ArticleOncoimmunology
Predicting T cell recognition of MHC class I restricted neoepitopes.
Article in Oncoimmunology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers.
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Who cites it
62 citing papers in PubMed.
- Current landscape and future directions of neoantigen vaccines: A new era of personalized cancer immunotherapy.Innovation (Cambridge (Mass.)) · 2026Review
- TrambaHLApan: A Transformer and Mamba-based Neoantigen Prediction Method Considering both Antigen Presentation and Immunogenicity.Interdisciplinary sciences, computational life sciences · 2026Article
- Cancer epitope prediction tools and analysis pipelines in CEDAR.Nucleic acids research · 2026Article
- The research progress of gastric cancer vaccines: a narrative review.Translational cancer research · 2026Review
- NeoGuider: neoepitope prediction using advanced feature engineering.Genome medicine · 2025Article
- Single-cell multi-omics in cancer immunotherapy: from tumor heterogeneity to personalized precision treatment.Molecular cancer · 2025Review
- Neoantigen load as a predictor of relapse in early-stage NSCLC: features that agonise and antagonise prognosis.Cancer immunology, immunotherapy : CII · 2025Article
- Computational methods and data resources for predicting tumor neoantigens.Briefings in bioinformatics · 2025Review
- Comparative performance analysis of neoepitope prediction algorithms in head and neck cancer.Frontiers in immunology · 2025Article
- A Computational Strategy for the Rapid Identification and Ranking of Patient-Specific T Cell Receptors Bound to Neoantigens.Macromolecular rapid communications · 2024Article
- PepCARES: A Comprehensive Advanced Refinement and Evaluation System for Peptide Design and Affinity Screening.ACS omega · 2024Article
- Identification of diagnostic candidate genes in COVID-19 patients with sepsis.Immunity, inflammation and disease · 2024Article
- Reverse vaccinology approaches to design a potent multiepitope vaccine against the HIV whole genome: immunoinformatic, bioinformatics, and molecular dynamics approaches.BMC infectious diseases · 2024Article
- Germline homozygosity and allelic imbalance of HLA-I are common in esophagogastric adenocarcinoma and impair the repertoire of immunogenic peptides.Journal for immunotherapy of cancer · 2024Article
- Modification of the tumor microenvironment enhances immunity with plasmid gene therapy.Cancer gene therapy · 2024Article
- Transfer learning improves pMHC kinetic stability and immunogenicity predictions.Immunoinformatics (Amsterdam, Netherlands) · 2024Article
- Cancer neoepitopes viewed through negative selection and peripheral tolerance: a new path to cancer vaccines.The Journal of clinical investigation · 2024Review
- In silico analysis of molecular mimicry between human aquaporin 3,F1000Research · 2024Article
- Structural and physical features that distinguish tumor-controlling from inactive cancer neoepitopes.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Unsupervised and supervised AI on molecular dynamics simulations reveals complex characteristics of HLA-A2-peptide immunogenicity.Briefings in bioinformatics · 2023Article
2 more citing papers are in PubMed but not listed here.
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Authors and funding
11 authors.
Funding
Abstract
Epitopes that arise from a somatic mutation, also called neoepitopes, are now known to play a key role in cancer immunology and immunotherapy. Recent advances in high-throughput sequencing have made it possible to identify all mutations and thereby all potential neoepitope candidates in an individual cancer. However, most of these neoepitope candidates are not recognized by T cells of cancer patients when tested in vivo or in vitro, meaning they are not immunogenic. Especially in patients with a high mutational load, usually hundreds of potential neoepitopes are detected, highlighting the need to further narrow down this candidate list. In our study, we assembled a dataset of known, naturally processed, immunogenic neoepitopes to dissect the properties that make these neoepitopes immunogenic. The tools to use and thresholds to apply for prioritizing neoepitopes have so far been largely based on experience with epitope identification in other settings such as infectious disease and allergy. Here, we performed a detailed analysis on our dataset of curated immunogenic neoepitopes to establish the appropriate tools and thresholds in the cancer setting. To this end, we evaluated different predictors for parameters that play a role in a neoepitope's immunogenicity and suggest that using binding predictions and length-rescaling yields the best performance in discriminating immunogenic neoepitopes from a background set of mutated peptides. We furthermore show that almost all neoepitopes had strong predicted binding affinities (as expected), but more surprisingly, the corresponding non-mutated peptides had nearly as high affinities. Our results provide a rational basis for parameters in neoepitope filtering approaches that are being commonly used.
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