PCI-DB: a novel primary tissue immunopeptidome database to guide next-generation peptide-based immunotherapy...

Area of Science:

• Immunology
• Oncology
• Bioinformatics

Background:

• Cancer immunotherapies depend on T cell recognition of peptide antigens presented by human leukocyte antigens (HLA).
• Identifying naturally presented peptide targets for personalized and off-the-shelf immunotherapies is challenging.

Purpose of the Study:

• To introduce the Peptides for Cancer Immunotherapy Database (PCI-DB) as an open-access resource for identifying peptide targets for cancer immunotherapy development.
• To facilitate the identification and characterization of tumor-associated antigens (TAAs) and neoepitopes.

Main Methods:

• Analysis of over 10,000 mass spectrometry (MS) files from 3,000+ tissue samples.
• Standardized data processing using nf-core pipelines MHCquant2 and epitopeprediction.
• Application of a global false discovery rate to minimize false positives.

Main Results:

• The PCI-DB contains over 6.6 million HLA class I and 3.4 million HLA class II peptides from 40+ tissue types.
• Insights into cancer-testis antigen representation and identification of cross-tumor and entity-specific TAAs and neoepitopes.
• Successful design of personalized peptide vaccines for two metastatic cancer patients, inducing potent T-cell responses and long-term survival.

Conclusions:

• PCI-DB is a versatile tool for understanding cancer antigen presentation.
• PCI-DB supports the development of novel immunotherapies.