Various cancer immunotherapies rely on the T cell recognition of peptide antigens presented on human leukocyte antigens (HLA). However, the identification and selection of naturally presented peptide targets for the development of personalized as well as off-the-shelf immunotherapy approaches remains challenging. Here, we introduce the open-access Peptides for Cancer Immunotherapy Database (PCI-DB, https://pci-db.org/), a comprehensive resource of immunopeptidome data originating from various malignant and benign primary tissues that provides the research community with a convenient tool to facilitate the identification of peptide targets for immunotherapy development. The PCI-DB includes > 6.6 million HLA class I and > 3.4 million HLA class II peptides from over 40 tissue types and cancer entities analyzed uniformly using high-sensitive nf-core bioinformatics pipelines and applying a global peptide false discovery rate (FDR) approach. First application of the database provided insights into the representation of cancer-testis antigens (CTA) across malignant and benign tissues and enabled the identification and characterization of the cross-tumor entity and entity-specific tumor-associated antigens as well as naturally presented neoepitopes from frequent cancer mutations. Further, we used the PCI-DB to design personalized peptide vaccines for two patients suffering from metastatic cancer. PCI-DB enabled the composition of both a multi-peptide vaccine comprising non-mutated, highly frequent tumor-associated antigens matching the immunopeptidome of the individual patient´s tumor and a neoepitope-based vaccine matching the mutational profile of the cancer patient. Both vaccine approaches induced potent and long-lasting T-cell responses, accompanied by long-term survival of these advanced cancer patients. In conclusion, the PCI-DB provides a highly versatile tool to broaden the understanding of cancer-related antigen presentation and, ultimately, supports the development of novel immunotherapies.
[dataset license: CC0 1.0 Universal (CC0 1.0)]
Keywords: Human ; Lc-msms ; Synthetic peptides ; DatasetType:Proteomics
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Prof. Dr. med. Juliane Walz, Department of Peptide-based Immunotherapy, N/A |
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