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Relevant bibliographies by topics / Immunopeptidomics

Academic literature on the topic 'Immunopeptidomics'

Author: Grafiati

Published: 20 July 2024

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Journal articles on the topic "Immunopeptidomics"

1

Ternette, Nicola, and Anthony W. Purcell. "Immunopeptidomics Special Issue." PROTEOMICS 18, no. 12 (June 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, and 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, no. 6_Supplement (March 22, 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., and Karl Mechtler. "Immunopeptidomics in the Era of Single-Cell Proteomics." Biology 12, no. 12 (December 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, et al. "Immunopeptidomic Analysis of BoLA-I and BoLA-DR Presented Peptides from Theileria parva Infected Cells." Vaccines 10, no. 11 (November 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, and Michal Bassani-Sternberg. "Identification of tumor antigens with immunopeptidomics." Nature Biotechnology 40, no. 2 (October 11, 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, and Sean Boyle. "57 Precision neoantigen discovery using novel algorithms and expanded HLA-ligandome datasets." Journal for ImmunoTherapy of Cancer 8, Suppl 3 (November 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, and Hong Bing Yu. "A framework for unbiased, robust and system-wide characterization of MHC-bound peptides and epitopes (APP5P.111)." Journal of Immunology 194, no. 1_Supplement (May 1, 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, and Noriyuki Sato. "Human cancer immunopeptidomics for efficient CTL immunotherapy." Annals of Oncology 26 (November 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, and S. L. Hoffman. "Comparative immunopeptidomics of humans and their pathogens." Proceedings of the National Academy of Sciences 101, no. 36 (August 23, 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, and Candelaria Gomez‐Manzano. "Oncolytic adenoviruses and immunopeptidomics: a convenient marriage." Molecular Oncology 18, no. 4 (April 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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Dissertations / Theses on the topic "Immunopeptidomics"

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Ghosh, Michael [Verfasser]. "Advancing immunopeptidomics : validation of the method, improved epitope prediction, peptide-based HLA typing and discrimination of healthy and malignant tissue / Michael Ghosh." Tübingen : Universitätsbibliothek Tübingen, 2020. http://d-nb.info/1218073012/34.

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Rijal, Jeewan Babu. "Development and optimization of high-performing quantitative proteomics methods : application to the discovery of biomarkers for the early diagnosis of multiple sclerosis." Electronic Thesis or Diss., Strasbourg, 2024. http://www.theses.fr/2024STRAF015.

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De récentes et importantes innovations instrumentales, analytiques et bioinformatiques permettent aujourd’hui à l’analyse protéomique par spectrométrie de masse (MS) d'atteindre des niveaux de sensibilité et de couverture du protéome jamais atteints auparavant, ce qui laisse entrevoir de nouveaux espoirs pour la découverte de biomarqueurs de pathologies.Ce travail de doctorat se concentre sur le développement et l'optimisation de méthodes enprotéomique, incluant la préparation des échantillons et leur automatisation, la mise au point de méthodes d'acquisition MS utilisant des modes diaPASEF sur un instrument timsTOF de dernière génération et enfin l'évaluation de stratégies d'interprétation des données adaptées. Les méthodes optimisées ont ensuite été appliquées et ont permis d’identifier 46 candidats biomarqueurs robustes pour la sclérose en plaques, au travers de divers types d'échantillons sur un modèle murin et des fluides corporels humains. Huit de ces biomarqueurs ont été validés avec succès par ELISA, soulignant la pertinence des méthodes développées
Recent major instrumental, analytical and computational innovations are enabling massspectrometry-based proteomics to achieve previously unmet levels of sensitivity and proteome coverage and thus hold new promises for biomarker discovery studies.This PhD work focuses on proteomics method developments and optimizations including the sample preparation and its automation, the fine tuning of MS acquisition methods using diaPASEF on a latest generation timsTOF instrument and the benchmarking of adapted data interpretation strategies. Optimized workflows were then applied to identify 46 robust biomarker candidates for Multiple Sclerosis, detected across various sample types on a mouse model and human body fluids. Eight of these biomarkers were successfully validated by orthogonal ELISA, underlining the effectiveness of our optimized MS workflows

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Di, Marco Moreno [Verfasser], and Stefan [Akademischer Betreuer] Stevanović. "The immunopeptidomic landscape of clear cell renal cell carcinoma : identification and characterization of T-cell epitopes for immunotherapeutic approaches / Moreno Di Marco ; Betreuer: Stefan Stevanović." Tübingen : Universitätsbibliothek Tübingen, 2018. http://d-nb.info/119926850X/34.

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Book chapters on the topic "Immunopeptidomics"

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Gabriel, Wassim, Mario Picciani, Matthew The, and Mathias Wilhelm. "Deep Learning-Assisted Analysis of Immunopeptidomics Data." In Methods in Molecular Biology, 457–83. New York, NY: Springer US, 2024. http://dx.doi.org/10.1007/978-1-0716-3646-6_25.

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Bassani-Sternberg, Michal. "Mass Spectrometry Based Immunopeptidomics for the Discovery of Cancer Neoantigens." In Methods in Molecular Biology, 209–21. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7537-2_14.

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Marino, Fabio, Chloe Chong, Justine Michaux, and Michal Bassani-Sternberg. "High-Throughput, Fast, and Sensitive Immunopeptidomics Sample Processing for Mass Spectrometry." In Methods in Molecular Biology, 67–79. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-8979-9_5.

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ElAbd, Hesham, and Andre Franke. "Mass Spectrometry-Based Immunopeptidomics of Peptides Presented on Human Leukocyte Antigen Proteins." In Methods in Molecular Biology, 425–43. New York, NY: Springer US, 2024. http://dx.doi.org/10.1007/978-1-0716-3646-6_23.

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Alvarez, Iñaki. "Purification of HLA Immunopeptidomes from Human Thymus." In Methods in Molecular Biology, 127–36. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1936-0_10.

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Marcilla, Miguel. "Immunopeptidomic Analysis of the Phosphopeptidome Displayed by HLA Class I Molecules." In Methods in Molecular Biology, 149–58. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1936-0_12.

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Shao, Wenguang, Etienne Caron, Patrick Pedrioli, and Ruedi Aebersold. "The SysteMHC Atlas: a Computational Pipeline, a Website, and a Data Repository for Immunopeptidomic Analyses." In Bioinformatics for Cancer Immunotherapy, 173–81. New York, NY: Springer US, 2020. http://dx.doi.org/10.1007/978-1-0716-0327-7_12.

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Kovalchik, Kevin, David Hamelin, and Etienne Caron. "Generation of HLA Allele-Specific Spectral Libraries to Identify and Quantify Immunopeptidomes by SWATH/DIA-MS." In Methods in Molecular Biology, 137–47. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1936-0_11.

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Bhattacharjee, Bedanta, Rajashri Bezbaruah, Damanbhalang Rynjah, Arzoo Newar, Disha Valu, Nasima Ahmed, and Prashant Kumar. "Proteogenomics and immunopeptidomics in the development of advanced vaccines." In Advanced Vaccination Technologies for Infectious and Chronic Diseases, 455–75. Elsevier, 2024. http://dx.doi.org/10.1016/b978-0-443-18564-9.00019-9.

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Barbosa, Camila R. R., and Paulo J. G. Bettencourt. "Harnessing the power of CD8+ T-cells: identification and validation of peptides bound to major histocompatibility complex class I by immunopeptidomics." In Vaccinology and Methods in Vaccine Research, 133–61. Elsevier, 2022. http://dx.doi.org/10.1016/b978-0-323-91146-7.00008-1.

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Conference papers on the topic "Immunopeptidomics"

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Bruno, Peter, Richard Timms, Nouran Abdelfattah, Yumei Leng, Felipe Lelis, Duane Wesemann, Xu Yu, and Stephen Elledge. "63 EpiScan: A synthetic biology platform for targeted immunopeptidomics." In SITC 37th Annual Meeting (SITC 2022) Abstracts. BMJ Publishing Group Ltd, 2022. http://dx.doi.org/10.1136/jitc-2022-sitc2022.0063.

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Bassani-Sternberg, Michal, Chloe Chong, Fabio Marino, HuiSong Pak, David Gfeller, Markus Müller, and George Coukos. "Abstract PL02-02: Immunopeptidomics: Accelerating the development of personalized cancer immunotherapy." In Abstracts: AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; October 26-30, 2017; Philadelphia, PA. American Association for Cancer Research, 2018. http://dx.doi.org/10.1158/1535-7163.targ-17-pl02-02.

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Zöhrer, Benedikt, Ákos Végvári, Johan Grunewald, and Åsa M. Wheelock. "Late Breaking Abstract - HLA-DR immunopeptidomics on cells from bronchoalveolar lavage." In ERS International Congress 2023 abstracts. European Respiratory Society, 2023. http://dx.doi.org/10.1183/13993003.congress-2023.pa2220.

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Xue, Liang, Mykola Bordyuh, Djork-Arne Clevert, and Robert Stanton. "Benchmarking deep learning models and classical de novo sequencing tools for immunopeptidomics." In BCB '23: 14th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3584371.3613055.

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Sinharay, Ricky, Andreas Neerincx, Arwen Altenburg, Jens Bauer, Mark R. Wills, Julianne S. Walz, William Gelson, and Louise H. Boyle. "O02 Discovery of novel HLA class I presented hepatitis B peptides using an immunopeptidomics approach." In Abstracts of the British Association for the Study of the Liver Annual Meeting, 20–23 September 2022. BMJ Publishing Group Ltd and British Society of Gastroenterology, 2022. http://dx.doi.org/10.1136/gutjnl-2022-basl.2.

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Mellacheruvu, Datta, Nick Phillips, Gabor Bartha, Jason Harris, Robert Power, Rena McClory, John West, Richard Chen, and Sean Michael Boyle. "Abstract 4536: Applying immunopeptidomics and machine learning to improve neoantigen prediction for therapeutic and diagnostic use." In Proceedings: AACR Annual Meeting 2019; March 29-April 3, 2019; Atlanta, GA. American Association for Cancer Research, 2019. http://dx.doi.org/10.1158/1538-7445.sabcs18-4536.

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Mellacheruvu, Datta, Nick Phillips, Gabor Bartha, Jason Harris, Robert Power, Rena McClory, John West, Richard Chen, and Sean Michael Boyle. "Abstract 4536: Applying immunopeptidomics and machine learning to improve neoantigen prediction for therapeutic and diagnostic use." In Proceedings: AACR Annual Meeting 2019; March 29-April 3, 2019; Atlanta, GA. American Association for Cancer Research, 2019. http://dx.doi.org/10.1158/1538-7445.am2019-4536.

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Fonseca, Andre, and Dinler Antunes. "1278 Crossdome: An interactive R package to predict T-cell cross-reactivity risk on immunopeptidomics databases." In SITC 37th Annual Meeting (SITC 2022) Abstracts. BMJ Publishing Group Ltd, 2022. http://dx.doi.org/10.1136/jitc-2022-sitc2022.1278.

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Hernandez, Gabrielle M., Hannah Taylor, Eva K. Verzani, Claudia Ctortecka, David A. Berrios Nolasco, Karl R. Clauser, Namrata D. Udeshi, Jennifer G. Abelin, Elisabet Manasanch, and Steven A. Carr. "1040-B Identifying tumor-associated antigens in different stages of multiple myeloma using low input HLA immunopeptidomics." In SITC 38th Annual Meeting (SITC 2023) Abstracts Supplement 2. BMJ Publishing Group Ltd, 2023. http://dx.doi.org/10.1136/jitc-2023-sitc2023.1040-b.

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Schöllhorn, Anna, Thorben Groß, Lucia Torres Fernandez, Jens Bauer, Ana Marcu, Juliane Walz, Reinhild Klein, et al. "213 Immunopeptidomics of small cell lung cancer reveals numerous tumor-specific HLA ligands as T cell antigen candidates." In SITC 38th Annual Meeting (SITC 2023) Abstracts. BMJ Publishing Group Ltd, 2023. http://dx.doi.org/10.1136/jitc-2023-sitc2023.0213.

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