ArticleNAR cancer2024
A large-scale study of peptide features defining immunogenicity of cancer neo-epitopes.
Article in NAR cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- 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
- Advances in predicting T cell epitope recognition for cancer immunotherapy.Nature cancer · 2026Review
- NeoPrecis: enhancing immunotherapy response prediction through integration of qualified immunogenicity and clonality-aware neoantigen landscapes.Nature communications · 2026Article
- A Reproducible Sequence-Level Strategy to Enhance Peptide Immunogenicity While Preserving Wild-Type Epitope Recognition.Antibodies (Basel, Switzerland) · 2025Article
- Intracellular Transport of Monomeric Peptides, (Poly)Peptide-Based Coacervates and Fibrils: Mechanisms and Prospects for Drug Delivery.International journal of molecular sciences · 2025Review
- Somatic Mutations in DNA Mismatch Repair Genes, Mutation Rate and Neoantigen Load in Acute Lymphoblastic Leukemia.Pharmaceuticals (Basel, Switzerland) · 2025Article
- NeoPrecis: Enhancing Immunotherapy Response Prediction through Integration of Qualified Immunogenicity and Clonality-Aware Neoantigen Landscapes.bioRxiv : the preprint server for biology · 2025Article
- Computation strategies and clinical applications in neoantigen discovery towards precision cancer immunotherapy.Biomarker research · 2025Review
- Integrative Computational Immunology Applied to Identify and Characterize CD8+ T-cell Neoepitopes of Shared KRAS Neoantigen Oncogenic Driver Hotspot Mutations.Asian Pacific journal of cancer prevention : APJCP · 2025Article
- Leveraging mRNA technology for antigen based immuno-oncology therapies.Journal for immunotherapy of cancer · 2025Review
- NetMHCpan-4.2: improved prediction of CD8+ epitopes by use of transfer learning and structural features.Frontiers in immunology · 2025Article
- 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
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Accurate prediction of immunogenicity for neo-epitopes arising from a cancer associated mutation is a crucial step in many bioinformatics pipelines that predict outcome of checkpoint blockade treatments or that aim to design personalised cancer immunotherapies and vaccines. In this study, we performed a comprehensive analysis of peptide features relevant for prediction of immunogenicity using the Cancer Epitope Database and Analysis Resource (CEDAR), a curated database of cancer epitopes with experimentally validated immunogenicity annotations from peer-reviewed publications. The developed model, ICERFIRE (ICore-based Ensemble Random Forest for neo-epitope Immunogenicity pREdiction), extracts the predicted ICORE from the full neo-epitope as input, i.e. the nested peptide with the highest predicted major histocompatibility complex (MHC) binding potential combined with its predicted likelihood of antigen presentation (%Rank). Key additional features integrated into the model include assessment of the BLOSUM mutation score of the neo-epitope, and antigen expression levels of the wild-type counterpart which is often reflecting a neo-epitope's abundance. We demonstrate improved and robust performance of ICERFIRE over existing immunogenicity and epitope prediction models, both in cross-validation and on external validation datasets.
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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.