Identification of the Cryptic HLA-I Immunopeptidome
peer-reviewed · Cancer Immunology Research · 2020
| Date | 2020-08-01 |
| Type | peer-reviewed |
| Venue | Cancer Immunology Research |
| Publisher | American Association for Cancer Research (AACR) |
| Contribution | adjacent |
| DOI | 10.1158/2326-6066.cir-19-0886 |
| Citations (OpenAlex) | 133 |
Abstract
The success of cancer immunotherapy relies on the ability of cytotoxic T cells to specifically recognize and eliminate tumor cells based on peptides presented by HLA-I. Although the peptide epitopes that elicit the corresponding immune response often remain unidentified, it is generally assumed that neoantigens, due to tumor-specific mutations, are the most common targets. Here, we used a mass spectrometric approach to show an underappreciated class of epitopes that accounts for up to 15% of HLA-I peptides for certain HLA alleles in various tumors and patients. These peptides are translated from cryptic open reading frames in supposedly noncoding regions in the genome and are mostly unidentifiable with conventional computational analyses of mass spectrometry (MS) data. Our approach, Peptide-PRISM, identified thousands of such cryptic peptides in tumor immunopeptidomes. About 20% of these HLA-I peptides represented the C-terminus of the corresponding translation product, suggesting frequent proteasome-independent processing. Our data also revealed HLA-I allele-dependent presentation of cryptic peptides, with HLA-A*03 and HLA-A*11 presenting the highest percentage of cryptic peptides. Our analyses refute the reported frequent presentation of HLA peptides generated by proteasome-catalyzed peptide splicing. Thus, Peptide-PRISM represents an important step toward comprehensive identification of HLA-I immunopeptidomes and reveals cryptic peptides as an abundant class of epitopes with potential relevance for novel immunotherapeutic approaches.
Methods and tools
- Peptide-PRISM: Identifies cryptic HLA-I peptides by matching the top de novo sequencing candidates for each spectrum against a six-frame translation of the genome and transcriptome, then scoring them in a stratified FDR; showed cryptic peptides are a substantial part of the HLA-I immunopeptidome.
Cites (4)
- Deep learning enables de novo peptide sequencing from data-independent-acquisition mass spectrometry (2018) crossref
- Estimating the Contribution of Proteasomal Spliced Peptides to the HLA-I Ligandome* (2018) crossref
- PEAKS DB: De Novo Sequencing Assisted Database Search for Sensitive and Accurate Peptide Identification (2012) crossref
- De Novo Peptide Sequencing and Identification with Precision Mass Spectrometry (2007) crossref
Cited by (4)
- SILAC-based quantification reveals modulation of the immunopeptidome in BRAF and MEK inhibitor sensitive and resistant melanoma cells (2025) both
- Exploring the Immunogenicity of Noncanonical HLA-I Tumor Ligands Identified through Proteogenomics (2023) both
- T cells of colorectal cancer patients’ stimulated by neoantigenic and cryptic peptides better recognize autologous tumor cells (2022) both
- Proteogenomic Analysis Unveils the HLA Class I-Presented Immunopeptidome in Melanoma and EGFR-Mutant Lung Adenocarcinoma (2021) both