KRAS Epitope Identification Immunopeptidomics
Ontology highlight
ABSTRACT:
INSTRUMENT(S): Orbitrap Fusion Lumos, Q Exactive HF
ORGANISM(S): Homo Sapiens (human)
TISSUE(S): T Cell
SUBMITTER: 
Beatriz Carreno
LAB HEAD: Beatriz M. Carreno
PROVIDER: PXD024412 | Pride | 2021-05-29
REPOSITORIES: Pride
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| 01112019_Core_Adham_7_A2.raw | Raw | |||
| 01112019_MHC_AdhamB_7_A11_PRM.raw | Raw | |||
| 01112019_MHC_AdhamB_7_B7_PRM.raw | Raw | |||
| 01112019_MHC_AdhamB_K5_A11_PRM.raw | Raw | |||
| 01112019_MHC_AdhamB_K5_A2_PRM.raw | Raw |
Items per page: 5 1 - 5 of 30 |
Similar Datasets
Project description:The recent success of monoclonal antibody checkpoint inhibitor therapies that enhance the ability of CD8+ T cells to detect cancer-related antigenic peptides has refocused the need to fully understand the repertoire of peptides being presented to the immune system. Whilst the peptide ligandome presented by cell surface HLA class I molecules on cancer cells has been studied extensively, the ligandome of extracellular vesicles remains poorly defined. Here we report the HLA class I ligandome of both cell surface and extracellular vesicles from eight breast cancer cell lines (MCF7, MDA-MB-231, MDA-MB-361, MDA-MB-415, MDA-MB-453, HCC 1806, HCC 1395, and HCC 1954), and additionally the melanoma cell line ESTDAB-056 and the multiple myeloma line RPMI 8226. Utilising HLA class I immunoisolation and mass spectrometry we detected a total of 6574 peptides from the cell surface and 2461 peptides from the extracellular vesicles of the cell lines studied. Within the extracellular vesicle HLA class I ligandome we identified 150 peptides derived from tumour associated antigenic proteins, of which 19 peptides have been shown to elicit T cell responses in previous studies. Our data thus confirms for the first time the presence of clinically relevant tumour-associated antigenic peptides in the HLA class I ligandome present on EV.
2022-04-01 | PXD025345 | Pride
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
Project description: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.
2019-12-16 | GSE131267 | GEO
Project description:Comprehensive knowledge of the HLA class-I and class-II peptides presented to T-cells is crucial for designing innovative therapeutics against cancer and other diseases. However methodologies for their extraction for mass-spectrometry analysis have been a major limitation. We designed a novel high-throughput, reproducible and sensitive method for sequential extraction of HLA-I and -II peptides from up to 96 samples in a plate format from cell lines or tissues. Our methodology drastically reduces sample-handling and can be completed within six hours. We report identification and quantification of thousands of peptides with excellent reproducibility (Pearson correlations reaching 0.98 and 0.97, class I and II, respectively). Due to improved sensitivity, as many as 1,846 HLA-I and 2,633 HLA-II peptides were accurately identified from only 107 B-cells. We demonstrate the feasibility of performing drug-screening by using ovarian cancer cells treated with interferon gamma. This straightforward method is applicable for basic and clinical applications.
2017-12-15 | PXD006939 | Pride
Project description:Comprehensive knowledge of the HLA class-I and class-II peptides presented to T-cells is crucial for designing innovative therapeutics against cancer and other diseases. However methodologies for their extraction for mass-spectrometry analysis have been a major limitation. We designed a novel high-throughput, reproducible and sensitive method for sequential extraction of HLA-I and -II peptides from up to 96 samples in a plate format from cell lines or tissues. Our methodology drastically reduces sample-handling and can be completed within six hours. We report identification and quantification of thousands of peptides with excellent reproducibility (Pearson correlations reaching 0.98 and 0.97, class I and II, respectively). Due to improved sensitivity, as many as 1,846 HLA-I and 2,633 HLA-II peptides were accurately identified from only 107 B-cells. We demonstrate the feasibility of performing drug-screening by using ovarian cancer cells treated with interferon gamma. This straightforward method is applicable for basic and clinical applications.
2018-03-19 | MSV000082184 | MassIVE
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
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 | GSE210998 | GEO
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
Project description:Characterizing the human leukocyte antigen (HLA) bound ligandome by mass spectrometry (MS) holds great promise for developing vaccines and drugs for immune oncology. Still, the identification of such non-tryptic peptides presents substantial computational challenges. To address these, we synthesized >300,000 peptides within the ProteomeTools project representing HLA class I & II ligands and products of the proteases AspN and LysN and analyzed these by multi-modal LC-MS/MS. The resulting data enabled training of a single model using the deep learning framework Prosit that shows outstanding prediction accuracy of fragment ion spectra for tryptic and non-tryptic peptides. Applying Prosit demonstrates that the identification of HLA peptides can be improved by 50-300% on average, that proteasomal HLA peptide splicing may not exist and that additional neo-epitopes that elicit an immune response can be identified from patient tumors. Together, the provided peptides, spectra and computational tools substantially expand the scope of immunopeptidomics workflows.
2021-04-26 | PXD021398 | Pride
Project description:Immunopeptidome analysis of patient-derived colorectal cancer cell lines HROC113 and HROC285 T0 M2 from the HROC collection [Mullins et al. Cancers (Basel). 2019;11(10):1520. doi: 10.3390/cancers11101520] was performed to characterize the natural HLA class I presented ligandome in native as well as IFNγ treated cells.
2023-03-11 | PXD037587 | Pride