ArticleScience immunology2023
Computational prediction of MHC anchor locations guides neoantigen identification and prioritization.
Article in Science immunology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
28 citing papers in PubMed, 25 citations in OpenAlex.
- Neoantigen cancer vaccines for gastrointestinal tumors: opportunities and challenges.MedScience · 2026Review
- Cross-task interpretability through unified modeling reveals a universal shortcut bias in neoantigen prediction.Cell genomics · 2026Article
- pVACtools v6: A comprehensive suite for neoantigen prediction, visualization, and therapy design.ArXiv · 2026Article
- PMGen: from peptide-MHC structure prediction to peptide generation.Bioinformatics (Oxford, England) · 2026Article
- Spatiotemporally engineered tumor-derived extracellular vesicle-based scaffold vaccine for personalized cancer immunotherapy.Nature communications · 2026Article
- BeitAI-pHLA: Multiallele Peptide-HLA Class I Binding Prediction Using Protein Language Model and Multi-Instance Learning.Computational and structural biotechnology journal · 2026Article
- Biological sequence analysis: Advances, medical applications, and challenges.Fundamental research · 2026Review
- ImmunoNX: a robust bioinformatics workflow to support personalized neoantigen vaccine trials.ArXiv · 2025Article
- RNA-based cancer vaccines: mechanisms, clinical progress, and translational challenges.Immunologic research · 2025Review
- Flagellin co-expression potentiates mRNA vaccine-induced cytotoxic T lymphocyte responses but not anti-tumor immunity.Scientific reports · 2025Article
- Personalizing DNA Cancer Vaccines.Journal of personalized medicine · 2025Review
- T cell receptor cross-reactivity prediction improved by a comprehensive mutational scan database.Cell systems · 2025Article
- Comprehensive assessment of computational methods for cancer immunoediting.Cell reports methods · 2025Article
- Dual mRNA nanoparticles strategy for enhanced pancreatic cancer treatment and β-elemene combination therapy.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Comprehensive epitope mutational scan database enables accurate T cell receptor cross-reactivity prediction.bioRxiv : the preprint server for biology · 2025Article
- OnmiMHC: a machine learning solution for UCEC tumor vaccine development through enhanced peptide-MHC binding prediction.Frontiers in immunology · 2025Article
- Relevance of mutation-derived neoantigens and non-classical antigens for anticancer therapies.Human vaccines & immunotherapeutics · 2024Review
- Genetically Engineered T Cells and Recombinant Antibodies to Target Intracellular Neoantigens: Current Status and Future Directions.International journal of molecular sciences · 2024Review
- pVACview: an interactive visualization tool for efficient neoantigen prioritization and selection.Genome medicine · 2024Article
- Molecular targets and strategies in the development of nucleic acid cancer vaccines: from shared to personalized antigens.Journal of biomedical science · 2024Review
Corrections and comments
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Authors and funding
25 authors at 5 institutions in 2 countries.
Funding
Abstract
Neoantigens are tumor-specific peptide sequences resulting from sources such as somatic DNA mutations. Upon loading onto major histocompatibility complex (MHC) molecules, they can trigger recognition by T cells. Accurate neoantigen identification is thus critical for both designing cancer vaccines and predicting response to immunotherapies. Neoantigen identification and prioritization relies on correctly predicting whether the presenting peptide sequence can successfully induce an immune response. Because most somatic mutations are single-nucleotide variants, changes between wild-type and mutated peptides are typically subtle and require cautious interpretation. A potentially underappreciated variable in neoantigen prediction pipelines is the mutation position within the peptide relative to its anchor positions for the patient's specific MHC molecules. Whereas a subset of peptide positions are presented to the T cell receptor for recognition, others are responsible for anchoring to the MHC, making these positional considerations critical for predicting T cell responses. We computationally predicted anchor positions for different peptide lengths for 328 common HLA alleles and identified unique anchoring patterns among them. Analysis of 923 tumor samples shows that 6 to 38% of neoantigen candidates are potentially misclassified and can be rescued using allele-specific knowledge of anchor positions. A subset of anchor results were orthogonally validated using protein crystallography structures. Representative anchor trends were experimentally validated using peptide-MHC stability assays and competition binding assays. By incorporating our anchor prediction results into neoantigen prediction pipelines, we hope to formalize, streamline, and improve the identification process for relevant clinical studies.
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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.