1
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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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2
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Ferreira HJ, Stevenson BJ, Pak H, Yu F, Almeida Oliveira J, Huber F, Taillandier-Coindard M, Michaux J, Ricart-Altimiras E, Kraemer AI, Kandalaft LE, Speiser DE, Nesvizhskii AI, Müller M, Bassani-Sternberg M. Immunopeptidomics-based identification of naturally presented non-canonical circRNA-derived peptides. Nat Commun 2024; 15:2357. [PMID: 38490980 PMCID: PMC10943130 DOI: 10.1038/s41467-024-46408-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 02/16/2024] [Indexed: 03/18/2024] Open
Abstract
Circular RNAs (circRNAs) are covalently closed non-coding RNAs lacking the 5' cap and the poly-A tail. Nevertheless, it has been demonstrated that certain circRNAs can undergo active translation. Therefore, aberrantly expressed circRNAs in human cancers could be an unexplored source of tumor-specific antigens, potentially mediating anti-tumor T cell responses. This study presents an immunopeptidomics workflow with a specific focus on generating a circRNA-specific protein fasta reference. The main goal of this workflow is to streamline the process of identifying and validating human leukocyte antigen (HLA) bound peptides potentially originating from circRNAs. We increase the analytical stringency of our workflow by retaining peptides identified independently by two mass spectrometry search engines and/or by applying a group-specific FDR for canonical-derived and circRNA-derived peptides. A subset of circRNA-derived peptides specifically encoded by the region spanning the back-splice junction (BSJ) are validated with targeted MS, and with direct Sanger sequencing of the respective source transcripts. Our workflow identifies 54 unique BSJ-spanning circRNA-derived peptides in the immunopeptidome of melanoma and lung cancer samples. Our approach enlarges the catalog of source proteins that can be explored for immunotherapy.
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Affiliation(s)
- Humberto J Ferreira
- Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Department of Oncology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
- Agora Cancer Research Centre, Lausanne, Switzerland
| | - Brian J Stevenson
- Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Agora Cancer Research Centre, Lausanne, Switzerland
- SIB Swiss Institute of Bioinformatics, University of Lausanne, Lausanne, Switzerland
| | - HuiSong Pak
- Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Department of Oncology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
- Agora Cancer Research Centre, Lausanne, Switzerland
| | - Fengchao Yu
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Jessica Almeida Oliveira
- Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Department of Oncology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
- Agora Cancer Research Centre, Lausanne, Switzerland
| | - Florian Huber
- Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Department of Oncology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
- Agora Cancer Research Centre, Lausanne, Switzerland
| | - Marie Taillandier-Coindard
- Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Department of Oncology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
- Agora Cancer Research Centre, Lausanne, Switzerland
| | - Justine Michaux
- Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Department of Oncology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
- Agora Cancer Research Centre, Lausanne, Switzerland
| | - Emma Ricart-Altimiras
- Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Department of Oncology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
- Agora Cancer Research Centre, Lausanne, Switzerland
| | - Anne I Kraemer
- Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Department of Oncology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
- Agora Cancer Research Centre, Lausanne, Switzerland
| | - Lana E Kandalaft
- Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Department of Oncology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
- Agora Cancer Research Centre, Lausanne, Switzerland
- Center of Experimental Therapeutics, Department of Oncology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - Daniel E Speiser
- Department of Oncology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - Alexey I Nesvizhskii
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Markus Müller
- Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Department of Oncology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
- Agora Cancer Research Centre, Lausanne, Switzerland
- SIB Swiss Institute of Bioinformatics, University of Lausanne, Lausanne, Switzerland
| | - Michal Bassani-Sternberg
- Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland.
- Department of Oncology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland.
- Agora Cancer Research Centre, Lausanne, Switzerland.
- Center of Experimental Therapeutics, Department of Oncology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland.
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3
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Lapin J, Yan X, Dong Q. UniSpec: Deep Learning for Predicting the Full Range of Peptide Fragment Ion Series to Enhance the Proteomics Data Analysis Workflow. Anal Chem 2024. [PMID: 38329031 DOI: 10.1021/acs.analchem.3c02321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
We present UniSpec, an attention-driven deep neural network designed to predict comprehensive collision-induced fragmentation spectra, thereby improving peptide identification in shotgun proteomics. Utilizing a training data set of 1.8 million unique high-quality tandem mass spectra (MS2) from 0.8 million unique peptide ions, UniSpec learned with a peptide fragmentation dictionary encompassing 7919 fragment peaks. Among these, 5712 are neutral loss peaks, with 2310 corresponding to modification-specific neutral losses. Remarkably, UniSpec can predict 73%-77% of fragment intensities based on our NIST reference library spectra, a significant leap from the 35%-45% coverage of only b and y ions. Comparative studies with Prosit elucidate that while both models are strong at predicting their respective fragment ion series, UniSpec particularly shines in generating more complex MS2 spectra with diverse ion annotations. The integration of UniSpec's predictions into shotgun proteomics data analysis boosts the identification rate of tryptic peptides by 48% at a 1% false discovery rate (FDR) and 60% at a more confident 0.1% FDR. Using UniSpec's predicted in-silico spectral library, the search results closely matched those from search engines and experimental spectral libraries used in peptide identification, highlighting its potential as a stand-alone identification tool. The source code and Python scripts are available on GitHub (https://github.com/usnistgov/UniSpec) and Zenodo (https://zenodo.org/records/10452792), and all data sets and analysis results generated in this work were deposited in Zenodo (https://zenodo.org/records/10052268).
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Affiliation(s)
- Joel Lapin
- Department of Physics, Georgetown University, Washington, D.C. 20057, United States
- Associate, Mass Spectrometry Data Center, Biomolecular Measurement Division, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, Maryland 20899, United States
| | - Xinjian Yan
- Mass Spectrometry Data Center, Biomolecular Measurement Division, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, Maryland 20899, United States
| | - Qian Dong
- Mass Spectrometry Data Center, Biomolecular Measurement Division, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, Maryland 20899, United States
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4
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Soh WT, Roetschke HP, Cormican JA, Teo BF, Chiam NC, Raabe M, Pflanz R, Henneberg F, Becker S, Chari A, Liu H, Urlaub H, Liepe J, Mishto M. Protein degradation by human 20S proteasomes elucidates the interplay between peptide hydrolysis and splicing. Nat Commun 2024; 15:1147. [PMID: 38326304 PMCID: PMC10850103 DOI: 10.1038/s41467-024-45339-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 01/17/2024] [Indexed: 02/09/2024] Open
Abstract
If and how proteasomes catalyze not only peptide hydrolysis but also peptide splicing is an open question that has divided the scientific community. The debate has so far been based on immunopeptidomics, in vitro digestions of synthetic polypeptides as well as ex vivo and in vivo experiments, which could only indirectly describe proteasome-catalyzed peptide splicing of full-length proteins. Here we develop a workflow-and cognate software - to analyze proteasome-generated non-spliced and spliced peptides produced from entire proteins and apply it to in vitro digestions of 15 proteins, including well-known intrinsically disordered proteins such as human tau and α-Synuclein. The results confirm that 20S proteasomes produce a sizeable variety of cis-spliced peptides, whereas trans-spliced peptides are a minority. Both peptide hydrolysis and splicing produce peptides with well-defined characteristics, which hint toward an intricate regulation of both catalytic activities. At protein level, both non-spliced and spliced peptides are not randomly localized within protein sequences, but rather concentrated in hotspots of peptide products, in part driven by protein sequence motifs and proteasomal preferences. At sequence level, the different peptide sequence preference of peptide hydrolysis and peptide splicing suggests a competition between the two catalytic activities of 20S proteasomes during protein degradation.
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Affiliation(s)
- Wai Tuck Soh
- Research Group of Quantitative and Systems Biology, Max-Planck-Institute for Multidisciplinary Sciences, 37077, Göttingen, Germany
| | - Hanna P Roetschke
- Research Group of Quantitative and Systems Biology, Max-Planck-Institute for Multidisciplinary Sciences, 37077, Göttingen, Germany
- Centre for Inflammation Biology and Cancer Immunology & Peter Gorer Department of Immunobiology, King's College London, SE1 1UL, London, UK
- Research Group of Molecular Immunology, Francis Crick Institute, NW1 1AT, London, UK
| | - John A Cormican
- Research Group of Quantitative and Systems Biology, Max-Planck-Institute for Multidisciplinary Sciences, 37077, Göttingen, Germany
| | - Bei Fang Teo
- Centre for Inflammation Biology and Cancer Immunology & Peter Gorer Department of Immunobiology, King's College London, SE1 1UL, London, UK
- Research Group of Molecular Immunology, Francis Crick Institute, NW1 1AT, London, UK
- Immunology Programme, Life Sciences Institute; Immunology Translational Research Program and Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117456, Singapore
| | - Nyet Cheng Chiam
- Research Group of Quantitative and Systems Biology, Max-Planck-Institute for Multidisciplinary Sciences, 37077, Göttingen, Germany
| | - Monika Raabe
- Research Group of Bioanalytical Mass Spectrometry, Max-Planck-Institute for Multidisciplinary Sciences, 37077, Göttingen, Germany
| | - Ralf Pflanz
- Research Group of Bioanalytical Mass Spectrometry, Max-Planck-Institute for Multidisciplinary Sciences, 37077, Göttingen, Germany
| | - Fabian Henneberg
- Department of Structural Dynamics, Max-Planck-Institute for Multidisciplinary Sciences, 37077, Göttingen, Germany
| | - Stefan Becker
- Department of NMR-based Structural Biology, Max-Planck-Institute for Multidisciplinary Sciences, 37077, Göttingen, Germany
| | - Ashwin Chari
- Research Group of Structural Biochemistry and Mechanisms, Max-Planck-Institute for Multidisciplinary Sciences, 37077, Göttingen, Germany
| | - Haiyan Liu
- Immunology Programme, Life Sciences Institute; Immunology Translational Research Program and Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117456, Singapore
| | - Henning Urlaub
- Research Group of Bioanalytical Mass Spectrometry, Max-Planck-Institute for Multidisciplinary Sciences, 37077, Göttingen, Germany
- Institute of Clinical Chemistry, University Medical Center Göttingen, 37075, Göttingen, Germany
| | - Juliane Liepe
- Research Group of Quantitative and Systems Biology, Max-Planck-Institute for Multidisciplinary Sciences, 37077, Göttingen, Germany.
| | - Michele Mishto
- Centre for Inflammation Biology and Cancer Immunology & Peter Gorer Department of Immunobiology, King's College London, SE1 1UL, London, UK.
- Research Group of Molecular Immunology, Francis Crick Institute, NW1 1AT, London, UK.
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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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Gabriel W, Picciani M, The M, Wilhelm M. Deep Learning-Assisted Analysis of Immunopeptidomics Data. Methods Mol Biol 2024; 2758:457-483. [PMID: 38549030 DOI: 10.1007/978-1-0716-3646-6_25] [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
Liquid chromatography-coupled mass spectrometry (LC-MS/MS) is the primary method to obtain direct evidence for the presentation of disease- or patient-specific human leukocyte antigen (HLA). However, compared to the analysis of tryptic peptides in proteomics, the analysis of HLA peptides still poses computational and statistical challenges. Recently, fragment ion intensity-based matching scores assessing the similarity between predicted and observed spectra were shown to substantially increase the number of confidently identified peptides, particularly in use cases where non-tryptic peptides are analyzed. In this chapter, we describe in detail three procedures on how to benefit from state-of-the-art deep learning models to analyze and validate single spectra, single measurements, and multiple measurements in mass spectrometry-based immunopeptidomics. For this, we explain how to use the Universal Spectrum Explorer (USE), online Oktoberfest, and offline Oktoberfest. For intensity-based scoring, Oktoberfest uses fragment ion intensity and retention time predictions from the deep learning framework Prosit, a deep neural network trained on a very large number of synthetic peptides and tandem mass spectra generated within the ProteomeTools project. The examples shown highlight how deep learning-assisted analysis can increase the number of identified HLA peptides, facilitate the discovery of confidently identified neo-epitopes, or provide assistance in the assessment of the presence of cryptic peptides, such as spliced peptides.
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Affiliation(s)
- Wassim Gabriel
- 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
| | - Matthew The
- Chair of Proteomics and Bioanalytics, 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.
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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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Yang KL, Yu F, Teo GC, Li K, Demichev V, Ralser M, Nesvizhskii AI. MSBooster: improving peptide identification rates using deep learning-based features. Nat Commun 2023; 14:4539. [PMID: 37500632 PMCID: PMC10374903 DOI: 10.1038/s41467-023-40129-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 07/06/2023] [Indexed: 07/29/2023] Open
Abstract
Peptide identification in liquid chromatography-tandem mass spectrometry (LC-MS/MS) experiments relies on computational algorithms for matching acquired MS/MS spectra against sequences of candidate peptides using database search tools, such as MSFragger. Here, we present a new tool, MSBooster, for rescoring peptide-to-spectrum matches using additional features incorporating deep learning-based predictions of peptide properties, such as LC retention time, ion mobility, and MS/MS spectra. We demonstrate the utility of MSBooster, in tandem with MSFragger and Percolator, in several different workflows, including nonspecific searches (immunopeptidomics), direct identification of peptides from data independent acquisition data, single-cell proteomics, and data generated on an ion mobility separation-enabled timsTOF MS platform. MSBooster is fast, robust, and fully integrated into the widely used FragPipe computational platform.
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Affiliation(s)
- Kevin L Yang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Fengchao Yu
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA.
| | - Guo Ci Teo
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Kai Li
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Vadim Demichev
- Department of Biochemistry, Charité Universitätsmedizin, Berlin, Germany
- Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Markus Ralser
- Department of Biochemistry, Charité Universitätsmedizin, Berlin, Germany
- Nuffield Department of Medicine, The Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Max Planck Institute for Molecular Genetics, Berlin, Germany
| | - Alexey I Nesvizhskii
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA.
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9
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Peng X, Woodhouse I, Hancock G, Parker R, Marx K, Müller J, Salatino S, Partridge T, Nicastri A, Liao H, Kruppa G, Hellner K, Dorrell L, Ternette N. Novel canonical and non-canonical viral antigens extend current targets for immunotherapy of HPV-driven cervical cancer. iScience 2023; 26:106101. [PMID: 36876126 PMCID: PMC9978627 DOI: 10.1016/j.isci.2023.106101] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Revised: 08/30/2022] [Accepted: 01/26/2023] [Indexed: 02/05/2023] Open
Abstract
Current immunotherapeutic approaches for human papillomavirus (HPV)-driven cervical cancer target the viral oncogenes E6 and E7. We report viral canonical and alternative reading frame (ARF)-derived sequences presented on cervical tumor cells, including antigens encoded by the conserved viral gene E1. We confirm immunogenicity of the identified viral peptides in HPV-positive women, and women with cervical intraepithelial neoplasia. We observe consistent transcription of the E1, E6, and E7 genes in 10 primary cervical tumor resections from the four most common high-risk HPV subtypes (HPV16, 18, 31, and 45), suggesting the suitability of E1 as therapeutic target. We finally confirm HLA presentation of canonical peptides derived from E6 and E7, and ARF-derived viral peptides from a reverse-strand transcript spanning the HPV E1 and E2 genes in primary human cervical tumor tissue. Our results extend currently known viral immunotherapeutic targets in cervical cancer and highlight E1 as an important cervical cancer antigen.
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Affiliation(s)
- Xu Peng
- Centre for Immuno-Oncology, Nuffield Department of Medicine, University of Oxford, OX3 7DQ Oxford, UK
| | - Isaac Woodhouse
- Centre for Immuno-Oncology, Nuffield Department of Medicine, University of Oxford, OX3 7DQ Oxford, UK
| | - Gemma Hancock
- Nuffield Department of Medicine, University of Oxford, OX3 7FZ Oxford, UK
| | - Robert Parker
- Centre for Immuno-Oncology, Nuffield Department of Medicine, University of Oxford, OX3 7DQ Oxford, UK
- The Jenner Institute, University of Oxford, OX3 7DQ Oxford, UK
| | - Kristina Marx
- Bruker Daltonics, Fahrenheitstraße 4, 28359 Bremen, Germany
| | - Julius Müller
- The Jenner Institute, University of Oxford, OX3 7DQ Oxford, UK
| | - Silvia Salatino
- Wellcome Centre Human Genetics, University of Oxford, OX3 7BN Oxford, UK
| | - Thomas Partridge
- Nuffield Department of Medicine, University of Oxford, OX3 7FZ Oxford, UK
| | - Annalisa Nicastri
- Centre for Immuno-Oncology, Nuffield Department of Medicine, University of Oxford, OX3 7DQ Oxford, UK
- The Jenner Institute, University of Oxford, OX3 7DQ Oxford, UK
| | - Hanqing Liao
- Centre for Immuno-Oncology, Nuffield Department of Medicine, University of Oxford, OX3 7DQ Oxford, UK
- The Jenner Institute, University of Oxford, OX3 7DQ Oxford, UK
| | - Gary Kruppa
- Bruker Daltonics, Fahrenheitstraße 4, 28359 Bremen, Germany
| | - Karin Hellner
- Nuffield Department of Women’s and Reproductive Health, University of Oxford, John Radcliffe Hospital, OX3 9DU Oxford, UK
| | - Lucy Dorrell
- Nuffield Department of Medicine, University of Oxford, OX3 7FZ Oxford, UK
- The Jenner Institute, University of Oxford, OX3 7DQ Oxford, UK
- Immunocore Ltd., OX14 4RY Abingdon, UK
| | - Nicola Ternette
- Centre for Immuno-Oncology, Nuffield Department of Medicine, University of Oxford, OX3 7DQ Oxford, UK
- The Jenner Institute, University of Oxford, OX3 7DQ Oxford, UK
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10
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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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11
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Tailor A, Estephan H, Parker R, Woodhouse I, Abdulghani M, Nicastri A, Jones K, Salatino S, Muschel R, Humphrey T, Giaccia A, Ternette N. Ionizing Radiation Drives Key Regulators of Antigen Presentation and a Global Expansion of the Immunopeptidome. Mol Cell Proteomics 2022; 21:100410. [PMID: 36089194 PMCID: PMC9579046 DOI: 10.1016/j.mcpro.2022.100410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 08/04/2022] [Accepted: 08/06/2022] [Indexed: 01/18/2023] Open
Abstract
Little is known about the pathways regulating MHC antigen presentation and the identity of treatment-specific T cell antigens induced by ionizing radiation. For this reason, we investigated the radiation-specific changes in the colorectal tumor cell proteome. We found an increase in DDX58 and ZBP1 protein expression, two nucleic acid sensing molecules likely involved in induction of the dominant interferon response signature observed after genotoxic insult. We further observed treatment-induced changes in key regulators and effector proteins of the antigen processing and presentation machinery. Differential regulation of MHC allele expression was further driving the presentation of a significantly broader MHC-associated peptidome postirradiation, defining a radiation-specific peptide repertoire. Interestingly, treatment-induced peptides originated predominantly from proteins involved in catecholamine synthesis and metabolic pathways. A nuanced relationship between protein expression and antigen presentation was observed where radiation-induced changes in proteins do not correlate with increased presentation of associated peptides. Finally, we detected an increase in the presentation of a tumor-specific neoantigen derived from Mtch1. This study provides new insights into how radiation enhances antigen processing and presentation that could be suitable for the development of combinatorial therapies. Data are available via ProteomeXchange with identifier PXD032003.
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Affiliation(s)
- Arun Tailor
- Oxford Cancer Centre for Immuno-Oncology, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom; The Jenner Institute, University of Oxford, Oxford, United Kingdom.
| | - Hala Estephan
- Oxford Institute of Radiation Oncology, Department of Oncology, University of Oxford, Oxford, United Kingdom
| | - Robert Parker
- Oxford Cancer Centre for Immuno-Oncology, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom; The Jenner Institute, University of Oxford, Oxford, United Kingdom
| | - Isaac Woodhouse
- Oxford Cancer Centre for Immuno-Oncology, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Majd Abdulghani
- Oxford Institute of Radiation Oncology, Department of Oncology, University of Oxford, Oxford, United Kingdom
| | - Annalisa Nicastri
- Oxford Cancer Centre for Immuno-Oncology, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom; The Jenner Institute, University of Oxford, Oxford, United Kingdom
| | - Keaton Jones
- Nuffield Department of Surgical Sciences, John Radcliffe Hospital, Oxford, United Kingdom
| | - Silvia Salatino
- Nuffield Department of Medicine, Wellcome Centre for Human Genetics, Oxford, Unitied Kingdom
| | - Ruth Muschel
- Oxford Institute of Radiation Oncology, Department of Oncology, University of Oxford, Oxford, United Kingdom
| | - Timothy Humphrey
- Oxford Institute of Radiation Oncology, Department of Oncology, University of Oxford, Oxford, United Kingdom
| | - Amato Giaccia
- Oxford Institute of Radiation Oncology, Department of Oncology, University of Oxford, Oxford, United Kingdom
| | - Nicola Ternette
- Oxford Cancer Centre for Immuno-Oncology, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom; The Jenner Institute, University of Oxford, Oxford, United Kingdom.
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12
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Illing PT, Ramarathinam SH, Purcell AW. New insights and approaches for analyses of immunopeptidomes. Curr Opin Immunol 2022; 77:102216. [PMID: 35716458 DOI: 10.1016/j.coi.2022.102216] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 05/10/2022] [Indexed: 11/03/2022]
Abstract
Human leucocyte antigen (HLA) molecules play a key role in health and disease by presenting antigen to T-lymphocytes for immunosurveillance. Immunopeptidomics involves the study of the collection of peptides presented within the antigen-binding groove of HLA molecules. Identifying their nature and diversity is crucial to understanding immunosurveillance especially during infection or for the recognition and potential eradication of tumours. This review discusses recent advances in the isolation, identification, and quantitation of these peptide antigens. New informatics approaches and databases have shed light on the extent of peptide antigens derived from unconventional sources including peptides derived from transcripts associated with frame shifts, long noncoding RNA, incorrectly annotated untranslated regions, post-translational modifications, and proteasomal splicing. Several challenges remain in successful analysis of immunopeptides, yet recent developments point to unexplored biology waiting to be unravelled.
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Affiliation(s)
- Patricia T Illing
- Department of Biochemistry and Molecular Biology and Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Melbourne, Victoria, Australia
| | - Sri H Ramarathinam
- Department of Biochemistry and Molecular Biology and Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Melbourne, Victoria, Australia
| | - Anthony W Purcell
- Department of Biochemistry and Molecular Biology and Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Melbourne, Victoria, Australia.
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13
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Thibault P, Perreault C. Immunopeptidomics: Reading the Immune Signal That Defines Self From Nonself. Mol Cell Proteomics 2022; 21:100234. [PMID: 35567924 PMCID: PMC9252926 DOI: 10.1016/j.mcpro.2022.100234] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Affiliation(s)
- Pierre Thibault
- Institute for Research in Immunology and Cancer, Université de Montréal, Montreal, Quebec, Canada; Department of Chemistry, Université de Montréal, Montreal, Quebec, Canada
| | - Claude Perreault
- Institute for Research in Immunology and Cancer, Université de Montréal, Montreal, Quebec, Canada; Department of Medicine, Université de Montréal, Montreal, Quebec, Canada
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14
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Hensen L, Illing PT, Rowntree LC, Davies J, Miller A, Tong SYC, Habel JR, van de Sandt CE, Flanagan K, Purcell AW, Kedzierska K, Clemens EB. T Cell Epitope Discovery in the Context of Distinct and Unique Indigenous HLA Profiles. Front Immunol 2022; 13:812393. [PMID: 35603215 PMCID: PMC9121770 DOI: 10.3389/fimmu.2022.812393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 03/28/2022] [Indexed: 11/13/2022] Open
Abstract
CD8+ T cells are a pivotal part of the immune response to viruses, playing a key role in disease outcome and providing long-lasting immunity to conserved pathogen epitopes. Understanding CD8+ T cell immunity in humans is complex due to CD8+ T cell restriction by highly polymorphic Human Leukocyte Antigen (HLA) proteins, requiring T cell epitopes to be defined for different HLA allotypes across different ethnicities. Here we evaluate strategies that have been developed to facilitate epitope identification and study immunogenic T cell responses. We describe an immunopeptidomics approach to sequence HLA-bound peptides presented on virus-infected cells by liquid chromatography with tandem mass spectrometry (LC-MS/MS). Using antigen presenting cell lines that stably express the HLA alleles characteristic of Indigenous Australians, this approach has been successfully used to comprehensively identify influenza-specific CD8+ T cell epitopes restricted by HLA allotypes predominant in Indigenous Australians, including HLA-A*24:02 and HLA-A*11:01. This is an essential step in ensuring high vaccine coverage and efficacy in Indigenous populations globally, known to be at high risk from influenza disease and other respiratory infections.
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Affiliation(s)
- Luca Hensen
- Department of Microbiology and Immunology, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Parkville, VIC, Australia
| | - Patricia T. Illing
- Department of Biochemistry and Molecular Biology & Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia
| | - Louise C. Rowntree
- Department of Microbiology and Immunology, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Parkville, VIC, Australia
| | - Jane Davies
- Menzies School of Health Research, Darwin, NT, Australia
| | - Adrian Miller
- Indigenous Engagement, CQUniversity, Townsville, QLD, Australia
| | - Steven Y. C. Tong
- Menzies School of Health Research, Darwin, NT, Australia
- Victorian Infectious Diseases Service, The Royal Melbourne Hospital at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
- Department of Infectious Diseases, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Jennifer R. Habel
- Department of Microbiology and Immunology, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Parkville, VIC, Australia
| | - Carolien E. van de Sandt
- Department of Microbiology and Immunology, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Parkville, VIC, Australia
- Department of Hematopoiesis, Sanquin Research and Landsteiner Laboratory, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
| | - Katie L. Flanagan
- Department of Infectious Diseases and Tasmanian Vaccine Trial Centre, Launceston General Hospital, Launceston, TAS, Australia
- School of Health Sciences and School of Medicine, University of Tasmania, Launceston, TAS, Australia
- Department of Immunology and Pathology, Monash University, Melbourne, VIC, Australia
- School of Health and Biomedical Science, RMIT University, Melbourne, VIC, Australia
| | - Anthony W. Purcell
- Department of Biochemistry and Molecular Biology & Infection and Immunity Program, Biomedicine Discovery Institute, Monash University, Clayton, VIC, Australia
| | - Katherine Kedzierska
- Department of Microbiology and Immunology, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Parkville, VIC, Australia
- *Correspondence: Katherine Kedzierska,
| | - E. Bridie Clemens
- Department of Microbiology and Immunology, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Parkville, VIC, Australia
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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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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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17
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Scull KE, Pandey K, Ramarathinam SH, Purcell AW. Immunopeptidogenomics: Harnessing RNA-Seq to Illuminate the Dark Immunopeptidome. Mol Cell Proteomics 2021; 20:100143. [PMID: 34509645 PMCID: PMC8724885 DOI: 10.1016/j.mcpro.2021.100143] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 08/10/2021] [Accepted: 08/24/2021] [Indexed: 01/08/2023] Open
Abstract
Human leukocyte antigen (HLA) molecules are cell-surface glycoproteins that present peptide antigens on the cell surface for surveillance by T lymphocytes, which contemporaneously seek signs of disease. Mass spectrometric analysis allows us to identify large numbers of these peptides (the immunopeptidome) following affinity purification of solubilized HLA-peptide complexes. However, in recent years, there has been a growing awareness of the "dark side" of the immunopeptidome: unconventional peptide epitopes, including neoepitopes, which elude detection by conventional search methods because their sequences are not present in reference protein databases (DBs). Here, we establish a bioinformatics workflow to aid identification of peptides generated by noncanonical translation of mRNA or by genome variants. The workflow incorporates both standard transcriptomics software and novel computer programs to produce cell line-specific protein DBs based on three-frame translation of the transcriptome. The final protein DB also includes sequences resulting from variants determined by variant calling on the same RNA-Seq data. We then searched our experimental data against both transcriptome-based and standard DBs using PEAKS Studio (Bioinformatics Solutions, Inc). Finally, further novel software helps to compare the various result sets arising for each sample, pinpoint putative genomic origins for unconventional sequences, and highlight potential neoepitopes. We applied the workflow to study the immunopeptidome of the acute myeloid leukemia cell line THP-1, using RNA-Seq and immunopeptidome data. We confidently identified over 14,000 peptides from three replicates of purified HLA peptides derived from THP-1 cells using the conventional UniProt human proteome. Using the transcriptome-based DB generated using our workflow, we recapitulated >85% of these and also identified 1029 unconventional peptides not explained by UniProt, including 16 sequences caused by nonsynonymous variants. Our workflow, which we term "immunopeptidogenomics," can provide DBs, which include pertinent unconventional sequences and allow neoepitope discovery, without becoming too large to search. Immunopeptidogenomics is a step toward unbiased search approaches that are needed to illuminate the dark side of the immunopeptidome.
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Affiliation(s)
- Katherine E Scull
- Department of Biochemistry and Molecular Biology and Infection and Immunity Program, Monash Biomedicine Discovery Institute, Monash University, Clayton, Victoria, Australia
| | - Kirti Pandey
- Department of Biochemistry and Molecular Biology and Infection and Immunity Program, Monash Biomedicine Discovery Institute, Monash University, Clayton, Victoria, Australia
| | - Sri H Ramarathinam
- Department of Biochemistry and Molecular Biology and Infection and Immunity Program, Monash Biomedicine Discovery Institute, Monash University, Clayton, Victoria, Australia.
| | - Anthony W Purcell
- Department of Biochemistry and Molecular Biology and Infection and Immunity Program, Monash Biomedicine Discovery Institute, Monash University, Clayton, Victoria, Australia.
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