1
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Camarena ME, Theunissen P, Ruiz M, Ruiz-Orera J, Calvo-Serra B, Castelo R, Castro C, Sarobe P, Fortes P, Perera-Bel J, Albà MM. Microproteins encoded by noncanonical ORFs are a major source of tumor-specific antigens in a liver cancer patient meta-cohort. SCIENCE ADVANCES 2024; 10:eadn3628. [PMID: 38985879 PMCID: PMC11235171 DOI: 10.1126/sciadv.adn3628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 06/04/2024] [Indexed: 07/12/2024]
Abstract
The expression of tumor-specific antigens during cancer progression can trigger an immune response against the tumor. Here, we investigate if microproteins encoded by noncanonical open reading frames (ncORFs) are a relevant source of tumor-specific antigens. We analyze RNA sequencing data from 117 hepatocellular carcinoma (HCC) tumors and matched healthy tissue together with ribosome profiling and immunopeptidomics data. Combining human leukocyte antigen-epitope binding predictions and experimental validation experiments, we conclude that around 40% of the tumor-specific antigens in HCC are likely to be derived from ncORFs, including two peptides that can trigger an immune response in humanized mice. We identify a subset of 33 tumor-specific long noncoding RNAs expressing novel cancer antigens shared by more than 10% of the HCC samples analyzed, which, when combined, cover a large proportion of the patients. The results of the study open avenues for extending the range of anticancer vaccines.
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Affiliation(s)
| | - Patrick Theunissen
- Center for Applied Medical Research (CIMA), University of Navarra (UNAV), Pamplona, Spain
| | - Marta Ruiz
- Center for Applied Medical Research (CIMA), University of Navarra (UNAV), Pamplona, Spain
| | - Jorge Ruiz-Orera
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125 Berlin, Germany
| | - Beatriz Calvo-Serra
- Department of Medicine and Life Sciences, Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Robert Castelo
- Department of Medicine and Life Sciences, Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Carla Castro
- Center for Applied Medical Research (CIMA), University of Navarra (UNAV), Pamplona, Spain
| | - Pablo Sarobe
- Center for Applied Medical Research (CIMA), University of Navarra (UNAV), Pamplona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Pamplona, Spain
- Instituto de Investigación Sanitaria de Navarra (IdiSNA), Pamplona, Spain
- Cancer Clinic University of Navarra (CCUN), Pamplona, Spain
| | - Puri Fortes
- Center for Applied Medical Research (CIMA), University of Navarra (UNAV), Pamplona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Pamplona, Spain
- Instituto de Investigación Sanitaria de Navarra (IdiSNA), Pamplona, Spain
- Cancer Clinic University of Navarra (CCUN), Pamplona, Spain
- Spanish Network for Advanced Therapies (TERAV ISCIII), Madrid, Spain
| | | | - M Mar Albà
- Hospital del Mar Research Institute, Barcelona, Spain
- Catalan Institute for Research and Advanced Studies (ICREA), Barcelona, Spain
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2
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Kalhor M, Lapin J, Picciani M, Wilhelm M. Rescoring Peptide Spectrum Matches: Boosting Proteomics Performance by Integrating Peptide Property Predictors Into Peptide Identification. Mol Cell Proteomics 2024; 23:100798. [PMID: 38871251 DOI: 10.1016/j.mcpro.2024.100798] [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: 02/02/2024] [Revised: 05/26/2024] [Accepted: 06/09/2024] [Indexed: 06/15/2024] Open
Abstract
Rescoring of peptide spectrum matches originating from database search engines enabled by peptide property predictors is exceeding the performance of peptide identification from traditional database search engines. In contrast to the peptide spectrum match scores calculated by traditional database search engines, rescoring peptide spectrum matches generates scores based on comparing observed and predicted peptide properties, such as fragment ion intensities and retention times. These newly generated scores enable a more efficient discrimination between correct and incorrect peptide spectrum matches. This approach was shown to lead to substantial improvements in the number of confidently identified peptides, facilitating the analysis of challenging datasets in various fields such as immunopeptidomics, metaproteomics, proteogenomics, and single-cell proteomics. In this review, we summarize the key elements leading up to the recent introduction of multiple data-driven rescoring pipelines. We provide an overview of relevant post-processing rescoring tools, introduce prominent data-driven rescoring pipelines for various applications, and highlight limitations, opportunities, and future perspectives of this approach and its impact on mass spectrometry-based proteomics.
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Affiliation(s)
- Mostafa Kalhor
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Joel Lapin
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Mario Picciani
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Mathias Wilhelm
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany; Munich Data Science Institute, Technical University of Munich, Garching, Germany.
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3
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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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4
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Adams C, Laukens K, Bittremieux W, Boonen K. Machine learning-based peptide-spectrum match rescoring opens up the immunopeptidome. Proteomics 2024; 24:e2300336. [PMID: 38009585 DOI: 10.1002/pmic.202300336] [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: 09/06/2023] [Revised: 10/18/2023] [Accepted: 10/23/2023] [Indexed: 11/29/2023]
Abstract
Immunopeptidomics is a key technology in the discovery of targets for immunotherapy and vaccine development. However, identifying immunopeptides remains challenging due to their non-tryptic nature, which results in distinct spectral characteristics. Moreover, the absence of strict digestion rules leads to extensive search spaces, further amplified by the incorporation of somatic mutations, pathogen genomes, unannotated open reading frames, and post-translational modifications. This inflation in search space leads to an increase in random high-scoring matches, resulting in fewer identifications at a given false discovery rate. Peptide-spectrum match rescoring has emerged as a machine learning-based solution to address challenges in mass spectrometry-based immunopeptidomics data analysis. It involves post-processing unfiltered spectrum annotations to better distinguish between correct and incorrect peptide-spectrum matches. Recently, features based on predicted peptidoform properties, including fragment ion intensities, retention time, and collisional cross section, have been used to improve the accuracy and sensitivity of immunopeptide identification. In this review, we describe the diverse bioinformatics pipelines that are currently available for peptide-spectrum match rescoring and discuss how they can be used for the analysis of immunopeptidomics data. Finally, we provide insights into current and future machine learning solutions to boost immunopeptide identification.
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Affiliation(s)
- Charlotte Adams
- Adrem Data Lab, Department of Computer Science, University of Antwerp, Antwerp, Belgium
- Laboratory of Protein Science, Proteomics and Epigenetic Signaling (PPES), Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Kris Laukens
- Adrem Data Lab, Department of Computer Science, University of Antwerp, Antwerp, Belgium
| | - Wout Bittremieux
- Adrem Data Lab, Department of Computer Science, University of Antwerp, Antwerp, Belgium
| | - Kurt Boonen
- Laboratory of Protein Science, Proteomics and Epigenetic Signaling (PPES), Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- ImmuneSpec BV, Niel, Belgium
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5
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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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6
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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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7
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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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8
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Buonaguro L, Tagliamonte M. Peptide-based vaccine for cancer therapies. Front Immunol 2023; 14:1210044. [PMID: 37654484 PMCID: PMC10467431 DOI: 10.3389/fimmu.2023.1210044] [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: 04/21/2023] [Accepted: 07/31/2023] [Indexed: 09/02/2023] Open
Abstract
Different strategies based on peptides are available for cancer treatment, in particular to counter-act the progression of tumor growth and disease relapse. In the last decade, in the context of therapeutic strategies against cancer, peptide-based vaccines have been evaluated in different tumor models. The peptides selected for cancer vaccine development can be classified in two main type: tumor-associated antigens (TAAs) and tumor-specific antigens (TSAs), which are captured, internalized, processed and presented by antigen-presenting cells (APCs) to cell-mediated immunity. Peptides loaded onto MHC class I are recognized by a specific TCR of CD8+ T cells, which are activated to exert their cytotoxic activity against tumor cells presenting the same peptide-MHC-I complex. This process is defined as active immunotherapy as the host's immune system is either de novo activated or restimulated to mount an effective, tumor-specific immune reaction that may ultimately lead to tu-mor regression. However, while the preclinical data have frequently shown encouraging results, therapeutic cancer vaccines clinical trials, including those based on peptides have not provided satisfactory data to date. The limited efficacy of peptide-based cancer vaccines is the consequence of several factors, including the identification of specific target tumor antigens, the limited immunogenicity of peptides and the highly immunosuppressive tumor microenvironment (TME). An effective cancer vaccine can be developed only by addressing all such different aspects. The present review describes the state of the art for each of such factors.
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Affiliation(s)
| | - Maria Tagliamonte
- Innovative Immunological Models Unit, Istituto Nazionale Tumori - IRCCS - “Fond G. Pascale”, Naples, Italy
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9
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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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10
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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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11
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ElAbd H, Bacher P, Tholey A, Lenz TL, Franke A. Challenges and opportunities in analyzing and modeling peptide presentation by HLA-II proteins. Front Immunol 2023; 14:1107266. [PMID: 37063883 PMCID: PMC10090296 DOI: 10.3389/fimmu.2023.1107266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Accepted: 03/13/2023] [Indexed: 03/31/2023] Open
Abstract
The human leukocyte antigen (HLA) proteins are an indispensable component of adaptive immunity because of their role in presenting self and foreign peptides to T cells. Further, many complex diseases are associated with genetic variation in the HLA region, implying an important role for specific HLA-presented peptides in the etiology of these diseases. Identifying the specific set of peptides presented by an individual’s HLA proteins in vivo, as a whole being referred to as the immunopeptidome, has therefore gathered increasing attention for different reasons. For example, identifying neoepitopes for cancer immunotherapy, vaccine development against infectious pathogens, or elucidating the role of HLA in autoimmunity. Despite the tremendous progress made during the last decade in these areas, several questions remain unanswered. In this perspective, we highlight five remaining key challenges in the analysis of peptide presentation and T cell immunogenicity and discuss potential solutions to these problems. We believe that addressing these questions would not only improve our understanding of disease etiology but will also have a direct translational impact in terms of engineering better vaccines and in developing more potent immunotherapies.
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Affiliation(s)
- Hesham ElAbd
- Institute of Clinical Molecular Biology, University of Kiel, Kiel, Germany
| | - Petra Bacher
- Institute of Clinical Molecular Biology, University of Kiel, Kiel, Germany
- Institute of Immunology, University of Kiel, Kiel, Germany
| | - Andreas Tholey
- Proteomics & Bioanalytics, Institute for Experimental Medicine, University of Kiel, Kiel, Germany
| | - Tobias L. Lenz
- Research Unit for Evolutionary Immunogenomics, Department of Biology, University of Hamburg, Hamburg, Germany
| | - Andre Franke
- Institute of Clinical Molecular Biology, University of Kiel, Kiel, Germany
- *Correspondence: Andre Franke,
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12
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Ahn R, Cui Y, White FM. Antigen discovery for the development of cancer immunotherapy. Semin Immunol 2023; 66:101733. [PMID: 36841147 DOI: 10.1016/j.smim.2023.101733] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 02/15/2023] [Accepted: 02/16/2023] [Indexed: 02/25/2023]
Abstract
Central to successful cancer immunotherapy is effective T cell antitumor immunity. Multiple targeted immunotherapies engineered to invigorate T cell-driven antitumor immunity rely on identifying the repertoire of T cell antigens expressed on the tumor cell surface. Mass spectrometry-based survey of such antigens ("immunopeptidomics") combined with other omics platforms and computational algorithms has been instrumental in identifying and quantifying tumor-derived T cell antigens. In this review, we discuss the types of tumor antigens that have emerged for targeted cancer immunotherapy and the immunopeptidomics methods that are central in MHC peptide identification and quantification. We provide an overview of the strength and limitations of mass spectrometry-driven approaches and how they have been integrated with other technologies to discover targetable T cell antigens for cancer immunotherapy. We highlight some of the emerging cancer immunotherapies that successfully capitalized on immunopeptidomics, their challenges, and mass spectrometry-based strategies that can support their development.
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Affiliation(s)
- Ryuhjin Ahn
- David H. Koch Institute for Integrative Cancer Research, Cambridge, MA 02139, USA; Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Yufei Cui
- David H. Koch Institute for Integrative Cancer Research, Cambridge, MA 02139, USA; Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Forest M White
- David H. Koch Institute for Integrative Cancer Research, Cambridge, MA 02139, USA; Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
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13
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Cormican JA, Horokhovskyi Y, Soh WT, Mishto M, Liepe J. inSPIRE: An Open-Source Tool for Increased Mass Spectrometry Identification Rates Using Prosit Spectral Prediction. Mol Cell Proteomics 2022; 21:100432. [PMID: 36280141 PMCID: PMC9720494 DOI: 10.1016/j.mcpro.2022.100432] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 10/17/2022] [Accepted: 10/19/2022] [Indexed: 11/05/2022] Open
Abstract
Rescoring of mass spectrometry (MS) search results using spectral predictors can strongly increase peptide spectrum match (PSM) identification rates. This approach is particularly effective when aiming to search MS data against large databases, for example, when dealing with nonspecific cleavage in immunopeptidomics or inflation of the reference database for noncanonical peptide identification. Here, we present inSPIRE (in silico Spectral Predictor Informed REscoring), a flexible and performant open-source rescoring pipeline built on Prosit MS spectral prediction, which is compatible with common database search engines. inSPIRE allows large-scale rescoring with data from multiple MS search files, increases sensitivity to minor differences in amino acid residue position, and can be applied to various MS sample types, including tryptic proteome digestions and immunopeptidomes. inSPIRE boosts PSM identification rates in immunopeptidomics, leading to better performance than the original Prosit rescoring pipeline, as confirmed by benchmarking of inSPIRE performance on ground truth datasets. The integration of various features in the inSPIRE backbone further boosts the PSM identification in immunopeptidomics, with a potential benefit for the identification of noncanonical peptides.
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Affiliation(s)
- John A Cormican
- Max-Planck-Institute for Multidisciplinary Sciences (MPI-NAT), Göttingen, Germany
| | - Yehor Horokhovskyi
- Max-Planck-Institute for Multidisciplinary Sciences (MPI-NAT), Göttingen, Germany
| | - Wai Tuck Soh
- Max-Planck-Institute for Multidisciplinary Sciences (MPI-NAT), Göttingen, Germany
| | - Michele Mishto
- Centre for Inflammation Biology and Cancer Immunology (CIBCI) & Peter Gorer Department of Immunobiology, King's College London, London, United Kingdom; The Francis Crick Institute, London, United Kingdom.
| | - Juliane Liepe
- Max-Planck-Institute for Multidisciplinary Sciences (MPI-NAT), Göttingen, Germany.
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14
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Declercq A, Bouwmeester R, Hirschler A, Carapito C, Degroeve S, Martens L, Gabriels R. MS 2Rescore: Data-driven rescoring dramatically boosts immunopeptide identification rates. Mol Cell Proteomics 2022; 21:100266. [PMID: 35803561 PMCID: PMC9411678 DOI: 10.1016/j.mcpro.2022.100266] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 06/30/2022] [Accepted: 07/01/2022] [Indexed: 12/03/2022] Open
Abstract
Immunopeptidomics aims to identify major histocompatibility complex (MHC)-presented peptides on almost all cells that can be used in anti-cancer vaccine development. However, existing immunopeptidomics data analysis pipelines suffer from the nontryptic nature of immunopeptides, complicating their identification. Previously, peak intensity predictions by MS2PIP and retention time predictions by DeepLC have been shown to improve tryptic peptide identifications when rescoring peptide-spectrum matches with Percolator. However, as MS2PIP was tailored toward tryptic peptides, we have here retrained MS2PIP to include nontryptic peptides. Interestingly, the new models not only greatly improve predictions for immunopeptides but also yield further improvements for tryptic peptides. We show that the integration of new MS2PIP models, DeepLC, and Percolator in one software package, MS2Rescore, increases spectrum identification rate and unique identified peptides with 46% and 36% compared to standard Percolator rescoring at 1% FDR. Moreover, MS2Rescore also outperforms the current state-of-the-art in immunopeptide-specific identification approaches. Altogether, MS2Rescore thus allows substantially improved identification of novel epitopes from existing immunopeptidomics workflows. MS2Rescore significantly boosts immunopeptide identification rates Data-driven post-processing allows for a ten-fold increase in specificity MS2PIP and DeepLC predictors are integrated with Percolator post-processing MS2Rescore accepts identification results from MaxQuant, PEAKS, MS-GF+ and X!Tandem MS2Rescore shows great promise to extend current neo- and xeno-epitope landscapes
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Affiliation(s)
- Arthur Declercq
- VIB-UGent Center for Medical Biotechnology, VIB, Belgium; Department of Biomolecular Medicine, Ghent University, Belgium
| | - Robbin Bouwmeester
- VIB-UGent Center for Medical Biotechnology, VIB, Belgium; Department of Biomolecular Medicine, Ghent University, Belgium
| | - Aurélie Hirschler
- Laboratoire de Spectrométrie de Masse BioOrganique (LSMBO), Université de Strasbourg, CNRS
| | - Christine Carapito
- Laboratoire de Spectrométrie de Masse BioOrganique (LSMBO), Université de Strasbourg, CNRS
| | - Sven Degroeve
- VIB-UGent Center for Medical Biotechnology, VIB, Belgium; Department of Biomolecular Medicine, Ghent University, Belgium
| | - Lennart Martens
- VIB-UGent Center for Medical Biotechnology, VIB, Belgium; Department of Biomolecular Medicine, Ghent University, Belgium.
| | - Ralf Gabriels
- VIB-UGent Center for Medical Biotechnology, VIB, Belgium; Department of Biomolecular Medicine, Ghent University, Belgium
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15
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Mishto M, Horokhovskyi Y, Cormican JA, Yang X, Lynham S, Urlaub H, Liepe J. Database search engines and target database features impinge upon the identification of post-translationally cis-spliced peptides in HLA class I immunopeptidomes. Proteomics 2022; 22:e2100226. [PMID: 35184383 PMCID: PMC9286349 DOI: 10.1002/pmic.202100226] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 01/19/2022] [Accepted: 02/10/2022] [Indexed: 11/08/2022]
Abstract
Unconventional epitopes presented by HLA class I complexes are emerging targets for T cell targeted immunotherapies. Their identification by mass spectrometry (MS) required development of novel methods to cope with the large number of theoretical candidates. Methods to identify post-translationally spliced peptides led to a broad range of outcomes. We here investigated the impact of three common database search engines - that is, Mascot, Mascot+Percolator, and PEAKS DB - as final identification step, as well as the features of target database on the ability to correctly identify non-spliced and cis-spliced peptides. We used ground truth datasets measured by MS to benchmark methods' performance and extended the analysis to HLA class I immunopeptidomes. PEAKS DB showed better precision and recall of cis-spliced peptides and larger number of identified peptides in HLA class I immunopeptidomes than the other search engine strategies. The better performance of PEAKS DB appears to result from better discrimination between target and decoy hits and hence a more robust FDR estimation, and seems independent to peptide and spectrum features here investigated.
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Affiliation(s)
- Michele Mishto
- Centre for Inflammation Biology and Cancer Immunology (CIBCI) & Peter Gorer Department of ImmunobiologyKing's College LondonLondonUK
- Francis Crick InstituteLondonUK
| | | | - John A. Cormican
- Max‐Planck‐Institute for Multidisciplinary SciencesGöttingenGermany
| | - Xiaoping Yang
- Proteomics Core Facility, James Black CentreKing's CollegeLondonUK
| | - Steven Lynham
- Proteomics Core Facility, James Black CentreKing's CollegeLondonUK
| | - Henning Urlaub
- Max‐Planck‐Institute for Multidisciplinary SciencesGöttingenGermany
- Institute of Clinical ChemistryUniversity Medical Center GöttingenGöttingenGermany
| | - Juliane Liepe
- Max‐Planck‐Institute for Multidisciplinary SciencesGöttingenGermany
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16
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IntroSpect: Motif-Guided Immunopeptidome Database Building Tool to Improve the Sensitivity of HLA I Binding Peptide Identification by Mass Spectrometry. Biomolecules 2022; 12:biom12040579. [PMID: 35454168 PMCID: PMC9025654 DOI: 10.3390/biom12040579] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 04/11/2022] [Accepted: 04/12/2022] [Indexed: 01/02/2023] Open
Abstract
Although database search tools originally developed for shotgun proteome have been widely used in immunopeptidomic mass spectrometry identifications, they have been reported to achieve undesirably low sensitivities or high false positive rates as a result of the hugely inflated search space caused by the lack of specific enzymic digestions in immunopeptidome. To overcome such a problem, we developed a motif-guided immunopeptidome database building tool named IntroSpect, which is designed to first learn the peptide motifs from high confidence hits in the initial search, and then build a targeted database for refined search. Evaluated on 18 representative HLA class I datasets, IntroSpect can improve the sensitivity by an average of 76%, compared to conventional searches with unspecific digestions, while maintaining a very high level of accuracy (~96%), as confirmed by synthetic validation experiments. A distinct advantage of IntroSpect is that it does not depend on any external HLA data, so that it performs equally well on both well-studied and poorly-studied HLA types, unlike the previously developed method SpectMHC. We have also designed IntroSpect to keep a global FDR that can be conveniently controlled, similar to a conventional database search. Finally, we demonstrate the practical value of IntroSpect by discovering neoepitopes from MS data directly, an important application in cancer immunotherapies. IntroSpect is freely available to download and use.
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17
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Nielsen M, Ternette N, Barra C. The interdependence of machine learning and LC-MS approaches for an unbiased understanding of the cellular immunopeptidome. Expert Rev Proteomics 2022; 19:77-88. [PMID: 35390265 DOI: 10.1080/14789450.2022.2064278] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION The comprehensive collection of peptides presented by Major Histocompatibility Complex (MHC) molecules on the cell surface is collectively known as the immunopeptidome. The analysis and interpretation of such data sets holds great promise for furthering our understanding of basic immunology and adaptive immune activation and regulation, and for direct rational discovery of T cell antigens and the design of T-cell based therapeutics and vaccines. These applications are however challenged by the complex nature of immunopeptidome data. AREAS COVERED Here, we describe the benefits and shortcomings of applying liquid chromatography-tandem mass spectrometry (MS) to obtain large scale immunopeptidome data sets and illustrate how the accurate analysis and optimal interpretation of such data is reliant on the availability of refined and highly optimized machine learning approaches. EXPERT OPINION Further we demonstrate how the accuracy of immunoinformatics prediction methods within the field of MHC antigen presentation has benefited greatly from the availability of MS-immunopeptidomics data, and exemplify how optimal antigen discovery is best performed in a synergistic combination of MS experiments and such in silico models trained on large scale immunopeptidomics data.
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Affiliation(s)
- Morten Nielsen
- Department of Health technology, Technical University of Denmark, DK-2800 Lyngby, Denmark
| | - Nicola Ternette
- Centre for Cellular and Molecular Physiology, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK
| | - Carolina Barra
- Department of Health technology, Technical University of Denmark, DK-2800 Lyngby, Denmark
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18
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Kubiniok P, Marcu A, Bichmann L, Kuchenbecker L, Schuster H, Hamelin DJ, Duquette JD, Kovalchik KA, Wessling L, Kohlbacher O, Rammensee HG, Neidert MC, Sirois I, Caron E. Understanding the constitutive presentation of MHC class I immunopeptidomes in primary tissues. iScience 2022; 25:103768. [PMID: 35141507 PMCID: PMC8810409 DOI: 10.1016/j.isci.2022.103768] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 06/15/2021] [Accepted: 01/11/2022] [Indexed: 12/20/2022] Open
Abstract
Understanding the molecular principles that govern the composition of the MHC-I immunopeptidome across different primary tissues is fundamentally important to predict how T cells respond in different contexts in vivo. Here, we performed a global analysis of the MHC-I immunopeptidome from 29 to 19 primary human and mouse tissues, respectively. First, we observed that different HLA-A, HLA-B, and HLA-C allotypes do not contribute evenly to the global composition of the MHC-I immunopeptidome across multiple human tissues. Second, we found that tissue-specific and housekeeping MHC-I peptides share very distinct properties. Third, we discovered that proteins that are evolutionarily hyperconserved represent the primary source of the MHC-I immunopeptidome at the organism-wide scale. Fourth, we uncovered new components of the antigen processing and presentation network, including the carboxypeptidases CPE, CNDP1/2, and CPVL. Together, this study opens up new avenues toward a system-wide understanding of antigen presentation in vivo across mammalian species. Tissue-specific and housekeeping MHC class I peptides share distinct properties HLA-A, HLA-B, and HLA-C allotypes contribute very unevenly to the pool of class I peptides MHC-I immunopeptidomes are represented by evolutionarily conserved proteins An extended antigen processing and presentation pathway is uncovered
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Affiliation(s)
- Peter Kubiniok
- CHU Sainte-Justine Research Center, Montreal, QC H3T 1C5, Canada
| | - Ana Marcu
- Department of Immunology, Interfaculty Institute for Cell Biology, University of Tübingen, 72076 Tübingen, Baden-Württemberg, Germany
- Cluster of Excellence iFIT (EXC 2180), “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, 72076 Tübingen, Baden-Württemberg, Germany
| | - Leon Bichmann
- Department of Immunology, Interfaculty Institute for Cell Biology, University of Tübingen, 72076 Tübingen, Baden-Württemberg, Germany
- Applied Bioinformatics, Department of Computer Science, University of Tübingen, 72074 Tübingen, Baden-Württemberg, Germany
| | - Leon Kuchenbecker
- Applied Bioinformatics, Department of Computer Science, University of Tübingen, 72074 Tübingen, Baden-Württemberg, Germany
| | - Heiko Schuster
- Immatics Biotechnologies GmbH, 72076 Tübingen, Baden-Württemberg, Germany
| | - David J. Hamelin
- CHU Sainte-Justine Research Center, Montreal, QC H3T 1C5, Canada
| | | | | | - Laura Wessling
- CHU Sainte-Justine Research Center, Montreal, QC H3T 1C5, Canada
| | - Oliver Kohlbacher
- Applied Bioinformatics, Department of Computer Science, University of Tübingen, 72074 Tübingen, Baden-Württemberg, Germany
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, 72076 Tübingen, Baden-Württemberg, Germany
- Biomolecular Interactions, Max Planck Institute for Developmental Biology, 72076 Tübingen, Baden-Württemberg, Germany
- Cluster of Excellence Machine Learning in the Sciences (EXC 2064), University of Tübingen, 72074 Tübingen, Baden-Württemberg, Germany
- Translational Bioinformatics, University Hospital Tübingen, 72076 Tübingen, Baden-Württemberg, Germany
| | - Hans-Georg Rammensee
- Department of Immunology, Interfaculty Institute for Cell Biology, University of Tübingen, 72076 Tübingen, Baden-Württemberg, Germany
- Cluster of Excellence iFIT (EXC 2180), “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, 72076 Tübingen, Baden-Württemberg, Germany
- DKFZ Partner Site Tübingen, German Cancer Consortium (DKTK), 72076 Tübingen, Baden-Württemberg, Germany
| | - Marian C. Neidert
- Clinical Neuroscience Center and Department of Neurosurgery, University Hospital and University of Zürich, 8057&8091 Zürich, Switzerland
| | - Isabelle Sirois
- CHU Sainte-Justine Research Center, Montreal, QC H3T 1C5, Canada
| | - Etienne Caron
- CHU Sainte-Justine Research Center, Montreal, QC H3T 1C5, Canada
- Department of Pathology and Cellular Biology, Faculty of Medicine, Université de Montréal, QC H3T 1J4, Canada
- Corresponding author
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19
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Kovalchik KA, Ma Q, Wessling L, Saab F, Despault J, Kubiniok P, Hamelin DJ, Faridi P, Li C, Purcell AW, Jang A, Paramithiotis E, Tognetti M, Reiter L, Bruderer R, Lanoix J, Bonneil É, Courcelles M, Thibault P, Caron E, Sirois I. MhcVizPipe: A Quality Control Software for Rapid Assessment of Small- to Large-Scale Immunopeptidome Data Sets. Mol Cell Proteomics 2021; 21:100178. [PMID: 34798331 PMCID: PMC8717601 DOI: 10.1016/j.mcpro.2021.100178] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 10/28/2021] [Accepted: 11/01/2021] [Indexed: 12/12/2022] Open
Abstract
Mass spectrometry (MS)-based immunopeptidomics is maturing into an automatized, high-throughput technology, producing small- to large-scale datasets of clinically relevant MHC class I- and II-associated peptides. Consequently, the development of quality control (QC) and quality assurance (QA) systems capable of detecting sample and/or measurement issues is important for instrument operators and scientists in charge of downstream data interpretation. Here, we created MhcVizPipe (MVP), a semi-automated QC software tool that enables rapid and simultaneous assessment of multiple MHC class I and II immunopeptidomic datasets generated by MS, including datasets generated from large sample cohorts. In essence, MVP provides a rapid and consolidated view of sample quality, composition and MHC-specificity to greatly accelerate the 'pass-fail' QC decision-making process toward data interpretation. MVP parallelizes the use of well-established immunopeptidomic algorithms (NetMHCpan, NetMHCIIpan and GibbsCluster) and rapidly generates organized and easy-to-understand reports in HTML format. The reports are fully portable and can be viewed on any computer with a modern web browser. MVP is intuitive to use and will find utility in any specialized immunopeptidomic laboratory and proteomics core facility that provides immunopeptidomic services to the community.
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Affiliation(s)
| | - Qing Ma
- School of Electrical Engineering and Computer Science, Faculty of Engineering, University of Ottawa, ON K1N 6N5, Canada
| | - Laura Wessling
- CHU Sainte-Justine Research Center, Montreal, QC H3T 1C5, Canada
| | - Frederic Saab
- CHU Sainte-Justine Research Center, Montreal, QC H3T 1C5, Canada
| | - Jérôme Despault
- CHU Sainte-Justine Research Center, Montreal, QC H3T 1C5, Canada
| | - Peter Kubiniok
- CHU Sainte-Justine Research Center, Montreal, QC H3T 1C5, Canada
| | - David J Hamelin
- CHU Sainte-Justine Research Center, Montreal, QC H3T 1C5, Canada
| | - Pouya Faridi
- Infection and Immunity Program and Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria 3800, Australia
| | - Chen Li
- Infection and Immunity Program and Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria 3800, Australia
| | - Anthony W Purcell
- Infection and Immunity Program and Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria 3800, Australia
| | - Anne Jang
- CellCarta, Montreal, QC H2X 3Y7, Canada
| | | | | | - Lukas Reiter
- Biognosys, Wagistrasse 21, 8952 Schlieren, Switzerland
| | | | - Joël Lanoix
- Institute of Research in Immunology and Cancer, Montreal, QC H3T 1J4, Canada
| | - Éric Bonneil
- Institute of Research in Immunology and Cancer, Montreal, QC H3T 1J4, Canada
| | - Mathieu Courcelles
- Institute of Research in Immunology and Cancer, Montreal, QC H3T 1J4, Canada
| | - Pierre Thibault
- Institute of Research in Immunology and Cancer, Montreal, QC H3T 1J4, Canada; Department of Chemistry, Université de Montréal, Montreal, QC H3T 1J4, Canada
| | - Etienne Caron
- CHU Sainte-Justine Research Center, Montreal, QC H3T 1C5, Canada; Department of Pathology and Cellular Biology, Faculty of Medicine, Université de Montréal, QC H3T 1J4, Canada.
| | - Isabelle Sirois
- CHU Sainte-Justine Research Center, Montreal, QC H3T 1C5, Canada.
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20
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ElAbd H, Degenhardt F, Koudelka T, Kamps AK, Tholey A, Bacher P, Lenz TL, Franke A, Wendorff M. Immunopeptidomics toolkit library (IPTK): a python-based modular toolbox for analyzing immunopeptidomics data. BMC Bioinformatics 2021; 22:405. [PMID: 34404349 PMCID: PMC8369717 DOI: 10.1186/s12859-021-04315-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 08/03/2021] [Indexed: 01/12/2023] Open
Abstract
Background The human leukocyte antigen (HLA) proteins play a fundamental role in the adaptive immune system as they present peptides to T cells. Mass-spectrometry-based immunopeptidomics is a promising and powerful tool for characterizing the immunopeptidomic landscape of HLA proteins, that is the peptides presented on HLA proteins. Despite the growing interest in the technology, and the recent rise of immunopeptidomics-specific identification pipelines, there is still a gap in data-analysis and software tools that are specialized in analyzing and visualizing immunopeptidomics data. Results We present the IPTK library which is an open-source Python-based library for analyzing, visualizing, comparing, and integrating different omics layers with the identified peptides for an in-depth characterization of the immunopeptidome. Using different datasets, we illustrate the ability of the library to enrich the result of the identified peptidomes. Also, we demonstrate the utility of the library in developing other software and tools by developing an easy-to-use dashboard that can be used for the interactive analysis of the results. Conclusion IPTK provides a modular and extendable framework for analyzing and integrating immunopeptidomes with different omics layers. The library is deployed into PyPI at https://pypi.org/project/IPTKL/ and into Bioconda at https://anaconda.org/bioconda/iptkl, while the source code of the library and the dashboard, along with the online tutorials are available at https://github.com/ikmb/iptoolkit. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04315-0.
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Affiliation(s)
- Hesham ElAbd
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany
| | - Frauke Degenhardt
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany
| | - Tomas Koudelka
- Proteomics and Bioanalytics, Institute for Experimental Medicine, Christian-Albrechts-University of Kiel, Kiel, Germany
| | - Ann-Kristin Kamps
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany.,Institute of Immunology, Christian-Albrechts-University of Kiel, Kiel, Germany
| | - Andreas Tholey
- Proteomics and Bioanalytics, Institute for Experimental Medicine, Christian-Albrechts-University of Kiel, Kiel, Germany
| | - Petra Bacher
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany.,Institute of Immunology, Christian-Albrechts-University of Kiel, Kiel, Germany
| | - Tobias L Lenz
- Research Unit for Evolutionary Immunogenomics, Department of Biology, University of Hamburg, Hamburg, Germany
| | - Andre Franke
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany.
| | - Mareike Wendorff
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany
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21
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Parker R, Tailor A, Peng X, Nicastri A, Zerweck J, Reimer U, Wenschuh H, Schnatbaum K, Ternette N. The Choice of Search Engine Affects Sequencing Depth and HLA Class I Allele-Specific Peptide Repertoires. Mol Cell Proteomics 2021; 20:100124. [PMID: 34303857 PMCID: PMC8724928 DOI: 10.1016/j.mcpro.2021.100124] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 07/09/2021] [Accepted: 07/12/2021] [Indexed: 12/26/2022] Open
Abstract
Standardization of immunopeptidomics experiments across laboratories is a pressing issue within the field, and currently a variety of different methods for sample preparation and data analysis tools are applied. Here, we compared different software packages to interrogate immunopeptidomics datasets and found that Peaks reproducibly reports substantially more peptide sequences (~30-70%) compared with Maxquant, Comet, and MS-GF+ at a global false discovery rate (FDR) of <1%. We noted that these differences are driven by search space and spectral ranking. Furthermore, we observed differences in the proportion of peptides binding the human leukocyte antigen (HLA) alleles present in the samples, indicating that sequence-related differences affected the performance of each tested engine. Utilizing data from single HLA allele expressing cell lines, we observed significant differences in amino acid frequency among the peptides reported, with a broadly higher representation of hydrophobic amino acids L, I, P, and V reported by Peaks. We validated these results using data generated with a synthetic library of 2000 HLA-associated peptides from four common HLA alleles with distinct anchor residues. Our investigation highlights that search engines create a bias in peptide sequence depth and peptide amino acid composition, and resulting data should be interpreted with caution.
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Affiliation(s)
- Robert Parker
- Nuffield Department of Medicine, Centre for Cellar and Medical Physiology, University of Oxford, Oxford, UK.
| | - Arun Tailor
- Nuffield Department of Medicine, Centre for Cellar and Medical Physiology, University of Oxford, Oxford, UK
| | - Xu Peng
- Nuffield Department of Medicine, Centre for Cellar and Medical Physiology, University of Oxford, Oxford, UK
| | - Annalisa Nicastri
- Nuffield Department of Medicine, Centre for Cellar and Medical Physiology, University of Oxford, Oxford, UK
| | | | - Ulf Reimer
- JPT Peptide Technologies GmbH, Berlin, Germany
| | | | | | - Nicola Ternette
- Nuffield Department of Medicine, Centre for Cellar and Medical Physiology, University of Oxford, Oxford, UK.
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22
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Empirical Evaluation of the Use of Computational HLA Binding as an Early Filter to the Mass Spectrometry-Based Epitope Discovery Workflow. Cancers (Basel) 2021; 13:cancers13102307. [PMID: 34065814 PMCID: PMC8150281 DOI: 10.3390/cancers13102307] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 05/06/2021] [Accepted: 05/06/2021] [Indexed: 12/22/2022] Open
Abstract
Immunopeptidomics is used to identify novel epitopes for (therapeutic) vaccination strategies in cancer and infectious disease. Various false discovery rates (FDRs) are applied in the field when converting liquid chromatography-tandem mass spectrometry (LC-MS/MS) spectra to peptides. Subsequently, large efforts have recently been made to rescue peptides of lower confidence. However, it remains unclear what the overall relation is between the FDR threshold and the percentage of obtained HLA-binders. We here directly evaluated the effect of varying FDR thresholds on the resulting immunopeptidomes of HLA-eluates from human cancer cell lines and primary hepatocyte isolates using HLA-binding algorithms. Additional peptides obtained using less stringent FDR-thresholds, although generally derived from poorer spectra, still contained a high amount of HLA-binders and confirmed recently developed tools that tap into this pool of otherwise ignored peptides. Most of these peptides were identified with improved confidence when cell input was increased, supporting the validity and potential of these identifications. Altogether, our data suggest that increasing the FDR threshold for peptide identification in conjunction with data filtering by HLA-binding prediction, is a valid and highly potent method to more efficient exhaustion of immunopeptidome datasets for epitope discovery and reveals the extent of peptides to be rescued by recently developed algorithms.
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23
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Marcu A, Bichmann L, Kuchenbecker L, Kowalewski DJ, Freudenmann LK, Backert L, Mühlenbruch L, Szolek A, Lübke M, Wagner P, Engler T, Matovina S, Wang J, Hauri-Hohl M, Martin R, Kapolou K, Walz JS, Velz J, Moch H, Regli L, Silginer M, Weller M, Löffler MW, Erhard F, Schlosser A, Kohlbacher O, Stevanović S, Rammensee HG, Neidert MC. HLA Ligand Atlas: a benign reference of HLA-presented peptides to improve T-cell-based cancer immunotherapy. J Immunother Cancer 2021; 9:e002071. [PMID: 33858848 PMCID: PMC8054196 DOI: 10.1136/jitc-2020-002071] [Citation(s) in RCA: 100] [Impact Index Per Article: 33.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/25/2021] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND The human leucocyte antigen (HLA) complex controls adaptive immunity by presenting defined fractions of the intracellular and extracellular protein content to immune cells. Understanding the benign HLA ligand repertoire is a prerequisite to define safe T-cell-based immunotherapies against cancer. Due to the poor availability of benign tissues, if available, normal tissue adjacent to the tumor has been used as a benign surrogate when defining tumor-associated antigens. However, this comparison has proven to be insufficient and even resulted in lethal outcomes. In order to match the tumor immunopeptidome with an equivalent counterpart, we created the HLA Ligand Atlas, the first extensive collection of paired HLA-I and HLA-II immunopeptidomes from 227 benign human tissue samples. This dataset facilitates a balanced comparison between tumor and benign tissues on HLA ligand level. METHODS Human tissue samples were obtained from 16 subjects at autopsy, five thymus samples and two ovary samples originating from living donors. HLA ligands were isolated via immunoaffinity purification and analyzed in over 1200 liquid chromatography mass spectrometry runs. Experimentally and computationally reproducible protocols were employed for data acquisition and processing. RESULTS The initial release covers 51 HLA-I and 86 HLA-II allotypes presenting 90,428 HLA-I- and 142,625 HLA-II ligands. The HLA allotypes are representative for the world population. We observe that immunopeptidomes differ considerably between tissues and individuals on source protein and HLA-ligand level. Moreover, we discover 1407 HLA-I ligands from non-canonical genomic regions. Such peptides were previously described in tumors, peripheral blood mononuclear cells (PBMCs), healthy lung tissues and cell lines. In a case study in glioblastoma, we show that potential on-target off-tumor adverse events in immunotherapy can be avoided by comparing tumor immunopeptidomes to the provided multi-tissue reference. CONCLUSION Given that T-cell-based immunotherapies, such as CAR-T cells, affinity-enhanced T cell transfer, cancer vaccines and immune checkpoint inhibition, have significant side effects, the HLA Ligand Atlas is the first step toward defining tumor-associated targets with an improved safety profile. The resource provides insights into basic and applied immune-associated questions in the context of cancer immunotherapy, infection, transplantation, allergy and autoimmunity. It is publicly available and can be browsed in an easy-to-use web interface at https://hla-ligand-atlas.org .
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Affiliation(s)
- Ana Marcu
- Department of Immunology, Interfaculty Institute for Cell Biology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
| | - Leon Bichmann
- Department of Immunology, Interfaculty Institute for Cell Biology, University of Tübingen, Tübingen, Germany
- Applied Bioinformatics, Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Leon Kuchenbecker
- Applied Bioinformatics, Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Daniel Johannes Kowalewski
- Department of Immunology, Interfaculty Institute for Cell Biology, University of Tübingen, Tübingen, Germany
| | - Lena Katharina Freudenmann
- Department of Immunology, Interfaculty Institute for Cell Biology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
- DKFZ Partner Site Tübingen, German Cancer Consortium (DKTK), Tübingen, Germany
| | - Linus Backert
- Department of Immunology, Interfaculty Institute for Cell Biology, University of Tübingen, Tübingen, Germany
- Applied Bioinformatics, Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Lena Mühlenbruch
- Department of Immunology, Interfaculty Institute for Cell Biology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
- DKFZ Partner Site Tübingen, German Cancer Consortium (DKTK), Tübingen, Germany
| | - András Szolek
- Applied Bioinformatics, Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Maren Lübke
- Department of Immunology, Interfaculty Institute for Cell Biology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
| | - Philipp Wagner
- Department of Immunology, Interfaculty Institute for Cell Biology, University of Tübingen, Tübingen, Germany
- Department of Obstetrics and Gynecology, University Hospital of Tübingen, Tübingen, Germany
| | - Tobias Engler
- Department of Obstetrics and Gynecology, University Hospital of Tübingen, Tübingen, Germany
| | - Sabine Matovina
- Department of Obstetrics and Gynecology, University Hospital of Tübingen, Tübingen, Germany
| | - Jian Wang
- Neuroimmunology and MS Research, Neurology Clinic, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Mathias Hauri-Hohl
- Pediatric Stem Cell Transplantation, University Children's Hospital Zurich, Zurich, Switzerland
| | - Roland Martin
- Neuroimmunology and MS Research, Neurology Clinic, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Konstantina Kapolou
- Clinical Neuroscience Center and Department of Neurosurgery, University Hospital and University of Zurich, Zurich, Switzerland
| | - Juliane Sarah Walz
- Department of Immunology, Interfaculty Institute for Cell Biology, University of 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), University Hospital of Tübingen, Tübingen, Germany
- Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology (IKP) and Robert Bosch Center for Tumor Diseases (RBCT), Stuttgart, Germany
| | - Julia Velz
- Clinical Neuroscience Center and Department of Neurosurgery, University Hospital and University of Zurich, Zurich, Switzerland
| | - Holger Moch
- Department of Pathology and Molecular Pathology, University Hospital and University of Zurich, Zurich, Switzerland
| | - Luca Regli
- Clinical Neuroscience Center and Department of Neurosurgery, University Hospital and University of Zurich, Zurich, Switzerland
| | - Manuela Silginer
- Clinical Neuroscience Center and Department of Neurology, University Hospital and University of Zurich, Zurich, Switzerland
| | - Michael Weller
- Clinical Neuroscience Center and Department of Neurology, University Hospital and University of Zurich, Zurich, Switzerland
| | - Markus W Löffler
- Department of Immunology, Interfaculty Institute for Cell Biology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
- DKFZ Partner Site Tübingen, German Cancer Consortium (DKTK), Tübingen, Germany
- Department of General, Visceral and Transplant Surgery, University Hospital of Tübingen, Tübingen, Germany
- Department of Clinical Pharmacology, University of Hospital Tübingen, Tübingen, Germany
| | - Florian Erhard
- Institute for Virology and Immunobiology, Julius-Maximilians-University Würzburg, Würzburg, Bayern, Germany
| | - Andreas Schlosser
- Rudolf Virchow Center - Center for Integrative and Translational Bioimaging, Julius-Maximilians-University Würzburg, Würzburg, Germany
| | - Oliver Kohlbacher
- Applied Bioinformatics, Department of Computer Science, University of Tübingen, Tübingen, Germany
- DKFZ Partner Site Tübingen, German Cancer Consortium (DKTK), Tübingen, Germany
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen, Germany
- Quantitative Biology Center (QBiC), University of Tübingen, Tübingen, Germany
- Biomolecular Interactions, Max Planck Institute for Developmental Biology, Tübingen, Germany
- Cluster of Excellence Machine Learning in the Sciences (EXC 2064), University of Tübingen, Tübingen, Germany
- Institute for Translational Bioinformatics, University Hospital Tübingen, Tübingen, Germany
| | - Stefan Stevanović
- Department of Immunology, Interfaculty Institute for Cell Biology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
- DKFZ Partner Site Tübingen, German Cancer Consortium (DKTK), Tübingen, Germany
| | - Hans-Georg Rammensee
- Department of Immunology, Interfaculty Institute for Cell Biology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
- DKFZ Partner Site Tübingen, German Cancer Consortium (DKTK), Tübingen, Germany
| | - Marian Christoph Neidert
- Clinical Neuroscience Center and Department of Neurosurgery, University Hospital and University of Zurich, Zurich, Switzerland
- Department of Neurosurgery, Cantonal Hospital St.Gallen, St.Gallen, Switzerland
- Neuroscience Center Zurich (ZNZ), University of Zurich and ETH Zurich, Zurich, Switzerland
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24
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Li K, Jain A, Malovannaya A, Wen B, Zhang B. DeepRescore: Leveraging Deep Learning to Improve Peptide Identification in Immunopeptidomics. Proteomics 2020; 20:e1900334. [PMID: 32864883 PMCID: PMC7718998 DOI: 10.1002/pmic.201900334] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Revised: 08/27/2020] [Indexed: 12/23/2022]
Abstract
The identification of major histocompatibility complex (MHC)-binding peptides in mass spectrometry (MS)-based immunopeptideomics relies largely on database search engines developed for proteomics data analysis. However, because immunopeptidomics experiments do not involve enzymatic digestion at specific residues, an inflated search space leads to a high false positive rate and low sensitivity in peptide identification. In order to improve the sensitivity and reliability of peptide identification, a post-processing tool named DeepRescore is developed. DeepRescore combines peptide features derived from deep learning predictions, namely accurate retention timeand MS/MS spectra predictions, with previously used features to rescore peptide-spectrum matches. Using two public immunopeptidomics datasets, it is shown that rescoring by DeepRescore increases both the sensitivity and reliability of MHC-binding peptide and neoantigen identifications compared to existing methods. It is also shown that the performance improvement is, to a large extent, driven by the deep learning-derived features. DeepRescore is developed using NextFlow and Docker and is available at https://github.com/bzhanglab/DeepRescore.
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Affiliation(s)
- Kai Li
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Antrix Jain
- Mass Spectrometry Proteomics Core, Baylor College of Medicine, Houston, TX 77030, USA
| | - Anna Malovannaya
- Mass Spectrometry Proteomics Core, Baylor College of Medicine, Houston, TX 77030, USA
- Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Bo Wen
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Bing Zhang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
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25
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Wen B, Li K, Zhang Y, Zhang B. Cancer neoantigen prioritization through sensitive and reliable proteogenomics analysis. Nat Commun 2020; 11:1759. [PMID: 32273506 PMCID: PMC7145864 DOI: 10.1038/s41467-020-15456-w] [Citation(s) in RCA: 69] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Accepted: 03/10/2020] [Indexed: 01/01/2023] Open
Abstract
Genomics-based neoantigen discovery can be enhanced by proteomic evidence, but there remains a lack of consensus on the performance of different quality control methods for variant peptide identification in proteogenomics. We propose to use the difference between accurately predicted and observed retention times for each peptide as a metric to evaluate different quality control methods. To this end, we develop AutoRT, a deep learning algorithm with high accuracy in retention time prediction. Analysis of three cancer data sets with a total of 287 tumor samples using different quality control strategies results in substantially different numbers of identified variant peptides and putative neoantigens. Our systematic evaluation, using the proposed retention time metric, provides insights and practical guidance on the selection of quality control strategies. We implement the recommended strategy in a computational workflow named NeoFlow to support proteogenomics-based neoantigen prioritization, enabling more sensitive discovery of putative neoantigens. Identifying mutation-derived neoantigens by proteogenomics requires robust strategies for quality control. Here, the authors propose peptide retention time as an evaluation metric for proteogenomics quality control methods, and develop a deep learning algorithm for accurate retention time prediction.
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Affiliation(s)
- Bo Wen
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, 77030, USA.,Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Kai Li
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, 77030, USA.,Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Yun Zhang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, 77030, USA.,Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Bing Zhang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, 77030, USA. .,Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA.
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26
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Courcelles M, Durette C, Daouda T, Laverdure JP, Vincent K, Lemieux S, Perreault C, Thibault P. MAPDP: A Cloud-Based Computational Platform for Immunopeptidomics Analyses. J Proteome Res 2020; 19:1873-1881. [PMID: 32108478 DOI: 10.1021/acs.jproteome.9b00859] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
The immunopeptidome corresponds to the repertoire of peptides presented at the cell surface by the major histocompatibility complex (MHC) molecules. Cytotoxic T cells scan this repertoire to identify nonself antigens that can arise from tumors or infected cells. The identification of actionable antigenic targets is key to the development of therapeutic cancer vaccines, T-cell therapy, and other T-cell receptor-based biologics. The growing clinical interest for immunopeptidomics has accelerated the development of high throughput proteogenomic platforms that provide a system-level analysis of MHC-associated peptides. Improvement in sensitivity and throughput of mass spectrometers now allows the detection of a few thousands of peptides from less than 100 million cells. To manage the amount of data generated by these instruments, we have developed the MHC-associated peptide discovery platform (MAPDP), a novel open-source cloud-based computational platform for immunopeptidomic analyses. It provides convenient access from a web portal to immunopeptidomes stored in the database, filtering tools, various visualizations, annotations (e.g., IEDB, dbSNP, gnomAD), peptide-binding affinity prediction (mhcflurry, NetMHC), HLA genotyping, and the generation of personalized proteome databases. MAPDP functionalities are demonstrated here by the discovery of MHC peptides featuring new genetic variants identified in two previously published ovarian carcinoma data sets.
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Affiliation(s)
- Mathieu Courcelles
- Institute for Research in Immunology and Cancer, Université de Montréal, Montréal, Québec H3C 3J7, Canada
| | - Chantal Durette
- Institute for Research in Immunology and Cancer, Université de Montréal, Montréal, Québec H3C 3J7, Canada
| | - Tariq Daouda
- Institute for Research in Immunology and Cancer, Université de Montréal, Montréal, Québec H3C 3J7, Canada.,Department of Biochemistry, Université de Montréal, Montréal, Québec H3C 3J7, Canada
| | - Jean-Philippe Laverdure
- Institute for Research in Immunology and Cancer, Université de Montréal, Montréal, Québec H3C 3J7, Canada
| | - Krystel Vincent
- Institute for Research in Immunology and Cancer, Université de Montréal, Montréal, Québec H3C 3J7, Canada
| | - Sébastien Lemieux
- Institute for Research in Immunology and Cancer, Université de Montréal, Montréal, Québec H3C 3J7, Canada.,Department of Biochemistry, Université de Montréal, Montréal, Québec H3C 3J7, Canada
| | - Claude Perreault
- Institute for Research in Immunology and Cancer, Université de Montréal, Montréal, Québec H3C 3J7, Canada.,Department of Medicine, Université de Montréal, Montréal, Québec H3C 3J7, Canada
| | - Pierre Thibault
- Institute for Research in Immunology and Cancer, Université de Montréal, Montréal, Québec H3C 3J7, Canada.,Department of Chemistry, Université de Montréal, Montréal, Québec H3C 3J7, Canada
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27
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Ghosh M, Gauger M, Marcu A, Nelde A, Denk M, Schuster H, Rammensee HG, Stevanović S. Guidance Document: Validation of a High-Performance Liquid Chromatography-Tandem Mass Spectrometry Immunopeptidomics Assay for the Identification of HLA Class I Ligands Suitable for Pharmaceutical Therapies. Mol Cell Proteomics 2020; 19:432-443. [PMID: 31937595 PMCID: PMC7050110 DOI: 10.1074/mcp.c119.001652] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Revised: 01/09/2020] [Indexed: 12/30/2022] Open
Abstract
For more than two decades naturally presented, human leukocyte antigen (HLA)-restricted peptides (immunopeptidome) have been eluted and sequenced using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Since, identified disease-associated HLA ligands have been characterized and evaluated as potential active substances. Treatments based on HLA-presented peptides have shown promising results in clinical application as personalized T cell-based immunotherapy. Peptide vaccination cocktails are produced as investigational medicinal products under GMP conditions. To support clinical trials based on HLA-presented tumor-associated antigens, in this study the sensitive LC-MS/MS HLA class I antigen identification pipeline was fully validated for our technical equipment according to the current US Food and Drug Administration (FDA) and European Medicines Agency (EMA) guidelines.The immunopeptidomes of JY cells with or without spiked-in, isotope labeled peptides, of peripheral blood mononuclear cells of healthy volunteers as well as a chronic lymphocytic leukemia and a bladder cancer sample were reliably identified using a data-dependent acquisition method. As the LC-MS/MS pipeline is used for identification purposes, the validation parameters include accuracy, precision, specificity, limit of detection and robustness.
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Affiliation(s)
- Michael Ghosh
- Department of Immunology, Institute for Cell Biology, University of Tübingen, Tübingen, Germany; Natural and Medical Science Institute at the University of Tübingen (NMI), Reutlingen, Germany
| | - Marion Gauger
- Department of Immunology, Institute for Cell Biology, University of Tübingen, Tübingen, Germany
| | - Ana Marcu
- Department of Immunology, Institute for Cell Biology, University of Tübingen, Tübingen, Germany
| | - Annika Nelde
- Department of Immunology, Institute for Cell Biology, University of Tübingen, Tübingen, Germany
| | - Monika Denk
- Department of Immunology, Institute for Cell Biology, University of Tübingen, Tübingen, Germany; German Cancer Research Center (DKFZ) partner site and German Cancer Consortium (DKTK) Tübingen, Tübingen, Germany
| | - Heiko Schuster
- Department of Immunology, Institute for Cell Biology, University of Tübingen, Tübingen, Germany; Immatics Biotechnologies GmbH, Tübingen, Germany
| | - Hans-Georg Rammensee
- Department of Immunology, Institute for Cell Biology, University of Tübingen, Tübingen, Germany; German Cancer Research Center (DKFZ) partner site and German Cancer Consortium (DKTK) Tübingen, Tübingen, Germany
| | - Stefan Stevanović
- Department of Immunology, Institute for Cell Biology, University of Tübingen, Tübingen, Germany; German Cancer Research Center (DKFZ) partner site and German Cancer Consortium (DKTK) Tübingen, Tübingen, Germany.
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28
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Kote S, Pirog A, Bedran G, Alfaro J, Dapic I. Mass Spectrometry-Based Identification of MHC-Associated Peptides. Cancers (Basel) 2020; 12:cancers12030535. [PMID: 32110973 PMCID: PMC7139412 DOI: 10.3390/cancers12030535] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Revised: 02/15/2020] [Accepted: 02/20/2020] [Indexed: 02/06/2023] Open
Abstract
Neoantigen-based immunotherapies promise to improve patient outcomes over the current standard of care. However, detecting these cancer-specific antigens is one of the significant challenges in the field of mass spectrometry. Even though the first sequencing of the immunopeptides was done decades ago, today there is still a diversity of the protocols used for neoantigen isolation from the cell surface. This heterogeneity makes it difficult to compare results between the laboratories and the studies. Isolation of the neoantigens from the cell surface is usually done by mild acid elution (MAE) or immunoprecipitation (IP) protocol. However, limited amounts of the neoantigens present on the cell surface impose a challenge and require instrumentation with enough sensitivity and accuracy for their detection. Detecting these neopeptides from small amounts of available patient tissue limits the scope of most of the studies to cell cultures. Here, we summarize protocols for the extraction and identification of the major histocompatibility complex (MHC) class I and II peptides. We aimed to evaluate existing methods in terms of the appropriateness of the isolation procedure, as well as instrumental parameters used for neoantigen detection. We also focus on the amount of the material used in the protocols as the critical factor to consider when analyzing neoantigens. Beyond experimental aspects, there are numerous readily available proteomics suits/tools applicable for neoantigen discovery; however, experimental validation is still necessary for neoantigen characterization.
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