ArticleFrontiers in immunology2024
IMPROVE: a feature model to predict neoepitope immunogenicity through broad-scale validation of T-cell recognition.
Article in Frontiers in immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A meta-analysis of experimentally validated neo-epitopes: patterns, biases, and opportunities.Cancer immunology, immunotherapy : CII · 2025Pooled it
- A Phase Ib/II Study of Atezolizumab in Combination with Thiopurine Therapy in Patients with Metastatic Solid Tumors and Intermediate Tumor Mutational Burden.Cancer research communications · 2026Trial
- Technical review of artificial intelligence in TCR-T therapy.Journal of the National Cancer Center · 2026Review
- 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
- Safety and feasibility of blood-derived multiple antigen-specific endogenously derived T cells (MASE-T) for metastatic melanoma.Immuno-oncology technology · 2026Article
- Engineering TCR-directed T-cells for the treatment of multiple myeloma.Frontiers in immunology · 2026Review
- Unlocking the undruggable spliceosome: generative AI and structural dynamics in cancer therapy.Frontiers in cell and developmental biology · 2026Review
- NeoGuider: neoepitope prediction using advanced feature engineering.Genome medicine · 2025Article
- Alternative splicing: from tumorigenesis to neoantigen-mediated cancer immunotherapy.Biomarker research · 2025Review
- TCR-engineered T cells targeting a shared β-catenin mutation eradicate solid tumors.Nature immunology · 2025Article
- Computation strategies and clinical applications in neoantigen discovery towards precision cancer immunotherapy.Biomarker research · 2025Review
- Leveraging mRNA technology for antigen based immuno-oncology therapies.Journal for immunotherapy of cancer · 2025Review
- NeoTImmuML: a machine learning-based prediction model for human tumor neoantigen immunogenicity.Frontiers in immunology · 2025Article
- NetMHCpan-4.2: improved prediction of CD8+ epitopes by use of transfer learning and structural features.Frontiers in immunology · 2025Article
- Innovative Immunoinformatics Tools for Enhancing MHC (Major Histocompatibility Complex) Class I Epitope Prediction in Immunoproteomics.Protein and peptide letters · 2025Review
- Neoantigen-based immunotherapy: advancing precision medicine in cancer and glioblastoma treatment through discovery and innovation.Exploration of targeted anti-tumor therapy · 2025Review
- Neoantigen immunogenicity landscapes and evolution of tumor ecosystems during immunotherapy with nivolumab.Nature medicine · 2024Article
- A modular protein language modelling approach to immunogenicity prediction.PLoS computational biology · 2024Article
- VACCIMEL, an allogeneic melanoma vaccine, efficiently triggers T cell immune responses against neoantigens and alloantigens, as well as against tumor-associated antigens.Frontiers in immunology · 2024Article
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Authors and funding
23 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Mutation-derived neoantigens are critical targets for tumor rejection in cancer immunotherapy, and better tools for neoepitope identification and prediction are needed to improve neoepitope targeting strategies. Computational tools have enabled the identification of patient-specific neoantigen candidates from sequencing data, but limited data availability has hindered their capacity to predict which of the many neoepitopes will most likely give rise to T cell recognition. Method: To address this, we make use of experimentally validated T cell recognition towards 17,500 neoepitope candidates, with 467 being T cell recognized, across 70 cancer patients undergoing immunotherapy. Results: We evaluated 27 neoepitope characteristics, and created a random forest model, IMPROVE, to predict neoepitope immunogenicity. The presence of hydrophobic and aromatic residues in the peptide binding core were the most important features for predicting neoepitope immunogenicity. Conclusion: Overall, IMPROVE was found to significantly advance the identification of neoepitopes compared to other current methods.
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