An integrated database of experimentally validated major histocompatibility complex epitopes for antigen-specific cancer therapy.
Satoru KawakitaAidan ShenCheng-Chi ChaoZhaohui WangSiliangyu ChengBingbing LiChongming JiangPublished in: Antibody therapeutics (2024)
Cancer immunotherapy represents a paradigm shift in oncology, offering a superior anti-tumor efficacy and the potential for durable remission. The success of personalized vaccines and cell therapies hinges on the identification of immunogenic epitopes capable of eliciting an effective immune response. Current limitations in the availability of immunogenic epitopes restrict the broader application of such therapies. A critical criterion for serving as potential cancer antigens is their ability to stably bind to the major histocompatibility complex (MHC) for presentation on the surface of tumor cells. To address this, we have developed a comprehensive database of MHC epitopes, experimentally validated for their MHC binding and cell surface presentation. Our database catalogs 451 065 MHC peptide epitopes, each with experimental evidence for MHC binding, along with detailed information on human leukocyte antigen allele specificity, source peptides, and references to original studies. We also provide the grand average of hydropathy scores and predicted immunogenicity for the epitopes. The database (MHCepitopes) has been made available on the web and can be accessed at https://github.com/jcm1201/MHCepitopes.git. By consolidating empirical data from various sources coupled with calculated immunogenicity and hydropathy values, our database offers a robust resource for selecting actionable tumor antigens and advancing the design of antigen-specific cancer immunotherapies. It streamlines the process of identifying promising immunotherapeutic targets, potentially expediting the development of effective antigen-based cancer immunotherapies.
Keyphrases
- papillary thyroid
- immune response
- adverse drug
- squamous cell
- cancer therapy
- endothelial cells
- cell surface
- dendritic cells
- palliative care
- healthcare
- lymph node metastasis
- drug delivery
- drinking water
- stem cells
- bone marrow
- emergency department
- squamous cell carcinoma
- transcription factor
- machine learning
- inflammatory response
- young adults
- big data
- dna binding
- climate change
- deep learning
- systemic lupus erythematosus