MHC class I–associated peptides derive from selective regions of the human genome

Pearson, Hillary; Daouda, Tariq; Granados, Diana Paola; Durette, Chantal; Bonneil, Éric; Courcelles, Mathieu; Rodenbrock, Anja; Laverdure, Jean‐Philippe; Côté, Caroline; Mader, Sylvie; Lemieux, Sébastien; Thibault, Pierre; Perreault, Claude

doi:10.1172/jci88590

Cited by 175 publications

(239 citation statements)

References 81 publications

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“…The tool was benchmarked on two large independent data sets; one consisting of ~16,000 MS identified MHC restricted ligands (17) and one consisting of more than 1,250 validated T cell epitopes described in the IEDB. For both data sets, the updated version 4.0 of NetMHCpan significantly outperformed the earlier NetMHCpan 3.0 method.…”

Section: Discussionmentioning

confidence: 99%

“…A dataset of eluted ligands was obtained from Pearson et al (17). Also, a set of positive CD8 epitopes was downloaded from the IEDB.…”

Section: Methodsmentioning

confidence: 99%

“…Sensitivity of different models as a function of the Frank threshold on a ) eluted ligands published by Pearson et al (17) and b ) T-cell epitope data downloaded from IEDB.…”

Section: Figurementioning

confidence: 99%

“…Curves are estimated from a balanced set of eluted ligands from the (17) data set. The insert shows the complete sensitivity and specificity curves as a function of the percentile rank score.…”

Section: Figurementioning

confidence: 99%

“…Identification of MHC bound peptides by mass spectrometry thus holds great promise for the generation of large scale data sets characterizing the peptidome specific for individual MHC molecules (15, 17), and potentially also for the identification of T cell epitopes (18). It is however clear that, within the foreseeable future, the number of MHC molecules characterized by such MS studies will remain limited.…”

Section: Introductionmentioning

confidence: 99%

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NetMHCpan-4.0: Improved Peptide–MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data

Jurtz

Paul

Andreatta

et al. 2017

The Journal of Immunology

1,106

906

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Cytotoxic T cells are of central importance in the immune system’s response to disease. They recognize defective cells by binding to peptides presented on the cell surface by MHC (major histocompatibility complex) class I molecules. Peptide binding to MHC molecules is the single most selective step in the antigen presentation pathway. On the quest for T cell epitopes, the prediction of peptide binding to MHC molecules has therefore attracted large attention. In the past, predictors of peptide-MHC interaction have in most cases been trained on binding affinity data. Recently an increasing amount of MHC presented peptides identified by mass spectrometry has been published containing information about peptide processing steps in the presentation pathway and the length distribution of naturally presented peptides. Here, we present NetMHCpan-4.0, a method trained on both binding affinity and eluted ligand data leveraging the information from both data types. Large-scale benchmarking of the method demonstrates an increased predictive performance compared to state-of-the-art when it comes to identification of naturally processed ligands, cancer neoantigens, and T cell epitopes.

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Section: Discussionmentioning

confidence: 99%

“…A dataset of eluted ligands was obtained from Pearson et al (17). Also, a set of positive CD8 epitopes was downloaded from the IEDB.…”

Section: Methodsmentioning

confidence: 99%

“…Sensitivity of different models as a function of the Frank threshold on a ) eluted ligands published by Pearson et al (17) and b ) T-cell epitope data downloaded from IEDB.…”

Section: Figurementioning

confidence: 99%

Section: Figurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

NetMHCpan-4.0: Improved Peptide–MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data

Jurtz

Paul

Andreatta

et al. 2017

The Journal of Immunology

1,106

906

View full text Add to dashboard Cite

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Identification of MHC Peptides Using Mass Spectrometry for Neoantigen Discovery and Cancer Vaccine Development

Chen

Fulton

Twine

et al. 2019

Mass Spectrometry Reviews

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Immunotherapy with neoantigens presented by major histocompatibility complex (MHC) is one of the most promising approaches in cancer treatment. Using this approach, cancer vaccines can be designed to target tumor‐specific mutations that are not found in normal tissues. Clinical trials have demonstrated an increased immune response and eradication of tumors after injecting synthetic peptides selected from the immunopeptidome. Although the sequence of MHC binding peptides can be predicted from genome sequencing and prediction algorithms, this approach results in large numbers of predicted peptides, requiring the confirmation by mass spectrometry (MS) analysis. Identification of MHC peptides by direct MS analysis of immunopeptidome is accurate and sensitive, with tens of thousands of unique peptides potentially identified from either cancer cell line or tumor tissue. Peptides with mutations can also be identified with patient‐specific protein databases constructed from genome or transcriptome sequencing data. MS analysis also enables the characterization of the post‐translational modifications (PTMs) of those antigens that cannot be predicted. Moreover, PTMs were found to be more efficient in triggering an immune response. In addition to reviewing recent advances in the identification of neoantigens using MS, the techniques for cancer vaccine candidate selection and formulation, vaccine delivery systems, and the potential for use in combination with other therapeutics are also discussed. It is anticipated that MS‐based techniques will play an important role in future cancer vaccine development. © 2019 John Wiley & Sons Ltd. Mass Spec Rev 40:110–125, 2021.

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Computational Methods for Identification of T Cell Neoepitopes in Tumors

Jurtz

Olsen

2018

Methods in Molecular Biology

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MHC class I–associated peptides derive from selective regions of the human genome

Cited by 175 publications

References 81 publications

NetMHCpan-4.0: Improved Peptide–MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data

NetMHCpan-4.0: Improved Peptide–MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data

Identification of MHC Peptides Using Mass Spectrometry for Neoantigen Discovery and Cancer Vaccine Development

Computational Methods for Identification of T Cell Neoepitopes in Tumors

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