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The Dark Matter of the Immunopetidome: Cryptic Peptides as Tumor Antigens in Melanoma.

Subject Area Dermatology
Hematology, Oncology
Immunology
Term from 2019 to 2023
Project identifier Deutsche Forschungsgemeinschaft (DFG) - Project number 420185699
 
Final Report Year 2023

Final Report Abstract

A major goal of this project was to identify novel tumor-specific recurrent T-cell epitopes as targets for cancer immunotherapy in melanoma. The focus of our efforts here was on cryptic HLA peptides that originate from non-canonical open reading frames (ORFs) outside the protein coding sequence (CDS). These have only recently been discovered as a potentially high-yield source of tumor-specific T-cell antigens. In this project, we analyzed the immunopeptidomes of 10 melanoma samples (9 metastases & 1 primary tumor) and 5 melanoma cell lines by mass spectrometry. Data analysis was performed using our software tool Peptide-PRISM, which allows for the comprehensive identification of cryptic HLA peptides. To identify the genomic origin of the cryptic peptides, and to characterize their tumor specificity, Whole Genome Sequencing (WGS) and RNA-seq (RiboMinus) data were generated from the identical melanoma samples and combined with the immunopeptidome data. We will make these data sets (immunopeptidome, WGS, RNA-seq, clinical data) available to the scientific community after primary publication. In order to identify the most promising tumor-specific recurrent cryptic peptides (so-called TURCs), we have created an extensive immunopeptidome database using both healthy donor data (including the HLA-Ligand Atlas Project) and numerous tumor data from a wide variety of entities. This tumor/healthy database (TvHdb) currently includes more than 2500 MS raw data files from ~300 different patients/cell lines and more than 350k HLA-I peptides, including approximately 16k cryptic peptides. Using this database, we have identified ~50 TURCs for the analyzed patient cohort whose immunological characterization (immunogenicity by in vitro priming assay, TCR isolation, T-cell killing assay) is ongoing. These ongoing analyses are the first step towards clinical application. Other important outcomes of this project are the development and refinement of tools for immunopeptidome data analysis, as well as methodological improvement in sample preparation (improved detection of hydrophobic HLA peptides) and mass spectrometric analysis, in particular the development of new methods for quantitative immunopeptidome analysis using pSILAC. By creating innovative computational approaches, we have improved the accuracy and efficiency of immunopeptidome data analysis. These tools provide researchers with expanded capabilities to explore the landscape of the immunopeptidome and identify potential targets for cancer immunotherapy. Overall, the findings obtained in this project have contributed to a significantly improved understanding of cryptic HLA peptides. Our results indicate that tumor-specific T-cell epitopes are found significantly more frequently among cryptic HLA peptides than among conventional HLA peptides, and that cryptic peptides in principle represent suitable targets for cancer immunotherapy.

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