Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Sensitive direct detection of cancer antigens enabled by user-defined peptide libraries

ABSTRACT: Data dependent mass spectrometry (MS) is routinely used to identify HLA-bound peptides but it can have limitations in sensitivity and reproducibility. We introduce Pepyrus, a method that uses E. coli to generate large-scale user-defined peptide libraries that can be utilized to improve the confidence in identification of HLA-bound peptides, including lowly abundant neoantigens. Using a Pepyrus peptide library paired with an HLA-specific data independent acquisition (DIA) MS strategy, we recovered >75% of the expected sequences per single injection for libraries of >10,000 peptides. Pepyrus peptide libraries also enabled the identification of 0.1 fmol spiked-in peptides in a complex background. Application of Pepyrus to create personalized peptide libraries facilitated the identification of clinically relevant HLA antigens from melanoma and renal cell carcinoma patient derived cell lines, several of which were previously undetected. Pepyrus customization enables rapid creation of patient- or disease-specific peptide libraries facilitating the confident identification of rare HLA peptides from immunopeptidomics data and the generation of large training datasets to improve spectrum, retention time, and IM prediction tools.

ORGANISM(S): unidentified plasmid

PROVIDER: GSE309497 | GEO | 2025/11/20

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other
	GSE309497_LibrarySequencing.tsv.gz	Tabular
	GSE309497_Peptide_Reference.fasta.gz	Fasta.gz

Items per page:

1 - 3 of 3

Similar Datasets

Improved sensitivity of immunopeptidomics by leveraging available large-scale pan-HLA spectral libraries and data-independent acquisition

Project description:Mass spectrometry is the state-of-the-art methodology for capturing the breadth and depth of the immunopeptidome across HLA allotypes and cell types. The majority of studies in the immunopeptidomics field are discovery-driven and data-dependent tandem mass spectrometry acquisition is commonly used, as it generates high quality references of peptide fingerprints. However, DDA suffers from the stochastic selection of abundant ions that leads to lower sensitivity and reproducibility. In contrast, in data-independent acquisition, the fragmentation and acquisition of all fragment ions from a predefined list of precursor isolation windows yields a comprehensive map for a given sample. Because often the amount of HLA peptides eluted from biological samples, is typically not sufficient for acquiring both meaningful DDA data necessary for generating comprehensive spectral libraries and DIA MS measurements, the implementation of DIA in the immunopeptidomics translational research domain has remained limited. We implemented a DIA immunopeptidomics workflow and assessed its sensitivity and accuracy by matching DIA data against libraries with growing complexity - from sample-specific libraries to libraries combining from two to forty different immunopeptidomics samples. Matching DIA immunopeptidomics data against complex pan-HLA spectral library resulted in a two-fold increase in peptide identification compared with sample-specific library and in a three-fold increase compared with DDA measurements, yet with no detrimental effect on the specificity. We concluded that a comprehensive pan-HLA library for DIA approach is highly advantageous for the clinical Immunopeptidomics where low amount of biological sample is available for immunopeptidomics.

2021-04-13 | PXD022950 | Pride

DDA acquisition of the immunopeptidomes of C1R-B*57:01 and C1R-A*02:01 cell lines

Project description:Immunopeptidomes are the peptide repertoires bound by the molecules encoded by the major histocompatibility complex (MHC) (human leukocyte antigen (HLA) in humans). These HLA-peptide complexes are presented on the cell surface for immune T cell recognition. Immunopeptidomics utilizes tandem mass spectrometry (MS/MS) to identify and quantify peptides bound to HLA molecules. The dataset deposited here contains the DDA runs used to generate the spectral libraries of the HLA-bound peptides purified and isolated from C1R-B*57:01 and C1R-A*02:01 cell lines.

2023-02-13 | PXD034429 | 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

MHCquant2 refines immunopeptidomics tumor antigen discovery

Project description:The identification of human leukocyte antigen (HLA)-presented peptides as targets of anti-cancer T cell response is pivotal for the development of novel immunotherapies. Mass spectrometry (MS)-based immunopeptidomics enables the detection of these peptides, yet confident identifications and thus implementation in immunotherapy design are hampered by the high diversity and low abundance of naturally presented HLA peptides. Here, we introduce MHCquant2, a Nextflow-based open-source pipeline that leverages OpenMS tools and peptide property predictors (DeepLC, MS2PIP) for highly sensitive and scalable HLA peptide identification and quantification across various MS platforms. MHCquant2 increased peptide identifications up to 27% with significant expansion of low-abundant peptides, outperforming state-of-the-art pipelines. Using MHCquant2 we build a comprehensive benign tissue repository comprising re-analyzed data from available benign immunopeptidomes and a novel benignMHCquant2 dataset, adding more than 160,000 novel naturally presented HLA peptides. First applications of this benign repository and the MHCquant2 pipeline enabled (i) the refinement of tumor-associated antigens, (ii) as well as the detection of novel, high-frequent tumor-exclusive peptide antigens for multiple tumor entities, and (iii) the identification and quantification of mutation-derived low-abundant neoepitopes. MHCquant2 refines tumor antigen discovery in immunopeptidomics, paving the way for the implementation of off-the-shelf and personalized immunotherapy design.

2025-08-22 | PXD058436 | Pride

TOFIMS mass spectrometry-based immunopeptidomics refines tumor antigen identification

Project description:T cell recognition of human leukocyte antigen (HLA)-presented tumor-associated peptides is central for cancer immune surveillance. Mass spectrometry (MS)-based immunopeptidomics represents the only unbiased method for directly identifying and characterizing naturally presented tumor-associated peptides, which represent a key prerequisite for the development of T cell-based immunotherapies. This study reports on the de novo implementation of ion mobility separation-based timsTOF MS for next-generation immunopeptidomics, enabling high-speed and sensitive detection of HLA peptides. A direct comparison of timsTOF-based with state-of-the-art immunopeptidomics using orbitrap technology showed significantly increased HLA peptide identifications from benign and malignant primary samples of solid tissue and hematological origin. First application of timsTOF immunopeptidomics for tumor antigen discovery enabled (i) the expansion of benign reference immunopeptidome databases with more than 150,000 HLA peptides from 94 primary benign tissue samples, (ii) the refinement of previously described tumor antigens, and (iii) the identification of a vast array of novel tumor antigens comprising low abundant neoepitopes that might serve as targets for future cancer immunotherapy development.

2023-11-21 | PXD038782 | Pride

MS²Rescore: Data-driven rescoring dramatically boosts immunopeptide identification rates

Project description:Immunopeptidomics aims to identify Major Histocompatibility Complex-presented peptides on every cell that can be used in anti-cancer vaccine development. However, existing immunopeptidomics data analysis pipelines suffer from the non-tryptic nature of immunopeptides, complicating their identification. Previously, peak intensity predictions by MS²PIP and retention time predictions by DeepLC, have been shown to improve tryptic peptide identifications when rescoring peptide-spectrum matches with Percolator. However, as MS²PIP was tailored towards tryptic peptides, we have here retrained MS²PIP to include non-tryptic peptides. Interestingly, the new models not only greatly improve predictions for immunopeptides, but also yield further improvements for tryptic peptides. We show that the integration of new MS²PIP models, DeepLC, and Percolator in one software package, MS²Rescore, increases spectrum identification rate and unique identified peptides with 46% and 36% compared to standard Percolator rescoring at 1% FDR. Moreover, MS²Rescore also outperforms the current state-of-the-art in immunopeptide-specific identification approaches. Integration of immunopeptide MS²PIP models, DeepLC, and Percolator into MS²Rescore thus allows substantial improved identification of novel epitopes from existing immunopeptidomics workflows.

2022-07-11 | PXD033868 | Pride

Data-independent immunopeptidomics discovery of low-abundant bacterial epitopes

Project description:Mass spectrometry-based immunopeptidomics is a powerful approach to uncover peptides presented by human leukocyte antigen (HLA) molecules and serves as an important discovery tool guiding vaccine design and immunotherapies. While data-dependent acquisition (DDA) has been the standard for navigating through the complexity associated with non-enzymatic immunopeptide database searches, data-independent acquisition (DIA) is increasingly adopted in immunopeptidomics research. In this work, we compare diaPASEF to conventional ddaPASEF in terms of global immunopeptidome profiling and bacterial epitope discovery of the model intracellular pathogen Listeria monocytogenes. We show that DIA spectrum-centric searches of pseudo-MS/MS spectra complements DDA analysis by uncovering additional human and bacterial immunopeptides. Furthermore, we leveraged the latest DIA-NN release for proteome-wide predicted spectral library searches of all HLA class I peptides, scoring approximately 150 million immunopeptide peptide precursors. This approached outperformed other methods in identification of MHC class I peptides and recovered low-abundant peptide precursors missed by other methods. Taken together, our results demonstrate how both DIA spectrum- and peptide-centric immunopeptidomics analysis are promising strategies to recover low-abundant immunopeptides.

2025-08-21 | PXD063560 | Pride

DIA acquisition of the immunopeptidomes of C1R-B*57:01 and C1R-A*02:01 cell lines

Project description:Immunopeptidomes are the peptide repertoires bound by the molecules encoded by the major histocompatibility complex (MHC) (human leukocyte antigen (HLA) in humans). These HLA-peptide complexes are presented on the cell surface for immune T cell recognition. Immunopeptidomics utilizes tandem mass spectrometry (MS/MS) to identify and quantify peptides bound to HLA molecules. Data-independent acquisition (DIA) has emerged as a powerful strategy for deep proteome-wide profiling, but DIA application to immunopeptidomics analyses has so far seen limited use. The dataset deposited here contains the DIA data of the HLA-bound peptides purified and isolated from C1R-B*57:01 and C1R-A*02:01 cell lines.

2023-02-13 | PXD034539 | Pride

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

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

Deep learning boosts immunopeptidomics one mass spectrum at a time

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

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data