ArticleNature biotechnology2020
A large peptidome dataset improves HLA class I epitope prediction across most of the human population.
Article in Nature biotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 337 papers, 2 of them syntheses that pooled it.
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Who cites it
337 citing papers in PubMed, 2 syntheses or guidelines pooled it, 522 citations in OpenAlex.
- A meta-analysis of experimentally validated neo-epitopes: patterns, biases, and opportunities.Cancer immunology, immunotherapy : CII · 2025Pooled it
- Analysis of the different subpeptidomes presented by the HLA class I molecules of the B7 supertype.Cellular immunology · 2023Pooled it
- Personalized neoantigen-targeting cancer vaccines in ovarian cancer: a phase 1 trial.Nature medicine · 2026Article
- Self-Assembling Peptide-Adjuvant Conjugate (SaPAC) Platform for Precision Cancer Immunotherapy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Review
- A p53Molecular therapy. Oncology · 2026Article
- Review
- The Ratio of Immunogenicity: A quantitative metric to identify highly immunogenic protein antigens to improve tissue-engineered biomaterials.Biomaterials · 2026Article
- Article
- Single-nucleus multimodal spatial transcriptomics reveals spatial colocalization of neoantigen-expressing tumor cells and cognate T cells.Nature biotechnology · 2026Article
- A spatial view of tumor neoantigen recognition.Nature biotechnology · 2026Article
- Cancer epitope prediction tools and analysis pipelines in CEDAR.Nucleic acids research · 2026Article
- Cross-task interpretability through unified modeling reveals a universal shortcut bias in neoantigen prediction.Cell genomics · 2026Article
- AI-driven neoantigen identification: a comprehensive review from somatic variant calling to T cell recognition.Journal of translational medicine · 2026Review
- Pre-existing antibody and T cell responses to SaCas9, AsCas12a and CasΦ are comparable in naïve individuals.Nature communications · 2026Article
- Dual-degenerate TCRs target multiple KRAS hotspot and HLA-A3 family combinations.Research square · 2026Article
- A Deep Learning-Driven Framework Integrating Organoid-Based Functional Validation Identifies Universal Neoantigens from Recurrent Glioma Mutations.Cancer research · 2026Article
- MHC Attention: Identifying HLA-E presented cancer antigens through deep learning and high-throughput screening.bioRxiv : the preprint server for biology · 2026Article
- Innovative CRISPR/Cas9-Based Strategy for Allele-Specific HLA Peptidome Analysis Using a Pan-HLA Antibody.Molecular & cellular proteomics : MCP · 2026Article
- PMGen: from peptide-MHC structure prediction to peptide generation.Bioinformatics (Oxford, England) · 2026Article
277 more citing papers are in PubMed but not listed here.
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Authors and funding
25 authors at 5 institutions in 1 country.
Funding
Abstract
Prediction of HLA epitopes is important for the development of cancer immunotherapies and vaccines. However, current prediction algorithms have limited predictive power, in part because they were not trained on high-quality epitope datasets covering a broad range of HLA alleles. To enable prediction of endogenous HLA class I-associated peptides across a large fraction of the human population, we used mass spectrometry to profile >185,000 peptides eluted from 95 HLA-A, -B, -C and -G mono-allelic cell lines. We identified canonical peptide motifs per HLA allele, unique and shared binding submotifs across alleles and distinct motifs associated with different peptide lengths. By integrating these data with transcript abundance and peptide processing, we developed HLAthena, providing allele-and-length-specific and pan-allele-pan-length prediction models for endogenous peptide presentation. These models predicted endogenous HLA class I-associated ligands with 1.5-fold improvement in positive predictive value compared with existing tools and correctly identified >75% of HLA-bound peptides that were observed experimentally in 11 patient-derived tumor cell lines.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.