1
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Flender D, Vilenne F, Adams C, Boonen K, Valkenborg D, Baggerman G. Exploring the dynamic landscape of immunopeptidomics: Unravelling posttranslational modifications and navigating bioinformatics terrain. MASS SPECTROMETRY REVIEWS 2024. [PMID: 39152539 DOI: 10.1002/mas.21905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Revised: 07/30/2024] [Accepted: 08/01/2024] [Indexed: 08/19/2024]
Abstract
Immunopeptidomics is becoming an increasingly important field of study. The capability to identify immunopeptides with pivotal roles in the human immune system is essential to shift the current curative medicine towards personalized medicine. Throughout the years, the field has matured, giving insight into the current pitfalls. Nowadays, it is commonly accepted that generalizing shotgun proteomics workflows is malpractice because immunopeptidomics faces numerous challenges. While many of these difficulties have been addressed, the road towards the ideal workflow remains complicated. Although the presence of Posttranslational modifications (PTMs) in the immunopeptidome has been demonstrated, their identification remains highly challenging despite their significance for immunotherapies. The large number of unpredictable modifications in the immunopeptidome plays a pivotal role in the functionality and these challenges. This review provides a comprehensive overview of the current advancements in immunopeptidomics. We delve into the challenges associated with identifying PTMs within the immunopeptidome, aiming to address the current state of the field.
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Affiliation(s)
- Daniel Flender
- Centre for Proteomics, University of Antwerp, Antwerpen, Belgium
- Health Unit, VITO, Mol, Belgium
| | - Frédérique Vilenne
- Health Unit, VITO, Mol, Belgium
- Data Science Institute, University of Hasselt, Hasselt, Belgium
| | - Charlotte Adams
- Department of Computer Science, University of Antwerp, Antwerp, Belgium
| | - Kurt Boonen
- Centre for Proteomics, University of Antwerp, Antwerpen, Belgium
- ImmuneSpec, Niel, Belgium
| | - Dirk Valkenborg
- Data Science Institute, University of Hasselt, Hasselt, Belgium
| | - Geert Baggerman
- Department of Computer Science, University of Antwerp, Antwerp, Belgium
- ImmuneSpec, Niel, Belgium
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2
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Cai Y, Li D, Lv D, Yu J, Ma Y, Jiang T, Ding N, Liu Z, Li Y, Xu J. MHC-I-presented non-canonical antigens expand the cancer immunotherapy targets in acute myeloid leukemia. Sci Data 2024; 11:831. [PMID: 39090129 PMCID: PMC11294462 DOI: 10.1038/s41597-024-03660-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 07/18/2024] [Indexed: 08/04/2024] Open
Abstract
Identification of tumor neoantigens is indispensable for the development of cancer immunotherapies. However, we are still lacking knowledge about the potential neoantigens derived from sequences outside protein-coding regions. Here, we comprehensively characterized the immunopeptidome landscape by integrating multi-omics data in acute myeloid leukemia (AML). Both canonical and non-canonical MHC-associated peptides (MAPs) in AML were identified. We found that the quality and characteristics of ncMAPs are comparable or superior to cMAPs, suggesting ncMAPs are indispensable sources for tumor neoantigens. We further proposed a computational framework to prioritize the neoantigens by integrating additional transcriptome and immunopeptidome in normal tissues. Notably, 6 of prioritized 13 neoantigens were derived from ncMAPs. The expressions of corresponding source genes are highly related to infiltrations of immune cells. Finally, a risk model was developed, which exhibited good performance for clinical prognosis in AML. Our findings expand potential cancer immunotherapy targets and provide in-depth insights into AML treatment, laying a new foundation for precision therapies in AML.
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Affiliation(s)
- Yangyang Cai
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, 150001, China
| | - Donghao Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, 150001, China
| | - Dezhong Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, 150001, China
| | - Jiaxin Yu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, 150001, China
| | - Yingying Ma
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, 150001, China
| | - Tiantongfei Jiang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, 150001, China
| | - Na Ding
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, 150001, China
| | - Zhigang Liu
- Affiliated Foshan Maternity & Child Healthcare Hospital, Southern Medical University, Guangzhou, China.
| | - Yongsheng Li
- School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin, 150081, China.
| | - Juan Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, 150001, China.
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3
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Killian JT, Glenn King R, Lucander ACK, Kizziah JL, Fucile CF, Diaz-Avalos R, Qiu S, Silva-Sanchez A, Mousseau BJ, Macon KJ, Callahan AR, Yang G, Emon Hossain M, Akther J, Good DB, Kelso S, Houp JA, Rosenblum F, Porrett PM, Ong SC, Kumar V, Saphire EO, Kearney JF, Randall TD, Rosenberg AF, Green TJ, Lund FE. HLA topography enforces shared and convergent immunodominant B cell and antibody alloresponses in transplant recipients. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.03.31.534734. [PMID: 37034637 PMCID: PMC10081326 DOI: 10.1101/2023.03.31.534734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Donor-specific antibody (DSA) responses against human leukocyte antigen (HLA) proteins mismatched between kidney transplant donors and recipients cause allograft loss. The rules governing the immunogenicity of non-self donor HLA are poorly understood. Using single-cell, molecular, structural, and proteomic techniques, we profiled the HLA-specific B cell response in the kidney and blood of a transplant recipient with antibody-mediated rejection (AMR). We observed an immunodominant B cell antibody response focused on topographically exposed, solvent-accessible mismatched HLA residues along the peptide-binding groove - a subregion comprising only 20% of the HLA molecule. We further demonstrated that, even within a diverse cohort of transplant recipients, the B cell alloresponse consistently converges on this same immunodominant subregion on the crown of the HLA molecule. Based on these findings, we propose that B cell immunodominance in transplant rejection relies on antigenic topography, and we suggest that this link could be exploited for organ matching and therapeutics.
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Lautenbacher L, Yang KL, Kockmann T, Panse C, Chambers M, Kahl E, Yu F, Gabriel W, Bold D, Schmidt T, Li K, MacLean B, Nesvizhskii AI, Wilhelm M. Koina: Democratizing machine learning for proteomics research. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.01.596953. [PMID: 38895358 PMCID: PMC11185529 DOI: 10.1101/2024.06.01.596953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Recent developments in machine-learning (ML) and deep-learning (DL) have immense potential for applications in proteomics, such as generating spectral libraries, improving peptide identification, and optimizing targeted acquisition modes. Although new ML/DL models for various applications and peptide properties are frequently published, the rate at which these models are adopted by the community is slow, which is mostly due to technical challenges. We believe that, for the community to make better use of state-of-the-art models, more attention should be spent on making models easy to use and accessible by the community. To facilitate this, we developed Koina, an open-source containerized, decentralized and online-accessible high-performance prediction service that enables ML/DL model usage in any pipeline. Using the widely used FragPipe computational platform as example, we show how Koina can be easily integrated with existing proteomics software tools and how these integrations improve data analysis.
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Affiliation(s)
- Ludwig Lautenbacher
- Computational Mass Spectrometry, Technical University of Munich (TUM), Freising, Germany
| | - Kevin L. Yang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Tobias Kockmann
- Functional Genomics Center Zurich (FGCZ) - University of Zurich | ETH Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland
| | - Christian Panse
- Functional Genomics Center Zurich (FGCZ) - University of Zurich | ETH Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland
- Swiss Institute of Bioinformatics (SIB), Quartier Sorge - Batiment Amphipole, CH-1015 Lausanne, Switzerland
| | - Matthew Chambers
- Department of Genome Sciences, University of Washington, Seattle, WA 98195
| | - Elias Kahl
- Computational Mass Spectrometry, Technical University of Munich (TUM), Freising, Germany
| | - Fengchao Yu
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Wassim Gabriel
- Computational Mass Spectrometry, Technical University of Munich (TUM), Freising, Germany
| | - Dulguun Bold
- Computational Mass Spectrometry, Technical University of Munich (TUM), Freising, Germany
| | | | - Kai Li
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Brendan MacLean
- Department of Genome Sciences, University of Washington, Seattle, WA 98195
| | - Alexey I. Nesvizhskii
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Mathias Wilhelm
- Computational Mass Spectrometry, Technical University of Munich (TUM), Freising, Germany
- Munich Data Science Institute, Technical University of Munich, 85748, Garching, Germany
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5
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Bulashevska A, Nacsa Z, Lang F, Braun M, Machyna M, Diken M, Childs L, König R. Artificial intelligence and neoantigens: paving the path for precision cancer immunotherapy. Front Immunol 2024; 15:1394003. [PMID: 38868767 PMCID: PMC11167095 DOI: 10.3389/fimmu.2024.1394003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 05/13/2024] [Indexed: 06/14/2024] Open
Abstract
Cancer immunotherapy has witnessed rapid advancement in recent years, with a particular focus on neoantigens as promising targets for personalized treatments. The convergence of immunogenomics, bioinformatics, and artificial intelligence (AI) has propelled the development of innovative neoantigen discovery tools and pipelines. These tools have revolutionized our ability to identify tumor-specific antigens, providing the foundation for precision cancer immunotherapy. AI-driven algorithms can process extensive amounts of data, identify patterns, and make predictions that were once challenging to achieve. However, the integration of AI comes with its own set of challenges, leaving space for further research. With particular focus on the computational approaches, in this article we have explored the current landscape of neoantigen prediction, the fundamental concepts behind, the challenges and their potential solutions providing a comprehensive overview of this rapidly evolving field.
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Affiliation(s)
- Alla Bulashevska
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Zsófia Nacsa
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Franziska Lang
- TRON - Translational Oncology at the University Medical Center of the Johannes Gutenberg University gGmbH, Mainz, Germany
| | - Markus Braun
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Martin Machyna
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Mustafa Diken
- TRON - Translational Oncology at the University Medical Center of the Johannes Gutenberg University gGmbH, Mainz, Germany
| | - Liam Childs
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Renate König
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
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6
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Wilson E, Cava JK, Chowell D, Raja R, Mangalaparthi KK, Pandey A, Curtis M, Anderson KS, Singharoy A. The electrostatic landscape of MHC-peptide binding revealed using inception networks. Cell Syst 2024; 15:362-373.e7. [PMID: 38554709 DOI: 10.1016/j.cels.2024.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 11/24/2023] [Accepted: 03/05/2024] [Indexed: 04/02/2024]
Abstract
Predictive modeling of macromolecular recognition and protein-protein complementarity represents one of the cornerstones of biophysical sciences. However, such models are often hindered by the combinatorial complexity of interactions at the molecular interfaces. Exemplary of this problem is peptide presentation by the highly polymorphic major histocompatibility complex class I (MHC-I) molecule, a principal component of immune recognition. We developed human leukocyte antigen (HLA)-Inception, a deep biophysical convolutional neural network, which integrates molecular electrostatics to capture non-bonded interactions for predicting peptide binding motifs across 5,821 MHC-I alleles. These predictions of generated motifs correlate strongly with experimental peptide binding and presentation data. Beyond molecular interactions, the study demonstrates the application of predicted motifs in analyzing MHC-I allele associations with HIV disease progression and patient response to immune checkpoint inhibitors. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Eric Wilson
- School of Molecular Sciences, Arizona State University, Tempe, AZ 85207, USA; The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - John Kevin Cava
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85207, USA
| | - Diego Chowell
- The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Remya Raja
- Department of Immunology, Mayo Clinic, Scottsdale, AZ 85259, USA
| | - Kiran K Mangalaparthi
- Department of Laboratory Medicine and Pathology, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA
| | - Akhilesh Pandey
- Department of Laboratory Medicine and Pathology, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA; Center for Individualized Medicine, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA; Manipal Academy of Higher Education, Manipal 576104, Karnataka, India
| | - Marion Curtis
- Department of Immunology, Mayo Clinic, Scottsdale, AZ 85259, USA; College of Medicine and Science, Mayo Clinic, Scottsdale, AZ 85259, USA; Department of Cancer Biology, Mayo Clinic, Scottsdale, AZ 85259, USA
| | - Karen S Anderson
- School of Life Sciences, Arizona State University, Tempe, AZ 85207, USA.
| | - Abhishek Singharoy
- School of Molecular Sciences, Arizona State University, Tempe, AZ 85207, USA.
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7
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Choi S, Paek E. pXg: Comprehensive Identification of Noncanonical MHC-I-Associated Peptides From De Novo Peptide Sequencing Using RNA-Seq Reads. Mol Cell Proteomics 2024; 23:100743. [PMID: 38403075 PMCID: PMC10979277 DOI: 10.1016/j.mcpro.2024.100743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 02/19/2024] [Accepted: 02/21/2024] [Indexed: 02/27/2024] Open
Abstract
Discovering noncanonical peptides has been a common application of proteogenomics. Recent studies suggest that certain noncanonical peptides, known as noncanonical major histocompatibility complex-I (MHC-I)-associated peptides (ncMAPs), that bind to MHC-I may make good immunotherapeutic targets. De novo peptide sequencing is a great way to find ncMAPs since it can detect peptide sequences from their tandem mass spectra without using any sequence databases. However, this strategy has not been widely applied for ncMAP identification because there is not a good way to estimate its false-positive rates. In order to completely and accurately identify immunopeptides using de novo peptide sequencing, we describe a unique pipeline called proteomics X genomics. In contrast to current pipelines, it makes use of genomic data, RNA-Seq abundance and sequencing quality, in addition to proteomic features to increase the sensitivity and specificity of peptide identification. We show that the peptide-spectrum match quality and genetic traits have a clear relationship, showing that they can be utilized to evaluate peptide-spectrum matches. From 10 samples, we found 24,449 canonical MHC-I-associated peptides and 956 ncMAPs by using a target-decoy competition. Three hundred eighty-seven ncMAPs and 1611 canonical MHC-I-associated peptides were new identifications that had not yet been published. We discovered 11 ncMAPs produced from a squirrel monkey retrovirus in human cell lines in addition to the two ncMAPs originating from a complementarity determining region 3 in an antibody thanks to the unrestricted search space assumed by de novo sequencing. These entirely new identifications show that proteomics X genomics can make the most of de novo peptide sequencing's advantages and its potential use in the search for new immunotherapeutic targets.
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Affiliation(s)
- Seunghyuk Choi
- Department of Computer Science, Hanyang University, Seoul, Republic of Korea
| | - Eunok Paek
- Department of Computer Science, Hanyang University, Seoul, Republic of Korea; Institute for Artificial Intelligence Research, Hanyang University, Seoul, Republic of Korea.
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8
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Thrift WJ, Perera J, Cohen S, Lounsbury NW, Gurung HR, Rose CM, Chen J, Jhunjhunwala S, Liu K. Graph-pMHC: graph neural network approach to MHC class II peptide presentation and antibody immunogenicity. Brief Bioinform 2024; 25:bbae123. [PMID: 38555476 PMCID: PMC10981672 DOI: 10.1093/bib/bbae123] [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: 01/02/2024] [Revised: 02/20/2024] [Accepted: 02/27/2024] [Indexed: 04/02/2024] Open
Abstract
Antigen presentation on MHC class II (pMHCII presentation) plays an essential role in the adaptive immune response to extracellular pathogens and cancerous cells. But it can also reduce the efficacy of large-molecule drugs by triggering an anti-drug response. Significant progress has been made in pMHCII presentation modeling due to the collection of large-scale pMHC mass spectrometry datasets (ligandomes) and advances in machine learning. Here, we develop graph-pMHC, a graph neural network approach to predict pMHCII presentation. We derive adjacency matrices for pMHCII using Alphafold2-multimer and address the peptide-MHC binding groove alignment problem with a simple graph enumeration strategy. We demonstrate that graph-pMHC dramatically outperforms methods with suboptimal inductive biases, such as the multilayer-perceptron-based NetMHCIIpan-4.0 (+20.17% absolute average precision). Finally, we create an antibody drug immunogenicity dataset from clinical trial data and develop a method for measuring anti-antibody immunogenicity risk using pMHCII presentation models. Our model increases receiver operating characteristic curve (ROC)-area under the ROC curve (AUC) by 2.57% compared to just filtering peptides by hits in OASis alone for predicting antibody drug immunogenicity.
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Affiliation(s)
| | - Jason Perera
- Genentech, 1 DNA Way, South San Francisco, California 94080, USA
| | - Sivan Cohen
- Genentech, 1 DNA Way, South San Francisco, California 94080, USA
| | | | - Hem R Gurung
- Genentech, 1 DNA Way, South San Francisco, California 94080, USA
| | | | - Jieming Chen
- Genentech, 1 DNA Way, South San Francisco, California 94080, USA
| | | | - Kai Liu
- Genentech, 1 DNA Way, South San Francisco, California 94080, USA
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9
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Ding H, Teng Y, Gao P, Zhang Q, Wang M, Yu Y, Fan Y, Zhu L. Construction of a prognostic model for lung adenocarcinoma based on m6A/m5C/m1A genes. Hum Mol Genet 2024; 33:563-582. [PMID: 38142284 DOI: 10.1093/hmg/ddad208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 11/15/2023] [Accepted: 12/07/2023] [Indexed: 12/25/2023] Open
Abstract
BACKGROUND Developing a prognostic model for lung adenocarcinoma (LUAD) that utilizes m6A/m5C/m1A genes holds immense importance in providing precise prognosis predictions for individuals. METHODS This study mined m6A/m5C/m1A-related differential genes in LUAD based on public databases, identified LUAD tumor subtypes based on these genes, and further built a risk prognostic model grounded in differential genes between subtypes. The immune status between high- and low-risk groups was investigated, and the distribution of feature genes in tumor immune cells was analyzed using single-cell analysis. Based on the expression levels of feature genes, a projection of chemotherapeutic and targeted drugs was made for individuals identified as high-risk. Ultimately, cell experiments were further verified. RESULTS The 6-gene risk prognosis model based on differential genes between tumor subtypes had good predictive performance. Individuals classified as low-risk exhibited a higher (P < 0.05) abundance of infiltrating immune cells. Feature genes were mainly distributed in tumor immune cells like CD4+T cells, CD8+T cells, and regulatory T cells. Four drugs with relatively low IC50 values were found in the high-risk group: Elesclomol, Pyrimethamine, Saracatinib, and Temsirolimus. In addition, four drugs with significant positive correlation (P < 0.001) between IC50 values and feature gene expression were found, including Alectinib, Estramustine, Brigatinib, and Elesclomol. The low expression of key gene NTSR1 reduced the IC50 value of irinotecan. CONCLUSION Based on the m6A/m5C/m1A-related genes in LUAD, LUAD patients were divided into 2 subtypes, and a m6A/m5C/m1A-related LUAD prognostic model was constructed to provide a reference for the prognosis prediction of LUAD.
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Affiliation(s)
- Hao Ding
- Department of Respiratory Disease, Affiliated People's Hospital of Jiangsu University, NO. 8 Dianli Road, Runzhou District, Zhenjiang City, Jiangsu Province 212002, China
| | - Yuanyuan Teng
- Department of Respiratory Disease, Affiliated People's Hospital of Jiangsu University, NO. 8 Dianli Road, Runzhou District, Zhenjiang City, Jiangsu Province 212002, China
| | - Ping Gao
- Department of Respiratory Disease, Affiliated People's Hospital of Jiangsu University, NO. 8 Dianli Road, Runzhou District, Zhenjiang City, Jiangsu Province 212002, China
| | - Qi Zhang
- Department of Respiratory Disease, Affiliated People's Hospital of Jiangsu University, NO. 8 Dianli Road, Runzhou District, Zhenjiang City, Jiangsu Province 212002, China
| | - Mengdi Wang
- Department of Respiratory Disease, Affiliated People's Hospital of Jiangsu University, NO. 8 Dianli Road, Runzhou District, Zhenjiang City, Jiangsu Province 212002, China
| | - Yi Yu
- Department of General Practice, Jiankang Road Community Health Service Center, NO. 239 Zhongshan East Road, Jingkou District, Zhenjiang City, Jiangsu Province 212008, China
| | - Yueping Fan
- Department of Respiratory, Jurong Branch Hospital, Affiliated Hospital of Jiangsu University, NO. 8 Huayang South Road, Jurong City, Zhenjiang City, Jiangsu Province 212400, China
| | - Li Zhu
- Department of Nephrology, Affiliated People's Hospital of Jiangsu University, NO. 8 Dianli Road, Runzhou District, Zhenjiang City, Jiangsu Province 212002, China
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10
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Ferreira HJ, Stevenson BJ, Pak H, Yu F, Almeida Oliveira J, Huber F, Taillandier-Coindard M, Michaux J, Ricart-Altimiras E, Kraemer AI, Kandalaft LE, Speiser DE, Nesvizhskii AI, Müller M, Bassani-Sternberg M. Immunopeptidomics-based identification of naturally presented non-canonical circRNA-derived peptides. Nat Commun 2024; 15:2357. [PMID: 38490980 PMCID: PMC10943130 DOI: 10.1038/s41467-024-46408-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 02/16/2024] [Indexed: 03/18/2024] Open
Abstract
Circular RNAs (circRNAs) are covalently closed non-coding RNAs lacking the 5' cap and the poly-A tail. Nevertheless, it has been demonstrated that certain circRNAs can undergo active translation. Therefore, aberrantly expressed circRNAs in human cancers could be an unexplored source of tumor-specific antigens, potentially mediating anti-tumor T cell responses. This study presents an immunopeptidomics workflow with a specific focus on generating a circRNA-specific protein fasta reference. The main goal of this workflow is to streamline the process of identifying and validating human leukocyte antigen (HLA) bound peptides potentially originating from circRNAs. We increase the analytical stringency of our workflow by retaining peptides identified independently by two mass spectrometry search engines and/or by applying a group-specific FDR for canonical-derived and circRNA-derived peptides. A subset of circRNA-derived peptides specifically encoded by the region spanning the back-splice junction (BSJ) are validated with targeted MS, and with direct Sanger sequencing of the respective source transcripts. Our workflow identifies 54 unique BSJ-spanning circRNA-derived peptides in the immunopeptidome of melanoma and lung cancer samples. Our approach enlarges the catalog of source proteins that can be explored for immunotherapy.
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Affiliation(s)
- Humberto J Ferreira
- Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Department of Oncology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
- Agora Cancer Research Centre, Lausanne, Switzerland
| | - Brian J Stevenson
- Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Agora Cancer Research Centre, Lausanne, Switzerland
- SIB Swiss Institute of Bioinformatics, University of Lausanne, Lausanne, Switzerland
| | - HuiSong Pak
- Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Department of Oncology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
- Agora Cancer Research Centre, Lausanne, Switzerland
| | - Fengchao Yu
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Jessica Almeida Oliveira
- Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Department of Oncology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
- Agora Cancer Research Centre, Lausanne, Switzerland
| | - Florian Huber
- Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Department of Oncology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
- Agora Cancer Research Centre, Lausanne, Switzerland
| | - Marie Taillandier-Coindard
- Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Department of Oncology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
- Agora Cancer Research Centre, Lausanne, Switzerland
| | - Justine Michaux
- Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Department of Oncology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
- Agora Cancer Research Centre, Lausanne, Switzerland
| | - Emma Ricart-Altimiras
- Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Department of Oncology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
- Agora Cancer Research Centre, Lausanne, Switzerland
| | - Anne I Kraemer
- Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Department of Oncology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
- Agora Cancer Research Centre, Lausanne, Switzerland
| | - Lana E Kandalaft
- Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Department of Oncology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
- Agora Cancer Research Centre, Lausanne, Switzerland
- Center of Experimental Therapeutics, Department of Oncology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - Daniel E Speiser
- Department of Oncology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - Alexey I Nesvizhskii
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Markus Müller
- Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Department of Oncology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
- Agora Cancer Research Centre, Lausanne, Switzerland
- SIB Swiss Institute of Bioinformatics, University of Lausanne, Lausanne, Switzerland
| | - Michal Bassani-Sternberg
- Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland.
- Department of Oncology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland.
- Agora Cancer Research Centre, Lausanne, Switzerland.
- Center of Experimental Therapeutics, Department of Oncology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland.
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11
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Omenn GS, Lane L, Overall CM, Lindskog C, Pineau C, Packer NH, Cristea IM, Weintraub ST, Orchard S, Roehrl MHA, Nice E, Guo T, Van Eyk JE, Liu S, Bandeira N, Aebersold R, Moritz RL, Deutsch EW. The 2023 Report on the Proteome from the HUPO Human Proteome Project. J Proteome Res 2024; 23:532-549. [PMID: 38232391 PMCID: PMC11026053 DOI: 10.1021/acs.jproteome.3c00591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Since 2010, the Human Proteome Project (HPP), the flagship initiative of the Human Proteome Organization (HUPO), has pursued two goals: (1) to credibly identify the protein parts list and (2) to make proteomics an integral part of multiomics studies of human health and disease. The HPP relies on international collaboration, data sharing, standardized reanalysis of MS data sets by PeptideAtlas and MassIVE-KB using HPP Guidelines for quality assurance, integration and curation of MS and non-MS protein data by neXtProt, plus extensive use of antibody profiling carried out by the Human Protein Atlas. According to the neXtProt release 2023-04-18, protein expression has now been credibly detected (PE1) for 18,397 of the 19,778 neXtProt predicted proteins coded in the human genome (93%). Of these PE1 proteins, 17,453 were detected with mass spectrometry (MS) in accordance with HPP Guidelines and 944 by a variety of non-MS methods. The number of neXtProt PE2, PE3, and PE4 missing proteins now stands at 1381. Achieving the unambiguous identification of 93% of predicted proteins encoded from across all chromosomes represents remarkable experimental progress on the Human Proteome parts list. Meanwhile, there are several categories of predicted proteins that have proved resistant to detection regardless of protein-based methods used. Additionally there are some PE1-4 proteins that probably should be reclassified to PE5, specifically 21 LINC entries and ∼30 HERV entries; these are being addressed in the present year. Applying proteomics in a wide array of biological and clinical studies ensures integration with other omics platforms as reported by the Biology and Disease-driven HPP teams and the antibody and pathology resource pillars. Current progress has positioned the HPP to transition to its Grand Challenge Project focused on determining the primary function(s) of every protein itself and in networks and pathways within the context of human health and disease.
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Affiliation(s)
- Gilbert S. Omenn
- University of Michigan, Ann Arbor, Michigan 48109, United States
- Institute for Systems Biology, Seattle, Washington 98109, United States
| | - Lydie Lane
- CALIPHO Group, SIB Swiss Institute of Bioinformatics and University of Geneva, 1015 Lausanne, Switzerland
| | - Christopher M. Overall
- University of British Columbia, Vancouver, BC V6T 1Z4, Canada, Yonsei University Republic of Korea
| | | | - Charles Pineau
- University Rennes, Inserm U1085, Irset, 35042 Rennes, France
| | | | | | - Susan T. Weintraub
- University of Texas Health Science Center-San Antonio, San Antonio, Texas 78229-3900, United States
| | | | - Michael H. A. Roehrl
- Department of Pathology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, United States
| | | | - Tiannan Guo
- Westlake Center for Intelligent Proteomics, Westlake Laboratory, Westlake University, Hangzhou 310024, Zhejiang Province, China
| | - Jennifer E. Van Eyk
- Advanced Clinical Biosystems Research Institute, Smidt Heart Institute, Cedars-Sinai Medical Center, 127 South San Vicente Boulevard, Pavilion, 9th Floor, Los Angeles, CA, 90048, United States
| | - Siqi Liu
- BGI Group, Shenzhen 518083, China
| | - Nuno Bandeira
- University of California, San Diego, La Jolla, CA, 92093, United States
| | - Ruedi Aebersold
- Institute of Molecular Systems Biology in ETH Zurich, 8092 Zurich, Switzerland
- University of Zurich, 8092 Zurich, Switzerland
| | - Robert L. Moritz
- Institute for Systems Biology, Seattle, Washington 98109, United States
| | - Eric W. Deutsch
- Institute for Systems Biology, Seattle, Washington 98109, United States
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12
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Liao H, Barra C, Zhou Z, Peng X, Woodhouse I, Tailor A, Parker R, Carré A, Borrow P, Hogan MJ, Paes W, Eisenlohr LC, Mallone R, Nielsen M, Ternette N. MARS an improved de novo peptide candidate selection method for non-canonical antigen target discovery in cancer. Nat Commun 2024; 15:661. [PMID: 38253617 PMCID: PMC10803737 DOI: 10.1038/s41467-023-44460-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Accepted: 12/14/2023] [Indexed: 01/24/2024] Open
Abstract
Understanding the nature and extent of non-canonical human leukocyte antigen (HLA) presentation in tumour cells is a priority for target antigen discovery for the development of next generation immunotherapies in cancer. We here employ a de novo mass spectrometric sequencing approach with a refined, MHC-centric analysis strategy to detect non-canonical MHC-associated peptides specific to cancer without any prior knowledge of the target sequence from genomic or RNA sequencing data. Our strategy integrates MHC binding rank, Average local confidence scores, and peptide Retention time prediction for improved de novo candidate Selection; culminating in the machine learning model MARS. We benchmark our model on a large synthetic peptide library dataset and reanalysis of a published dataset of high-quality non-canonical MHC-associated peptide identifications in human cancer. We achieve almost 2-fold improvement for high quality spectral assignments in comparison to de novo sequencing alone with an estimated accuracy of above 85.7% when integrated with a stepwise peptide sequence mapping strategy. Finally, we utilize MARS to detect and validate lncRNA-derived peptides in human cervical tumour resections, demonstrating its suitability to discover novel, immunogenic, non-canonical peptide sequences in primary tumour tissue.
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Affiliation(s)
- Hanqing Liao
- The Jenner Institute, University of Oxford, Oxford, OX3 7BN, UK
- Centre for Immuno-Oncology, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7DQ, UK
| | | | - Zhicheng Zhou
- Université Paris Cité, Institut Cochin, CNRS, INSERM, 75014, Paris, France
| | - Xu Peng
- The Jenner Institute, University of Oxford, Oxford, OX3 7BN, UK
| | - Isaac Woodhouse
- The Jenner Institute, University of Oxford, Oxford, OX3 7BN, UK
- Centre for Immuno-Oncology, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7DQ, UK
| | - Arun Tailor
- The Jenner Institute, University of Oxford, Oxford, OX3 7BN, UK
- Centre for Immuno-Oncology, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7DQ, UK
| | - Robert Parker
- The Jenner Institute, University of Oxford, Oxford, OX3 7BN, UK
- Centre for Immuno-Oncology, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7DQ, UK
| | - Alexia Carré
- Université Paris Cité, Institut Cochin, CNRS, INSERM, 75014, Paris, France
| | - Persephone Borrow
- Centre for Immuno-Oncology, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7DQ, UK
| | - Michael J Hogan
- Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Wayne Paes
- The Jenner Institute, University of Oxford, Oxford, OX3 7BN, UK
- Centre for Immuno-Oncology, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7DQ, UK
| | - Laurence C Eisenlohr
- Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Roberto Mallone
- Université Paris Cité, Institut Cochin, CNRS, INSERM, 75014, Paris, France
- Assistance Publique Hôpitaux de Paris, Service de Diabétologie et Immunologie Clinique, Cochin Hospital, 75014, Paris, France
| | | | - Nicola Ternette
- The Jenner Institute, University of Oxford, Oxford, OX3 7BN, UK.
- Centre for Immuno-Oncology, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7DQ, UK.
- University of Utrecht, Department of Pharmaceutical Sciences, 3584 CH, Utrecht, The Netherlands.
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13
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Shahbazy M, Ramarathinam SH, Li C, Illing PT, Faridi P, Croft NP, Purcell AW. MHCpLogics: an interactive machine learning-based tool for unsupervised data visualization and cluster analysis of immunopeptidomes. Brief Bioinform 2024; 25:bbae087. [PMID: 38487848 PMCID: PMC10940831 DOI: 10.1093/bib/bbae087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 12/12/2023] [Accepted: 02/15/2024] [Indexed: 03/18/2024] Open
Abstract
The major histocompatibility complex (MHC) encodes a range of immune response genes, including the human leukocyte antigens (HLAs) in humans. These molecules bind peptide antigens and present them on the cell surface for T cell recognition. The repertoires of peptides presented by HLA molecules are termed immunopeptidomes. The highly polymorphic nature of the genres that encode the HLA molecules confers allotype-specific differences in the sequences of bound ligands. Allotype-specific ligand preferences are often defined by peptide-binding motifs. Individuals express up to six classical class I HLA allotypes, which likely present peptides displaying different binding motifs. Such complex datasets make the deconvolution of immunopeptidomic data into allotype-specific contributions and further dissection of binding-specificities challenging. Herein, we developed MHCpLogics as an interactive machine learning-based tool for mining peptide-binding sequence motifs and visualization of immunopeptidome data across complex datasets. We showcase the functionalities of MHCpLogics by analyzing both in-house and published mono- and multi-allelic immunopeptidomics data. The visualization modalities of MHCpLogics allow users to inspect clustered sequences down to individual peptide components and to examine broader sequence patterns within multiple immunopeptidome datasets. MHCpLogics can deconvolute large immunopeptidome datasets enabling the interrogation of clusters for the segregation of allotype-specific peptide sequence motifs, identification of sub-peptidome motifs, and the exportation of clustered peptide sequence lists. The tool facilitates rapid inspection of immunopeptidomes as a resource for the immunology and vaccine communities. MHCpLogics is a standalone application available via an executable installation at: https://github.com/PurcellLab/MHCpLogics.
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Affiliation(s)
- Mohammad Shahbazy
- Department of Biochemistry and Molecular Biology and Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Melbourne, VIC 3800, Australia
| | - Sri H Ramarathinam
- Department of Biochemistry and Molecular Biology and Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Melbourne, VIC 3800, Australia
| | - Chen Li
- Department of Biochemistry and Molecular Biology and Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Melbourne, VIC 3800, Australia
| | - Patricia T Illing
- Department of Biochemistry and Molecular Biology and Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Melbourne, VIC 3800, Australia
| | - Pouya Faridi
- Centre for Cancer Research, Hudson Institute of Medical Research, Clayton, VIC 3168, Australia
- Monash Proteomics and Metabolomics Platform, Department of Medicine, School of Clinical Sciences, Monash University, Clayton, VIC 3800, Australia
| | - Nathan P Croft
- Department of Biochemistry and Molecular Biology and Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Melbourne, VIC 3800, Australia
| | - Anthony W Purcell
- Department of Biochemistry and Molecular Biology and Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Melbourne, VIC 3800, Australia
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14
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Huang X, Gan Z, Cui H, Lan T, Liu Y, Caron E, Shao W. The SysteMHC Atlas v2.0, an updated resource for mass spectrometry-based immunopeptidomics. Nucleic Acids Res 2024; 52:D1062-D1071. [PMID: 38000392 PMCID: PMC10767952 DOI: 10.1093/nar/gkad1068] [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: 09/15/2023] [Revised: 10/16/2023] [Accepted: 10/25/2023] [Indexed: 11/26/2023] Open
Abstract
The SysteMHC Atlas v1.0 was the first public repository dedicated to mass spectrometry-based immunopeptidomics. Here we introduce a newly released version of the SysteMHC Atlas v2.0 (https://systemhc.sjtu.edu.cn), a comprehensive collection of 7190 MS files from 303 allotypes. We extended and optimized a computational pipeline that allows the identification of MHC-bound peptides carrying on unexpected post-translational modifications (PTMs), thereby resulting in 471K modified peptides identified over 60 distinct PTM types. In total, we identified approximately 1.0 million and 1.1 million unique peptides for MHC class I and class II immunopeptidomes, respectively, indicating a 6.8-fold increase and a 28-fold increase to those in v1.0. The SysteMHC Atlas v2.0 introduces several new features, including the inclusion of non-UniProt peptides, and the incorporation of several novel computational tools for FDR estimation, binding affinity prediction and motif deconvolution. Additionally, we enhanced the user interface, upgraded website framework, and provided external links to other resources related. Finally, we built and provided various spectral libraries as community resources for data mining and future immunopeptidomic and proteomic analysis. We believe that the SysteMHC Atlas v2.0 is a unique resource to provide key insights to the immunology and proteomics community and will accelerate the development of vaccines and immunotherapies.
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Affiliation(s)
- Xiaoxiang Huang
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Science & Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Ziao Gan
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Science & Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Haowei Cui
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Science & Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Tian Lan
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Science & Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yansheng Liu
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT 06511, USA
| | - Etienne Caron
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT 06511, USA
| | - Wenguang Shao
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Science & Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
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15
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Salyer LG, Salhi HE, Brundage EA, Shettigar V, Sturgill SL, Zanella H, Templeton B, Abay E, Emmer KM, Lowe J, Rafael-Fortney JA, Parinandi N, Foster DB, McKinsey TA, Woulfe KC, Ziolo MT, Biesiadecki BJ. Troponin I Tyrosine Phosphorylation Beneficially Accelerates Diastolic Function. Circ Res 2024; 134:33-45. [PMID: 38095088 PMCID: PMC10872382 DOI: 10.1161/circresaha.123.323132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 11/28/2023] [Indexed: 01/06/2024]
Abstract
BACKGROUND A healthy heart is able to modify its function and increase relaxation through post-translational modifications of myofilament proteins. While there are known examples of serine/threonine kinases directly phosphorylating myofilament proteins to modify heart function, the roles of tyrosine (Y) phosphorylation to directly modify heart function have not been demonstrated. The myofilament protein TnI (troponin I) is the inhibitory subunit of the troponin complex and is a key regulator of cardiac contraction and relaxation. We previously demonstrated that TnI-Y26 phosphorylation decreases calcium-sensitive force development and accelerates calcium dissociation, suggesting a novel role for tyrosine kinase-mediated TnI-Y26 phosphorylation to regulate cardiac relaxation. Therefore, we hypothesize that increasing TnI-Y26 phosphorylation will increase cardiac relaxation in vivo and be beneficial during pathological diastolic dysfunction. METHODS The signaling pathway involved in TnI-Y26 phosphorylation was predicted in silico and validated by tyrosine kinase activation and inhibition in primary adult murine cardiomyocytes. To investigate how TnI-Y26 phosphorylation affects cardiac muscle, structure, and function in vivo, we developed a novel TnI-Y26 phosphorylation-mimetic mouse that was subjected to echocardiography, pressure-volume loop hemodynamics, and myofibril mechanical studies. TnI-Y26 phosphorylation-mimetic mice were further subjected to the nephrectomy/DOCA (deoxycorticosterone acetate) model of diastolic dysfunction to investigate the effects of increased TnI-Y26 phosphorylation in disease. RESULTS Src tyrosine kinase is sufficient to phosphorylate TnI-Y26 in cardiomyocytes. TnI-Y26 phosphorylation accelerates in vivo relaxation without detrimental structural or systolic impairment. In a mouse model of diastolic dysfunction, TnI-Y26 phosphorylation is beneficial and protects against the development of disease. CONCLUSIONS We have demonstrated that tyrosine kinase phosphorylation of TnI is a novel mechanism to directly and beneficially accelerate myocardial relaxation in vivo.
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Affiliation(s)
- Lorien G Salyer
- Department of Physiology and Cell Biology, Davis Heart and Lung Research Institute (L.G.S., H.E.S., E.A.B., V.S., S.L.S., H.Z., B.T., E.A., J.L., J.A.R.-F., M.T.Z., B.J.B.), Ohio State University, Columbus
| | - Hussam E Salhi
- Department of Physiology and Cell Biology, Davis Heart and Lung Research Institute (L.G.S., H.E.S., E.A.B., V.S., S.L.S., H.Z., B.T., E.A., J.L., J.A.R.-F., M.T.Z., B.J.B.), Ohio State University, Columbus
| | - Elizabeth A Brundage
- Department of Physiology and Cell Biology, Davis Heart and Lung Research Institute (L.G.S., H.E.S., E.A.B., V.S., S.L.S., H.Z., B.T., E.A., J.L., J.A.R.-F., M.T.Z., B.J.B.), Ohio State University, Columbus
| | - Vikram Shettigar
- Department of Physiology and Cell Biology, Davis Heart and Lung Research Institute (L.G.S., H.E.S., E.A.B., V.S., S.L.S., H.Z., B.T., E.A., J.L., J.A.R.-F., M.T.Z., B.J.B.), Ohio State University, Columbus
| | - Sarah L Sturgill
- Department of Physiology and Cell Biology, Davis Heart and Lung Research Institute (L.G.S., H.E.S., E.A.B., V.S., S.L.S., H.Z., B.T., E.A., J.L., J.A.R.-F., M.T.Z., B.J.B.), Ohio State University, Columbus
| | - Helena Zanella
- Department of Physiology and Cell Biology, Davis Heart and Lung Research Institute (L.G.S., H.E.S., E.A.B., V.S., S.L.S., H.Z., B.T., E.A., J.L., J.A.R.-F., M.T.Z., B.J.B.), Ohio State University, Columbus
| | - Benjamin Templeton
- Department of Physiology and Cell Biology, Davis Heart and Lung Research Institute (L.G.S., H.E.S., E.A.B., V.S., S.L.S., H.Z., B.T., E.A., J.L., J.A.R.-F., M.T.Z., B.J.B.), Ohio State University, Columbus
| | - Eaman Abay
- Department of Physiology and Cell Biology, Davis Heart and Lung Research Institute (L.G.S., H.E.S., E.A.B., V.S., S.L.S., H.Z., B.T., E.A., J.L., J.A.R.-F., M.T.Z., B.J.B.), Ohio State University, Columbus
| | - Kathryn M Emmer
- University Laboratory Animal Resources (K.M.E.), Ohio State University, Columbus
| | - Jeovanna Lowe
- Department of Physiology and Cell Biology, Davis Heart and Lung Research Institute (L.G.S., H.E.S., E.A.B., V.S., S.L.S., H.Z., B.T., E.A., J.L., J.A.R.-F., M.T.Z., B.J.B.), Ohio State University, Columbus
| | - Jill A Rafael-Fortney
- Department of Physiology and Cell Biology, Davis Heart and Lung Research Institute (L.G.S., H.E.S., E.A.B., V.S., S.L.S., H.Z., B.T., E.A., J.L., J.A.R.-F., M.T.Z., B.J.B.), Ohio State University, Columbus
| | - Narasimham Parinandi
- Division of Pulmonary, Critical Care and Sleep Medicine (N.P.), Ohio State University, Columbus
| | - D Brian Foster
- Division of Cardiology, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD (D.B.F.)
| | - Timothy A McKinsey
- Department of Medicine, Division of Cardiology (T.A.M., K.C.W.), University of Colorado Anschutz Medical Campus, Aurora
- Consortium for Fibrosis Research and Translation (T.A.M.), University of Colorado Anschutz Medical Campus, Aurora
| | - Kathleen C Woulfe
- Department of Medicine, Division of Cardiology (T.A.M., K.C.W.), University of Colorado Anschutz Medical Campus, Aurora
| | - Mark T Ziolo
- Department of Physiology and Cell Biology, Davis Heart and Lung Research Institute (L.G.S., H.E.S., E.A.B., V.S., S.L.S., H.Z., B.T., E.A., J.L., J.A.R.-F., M.T.Z., B.J.B.), Ohio State University, Columbus
| | - Brandon J Biesiadecki
- Department of Physiology and Cell Biology, Davis Heart and Lung Research Institute (L.G.S., H.E.S., E.A.B., V.S., S.L.S., H.Z., B.T., E.A., J.L., J.A.R.-F., M.T.Z., B.J.B.), Ohio State University, Columbus
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16
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Kina E, Laverdure JP, Durette C, Lanoix J, Courcelles M, Zhao Q, Apavaloaei A, Larouche JD, Hardy MP, Vincent K, Gendron P, Hesnard L, Thériault C, Ruiz Cuevas MV, Ehx G, Thibault P, Perreault C. Breast cancer immunopeptidomes contain numerous shared tumor antigens. J Clin Invest 2024; 134:e166740. [PMID: 37906288 PMCID: PMC10760959 DOI: 10.1172/jci166740] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 10/23/2023] [Indexed: 11/02/2023] Open
Abstract
Hormone receptor-positive breast cancer (HR+) is immunologically cold and has not benefited from advances in immunotherapy. In contrast, subsets of triple-negative breast cancer (TNBC) display high leukocytic infiltration and respond to checkpoint blockade. CD8+ T cells, the main effectors of anticancer responses, recognize MHC I-associated peptides (MAPs). Our work aimed to characterize the repertoire of MAPs presented by HR+ and TNBC tumors. Using mass spectrometry, we identified 57,094 unique MAPs in 26 primary breast cancer samples. MAP source genes highly overlapped between both subtypes. We identified 25 tumor-specific antigens (TSAs) mainly deriving from aberrantly expressed regions. TSAs were most frequently identified in TNBC samples and were more shared among The Cancer Genome Atlas (TCGA) database TNBC than HR+ samples. In the TNBC cohort, the predicted number of TSAs positively correlated with leukocytic infiltration and overall survival, supporting their immunogenicity in vivo. We detected 49 tumor-associated antigens (TAAs), some of which derived from cancer-associated fibroblasts. Functional expansion of specific T cell assays confirmed the in vitro immunogenicity of several TSAs and TAAs. Our study identified attractive targets for cancer immunotherapy in both breast cancer subtypes. The higher prevalence of TSAs in TNBC tumors provides a rationale for their responsiveness to checkpoint blockade.
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Affiliation(s)
- Eralda Kina
- Institute for Research in Immunology and Cancer (IRIC), and
- Department of Medicine, University of Montreal, Montreal, Quebec, Canada
| | | | | | - Joël Lanoix
- Institute for Research in Immunology and Cancer (IRIC), and
| | | | - Qingchuan Zhao
- Institute for Research in Immunology and Cancer (IRIC), and
- Department of Medicine, University of Montreal, Montreal, Quebec, Canada
| | - Anca Apavaloaei
- Institute for Research in Immunology and Cancer (IRIC), and
- Department of Medicine, University of Montreal, Montreal, Quebec, Canada
| | - Jean-David Larouche
- Institute for Research in Immunology and Cancer (IRIC), and
- Department of Medicine, University of Montreal, Montreal, Quebec, Canada
| | | | | | | | - Leslie Hesnard
- Institute for Research in Immunology and Cancer (IRIC), and
| | | | - Maria Virginia Ruiz Cuevas
- Institute for Research in Immunology and Cancer (IRIC), and
- Department of Medicine, University of Montreal, Montreal, Quebec, Canada
| | - Grégory Ehx
- Laboratory of Hematology, GIGA-I3, University of Liege and CHU of Liège, Liege, Belgium
| | - Pierre Thibault
- Institute for Research in Immunology and Cancer (IRIC), and
- Department of Chemistry, University of Montreal, Montreal, Quebec, Canada
| | - Claude Perreault
- Institute for Research in Immunology and Cancer (IRIC), and
- Department of Medicine, University of Montreal, Montreal, Quebec, Canada
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17
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Racle J, Gfeller D. How to Predict Binding Specificity and Ligands for New MHC-II Alleles with MixMHC2pred. Methods Mol Biol 2024; 2809:215-235. [PMID: 38907900 DOI: 10.1007/978-1-0716-3874-3_14] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/24/2024]
Abstract
MHC-II molecules are key mediators of antigen presentation in vertebrate species and bind to their ligands with high specificity. The very high polymorphism of MHC-II genes within species and the fast-evolving nature of these genes across species has resulted in tens of thousands of different alleles, with hundreds of new alleles being discovered yearly through large sequencing projects in different species. Here we describe how to use MixMHC2pred to predict the binding specificity of any MHC-II allele directly from its amino acid sequence. We then show how both MHC-II ligands and CD4+ T cell epitopes can be predicted in different species with our approach. MixMHC2pred is available at http://mixmhc2pred.gfellerlab.org/ .
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Affiliation(s)
- Julien Racle
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
- Agora Cancer Research Centre, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
| | - David Gfeller
- Department of Oncology UNIL CHUV, Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland.
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland.
- Agora Cancer Research Centre, Lausanne, Switzerland.
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland.
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18
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ElAbd H, Franke A. Mass Spectrometry-Based Immunopeptidomics of Peptides Presented on Human Leukocyte Antigen Proteins. Methods Mol Biol 2024; 2758:425-443. [PMID: 38549028 DOI: 10.1007/978-1-0716-3646-6_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/02/2024]
Abstract
Human leukocyte antigen (HLA) proteins are a group of glycoproteins that are expressed at the cell surface, where they present peptides to T cells through physical interactions with T-cell receptors (TCRs). Hence, characterizing the set of peptides presented by HLA proteins, referred to hereafter as the immunopeptidome, is fundamental for neoantigen identification, immunotherapy, and vaccine development. As a result, different methods have been used over the years to identify peptides presented by HLA proteins, including competition assays, peptide microarrays, and yeast display systems. Nonetheless, over the last decade, mass spectrometry-based immunopeptidomics (MS-immunopeptidomics) has emerged as the gold-standard method for identifying peptides presented by HLA proteins. MS-immunopeptidomics enables the direct identification of the immunopeptidome in different tissues and cell types in different physiological and pathological states, for example, solid tumors or virally infected cells. Despite its advantages, it is still an experimentally and computationally challenging technique with different aspects that need to be considered before planning an MS-immunopeptidomics experiment, while conducting the experiment and with analyzing and interpreting the results. Hence, we aim in this chapter to provide an overview of this method and discuss different practical considerations at different stages starting from sample collection until data analysis. These points should aid different groups aiming at utilizing MS-immunopeptidomics, as well as, identifying future research directions to improve the method.
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Affiliation(s)
- Hesham ElAbd
- Institute of Clinical Molecular Biology, University of Kiel, Kiel, Germany
| | - Andre Franke
- Institute of Clinical Molecular Biology, University of Kiel, Kiel, Germany.
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19
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Wahle M, Thielert M, Zwiebel M, Skowronek P, Zeng WF, Mann M. IMBAS-MS Discovers Organ-Specific HLA Peptide Patterns in Plasma. Mol Cell Proteomics 2024; 23:100689. [PMID: 38043703 PMCID: PMC10765297 DOI: 10.1016/j.mcpro.2023.100689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 11/24/2023] [Accepted: 11/28/2023] [Indexed: 12/05/2023] Open
Abstract
Distinction of non-self from self is the major task of the immune system. Immunopeptidomics studies the peptide repertoire presented by the human leukocyte antigen (HLA) protein, usually on tissues. However, HLA peptides are also bound to plasma soluble HLA (sHLA), but little is known about their origin and potential for biomarker discovery in this readily available biofluid. Currently, immunopeptidomics is hampered by complex workflows and limited sensitivity, typically requiring several mL of plasma. Here, we take advantage of recent improvements in the throughput and sensitivity of mass spectrometry (MS)-based proteomics to develop a highly sensitive, automated, and economical workflow for HLA peptide analysis, termed Immunopeptidomics by Biotinylated Antibodies and Streptavidin (IMBAS). IMBAS-MS quantifies more than 5000 HLA class I peptides from only 200 μl of plasma, in just 30 min. Our technology revealed that the plasma immunopeptidome of healthy donors is remarkably stable throughout the year and strongly correlated between individuals with overlapping HLA types. Immunopeptides originating from diverse tissues, including the brain, are proportionately represented. We conclude that sHLAs are a promising avenue for immunology and potentially for precision oncology.
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Affiliation(s)
- Maria Wahle
- Department Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Marvin Thielert
- Department Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Maximilian Zwiebel
- Department Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Patricia Skowronek
- Department Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Wen-Feng Zeng
- Department Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Matthias Mann
- Department Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany.
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20
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Yu KKH, Basu S, Baquer G, Ahn R, Gantchev J, Jindal S, Regan MS, Abou-Mrad Z, Prabhu MC, Williams MJ, D'Souza AD, Malinowski SW, Hopland K, Elhanati Y, Stopka SA, Stortchevoi A, He Z, Sun J, Chen Y, Espejo AB, Chow KH, Yerrum S, Kao PL, Kerrigan BP, Norberg L, Nielsen D, Puduvalli VK, Huse J, Beroukhim R, Kim YSB, Goswami S, Boire A, Frisken S, Cima MJ, Holdhoff M, Lucas CHG, Bettegowda C, Levine SS, Bale TA, Brennan C, Reardon DA, Lang FF, Antonio Chiocca E, Ligon KL, White FM, Sharma P, Tabar V, Agar NYR. Investigative needle core biopsies for multi-omics in Glioblastoma. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.29.23300541. [PMID: 38234840 PMCID: PMC10793534 DOI: 10.1101/2023.12.29.23300541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Glioblastoma (GBM) is a primary brain cancer with an abysmal prognosis and few effective therapies. The ability to investigate the tumor microenvironment before and during treatment would greatly enhance both understanding of disease response and progression, as well as the delivery and impact of therapeutics. Stereotactic biopsies are a routine surgical procedure performed primarily for diagnostic histopathologic purposes. The role of investigative biopsies - tissue sampling for the purpose of understanding tumor microenvironmental responses to treatment using integrated multi-modal molecular analyses ('Multi-omics") has yet to be defined. Secondly, it is unknown whether comparatively small tissue samples from brain biopsies can yield sufficient information with such methods. Here we adapt stereotactic needle core biopsy tissue in two separate patients. In the first patient with recurrent GBM we performed highly resolved multi-omics analysis methods including single cell RNA sequencing, spatial-transcriptomics, metabolomics, proteomics, phosphoproteomics, T-cell clonotype analysis, and MHC Class I immunopeptidomics from biopsy tissue that was obtained from a single procedure. In a second patient we analyzed multi-regional core biopsies to decipher spatial and genomic variance. We also investigated the utility of stereotactic biopsies as a method for generating patient derived xenograft models in a separate patient cohort. Dataset integration across modalities showed good correspondence between spatial modalities, highlighted immune cell associated metabolic pathways and revealed poor correlation between RNA expression and the tumor MHC Class I immunopeptidome. In conclusion, stereotactic needle biopsy cores are of sufficient quality to generate multi-omics data, provide data rich insight into a patient's disease process and tumor immune microenvironment and can be of value in evaluating treatment responses. One sentence summary Integrative multi-omics analysis of stereotactic needle core biopsies in glioblastoma.
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21
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Hoenisch Gravel N, Nelde A, Bauer J, Mühlenbruch L, Schroeder SM, Neidert MC, Scheid J, Lemke S, Dubbelaar ML, Wacker M, Dengler A, Klein R, Mauz PS, Löwenheim H, Hauri-Hohl M, Martin R, Hennenlotter J, Stenzl A, Heitmann JS, Salih HR, Rammensee HG, Walz JS. TOF IMS mass spectrometry-based immunopeptidomics refines tumor antigen identification. Nat Commun 2023; 14:7472. [PMID: 37978195 PMCID: PMC10656517 DOI: 10.1038/s41467-023-42692-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 10/18/2023] [Indexed: 11/19/2023] Open
Abstract
T cell recognition of human leukocyte antigen (HLA)-presented tumor-associated peptides is central for cancer immune surveillance. Mass spectrometry (MS)-based immunopeptidomics represents the only unbiased method for the direct identification and characterization of naturally presented tumor-associated peptides, a key prerequisite for the development of T cell-based immunotherapies. This study reports on the implementation of ion mobility separation-based time-of-flight (TOFIMS) MS for next-generation immunopeptidomics, enabling high-speed and sensitive detection of HLA-presented peptides. Applying TOFIMS-based immunopeptidomics, a novel extensive benignTOFIMS dataset was generated from 94 primary benign samples of solid tissue and hematological origin, which enabled the expansion of benign reference immunopeptidome databases with > 150,000 HLA-presented peptides, the refinement of previously described tumor antigens, as well as the identification of frequently presented self antigens and not yet described tumor antigens comprising low abundant mutation-derived neoepitopes that might serve as targets for future cancer immunotherapy development.
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Affiliation(s)
- Naomi Hoenisch Gravel
- Department of Peptide-based Immunotherapy, University and University Hospital Tübingen, Tübingen, Germany
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
| | - Annika Nelde
- Department of Peptide-based Immunotherapy, University and University Hospital Tübingen, Tübingen, Germany
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
| | - Jens Bauer
- Department of Peptide-based Immunotherapy, University and University Hospital Tübingen, Tübingen, Germany
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
| | - Lena Mühlenbruch
- Department of Peptide-based Immunotherapy, University and University Hospital Tübingen, Tübingen, Germany
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), partner site Tübingen, Tübingen, Germany
| | - Sarah M Schroeder
- Department of Peptide-based Immunotherapy, University and University Hospital Tübingen, Tübingen, Germany
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- Department of Otorhinolaryngology, Head and Neck Surgery, University of Tübingen, Tübingen, Germany
| | - Marian C Neidert
- Neuroscience Center Zürich (ZNZ), University of Zürich and ETH Zürich, Zürich, Switzerland
- Clinical Neuroscience Center and Department of Neurosurgery, University Hospital and University of Zurich, Zürich, Switzerland
- Department of Neurosurgery, Cantonal Hospital St. Gallen, Zürich, Switzerland
| | - Jonas Scheid
- Department of Peptide-based Immunotherapy, University and University Hospital Tübingen, Tübingen, Germany
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
- Quantitative Biology Center (QBIC), University of Tübingen, Tübingen, Germany
| | - Steffen Lemke
- Department of Peptide-based Immunotherapy, University and University Hospital Tübingen, Tübingen, Germany
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
- Quantitative Biology Center (QBIC), University of Tübingen, Tübingen, Germany
| | - Marissa L Dubbelaar
- Department of Peptide-based Immunotherapy, University and University Hospital Tübingen, Tübingen, Germany
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
- Quantitative Biology Center (QBIC), University of Tübingen, Tübingen, Germany
| | - Marcel Wacker
- Department of Peptide-based Immunotherapy, University and University Hospital Tübingen, Tübingen, Germany
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
| | - Anna Dengler
- Department of Peptide-based Immunotherapy, University and University Hospital Tübingen, Tübingen, Germany
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
| | - Reinhild Klein
- Department of Hematology, Oncology, Clinical Immunology and Rheumatology, University Hospital Tübingen, Tübingen, Germany
| | - Paul-Stefan Mauz
- Department of Otorhinolaryngology, Head and Neck Surgery, University of Tübingen, Tübingen, Germany
| | - Hubert Löwenheim
- Department of Otorhinolaryngology, Head and Neck Surgery, University of Tübingen, Tübingen, Germany
| | - Mathias Hauri-Hohl
- Pediatric Stem Cell Transplantation, University Children's Hospital Zürich, Zürich, Switzerland
| | - Roland Martin
- Neuroimmunology and MS Research, Neurology Clinic, University and University Hospital Zürich, Zürich, Switzerland
| | - Jörg Hennenlotter
- Department of Urology, University Hospital Tübingen, Tübingen, Germany
| | - Arnulf Stenzl
- Department of Urology, University Hospital Tübingen, Tübingen, Germany
| | - Jonas S Heitmann
- Cluster of Excellence iFIT (EXC2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
- Clinical Collaboration Unit Translational Immunology, German Cancer Consortium (DKTK), Department of Internal Medicine, University Hospital Tübingen, Tübingen, Germany
| | - Helmut R Salih
- Cluster of Excellence iFIT (EXC2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), partner site Tübingen, Tübingen, Germany
- Clinical Collaboration Unit Translational Immunology, German Cancer Consortium (DKTK), Department of Internal Medicine, University Hospital Tübingen, Tübingen, Germany
| | - Hans-Georg Rammensee
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), partner site Tübingen, Tübingen, Germany
| | - Juliane S Walz
- Department of Peptide-based Immunotherapy, University and University Hospital Tübingen, Tübingen, Germany.
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany.
- Cluster of Excellence iFIT (EXC2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany.
- Clinical Collaboration Unit Translational Immunology, German Cancer Consortium (DKTK), Department of Internal Medicine, University Hospital Tübingen, Tübingen, Germany.
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22
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Chiaro J, Antignani G, Feola S, Feodoroff M, Martins B, Cojoc H, Russo S, Fusciello M, Hamdan F, Ferrari V, Ciampi D, Ilonen I, Räsänen J, Mäyränpää M, Partanen J, Koskela S, Honkanen J, Halonen J, Kuryk L, Rescigno M, Grönholm M, Branca RM, Lehtiö J, Cerullo V. Development of mesothelioma-specific oncolytic immunotherapy enabled by immunopeptidomics of murine and human mesothelioma tumors. Nat Commun 2023; 14:7056. [PMID: 37923723 PMCID: PMC10624665 DOI: 10.1038/s41467-023-42668-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 10/18/2023] [Indexed: 11/06/2023] Open
Abstract
Malignant pleural mesothelioma (MPM) is an aggressive tumor with a poor prognosis. As the available therapeutic options show a lack of efficacy, novel therapeutic strategies are urgently needed. Given its T-cell infiltration, we hypothesized that MPM is a suitable target for therapeutic cancer vaccination. To date, research on mesothelioma has focused on the identification of molecular signatures to better classify and characterize the disease, and little is known about therapeutic targets that engage cytotoxic (CD8+) T cells. In this study we investigate the immunopeptidomic antigen-presented landscape of MPM in both murine (AB12 cell line) and human cell lines (H28, MSTO-211H, H2452, and JL1), as well as in patients' primary tumors. Applying state-of-the-art immuno-affinity purification methodologies, we identify MHC I-restricted peptides presented on the surface of malignant cells. We characterize in vitro the immunogenicity profile of the eluted peptides using T cells from human healthy donors and cancer patients. Furthermore, we use the most promising peptides to formulate an oncolytic virus-based precision immunotherapy (PeptiCRAd) and test its efficacy in a mouse model of mesothelioma in female mice. Overall, we demonstrate that the use of immunopeptidomic analysis in combination with oncolytic immunotherapy represents a feasible and effective strategy to tackle untreatable tumors.
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Affiliation(s)
- Jacopo Chiaro
- Drug Research Program (DRP), ImmunoViroTherapy Lab (IVT), Division of Pharmaceutical Biosciences, Faculty of Pharmacy, University of Helsinki, Viikinkaari 5E, 00790, Helsinki, Finland
- Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Fabianinkatu 33, 00710, Helsinki, Finland
- Translational Immunology Program (TRIMM), Faculty of Medicine Helsinki University, University of Helsinki, Haartmaninkatu 8, 00290, Helsinki, Finland
- Digital Precision Cancer Medicine Flagship (iCAN), University of Helsinki, 00014, Helsinki, Finland
| | - Gabriella Antignani
- Drug Research Program (DRP), ImmunoViroTherapy Lab (IVT), Division of Pharmaceutical Biosciences, Faculty of Pharmacy, University of Helsinki, Viikinkaari 5E, 00790, Helsinki, Finland
- Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Fabianinkatu 33, 00710, Helsinki, Finland
- Translational Immunology Program (TRIMM), Faculty of Medicine Helsinki University, University of Helsinki, Haartmaninkatu 8, 00290, Helsinki, Finland
- Digital Precision Cancer Medicine Flagship (iCAN), University of Helsinki, 00014, Helsinki, Finland
| | - Sara Feola
- Drug Research Program (DRP), ImmunoViroTherapy Lab (IVT), Division of Pharmaceutical Biosciences, Faculty of Pharmacy, University of Helsinki, Viikinkaari 5E, 00790, Helsinki, Finland
- Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Fabianinkatu 33, 00710, Helsinki, Finland
- Translational Immunology Program (TRIMM), Faculty of Medicine Helsinki University, University of Helsinki, Haartmaninkatu 8, 00290, Helsinki, Finland
- Digital Precision Cancer Medicine Flagship (iCAN), University of Helsinki, 00014, Helsinki, Finland
| | - Michaela Feodoroff
- Drug Research Program (DRP), ImmunoViroTherapy Lab (IVT), Division of Pharmaceutical Biosciences, Faculty of Pharmacy, University of Helsinki, Viikinkaari 5E, 00790, Helsinki, Finland
- Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Fabianinkatu 33, 00710, Helsinki, Finland
- Translational Immunology Program (TRIMM), Faculty of Medicine Helsinki University, University of Helsinki, Haartmaninkatu 8, 00290, Helsinki, Finland
- Digital Precision Cancer Medicine Flagship (iCAN), University of Helsinki, 00014, Helsinki, Finland
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Beatriz Martins
- Drug Research Program (DRP), ImmunoViroTherapy Lab (IVT), Division of Pharmaceutical Biosciences, Faculty of Pharmacy, University of Helsinki, Viikinkaari 5E, 00790, Helsinki, Finland
- Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Fabianinkatu 33, 00710, Helsinki, Finland
- Translational Immunology Program (TRIMM), Faculty of Medicine Helsinki University, University of Helsinki, Haartmaninkatu 8, 00290, Helsinki, Finland
- Digital Precision Cancer Medicine Flagship (iCAN), University of Helsinki, 00014, Helsinki, Finland
| | - Hanne Cojoc
- Valo Therapeutics Oy, Viikinkaari 6, Helsinki, Finland, 00790, Helsinki, Finland
| | - Salvatore Russo
- Drug Research Program (DRP), ImmunoViroTherapy Lab (IVT), Division of Pharmaceutical Biosciences, Faculty of Pharmacy, University of Helsinki, Viikinkaari 5E, 00790, Helsinki, Finland
- Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Fabianinkatu 33, 00710, Helsinki, Finland
- Translational Immunology Program (TRIMM), Faculty of Medicine Helsinki University, University of Helsinki, Haartmaninkatu 8, 00290, Helsinki, Finland
- Digital Precision Cancer Medicine Flagship (iCAN), University of Helsinki, 00014, Helsinki, Finland
| | - Manlio Fusciello
- Drug Research Program (DRP), ImmunoViroTherapy Lab (IVT), Division of Pharmaceutical Biosciences, Faculty of Pharmacy, University of Helsinki, Viikinkaari 5E, 00790, Helsinki, Finland
- Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Fabianinkatu 33, 00710, Helsinki, Finland
- Translational Immunology Program (TRIMM), Faculty of Medicine Helsinki University, University of Helsinki, Haartmaninkatu 8, 00290, Helsinki, Finland
- Digital Precision Cancer Medicine Flagship (iCAN), University of Helsinki, 00014, Helsinki, Finland
| | - Firas Hamdan
- Drug Research Program (DRP), ImmunoViroTherapy Lab (IVT), Division of Pharmaceutical Biosciences, Faculty of Pharmacy, University of Helsinki, Viikinkaari 5E, 00790, Helsinki, Finland
- Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Fabianinkatu 33, 00710, Helsinki, Finland
- Translational Immunology Program (TRIMM), Faculty of Medicine Helsinki University, University of Helsinki, Haartmaninkatu 8, 00290, Helsinki, Finland
- Digital Precision Cancer Medicine Flagship (iCAN), University of Helsinki, 00014, Helsinki, Finland
| | - Valentina Ferrari
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20090, Pieve Emanuele, MI, Italy
| | - Daniele Ciampi
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20090, Pieve Emanuele, MI, Italy
| | - Ilkka Ilonen
- Department of General Thoracic and Esophageal Surgery, Heart and Lung Center, Helsinki University Hospital, 00029, Helsinki, Finland
- Department of Surgery, Clinicum, University of Helsinki, 00029, Helsinki, Finland
| | - Jari Räsänen
- Department of General Thoracic and Esophageal Surgery, Heart and Lung Center, Helsinki University Hospital, 00029, Helsinki, Finland
- Department of Surgery, Clinicum, University of Helsinki, 00029, Helsinki, Finland
| | - Mikko Mäyränpää
- Department of Pathology, Helsinki University Hospital, Helsinki, Finland
| | - Jukka Partanen
- Research & Development Finnish Red Cross Blood Service Helsinki, Kivihaantie 7, 00310, Helsinki, Finland
| | - Satu Koskela
- Finnish Red Cross Blood Service Biobank, Härkälenkki 13, 01730, Vantaa, Finland
| | - Jarno Honkanen
- Finnish Red Cross Blood Service Biobank, Härkälenkki 13, 01730, Vantaa, Finland
| | - Jussi Halonen
- Finnish Red Cross Blood Service Biobank, Härkälenkki 13, 01730, Vantaa, Finland
| | - Lukasz Kuryk
- Valo Therapeutics Oy, Viikinkaari 6, Helsinki, Finland, 00790, Helsinki, Finland
- Department of Virology, National Institute of Public Health NIH-National Research Institute, 24 Chocimska Str., 00-791, Warsaw, Poland
| | - Maria Rescigno
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20090, Pieve Emanuele, MI, Italy
- IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089, Rozzano, MI, Italy
| | - Mikaela Grönholm
- Drug Research Program (DRP), ImmunoViroTherapy Lab (IVT), Division of Pharmaceutical Biosciences, Faculty of Pharmacy, University of Helsinki, Viikinkaari 5E, 00790, Helsinki, Finland
- Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Fabianinkatu 33, 00710, Helsinki, Finland
- Translational Immunology Program (TRIMM), Faculty of Medicine Helsinki University, University of Helsinki, Haartmaninkatu 8, 00290, Helsinki, Finland
- Digital Precision Cancer Medicine Flagship (iCAN), University of Helsinki, 00014, Helsinki, Finland
| | - Rui M Branca
- Science for Life Laboratory, Department of Oncology-Pathology, Karolinska Institutet, Solna, Sweden
| | - Janne Lehtiö
- Science for Life Laboratory, Department of Oncology-Pathology, Karolinska Institutet, Solna, Sweden
| | - Vincenzo Cerullo
- Drug Research Program (DRP), ImmunoViroTherapy Lab (IVT), Division of Pharmaceutical Biosciences, Faculty of Pharmacy, University of Helsinki, Viikinkaari 5E, 00790, Helsinki, Finland.
- Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Fabianinkatu 33, 00710, Helsinki, Finland.
- Translational Immunology Program (TRIMM), Faculty of Medicine Helsinki University, University of Helsinki, Haartmaninkatu 8, 00290, Helsinki, Finland.
- Digital Precision Cancer Medicine Flagship (iCAN), University of Helsinki, 00014, Helsinki, Finland.
- Department of Molecular Medicine and Medical Biotechnology and CEINGE, Naples University Federico II, 80131, Naples, Italy.
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23
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Nelde A, Schuster H, Heitmann JS, Bauer J, Maringer Y, Zwick M, Volkmer JP, Chen JY, Stanger AMP, Lehmann A, Appiah B, Märklin M, Rücker-Braun E, Salih HR, Roerden M, Schroeder SM, Häring MF, Schlosser A, Schetelig J, Schmitz M, Boerries M, Köhler N, Lengerke C, Majeti R, Weissman IL, Rammensee HG, Walz JS. Immune Surveillance of Acute Myeloid Leukemia Is Mediated by HLA-Presented Antigens on Leukemia Progenitor Cells. Blood Cancer Discov 2023; 4:468-489. [PMID: 37847741 PMCID: PMC10618727 DOI: 10.1158/2643-3230.bcd-23-0020] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 06/13/2023] [Accepted: 09/28/2023] [Indexed: 10/19/2023] Open
Abstract
Therapy-resistant leukemia stem and progenitor cells (LSC) are a main cause of acute myeloid leukemia (AML) relapse. LSC-targeting therapies may thus improve outcome of patients with AML. Here we demonstrate that LSCs present HLA-restricted antigens that induce T-cell responses allowing for immune surveillance of AML. Using a mass spectrometry-based immunopeptidomics approach, we characterized the antigenic landscape of patient LSCs and identified AML- and AML/LSC-associated HLA-presented antigens absent from normal tissues comprising nonmutated peptides, cryptic neoepitopes, and neoepitopes of common AML driver mutations of NPM1 and IDH2. Functional relevance of shared AML/LSC antigens is illustrated by presence of their cognizant memory T cells in patients. Antigen-specific T-cell recognition and HLA class II immunopeptidome diversity correlated with clinical outcome. Together, these antigens shared among AML and LSCs represent prime targets for T cell-based therapies with potential of eliminating residual LSCs in patients with AML. SIGNIFICANCE The elimination of therapy-resistant leukemia stem and progenitor cells (LSC) remains a major challenge in the treatment of AML. This study identifies and functionally validates LSC-associated HLA class I and HLA class II-presented antigens, paving the way to the development of LSC-directed T cell-based immunotherapeutic approaches for patients with AML. See related commentary by Ritz, p. 430 . This article is featured in Selected Articles from This Issue, p. 419.
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Affiliation(s)
- Annika Nelde
- Department of Peptide-Based Immunotherapy, University and University Hospital Tübingen, Tübingen, Germany
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, Tübingen, Germany
| | - Heiko Schuster
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
| | - Jonas S. Heitmann
- Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, Tübingen, Germany
- Clinical Collaboration Unit Translational Immunology, German Cancer Consortium (DKTK), Department of Internal Medicine, University Hospital Tübingen, Tübingen, Germany
| | - Jens Bauer
- Department of Peptide-Based Immunotherapy, University and University Hospital Tübingen, Tübingen, Germany
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, Tübingen, Germany
| | - Yacine Maringer
- Department of Peptide-Based Immunotherapy, University and University Hospital Tübingen, Tübingen, Germany
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, Tübingen, Germany
| | - Melissa Zwick
- Department of Medicine I, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Jens-Peter Volkmer
- Institute for Stem Cell Biology and Regenerative Medicine and the Ludwig Cancer Center, Stanford University School of Medicine, Stanford, California
| | - James Y. Chen
- Institute for Stem Cell Biology and Regenerative Medicine and the Ludwig Cancer Center, Stanford University School of Medicine, Stanford, California
| | - Anna M. Paczulla Stanger
- Department of Hematology, Oncology, Clinical Immunology and Rheumatology, University Hospital Tübingen, Tübingen, Germany
- Department of Biomedicine, University of Basel and University Hospital Basel, Basel, Switzerland
| | - Ariane Lehmann
- Faculty of Medicine, Medical Center, Institute of Medical Bioinformatics and Systems Medicine (IBSM), University of Freiburg, Germany
| | - Bismark Appiah
- Faculty of Medicine, Medical Center, Institute of Medical Bioinformatics and Systems Medicine (IBSM), University of Freiburg, Germany
| | - Melanie Märklin
- Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, Tübingen, Germany
- Clinical Collaboration Unit Translational Immunology, German Cancer Consortium (DKTK), Department of Internal Medicine, University Hospital Tübingen, Tübingen, Germany
| | - Elke Rücker-Braun
- Department of Medicine I, University Hospital of Dresden, Dresden, Germany
- Center for Regenerative Therapies Dresden, Technische Universität Dresden, Dresden, Germany
| | - Helmut R. Salih
- Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, Tübingen, Germany
- Clinical Collaboration Unit Translational Immunology, German Cancer Consortium (DKTK), Department of Internal Medicine, University Hospital Tübingen, Tübingen, Germany
| | - Malte Roerden
- Department of Peptide-Based Immunotherapy, University and University Hospital Tübingen, Tübingen, Germany
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- Department of Hematology, Oncology, Clinical Immunology and Rheumatology, University Hospital Tübingen, Tübingen, Germany
| | - Sarah M. Schroeder
- Department of Peptide-Based Immunotherapy, University and University Hospital Tübingen, Tübingen, Germany
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- Department of Otorhinolaryngology, Head and Neck Surgery, University of Tübingen, Tübingen, Germany
| | - Max-Felix Häring
- Department of Peptide-Based Immunotherapy, University and University Hospital Tübingen, Tübingen, Germany
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- Department of Hematology, Oncology, Clinical Immunology and Rheumatology, University Hospital Tübingen, Tübingen, Germany
| | | | - Johannes Schetelig
- Department of Medicine I, University Hospital of Dresden, Dresden, Germany
- German Bone Marrow Donor Center (DKMS), Clinical Trials Unit, Dresden, Germany
| | - Marc Schmitz
- Institute of Immunology, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- National Center for Tumor Diseases (NCT), University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- German Cancer Consortium (DKTK), partner site Dresden, and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Melanie Boerries
- Faculty of Medicine, Medical Center, Institute of Medical Bioinformatics and Systems Medicine (IBSM), University of Freiburg, Germany
- Comprehensive Cancer Center Freiburg (CCCF), Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- German Cancer Consortium (DKTK), Partner Site, Freiburg, and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Natalie Köhler
- Department of Medicine I, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Centre for Integrative Biological Signalling Studies (CIBSS), University of Freiburg, Freiburg, Germany
| | - Claudia Lengerke
- Department of Hematology, Oncology, Clinical Immunology and Rheumatology, University Hospital Tübingen, Tübingen, Germany
- Department of Biomedicine, University of Basel and University Hospital Basel, Basel, Switzerland
- Clinic for Hematology, University of Basel and University Hospital Basel, Basel, Switzerland
- German Cancer Consortium (DKTK), DKFZ partner site Tübingen, Germany
| | - Ravindra Majeti
- Institute for Stem Cell Biology and Regenerative Medicine and the Ludwig Cancer Center, Stanford University School of Medicine, Stanford, California
- Division of Hematology, Department of Medicine, Stanford University School of Medicine, Stanford, California
| | - Irving L. Weissman
- Institute for Stem Cell Biology and Regenerative Medicine and the Ludwig Cancer Center, Stanford University School of Medicine, Stanford, California
| | - Hans-Georg Rammensee
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, Tübingen, Germany
- German Cancer Consortium (DKTK), DKFZ partner site Tübingen, Germany
| | - Juliane S. Walz
- Department of Peptide-Based Immunotherapy, University and University Hospital Tübingen, Tübingen, Germany
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, Tübingen, Germany
- Clinical Collaboration Unit Translational Immunology, German Cancer Consortium (DKTK), Department of Internal Medicine, University Hospital Tübingen, Tübingen, Germany
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Malviya M, Aretz Z, Molvi Z, Lee J, Pierre S, Wallisch P, Dao T, Scheinberg DA. Challenges and solutions for therapeutic TCR-based agents. Immunol Rev 2023; 320:58-82. [PMID: 37455333 PMCID: PMC11141734 DOI: 10.1111/imr.13233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 06/18/2023] [Indexed: 07/18/2023]
Abstract
Recent development of methods to discover and engineer therapeutic T-cell receptors (TCRs) or antibody mimics of TCRs, and to understand their immunology and pharmacology, lag two decades behind therapeutic antibodies. Yet we have every expectation that TCR-based agents will be similarly important contributors to the treatment of a variety of medical conditions, especially cancers. TCR engineered cells, soluble TCRs and their derivatives, TCR-mimic antibodies, and TCR-based CAR T cells promise the possibility of highly specific drugs that can expand the scope of immunologic agents to recognize intracellular targets, including mutated proteins and undruggable transcription factors, not accessible by traditional antibodies. Hurdles exist regarding discovery, specificity, pharmacokinetics, and best modality of use that will need to be overcome before the full potential of TCR-based agents is achieved. HLA restriction may limit each agent to patient subpopulations and off-target reactivities remain important barriers to widespread development and use of these new agents. In this review we discuss the unique opportunities for these new classes of drugs, describe their unique antigenic targets, compare them to traditional antibody therapeutics and CAR T cells, and review the various obstacles that must be overcome before full application of these drugs can be realized.
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Affiliation(s)
- Manish Malviya
- Molecular Pharmacology Program, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065
| | - Zita Aretz
- Molecular Pharmacology Program, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065
- Physiology, Biophysics & Systems Biology Program, Weill Cornell Graduate School of Medical Sciences, 1300 York Avenue, New York, NY 10021
| | - Zaki Molvi
- Molecular Pharmacology Program, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065
- Physiology, Biophysics & Systems Biology Program, Weill Cornell Graduate School of Medical Sciences, 1300 York Avenue, New York, NY 10021
| | - Jayop Lee
- Molecular Pharmacology Program, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065
| | - Stephanie Pierre
- Molecular Pharmacology Program, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065
- Tri-Institutional Medical Scientist Program, 1300 York Avenue, New York, NY 10021
| | - Patrick Wallisch
- Molecular Pharmacology Program, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065
- Pharmacology Program, Weill Cornell Graduate School of Medical Sciences, 1300 York Avenue, New York, NY 10021
| | - Tao Dao
- Molecular Pharmacology Program, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065
| | - David A. Scheinberg
- Molecular Pharmacology Program, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065
- Pharmacology Program, Weill Cornell Graduate School of Medical Sciences, 1300 York Avenue, New York, NY 10021
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25
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Yin S, Klaeger S, Chea VA, Carulli IP, Rachimi S, Black KE, Filbin M, Hariri LP, Knipe RS, Padera RF, Stevens JD, Lane WJ, Carr SA, Wu CJ, Kim EY, Keskin DB. Integrated Immunopeptidomic and Proteomic Analysis of COVID-19 lung biopsies. Front Immunol 2023; 14:1269335. [PMID: 37942334 PMCID: PMC10628763 DOI: 10.3389/fimmu.2023.1269335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 10/09/2023] [Indexed: 11/10/2023] Open
Abstract
Introduction Severe respiratory illness is the most prominent manifestation of patients infected with SARS-CoV-2, and yet the molecular mechanisms underlying severe lung disease in COVID-19 affected patients still require elucidation. Human leukocyte antigen class I (HLA-I) expression is crucial for antigen presentation and the host's response to SARS-CoV-2. Methods To gain insights into the immune response and molecular pathways involved in severe lung disease, we performed immunopeptidomic and proteomic analyses of lung tissues recovered at four COVID-19 autopsy and six non-COVID-19 transplants. Results We found signals of tissue injury and regeneration in lung fibroblast and alveolar type I/II cells, resulting in the production of highly immunogenic self-antigens within the lungs of COVID-19 patients. We also identified immune activation of the M2c macrophage as the primary source of HLA-I presentation and immunogenicity in this context. Additionally, we identified 28 lung signatures that can serve as early plasma markers for predicting infection and severe COVID-19 disease. These protein signatures were predominantly expressed in macrophages and epithelial cells and were associated with complement and coagulation cascades. Discussion Our findings emphasize the significant role of macrophage-mediated immunity in the development of severe lung disease in COVID-19 patients.
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Affiliation(s)
- Shanye Yin
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
| | - Susan Klaeger
- Broad Institute of MIT and Harvard, Cambridge, MA, United States
| | - Vipheaviny A. Chea
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, United States
- Translational Immunogenomics Laboratory, Dana-Farber Cancer Institute, Boston, MA, United States
| | - Isabel P. Carulli
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, United States
- Translational Immunogenomics Laboratory, Dana-Farber Cancer Institute, Boston, MA, United States
| | - Suzanna Rachimi
- Broad Institute of MIT and Harvard, Cambridge, MA, United States
| | - Katharine E. Black
- Harvard Medical School, Boston, MA, United States
- Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston, MA, United States
| | - Michael Filbin
- Harvard Medical School, Boston, MA, United States
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA, United States
| | - Lida P. Hariri
- Harvard Medical School, Boston, MA, United States
- Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston, MA, United States
- Department of Pathology, Massachusetts General Hospital, Boston, MA, United States
| | - Rachel S. Knipe
- Harvard Medical School, Boston, MA, United States
- Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston, MA, United States
| | - Robert F. Padera
- Harvard Medical School, Boston, MA, United States
- Department of Pathology, Brigham and Women’s Hospital, Boston, MA, United States
| | - Jonathan D. Stevens
- Department of Pathology, Brigham and Women’s Hospital, Boston, MA, United States
| | - William J. Lane
- Harvard Medical School, Boston, MA, United States
- Department of Pathology, Brigham and Women’s Hospital, Boston, MA, United States
| | - Steven A. Carr
- Broad Institute of MIT and Harvard, Cambridge, MA, United States
| | - Catherine J. Wu
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
- Broad Institute of MIT and Harvard, Cambridge, MA, United States
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA, United States
| | - Edy Yong Kim
- Harvard Medical School, Boston, MA, United States
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, MA, United States
| | - Derin B. Keskin
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
- Broad Institute of MIT and Harvard, Cambridge, MA, United States
- Translational Immunogenomics Laboratory, Dana-Farber Cancer Institute, Boston, MA, United States
- Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
- Department of Computer Science, Metropolitan College, Boston University, Boston, MA, United States
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26
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Zhang B, Bassani-Sternberg M. Current perspectives on mass spectrometry-based immunopeptidomics: the computational angle to tumor antigen discovery. J Immunother Cancer 2023; 11:e007073. [PMID: 37899131 PMCID: PMC10619091 DOI: 10.1136/jitc-2023-007073] [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] [Accepted: 07/21/2023] [Indexed: 10/31/2023] Open
Abstract
Identification of tumor antigens presented by the human leucocyte antigen (HLA) molecules is essential for the design of effective and safe cancer immunotherapies that rely on T cell recognition and killing of tumor cells. Mass spectrometry (MS)-based immunopeptidomics enables high-throughput, direct identification of HLA-bound peptides from a variety of cell lines, tumor tissues, and healthy tissues. It involves immunoaffinity purification of HLA complexes followed by MS profiling of the extracted peptides using data-dependent acquisition, data-independent acquisition, or targeted approaches. By incorporating DNA, RNA, and ribosome sequencing data into immunopeptidomics data analysis, the proteogenomic approach provides a powerful means for identifying tumor antigens encoded within the canonical open reading frames of annotated coding genes and non-canonical tumor antigens derived from presumably non-coding regions of our genome. We discuss emerging computational challenges in immunopeptidomics data analysis and tumor antigen identification, highlighting key considerations in the proteogenomics-based approach, including accurate DNA, RNA and ribosomal sequencing data analysis, careful incorporation of predicted novel protein sequences into reference protein database, special quality control in MS data analysis due to the expanded and heterogeneous search space, cancer-specificity determination, and immunogenicity prediction. The advancements in technology and computation is continually enabling us to identify tumor antigens with higher sensitivity and accuracy, paving the way toward the development of more effective cancer immunotherapies.
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Affiliation(s)
- Bing Zhang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, Texas, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA
| | - Michal Bassani-Sternberg
- Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Department of Oncology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
- Agora Cancer Research Centre, Lausanne, Switzerland
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27
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Giannakopoulou E, Lehander M, Virding Culleton S, Yang W, Li Y, Karpanen T, Yoshizato T, Rustad EH, Nielsen MM, Bollineni RC, Tran TT, Delic-Sarac M, Gjerdingen TJ, Douvlataniotis K, Laos M, Ali M, Hillen A, Mazzi S, Chin DWL, Mehta A, Holm JS, Bentzen AK, Bill M, Griffioen M, Gedde-Dahl T, Lehmann S, Jacobsen SEW, Woll PS, Olweus J. A T cell receptor targeting a recurrent driver mutation in FLT3 mediates elimination of primary human acute myeloid leukemia in vivo. NATURE CANCER 2023; 4:1474-1490. [PMID: 37783807 PMCID: PMC10597840 DOI: 10.1038/s43018-023-00642-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 08/28/2023] [Indexed: 10/04/2023]
Abstract
Acute myeloid leukemia (AML), the most frequent leukemia in adults, is driven by recurrent somatically acquired genetic lesions in a restricted number of genes. Treatment with tyrosine kinase inhibitors has demonstrated that targeting of prevalent FMS-related receptor tyrosine kinase 3 (FLT3) gain-of-function mutations can provide significant survival benefits for patients, although the efficacy of FLT3 inhibitors in eliminating FLT3-mutated clones is variable. We identified a T cell receptor (TCR) reactive to the recurrent D835Y driver mutation in the FLT3 tyrosine kinase domain (TCRFLT3D/Y). TCRFLT3D/Y-redirected T cells selectively eliminated primary human AML cells harboring the FLT3D835Y mutation in vitro and in vivo. TCRFLT3D/Y cells rejected both CD34+ and CD34- AML in mice engrafted with primary leukemia from patients, reaching minimal residual disease-negative levels, and eliminated primary CD34+ AML leukemia-propagating cells in vivo. Thus, T cells targeting a single shared mutation can provide efficient immunotherapy toward selective elimination of clonally involved primary AML cells in vivo.
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Grants
- G0801073 Medical Research Council
- MC_UU_00016/5 Medical Research Council
- MC_UU_12009/5 Medical Research Council
- South-Eastern Regional Health Authority Norway, the Research Council of Norway, the Norwegian Cancer Society, the Norwegian Childhood Cancer Foundation, Stiftelsen Kristian Gerhard Jebsen, European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 865805), the University of Oslo and Oslo University Hospital and Novo Nordisk Foundation.
- Knut and Alice Wallenberg Foundation, The Swedish Research Council, Tobias Foundation, Torsten Söderberg Foundation, Center for Innovative Medicine (CIMED) at Karolinska Institutet, and The UK Medical Research Council
- Technical University of Denmark (DTU)
- Aarhus University Hospital
- Leiden University Medical Center
- Oslo University Hospital
- Karolinska University Hospital
- University of Oslo and Oslo University Hospital
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Affiliation(s)
- Eirini Giannakopoulou
- Department of Cancer Immunology, Oslo University Hospital Radiumhospitalet, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Madeleine Lehander
- Department of Medicine Huddinge, Center for Hematology and Regenerative Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Stina Virding Culleton
- Department of Medicine Huddinge, Center for Hematology and Regenerative Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Weiwen Yang
- Department of Cancer Immunology, Oslo University Hospital Radiumhospitalet, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Yingqian Li
- Department of Cancer Immunology, Oslo University Hospital Radiumhospitalet, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Terhi Karpanen
- Department of Cancer Immunology, Oslo University Hospital Radiumhospitalet, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Genomics Group, Faculty of Biosciences and Aquaculture, Nord University, Bodø, Norway
| | - Tetsuichi Yoshizato
- Department of Medicine Huddinge, Center for Hematology and Regenerative Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Even H Rustad
- Department of Cancer Immunology, Oslo University Hospital Radiumhospitalet, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Morten Milek Nielsen
- Department of Cancer Immunology, Oslo University Hospital Radiumhospitalet, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Ravi Chand Bollineni
- Department of Cancer Immunology, Oslo University Hospital Radiumhospitalet, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Trung T Tran
- Department of Immunology, Oslo University Hospital, Oslo, Norway
| | - Marina Delic-Sarac
- Department of Cancer Immunology, Oslo University Hospital Radiumhospitalet, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Thea Johanne Gjerdingen
- Department of Cancer Immunology, Oslo University Hospital Radiumhospitalet, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Karolos Douvlataniotis
- Department of Cancer Immunology, Oslo University Hospital Radiumhospitalet, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Maarja Laos
- Department of Cancer Immunology, Oslo University Hospital Radiumhospitalet, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Muhammad Ali
- Department of Cancer Immunology, Oslo University Hospital Radiumhospitalet, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Amy Hillen
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
| | - Stefania Mazzi
- Department of Medicine Huddinge, Center for Hematology and Regenerative Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Desmond Wai Loon Chin
- Department of Medicine Huddinge, Center for Hematology and Regenerative Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Adi Mehta
- Department of Immunology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Jeppe Sejerø Holm
- Section for Experimental and Translational Immunology, Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Amalie Kai Bentzen
- Section for Experimental and Translational Immunology, Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Marie Bill
- Department of Hematology, Aarhus University Hospital, Aarhus, Denmark
| | - Marieke Griffioen
- Department of Hematology, Leiden University Medical Center, Leiden, the Netherlands
| | - Tobias Gedde-Dahl
- Hematology Department, Section for Stem Cell Transplantation, Oslo University Hospital, Rikshospitalet, Clinic for Cancer Medicine, Oslo, Norway
| | - Sören Lehmann
- Department of Medicine Huddinge, Center for Hematology and Regenerative Medicine, Karolinska Institutet, Stockholm, Sweden
- Karolinska University Hospital, Stockholm, Sweden
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Sten Eirik W Jacobsen
- Department of Medicine Huddinge, Center for Hematology and Regenerative Medicine, Karolinska Institutet, Stockholm, Sweden.
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden.
- Karolinska University Hospital, Stockholm, Sweden.
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.
| | - Petter S Woll
- Department of Medicine Huddinge, Center for Hematology and Regenerative Medicine, Karolinska Institutet, Stockholm, Sweden.
| | - Johanna Olweus
- Department of Cancer Immunology, Oslo University Hospital Radiumhospitalet, Oslo, Norway.
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
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28
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Apavaloaei A, Perreault C. Immunotargeting of a recurrent AML-specific neoantigen. NATURE CANCER 2023; 4:1403-1405. [PMID: 37783806 DOI: 10.1038/s43018-023-00634-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
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29
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Nicholas B, Skipp P. What do cancer-specific CD8+ T cells see? The contribution of immunopeptidomics. Essays Biochem 2023; 67:957-965. [PMID: 37503576 DOI: 10.1042/ebc20220246] [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: 02/24/2023] [Revised: 07/03/2023] [Accepted: 07/13/2023] [Indexed: 07/29/2023]
Abstract
Immunopeptidomics is the survey of all peptides displayed on a cell or tissue when bound to human leukocyte antigen (HLA) molecules using tandem mass spectrometry. When attempting to determine the targets of tumour-specific CD8+ T cells, a survey of the potential ligands in tumour tissues is invaluable, and, in comparison with in-silico predictions, provides greater certainty of the existence of individual epitopes, as immunopeptidomics-confirmed CD8+ T-cell epitopes are known to be immunogenic, and direct observation should avoid the risk of autoreactivity which could arise following immunisation with structural homologues. The canonical sources of CD8+ T-cell tumour specific epitopes, such as tumour associated antigens, may be well conserved between patients and tumour types, but are often only weakly immunogenic. Direct observation of tumour-specific neoantigens by immunopeptidomics is rare, although valuable. Thus, there has been increasing interest in the non-canonical origins of tumour-reactive CD8+ T-cell epitopes, such as those arising from proteasomal splicing events, translational/turnover defects and alternative open reading frame reads. Such epitopes can be identified in silico, although validation is more challenging. Non-self CD8+ T-cell epitopes such as viral epitopes may be useful in certain cancer types with known viral origins, however these have been relatively unexplored with immunopeptidomics to date, possibly due to the paucity of source viral proteins in tumour tissues. This review examines the latest evidence for canonical, non-canonical and non-human CD8+ T-cell epitopes identified by immunopeptidomics, and concludes that the relative contribution for each of these sources to anti-tumour CD8+ T-cell reactivity is currently uncertain.
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Affiliation(s)
- Ben Nicholas
- Centre for Proteomic Research, Biological Sciences and Institute for Life Sciences, Building 85, University of Southampton, SO17 1BJ, U.K
| | - Paul Skipp
- Centre for Proteomic Research, Biological Sciences and Institute for Life Sciences, Building 85, University of Southampton, SO17 1BJ, U.K
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30
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Massa C, Seliger B. Combination of multiple omics techniques for a personalized therapy or treatment selection. Front Immunol 2023; 14:1258013. [PMID: 37828984 PMCID: PMC10565668 DOI: 10.3389/fimmu.2023.1258013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 09/05/2023] [Indexed: 10/14/2023] Open
Abstract
Despite targeted therapies and immunotherapies have revolutionized the treatment of cancer patients, only a limited number of patients have long-term responses. Moreover, due to differences within cancer patients in the tumor mutational burden, composition of the tumor microenvironment as well as of the peripheral immune system and microbiome, and in the development of immune escape mechanisms, there is no "one fit all" therapy. Thus, the treatment of patients must be personalized based on the specific molecular, immunologic and/or metabolic landscape of their tumor. In order to identify for each patient the best possible therapy, different approaches should be employed and combined. These include (i) the use of predictive biomarkers identified on large cohorts of patients with the same tumor type and (ii) the evaluation of the individual tumor with "omics"-based analyses as well as its ex vivo characterization for susceptibility to different therapies.
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Affiliation(s)
- Chiara Massa
- Institute for Translational Immunology, Brandenburg Medical School Theodor Fontane, Brandenburg an der Havel, Germany
| | - Barbara Seliger
- Institute for Translational Immunology, Brandenburg Medical School Theodor Fontane, Brandenburg an der Havel, Germany
- Institute of Medical Immunology, Martin Luther University Halle-Wittenberg, Halle, Germany
- Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany
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31
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Hartman K, Steiner G, Siegel M, Looney CM, Hickling TP, Bray-French K, Springer S, Marban-Doran C, Ducret A. Expanding the MAPPs Assay to Accommodate MHC-II Pan Receptors for Improved Predictability of Potential T Cell Epitopes. BIOLOGY 2023; 12:1265. [PMID: 37759665 PMCID: PMC10525474 DOI: 10.3390/biology12091265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Revised: 09/08/2023] [Accepted: 09/11/2023] [Indexed: 09/29/2023]
Abstract
A critical step in the immunogenicity cascade is attributed to human leukocyte antigen (HLA) II presentation triggering T cell immune responses. The liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based major histocompatibility complex (MHC) II-associated peptide proteomics (MAPPs) assay is implemented during preclinical risk assessments to identify biotherapeutic-derived T cell epitopes. Although studies indicate that HLA-DP and HLA-DQ alleles are linked to immunogenicity, most MAPPs studies are restricted to using HLA-DR as the dominant HLA II genotype due to the lack of well-characterized immunoprecipitating antibodies. Here, we address this issue by testing various commercially available clones of MHC-II pan (CR3/43, WR18, and Tü39), HLA-DP (B7/21), and HLA-DQ (SPV-L3 and 1a3) antibodies in the MAPPs assay, and characterizing identified peptides according to binding specificity. Our results reveal that HLA II receptor-precipitating reagents with similar reported specificities differ based on clonality and that MHC-II pan antibodies do not entirely exhibit pan-specific tendencies. Since no individual antibody clone is able to recover the complete HLA II peptide repertoire, we recommend a mixed strategy of clones L243, WR18, and SPV-L3 in a single immunoprecipitation step for more robust compound-specific peptide detection. Ultimately, our optimized MAPPs strategy improves the predictability and additional identification of T cell epitopes in immunogenicity risk assessments.
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Affiliation(s)
- Katharina Hartman
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Grenzacherstrasse 124, 4070 Basel, Switzerland (C.M.L.)
| | - Guido Steiner
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Grenzacherstrasse 124, 4070 Basel, Switzerland (C.M.L.)
| | - Michel Siegel
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Grenzacherstrasse 124, 4070 Basel, Switzerland (C.M.L.)
| | - Cary M. Looney
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Grenzacherstrasse 124, 4070 Basel, Switzerland (C.M.L.)
| | - Timothy P. Hickling
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Grenzacherstrasse 124, 4070 Basel, Switzerland (C.M.L.)
| | - Katharine Bray-French
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Grenzacherstrasse 124, 4070 Basel, Switzerland (C.M.L.)
| | - Sebastian Springer
- School of Science, Department of Biochemistry and Cell Biology, Constructor University, Campus Ring 1, 28759 Bremen, Germany
| | - Céline Marban-Doran
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Grenzacherstrasse 124, 4070 Basel, Switzerland (C.M.L.)
| | - Axel Ducret
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, Grenzacherstrasse 124, 4070 Basel, Switzerland (C.M.L.)
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Schendel DJ. Evolution by innovation as a driving force to improve TCR-T therapies. Front Oncol 2023; 13:1216829. [PMID: 37810959 PMCID: PMC10552759 DOI: 10.3389/fonc.2023.1216829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 08/16/2023] [Indexed: 10/10/2023] Open
Abstract
Adoptive cell therapies continually evolve through science-based innovation. Specialized innovations for TCR-T therapies are described here that are embedded in an End-to-End Platform for TCR-T Therapy Development which aims to provide solutions for key unmet patient needs by addressing challenges of TCR-T therapy, including selection of target antigens and suitable T cell receptors, generation of TCR-T therapies that provide long term, durable efficacy and safety and development of efficient and scalable production of patient-specific (personalized) TCR-T therapy for solid tumors. Multiple, combinable, innovative technologies are used in a systematic and sequential manner in the development of TCR-T therapies. One group of technologies encompasses product enhancements that enable TCR-T therapies to be safer, more specific and more effective. The second group of technologies addresses development optimization that supports discovery and development processes for TCR-T therapies to be performed more quickly, with higher quality and greater efficiency. Each module incorporates innovations layered onto basic technologies common to the field of immunology. An active approach of "evolution by innovation" supports the overall goal to develop best-in-class TCR-T therapies for treatment of patients with solid cancer.
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Affiliation(s)
- Dolores J. Schendel
- Medigene Immunotherapies GmbH, Planegg, Germany
- Medigene AG, Planegg, Germany
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Lee CH, Huh J, Buckley PR, Jang M, Pinho MP, Fernandes RA, Antanaviciute A, Simmons A, Koohy H. A robust deep learning workflow to predict CD8 + T-cell epitopes. Genome Med 2023; 15:70. [PMID: 37705109 PMCID: PMC10498576 DOI: 10.1186/s13073-023-01225-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 08/30/2023] [Indexed: 09/15/2023] Open
Abstract
BACKGROUND T-cells play a crucial role in the adaptive immune system by triggering responses against cancer cells and pathogens, while maintaining tolerance against self-antigens, which has sparked interest in the development of various T-cell-focused immunotherapies. However, the identification of antigens recognised by T-cells is low-throughput and laborious. To overcome some of these limitations, computational methods for predicting CD8 + T-cell epitopes have emerged. Despite recent developments, most immunogenicity algorithms struggle to learn features of peptide immunogenicity from small datasets, suffer from HLA bias and are unable to reliably predict pathology-specific CD8 + T-cell epitopes. METHODS We developed TRAP (T-cell recognition potential of HLA-I presented peptides), a robust deep learning workflow for predicting CD8 + T-cell epitopes from MHC-I presented pathogenic and self-peptides. TRAP uses transfer learning, deep learning architecture and MHC binding information to make context-specific predictions of CD8 + T-cell epitopes. TRAP also detects low-confidence predictions for peptides that differ significantly from those in the training datasets to abstain from making incorrect predictions. To estimate the immunogenicity of pathogenic peptides with low-confidence predictions, we further developed a novel metric, RSAT (relative similarity to autoantigens and tumour-associated antigens), as a complementary to 'dissimilarity to self' from cancer studies. RESULTS TRAP was used to identify epitopes from glioblastoma patients as well as SARS-CoV-2 peptides, and it outperformed other algorithms in both cancer and pathogenic settings. TRAP was especially effective at extracting immunogenicity-associated properties from restricted data of emerging pathogens and translating them onto related species, as well as minimising the loss of likely epitopes in imbalanced datasets. We also demonstrated that the novel metric termed RSAT was able to estimate immunogenic of pathogenic peptides of various lengths and species. TRAP implementation is available at: https://github.com/ChloeHJ/TRAP . CONCLUSIONS This study presents a novel computational workflow for accurately predicting CD8 + T-cell epitopes to foster a better understanding of antigen-specific T-cell response and the development of effective clinical therapeutics.
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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, OX3 9DS, UK
- MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DS, UK
| | - Jaesung Huh
- Visual Geometry Group, Department of Engineering Science, University of Oxford, Oxford, OX2 6NN, UK
| | - Paul R Buckley
- MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DS, UK
- MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DS, UK
| | - Myeongjun Jang
- Intelligent Systems Lab, Department of Computer Science, University of Oxford, Oxford, OX1 3QG, UK
| | - Mariana Pereira Pinho
- MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DS, UK
| | - Ricardo A Fernandes
- Chinese Academy of Medical Sciences (CAMS) Oxford Institute (COI), University of Oxford, Oxford, OX3 7BN, UK
| | - Agne Antanaviciute
- MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DS, UK
- MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DS, UK
| | - Alison Simmons
- MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DS, UK
- Translational Gastroenterology Unit, John Radcliffe Hospital, Oxford, OX3 9DS, UK
| | - Hashem Koohy
- MRC Human Immunology Unit, Medical Research Council (MRC) Weatherall Institute of Molecular Medicine (WIMM), John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DS, UK.
- MRC WIMM Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DS, UK.
- Alan Turning Fellow in Health and Medicine, The Alan Turing Institute, London, UK.
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Molvi Z, Klatt MG, Dao T, Urraca J, Scheinberg DA, O'Reilly RJ. The landscape of MHC-presented phosphopeptides yields actionable shared tumor antigens for cancer immunotherapy across multiple HLA alleles. J Immunother Cancer 2023; 11:e006889. [PMID: 37775115 PMCID: PMC10546156 DOI: 10.1136/jitc-2023-006889] [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] [Accepted: 08/23/2023] [Indexed: 10/01/2023] Open
Abstract
BACKGROUND Certain phosphorylated peptides are differentially presented by major histocompatibility complex (MHC) molecules on cancer cells characterized by aberrant phosphorylation. Phosphopeptides presented in complex with the human leukocyte antigen HLA-A*02:01 provide a stability advantage over their non-phosphorylated counterparts. This stability is thought to contribute to enhanced immunogenicity. Whether tumor-associated phosphopeptides presented by other common alleles exhibit immunogenicity and structural characteristics similar to those presented by A*02:01 is unclear. Therefore, we determined the identity, structural features, and immunogenicity of phosphopeptides presented by the prevalent alleles HLA-A*03:01, HLA-A*11:01, HLA-C*07:01, and HLA-C*07:02. METHODS We isolated peptide-MHC complexes by immunoprecipitation from 11 healthy and neoplastic tissue samples using mass spectrometry, and then combined the resulting data with public immunopeptidomics data sets to assemble a curated set of phosphopeptides presented by 96 samples spanning 20 distinct healthy and neoplastic tissue types. We determined the biochemical features of selected phosphopeptides by in vitro binding assays and in silico docking, and their immunogenicity by analyzing healthy donor T cells for phosphopeptide-specific multimer binding and cytokine production. RESULTS We identified a subset of phosphopeptides presented by HLA-A*03:01, A*11:01, C*07:01 and C*07:02 on multiple tumor types, particularly lymphomas and leukemias, but not healthy tissues. These phosphopeptides are products of genes essential to lymphoma and leukemia survival. The presented phosphopeptides generally exhibited similar or worse binding to A*03:01 than their non-phosphorylated counterparts. HLA-C*07:01 generally presented phosphopeptides but not their unmodified counterparts. Phosphopeptide binding to HLA-C*07:01 was dependent on B-pocket interactions that were absent in HLA-C*07:02. While HLA-A*02:01 and HLA-A*11:01 phosphopeptide-specific T cells could be readily detected in an autologous setting even when the non-phosphorylated peptide was co-presented, HLA-A*03:01 or HLA-C*07:01 phosphopeptides were repeatedly non-immunogenic, requiring use of allogeneic T cells to induce phosphopeptide-specific T cells. CONCLUSIONS Phosphopeptides presented by multiple alleles that are differentially expressed on tumors constitute tumor-specific antigens that could be targeted for cancer immunotherapy, but the immunogenicity of such phosphopeptides is not a general feature. In particular, phosphopeptides presented by HLA-A*02:01 and A*11:01 exhibit consistent immunogenicity, while phosphopeptides presented by HLA-A*03:01 and C*07:01, although appropriately presented, are not immunogenic. Thus, to address an expanded patient population, phosphopeptide-targeted immunotherapies should be wary of allele-specific differences.
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Affiliation(s)
- Zaki Molvi
- Immunology Program, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Martin G Klatt
- Department of Hematology, Oncology and Tumor Immunology, Charite Universitatsmedizin Berlin, Berlin, Germany
- German Cancer Research Center, Heidelberg, Baden-Württemberg, Germany
- Berlin Institute of Health at Charité -Universitätsmedizin Berlin, BIH Biomedical 13 Innovation Academy, BIH Charité Clinician Scientist Program, Berlin, Germany
| | - Tao Dao
- Department of Pediatrics, Molecular Pharmacology Program, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Jessica Urraca
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - David A Scheinberg
- Department of Pediatrics, Molecular Pharmacology Program, Memorial Sloan Kettering Cancer Center, New York, New York, USA
- Weill Cornell Medicine, New York, New York, USA
| | - Richard J O'Reilly
- Immunology Program, Memorial Sloan Kettering Cancer Center, New York, New York, USA
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
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Yang K, Halima A, Chan TA. Antigen presentation in cancer - mechanisms and clinical implications for immunotherapy. Nat Rev Clin Oncol 2023; 20:604-623. [PMID: 37328642 DOI: 10.1038/s41571-023-00789-4] [Citation(s) in RCA: 27] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/25/2023] [Indexed: 06/18/2023]
Abstract
Over the past decade, the emergence of effective immunotherapies has revolutionized the clinical management of many types of cancers. However, long-term durable tumour control is only achieved in a fraction of patients who receive these therapies. Understanding the mechanisms underlying clinical response and resistance to treatment is therefore essential to expanding the level of clinical benefit obtained from immunotherapies. In this Review, we describe the molecular mechanisms of antigen processing and presentation in tumours and their clinical consequences. We examine how various aspects of the antigen-presentation machinery (APM) shape tumour immunity. In particular, we discuss genomic variants in HLA alleles and other APM components, highlighting their influence on the immunopeptidomes of both malignant cells and immune cells. Understanding the APM, how it is regulated and how it changes in tumour cells is crucial for determining which patients will respond to immunotherapy and why some patients develop resistance. We focus on recently discovered molecular and genomic alterations that drive the clinical outcomes of patients receiving immune-checkpoint inhibitors. An improved understanding of how these variables mediate tumour-immune interactions is expected to guide the more precise administration of immunotherapies and reveal potentially promising directions for the development of new immunotherapeutic approaches.
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Affiliation(s)
- Kailin Yang
- Department of Radiation Oncology, Taussig Cancer Center, Cleveland Clinic, Cleveland, OH, USA
| | - Ahmed Halima
- Department of Radiation Oncology, Taussig Cancer Center, Cleveland Clinic, Cleveland, OH, USA
| | - Timothy A Chan
- Department of Radiation Oncology, Taussig Cancer Center, Cleveland Clinic, Cleveland, OH, USA.
- Center for Immunotherapy and Precision Immuno-Oncology, Cleveland Clinic, Cleveland, OH, USA.
- National Center for Regenerative Medicine, Cleveland, OH, USA.
- Case Comprehensive Cancer Center, Cleveland, OH, USA.
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36
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Cuevas MVR, Hardy MP, Larouche JD, Apavaloaei A, Kina E, Vincent K, Gendron P, Laverdure JP, Durette C, Thibault P, Lemieux S, Perreault C, Ehx G. BamQuery: a proteogenomic tool to explore the immunopeptidome and prioritize actionable tumor antigens. Genome Biol 2023; 24:188. [PMID: 37582761 PMCID: PMC10426134 DOI: 10.1186/s13059-023-03029-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 07/31/2023] [Indexed: 08/17/2023] Open
Abstract
MHC-I-associated peptides deriving from non-coding genomic regions and mutations can generate tumor-specific antigens, including neoantigens. Quantifying tumor-specific antigens' RNA expression in malignant and benign tissues is critical for discriminating actionable targets. We present BamQuery, a tool attributing an exhaustive RNA expression to MHC-I-associated peptides of any origin from bulk and single-cell RNA-sequencing data. We show that many cryptic and mutated tumor-specific antigens can derive from multiple discrete genomic regions, abundantly expressed in normal tissues. BamQuery can also be used to predict MHC-I-associated peptides immunogenicity and identify actionable tumor-specific antigens de novo.
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Affiliation(s)
- Maria Virginia Ruiz Cuevas
- Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, QC, H3C 3J7, Canada
- Department of Biochemistry and Molecular Medicine, Université de Montréal, Montreal, QC, H3C 3J7, Canada
| | - Marie-Pierre Hardy
- Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, QC, H3C 3J7, Canada
| | - Jean-David Larouche
- Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, QC, H3C 3J7, Canada
- Department of Medicine, Université de Montréal, Montreal, QC, H3C 3J7, Canada
| | - Anca Apavaloaei
- Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, QC, H3C 3J7, Canada
- Department of Medicine, Université de Montréal, Montreal, QC, H3C 3J7, Canada
| | - Eralda Kina
- Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, QC, H3C 3J7, Canada
- Department of Medicine, Université de Montréal, Montreal, QC, H3C 3J7, Canada
| | - Krystel Vincent
- Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, QC, H3C 3J7, Canada
| | - Patrick Gendron
- Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, QC, H3C 3J7, Canada
| | - Jean-Philippe Laverdure
- Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, QC, H3C 3J7, Canada
| | - Chantal Durette
- Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, QC, H3C 3J7, Canada
| | - Pierre Thibault
- Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, QC, H3C 3J7, Canada
- Department of Chemistry, Université de Montréal, Montreal, QC, H3C 3J7, Canada
| | - Sébastien Lemieux
- Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, QC, H3C 3J7, Canada
- Department of Biochemistry and Molecular Medicine, Université de Montréal, Montreal, QC, H3C 3J7, Canada
| | - Claude Perreault
- Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, QC, H3C 3J7, Canada
- Department of Medicine, Université de Montréal, Montreal, QC, H3C 3J7, Canada
| | - Grégory Ehx
- Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, QC, H3C 3J7, Canada.
- Laboratory of Hematology, GIGA-I3, University of Liege, CHU of Liege, Liege, Belgium.
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37
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Medici G, Freudenmann LK, Velz J, Wang SSY, Kapolou K, Paramasivam N, Mühlenbruch L, Kowalewski DJ, Vasella F, Bilich T, Frey BM, Dubbelaar ML, Patterson AB, Zeitlberger AM, Silginer M, Roth P, Weiss T, Wirsching HG, Krayenbühl N, Bozinov O, Regli L, Rammensee HG, Rushing EJ, Sahm F, Walz JS, Weller M, Neidert MC. A T-cell antigen atlas for meningioma: novel options for immunotherapy. Acta Neuropathol 2023; 146:173-190. [PMID: 37368072 PMCID: PMC10329067 DOI: 10.1007/s00401-023-02605-w] [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: 02/13/2023] [Revised: 06/16/2023] [Accepted: 06/17/2023] [Indexed: 06/28/2023]
Abstract
Meningiomas are the most common primary intracranial tumors. Although most symptomatic cases can be managed by surgery and/or radiotherapy, a relevant number of patients experience an unfavorable clinical course and additional treatment options are needed. As meningiomas are often perfused by dural branches of the external carotid artery, which is located outside the blood-brain barrier, they might be an accessible target for immunotherapy. However, the landscape of naturally presented tumor antigens in meningioma is unknown. We here provide a T-cell antigen atlas for meningioma by in-depth profiling of the naturally presented immunopeptidome using LC-MS/MS. Candidate target antigens were selected based on a comparative approach using an extensive immunopeptidome data set of normal tissues. Meningioma-exclusive antigens for HLA class I and II are described here for the first time. Top-ranking targets were further functionally characterized by showing their immunogenicity through in vitro T-cell priming assays. Thus, we provide an atlas of meningioma T-cell antigens which will be publicly available for further research. In addition, we have identified novel actionable targets that warrant further investigation as an immunotherapy option for meningioma.
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Affiliation(s)
- Gioele Medici
- Laboratory of Molecular Neuro-Oncology, Department of Neurology, Clinical Neuroscience Center, University Hospital and University of Zurich, Frauenklinikstrasse 26, 8091, Zurich, Switzerland.
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital and University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland.
| | - Lena K Freudenmann
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- DKFZ Partner Site Tübingen, German Cancer Consortium (DKTK), Tübingen, Germany
- Cluster of Excellence iFIT (EXC2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
| | - Julia Velz
- Laboratory of Molecular Neuro-Oncology, Department of Neurology, Clinical Neuroscience Center, University Hospital and University of Zurich, Frauenklinikstrasse 26, 8091, Zurich, Switzerland
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital and University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland
| | - Sophie Shih-Yüng Wang
- Laboratory of Molecular Neuro-Oncology, Department of Neurology, Clinical Neuroscience Center, University Hospital and University of Zurich, Frauenklinikstrasse 26, 8091, Zurich, Switzerland
- Department of Neurosurgery and Neurotechnology, Eberhard Karls University Tübingen, Tübingen, Germany
| | - Konstantina Kapolou
- Laboratory of Molecular Neuro-Oncology, Department of Neurology, Clinical Neuroscience Center, University Hospital and University of Zurich, Frauenklinikstrasse 26, 8091, Zurich, Switzerland
- Roche Diagnostics International Ltd, Rotkreuz, Switzerland
| | - Nagarajan Paramasivam
- Computational Oncology Group, Molecular Precision Oncology Program, NCT Heidelberg and DKFZ, Heidelberg, Germany
| | - Lena Mühlenbruch
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
- Department of Peptide-Based Immunotherapy, University and University Hospital Tübingen, Tübingen, Germany
| | - Daniel J Kowalewski
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- DKFZ Partner Site Tübingen, German Cancer Consortium (DKTK), Tübingen, Germany
| | - Flavio Vasella
- Laboratory of Molecular Neuro-Oncology, Department of Neurology, Clinical Neuroscience Center, University Hospital and University of Zurich, Frauenklinikstrasse 26, 8091, Zurich, Switzerland
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital and University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland
| | - Tatjana Bilich
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
- Clinical Collaboration Unit Translational Immunology, German Cancer Consortium (DKTK), Department of Internal Medicine, University Hospital Tübingen, Tübingen, Germany
- Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA, USA
| | - Beat M Frey
- Blood Transfusion Service, Swiss Red Cross, Schlieren, Switzerland
| | - Marissa L Dubbelaar
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
- Department of Peptide-Based Immunotherapy, University and University Hospital Tübingen, Tübingen, Germany
- Quantitative Biology Center (QBiC), Eberhard Karls University Tübingen, 72076, Tübingen, Baden-Württemberg, Germany
| | | | - Anna Maria Zeitlberger
- Department of Neurosurgery, Cantonal Hospital St. Gallen, Rorschacher Strasse 95, 9007, St. Gallen, Switzerland
| | - Manuela Silginer
- Laboratory of Molecular Neuro-Oncology, Department of Neurology, Clinical Neuroscience Center, University Hospital and University of Zurich, Frauenklinikstrasse 26, 8091, Zurich, Switzerland
| | - Patrick Roth
- Laboratory of Molecular Neuro-Oncology, Department of Neurology, Clinical Neuroscience Center, University Hospital and University of Zurich, Frauenklinikstrasse 26, 8091, Zurich, Switzerland
| | - Tobias Weiss
- Laboratory of Molecular Neuro-Oncology, Department of Neurology, Clinical Neuroscience Center, University Hospital and University of Zurich, Frauenklinikstrasse 26, 8091, Zurich, Switzerland
| | - Hans-Georg Wirsching
- Laboratory of Molecular Neuro-Oncology, Department of Neurology, Clinical Neuroscience Center, University Hospital and University of Zurich, Frauenklinikstrasse 26, 8091, Zurich, Switzerland
| | - Niklaus Krayenbühl
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital and University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland
| | - Oliver Bozinov
- Department of Neurosurgery, Cantonal Hospital St. Gallen, Rorschacher Strasse 95, 9007, St. Gallen, Switzerland
| | - Luca Regli
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital and University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland
| | - Hans-Georg Rammensee
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- DKFZ Partner Site Tübingen, German Cancer Consortium (DKTK), Tübingen, Germany
| | - Elisabeth Jane Rushing
- Department of Neuropathology, University Hospital and University of Zurich, Schmelzbergstrasse 12, 8091, Zurich, Switzerland
| | - Felix Sahm
- Department of Neuropathology, Heidelberg University Hospital, Heidelberg, Germany
- CCU Neuropathology, German Consortium for Translational Cancer Research (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Juliane S Walz
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
- Department of Peptide-Based Immunotherapy, University and University Hospital Tübingen, Tübingen, Germany
- Clinical Collaboration Unit Translational Immunology, German Cancer Consortium (DKTK), Department of Internal Medicine, University Hospital Tübingen, Tübingen, Germany
| | - Michael Weller
- Laboratory of Molecular Neuro-Oncology, Department of Neurology, Clinical Neuroscience Center, University Hospital and University of Zurich, Frauenklinikstrasse 26, 8091, Zurich, Switzerland
| | - Marian C Neidert
- Laboratory of Molecular Neuro-Oncology, Department of Neurology, Clinical Neuroscience Center, University Hospital and University of Zurich, Frauenklinikstrasse 26, 8091, Zurich, Switzerland
- Department of Neurosurgery, Clinical Neuroscience Center, University Hospital and University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland
- Department of Neurosurgery, Cantonal Hospital St. Gallen, Rorschacher Strasse 95, 9007, St. Gallen, Switzerland
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Hartout P, Počuča B, Méndez-García C, Schleberger C. Investigating the human and nonobese diabetic mouse MHC class II immunopeptidome using protein language modeling. Bioinformatics 2023; 39:btad469. [PMID: 37527005 PMCID: PMC10421966 DOI: 10.1093/bioinformatics/btad469] [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: 11/22/2022] [Revised: 06/17/2023] [Accepted: 07/31/2023] [Indexed: 08/03/2023] Open
Abstract
MOTIVATION Identifying peptides associated with the major histocompability complex class II (MHCII) is a central task in the evaluation of the immunoregulatory function of therapeutics and drug prototypes. MHCII-peptide presentation prediction has multiple biopharmaceutical applications, including the safety assessment of biologics and engineered derivatives in silico, or the fast progression of antigen-specific immunomodulatory drug discovery programs in immune disease and cancer. This has resulted in the collection of large-scale datasets on adaptive immune receptor antigenic responses and MHC-associated peptide proteomics. In parallel, recent deep learning algorithmic advances in protein language modeling have shown potential in leveraging large collections of sequence data and improve MHC presentation prediction. RESULTS Here, we train a compact transformer model (AEGIS) on human and mouse MHCII immunopeptidome data, including a preclinical murine model, and evaluate its performance on the peptide presentation prediction task. We show that the transformer performs on par with existing deep learning algorithms and that combining datasets from multiple organisms increases model performance. We trained variants of the model with and without MHCII information. In both alternatives, the inclusion of peptides presented by the I-Ag7 MHC class II molecule expressed by nonobese diabetic mice enabled for the first time the accurate in silico prediction of presented peptides in a preclinical type 1 diabetes model organism, which has promising therapeutic applications. AVAILABILITY AND IMPLEMENTATION The source code is available at https://github.com/Novartis/AEGIS.
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Affiliation(s)
- Philip Hartout
- Discovery Sciences, Novartis Institutes for Biomedical Research, Basel 4056, Switzerland
| | - Bojana Počuča
- NIBR Research Informatics, Novartis Institutes for Biomedical Research, Basel 4056, Switzerland
| | - Celia Méndez-García
- Discovery Sciences, Novartis Institutes for Biomedical Research, Basel 4056, Switzerland
| | - Christian Schleberger
- Discovery Sciences, Novartis Institutes for Biomedical Research, Basel 4056, Switzerland
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Lerner A, Benzvi C, Vojdani A. SARS-CoV-2 Gut-Targeted Epitopes: Sequence Similarity and Cross-Reactivity Join Together for Molecular Mimicry. Biomedicines 2023; 11:1937. [PMID: 37509576 PMCID: PMC10376948 DOI: 10.3390/biomedicines11071937] [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: 06/14/2023] [Revised: 07/02/2023] [Accepted: 07/06/2023] [Indexed: 07/30/2023] Open
Abstract
The gastrointestinal tract can be heavily infected by SARS-CoV-2. Being an auto-immunogenic virus, SARS-CoV-2 represents an environmental factor that might play a role in gut-associated autoimmune diseases. However, molecular mimicry between the virus and the intestinal epitopes is under-investigated. The present study aims to elucidate sequence similarity between viral antigens and human enteric sequences, based on known cross-reactivity. SARS-CoV-2 epitopes that cross-react with human gut antigens were explored, and sequence alignment was performed against self-antigens implicated in enteric autoimmune conditions. Experimental SARS-CoV-2 epitopes were aggregated from the Immune Epitope Database (IEDB), while enteric antigens were obtained from the UniProt Knowledgebase. A Pairwise Local Alignment tool, EMBOSS Matcher, was employed for the similarity search. Sequence similarity and targeted cross-reactivity were depicted between 10 pairs of immunoreactive epitopes. Similar pairs were found in four viral proteins and seven enteric antigens related to ulcerative colitis, primary biliary cholangitis, celiac disease, and autoimmune hepatitis. Antibodies made against the viral proteins that were cross-reactive with human gut antigens are involved in several essential cellular functions. The relationship and contribution of those intestinal cross-reactive epitopes to SARS-CoV-2 or its potential contribution to gut auto-immuno-genesis are discussed.
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Affiliation(s)
- Aaron Lerner
- Chaim Sheba Medical Center, The Zabludowicz Research Center for Autoimmune Diseases, Ramat Gan 52621, Israel
- Research Department, Ariel University, Ariel 40700, Israel
| | - Carina Benzvi
- Chaim Sheba Medical Center, The Zabludowicz Research Center for Autoimmune Diseases, Ramat Gan 52621, Israel
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Talwar JV, Laub D, Pagadala MS, Castro A, Lewis M, Luebeck GE, Gorman BR, Pan C, Dong FN, Markianos K, Teerlink CC, Lynch J, Hauger R, Pyarajan S, Tsao PS, Morris GP, Salem RM, Thompson WK, Curtius K, Zanetti M, Carter H. Autoimmune alleles at the major histocompatibility locus modify melanoma susceptibility. Am J Hum Genet 2023; 110:1138-1161. [PMID: 37339630 PMCID: PMC10357503 DOI: 10.1016/j.ajhg.2023.05.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 05/20/2023] [Accepted: 05/22/2023] [Indexed: 06/22/2023] Open
Abstract
Autoimmunity and cancer represent two different aspects of immune dysfunction. Autoimmunity is characterized by breakdowns in immune self-tolerance, while impaired immune surveillance can allow for tumorigenesis. The class I major histocompatibility complex (MHC-I), which displays derivatives of the cellular peptidome for immune surveillance by CD8+ T cells, serves as a common genetic link between these conditions. As melanoma-specific CD8+ T cells have been shown to target melanocyte-specific peptide antigens more often than melanoma-specific antigens, we investigated whether vitiligo- and psoriasis-predisposing MHC-I alleles conferred a melanoma-protective effect. In individuals with cutaneous melanoma from both The Cancer Genome Atlas (n = 451) and an independent validation set (n = 586), MHC-I autoimmune-allele carrier status was significantly associated with a later age of melanoma diagnosis. Furthermore, MHC-I autoimmune-allele carriers were significantly associated with decreased risk of developing melanoma in the Million Veteran Program (OR = 0.962, p = 0.024). Existing melanoma polygenic risk scores (PRSs) did not predict autoimmune-allele carrier status, suggesting these alleles provide orthogonal risk-relevant information. Mechanisms of autoimmune protection were neither associated with improved melanoma-driver mutation association nor improved gene-level conserved antigen presentation relative to common alleles. However, autoimmune alleles showed higher affinity relative to common alleles for particular windows of melanocyte-conserved antigens and loss of heterozygosity of autoimmune alleles caused the greatest reduction in presentation for several conserved antigens across individuals with loss of HLA alleles. Overall, this study presents evidence that MHC-I autoimmune-risk alleles modulate melanoma risk unaccounted for by current PRSs.
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Affiliation(s)
- James V Talwar
- Department of Medicine, Division of Medical Genetics, University of California San Diego, La Jolla, CA 92093, USA; Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA 92093, USA
| | - David Laub
- Department of Medicine, Division of Medical Genetics, University of California San Diego, La Jolla, CA 92093, USA; Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA 92093, USA
| | - Meghana S Pagadala
- Biomedical Science Program, University of California San Diego, La Jolla, CA 92093, USA
| | - Andrea Castro
- Department of Medicine, Division of Medical Genetics, University of California San Diego, La Jolla, CA 92093, USA; Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA 92093, USA
| | - McKenna Lewis
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Georg E Luebeck
- Public Health Sciences Division, Herbold Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Bryan R Gorman
- Center for Data and Computational Sciences (C-DACS), VA Boston Healthcare System, Boston, MA 02130, USA; Booz Allen Hamilton, Inc., McLean, VA 22102, USA
| | - Cuiping Pan
- Palo Alto Epidemiology Research and Information Center for Genomics, VA Palo Alto, CA, USA
| | - Frederick N Dong
- Center for Data and Computational Sciences (C-DACS), VA Boston Healthcare System, Boston, MA 02130, USA; Booz Allen Hamilton, Inc., McLean, VA 22102, USA
| | - Kyriacos Markianos
- Center for Data and Computational Sciences (C-DACS), VA Boston Healthcare System, Boston, MA 02130, USA; Division of Genetics and Genomics, Department of Pediatrics, Boston Children's Hospital, Boston, MA 02115, USA; Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02115, USA
| | - Craig C Teerlink
- Department of Veterans Affairs Informatics and Computing Infrastructure (VINCI), VA Salt Lake City Healthcare System, Salt Lake City, UT, USA; Department of Internal Medicine, Division of Epidemiology, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Julie Lynch
- Department of Veterans Affairs Informatics and Computing Infrastructure (VINCI), VA Salt Lake City Healthcare System, Salt Lake City, UT, USA; Department of Internal Medicine, Division of Epidemiology, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Richard Hauger
- VA San Diego Healthcare System, La Jolla, CA, USA; Center for Behavioral Genetics of Aging, University of California San Diego, La Jolla, CA, USA; Center of Excellence for Stress and Mental Health (CESAMH), VA San Diego Healthcare System, San Diego, CA, USA
| | - Saiju Pyarajan
- Center for Data and Computational Sciences (C-DACS), VA Boston Healthcare System, Boston, MA 02130, USA; Department of Medicine, Brigham Women's Hospital, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Philip S Tsao
- Palo Alto Epidemiology Research and Information Center for Genomics, VA Palo Alto, CA, USA; Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Gerald P Morris
- Department of Pathology, University of California San Diego, La Jolla, CA 92093, USA
| | - Rany M Salem
- Division of Epidemiology, Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA 92093, USA
| | - Wesley K Thompson
- Center for Population Neuroscience and Genetics, Laureate Institute for Brain Research, Tulsa, OK 74136, USA
| | - Kit Curtius
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA 92093, USA; Moores Cancer Center, University of California San Diego, La Jolla, CA 92093, USA; Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA
| | - Maurizio Zanetti
- Moores Cancer Center, University of California San Diego, La Jolla, CA 92093, USA; The Laboratory of Immunology, University of California San Diego, La Jolla, CA 92093, USA; Department of Medicine, Division of Hematology and Oncology, University of California San Diego, La Jolla, CA 92093, USA
| | - Hannah Carter
- Department of Medicine, Division of Medical Genetics, University of California San Diego, La Jolla, CA 92093, USA; Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA 92093, USA; Moores Cancer Center, University of California San Diego, La Jolla, CA 92093, USA.
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Li X, Pak HS, Huber F, Michaux J, Taillandier-Coindard M, Altimiras ER, Bassani-Sternberg M. A microfluidics-enabled automated workflow of sample preparation for MS-based immunopeptidomics. CELL REPORTS METHODS 2023; 3:100479. [PMID: 37426762 PMCID: PMC10326370 DOI: 10.1016/j.crmeth.2023.100479] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 03/22/2023] [Accepted: 04/19/2023] [Indexed: 07/11/2023]
Abstract
Mass spectrometry (MS)-based immunopeptidomics is an attractive antigen discovery method with growing clinical implications. However, the current experimental approach to extract HLA-restricted peptides requires a bulky sample source, which remains a challenge for obtaining clinical specimens. We present an innovative workflow that requires a low sample volume, which streamlines the immunoaffinity purification (IP) and C18 peptide cleanup on a single microfluidics platform with automated liquid handling and minimal sample transfers, resulting in higher assay sensitivity. We also demonstrate how the state-of-the-art data-independent acquisition (DIA) method further enhances the depth of tandem MS spectra-based peptide sequencing. Consequently, over 4,000 and 5,000 HLA-I-restricted peptides were identified from as few as 0.2 million RA957 cells and a melanoma tissue of merely 5 mg, respectively. We also identified multiple immunogenic tumor-associated antigens and hundreds of peptides derived from non-canonical protein sources. This workflow represents a powerful tool for identifying the immunopeptidome of sparse samples.
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Affiliation(s)
- Xiaokang Li
- Ludwig Institute for Cancer Research, University of Lausanne, Rue du Bugnon 25A, 1005 Lausanne, Switzerland
- Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland
- Agora Cancer Research Centre, Rue du Bugnon 25A, 1005 Lausanne, Switzerland
| | - Hui Song Pak
- Ludwig Institute for Cancer Research, University of Lausanne, Rue du Bugnon 25A, 1005 Lausanne, Switzerland
- Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland
- Agora Cancer Research Centre, Rue du Bugnon 25A, 1005 Lausanne, Switzerland
| | - Florian Huber
- Ludwig Institute for Cancer Research, University of Lausanne, Rue du Bugnon 25A, 1005 Lausanne, Switzerland
- Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland
- Agora Cancer Research Centre, Rue du Bugnon 25A, 1005 Lausanne, Switzerland
| | - Justine Michaux
- Ludwig Institute for Cancer Research, University of Lausanne, Rue du Bugnon 25A, 1005 Lausanne, Switzerland
- Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland
- Agora Cancer Research Centre, Rue du Bugnon 25A, 1005 Lausanne, Switzerland
| | - Marie Taillandier-Coindard
- Ludwig Institute for Cancer Research, University of Lausanne, Rue du Bugnon 25A, 1005 Lausanne, Switzerland
- Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland
- Agora Cancer Research Centre, Rue du Bugnon 25A, 1005 Lausanne, Switzerland
| | - Emma Ricart Altimiras
- Ludwig Institute for Cancer Research, University of Lausanne, Rue du Bugnon 25A, 1005 Lausanne, Switzerland
- Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland
- Agora Cancer Research Centre, Rue du Bugnon 25A, 1005 Lausanne, Switzerland
| | - Michal Bassani-Sternberg
- Ludwig Institute for Cancer Research, University of Lausanne, Rue du Bugnon 25A, 1005 Lausanne, Switzerland
- Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland
- Agora Cancer Research Centre, Rue du Bugnon 25A, 1005 Lausanne, Switzerland
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Gouttefangeas C, Klein R, Maia A. The good and the bad of T cell cross-reactivity: challenges and opportunities for novel therapeutics in autoimmunity and cancer. Front Immunol 2023; 14:1212546. [PMID: 37409132 PMCID: PMC10319254 DOI: 10.3389/fimmu.2023.1212546] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 05/24/2023] [Indexed: 07/07/2023] Open
Abstract
T cells are main actors of the immune system with an essential role in protection against pathogens and cancer. The molecular key event involved in this absolutely central task is the interaction of membrane-bound specific T cell receptors with peptide-MHC complexes which initiates T cell priming, activation and recall, and thus controls a range of downstream functions. While textbooks teach us that the repertoire of mature T cells is highly diverse, it is clear that this diversity cannot possibly cover all potential foreign peptides that might be encountered during life. TCR cross-reactivity, i.e. the ability of a single TCR to recognise different peptides, offers the best solution to this biological challenge. Reports have shown that indeed, TCR cross-reactivity is surprisingly high. Hence, the T cell dilemma is the following: be as specific as possible to target foreign danger and spare self, while being able to react to a large spectrum of body-threatening situations. This has major consequences for both autoimmune diseases and cancer, and significant implications for the development of T cell-based therapies. In this review, we will present essential experimental evidence of T cell cross-reactivity, implications for two opposite immune conditions, i.e. autoimmunity vs cancer, and how this can be differently exploited for immunotherapy approaches. Finally, we will discuss the tools available for predicting cross-reactivity and how improvements in this field might boost translational approaches.
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Affiliation(s)
- Cécile Gouttefangeas
- Nencki Institute of Experimental Biology of the Polish Academy of Sciences, Warsaw, Poland
- Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, Tübingen, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ) partner site Tübingen, Tübingen, Germany
| | - Reinhild Klein
- Department of Hematology, Oncology, Clinical Immunology and Rheumatology, University Hospital Tübingen, Tübingen, Germany
| | - Ana Maia
- Nencki Institute of Experimental Biology of the Polish Academy of Sciences, Warsaw, Poland
- Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, Tübingen, Germany
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Lozano-Rabella M, Garcia-Garijo A, Palomero J, Yuste-Estevanez A, Erhard F, Farriol-Duran R, Martín-Liberal J, Ochoa-de-Olza M, Matos I, Gartner JJ, Ghosh M, Canals F, Vidal A, Piulats JM, Matías-Guiu X, Brana I, Muñoz-Couselo E, Garralda E, Schlosser A, Gros A. Exploring the Immunogenicity of Noncanonical HLA-I Tumor Ligands Identified through Proteogenomics. Clin Cancer Res 2023; 29:2250-2265. [PMID: 36749875 PMCID: PMC10261919 DOI: 10.1158/1078-0432.ccr-22-3298] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 12/20/2022] [Accepted: 02/03/2023] [Indexed: 02/09/2023]
Abstract
PURPOSE Tumor antigens are central to antitumor immunity. Recent evidence suggests that peptides from noncanonical (nonC) aberrantly translated proteins can be presented on HLA-I by tumor cells. Here, we investigated the immunogenicity of nonC tumor HLA-I ligands (nonC-TL) to better understand their contribution to cancer immunosurveillance and their therapeutic applicability. EXPERIMENTAL DESIGN Peptides presented on HLA-I were identified in 9 patient-derived tumor cell lines from melanoma, gynecologic, and head and neck cancer through proteogenomics. A total of 507 candidate tumor antigens, including nonC-TL, neoantigens, cancer-germline, or melanocyte differentiation antigens, were tested for T-cell recognition of preexisting responses in patients with cancer. Donor peripheral blood lymphocytes (PBL) were in vitro sensitized against 170 selected nonC-TL to isolate antigen-specific T-cell receptors (TCR) and evaluate their therapeutic potential. RESULTS We found no recognition of the 507 nonC-TL tested by autologous ex vivo expanded tumor-reactive T-cell cultures while the same cultures demonstrated reactivity to mutated, cancer-germline, or melanocyte differentiation antigens. However, in vitro sensitization of donor PBL against 170 selected nonC-TL, led to the identification of TCRs specific to three nonC-TL, two of which mapped to the 5' UTR regions of HOXC13 and ZKSCAN1, and one mapping to a noncoding spliced variant of C5orf22C. T cells targeting these nonC-TL recognized cancer cell lines naturally presenting their corresponding antigens. Expression of the three immunogenic nonC-TL was shared across tumor types and barely or not detected in normal cells. CONCLUSIONS Our findings predict a limited contribution of nonC-TL to cancer immunosurveillance but demonstrate they may be attractive novel targets for widely applicable immunotherapies. See related commentary by Fox et al., p. 2173.
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Affiliation(s)
- Maria Lozano-Rabella
- Tumor Immunology and Immunotherapy, Vall d'Hebron Institute of Oncology (VHIO), Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Andrea Garcia-Garijo
- Tumor Immunology and Immunotherapy, Vall d'Hebron Institute of Oncology (VHIO), Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Jara Palomero
- Tumor Immunology and Immunotherapy, Vall d'Hebron Institute of Oncology (VHIO), Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Anna Yuste-Estevanez
- Tumor Immunology and Immunotherapy, Vall d'Hebron Institute of Oncology (VHIO), Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Florian Erhard
- Institute for Virology and Immunobiology, University of Würzburg, Würzburg, Germany
| | - Roc Farriol-Duran
- Tumor Immunology and Immunotherapy, Vall d'Hebron Institute of Oncology (VHIO), Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Juan Martín-Liberal
- Early Drug Development Unit (UITM) Vall d'Hebron Institute of Oncology (VHIO), Vall d'Hebron Barcelona Hospital, Barcelona, Spain
| | - Maria Ochoa-de-Olza
- Early Drug Development Unit (UITM) Vall d'Hebron Institute of Oncology (VHIO), Vall d'Hebron Barcelona Hospital, Barcelona, Spain
| | - Ignacio Matos
- Early Drug Development Unit (UITM) Vall d'Hebron Institute of Oncology (VHIO), Vall d'Hebron Barcelona Hospital, Barcelona, Spain
| | - Jared J. Gartner
- Surgery Branch, National Cancer Institute (NCI), National Institutes of Health, Bethesda, Maryland
| | - Michael Ghosh
- Institute for Cell Biology Department of Immunology, University of Tübingen, Tübingen, Germany
| | - Francesc Canals
- Proteomics, Vall d'Hebron Institute of Oncology (VHIO), Vall d'Hebron Barcelona Hospital, Barcelona, Spain
| | - August Vidal
- Department of Pathology. Hospital Universitari de Bellvitge-IDIBELL, CIBERONC, Barcelona, Spain
| | - Josep Maria Piulats
- Medical Oncology, Catalan Institute of Cancer (ICO), IDIBELL-Oncobell, Hospitalet de Llobregat, Spain
| | - Xavier Matías-Guiu
- Department of Pathology. Hospital Universitari de Bellvitge-IDIBELL, CIBERONC, Barcelona, Spain
| | - Irene Brana
- Early Drug Development Unit (UITM) Vall d'Hebron Institute of Oncology (VHIO), Vall d'Hebron Barcelona Hospital, Barcelona, Spain
| | - Eva Muñoz-Couselo
- Melanoma and other skin tumors unit, Vall d'Hebron Institute of Oncology (VHIO), Vall d'Hebron Barcelona Hospital, Barcelona, Spain
| | - Elena Garralda
- Early Drug Development Unit (UITM) Vall d'Hebron Institute of Oncology (VHIO), Vall d'Hebron Barcelona Hospital, Barcelona, Spain
| | - Andreas Schlosser
- Rudolf Virchow Center, Center for Integrative and Translational Bioimaging, Julius-Maximilians-University Würzburg, Würzburg, Germany
| | - Alena Gros
- Tumor Immunology and Immunotherapy, Vall d'Hebron Institute of Oncology (VHIO), Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
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Bedran G, Polasky DA, Hsiao Y, Yu F, da Veiga Leprevost F, Alfaro JA, Cieslik M, Nesvizhskii AI. Unraveling the glycosylated immunopeptidome with HLA-Glyco. Nat Commun 2023; 14:3461. [PMID: 37308510 PMCID: PMC10258777 DOI: 10.1038/s41467-023-39270-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 06/02/2023] [Indexed: 06/14/2023] Open
Abstract
Recent interest in targeted therapies has been sparked by the study of MHC-associated peptides (MAPs) that undergo post-translational modifications (PTMs), particularly glycosylation. In this study, we introduce a fast computational workflow that merges the MSFragger-Glyco search algorithm with a false discovery rate control for glycopeptide analysis from mass spectrometry-based immunopeptidome data. By analyzing eight large-scale publicly available studies, we find that glycosylated MAPs are predominantly presented by MHC class II. Here, we present HLA-Glyco, a comprehensive resource containing over 3,400 human leukocyte antigen (HLA) class II N-glycopeptides from 1,049 distinct protein glycosylation sites. This resource provides valuable insights, including high levels of truncated glycans, conserved HLA-binding cores, and differences in glycosylation positional specificity between HLA allele groups. We integrate the workflow within the FragPipe computational platform and provide HLA-Glyco as a free web resource. Overall, our work provides a valuable tool and resource to aid the nascent field of glyco-immunopeptidomics.
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Affiliation(s)
- Georges Bedran
- International Centre for Cancer Vaccine Science, University of Gdansk, Gdansk, Poland
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Daniel A Polasky
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Yi Hsiao
- Department of Computational Medicine and Bioinformatics, Ann Arbor, MI, USA
| | - Fengchao Yu
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | | | - Javier A Alfaro
- International Centre for Cancer Vaccine Science, University of Gdansk, Gdansk, Poland
- Department of Biochemistry and Microbiology, University of Victoria, Victoria, BC, Canada
- School of Informatics, University of Edinburgh, Edinburgh, UK
| | - Marcin Cieslik
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
- Department of Computational Medicine and Bioinformatics, Ann Arbor, MI, USA
- Michigan Center for Translational Pathology, University of Michigan School of Medicine, Ann Arbor, MI, USA
| | - Alexey I Nesvizhskii
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA.
- Department of Computational Medicine and Bioinformatics, Ann Arbor, MI, USA.
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Fonseca AF, Antunes DA. CrossDome: an interactive R package to predict cross-reactivity risk using immunopeptidomics databases. Front Immunol 2023; 14:1142573. [PMID: 37377956 PMCID: PMC10291144 DOI: 10.3389/fimmu.2023.1142573] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 05/23/2023] [Indexed: 06/29/2023] Open
Abstract
T-cell-based immunotherapies hold tremendous potential in the fight against cancer, thanks to their capacity to specifically targeting diseased cells. Nevertheless, this potential has been tempered with safety concerns regarding the possible recognition of unknown off-targets displayed by healthy cells. In a notorious example, engineered T-cells specific to MAGEA3 (EVDPIGHLY) also recognized a TITIN-derived peptide (ESDPIVAQY) expressed by cardiac cells, inducing lethal damage in melanoma patients. Such off-target toxicity has been related to T-cell cross-reactivity induced by molecular mimicry. In this context, there is growing interest in developing the means to avoid off-target toxicity, and to provide safer immunotherapy products. To this end, we present CrossDome, a multi-omics suite to predict the off-target toxicity risk of T-cell-based immunotherapies. Our suite provides two alternative protocols, i) a peptide-centered prediction, or ii) a TCR-centered prediction. As proof-of-principle, we evaluate our approach using 16 well-known cross-reactivity cases involving cancer-associated antigens. With CrossDome, the TITIN-derived peptide was predicted at the 99+ percentile rank among 36,000 scored candidates (p-value < 0.001). In addition, off-targets for all the 16 known cases were predicted within the top ranges of relatedness score on a Monte Carlo simulation with over 5 million putative peptide pairs, allowing us to determine a cut-off p-value for off-target toxicity risk. We also implemented a penalty system based on TCR hotspots, named contact map (CM). This TCR-centered approach improved upon the peptide-centered prediction on the MAGEA3-TITIN screening (e.g., from 27th to 6th, out of 36,000 ranked peptides). Next, we used an extended dataset of experimentally-determined cross-reactive peptides to evaluate alternative CrossDome protocols. The level of enrichment of validated cases among top 50 best-scored peptides was 63% for the peptide-centered protocol, and up to 82% for the TCR-centered protocol. Finally, we performed functional characterization of top ranking candidates, by integrating expression data, HLA binding, and immunogenicity predictions. CrossDome was designed as an R package for easy integration with antigen discovery pipelines, and an interactive web interface for users without coding experience. CrossDome is under active development, and it is available at https://github.com/AntunesLab/crossdome.
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46
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Bedran G, Gasser HC, Weke K, Wang T, Bedran D, Laird A, Battail C, Zanzotto FM, Pesquita C, Axelson H, Rajan A, Harrison DJ, Palkowski A, Pawlik M, Parys M, O'Neill JR, Brennan PM, Symeonides SN, Goodlett DR, Litchfield K, Fahraeus R, Hupp TR, Kote S, Alfaro JA. The Immunopeptidome from a Genomic Perspective: Establishing the Noncanonical Landscape of MHC Class I-Associated Peptides. Cancer Immunol Res 2023; 11:747-762. [PMID: 36961404 PMCID: PMC10236148 DOI: 10.1158/2326-6066.cir-22-0621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 11/25/2022] [Accepted: 03/16/2023] [Indexed: 03/25/2023]
Abstract
Tumor antigens can emerge through multiple mechanisms, including translation of noncoding genomic regions. This noncanonical category of tumor antigens has recently gained attention; however, our understanding of how they recur within and between cancer types is still in its infancy. Therefore, we developed a proteogenomic pipeline based on deep learning de novo mass spectrometry (MS) to enable the discovery of noncanonical MHC class I-associated peptides (ncMAP) from noncoding regions. Considering that the emergence of tumor antigens can also involve posttranslational modifications (PTM), we included an open search component in our pipeline. Leveraging the wealth of MS-based immunopeptidomics, we analyzed data from 26 MHC class I immunopeptidomic studies across 11 different cancer types. We validated the de novo identified ncMAPs, along with the most abundant PTMs, using spectral matching and controlled their FDR to 1%. The noncanonical presentation appeared to be 5 times enriched for the A03 HLA supertype, with a projected population coverage of 55%. The data reveal an atlas of 8,601 ncMAPs with varying levels of cancer selectivity and suggest 17 cancer-selective ncMAPs as attractive therapeutic targets according to a stringent cutoff. In summary, the combination of the open-source pipeline and the atlas of ncMAPs reported herein could facilitate the identification and screening of ncMAPs as targets for T-cell therapies or vaccine development.
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Affiliation(s)
- Georges Bedran
- International Centre for Cancer Vaccine Science, University of Gdansk, Gdansk, Poland
| | | | - Kenneth Weke
- International Centre for Cancer Vaccine Science, University of Gdansk, Gdansk, Poland
| | - Tongjie Wang
- School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
| | - Dominika Bedran
- International Centre for Cancer Vaccine Science, University of Gdansk, Gdansk, Poland
| | - Alexander Laird
- Urology Department, Western General Hospital, NHS Lothian, Edinburgh, United Kingdom
- Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom
| | - Christophe Battail
- CEA, Grenoble Alpes University, INSERM, IRIG, Biosciences and Bioengineering for Health Laboratory (BGE) - UA13 INSERM-CEA-UGA, Grenoble, France
| | | | - Catia Pesquita
- LASIGE, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal
| | - Håkan Axelson
- Division of Translational Cancer Research, Department of Laboratory Medicine, Lund University, Lund, Sweden
| | - Ajitha Rajan
- School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
| | - David J. Harrison
- School of Medicine, University of St Andrews, St Andrews, United Kingdom
| | - Aleksander Palkowski
- International Centre for Cancer Vaccine Science, University of Gdansk, Gdansk, Poland
| | - Maciej Pawlik
- Academic Computer Centre CYFRONET, AGH University of Science and Technology, Cracow, Poland
| | - Maciej Parys
- Royal (Dick) School of Veterinary Studies and The Roslin Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - J. Robert O'Neill
- Cambridge Oesophagogastric Centre, Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
| | - Paul M. Brennan
- Translational Neurosurgery, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Stefan N. Symeonides
- Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom
| | - David R. Goodlett
- International Centre for Cancer Vaccine Science, University of Gdansk, Gdansk, Poland
- Department of Biochemistry and Microbiology, University of Victoria, Victoria, Canada
- University of Victoria Genome BC Proteome Centre, Victoria, Canada
| | - Kevin Litchfield
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, United Kingdom
- Tumour Immunogenomics and Immunosurveillance Laboratory, University College London Cancer Institute, London, United Kingdom
| | - Robin Fahraeus
- International Centre for Cancer Vaccine Science, University of Gdansk, Gdansk, Poland
- Inserm UMRS1131, Institut de Génétique Moléculaire, Université Paris 7, Paris, France
| | - Ted R. Hupp
- International Centre for Cancer Vaccine Science, University of Gdansk, Gdansk, Poland
- Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, United Kingdom
| | - Sachin Kote
- International Centre for Cancer Vaccine Science, University of Gdansk, Gdansk, Poland
| | - Javier A. Alfaro
- International Centre for Cancer Vaccine Science, University of Gdansk, Gdansk, Poland
- School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
- Department of Biochemistry and Microbiology, University of Victoria, Victoria, Canada
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47
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Admon A. The biogenesis of the immunopeptidome. Semin Immunol 2023; 67:101766. [PMID: 37141766 DOI: 10.1016/j.smim.2023.101766] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 04/26/2023] [Accepted: 04/26/2023] [Indexed: 05/06/2023]
Abstract
The immunopeptidome is the repertoire of peptides bound and presented by the MHC class I, class II, and non-classical molecules. The peptides are produced by the degradation of most cellular proteins, and in some cases, peptides are produced from extracellular proteins taken up by the cells. This review attempts to first describe some of its known and well-accepted concepts, and next, raise some questions about a few of the established dogmas in this field: The production of novel peptides by splicing is questioned, suggesting here that spliced peptides are extremely rare, if existent at all. The degree of the contribution to the immunopeptidome by degradation of cellular protein by the proteasome is doubted, therefore this review attempts to explain why it is likely that this contribution to the immunopeptidome is possibly overstated. The contribution of defective ribosome products (DRiPs) and non-canonical peptides to the immunopeptidome is noted and methods are suggested to quantify them. In addition, the common misconception that the MHC class II peptidome is mostly derived from extracellular proteins is noted, and corrected. It is stressed that the confirmation of sequence assignments of non-canonical and spliced peptides should rely on targeted mass spectrometry using spiking-in of heavy isotope-labeled peptides. Finally, the new methodologies and modern instrumentation currently available for high throughput kinetics and quantitative immunopeptidomics are described. These advanced methods open up new possibilities for utilizing the big data generated and taking a fresh look at the established dogmas and reevaluating them critically.
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Affiliation(s)
- Arie Admon
- Faculty of Biology, Technion-Israel Institute of Technology, Israel.
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48
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Phulphagar KM, Ctortecka C, Jacome ASV, Klaeger S, Verzani EK, Hernandez GM, Udeshi ND, Clauser KR, Abelin JG, Carr SA. Sensitive, high-throughput HLA-I and HLA-II immunopeptidomics using parallel accumulation-serial fragmentation mass spectrometry. Mol Cell Proteomics 2023:100563. [PMID: 37142057 DOI: 10.1016/j.mcpro.2023.100563] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 04/21/2023] [Accepted: 04/24/2023] [Indexed: 05/06/2023] Open
Abstract
Comprehensive, in-depth identification of the human leukocyte antigen HLA-I and HLA-II tumor immunopeptidome can inform the development of cancer immunotherapies. Mass spectrometry (MS) is powerful technology for direct identification of HLA peptides from patient derived tumor samples or cell lines. However, achieving sufficient coverage to detect rare, clinically relevant antigens requires highly sensitive MS-based acquisition methods and large amounts of sample. While immunopeptidome depth can be increased by off-line fractionation prior to MS, its use is impractical when analyzing limited amounts of primary tissue biopsies. To address this challenge, we developed and applied a high throughput, sensitive, single-shot MS-based immunopeptidomics workflow that leverages trapped ion mobility time-of-flight mass spectrometry on the Bruker timsTOF SCP. We demonstrate >2-fold improved coverage of HLA immunopeptidomes relative to prior methods with up to 15,000 distinct HLA-I and HLA-II peptides from 4e7 cells. Our optimized single-shot MS acquisition method on the timsTOF SCP maintains high coverage, eliminates the need for off-line fractionation and reduces input requirements to as few as 1e6 A375 cells for > 800 distinct HLA-I peptides. This depth is sufficient to identify HLA-I peptides derived from cancer-testis antigen, and non-canonical proteins. We also apply our optimized single-shot SCP acquisition methods to tumor derived samples, enabling sensitive, high throughput and reproducible immunopeptidome profiling with detection of clinically relevant peptides from less than 4e7 cells or 15 mg wet weight tissue.
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49
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Naghavian R, Faigle W, Oldrati P, Wang J, Toussaint NC, Qiu Y, Medici G, Wacker M, Freudenmann LK, Bonté PE, Weller M, Regli L, Amigorena S, Rammensee HG, Walz JS, Brugger SD, Mohme M, Zhao Y, Sospedra M, Neidert MC, Martin R. Microbial peptides activate tumour-infiltrating lymphocytes in glioblastoma. Nature 2023; 617:807-817. [PMID: 37198490 PMCID: PMC10208956 DOI: 10.1038/s41586-023-06081-w] [Citation(s) in RCA: 37] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 04/13/2023] [Indexed: 05/19/2023]
Abstract
Microbial organisms have key roles in numerous physiological processes in the human body and have recently been shown to modify the response to immune checkpoint inhibitors1,2. Here we aim to address the role of microbial organisms and their potential role in immune reactivity against glioblastoma. We demonstrate that HLA molecules of both glioblastoma tissues and tumour cell lines present bacteria-specific peptides. This finding prompted us to examine whether tumour-infiltrating lymphocytes (TILs) recognize tumour-derived bacterial peptides. Bacterial peptides eluted from HLA class II molecules are recognized by TILs, albeit very weakly. Using an unbiased antigen discovery approach to probe the specificity of a TIL CD4+ T cell clone, we show that it recognizes a broad spectrum of peptides from pathogenic bacteria, commensal gut microbiota and also glioblastoma-related tumour antigens. These peptides were also strongly stimulatory for bulk TILs and peripheral blood memory cells, which then respond to tumour-derived target peptides. Our data hint at how bacterial pathogens and bacterial gut microbiota can be involved in specific immune recognition of tumour antigens. The unbiased identification of microbial target antigens for TILs holds promise for future personalized tumour vaccination approaches.
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Affiliation(s)
- Reza Naghavian
- Neuroimmunology and MS Research Section (NIMS), Neurology Clinic, University of Zurich, University Hospital Zurich, Zurich, Switzerland
- Cellerys AG, Schlieren, Switzerland
| | - Wolfgang Faigle
- Neuroimmunology and MS Research Section (NIMS), Neurology Clinic, University of Zurich, University Hospital Zurich, Zurich, Switzerland
- Cellerys AG, Schlieren, Switzerland
- Immunity and Cancer, Institut Curie, PSL University, INSERM U932, Paris, France
| | - Pietro Oldrati
- Neuroimmunology and MS Research Section (NIMS), Neurology Clinic, University of Zurich, University Hospital Zurich, Zurich, Switzerland
| | - Jian Wang
- Neuroimmunology and MS Research Section (NIMS), Neurology Clinic, University of Zurich, University Hospital Zurich, Zurich, Switzerland
- School of Basic Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Nora C Toussaint
- NEXUS Personalized Health Technologies, ETH Zurich, Schlieren, Switzerland
- Swiss Institute of Bioinformatics, Zurich, Switzerland
| | - Yuhan Qiu
- Neuroimmunology and MS Research Section (NIMS), Neurology Clinic, University of Zurich, University Hospital Zurich, Zurich, Switzerland
| | - Gioele Medici
- Clinical Neuroscience Center, Department of Neurosurgery, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Marcel Wacker
- Department of Peptide-based Immunotherapy, University of Tübingen, University Hospital Tübingen, Tübingen, Germany
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), partner site Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) 'Image-Guided and Functionally Instructed Tumor Therapies', University of Tübingen, Tübingen, Germany
| | - Lena K Freudenmann
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), partner site Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) 'Image-Guided and Functionally Instructed Tumor Therapies', University of Tübingen, Tübingen, Germany
| | | | - Michael Weller
- Laboratory of Molecular Neuro-Oncology, Department of Neurology and Clinical Neuroscience, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Luca Regli
- Clinical Neuroscience Center, Department of Neurosurgery, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Sebastian Amigorena
- Immunity and Cancer, Institut Curie, PSL University, INSERM U932, Paris, France
| | - Hans-Georg Rammensee
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), partner site Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) 'Image-Guided and Functionally Instructed Tumor Therapies', University of Tübingen, Tübingen, Germany
| | - Juliane S Walz
- Department of Peptide-based Immunotherapy, University of Tübingen, University Hospital Tübingen, Tübingen, Germany
- Institute for Cell Biology, Department of Immunology, University of Tübingen, Tübingen, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), partner site Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) 'Image-Guided and Functionally Instructed Tumor Therapies', University of Tübingen, Tübingen, Germany
- Clinical Collaboration Unit Translational Immunology, German Cancer Consortium (DKTK), Department of Internal Medicine, University Hospital Tübingen, Tübingen, Germany
| | - Silvio D Brugger
- Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Malte Mohme
- Department of Neurosurgery, University Hospital Hamburg Eppendorf, University of Hamburg, Hamburg, Germany
| | - Yingdong Zhao
- Computational and Systems Biology Branch, Biometric Research Program, Division of Cancer Treatment and Diagnosis, NCI, NIH, Rockville, MD, USA
| | - Mireia Sospedra
- Neuroimmunology and MS Research Section (NIMS), Neurology Clinic, University of Zurich, University Hospital Zurich, Zurich, Switzerland
- Cellerys AG, Schlieren, Switzerland
| | - Marian C Neidert
- Clinical Neuroscience Center, Department of Neurosurgery, University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Department of Neurosurgery, Cantonal Hospital St. Gallen, St. Gallen, Switzerland
| | - Roland Martin
- Neuroimmunology and MS Research Section (NIMS), Neurology Clinic, University of Zurich, University Hospital Zurich, Zurich, Switzerland.
- Cellerys AG, Schlieren, Switzerland.
- Institute of Experimental Immunology, University of Zurich, Zurich, Switzerland.
- Therapeutic Immune Design Unit, Center for Molecular Medicine, Department of Clinical Neurosciences, Karolinska Institutet, Stockholm, Sweden.
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50
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Oreper D, Klaeger S, Jhunjhunwala S, Delamarre L. The peptide woods are lovely, dark and deep: Hunting for novel cancer antigens. Semin Immunol 2023; 67:101758. [PMID: 37027981 DOI: 10.1016/j.smim.2023.101758] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Revised: 03/22/2023] [Accepted: 03/22/2023] [Indexed: 04/08/2023]
Abstract
Harnessing the patient's immune system to control a tumor is a proven avenue for cancer therapy. T cell therapies as well as therapeutic vaccines, which target specific antigens of interest, are being explored as treatments in conjunction with immune checkpoint blockade. For these therapies, selecting the best suited antigens is crucial. Most of the focus has thus far been on neoantigens that arise from tumor-specific somatic mutations. Although there is clear evidence that T-cell responses against mutated neoantigens are protective, the large majority of these mutations are not immunogenic. In addition, most somatic mutations are unique to each individual patient and their targeting requires the development of individualized approaches. Therefore, novel antigen types are needed to broaden the scope of such treatments. We review high throughput approaches for discovering novel tumor antigens and some of the key challenges associated with their detection, and discuss considerations when selecting tumor antigens to target in the clinic.
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Affiliation(s)
- Daniel Oreper
- Genentech, 1 DNA way, South San Francisco, 94080 CA, USA.
| | - Susan Klaeger
- Genentech, 1 DNA way, South San Francisco, 94080 CA, USA.
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