Proteogenomic Analysis Unveils the HLA Class I-Presented Immunopeptidome in Melanoma and EGFR-Mutant Lung Adenocarcinoma
peer-reviewed · Molecular & Cellular Proteomics · 2021
| Date | 2021-08-13 |
| Type | peer-reviewed |
| Venue | Molecular & Cellular Proteomics |
| Publisher | Elsevier BV |
| Contribution | downstream-application |
| DOI | 10.1016/j.mcpro.2021.100136 |
| Citations (OpenAlex) | 37 |
| Venue 2-year citedness | 4.69 |
Abstract
Immune checkpoint inhibitors and adoptive lymphocyte transfer-based therapies have shown great therapeutic potential in cancers with high tumor mutational burden (TMB), such as melanoma, but not in cancers with low TMB, such as mutant epidermal growth factor receptor (EGFR)-driven lung adenocarcinoma. Precision immunotherapy is an unmet need for most cancers, particularly for cancers that respond inadequately to immune checkpoint inhibitors. Here, we employed large-scale MS-based proteogenomic profiling to identify potential immunogenic human leukocyte antigen (HLA) class I-presented peptides in melanoma and EGFR-mutant lung adenocarcinoma. Similar numbers of peptides were identified from both tumor types. Cell line and patient-specific databases (DBs) were constructed using variants identified from whole-exome sequencing. A de novo search algorithm was used to interrogate the HLA class I immunopeptidome MS data. We identified 12 variant peptides and several classes of tumor-associated antigen-derived peptides. We constructed a cancer germ line (CG) antigen DB with 285 antigens. This allowed us to identify 40 class I-presented CG antigen-derived peptides. The class I immunopeptidome comprised more than 1000 post-translationally modified (PTM) peptides representing 58 different PTMs, underscoring the critical role PTMs may play in HLA binding. Finally, leveraging de novo search algorithm and an annotated long noncoding RNA (lncRNA) DB, we developed a novel lncRNA-encoded peptide discovery pipeline to identify 44 lncRNA-derived peptides that are presented by class I. We validated tandem MS spectra of select variant, CG antigen, and lncRNA-derived peptides using synthetic peptides and performed HLA class I-binding assays to demonstrate binding to class I proteins. In summary, we provide direct evidence of HLA class I presentation of a large number of variant and tumor-associated peptides in both low and high TMB cancer. These results can potentially be useful for precision immunotherapies, such as vaccine or adoptive cell therapies in melanoma and EGFR-mutant lung cancers.
Methods and tools
- Melanoma and EGFR lung cancer HLA-I proteogenomics: Proteogenomic HLA class I immunopeptidomics in melanoma and EGFR-mutant lung adenocarcinoma, using de novo search to find variant, PTM and lncRNA-encoded peptides.
Methods it uses
- PEAKS: Commercial DP-based de novo
Data deposited
- Proteogenomic analysis unveils the HLA Class I presented immunopeptidome in melanoma and EGFR mutant lung adenocarcinoma — as deposited · PXD022949
- Proteogenomic analysis unveils the expression of cancer associated and specific antigen-derived HLA Class ǀ immunopeptides in melanoma and EGFR mutant lung adenocarcinoma — as deposited · PXD019774
Cites (4)
- Identification of the Cryptic HLA-I Immunopeptidome (2020) both
- Deep learning enables de novo peptide sequencing from data-independent-acquisition mass spectrometry (2018) both
- De novo peptide sequencing by deep learning (2017) both
- Error-Tolerant Identification of Peptides in Sequence Databases by Peptide Sequence Tags (1994) both