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Weisbrod L, Capriotti L, Hofmann M, Spieler V, Dersch H, Voedisch B, Schmidt P, Knake S. FASTMAP-a flexible and scalable immunopeptidomics pipeline for HLA- and antigen-specific T-cell epitope mapping based on artificial antigen-presenting cells. Front Immunol 2024; 15:1386160. [PMID: 38779658 PMCID: PMC11109385 DOI: 10.3389/fimmu.2024.1386160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Accepted: 04/17/2024] [Indexed: 05/25/2024] Open
Abstract
The study of peptide repertoires presented by major histocompatibility complex (MHC) molecules and the identification of potential T-cell epitopes contribute to a multitude of immunopeptidome-based treatment approaches. Epitope mapping is essential for the development of promising epitope-based approaches in vaccination as well as for innovative therapeutics for autoimmune diseases, infectious diseases, and cancer. It also plays a critical role in the immunogenicity assessment of protein therapeutics with regard to safety and efficacy concerns. The main challenge emerges from the highly polymorphic nature of the human leukocyte antigen (HLA) molecules leading to the requirement of a peptide mapping strategy for a single HLA allele. As many autoimmune diseases are linked to at least one specific antigen, we established FASTMAP, an innovative strategy to transiently co-transfect a single HLA allele combined with a disease-specific antigen into a human cell line. This approach allows the specific identification of HLA-bound peptides using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Using FASTMAP, we found a comparable spectrum of endogenous peptides presented by the most frequently expressed HLA alleles in the world's population compared to what has been described in literature. To ensure a reliable peptide mapping workflow, we combined the HLA alleles with well-known human model antigens like coagulation factor VIII, acetylcholine receptor subunit alpha, protein structures of the SARS-CoV-2 virus, and myelin basic protein. Using these model antigens, we have been able to identify a broad range of peptides that are in line with already published and in silico predicted T-cell epitopes of the specific HLA/model antigen combination. The transient co-expression of a single affinity-tagged MHC molecule combined with a disease-specific antigen in a human cell line in our FASTMAP pipeline provides the opportunity to identify potential T-cell epitopes/endogenously processed MHC-bound peptides in a very cost-effective, fast, and customizable system with high-throughput potential.
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Affiliation(s)
- Luisa Weisbrod
- Recombinant Protein Discovery, CSL Innovation GmbH, Marburg, Germany
| | - Luigi Capriotti
- Analytical Biochemistry, Research and Development, CSL Behring AG, Bern, Switzerland
| | - Marco Hofmann
- Recombinant Protein Discovery, CSL Innovation GmbH, Marburg, Germany
| | - Valerie Spieler
- Recombinant Protein Discovery, CSL Innovation GmbH, Marburg, Germany
| | - Herbert Dersch
- Recombinant Protein Discovery, CSL Innovation GmbH, Marburg, Germany
| | - Bernd Voedisch
- Recombinant Protein Discovery, CSL Innovation GmbH, Marburg, Germany
| | - Peter Schmidt
- Protein Biochemistry, Bio21 Institute, CSL Limited, Parkville, VIC, Australia
| | - Susanne Knake
- Department of Neurology, Epilepsy Center Hessen, Philipps University Marburg, Marburg, Germany
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Borch A, Carri I, Reynisson B, Alvarez HMG, Munk KK, Montemurro A, Kristensen NP, Tvingsholm SA, Holm JS, Heeke C, Moss KH, Hansen UK, Schaap-Johansen AL, Bagger FO, de Lima VAB, Rohrberg KS, Funt SA, Donia M, Svane IM, Lassen U, Barra C, Nielsen M, Hadrup SR. IMPROVE: a feature model to predict neoepitope immunogenicity through broad-scale validation of T-cell recognition. Front Immunol 2024; 15:1360281. [PMID: 38633261 PMCID: PMC11021644 DOI: 10.3389/fimmu.2024.1360281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 03/07/2024] [Indexed: 04/19/2024] Open
Abstract
Background Mutation-derived neoantigens are critical targets for tumor rejection in cancer immunotherapy, and better tools for neoepitope identification and prediction are needed to improve neoepitope targeting strategies. Computational tools have enabled the identification of patient-specific neoantigen candidates from sequencing data, but limited data availability has hindered their capacity to predict which of the many neoepitopes will most likely give rise to T cell recognition. Method To address this, we make use of experimentally validated T cell recognition towards 17,500 neoepitope candidates, with 467 being T cell recognized, across 70 cancer patients undergoing immunotherapy. Results We evaluated 27 neoepitope characteristics, and created a random forest model, IMPROVE, to predict neoepitope immunogenicity. The presence of hydrophobic and aromatic residues in the peptide binding core were the most important features for predicting neoepitope immunogenicity. Conclusion Overall, IMPROVE was found to significantly advance the identification of neoepitopes compared to other current methods.
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Affiliation(s)
- Annie Borch
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Ibel Carri
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina
| | - Birkir Reynisson
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Heli M. Garcia Alvarez
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina
| | - Kamilla K. Munk
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | | | | | - Siri A. Tvingsholm
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Jeppe Sejerø Holm
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Christina Heeke
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Keith Henry Moss
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Ulla Kring Hansen
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | | | | | | | | | - Samuel A. Funt
- Department of Medicine, Weill Cornell Medical College, New York, NY, United States
| | - Marco Donia
- National Center for Cancer Immune Therapy, Copenhagen University Hospital, Herlev, Denmark
| | - Inge Marie Svane
- National Center for Cancer Immune Therapy, Copenhagen University Hospital, Herlev, Denmark
| | - Ulrik Lassen
- Department of Oncology, Phase 1 Unit, Rigshospitalet, Copenhagen, Denmark
| | - Carolina Barra
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
| | - Morten Nielsen
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
- Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martín, Buenos Aires, Argentina
| | - Sine Reker Hadrup
- Department of Health Technology, Technical University of Denmark, Lyngby, Denmark
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Wan YTR, Koşaloğlu‐Yalçın Z, Peters B, Nielsen M. A large-scale study of peptide features defining immunogenicity of cancer neo-epitopes. NAR Cancer 2024; 6:zcae002. [PMID: 38288446 PMCID: PMC10823584 DOI: 10.1093/narcan/zcae002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Revised: 01/03/2024] [Accepted: 01/12/2024] [Indexed: 01/31/2024] Open
Abstract
Accurate prediction of immunogenicity for neo-epitopes arising from a cancer associated mutation is a crucial step in many bioinformatics pipelines that predict outcome of checkpoint blockade treatments or that aim to design personalised cancer immunotherapies and vaccines. In this study, we performed a comprehensive analysis of peptide features relevant for prediction of immunogenicity using the Cancer Epitope Database and Analysis Resource (CEDAR), a curated database of cancer epitopes with experimentally validated immunogenicity annotations from peer-reviewed publications. The developed model, ICERFIRE (ICore-based Ensemble Random Forest for neo-epitope Immunogenicity pREdiction), extracts the predicted ICORE from the full neo-epitope as input, i.e. the nested peptide with the highest predicted major histocompatibility complex (MHC) binding potential combined with its predicted likelihood of antigen presentation (%Rank). Key additional features integrated into the model include assessment of the BLOSUM mutation score of the neo-epitope, and antigen expression levels of the wild-type counterpart which is often reflecting a neo-epitope's abundance. We demonstrate improved and robust performance of ICERFIRE over existing immunogenicity and epitope prediction models, both in cross-validation and on external validation datasets.
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Affiliation(s)
- Yat-tsai Richie Wan
- Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, DK 28002, Denmark
| | - Zeynep Koşaloğlu‐Yalçın
- Center for Infectious Disease and Vaccine Research, La Jolla Institute of Immunology, La Jolla, CA 92037, USA
| | - Bjoern Peters
- Center for Infectious Disease and Vaccine Research, La Jolla Institute of Immunology, La Jolla, CA 92037, USA
| | - Morten Nielsen
- Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, DK 28002, Denmark
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Contemplating immunopeptidomes to better predict them. Semin Immunol 2023; 66:101708. [PMID: 36621290 DOI: 10.1016/j.smim.2022.101708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 12/16/2022] [Accepted: 12/20/2022] [Indexed: 01/09/2023]
Abstract
The identification of T-cell epitopes is key for a complete molecular understanding of immune recognition mechanisms in infectious diseases, autoimmunity and cancer. T-cell epitopes further provide targets for personalized vaccines and T-cell therapy, with several therapeutic applications in cancer immunotherapy and elsewhere. T-cell epitopes consist of short peptides displayed on Major Histocompatibility Complex (MHC) molecules. The recent advances in mass spectrometry (MS) based technologies to profile the ensemble of peptides displayed on MHC molecules - the so-called immunopeptidome - had a major impact on our understanding of antigen presentation and MHC ligands. On the one hand, these techniques enabled researchers to directly identify hundreds of thousands of peptides presented on MHC molecules, including some that elicited T-cell recognition. On the other hand, the data collected in these experiments revealed fundamental properties of antigen presentation pathways and significantly improved our ability to predict naturally presented MHC ligands and T-cell epitopes across the wide spectrum of MHC alleles found in human and other organisms. Here we review recent computational developments to analyze experimentally determined immunopeptidomes and harness these data to improve our understanding of antigen presentation and MHC binding specificities, as well as our ability to predict MHC ligands. We further discuss the strengths and limitations of the latest approaches to move beyond predictions of antigen presentation and tackle the challenges of predicting TCR recognition and immunogenicity.
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Frentzen A, Greenbaum JA, Kim H, Peters B, Koşaloğlu-Yalçın Z. Estimating tissue-specific peptide abundance from public RNA-Seq data. Front Genet 2023; 14:1082168. [PMID: 36713080 PMCID: PMC9878344 DOI: 10.3389/fgene.2023.1082168] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 01/04/2023] [Indexed: 01/15/2023] Open
Abstract
Several novel MHC class I epitope prediction tools additionally incorporate the abundance levels of the peptides' source antigens and have shown improved performance for predicting immunogenicity. Such tools require the user to input the MHC alleles and peptide sequences of interest, as well as the abundance levels of the peptides' source proteins. However, such expression data is often not directly available to users, and retrieving the expression level of a peptide's source antigen from public databases is not trivial. We have developed the Peptide eXpression annotator (pepX), which takes a peptide as input, identifies from which proteins the peptide can be derived, and returns an estimate of the expression level of those source proteins from selected public databases. We have also investigated how the abundance level of a peptide can be best estimated in cases when it can originate from multiple transcripts and proteins and found that summing up transcript-level expression values performs best in distinguishing ligands from decoy peptides.
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Affiliation(s)
- Angela Frentzen
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, San Diego, CA, United States
| | - Jason A. Greenbaum
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, San Diego, CA, United States
| | - Haeuk Kim
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, San Diego, CA, United States
| | - Bjoern Peters
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, San Diego, CA, United States,Department of Medicine, University of California, San Diego, San Diego, CA, United States
| | - Zeynep Koşaloğlu-Yalçın
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, San Diego, CA, United States,*Correspondence: Zeynep Koşaloğlu-Yalçın,
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