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Gene and protein sequence features augment HLA class I ligand predictions (ribosome profiling)

ABSTRACT: The sensitivity of malignant tissues to T cell-based cancer immunotherapies is dependent on the presence of targetable HLA class I ligands on the tumor cell surface. Peptide intrinsic factors, such as HLA class I affinity, likelihood of proteasomal processing, and transport into the ER lumen have all been established as determinants of HLA ligand presentation. However, the role of sequence features at the gene and protein level as determinants of epitope presentation has not been systematically evaluated. To address this, we performed HLA ligandome mass spectrometry on patient-derived melanoma lines and used this data-set to evaluate the contribution of 7,124 gene and protein sequence features to HLA sampling. This analysis reveals that a number of predicted modifiers of mRNA and protein abundance and turn-over, including predicted mRNA methylation and protein ubiquitination sites, inform on the presence of HLA ligands. Importantly, integration of gene and protein sequence features into a machine learning approach augments HLA ligand predictions to a comparable degree as predictive models that include experimental measures of gene expression. Our study highlights the value of gene and protein features to HLA ligand predictions.

ORGANISM(S): Homo sapiens

PROVIDER: GSE210998 | GEO | 2023/01/08

REPOSITORIES: GEO

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Gene and protein sequence features augment HLA class I ligand predictions (mRNA sequencing)

Project description:The sensitivity of malignant tissues to T cell-based cancer immunotherapies is dependent on the presence of targetable HLA class I ligands on the tumor cell surface. Peptide intrinsic factors, such as HLA class I affinity, likelihood of proteasomal processing, and transport into the ER lumen have all been established as determinants of HLA ligand presentation. However, the role of sequence features at the gene and protein level as determinants of epitope presentation has not been systematically evaluated. To address this, we performed HLA ligandome mass spectrometry on patient-derived melanoma lines and used this data-set to evaluate the contribution of 7,124 gene and protein sequence features to HLA sampling. This analysis reveals that a number of predicted modifiers of mRNA and protein abundance and turn-over, including predicted mRNA methylation and protein ubiquitination sites, inform on the presence of HLA ligands. Importantly, integration of gene and protein sequence features into a machine learning approach augments HLA ligand predictions to a comparable degree as predictive models that include experimental measures of gene expression. Our study highlights the value of gene and protein features to HLA ligand predictions.

2023-01-08 | GSE210999 | GEO

Characterization of the HLA class I Ligandome from Extracellular vesicles

Project description:Extracellular vesicles have shown promise in the field of anti-tumor vaccines. EVs are antigen-presenting entities capable of eliciting potent T-cell responses and have therefore being explored as cell-free based peptide vaccine vectors. However, whether the EV ligandome sufficiently resembles the cell ligandome as to preserve antigenicity and immunogenicity of the ligands is still a crucial aspect to be addressed. In particular, when aiming to use EVs as cell-free vaccine vectors. To understand whether HLA-I complexes homing to EVs display the same ligand sequence features and characteristics than those homing to the cell surface, we used a highly sensitive MS-based immunopeptidomics approach to generate a comprehensive side-by-side analysis of the EV and whole-cell (WC) ligandomes. In this work we identified thousands of ligands derived from both EVs and WCs, which allowed a deep characterization of important ligand properties such as the occurrence of PTMs in both ligandomes. Our results indicate that EVs are as good as cells when it comes to antigen presentation, since the sequence properties of the antigens were conserved in EVs and WCs ligandomes. Interestingly, EVs were enriched in HLA-B ligands and cysteinylated peptides, which can ultimately affect the antigenicity and immunogenicity of the ligands and should be taken into consideration when developing cell-free EV-based peptide vaccination approaches.

2021-07-05 | PXD021177 | Pride

HLA ligand profiles of primary renal cell carcinoma maintained in metastases.sue

Project description:In recent years, several approaches have been taken in the peptide-based immunotherapy of metastatic renal cell carcinoma (RCC), although little is known about HLA presentation on metastases compared to primary tumor and normal tissue of RCC. In this study we compared primary tumor, normal tissue and metastases with the aim of identifying similarities and differences between these tissues. We performed this comparison for two RCC patients on the level of the HLA ligandome using mass spectrometry and for three patients on the level of the transcriptome using oligonucleotide microarrays. The quantitative results show that primary tumor is more similar to metastasis than to normal tissue, both on the level of HLA ligand presentation and mRNA. We were able to characterize a total of 142 peptides in the qualitative analysis of HLA-presented peptides. Six of them were significantly overpresented on metastasis, among them a peptide derived from CD151; fourteen were overpresented on both primary tumor and metastasis compared to normal tissue, among them an HLA ligand derived from tumor protein p53. Thus, we could demonstrate that peptide-based immunotherapy might affect tumor as well as metastasis of RCC, but not healthy kidney tissue. Furthermore we were able to identify several peptides derived from tumor-associated antigens that are suitable for vaccination of metastatic RCC. Three clear cell renal cell carcinomas including autologous normal tissue and autologous metastasis were analyzed. This dataset is part of the TransQST collection.

2008-09-03 | E-GEOD-12606 | biostudies-arrayexpress

Mapping the HLA ligandome landscape of acute myeloid leukemia: a targeted approach toward peptide-based immunotherapy

Project description:Identification of physiologically relevant peptide vaccine targets calls for the direct analysis of the entirety of naturally presented human leukocyte antigen (HLA) ligands, termed the HLA ligandome. In this study, we implemented this direct approach using immunoprecipitation and mass spectrometry to define acute myeloid leukemia (AML)-associated peptide vaccine targets. Mapping the HLA class I ligandomes of 15 AML patients and 35 healthy controls, more than 25 000 different naturally presented HLA ligands were identified. Target prioritization based on AML exclusivity and high presentation frequency in the AML cohort identified a panel of 132 LiTAAs (ligandome-derived tumor-associated antigens), and 341 corresponding HLA ligands (LiTAPs (ligandome-derived tumor-associated peptides)) represented subset independently in >20% of AML patients. Functional characterization of LiTAPs by interferon-γ ELISPOT (Enzyme-Linked ImmunoSpot) and intracellular cytokine staining confirmed AML-specific CD8+ T-cell recognition. Of note, our platform identified HLA ligands representing several established AML-associated antigens (e.g. NPM1, MAGED1, PRTN3, MPO, WT1), but found 80% of them to be also represented in healthy control samples. Mapping of HLA class II ligandomes provided additional CD4+ T-cell epitopes and potentially synergistic embedded HLA ligands, allowing for complementation of a multipeptide vaccine for the immunotherapy of AML.

2019-10-14 | PXD015748 | Pride

HLA ligand profiles of primary renal cell carcinoma maintained in metastases.sue

2008-09-03 | GSE12606 | GEO

Pre-fractionation extends, but creates a bias in the detectable HLA class Ι ligandome

Project description:HLA class Ι molecules on the cell surface enable CD8+ T lymphocytes to recognize cellular alterations in the form of antigens, including mutations, protein copy number alterations, aberrant post-translational modifications or pathogen proteins. At any given moment, tens of thousands of different self and foreign HLA class Ι peptide ligands may be presented on the cell surface by MHC class Ι complexes. Analysis of the HLA ligandome thrusts therefore unique challenges due to their enormous biochemical diversity and inherently wide range of abundances. Despite advances in enrichment, separation, MS instrumentation and fragmentation, it is still not achievable to cover the HLA class Ι ligandome in sufficient depth to support routine identification of e.g. viral pathogens or immuno-therapeutically important tumor neo-antigens. In this study, we evaluate two pre-fractionation techniques, high pH reversed phase and strong cation exchange for complementary analysis of HLA class Ι peptide ligands, benchmarking them against analyses circumventing pre-fractionation. We observe that pre-fractionation substantially extends the detectable HLA class Ι ligandome, but also creates an identification bias. We advocate a rational choice between no-fractionation, high pH reversed phase or strong cation exchange pre-fractionation for deeper HLA class Ι ligandome analysis depending on the targeted HLA locus, allele or peptide ligand modification

2019-02-25 | PXD011257 | Pride

Deep motif deconvolution of HLA-II peptidomes

Project description:CD4 T cells are key for priming and regulating immune recognition of infected and cancer cells, but predictions of class II epitopes have limited accuracy. We profiled over 100’000 HLA-II ligands by Mass Spectrometry and developed a novel motif deconvolution algorithm to analyze these data. Our work demonstrates substantial improvements in the definition of HLA-II binding motifs and enhanced accuracy in HLA-II ligand and class II epitope predictions.

2019-10-16 | PXD012308 | Pride

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

HLA-I peptidomes and phosphopeptidomes (B*40, B*27, B*07, B*39)

Project description:As aberrant phosphorylation is a hallmark of tumor cells, the display of tumor-specific phosphopeptides by Human Leukocyte Antigen (HLA) class I molecules can be exploited in the treatment of cancer by T-cell-based immunotherapy. Yet, the characterization and prediction of HLA-I phospholigands is challenging as the molecular determinants of the presentation of such post-translationally modified peptides are not fully understood. Here, we employed a peptidomic workflow to identify phosphorylated ligands associated with HLA-B*40, -B*27, -B*39 and -B*07. Remarkably, these phosphopeptides showed similar molecular features. Besides the specific anchor motifs imposed by the binding groove of each allotype, the predominance of phosphorylation at peptide position 4 (P4) became strikingly evident, as was the enrichment of basic residues at P1. This molecular understanding of the presentation of phosphopeptides by HLA-B molecules can help in predicting tumor-specific neo-antigens that arise from aberrant phosphorylation in cancer cells.

2016-12-12 | PXD005084 | Pride

B27 inmunopeptidome in vaccinica infected cells

Project description:Identification of natural human leukocyte antigen (HLA) ligandome is a key element to understand the cellular immune responses. Advanced high throughput mass spectrometry analyses identify a relevant, but not complete, fraction of the many tens of thousands of self-peptides generated by the antigen processing in live cells. In infected cells, in addition to this complex HLA ligandome, a minority of peptides from degradation of the few proteins encoded by the viral genome are also bound to HLA class I molecules. In this study, the standard immunoproteomics strategy was modified to include the classical acid stripping treatment after virus infection to enrich the HLA ligandome in virus ligands. Complexes of HLA-B*27:05-bound peptide pools were isolated from vaccinia virus (VACV)-infected cells treated with acid stripping after virus infection. The HLA class I ligandome was identified using high throughput mass spectrometry analyses, yielding 42 and 52 natural peptides processed and presented in untreated and after acid stripping treatment VACV-infected human cells, respectively. Most of these virus ligands were identified in both conditions, but a relevant fraction of VACV ligands detected by mass spectrometry was dependent of acid stripping treatment with almost twice more exclusive viral ligands that the untreated VACV-infected condition. Theoretical binding affinity prediction of the VACV HLA-B*27:05 ligands and acute antiviral T cell response characterization in the HLA transgenic mice model showed no differences between HLA ligands identified under the two conditions: untreated and acid stripping condition. These findings indicated that acid stripping treatment could be useful to identify HLA class I ligands from virus-infected cells.

2021-12-22 | PXD027859 | Pride

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