclear cells; STR: short tandem repeat; PBLs: peripheral blood lymphocytes; PBS: phosphate-buffered saline; PD-1: programmed cell death protein 1; TILs: tumor-infiltrating lymphocytes; RNA-seq: RNA sequencing; Tg: transgenic; TMGs: tandem minigenes; WES: whole-exome sequencing; WT: wild-type.
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Affiliation(s)
- Yaojun Yu
- Department of Gastrointestinal Surgery, Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Jing Zhang
- Department of Gastroenterology, Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Leyi Ni
- Department of Gastroenterology, Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Yuesheng Zhu
- Department of Gastroenterology, Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Hejie Yu
- Department of Gastroenterology, Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Yangyang Teng
- Department of Gastroenterology, Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Limiao Lin
- Department of Gastroenterology, Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Zhanxiong Xue
- Department of Gastroenterology, Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Xiangyang Xue
- Department of Oncology, Wenzhou Medical University School of Basic Medicine, Wenzhou, Zhejiang, China
| | - Xian Shen
- Department of Gastrointestinal Surgery, Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Haiping Song
- Department of Oncology, Qingdao Central Hospital, The Second Affiliated Hospital, Qingdao University, Qingdao, China
| | - Xiaoping Su
- Department of Oncology, Wenzhou Medical University School of Basic Medicine, Wenzhou, Zhejiang, China
| | - Weihong Sun
- Department of Oncology, Biotherapy Center, Qingdao Central Hospital, The Second Affiliated Hospital, Qingdao University, Qingdao, China
| | - Zhenzhai Cai
- Department of Gastroenterology, Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
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37
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Schmidt J, Smith AR, Magnin M, Racle J, Devlin JR, Bobisse S, Cesbron J, Bonnet V, Carmona SJ, Huber F, Ciriello G, Speiser DE, Bassani-Sternberg M, Coukos G, Baker BM, Harari A, Gfeller D. Prediction of neo-epitope immunogenicity reveals TCR recognition determinants and provides insight into immunoediting. CELL REPORTS MEDICINE 2021; 2:100194. [PMID: 33665637 PMCID: PMC7897774 DOI: 10.1016/j.xcrm.2021.100194] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 12/11/2020] [Accepted: 01/11/2021] [Indexed: 02/07/2023]
Abstract
CD8+ T cell recognition of peptide epitopes plays a central role in immune responses against pathogens and tumors. However, the rules that govern which peptides are truly recognized by existing T cell receptors (TCRs) remain poorly understood, precluding accurate predictions of neo-epitopes for cancer immunotherapy. Here, we capitalize on recent (neo-)epitope data to train a predictor of immunogenic epitopes (PRIME), which captures molecular properties of both antigen presentation and TCR recognition. PRIME not only improves prioritization of neo-epitopes but also correlates with T cell potency and unravels biophysical determinants of TCR recognition that we experimentally validate. Analysis of cancer genomics data reveals that recurrent mutations tend to be less frequent in patients where they are predicted to be immunogenic, providing further evidence for immunoediting in human cancer. PRIME will facilitate identification of pathogen epitopes in infectious diseases and neo-epitopes in cancer immunotherapy.
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Affiliation(s)
- Julien Schmidt
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University Hospital of Lausanne, Lausanne, Switzerland
| | - Angela R Smith
- Department of Chemistry and Biochemistry and the Harper Cancer Research Institute, University of Notre Dame, Notre Dame, IN, USA
| | - Morgane Magnin
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University Hospital of Lausanne, Lausanne, Switzerland
| | - Julien Racle
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, Lausanne, Switzerland.,Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Jason R Devlin
- Department of Chemistry and Biochemistry and the Harper Cancer Research Institute, University of Notre Dame, Notre Dame, IN, USA
| | - Sara Bobisse
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University Hospital of Lausanne, Lausanne, Switzerland
| | - Julien Cesbron
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University Hospital of Lausanne, Lausanne, Switzerland
| | | | - Santiago J Carmona
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, Lausanne, Switzerland.,Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Florian Huber
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University Hospital of Lausanne, Lausanne, Switzerland
| | - Giovanni Ciriello
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland.,Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - Daniel E Speiser
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, Lausanne, Switzerland
| | - Michal Bassani-Sternberg
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University Hospital of Lausanne, Lausanne, Switzerland
| | - George Coukos
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University Hospital of Lausanne, Lausanne, Switzerland
| | - Brian M Baker
- Department of Chemistry and Biochemistry and the Harper Cancer Research Institute, University of Notre Dame, Notre Dame, IN, USA
| | - Alexandre Harari
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University Hospital of Lausanne, Lausanne, Switzerland.,Center of Experimental Therapeutics, Department of Oncology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - David Gfeller
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, Lausanne, Switzerland.,Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
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38
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Marofi F, Motavalli R, Safonov VA, Thangavelu L, Yumashev AV, Alexander M, Shomali N, Chartrand MS, Pathak Y, Jarahian M, Izadi S, Hassanzadeh A, Shirafkan N, Tahmasebi S, Khiavi FM. CAR T cells in solid tumors: challenges and opportunities. Stem Cell Res Ther 2021; 12:81. [PMID: 33494834 PMCID: PMC7831265 DOI: 10.1186/s13287-020-02128-1] [Citation(s) in RCA: 257] [Impact Index Per Article: 85.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 12/28/2020] [Indexed: 01/11/2023] Open
Abstract
BACKGROUND CARs are simulated receptors containing an extracellular single-chain variable fragment (scFv), a transmembrane domain, as well as an intracellular region of immunoreceptor tyrosine-based activation motifs (ITAMs) in association with a co-stimulatory signal. MAIN BODY Chimeric antigen receptor (CAR) T cells are genetically engineered T cells to express a receptor for the recognition of the particular surface marker that has given rise to advances in the treatment of blood disorders. The CAR T cells obtain supra-physiological properties and conduct as "living drugs" presenting both immediate and steady effects after expression in T cells surface. But, their efficacy in solid tumor treatment has not yet been supported. The pivotal challenges in the field of solid tumor CAR T cell therapy can be summarized in three major parts: recognition, trafficking, and surviving in the tumor. On the other hand, the immunosuppressive tumor microenvironment (TME) interferes with T cell activity in terms of differentiation and exhaustion, and as a result of the combined use of CARs and checkpoint blockade, as well as the suppression of other inhibitor factors in the microenvironment, very promising results were obtained from the reduction of T cell exhaustion. CONCLUSION Nowadays, identifying and defeating the mechanisms associated with CAR T cell dysfunction is crucial to establish CAR T cells that can proliferate and lyse tumor cells severely. In this review, we discuss the CAR signaling and efficacy T in solid tumors and evaluate the most significant barriers in this process and describe the most novel therapeutic methods aiming to the acquirement of the promising therapeutic outcome in non-hematologic malignancies.
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Affiliation(s)
- Faroogh Marofi
- Department of Hematology, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Roza Motavalli
- Stem Cell Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
- Kidney Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Vladimir A. Safonov
- The Laboratory of Biogeochemistry and Environment, Vernadsky Institute of Geochemistry and Analytical Chemistry of Russian Academy of Sciences, Kosygina 19 Street, Moscow, Russian Federation 119991
| | - Lakshmi Thangavelu
- Department of Pharmacology, Saveetha Dental College and Hospital, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, India
| | | | - Markov Alexander
- Tyumen State Medical University, Tyumen Industrial University, Tyumen, Russian Federation
| | - Navid Shomali
- Toxicology and Chemotherapy Unit (G401), German Cancer Research Center, 69120 Heidelberg, Germany
| | | | - Yashwant Pathak
- Taneja College of Pharmacy, University of South Florida, Tampa, FL USA
| | - Mostafa Jarahian
- Department of Immunology, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Sepideh Izadi
- Toxicology and Chemotherapy Unit (G401), German Cancer Research Center, 69120 Heidelberg, Germany
| | - Ali Hassanzadeh
- Toxicology and Chemotherapy Unit (G401), German Cancer Research Center, 69120 Heidelberg, Germany
| | - Naghmeh Shirafkan
- Toxicology and Chemotherapy Unit (G401), German Cancer Research Center, 69120 Heidelberg, Germany
| | - Safa Tahmasebi
- Toxicology and Chemotherapy Unit (G401), German Cancer Research Center, 69120 Heidelberg, Germany
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39
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Borrman T, Pierce BG, Vreven T, Baker BM, Weng Z. High-throughput modeling and scoring of TCR-pMHC complexes to predict cross-reactive peptides. Bioinformatics 2020; 36:5377-5385. [PMID: 33355667 PMCID: PMC8016493 DOI: 10.1093/bioinformatics/btaa1050] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Revised: 11/23/2020] [Accepted: 12/08/2020] [Indexed: 01/14/2023] Open
Abstract
MOTIVATION The binding of T cell receptors (TCRs) to their target peptide MHC (pMHC) ligands initializes the cell-mediated immune response. In autoimmune diseases such as multiple sclerosis, the TCR erroneously recognizes self-peptides as foreign and activates an immune response against healthy cells. Such responses can be triggered by cross-recognition of the autoreactive TCR with foreign peptides. Hence, it would be desirable to identify such foreign-antigen triggers to provide a mechanistic understanding of autoimmune diseases. However, the large sequence space of foreign antigens presents an obstacle in the identification of cross-reactive peptides. RESULTS Here, we present an in silico modeling and scoring method which exploits the structural properties of TCR-pMHC complexes to predict the binding of cross-reactive peptides. We analyzed three mouse TCRs and one human TCR isolated from a patient with multiple sclerosis. Cross-reactive peptides for these TCRs were previously identified via yeast display coupled with deep sequencing, providing a robust dataset for evaluating our method. Modeling query peptides in their associated TCR-pMHC crystal structures, our method accurately selected the top binding peptides from sets containing more than a hundred thousand unique peptides. AVAILABILITY AND IMPLEMENTATION Analyses were performed using custom Python and R scripts available at https://github.com/tborrman/antigen-predict. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Tyler Borrman
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA, USA
| | - Brian G Pierce
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD, USA.,Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, USA
| | - Thom Vreven
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA, USA
| | - Brian M Baker
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN, USA.,Harper Cancer Research Institute, University of Notre Dame, Notre Dame, IN, USA
| | - Zhiping Weng
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA, USA
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40
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Lee CH, Salio M, Napolitani G, Ogg G, Simmons A, Koohy H. Predicting Cross-Reactivity and Antigen Specificity of T Cell Receptors. Front Immunol 2020; 11:565096. [PMID: 33193332 PMCID: PMC7642207 DOI: 10.3389/fimmu.2020.565096] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2020] [Accepted: 09/07/2020] [Indexed: 12/13/2022] Open
Abstract
Adaptive immune recognition is mediated by specific interactions between heterodimeric T cell receptors (TCRs) and their cognate peptide-MHC (pMHC) ligands, and the methods to accurately predict TCR:pMHC interaction would have profound clinical, therapeutic and pharmaceutical applications. Herein, we review recent developments in predicting cross-reactivity and antigen specificity of TCR recognition. We discuss current experimental and computational approaches to investigate cross-reactivity and antigen-specificity of TCRs and highlight how integrating kinetic, biophysical and structural features may offer valuable insights in modeling immunogenicity. We further underscore the close inter-relationship of these two interconnected notions and the need to investigate each in the light of the other for a better understanding of T cell responsiveness for the effective clinical applications.
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Affiliation(s)
- Chloe H. Lee
- MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
- MRC WIMM Centre for Computational Biology, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
| | - Mariolina Salio
- MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
| | - Giorgio Napolitani
- MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
| | - Graham Ogg
- MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
| | - Alison Simmons
- MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
- Translational Gastroenterology Unit, John Radcliffe Hospital, Oxford, United Kingdom
| | - Hashem Koohy
- MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
- MRC WIMM Centre for Computational Biology, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
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41
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Punta M, Jennings VA, Melcher AA, Lise S. The Immunogenic Potential of Recurrent Cancer Drug Resistance Mutations: An In Silico Study. Front Immunol 2020; 11:524968. [PMID: 33133066 PMCID: PMC7578429 DOI: 10.3389/fimmu.2020.524968] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Accepted: 09/14/2020] [Indexed: 12/13/2022] Open
Abstract
Cancer somatic mutations have been identified as a source of antigens that can be targeted by cancer immunotherapy. In this work, expanding on previous studies, we analyze the HLA-presentation properties of mutations that are known to drive resistance to cancer targeted-therapies. We survey a large dataset of mutations that confer resistance to different drugs and occur in numerous genes and tumor types. We show that a significant number of them are predicted in silico to be potentially immunogenic across a large proportion of the human population. Further, by analyzing a cohort of patients carrying a small subset of these resistance mutations, we provide evidence that what is observed in the general population may be indicative of the mutations' immunogenic potential in resistant patients. Two of the mutations in our dataset had previously been experimentally validated by others and it was confirmed that some of their associated neopeptides elicit T-cell responses in vitro. The identification of potent cancer-specific antigens can be instrumental for developing more effective immunotherapies. In this work, we propose a novel list of drug-resistance mutations, several of which are recurrent, that could be of particular interest in the context of off-the-shelf precision immunotherapies such as therapeutic cancer vaccines.
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Affiliation(s)
- Marco Punta
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, United Kingdom
| | - Victoria A. Jennings
- Department of Immunity and Infection, Leeds Institute of Medical Research, Leeds, United Kingdom
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom
| | - Alan A. Melcher
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom
| | - Stefano Lise
- Centre for Evolution and Cancer, The Institute of Cancer Research, London, United Kingdom
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42
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Gopanenko AV, Kosobokova EN, Kosorukov VS. Main Strategies for the Identification of Neoantigens. Cancers (Basel) 2020; 12:E2879. [PMID: 33036391 PMCID: PMC7600129 DOI: 10.3390/cancers12102879] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 10/01/2020] [Accepted: 10/05/2020] [Indexed: 12/24/2022] Open
Abstract
Genetic instability of tumors leads to the appearance of numerous tumor-specific somatic mutations that could potentially result in the production of mutated peptides that are presented on the cell surface by the MHC molecules. Peptides of this kind are commonly called neoantigens. Their presence on the cell surface specifically distinguishes tumors from healthy tissues. This feature makes neoantigens a promising target for immunotherapy. The rapid evolution of high-throughput genomics and proteomics makes it possible to implement these techniques in clinical practice. In particular, they provide useful tools for the investigation of neoantigens. The most valuable genomic approach to this problem is whole-exome sequencing coupled with RNA-seq. High-throughput mass-spectrometry is another option for direct identification of MHC-bound peptides, which is capable of revealing the entire MHC-bound peptidome. Finally, structure-based predictions could significantly improve the understanding of physicochemical and structural features that affect the immunogenicity of peptides. The development of pipelines combining such tools could improve the accuracy of the peptide selection process and decrease the required time. Here we present a review of the main existing approaches to investigating the neoantigens and suggest a possible ideal pipeline that takes into account all modern trends in the context of neoantigen discovery.
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Affiliation(s)
| | | | - Vyacheslav S. Kosorukov
- N.N. Blokhin National Medical Research Center of Oncology, Ministry of Health of the Russian Federation, 115478 Moscow, Russia; (A.V.G.); (E.N.K.)
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43
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Domínguez‐Romero AN, Martínez‐Cortés F, Munguía ME, Odales J, Gevorkian G, Manoutcharian K. Generation of multiepitope cancer vaccines based on large combinatorial libraries of survivin-derived mutant epitopes. Immunology 2020; 161:123-138. [PMID: 32619293 PMCID: PMC7496785 DOI: 10.1111/imm.13233] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Revised: 06/19/2020] [Accepted: 06/21/2020] [Indexed: 12/26/2022] Open
Abstract
Immune tolerance is the main challenge in the field of cancer vaccines, so modified peptide sequences or naturally occurring mutated versions of cancer-related wild-type (WT) antigens represent a promising pathway. However, the low immunogenicity of mutation-induced neoantigens and, particularly, their incapacity to activate CD8+ T cells are generating doubts on the success of neoantigen-based cancer vaccines in clinical trials. We developed a novel vaccine approach based on a new class of vaccine immunogens, called variable epitope libraries (VELs). We used three regions of survivin (SVN), composed of 40, 49 and 51 amino acids, along with the complete SVN protein to generate the VELs as multiepitope vaccines. BALB/c mice, challenged with the aggressive and highly metastatic 4T1 cell line, were vaccinated in a therapeutic setting. We showed significant tumor growth inhibition and, most importantly, strong suppression of lung metastasis after a single immunization using VEL vaccines. We demonstrated vaccine-induced broad cellular immune responses concomitant with extensive tumor infiltration of T cells, the activation of CD107a+ IFN-γ+ T cells in the spleen and a significant increase in the number of CD3+ CD8+ Ly6C+ effector T cells. In addition, we observed the presence of interferon-γ-, granzyme B- and perforin-producing lymphocytes along with modifications in the amount of CD11b+ Ly6Cint/low Ly6G+ granulocytic myeloid-derived suppressor cells and CD4+ CD25+ FoxP3+ regulatory T cells in the lungs and tumors of mice. In summary, we showed that the VELs represent a potent new class of cancer immunotherapy and propose the application of the VEL vaccine concept as a true alternative to currently available vaccine platforms.
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Affiliation(s)
| | - Fernando Martínez‐Cortés
- Instituto de Investigaciones BiomédicasUniversidad Nacional Autónoma de México (UNAM)MéxicoDFMéxico
| | - María Elena Munguía
- Instituto de Investigaciones BiomédicasUniversidad Nacional Autónoma de México (UNAM)MéxicoDFMéxico
| | - Josué Odales
- Instituto de Investigaciones BiomédicasUniversidad Nacional Autónoma de México (UNAM)MéxicoDFMéxico
| | - Goar Gevorkian
- Instituto de Investigaciones BiomédicasUniversidad Nacional Autónoma de México (UNAM)MéxicoDFMéxico
| | - Karen Manoutcharian
- Instituto de Investigaciones BiomédicasUniversidad Nacional Autónoma de México (UNAM)MéxicoDFMéxico
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44
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Aranha MP, Jewel YSM, Beckman RA, Weiner LM, Mitchell JC, Parks JM, Smith JC. Combining Three-Dimensional Modeling with Artificial Intelligence to Increase Specificity and Precision in Peptide-MHC Binding Predictions. JOURNAL OF IMMUNOLOGY (BALTIMORE, MD. : 1950) 2020; 205:1962-1977. [PMID: 32878910 PMCID: PMC7511449 DOI: 10.4049/jimmunol.1900918] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Accepted: 08/01/2020] [Indexed: 02/06/2023]
Abstract
The reliable prediction of the affinity of candidate peptides for the MHC is important for predicting their potential antigenicity and thus influences medical applications, such as decisions on their inclusion in T cell-based vaccines. In this study, we present a rapid, predictive computational approach that combines a popular, sequence-based artificial neural network method, NetMHCpan 4.0, with three-dimensional structural modeling. We find that the ensembles of bound peptide conformations generated by the programs MODELLER and Rosetta FlexPepDock are less variable in geometry for strong binders than for low-affinity peptides. In tests on 1271 peptide sequences for which the experimental dissociation constants of binding to the well-characterized murine MHC allele H-2Db are known, by applying thresholds for geometric fluctuations the structure-based approach in a standalone manner drastically improves the statistical specificity, reducing the number of false positives. Furthermore, filtering candidates generated with NetMHCpan 4.0 with the structure-based predictor led to an increase in the positive predictive value (PPV) of the peptides correctly predicted to bind very strongly (i.e., K d < 100 nM) from 40 to 52% (p = 0.027). The combined method also significantly improved the PPV when tested on five human alleles, including some with limited data for training. Overall, an average increase of 10% in the PPV was found over the standalone sequence-based method. The combined method should be useful in the rapid design of effective T cell-based vaccines.
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Affiliation(s)
- Michelle P Aranha
- Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, TN 37916
- Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, TN 37830
| | - Yead S M Jewel
- Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, TN 37916
- Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, TN 37830
| | - Robert A Beckman
- Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC 20007
- Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, DC 20007
- Department of Oncology and Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC 20057
| | - Louis M Weiner
- Department of Oncology and Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC 20057
| | - Julie C Mitchell
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830
| | - Jerry M Parks
- Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, TN 37830
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830
| | - Jeremy C Smith
- Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, TN 37916;
- Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, TN 37830
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45
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Structural dissimilarity from self drives neoepitope escape from immune tolerance. Nat Chem Biol 2020; 16:1269-1276. [PMID: 32807968 DOI: 10.1038/s41589-020-0610-1] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Accepted: 07/02/2020] [Indexed: 02/08/2023]
Abstract
T-cell recognition of peptides incorporating nonsynonymous mutations, or neoepitopes, is a cornerstone of tumor immunity and forms the basis of new immunotherapy approaches including personalized cancer vaccines. Yet as they are derived from self-peptides, the means through which immunogenic neoepitopes overcome immune self-tolerance are often unclear. Here we show that a point mutation in a non-major histocompatibility complex anchor position induces structural and dynamic changes in an immunologically active ovarian cancer neoepitope. The changes pre-organize the peptide into a conformation optimal for recognition by a neoepitope-specific T-cell receptor, allowing the receptor to bind the neoepitope with high affinity and deliver potent T-cell signals. Our results emphasize the importance of structural and physical changes relative to self in neoepitope immunogenicity. Considered broadly, these findings can help explain some of the difficulties in identifying immunogenic neoepitopes from sequence alone and provide guidance for developing novel, neoepitope-based personalized therapies.
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46
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Harari A, Graciotti M, Bassani-Sternberg M, Kandalaft LE. Antitumour dendritic cell vaccination in a priming and boosting approach. Nat Rev Drug Discov 2020; 19:635-652. [PMID: 32764681 DOI: 10.1038/s41573-020-0074-8] [Citation(s) in RCA: 139] [Impact Index Per Article: 34.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/19/2020] [Indexed: 02/06/2023]
Abstract
Mobilizing antitumour immunity through vaccination potentially constitutes a powerful anticancer strategy but has not yet provided robust clinical benefits in large patient populations. Although major hurdles still exist, we believe that currently available strategies for vaccines that target dendritic cells or use them to present antitumour antigens could be integrated into existing clinical practice using prime-boost approaches. In the priming phase, these approaches capitalize on either standard treatment modalities to trigger in situ vaccination and release tumour antigens or vaccination with dendritic cells loaded with tumour lysates or patient-specific neoantigens. In a second boost phase, personalized synthetic vaccines specifically boost T cells that were triggered during the priming phase. This immunotherapy approach has been enabled by the substantial recent improvements in dendritic cell vaccines. In this Perspective, we discuss these improvements, highlight how the prime-boost approach can be translated into clinical practice and provide solutions for various anticipated hurdles.
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Affiliation(s)
- Alexandre Harari
- Center of Experimental Therapeutics, Department of Oncology, University Hospital of Lausanne, Lausanne, Switzerland.,Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
| | - Michele Graciotti
- Center of Experimental Therapeutics, Department of Oncology, University Hospital of Lausanne, Lausanne, Switzerland.,Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
| | - Michal Bassani-Sternberg
- Center of Experimental Therapeutics, Department of Oncology, University Hospital of Lausanne, Lausanne, Switzerland.,Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
| | - Lana E Kandalaft
- Center of Experimental Therapeutics, Department of Oncology, University Hospital of Lausanne, Lausanne, Switzerland. .,Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland.
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47
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Abstract
Immunoinformatics is a discipline that applies methods of computer science to study and model the immune system. A fundamental question addressed by immunoinformatics is how to understand the rules of antigen presentation by MHC molecules to T cells, a process that is central to adaptive immune responses to infections and cancer. In the modern era of personalized medicine, the ability to model and predict which antigens can be presented by MHC is key to manipulating the immune system and designing strategies for therapeutic intervention. Since the MHC is both polygenic and extremely polymorphic, each individual possesses a personalized set of MHC molecules with different peptide-binding specificities, and collectively they present a unique individualized peptide imprint of the ongoing protein metabolism. Mapping all MHC allotypes is an enormous undertaking that cannot be achieved without a strong bioinformatics component. Computational tools for the prediction of peptide-MHC binding have thus become essential in most pipelines for T cell epitope discovery and an inescapable component of vaccine and cancer research. Here, we describe the development of several such tools, from pioneering efforts to the current state-of-the-art methods, that have allowed for accurate predictions of peptide binding of all MHC molecules, even including those that have not yet been characterized experimentally.
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Affiliation(s)
- Morten Nielsen
- Department of Health Technology, Technical University of Denmark, DK-2800 Kongens Lyngby, Denmark
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, CP 1650 San Martin, Buenos Aires, Argentina
| | - Massimo Andreatta
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, CP 1650 San Martin, Buenos Aires, Argentina
| | - Bjoern Peters
- Division of Vaccine Discovery, La Jolla Institute for Immunology, La Jolla, California 92037, USA
- Department of Medicine, University of California, San Diego, La Jolla, California 92093, USA
| | - Søren Buus
- Department of Immunology and Microbiology, Faculty of Health Sciences, University of Copenhagen, DK-2200 Copenhagen, Denmark
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48
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Towards new horizons: characterization, classification and implications of the tumour antigenic repertoire. Nat Rev Clin Oncol 2020; 17:595-610. [PMID: 32572208 PMCID: PMC7306938 DOI: 10.1038/s41571-020-0387-x] [Citation(s) in RCA: 105] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/05/2020] [Indexed: 12/21/2022]
Abstract
Immune-checkpoint inhibition provides an unmatched level of durable clinical efficacy in various malignancies. Such therapies promote the activation of antigen-specific T cells, although the precise targets of these T cells remain unknown. Exploiting these targets holds great potential to amplify responses to treatment, such as by combining immune-checkpoint inhibition with therapeutic vaccination or other antigen-directed treatments. In this scenario, the pivotal hurdle remains the definition of valid HLA-restricted tumour antigens, which requires several levels of evidence before targets can be established with sufficient confidence. Suitable antigens might include tumour-specific antigens with alternative or wild-type sequences, tumour-associated antigens and cryptic antigens that exceed exome boundaries. Comprehensive antigen classification is required to enable future clinical development and the definition of innovative treatment strategies. Furthermore, clinical development remains challenging with regard to drug manufacturing and regulation, as well as treatment feasibility. Despite these challenges, treatments based on diligently curated antigens combined with a suitable therapeutic platform have the potential to enable optimal antitumour efficacy in patients, either as monotherapies or in combination with other established immunotherapies. In this Review, we summarize the current state-of-the-art approaches for the identification of candidate tumour antigens and provide a structured terminology based on their underlying characteristics. Immune-checkpoint inhibition has transformed the treatment of patients with advanced-stage cancers. Nonetheless, the specific antigens targeted by T cells that are activated or reactivated by these agents remain largely unknown. In this Review, the authors describe the characterization and classification of tumour antigens including descriptions of the most appropriate detection methods, and discuss potential regulatory issues regarding the use of tumour antigen-based therapeutics. Immune-checkpoint inhibition has profoundly changed the paradigm for the care of several malignancies. Although these therapies activate antigen-specific T cells, the precise mechanisms of action and their specific targets remain largely unknown. Anticancer immunotherapies encompass two fundamentally different therapeutic principles based on knowledge of their therapeutic targets, that either have been characterized (antigen-aware) or have remained elusive (antigen-unaware). HLA-presented tumour antigens of potential therapeutic relevance can comprise alternative or wild-type amino acid sequences and can be subdivided into different categories based on their mechanisms of formation. The available methods for the detection of HLA-presented antigens come with intrinsic challenges and limitations and, therefore, warrant multiple lines of evidence of robust tumour specificity before being considered for clinical use. Knowledge obtained using various antigen-detection strategies can be combined with different therapeutic platforms to create individualized therapies that hold great promise, including when combined with already established immunotherapies. Tailoring immunotherapies while taking into account the substantial heterogeneity of malignancies as well as that of HLA loci not only requires innovative science, but also demands innovative approaches to trial design and drug regulation.
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49
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Wang G, Wan H, Jian X, Li Y, Ouyang J, Tan X, Zhao Y, Lin Y, Xie L. INeo-Epp: A Novel T-Cell HLA Class-I Immunogenicity or Neoantigenic Epitope Prediction Method Based on Sequence-Related Amino Acid Features. BIOMED RESEARCH INTERNATIONAL 2020; 2020:5798356. [PMID: 32626747 PMCID: PMC7315274 DOI: 10.1155/2020/5798356] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Accepted: 05/23/2020] [Indexed: 12/30/2022]
Abstract
In silico T-cell epitope prediction plays an important role in immunization experimental design and vaccine preparation. Currently, most epitope prediction research focuses on peptide processing and presentation, e.g., proteasomal cleavage, transporter associated with antigen processing (TAP), and major histocompatibility complex (MHC) combination. To date, however, the mechanism for the immunogenicity of epitopes remains unclear. It is generally agreed upon that T-cell immunogenicity may be influenced by the foreignness, accessibility, molecular weight, molecular structure, molecular conformation, chemical properties, and physical properties of target peptides to different degrees. In this work, we tried to combine these factors. Firstly, we collected significant experimental HLA-I T-cell immunogenic peptide data, as well as the potential immunogenic amino acid properties. Several characteristics were extracted, including the amino acid physicochemical property of the epitope sequence, peptide entropy, eluted ligand likelihood percentile rank (EL rank(%)) score, and frequency score for an immunogenic peptide. Subsequently, a random forest classifier for T-cell immunogenic HLA-I presenting antigen epitopes and neoantigens was constructed. The classification results for the antigen epitopes outperformed the previous research (the optimal AUC = 0.81, external validation data set AUC = 0.77). As mutational epitopes generated by the coding region contain only the alterations of one or two amino acids, we assume that these characteristics might also be applied to the classification of the endogenic mutational neoepitopes also called "neoantigens." Based on mutation information and sequence-related amino acid characteristics, a prediction model of a neoantigen was established as well (the optimal AUC = 0.78). Further, an easy-to-use web-based tool "INeo-Epp" was developed for the prediction of human immunogenic antigen epitopes and neoantigen epitopes.
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Affiliation(s)
- Guangzhi Wang
- College of Food Science and Technology, Shanghai Ocean University, Shanghai 201306, China
- Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, Shanghai 201203, China
| | - Huihui Wan
- Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, Shanghai 201203, China
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Xingxing Jian
- Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, Shanghai 201203, China
- Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education and Key Laboratory of Carcinogenesis, National Health and Family Planning Commission, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Yuyu Li
- College of Food Science and Technology, Shanghai Ocean University, Shanghai 201306, China
| | - Jian Ouyang
- Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, Shanghai 201203, China
| | - Xiaoxiu Tan
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Yong Zhao
- College of Food Science and Technology, Shanghai Ocean University, Shanghai 201306, China
| | - Yong Lin
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Lu Xie
- College of Food Science and Technology, Shanghai Ocean University, Shanghai 201306, China
- Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, Shanghai 201203, China
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50
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Abstract
Throughout the body, T cells monitor MHC-bound ligands expressed on the surface of essentially all cell types. MHC ligands that trigger a T cell immune response are referred to as T cell epitopes. Identifying such epitopes enables tracking, phenotyping, and stimulating T cells involved in immune responses in infectious disease, allergy, autoimmunity, transplantation, and cancer. The specific T cell epitopes recognized in an individual are determined by genetic factors such as the MHC molecules the individual expresses, in parallel to the individual's environmental exposure history. The complexity and importance of T cell epitope mapping have motivated the development of computational approaches that predict what T cell epitopes are likely to be recognized in a given individual or in a broader population. Such predictions guide experimental epitope mapping studies and enable computational analysis of the immunogenic potential of a given protein sequence region.
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Affiliation(s)
- Bjoern Peters
- Division of Vaccine Discovery, La Jolla Institute for Immunology, La Jolla, California 92037, USA; ,
- Department of Medicine, University of California San Diego, La Jolla, California 92093, USA
| | - Morten Nielsen
- Department of Health Technology, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark;
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, B1650 Buenos Aires, Argentina
| | - Alessandro Sette
- Division of Vaccine Discovery, La Jolla Institute for Immunology, La Jolla, California 92037, USA; ,
- Department of Medicine, University of California San Diego, La Jolla, California 92093, USA
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