ArticleFrontiers in immunology2023
Unraveling tumor specific neoantigen immunogenicity prediction: a comprehensive analysis.
Article in Frontiers in immunology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Artificial intelligence for translational personalized neoantigen cancer vaccine development.Journal of biomedical science · 2026Review
- Combinatorial in situ cancer vaccines: unlocking broad and enhanced antitumor responses.Signal transduction and targeted therapy · 2026Review
- In silico design and immunoinformatics evaluation of a multi-epitope vaccine targeting SEPT9 for gastrointestinal adenocarcinomas.BMC immunology · 2026Article
- Multi-strategy embedded framework for neoantigen vaccine maturation.Scientific reports · 2026Article
- Computational identification of B- and T-cell epitopes: a unified task taxonomy and review of databases, datasets, predictive pipelines, and gaps.Frontiers in immunology · 2026Review
- CNNeoPP: a large language model-enhanced deep learning pipeline for personalized neoantigen prediction and liquid biopsy applications.Frontiers in immunology · 2026Article
- Engineering Anti-Tumor Immunity: An Immunological Framework for mRNA Cancer Vaccines.Vaccines · 2025Review
- TMBclaw: tumor clone-aware graph learning improves immunotherapy response prediction across heterogeneous cohorts.Briefings in bioinformatics · 2025Article
- Integrating necroptosis and immune landscapes: a multi-omics-derived NecropImmScore stratifies prognosis and therapy in ovarian cancer.Cancer immunology, immunotherapy : CII · 2025Article
- Evaluating Gene Fusions as Prognostic Biomarkers and Therapeutic Targets in Immune Checkpoint Blockade-Treated Advanced Melanoma: A Retrospective Analysis.Cancer research communications · 2025Article
- Computation strategies and clinical applications in neoantigen discovery towards precision cancer immunotherapy.Biomarker research · 2025Review
- TumorAgDB1.0: tumor neoantigen database platform.Database : the journal of biological databases and curation · 2025Article
- The Immunogenic Tumor-Specific Neoantigen Database (ITSNdb): A Tool for Comprehensive Performance Evaluation of Neoantigen Immunogenicity Predictors.Methods in molecular biology (Clifton, N.J.) · 2025Article
- NeoTImmuML: a machine learning-based prediction model for human tumor neoantigen immunogenicity.Frontiers in immunology · 2025Article
- Cancer neoepitopes viewed through negative selection and peripheral tolerance: a new path to cancer vaccines.The Journal of clinical investigation · 2024Review
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Identification of tumor specific neoantigen (TSN) immunogenicity is crucial to develop peptide/mRNA based anti-tumoral vaccines and/or adoptive T-cell immunotherapies; thus, accurate in-silico classification/prioritization proves critical for cost-effective clinical applications. Several methods were proposed as TSNs immunogenicity predictors; however, comprehensive performance comparison is still lacking due to the absence of well documented and adequate TSN databases. Methods: Here, by developing a new curated database having 199 TSNs with experimentally-validated MHC-I presentation and positive/negative immune response (ITSNdb), sixteen metrics were evaluated as immunogenicity predictors. In addition, by using a dataset emulating patient derived TSNs and immunotherapy cohorts containing predicted TSNs for tumor neoantigen burden (TNB) with outcome association, the metrics were evaluated as TSNs prioritizers and as immunotherapy response biomarkers. Results: Our results show high performance variability among methods, highlighting the need for substantial improvement. Deep learning predictors were top ranked on ITSNdb but show discrepancy on validation databases. In overall, current predicted TNB did not outperform existing biomarkers. Conclusion: Recommendations for their clinical application and the ITSNdb are presented to promote development and comparison of computational TSNs immunogenicity predictors.
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