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A large peptidome dataset improves HLA class I epitope prediction across most of the human population

ABSTRACT: Prediction of HLA epitopes is important for the development of cancer immunotherapies and vaccines. However, current prediction algorithms have limited predictive power, in part because they were not trained on high-quality epitope datasets covering a broad range of HLA alleles. To enable prediction of endogenous HLA class I-associated peptides across a large fraction of the human population, we used mass spectrometry to profile >185,000 peptides eluted from 95 HLA-A, -B, -C and -G mono-allelic cell lines. We identified canonical peptide motifs per HLA allele, unique and shared binding submotifs across alleles and distinct motifs associated with different peptide lengths. By integrating these data with transcript abundance and peptide processing, we developed HLAthena, providing allele-and-length-specific and pan-allele-pan-length prediction models for endogenous peptide presentation. These models predicted endogenous HLA class I-associated ligands with 1.5-fold improvement in positive predictive value compared with existing tools and correctly identified >75% of HLA-bound peptides that were observed experimentally in 11 patient-derived tumor cell lines.

ORGANISM(S): Homo sapiens

PROVIDER: GSE131267 | GEO | 2019/12/16

REPOSITORIES: GEO

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Patient datasets for: A large peptidome dataset improves HLA class I epitope prediction across most of the human population

Project description:Sarkizova S, Klaeger S, Le PM, Li LW, Oliveira G, Keshishian H, Hartigan CH, Zhang W, Braun DA, Ligon KL, Bachireddy P, Zervantonakis IK, Rosenbluth JM, Ouspenskaia T, Law T, Justeson S, Stevens J, Lane WJ, Eisenhaure T, Zhang GL, Clauser KR, Hacohen N, Carr SA, Wu CJ, Keskin DB. Nature Biotechnology 2019. Prediction of HLA epitopes is important for the development of cancer immunotherapies and vaccines. However, current prediction algorithms have limited predictive power, in part because they were not trained on high-quality epitope datasets covering a broad range of HLA alleles. To enable prediction of endogenous HLA class I-associated peptides across a large fraction of the human population, we used mass spectrometry to profile >185,000 peptides eluted from 95 HLA-A, -B, -C and -G mono-allelic cell lines. We identified canonical peptide motifs per HLA allele, unique and shared binding submotifs across alleles and distinct motifs associated with different peptide lengths. By integrating these data with transcript abundance and peptide processing, we developed HLAthena, providing allele-and-length-specific and pan-allele-pan-length prediction models for endogenous peptide presentation. These models predicted endogenous HLA class I-associated ligands with 1.5-fold improvement in positive predictive value compared with existing tools and correctly identified >75% of HLA-bound peptides that were observed experimentally in 11 patient-derived tumor cell lines.

2019-10-09 | MSV000084442 | MassIVE

Mono-allelic datasets for: A large peptidome dataset improves HLA class I epitope prediction across most of the human population

2019-08-06 | MSV000084172 | MassIVE

Improved prediction of endogenous HLA-associated epitopes based on mono-allelic mass spectrometry profiling

Project description:LC-MS/MS-based identification of HLA-peptides is poised to provide a deep understanding of the rules underlying antigen presentation. However, a key obstacle limiting the utility of MS data is the ambiguity arising from the co-expression of multiple HLA alleles. Here, we introduce a strategy for profiling the HLA ligandome one allele at a time. By using cell lines expressing a single HLA allele, optimizing immunopurifications, and developing a novel spectral search algorithm, we identified thousands of peptides bound to 16 different HLA class I alleles. These data enabled the discovery of novel binding motifs, and an integrative analysis quantifying the contribution of factors critical to epitope presentation, such as protein cleavage and gene expression. We trained neural network prediction algorithms with our large dataset (>24,000 peptides) and outperformed algorithms trained on datasets of peptides with measured affinities. We thus demonstrate a scalable strategy for systematically learning the rules of endogenous antigen presentation.

2017-02-21 | GSE93315 | GEO

Unsupervised mining of HLA-I peptidomes reveals unsuspected false positives and new binding motifs

Project description:Modern antigen vaccine designs and studies of human leukocyte antigen (HLA)-mediated immune responses rely heavily on the knowledge of HLA allele-specific binding motifs and computational prediction of antigen-HLA binding affinity. Breakthroughs in HLA peptidomics have considerably expanded the databases of natural HLA antigens and enabled detailed characterizations of antigen-HLA binding specificity. However, cautions must be made when analyzing HLA peptidomics data because identified peptides may be contaminants or may weakly bind to the HLA molecules. Here, a hybrid de novo peptide sequencing approach was applied to large-scale mono-allelic HLA peptidomics datasets to uncover new antigens and refine current knowledge of HLA binding motifs. Up to 12-40% contaminations in the form of tryptic peptides were identified in the peptidomics data of HLA alleles whose binding motifs do not involve an arginine or a lysine at the C-terminus. Thousands of these peptides were reported in a community database as positive antigens and might be erroneously used to train prediction models. Furthermore, unsupervised clustering of identified antigens not only revealed additional binding motifs for several HLA class I alleles but also effectively isolated outliers which were confirmed to be false positives in a binding experiment. Overall, our findings expanded the knowledge of HLA binding specificity and indicated that a more careful HLA peptidomics data interpretation protocol is needed to ensure the high quality of community antigen databases.

2021-09-17 | PXD028088 | Pride

the landscape of phosphorylated HLA-I ligands

Project description:The identification and prediction of HLA-I–peptide interactions play an important role in our understanding of antigen recognition in 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. In this study, we collected in-depth phosphorylated HLA-I peptidomics data (1,920 unique phosphorylated peptides) from several studies covering 67 HLA-I alleles and expanded our motif deconvolution tool to identify precise binding motifs of phosphorylated HLA-I ligands for several alleles. In addition to the previously observed preferences for phosphorylation at P4, for proline next to the phosphosite and for arginine at P1, we could detect a clear enrichment of phosphorylated peptides among HLA-C ligands and among longer peptides. Binding assays were used to validate and interpret these observations. We then used these data to develop the first predictor of HLA-I– phosphorylated peptide interactions and demonstrated that combining phosphorylated and unmodified HLA-I ligands in the training of the predictor led to highest accuracy.

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Secreted HLA Fc-Fusion Profiles Immunopeptidome in Hypoxic PDAC and Cellular Senescence

Project description:Here, we describe a secreted HLA (sHLA) Fc-fusion construct for simple single HLA allele profiling in hypoxic pancreatic ductal adenocarcinoma (PDAC) and cellular senescence. This method streamlines sample preparation, enables temporal control, and provides allele-restricted target identification. Over 30,000 unique HLA-associated peptides were identified across two different HLA alleles and seven cell lines, with ~9,300 peptides newly discovered. The sHLA Fc-fusion capture technology holds potential to expedite immunopeptidomics and advance therapeutic interest in peptide-HLA complexes.

2024-01-26 | PXD045796 | Pride

Mass spectrometry profiling of HLA-associated peptidomes in mono-allelic cells enables more accurate epitope prediction

Project description:Abelin JG, Keskin DB, Sarkizova S, Hartigan CR, Zhang W, Sidney J, Stevens J, Lane W, Zhang GL, Eisenhaure T, Clauser KR, Hacohen N, Rooney MS, Carr SA, and Wu, CJ. Immunity, 2017. Identification of human leukocyte antigen (HLA)-bound peptides by liquid chromatography-tandem mass spectrometry (LC-MS/MS) is poised to provide a deep understanding of rules underlying antigen presentation. However, a key obstacle is the ambiguity that arises from the co-expression of multiple HLA alleles. Here, we have implemented a scalable mono-allelic strategy for profiling the HLA-peptidome. By using cell lines expressing a single HLA allele, optimizing immunopurifications, and developing an application-specific spectral search algorithm, we identified thousands of peptides bound to 16 different HLA class I alleles. These data enabled the discovery of subdominant binding motifs and an integrative analysis quantifying the contribution of factors critical to epitope presentation, such as protein cleavage and gene expression. We trained neural network prediction algorithms with our large dataset (>24,000 peptides) and outperformed algorithms trained on datasets of peptides with measured affinities. We thus demonstrate a strategy for systematically learning the rules of endogenous antigen presentation.

2017-02-01 | MSV000080527 | MassIVE

Deep learning using tumor HLA peptide mass spectrometry datasets improves neoantigen identification

Project description:Neoantigens, which are expressed on tumor cells, are one of the main targets of an effective anti-tumor T-cell response. Cancer immunotherapies to target neoantigens are of growing interest, and are currently in early human trials, but methods to identify neoantigens either require invasive or difficult-to-obtain clinical specimens, the screening of hundreds to thousands of synthetic peptides or tandem minigenes or are only relevant to specific human leukocyte antigen (HLA) alleles. We apply deep learning to a large (N=74 patients) HLA peptide and genomic dataset from various human tumors to create a computational model of antigen presentation for neoantigen prediction. We show that our model, named EDGE, increases the positive predictive value of HLA antigen prediction by up to 9 fold. We apply EDGE to enable identification of neoantigens and neoantigen-reactive T cells using routine clinical specimens and small numbers of synthetic peptides for most common HLA alleles. EDGE could enable an improved ability to develop neoantigen-targeted immunotherapies for cancer patients.

2018-07-21 | MSV000082648 | MassIVE

Defining HLA-II ligand processing and binding rules with mass spectrometry enhances cancer epitope prediction

Project description:Increasing evidence indicates CD4+ T cells can recognize cancer-specific antigens and control tumor growth. However, it remains difficult to predict the antigens that will be presented by human leukocyte antigen class II molecules (HLA-II) - hindering efforts to optimally target them therapeutically. Obstacles include inaccurate peptide-binding prediction and unsolved complexities of the HLA-II pathway. To address these challenges, we introduce an improved technology for discovering HLA-II binding motifs and conduct a comprehensive analysis of tumor-ligandomes to learn processing rules relevant in the tumor microenvironment (TME). We profiled HLA-II alleles and showed that binding motifs are highly sensitive to HLA-DM, a peptide loading chaperone. We also revealed that intratumoral HLA-II presentation is dominated by professional antigen presenting cells (APCs), rather than cancer cells. Integrating these observations, we developed algorithms that accurately predict APC ligandomes, including peptides from phagocytosed cancer cells. These tools and biological insights will enhance HLA-II directed cancer therapies.

2019-06-18 | MSV000083991 | MassIVE

SARS-CoV-2 infected cells present HLA-I peptides from canonical and out-of-frame ORFs

Project description:T cell-mediated immunity seems to play a critical role against SARS-CoV-2 infection and establishing protective memory. Yet the repertoire of viral epitopes responsible for activation of T cell responses remains mostly unknown. Identification and characterization of viral peptides presented on class I human leukocyte antigen (HLA-I) will reveal the viral signatures as seen by cytotoxic T cells that can then be harnessed for the development of effective vaccines. Here, we report the first HLA-I immunopeptidome of SARS-CoV-2 in two human cell lines at different times post-infection using mass spectrometry. We found HLA-I peptides derived not only from canonical ORFs, but also from internal out-of-frame ORFs in Spike and Nucleoprotein not currently captured by vaccine approaches. Whole proteome analysis revealed that expression of ubiquitination pathway proteins, and the proteasome maturation protein, POMP, were significantly altered in infected cells. Retrospective analysis of computational predictions highlighted shortcomings of in silico-only approaches in recovering the observed viral epitopes. Finally, given the experimental evidence for endogenous processing and presentation of the viral peptides we detected, we estimated that at least one HLA-A, -B, or -C allele is covered by at least one peptide for 99% of the population. These biological insights and the list of naturally presented SARS-CoV-2 peptides will facilitate data-driven selection of peptides for immune monitoring and vaccine development.

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