Artykuły w czasopismach: „Immunopeptidomics”

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Ternette, Nicola, i Anthony W. Purcell. "Immunopeptidomics Special Issue". PROTEOMICS 18, nr 12 (czerwiec 2018): 1800145. http://dx.doi.org/10.1002/pmic.201800145.

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Shapiro, Ilja E., Marco Tognetti, Tikira Temu, Oliver M. Bernhardt, Daniel Redfern, Yuehan Feng, Roland Bruderer i Lukas Reiter. "Abstract 5376: Quantitative profiling of HLA class I and class II antigens and neoantigens in tissue biopsy and PBMC samples using an optimized mass spectrometry-based workflow". Cancer Research 84, nr 6_Supplement (22.03.2024): 5376. http://dx.doi.org/10.1158/1538-7445.am2024-5376.

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Abstract Major histocompatibility complex (MHC) molecules play a central role in orchestrating immune responses by presenting antigenic peptides derived from both self and foreign proteins. In the context of cancer, understanding the repertoire of tumor-associated antigens (TAAs) presented by MHC molecules (or HLA molecules in human) is crucial for deciphering how the immune system recognizes and responds to malignant cells. The identification of neoantigens, unique to individual tumors due to somatic mutations, has become a focal point in immunopeptidomic studies. One significant hurdle in systematic immunopeptidomics analysis is the high input material requirement. Here, we present a semi-automated workflow to robustly identify and quantify immunopeptides from reduced amounts of clinical tissue biopsy and peripheral blood mononuclear cell samples. At the core of immunopeptidomics is the enrichment of HLA-associated peptides, followed by identification using mass spectrometry and bioinformatics tools. We optimized the native lysis and a sequential immunoprecipitation workflow for both class I and class II immunopeptides while ensuring scalability and reproducibility. Leveraging the magnetic properties of the beads, 1,000 samples can be processed within a week by a single operator. For both tissue and PBMC samples, we performed a systematic ramping experiment starting with as little as 2.5mg tissue or 5 million PBMCs. In all experiments, The established sample preparation offers high reproducibility and identifications of good quality: 1) Class-I immunopeptides: >60% of the peptides identified are 9-mers, >80% predicted strong binders, and the expected amino acids are enriched at the anchor positions; 2) Class-II immunopeptides, >50% of the peptides identified are 14-to-16-mers, and >50% are predicted strong binders. Furthermore, the pipeline is highly sensitive as we could still identify over 2,800 class-I immunopeptides when processing 2.5 mg fresh frozen tissue and 2,000 - 3,000 class-I immunopeptides when starting from 5 million PBMCs. Furthermore, the developed immunopeptidomics workflow was deployed to profile a cohort of 12 cancerous and matched healthy lung tissue samples. Their Class-I immunopeptidomes clearly displayed a pattern where matched tissues from each subject cluster together, further underlining the fact that the intricate immune-tumor interface is highly personalized. Overall, we established a robust pipeline for for class-I and II immunopeptidome profiling from clinically relevant sample types. Taking advantage of the nature of mass spectrometry-based methods, customized targeted assays can be developed without the need for affinity reagent, allowing specific and absolute quantification of any immunopeptide of interest. Citation Format: Ilja E. Shapiro, Marco Tognetti, Tikira Temu, Oliver M. Bernhardt, Daniel Redfern, Yuehan Feng, Roland Bruderer, Lukas Reiter. Quantitative profiling of HLA class I and class II antigens and neoantigens in tissue biopsy and PBMC samples using an optimized mass spectrometry-based workflow [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5376.

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Mayer, Rupert L., i Karl Mechtler. "Immunopeptidomics in the Era of Single-Cell Proteomics". Biology 12, nr 12 (12.12.2023): 1514. http://dx.doi.org/10.3390/biology12121514.

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Immunopeptidomics, as the analysis of antigen peptides being presented to the immune system via major histocompatibility complexes (MHC), is being seen as an imperative tool for identifying epitopes for vaccine development to treat cancer and viral and bacterial infections as well as parasites. The field has made tremendous strides over the last 25 years but currently still faces challenges in sensitivity and throughput for widespread applications in personalized medicine and large vaccine development studies. Cutting-edge technological advancements in sample preparation, liquid chromatography as well as mass spectrometry, and data analysis, however, are currently transforming the field. This perspective showcases how the advent of single-cell proteomics has accelerated this transformation of immunopeptidomics in recent years and will pave the way for even more sensitive and higher-throughput immunopeptidomics analyses.

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Connelley, Timothy, Annalisa Nicastri, Tara Sheldrake, Christina Vrettou, Andressa Fisch, Birkir Reynisson, Soren Buus i in. "Immunopeptidomic Analysis of BoLA-I and BoLA-DR Presented Peptides from Theileria parva Infected Cells". Vaccines 10, nr 11 (11.11.2022): 1907. http://dx.doi.org/10.3390/vaccines10111907.

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The apicomplexan parasite Theileria parva is the causative agent of East Coast fever, usually a fatal disease for cattle, which is prevalent in large areas of eastern, central, and southern Africa. Protective immunity against T. parva is mediated by CD8+ T cells, with CD4+ T-cells thought to be important in facilitating the full maturation and development of the CD8+ T-cell response. T. parva has a large proteome, with >4000 protein-coding genes, making T-cell antigen identification using conventional screening approaches laborious and expensive. To date, only a limited number of T-cell antigens have been described. Novel approaches for identifying candidate antigens for T. parva are required to replace and/or complement those currently employed. In this study, we report on the use of immunopeptidomics to study the repertoire of T. parva peptides presented by both BoLA-I and BoLA-DR molecules on infected cells. The study reports on peptides identified from the analysis of 13 BoLA-I and 6 BoLA-DR datasets covering a range of different BoLA genotypes. This represents the most comprehensive immunopeptidomic dataset available for any eukaryotic pathogen to date. Examination of the immunopeptidome data suggested the presence of a large number of coprecipitated and non-MHC-binding peptides. As part of the work, a pipeline to curate the datasets to remove these peptides was developed and used to generate a final list of 74 BoLA-I and 15 BoLA-DR-presented peptides. Together, the data demonstrated the utility of immunopeptidomics as a method to identify novel T-cell antigens for T. parva and the importance of careful curation and the application of high-quality immunoinformatics to parse the data generated.

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Chong, Chloe, George Coukos i Michal Bassani-Sternberg. "Identification of tumor antigens with immunopeptidomics". Nature Biotechnology 40, nr 2 (11.10.2021): 175–88. http://dx.doi.org/10.1038/s41587-021-01038-8.

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Mellacheruvu, Dattatreya, Rachel Pyke, Charles Abbott, Nick Phillips, Sejal Desai, Rena McClory, John West, Richard Chen i Sean Boyle. "57 Precision neoantigen discovery using novel algorithms and expanded HLA-ligandome datasets". Journal for ImmunoTherapy of Cancer 8, Suppl 3 (listopad 2020): A62. http://dx.doi.org/10.1136/jitc-2020-sitc2020.0057.

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BackgroundAccurately identified neoantigens can be effective therapeutic agents in both adjuvant and neoadjuvant settings. A key challenge for neoantigen discovery has been the availability of accurate prediction models for MHC peptide presentation. We have shown previously that our proprietary model based on (i) large-scale, in-house mono-allelic data, (ii) custom features that model antigen processing, and (iii) advanced machine learning algorithms has strong performance. We have extended upon our work by systematically integrating large quantities of high-quality, publicly available data, implementing new modelling algorithms, and rigorously testing our models. These extensions lead to substantial improvements in performance and generalizability. Our algorithm, named Systematic HLA Epitope Ranking Pan Algorithm (SHERPA™), is integrated into the ImmunoID NeXT Platform®, our immuno-genomics and transcriptomics platform specifically designed to enable the development of immunotherapies.MethodsIn-house immunopeptidomic data was generated using stably transfected HLA-null K562 cells lines that express a single HLA allele of interest, followed by immunoprecipitation using W6/32 antibody and LC-MS/MS. Public immunopeptidomics data was downloaded from repositories such as MassIVE and processed uniformly using in-house pipelines to generate peptide lists filtered at 1% false discovery rate. Other metrics (features) were either extracted from source data or generated internally by re-processing samples utilizing the ImmunoID NeXT Platform.ResultsWe have generated large-scale and high-quality immunopeptidomics data by using approximately 60 mono-allelic cell lines that unambiguously assign peptides to their presenting alleles to create our primary models. Briefly, our primary ‘binding’ algorithm models MHC-peptide binding using peptide and binding pockets while our primary ‘presentation’ model uses additional features to model antigen processing and presentation. Both primary models have significantly higher precision across all recall values in multiple test data sets, including mono-allelic cell lines and multi-allelic tissue samples. To further improve the performance of our model, we expanded the diversity of our training set using high-quality, publicly available mono-allelic immunopeptidomics data. Furthermore, multi-allelic data was integrated by resolving peptide-to-allele mappings using our primary models. We then trained a new model using the expanded training data and a new composite machine learning architecture. The resulting secondary model further improves performance and generalizability across several tissue samples.ConclusionsImproving technologies for neoantigen discovery is critical for many therapeutic applications, including personalized neoantigen vaccines, and neoantigen-based biomarkers for immunotherapies. Our new and improved algorithm (SHERPA) has significantly higher performance compared to a state-of-the-art public algorithm and furthers this objective.

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Foster, Leonard, Queenie Chan, Charlie Kuan i Hong Bing Yu. "A framework for unbiased, robust and system-wide characterization of MHC-bound peptides and epitopes (APP5P.111)". Journal of Immunology 194, nr 1_Supplement (1.05.2015): 183.13. http://dx.doi.org/10.4049/jimmunol.194.supp.183.13.

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Abstract Vaccines are the most inexpensive medicine in the long run, yet developing them remains a slow process. A major challenge is to identify proteins efficiently processed by the host and presented on major histocompatibility complexes (MHCs). Furthermore, the extreme polymorphism of the MHCs means that candidate antigens must be tested across many genotypes. Immunopeptidomics, the study of all peptides presented by the host’s immune system, holds promise but is limited by informatics:standard approaches result in high error rates for these samples. We describe here an experimentally validated informatic treatment of mass spectrometric data from peptides eluted from the surface of antigen-presenting cells that can identify epitopes bound by specific MHCs, predict their core binding regions, and reveal consensus binding motifs of MHC alleles. Our approach avoids the biases inherent with MHC immunopurification or prior consensus motifs and enables rational vaccine design by allowing rapid and sensitive screening of individual immunopeptidomes.

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Kochin, Vitaly, Takayuki Kanaseki, Sho Miyamoto, Daichi Morooka, Keigo Moniwa, Yutaro Ikeuchi, Akari Takaya, Yoshihiko Hirohashi, Toshihiko Torigoe i Noriyuki Sato. "Human cancer immunopeptidomics for efficient CTL immunotherapy". Annals of Oncology 26 (listopad 2015): vii30. http://dx.doi.org/10.1093/annonc/mdv424.02.

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Istrail, S., L. Florea, B. V. Halldorsson, O. Kohlbacher, R. S. Schwartz, V. B. Yap, J. W. Yewdell i S. L. Hoffman. "Comparative immunopeptidomics of humans and their pathogens". Proceedings of the National Academy of Sciences 101, nr 36 (23.08.2004): 13268–72. http://dx.doi.org/10.1073/pnas.0404740101.

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Garcia‐Moure, Marc, Andrew G. Gillard, Marta M. Alonso, Juan Fueyo i Candelaria Gomez‐Manzano. "Oncolytic adenoviruses and immunopeptidomics: a convenient marriage". Molecular Oncology 18, nr 4 (kwiecień 2024): 781–84. http://dx.doi.org/10.1002/1878-0261.13648.

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Oncolytic viruses (OVs) are biological therapeutic agents that selectively destroy cancer cells while sparing normal healthy cells. Besides direct oncolysis, OV infection induces a proinflammatory shift in the tumor microenvironment and the release of tumor‐associated antigens (TAAs) that might induce an anti‐tumor immunity. Due to their immunostimulatory effect, OVs have been explored for cancer vaccination against specific TAAs. However, this approach usually requires genetic modification of the virus and the production of a new viral vector for each target, which is difficult to implement for low prevalent antigens. In a recent study, Chiaro et al. presented an elegant proof of concept on how to implement the PeptiCRAd vaccination platform to overcome this limitation for the treatment of mesothelioma. Authors showed the feasibility of identifying immunogenic TAAs in human mesothelioma and using them to coat oncolytic adenovirus particles. The result was a customized virus‐based cancer vaccine that circumvents time and resource‐consuming steps incurred from genetically engineering viruses. Although some questions remain to be addressed, this interesting approach suggests novel strategies for personalized cancer medicine using oncolytic virotherapy.

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Tegeler, Christian M., Jonas S. Heitmann, Helmut R. Salih, Juliane S. Walz i Annika Nelde. "Abstract 1972: Clinical implications of HLA expression and immunopeptidome-presented tumor antigens in ovarian carcinoma". Cancer Research 82, nr 12_Supplement (15.06.2022): 1972. http://dx.doi.org/10.1158/1538-7445.am2022-1972.

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Abstract Ovarian carcinoma (OvCa) is the seventh most common malignancy in women and the eighth leading cause of cancer-related deaths worldwide. In a previous study, we characterized the antigenic landscape of ovarian carcinoma by mass spectrometry-based immunopeptidomics and identified novel OvCa-associated tumor antigens, including Mucin-16 (MUC-16) and Mesothelin (MSLN) with the aim to develop novel T cell-based immunotherapies (Schuster et al. PNAS 2017). Here, we analyzed the immunopeptidomics data of this OvCa cohort in relation to clinical patient characteristics and disease outcome. Analysis included 43 OvCa patients with respective immunopeptidomics and RNA sequencing data, comprising immunopeptidome diversity, tumor antigen presentation and expression (MUC16, MSLN) as well as HLA mRNA expression.Analyzing HLA class I-restricted tumor antigen presentation in relation to clinical data, we could show that nodal-positive patients presented more frequently HLA-restricted peptides derived from the tumor antigen MUC16 (p = 0.0087) and showed significantly increased numbers of unique MUC16-derived HLA-presented peptides within the total immunopeptidome (p = 0.042) compared to nodal-negative patients. No significant difference in HLA class I immunopeptidome diversity, overall tumor antigen presentation, and expression was observed for histological subtypes, grading, or the prevalence of distant metastases. For HLA class II-restricted tumor antigen presentation and HLA expression in relation to clinical data, we observed a more diverse HLA class II immunopeptidome in terms of different HLA class II-presented peptides (p = 0.011) for patients with high tumor grading (G3) compared to low/intermediate (G1/G2) grading. In line, the tumors of these patients also presented an increased number of different MSLN-derived HLA class II-restricted peptides (p = 0.021). No significant difference in HLA class II immunopeptidome diversity, tumor antigen presentation and expression was seen for the prevalence of distant metastasis, histological subtypes, or nodal positivity.Of note, patients presenting MSLN-derived peptides in their immunopeptidome showed a significantly prolonged recurrence-free survival (RFS, p = 0.011). In addition, patients exhibiting a high expression of HLA-DR showed a significantly increased RFS (p = 0.018 for HLA-DRA, p = 0.0031 for HLA-DRB).In conclusion, this work provides first insights on the relation of immunopeptidomic characteristics, comprising HLA expression and tumor antigen presentation, with clinical characteristics and disease outcome of OvCa patients. The observed correlation of HLA-DR expression and HLA class II tumor antigen presentation with prolonged RFS indicates a central role of CD4+ T cell responses for anti-tumor immune surveillance in ovarian cancer. Citation Format: Christian M. Tegeler, Jonas S. Heitmann, Helmut R. Salih, Juliane S. Walz, Annika Nelde. Clinical implications of HLA expression and immunopeptidome-presented tumor antigens in ovarian carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1972.

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Zhang, Bing, i Michal Bassani-Sternberg. "Current perspectives on mass spectrometry-based immunopeptidomics: the computational angle to tumor antigen discovery". Journal for ImmunoTherapy of Cancer 11, nr 10 (październik 2023): e007073. http://dx.doi.org/10.1136/jitc-2023-007073.

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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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Danner, Rebecca, Michael Pereckas, Joseph Rouse, Amanda Wahhab i Robert B. Lochhead. "Expanded presentation of Lyme autoantigens and identification of a novel immunogenic CD4+ T cell epitope from Borrelia burgdorferiMCP4 in murine Lyme arthritis". Journal of Immunology 210, nr 1_Supplement (1.05.2023): 221.17. http://dx.doi.org/10.4049/jimmunol.210.supp.221.17.

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Abstract Lyme arthritis (LA), caused by Borrelia burgdorferi (Bb), is often accompanied by autoimmune T and B cell responses, but the mechanisms of infection-induced autoimmunity are unclear. We used an immunopeptidomics approach to identify potential Lyme autoantigens and immunogenic Bb antigens, which were validated by histology and testing of T cell reactivity. C57BL/6 (B6) mice, which develop mild inflammatory LA, and B6 Il10−/− (IL10 KO) mice, which develop severe, persistent LA, were infected with Bb for 4 or 16 weeks, and MHCII-bound peptides from inguinal and popliteal lymph nodes were identified by LC-MS/MS. Joint inflammation, fibrosis, and vascular remodeling were analyzed by H&E, Masson’s trichrome, and anti-CD31 immunohistochemistry, respectively. Six Bb peptides were identified by LC-MS/MS, of which one epitope from methyl-accepting chemotaxis protein 4 (MCP4) was an immunogenic CD4+ T cell antigen. Over 10,000 self peptides, particularly from proteins involved in cholesterol metabolism, tissue damage, and vascular inflammation were identified in infected mice. Presentation of peptides from previously identified human Lyme autoantigens apolipoprotein B-100, fibronectin, and type V collagen were expanded in infected mice, suggestive of epitope spreading. Consistent with immunopeptidomics data, joints showed increased inflammatory infiltrate, fibrosis and neovascularization in infected mice, compared with joints from uninfected mice. In conclusion, this immunopeptidomics approach revealed key insights into potential mechanisms of infection-induced autoimmunity in LA and indentified a novel immunogenic CD4+ T cell antigen from Bb MCP4. Supported by grants from NIAID (R21AI148982)

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Bichmann, Leon, Annika Nelde, Michael Ghosh, Lukas Heumos, Christopher Mohr, Alexander Peltzer, Leon Kuchenbecker i in. "MHCquant: Automated and Reproducible Data Analysis for Immunopeptidomics". Journal of Proteome Research 18, nr 11 (7.10.2019): 3876–84. http://dx.doi.org/10.1021/acs.jproteome.9b00313.

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Chavda, Vivek P., i Elrashdy M. Redwan. "SARS-CoV-2: Immunopeptidomics and Other Immunological Studies". Vaccines 10, nr 11 (21.11.2022): 1975. http://dx.doi.org/10.3390/vaccines10111975.

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Courcelles, Mathieu, Chantal Durette, Tariq Daouda, Jean-Philippe Laverdure, Krystel Vincent, Sébastien Lemieux, Claude Perreault i Pierre Thibault. "MAPDP: A Cloud-Based Computational Platform for Immunopeptidomics Analyses". Journal of Proteome Research 19, nr 4 (28.02.2020): 1873–81. http://dx.doi.org/10.1021/acs.jproteome.9b00859.

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Bouzid, Rachid, Monique T. A. de Beijer, Robbie J. Luijten, Karel Bezstarosti, Amy L. Kessler, Marco J. Bruno, Maikel P. Peppelenbosch, Jeroen A. A. Demmers i Sonja I. Buschow. "Empirical Evaluation of the Use of Computational HLA Binding as an Early Filter to the Mass Spectrometry-Based Epitope Discovery Workflow". Cancers 13, nr 10 (12.05.2021): 2307. http://dx.doi.org/10.3390/cancers13102307.

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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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Thibault, Pierre, i Claude Perreault. "Immunopeptidomics: Reading the Immune Signal That Defines Self From Nonself". Molecular & Cellular Proteomics 21, nr 6 (czerwiec 2022): 100234. http://dx.doi.org/10.1016/j.mcpro.2022.100234.

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Purcell, Anthony W., Sri H. Ramarathinam i Nicola Ternette. "Mass spectrometry–based identification of MHC-bound peptides for immunopeptidomics". Nature Protocols 14, nr 6 (15.05.2019): 1687–707. http://dx.doi.org/10.1038/s41596-019-0133-y.

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Danner, Rebecca, Michael Pereckas, Joseph Rouse, Amanda Wahhab i Robert Lochhead. "Immunopeptidomics analysis of Lyme arthritis: insights into infection and autoimmunity". Journal of Immunology 206, nr 1_Supplement (1.05.2021): 93.10. http://dx.doi.org/10.4049/jimmunol.206.supp.93.10.

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Abstract Background: Lyme arthritis (LA) is caused by infection with the Lyme disease spirochete Borrelia burgdorferi (Bb) and in humans is often accompanied by autoimmune T and B cell responses. In this study, we used an immunopeptidomics approach to gain further insight into mechanisms of infection-induced autoimmunity. Methods: C57BL/6 (B6) mice, which develop mild, self-limiting LA, and B6 Il10−/− mice, which develop chronic, autoimmune-like LA, were inoculated with 2×104 Bb. Inguinal and popliteal lymph nodes (LN) were harvested from infected mice at 4 weeks and 16 weeks post-inoculation. MHC class II molecules were isolated by immunoaffinity capture and MHC-bound peptides were identified by LC/MS/MS. Results: Nearly 10,000 MHCII-bound peptides were identified. At 4 weeks post-inoculation, representing the peak of LA, proteins involved in leukocyte trans-endothelial migration, tissue repair, and immune activation, including a known Lyme autoantigen ApoB-100, were over-represented in both B6 and Il10−/− mice. Peptides from these proteins returned to near baseline levels in LN from B6 mice at 16 weeks post-inoculation, when arthritis resolves, but were further enriched in Il10−/− mice at 16 weeks, during the chronic, autoimmune-like phase. Surprisingly, only 27 peptides derived from Bb proteins were identified, all but one of which were from proteins found in the inner membrane, periplasm, or cytosol. Conclusions: This study identified immune-relevant proteins presented by APCs in draining LN that are associated with LA development, which included ApoB-100, a known Lyme autoantigen in humans. Further studies are underway to assess T cell responses to identified Bb and self-peptides, including ApoB-100, during Bb infection.

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Shapiro, Ilja E., i Michal Bassani-Sternberg. "The impact of immunopeptidomics: From basic research to clinical implementation". Seminars in Immunology 66 (marzec 2023): 101727. http://dx.doi.org/10.1016/j.smim.2023.101727.

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Faridi, Pouya, Anthony W. Purcell i Nathan Paul Croft. "In Immunopeptidomics We Need a Sniper Instead of a Shotgun". PROTEOMICS 18, nr 12 (7.03.2018): 1700464. http://dx.doi.org/10.1002/pmic.201700464.

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Li, Kai, Antrix Jain, Anna Malovannaya, Bo Wen i Bing Zhang. "DeepRescore: Leveraging Deep Learning to Improve Peptide Identification in Immunopeptidomics". PROTEOMICS 20, nr 21-22 (27.09.2020): 1900334. http://dx.doi.org/10.1002/pmic.201900334.

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Vaughan, Kerrie, Etienne Caron, Bjoern Peters i Alessandro Sette. "The future of the immunopeptidome in health and disease: a comprehensive analysis of naturally processed ligand data". Journal of Immunology 196, nr 1_Supplement (1.05.2016): 46.4. http://dx.doi.org/10.4049/jimmunol.196.supp.46.4.

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Abstract The promise of big data (‘OMICS’) in fundamental and translational science is clear. In this respect, high-throughput analysis of HLA class I and II associated immunopeptidomes by mass spectrometry is an important tool to establish T cell response repertoires in the context of disease. The last decade has seen tremendous growth in the breadth and number of these studies. The time is therefore ideal for initial assessment of these data as a whole. For this, we made use of all naturally eluted peptide data captured to date within the IEDB, representing more than 100,000 class I and II peptides. The goal of this analysis was to compare and contrast the overall nature of naturally eluted peptides (NP) from several standpoints, including molecular and biological function, sub-cellular location and protein class. Further, we investigated the extent to which the NP data overlap with existing T cell epitope data, MHC binding data, as well as output from class I and II prediction algorithms. The comprehensive collection of NP data will allow us to examine and bench mark the correlation with independently determined immunological response and MHC binding, and might also be useful to develop novel methods for the identification of optimal target antigens and for epitope prediction. These data support the potential value of immunopeptidomics in understanding T cell immunity.

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Peltonen, Karita, Sara Feola, Husen M. Umer, Jacopo Chiaro, Georgios Mermelekas, Erkko Ylösmäki, Sari Pesonen, Rui M. M. Branca, Janne Lehtiö i Vincenzo Cerullo. "Therapeutic Cancer Vaccination with Immunopeptidomics-Discovered Antigens Confers Protective Antitumor Efficacy". Cancers 13, nr 14 (7.07.2021): 3408. http://dx.doi.org/10.3390/cancers13143408.

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Knowledge of clinically targetable tumor antigens is becoming vital for broader design and utility of therapeutic cancer vaccines. This information is obtained reliably by directly interrogating the MHC-I presented peptide ligands, the immunopeptidome, with state-of-the-art mass spectrometry. Our manuscript describes direct identification of novel tumor antigens for an aggressive triple-negative breast cancer model. Immunopeptidome profiling revealed 2481 unique antigens, among them a novel ERV antigen originating from an endogenous retrovirus element. The clinical benefit and tumor control potential of the identified tumor antigens and ERV antigen were studied in a preclinical model using two vaccine platforms and therapeutic settings. Prominent control of established tumors was achieved using an oncolytic adenovirus platform designed for flexible and specific tumor targeting, namely PeptiCRAd. Our study presents a pipeline integrating immunopeptidome analysis-driven antigen discovery with a therapeutic cancer vaccine platform for improved personalized oncolytic immunotherapy.

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Shabani, Nor Raihan Mohammad, Che Muhammad Khairul Hisyam Ismail, Chiuan Herng Leow, Munirah Mokhtar, Kirnpal Kaur Banga Singh i Chiuan Yee Leow. "Identification of MHC Class II Immunopeptidomes from Shigella flexneri 2a-infected Macrophages as Potential Vaccine Candidates". Indonesian Biomedical Journal 14, nr 2 (28.06.2022): 139–47. http://dx.doi.org/10.18585/inabj.v14i2.1781.

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BACKGROUND: Shigella is a Gram-negative rod-shaped intracellular bacterial pathogen that causes bacterial dysentery or shigellosis among children under five years old. Antibiotics have been less effective in treating shigellosis due to the multi-drug resistance of Shigella. Therefore, an effective vaccine is urgently needed to prevent this disease. The present study aims to determine the peptides presented by major histocompatibility complex (MHC) class II molecules of Shigella-infected macrophages using mass spectrometry-based immunopeptidomics approaches. The MHC class II-associated peptides derived from Shigella-infected macrophages are candidates for developing subunit-based Shigella vaccine.METHODS: THP-1-derived macrophages were infected with Shigella flexneri 2a at the multiplicity of infection equal to 10. The lysate was immunoprecipitated and analyzed by liquid chromatography–tandem mass spectrometry (LC-MS/MS). The sequences retrieved were analyzed using bioinformatics tools.RESULTS: The Shigella-infected THP-1-derived macrophages contained sample peptides from source proteins of almost all subcellular localizations. Eight peptides from S. flexneri 2a-infected macrophages were predicted to be localized at the outer membrane proteins (OMPs) of S. flexneri 2a by the PSORTb server. Two of the OMP-associated peptides were predicted as antigenic, non-allergenic, and non-toxic by respective bioinformatics tools.CONCLUSION: The findings in this study showed two selected OMPs have great potential for vaccine development against shigellosis.KEYWORDS: immunopeptidomics, mass spectrometry, vaccine development, Shigella, MHC peptides

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Abd El-Baky, Nawal, Amro A. Amara i Elrashdy M. Redwan. "HLA-I and HLA-II Peptidomes of SARS-CoV-2: A Review". Vaccines 11, nr 3 (25.02.2023): 548. http://dx.doi.org/10.3390/vaccines11030548.

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The adaptive (T-cell-mediated) immune response is a key player in determining the clinical outcome, in addition to neutralizing antibodies, after SARS-CoV-2 infection, as well as supporting the efficacy of vaccines. T cells recognize viral-derived peptides bound to major histocompatibility complexes (MHCs) so that they initiate cell-mediated immunity against SARS-CoV-2 infection or can support developing a high-affinity antibody response. SARS-CoV-2-derived peptides bound to MHCs are characterized via bioinformatics or mass spectrometry on the whole proteome scale, named immunopeptidomics. They can identify potential vaccine targets or therapeutic approaches for SARS-CoV-2 or else may reveal the heterogeneity of clinical outcomes. SARS-CoV-2 epitopes that are naturally processed and presented on the human leukocyte antigen class I (HLA-I) and class II (HLA-II) were identified for immunopeptidomics. Most of the identified SARS-CoV-2 epitopes were canonical and out-of-frame peptides derived from spike and nucleocapsid proteins, followed by membrane proteins, whereby many of which are not caught by existing vaccines and could elicit effective responses of T cells in vivo. This review addresses the detection of SARS-CoV-2 viral epitopes on HLA-I and HLA-II using bioinformatics prediction and mass spectrometry (HLA peptidomics). Profiling the HLA-I and HLA-II peptidomes of SARS-CoV-2 is also detailed.

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Kallor, Ashwin Adrian, Michał Waleron, Georges Bedran, Patrícia Eugénio, Catia Pesquita, Daniel Faria, Fabio Massimo Zanzotto, Christophe Battail, Ajitha Rajan i Javier Alfaro. "Abstract 6577: CARMEN: A pan-HLA and pan-cancer proteogenomic database on antigen presentation to support cancer immunotherapy". Cancer Research 83, nr 7_Supplement (4.04.2023): 6577. http://dx.doi.org/10.1158/1538-7445.am2023-6577.

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Abstract Cancer immunotherapy has greatly improved the quality of life of cancer patients and it hinges on the discovery of novel cancer antigens that could be targeted to improve disease outcomes. The creation of databases such as IEDB, SysteMHC, TANTIGEN, caAtlas, HLA Ligand Atlas, Cancer Antigenic Peptide Database, SPENCER and IEAtlas support the immunopeptidomics community in understanding the landscape of antigen presentation. We have developed a pan-cancer, pan-HLA, and pan-tissue database containing immunopeptidomics data mapped to transcriptomic, genomic, immunological and biochemical data. The database was generated from 80 different publicly available immunopeptidomics mass spectrometry datasets collected between 2015-2022 (76 cancer and 4 normal datasets), covering 15 different types of cancers and 152 different HLA-I alleles. The peptides contained in our database were obtained by a combination of closed, open and de novo searches using an in-house developed computational pipeline. Following rigorous false discovery rate estimation at 1% and a second-round search to eliminate any false signals that may not have been detected in the previous round of FDR estimation, we obtained a list of 11.2 million peptide-HLA combinations comprising both coding and non-coding regions of the genome as well as bacterial peptides. These peptides have been mapped to chromosomal coordinates to facilitate adoption by the genomics community of this useful resource on antigen presentation. Pathway/biochemical analysis of each peptide was performed using the rWikiPathways package. Finally, mutations associated with each peptide were annotated using COSMIC and dbSNP resources. Our database includes a FAIR knowledge graph which contextualizes and enriches the data to enable clinicians to take effective therapeutic decisions on the appropriate form of treatment for cancer immunotherapy with the case study of clear cell renal cell carcinoma (ccRCC). We will continue to expand our database with new data over the next two years and expand the scope of its applications to facilitate uptake by the larger scientific community. Citation Format: Ashwin Adrian Kallor, Michał Waleron, Georges Bedran, Patrícia Eugénio, Catia Pesquita, Daniel Faria, Fabio Massimo Zanzotto, Christophe Battail, Ajitha Rajan, Javier Alfaro. CARMEN: A pan-HLA and pan-cancer proteogenomic database on antigen presentation to support cancer immunotherapy. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6577.

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Klein, Joshua, Daniel Sprague, Monica Lane, Meghan Hart, Olivia Petrillo, Italo Faria do Valle, Matthew Davis i in. "Abstract 904: AI platform provides an EDGE and enables state-of-the-art identification of peptide-HLAs for the development of T cell inducing vaccines". Cancer Research 84, nr 6_Supplement (22.03.2024): 904. http://dx.doi.org/10.1158/1538-7445.am2024-904.

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Abstract T cell inducing vaccines are key for the development of effective therapies against cancer and infectious diseases. Peptides, presented by Human Leukocyte Antigens (HLAs), are the targets of T cells, and their identification is therefore critical to the development of such vaccines. Here, we present the latest improvement in our EDGETM (Epitope Discovery for GEnomes) platform to address this critical need. EDGE is comprised of AI models that can predict peptide presentation by HLA class I and class II. Although the models are trained primarily using immunopeptidomics data, EDGE scores are predictive of peptide-HLA immunogenicity. There are three class I presentation models in EDGE: an allele-specific model, a pan-specific model, and a model specific for infectious diseases. The allele-specific model is applicable to a large but pre-defined set of HLA alleles. On a large test dataset, the allele-specific model achieved an average precision (AP) of 63% (PPV40=79%) compared to the AP of a standard best-available public model of 21% (PPV40=28%). A Ph1/2 clinical study of personalized cancer vaccines encoding neoantigens predicted from the allele-specific model demonstrated a ~50% molecular response (defined as >=30% reduction in circulating tumor DNA relative to baseline) rate with associated extended overall survival (vs non-responders) in metastatic, microsatellite stable colorectal cancer patients. We observed that >50% of the mutations were able to elicit T cell responses. The pan-specific class I model uses HLA sequences as input feature when training and, therefore, is applicable to any HLA. On the same test dataset as above, it achieved an AP of 65% (PPV40=81%) and performed better on average for ~40 less-common HLA alleles. Prediction of viral peptide presentation by HLA class I is challenging due to the lack of immunopeptidomics data. The class I model for infectious diseases was specifically optimized to predict for viral peptides and, therefore, performed better than available class I models on published HIV and Influenza A datasets. Prediction of peptide presentation by HLA class II is challenging due to the flexibility in how the longer peptides interact with open HLA grooves as well as the lack of immunopeptidomics data as compared to the class I peptides. The class II model in EDGE, EDGE-II, uses the latest developments in protein large language models, a novel learned HLA allele-deconvolution strategy, and in-house immunopeptidomics data, resulting in improved prediction of peptide presentation by HLA class II and immunogenicity driven by CD4+ T cells. On a benchmark validation dataset, EDGE-II achieved an AP of 71% as compared to AP of 62% of a leading published model. In summary, EDGETM provides a comprehensive state-of-the-art platform for the development of vaccines that can induce both CD8+ and CD4+ T cell responses to provide durable benefit to patients. Citation Format: Joshua Klein, Daniel Sprague, Monica Lane, Meghan Hart, Olivia Petrillo, Italo Faria do Valle, Matthew Davis, Andrew Ferguson, Andrew Allen, Karin Jooss, Ankur Dhanik. AI platform provides an EDGE and enables state-of-the-art identification of peptide-HLAs for the development of T cell inducing vaccines [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 904.

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Stopfer, Lauren E., Jason E. Conage-Pough i Forest M. White. "Quantitative Consequences of Protein Carriers in Immunopeptidomics and Tyrosine Phosphorylation MS2 Analyses". Molecular & Cellular Proteomics 20 (2021): 100104. http://dx.doi.org/10.1016/j.mcpro.2021.100104.

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Fritsche, Jens, Barbara Rakitsch, Franziska Hoffgaard, Michael Römer, Heiko Schuster, Daniel J. Kowalewski, Martin Priemer i in. "Translating Immunopeptidomics to Immunotherapy-Decision-Making for Patient and Personalized Target Selection". PROTEOMICS 18, nr 12 (10.04.2018): 1700284. http://dx.doi.org/10.1002/pmic.201700284.

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Li, Kai, Antrix Jain, Anna Malovannaya, Bo Wen i Bing Zhang. "Front Cover: DeepRescore: Leveraging Deep Learning to Improve Peptide Identification in Immunopeptidomics". PROTEOMICS 20, nr 21-22 (listopad 2020): 2070151. http://dx.doi.org/10.1002/pmic.202070151.

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Purcell, Anthony. "Mass spectrometry and immunopeptidomics – teaching us new lessons in antigen processing and presentation". Molecular Immunology 150 (październik 2022): 35. http://dx.doi.org/10.1016/j.molimm.2022.05.116.

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Abelin, Jennifer. "Abstract 1439 Mass spectrometry based immunopeptidomics as a tool for understanding antigen presentation". Journal of Biological Chemistry 300, nr 3 (marzec 2024): 106634. http://dx.doi.org/10.1016/j.jbc.2024.106634.

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Mohsen, Mona O., Daniel E. Speiser, Justine Michaux, HuiSong Pak, Brian J. Stevenson, Monique Vogel, Varghese Philipose Inchakalody i in. "Bedside formulation of a personalized multi-neoantigen vaccine against mammary carcinoma". Journal for ImmunoTherapy of Cancer 10, nr 1 (styczeń 2022): e002927. http://dx.doi.org/10.1136/jitc-2021-002927.

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BackgroundHarnessing the immune system to purposely recognize and destroy tumors represents a significant breakthrough in clinical oncology. Non-synonymous mutations (neoantigenic peptides) were identified as powerful cancer targets. This knowledge can be exploited for further improvements of active immunotherapies, including cancer vaccines, as T cells specific for neoantigens are not attenuated by immune tolerance mechanism and do not harm healthy tissues. The current study aimed at developing an optimized multitarget vaccine using short or long neoantigenic peptides utilizing virus-like particles (VLPs) as an efficient vaccine platform.MethodsMutations of murine mammary carcinoma cells were identified by integrating mass spectrometry-based immunopeptidomics and whole exome sequencing. Neoantigenic peptides were synthesized and covalently linked to virus-like nanoparticles using a Cu-free click chemistry method for easy preparation of vaccines against mouse mammary carcinoma.ResultsAs compared with short peptides, vaccination with long peptides was superior in the generation of neoantigen-specific CD4+ and CD8+ T cells, which readily produced interferon gamma (IFN-γ) and tumor-necrosis factor α (TNF-α). The resulting anti-tumor effect was associated with favorable immune re-polarization in the tumor microenvironment through reduction of myeloid-derived suppressor cells. Vaccination with long neoantigenic peptides also decreased post-surgical tumor recurrence and metastases, and prolonged mouse survival, despite the tumor’s low mutational burden.ConclusionIntegrating mass spectrometry-based immunopeptidomics and whole exome sequencing is an efficient approach for identifying neoantigenic peptides. Our multitarget VLP-based vaccine shows a promising anti-tumor effect in an aggressive murine mammary carcinoma model. Future clinical application using this strategy is readily feasible and practical, as click chemistry coupling of personalized synthetic peptides to the nanoparticles can be done at the bedside directly before injection.

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Wilder, Brandon Keith, Luna de Lacerda, Camila R. R. Barbosa, Maya Aleshnick, Thomas Martinson, David Morrow, Zeshou Zhao, Gaurav Gaiha i Caroline Junqueira. "Malaria antigens are presented to CD8 T cells via the non-classical HLA-E". Journal of Immunology 208, nr 1_Supplement (1.05.2022): 170.27. http://dx.doi.org/10.4049/jimmunol.208.supp.170.27.

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Abstract CD8 T cells have long been known to target infected hepatocytes during the intracellular liver stages of Plasmodium—the causative agent of malaria. Current T cell vaccine strategies are limited by the inability to obtain sufficient amounts of Plasmodium-infected hepatocytes to identify peptides presented on MHC-I (“immunopeptidomics”). Recently, we demonstrated that the blood stages of Plasmodium vivax are also susceptible to CD8 T cell killing and performed immunopeptidomics on Pv-infected reticulocytes. Peptides mapped to proteins conserved across Plasmodium spp. and were common across volunteers. Furthermore, ~50% of the peptides detected are not predicted to bind classical HLA-I molecules and thus could be presented by non-classical HLA. In support of this, blocking HLA-E during in vitro stimulation of PBMCs with a pool of newly-identified peptides substantially reduced the CD8 T cell response. Mechanistic studies conducted in the rhesus macaque/P. cynomolgi malaria model demonstrated that CD8 T cell killing of infected reticulocytes is conserved in this model as are CD8 responses to the newly identified peptides. In vitrostimulation assays showed that CD8 T cell responses to at least 3 of 9 peptides tested are completely restricted to MHC-E. This is supported by in vitro binding assays showing peptide binding to HLA-E. In summary, we demonstrate for the first time that malaria infection induces HLA/MHC-E-restricted CD8 T cell responses to newly identified peptides that are conserved across host and parasite species. Given the extreme conservation of MHC-E, these results pave the way to explore a universal, species-transcending vaccine for malaria based on MHC-E presentation of novel antigens.

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Maringer, Yacine, Lena Freudenmann, Annika Nelde, Jonas S. Heitmann, Helmut R. Salih, Marissa Dubbelaar, Jörg Hennenlotter i in. "Abstract 3556: Immunopeptidomics-guided tumor antigen warehouse design and first clinical application of a personalized peptide vaccine for prostate cancer". Cancer Research 82, nr 12_Supplement (15.06.2022): 3556. http://dx.doi.org/10.1158/1538-7445.am2022-3556.

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Abstract Prostate cancer is the second most common malignancy in men with radical prostatectomy and radio-therapy being the primary curative treatment modalities in localized disease. However, up to 30% of patients experience recurrence that frequently leads to the development of metastatic disease associated with poor outcome. Therefore, novel, effective and low-side effect treatment options are essential, in particular for elderly and frail prostate cancer patients not eligible for intense therapy. Whereas, immune checkpoint inhibitors showed limited efficacy in prostate cancer so far, targeted T cell-based approaches with bispecific antibodies and cancer vaccines achieved promising results in this tumor entity. In this study, we established a prostate-associated off-the-shelf peptide warehouse, using mass-spectrometry-based immunopeptidome analysis of a large cohort of primary prostate tissue (n = 51), for the development of a broadly applicable personalized vaccine. The prostate dataset (23 prostate cancer, 12 adjacent benign, 16 benign prostate samples) comprising 29,893 HLA class I and II molecules, was compared to the immunopeptidomes of various benign non-prostate tissues (HLA-Ligand Atlas) to identify prostate exclusive antigens. Further antigen selection was based on the high presentation frequency. In total, 30 frequently expressed and prostate exclusive HLA class I peptides restricted to the six common allotypes HLA-A*02, -A*03, -A*24, -B*07, -B*08 and -B*40, covering more than 76% of the world population for at least one allotype, and five promiscuous HLA-DR restricted peptides found in up to 37% of prostates were selected. Immunogenicity was validated by IFNγ ELISPOT screening for preexisting T cell responses, as well as by in vitro priming experiments of naïve T cells in prostate cancer patients and healthy volunteers. The peptide warehouse will enable the formulation of personalized vaccines based on individual HLA allotype and immunopeptidome analysis of patient’s tumor samples in a reasonable time and cost frame within large cohort studies. We provided first evidence for the feasibility of this approach by designing a personalized immunopeptidome-guided peptide vaccine for a patient with metastatic prostate cancer. The vaccine was adjuvanted with the toll-like receptor 1/2 agonist XS15 emulsified in Montanide࣪ ISA51 VG and applied three times within a 12-week interval. Induction of a profound T cell response targeting 7/9 (78%) vaccine peptides was observed one week after the second vaccination. T cell responses have been persisting for almost two years in the patient, and in combination with androgen deprivation therapy enabled persistent PSA remission. In conclusion, we designed an immunopeptidomics-guided peptide warehouse and provided first evidence for its personalized application in prostate cancer patients. Citation Format: Yacine Maringer, Lena Freudenmann, Annika Nelde, Jonas S. Heitmann, Helmut R. Salih, Marissa Dubbelaar, Jörg Hennenlotter, Arnulf Stenzl, Jens Bedke, Hans-Georg Rammensee, Juliane S. Walz. Immunopeptidomics-guided tumor antigen warehouse design and first clinical application of a personalized peptide vaccine for prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3556.

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Solleder, Marthe, Philippe Guillaume, Julien Racle, Justine Michaux, Hui-Song Pak, Markus Müller, George Coukos, Michal Bassani-Sternberg i David Gfeller. "Mass Spectrometry Based Immunopeptidomics Leads to Robust Predictions of Phosphorylated HLA Class I Ligands". Molecular & Cellular Proteomics 19, nr 2 (17.12.2019): 390–404. http://dx.doi.org/10.1074/mcp.tir119.001641.

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The presentation of peptides on class I human leukocyte antigen (HLA-I) molecules plays a central role in immune recognition of infected or malignant cells. In cancer, non-self HLA-I ligands can arise from many different alterations, including non-synonymous mutations, gene fusion, cancer-specific alternative mRNA splicing or aberrant post-translational modifications. Identifying HLA-I ligands remains a challenging task that requires either heavy experimental work for in vivo identification or optimized bioinformatics tools for accurate predictions. To date, no HLA-I ligand predictor includes post-translational modifications. To fill this gap, we curated phosphorylated HLA-I ligands from several immunopeptidomics studies (including six newly measured samples) covering 72 HLA-I alleles and retrieved a total of 2,066 unique phosphorylated peptides. We then expanded our motif deconvolution tool to identify precise binding motifs of phosphorylated HLA-I ligands. Our results reveal a clear enrichment of phosphorylated peptides among HLA-C ligands and demonstrate a prevalent role of both HLA-I motifs and kinase motifs on the presentation of phosphorylated peptides. These data further enabled us to develop and validate the first predictor of interactions between HLA-I molecules and phosphorylated peptides.

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Andreatta, Massimo, Annalisa Nicastri, Xu Peng, Gemma Hancock, Lucy Dorrell, Nicola Ternette i Morten Nielsen. "MS-Rescue: A Computational Pipeline to Increase the Quality and Yield of Immunopeptidomics Experiments". PROTEOMICS 19, nr 4 (18.01.2019): 1800357. http://dx.doi.org/10.1002/pmic.201800357.

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Fritsche, Jens, Barbara Rakitsch, Franziska Hoffgaard, Michael Römer, Heiko Schuster, Daniel J. Kowalewski, Martin Priemer i in. "Front Cover: Translating Immunopeptidomics to Immunotherapy-Decision-Making for Patient and Personalized Target Selection". PROTEOMICS 18, nr 12 (czerwiec 2018): 1870101. http://dx.doi.org/10.1002/pmic.201870101.

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Stutzmann, Charlotte, Jiaxi Peng, Zhaoguan Wu, Christopher Savoie, Isabelle Sirois, Pierre Thibault, Aaron R. Wheeler i Etienne Caron. "Unlocking the potential of microfluidics in mass spectrometry-based immunopeptidomics for tumor antigen discovery". Cell Reports Methods 3, nr 6 (czerwiec 2023): 100511. http://dx.doi.org/10.1016/j.crmeth.2023.100511.

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Tetens, Ashley R., Allison M. Martin, Antje Arnold, Orlandi V. Novak, Adrian Idrizi, Rakel Tryggvadottir, Jordyn Craig-Schwartz i in. "DIPG-58. TARGETING DISORDERED DNA METHYLATION IN DIPG TO CONSTRAIN VARIABILITY AND INDUCE IMMUNE SIGNALING". Neuro-Oncology 26, Supplement_4 (18.06.2024): 0. http://dx.doi.org/10.1093/neuonc/noae064.111.

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Abstract BACKGROUND Diffuse Intrinsic Pontine Glioma (DIPG) is a universally fatal pediatric brain cancer characterized by the histone H3 K27M mutation. The known downstream consequences of this mutation include loss of repressive chromatin marks, global DNA hypomethylation, and altered gene expression. METHODS We sought to investigate the role of epigenetic variability as a basis of DIPG cellular heterogeneity and plasticity by using Whole Genome Bisulfite Sequencing (WGBS). Existing methods of analysis of WGBS are limited in that they average/smooth methylation data and fail to capture intrinsic variability. We performed (WGBS) on 23 primary patient samples of DIPG and applied an analysis that captures the variability of DNA methylation reads to characterize methylation stochasticity. We then sought to modulate the epigenome pharmacologically using a DNA methyltransferase inhibitor and assessed changes through RNA-Seq and immunopeptidomics. RESULTS We find that DIPG has a marked increase in methylation stochasticity (quantified as methylation entropy). Additionally, while DIPG is globally hypomethylated, it retains DNA methylation at specific regulatory regions, such as bivalent promoters, and key genes, such as FOXG1, CXCR4, and KLF4. We sought to reverse these changes using the DNA methyltransferase inhibitor, decitabine, in 4 DIPG neurosphere cell lines. Treatment with decitabine reversed the entropy seen in DIPG and induced innate immune signaling and interferon signaling in DIPG cells. Using RNA-seq, we found that decitabine treatment dramatically alters gene expression, including activation of endogenous retroviral elements, activation of interferon signaling, de-repression of hypermethylated targets, and expression of putative neoantigens, such as PRAME and DAZL. Immunopeptidomics profiling of MHC Class I bound canonical and non-canonical peptides of DIPG cell lines after decitabine treatment also revealed decitabine-induced differences. CONCLUSIONS Taken together, our study finds that the methylome of DIPG is highly disordered but is also highly responsive to pharmacologic modulation by hypomethylating agents. Furthermore, hypomethylating drugs can increase the immunogenicity of DIPG, thus offering the potential for future combination with immunotherapy.

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Pyke, Rachel Marty, Steven Dea, Hima Anbunathan, Charles W. Abbott, Neeraja Ravi, Jason Harris, Gabor Bartha i in. "Abstract 5640: Mono-allelic immunopeptidomics data from 109 MHC-I alleles reveals variability in binding preferences and improves neoantigen prediction algorithm". Cancer Research 82, nr 12_Supplement (15.06.2022): 5640. http://dx.doi.org/10.1158/1538-7445.am2022-5640.

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Abstract Neoantigen-based biomarkers are a promising approach for stratifying patient response to immunotherapy; however, current neoantigen prediction methods are not accurate enough to optimize these biomarkers. Sequence variability in the major histocompatibility complex (MHC) leads to the presentation of diverse neoantigens to T cells, and accurately representing this diversity in neoantigen prediction is critical for improvement. Previously, we published data from 25 mono-allelic cell lines and built an associated MHC class I, pan-allelic neoantigen prediction algorithm (SHERPATM). Here, we profile an additional 84 MHC alleles including 37 that have never previously been profiled with mono-allelic immunopeptidomics, explore the impact of MHC variability on peptide binding and improve neoantigen prediction of the SHERPA algorithm. To generate the data, we stably and transiently transfected 109 different MHC alleles (43 HLA-A, 56 -B and 10 -C alleles) into independent K562 HLA-null cell lines, immunoprecipitated intact MHC complexes using a W6/32 antibody and profiled the bound peptides using LC/MS-MS. We recovered a median of 1430 peptides per allele, with yields from the transient transfections being consistently higher than the stable transfections. Nearly all alleles have a strong anchor residue in the ninth position, but the positions of the secondary anchor residue vary by gene. HLA-B showed a stronger preference for the second position while HLA-A exhibited more variability across the first, second and third positions. In addition to the 109 mono-allelic cell lines, SHERPA increases generalizability by systematically integrating an additional 104 mono-allelic and 384 multi-allelic samples with publicly available immunopeptidomics data. The 186 alleles in the resulting training dataset have an average allelic coverage of 98% across 18 different US-based ethnicities. We evaluated our updated performance on 10% held-out mono-allelic test data from multiple cell line sources. The positive predictive value (PPV) of SHERPA was markedly higher than either NetMHCPan 4.1 or MHCFlurry-2.0 (1.45 and 1.58-fold increase, respectively), with further gains when only the 37 previously unprofiled alleles were considered (1.51 and 1.79-fold increase, respectively). Furthermore, the SHERPA model was able to detect 1.38-fold more immunogenic epitopes than either other method. Finally, we performed predictions with SHERPA across millions of synthetic binding pockets and peptides to elucidate the impact of MHC variability on peptide diversity. We found a strong correlation between binding pocket positions that highly influence peptide binding and those that are evolutionarily divergent. In conclusion, we profiled 109 mono-allelic cell lines, showed key trends in MHC-associated peptides and improved the SHERPA neoantigen prediction model. Citation Format: Rachel Marty Pyke, Steven Dea, Hima Anbunathan, Charles W. Abbott, Neeraja Ravi, Jason Harris, Gabor Bartha, Sejal Desai, Rena McClory, John West, Michael P. Snyder, Richard O. Chen, Sean Michael Boyle. Mono-allelic immunopeptidomics data from 109 MHC-I alleles reveals variability in binding preferences and improves neoantigen prediction algorithm [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5640.

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Li, Chen, Jerico Revote, Sri H. Ramarathinam, Shan Zou Chung, Nathan P. Croft, Katherine E. Scull, Ziyi Huang i in. "Resourcing, annotating, and analysing synthetic peptides of SARS‐CoV‐2 for immunopeptidomics and other immunological studies". PROTEOMICS 21, nr 17-18 (14.04.2021): 2100036. http://dx.doi.org/10.1002/pmic.202100036.

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Graciotti, Michele, Fabio Marino, HuiSong Pak, Petra Baumgaertner, Anne-Christine Thierry, Johanna Chiffelle, Marta A. S. Perez i in. "Deciphering the Mechanisms of Improved Immunogenicity of Hypochlorous Acid-Treated Antigens in Anti-Cancer Dendritic Cell-Based Vaccines". Vaccines 8, nr 2 (2.06.2020): 271. http://dx.doi.org/10.3390/vaccines8020271.

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Hypochlorous acid (HOCl)-treated whole tumor cell lysates (Ox-L) have been shown to be more immunogenic when used as an antigen source for therapeutic dendritic cell (DC)-based vaccines, improving downstream immune responses both in vitro and in vivo. However, the mechanisms behind the improved immunogenicity are still elusive. To address this question, we conducted a proteomic and immunopeptidomics analyses to map modifications and alterations introduced by HOCl treatment using a human melanoma cell line as a model system. First, we show that one-hour HOCl incubation readily induces extensive protein oxidation, mitochondrial biogenesis, and increased expression of chaperones and antioxidant proteins, all features indicative of an activation of oxidative stress-response pathways. Characterization of the DC proteome after loading with HOCl treated tumor lysate (Ox-L) showed no significant difference compared to loading with untreated whole tumor lysate (FT-L). On the other hand, detailed immunopeptidomic analyses on monocyte-derived DCs (mo-DCs) revealed a great increase in human leukocyte antigen class II (HLA-II) presentation in mo-DCs loaded with Ox-L compared to the FT-L control. Further, 2026 HLA-II ligands uniquely presented on Ox-L-loaded mo-DCs were identified. In comparison, identities and intensities of HLA class I (HLA-I) ligands were overall comparable. We found that HLA-II ligands uniquely presented by DCs loaded with Ox-L were more solvent exposed in the structures of their source proteins, contrary to what has been hypothesized so far. Analyses from a phase I clinical trial showed that vaccinating patients using autologous Ox-L as an antigen source efficiently induces polyfunctional vaccine-specific CD4+ T cell responses. Hence, these results suggest that the increased immunogenicity of Ox-L is, at least in part, due to qualitative and quantitative changes in the HLA-II ligandome, potentially leading to an increased HLA-II dependent stimulation of the T cell compartment (i.e., CD4+ T cell responses). These results further contribute to the development of more effective and immunogenic DC-based vaccines and to the molecular understanding of the mechanism behind HOCl adjuvant properties.

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Stopfer, L. E., A. D. D'Souza i F. M. White. "1,2,3, MHC: a review of mass-spectrometry-based immunopeptidomics methods for relative and absolute quantification of pMHCs". Immuno-Oncology and Technology 11 (październik 2021): 100042. http://dx.doi.org/10.1016/j.iotech.2021.100042.

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Stopfer, L. E., A. D. D'Souza i F. M. White. "1,2,3, MHC: a review of mass-spectrometry-based immunopeptidomics methods for relative and absolute quantification of pMHCs". Immuno-Oncology and Technology 11 (październik 2021): 100042. http://dx.doi.org/10.1016/j.iotech.2021.100042.

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Ely, Zackery A., William A. Freed-Pastor, Zachary J. Kulstad, Jennifer G. Abelin, Eva Verzani, Kevin S. Kapner, Susan Klaeger i in. "Abstract C014: Broadening the repertoire of PDAC-specific targets for immune-based therapy through high-resolution immunopeptidomics". Cancer Research 82, nr 22_Supplement (15.11.2022): C014. http://dx.doi.org/10.1158/1538-7445.panca22-c014.

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Abstract Pancreatic adenocarcinoma (PDAC) is among the most lethal cancer types and has been largely recalcitrant to traditional immunotherapy. A large subset of PDAC tumors is computationally predicted to harbor potentially immunogenic peptides for MHC class I (MHC-I) presentation, but the nature, expression, and immunogenicity of these peptides has yet to be determined. By investigating the PDAC immunopeptidome, we can uncover and exploit novel immune-based targets for PDAC and render it vulnerable to immunotherapy. Prior efforts to study the immunopeptidome in PDAC have largely focused on profiling MHC-associated peptides (MAPs) from bulk tumor samples. This approach is severely limited by the contribution of MAPs from the non-malignant compartments, which constitutes most of the cellular mass in PDAC. We can overcome this limitation by using patient-derived organoids (PDOs) to expand a pure cancer cell population for MHC-I immunoprecipitation, followed by LC/MS-MS. We applied this approach and detected 17,000-20,500 unique MAPs per sample, a dramatic increase in depth and resolution over prior efforts. To ascertain which MAPs may be PDAC-restricted, we first analyzed bulk RNA-sequencing data from the Genotype-Tissue Expression Project (767 patients, 30 tissues) to generate a set of genes that are functionally undetectable (Q90 <1 TPM) in healthy somatic tissues. We cross-referenced this list with our set of PDO MAPs, yielding 143 PDAC-restricted MAPs. To further expand our search space, we implemented a tiered tissue-based filter to relax TPM cutoffs in less essential tissues (ex. prostate) while maintaining strict cutoffs in essential tissues (ex. brain), generating 85 additional MAPs. Both approaches uncovered cancer-restricted MAPs present in most PDO lines, which may represent shared therapeutic targets. While somatic mutations are a well-established source of tumor-specific neoantigens, these have yet to be investigated with immunopeptidomics in human PDAC. Despite harboring an intermediate mutational burden, we detected at least one mutation-derived neoepitope in most PDOs, a detection frequency much closer to the detection frequency of cancers with high mutational burden than previously expected. Additionally, non-canonical peptide sources, including retained introns (RI) and novel unannotated open reading frames (nuORF), may represent a source of cancer-restricted MAPs. Interestingly, we detect nuORF- and RI-derived MAPs (n=298) in all PDOs, suggesting that these too may be therapeutically relevant in PDAC. To assess the immunogenicity of candidate MAPs, future studies will utilize established protocols for priming and expanding MAP-specific human T cells with autologous DCs. Functional evaluation of MAP-specific T cells will help prioritize strategies for vaccination, and the generation of T cell receptor sequences for adoptive cell therapy. Collectively, these data deepen our understanding of the PDAC immunopeptidome and provide a novel set of targets for immunotherapy in PDAC. Citation Format: Zackery A. Ely, William A. Freed-Pastor, Zachary J. Kulstad, Jennifer G. Abelin, Eva Verzani, Kevin S. Kapner, Susan Klaeger, Karl R. Clauser, Miles Agus, Alex M. Jaeger, Nimisha B. Pattada, Arjun Bhutkar, Andrew J. Aguirre, Steven A. Carr, Tyler Jacks. Broadening the repertoire of PDAC-specific targets for immune-based therapy through high-resolution immunopeptidomics [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr C014.

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ElAbd, Hesham, Frauke Degenhardt, Tomas Koudelka, Ann-Kristin Kamps, Andreas Tholey, Petra Bacher, Tobias L. Lenz, Andre Franke i Mareike Wendorff. "Immunopeptidomics toolkit library (IPTK): a python-based modular toolbox for analyzing immunopeptidomics data". BMC Bioinformatics 22, nr 1 (17.08.2021). http://dx.doi.org/10.1186/s12859-021-04315-0.

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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.

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Wacker, Marcel, Jens Bauer, Laura Wessling, Marissa Dubbelaar, Annika Nelde, Hans-Georg Rammensee i Juliane S. Walz. "Immunoprecipitation methods impact the peptide repertoire in immunopeptidomics". Frontiers in Immunology 14 (21.07.2023). http://dx.doi.org/10.3389/fimmu.2023.1219720.

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IntroductionMass spectrometry-based immunopeptidomics is the only unbiased method to identify naturally presented HLA ligands, which is an indispensable prerequisite for characterizing novel tumor antigens for immunotherapeutic approaches. In recent years, improvements based on devices and methodology have been made to optimize sensitivity and throughput in immunopeptidomics. However, developments in ligand isolation, mass spectrometric analysis, and subsequent data processing can have a marked impact on the quality and quantity of immunopeptidomics data.MethodsIn this work, we compared the immunopeptidome composition in terms of peptide yields, spectra quality, hydrophobicity, retention time, and immunogenicity of two established immunoprecipitation methods (column-based and 96-well-based) using cell lines as well as primary solid and hematological tumor samples.ResultsAlthough, we identified comparable overall peptide yields, large proportions of method-exclusive peptides were detected with significantly higher hydrophobicity for the column-based method with potential implications for the identification of immunogenic tumor antigens. We showed that column preparation does not lose hydrophilic peptides in the hydrophilic washing step. In contrast, an additional 50% acetonitrile elution could partially regain lost hydrophobic peptides during 96-well preparation, suggesting a reduction of the bias towards the column-based method but not completely equalizing it.DiscussionTogether, this work showed how different immunoprecipitation methods and their adaptions can impact the peptide repertoire of immunopeptidomic analysis and therefore the identification of potential tumor-associated antigens.

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