A complete mass spectrometry-based immunopeptidomics pipeline for neoantigen identification and validation
preprint · Research Square · 2023
| Date | 2023-11-09 |
| Type | preprint |
| Venue | Research Square |
| Publisher | Research Square |
| Contribution | downstream-application |
| DOI | 10.21203/rs.3.rs-3520814/v1 |
| Citations (OpenAlex) | 3 |
Abstract
Here we proposed DeepImmu, a complete pipeline for neoantigen identification and validation that was based solely on mass spectrometry (MS) immunopeptidomics. In particular, a new enrichment kit was developed for HLA peptide purification from a small amount of biopsied tissue as low as 18mg. To identify candidate neoantigens from such a small amount of sample, we built DeepNovo Peptidome, a highly sensitive de novo sequencing-based workflow for HLA peptide identification. Furthermore, we developed DeepSelf, a personalized model for immunogenicity prediction based on the central tolerance of T cells, which could be used to prioritize candidate neoantigens from de novo HLA peptides for in vitro validation. Finally, we presented a new MS-based immunopeptidomics study of native tumor tissues from five patients with cervical cancer. We applied DeepImmu pipeline to identify and prioritize candidate neoantigens from low amounts of tumor tissues, and then performed in vitro validation of autologous neoantigen-specific T cell responses to confirm our results. Our MS-based de novo sequencing approach provides an unbiased solution for neoantigen discovery, because it does not depend on prior knowledge of protein databases, RNA sequencing data, or the source of neoantigens. By reducing the amount of sample to biopsy size, our DeepImmu pipeline can be easily performed in routine clinical applications.
Methods and tools
- DeepNovo Peptidome: Immunopeptidomics pipeline
Cited by (1)
- Deep Learning Methods for De Novo Peptide Sequencing (2024) crossref