Integration of proteomics profiling data to facilitate discovery of cancer neoantigens: a survey
peer-reviewed · Briefings in Bioinformatics · 2025
| Date | 2025-03-04 |
| Type | peer-reviewed |
| Venue | Briefings in Bioinformatics |
| Publisher | Oxford University Press (OUP) |
| Contribution | review |
| DOI | 10.1093/bib/bbaf087 |
| Citations (OpenAlex) | 2 |
| Venue 2-year citedness | 7.18 |
Abstract
Cancer neoantigens are peptides that originate from alterations in the genome, transcriptome, or proteome. These peptides can elicit cancer-specific T-cell recognition, making them potential candidates for cancer vaccines. The rapid advancement of proteomics technology holds tremendous potential for identifying these neoantigens. Here, we provided an up-to-date survey about database-based search methods and de novo peptide sequencing approaches in proteomics, and we also compared these methods to recommend reliable analytical tools for neoantigen identification. Unlike previous surveys on mass spectrometry-based neoantigen discovery, this survey summarizes the key advancements in de novo peptide sequencing approaches that utilize artificial intelligence. From a comparative study on a dataset of the HepG2 cell line and nine mixed hepatocellular carcinoma proteomics samples, we demonstrated the potential of proteomics for the identification of cancer neoantigens and conducted comparisons of the existing methods to illustrate their limits. Understanding these limits, we suggested a novel workflow for neoantigen discovery as perspectives.
Methods and tools
- Neoantigen proteomics survey: Surveys database search and deep learning de novo sequencing for cancer neoantigen discovery and compares tools on HepG2 and HCC samples.
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