ArticleSignal transduction and targeted therapy2023
Neo-intline: integrated pipeline enables neoantigen design through the in-silico presentation of T-cell epitope.
Article in Signal transduction and targeted therapy, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 18 citations in OpenAlex.
- AI-driven neoantigen identification: a comprehensive review from somatic variant calling to T cell recognition.Journal of translational medicine · 2026Review
- A Deep Learning-Driven Framework Integrating Organoid-Based Functional Validation Identifies Universal Neoantigens from Recurrent Glioma Mutations.Cancer research · 2026Article
- Molecular origin, discovery, validation and application of neoantigens.Asian journal of pharmaceutical sciences · 2026Review
- Review
- EpiMII: Structure-Aware Graph Neural Networks for MHC-II Epitope Generation.Research (Washington, D.C.) · 2026Article
- Computational Methods for Cancer Neoantigen Prediction.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Personalizing DNA Cancer Vaccines.Journal of personalized medicine · 2025Review
- Neoantigen-driven personalized tumor therapy: An update from discovery to clinical application.Chinese medical journal · 2025Review
- Identification of neoantigen epitopes in cervical cancer by multi-omics analysis.European journal of medical research · 2025Article
- Computation strategies and clinical applications in neoantigen discovery towards precision cancer immunotherapy.Biomarker research · 2025Review
- DNA-based immunotherapy for cancer: In vivo approaches for recalcitrant targets.Molecular therapy : the journal of the American Society of Gene Therapy · 2025Review
- EpiMII: Integrating Structure and Graph Neural Networks for MHC-II Epitope and Neoantigen Design.bioRxiv : the preprint server for biology · 2025Article
- OmniNeo: a multi-omics pipeline incorporating proteomics and AI selection for neoantigen optimization in tumor immunotherapy.Frontiers in immunology · 2025Article
- Advances of computational methods enhance the development of multi-epitope vaccines.Briefings in bioinformatics · 2024Review
- Molecular targets and strategies in the development of nucleic acid cancer vaccines: from shared to personalized antigens.Journal of biomedical science · 2024Review
- CD8Journal of translational medicine · 2024Review
- Stem-like CD8Frontiers in immunology · 2024Review
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Neoantigen vaccines are one of the most effective immunotherapies for personalized tumour treatment. The current immunogen design of neoantigen vaccines is usually based on whole-genome sequencing (WGS) and bioinformatics prediction that focuses on the prediction of binding affinity between peptide and MHC molecules, ignoring other peptide-presenting related steps. This may result in a gap between high prediction accuracy and relatively low clinical effectiveness. In this study, we designed an integrated in-silico pipeline, Neo-intline, which started from the SNPs and indels of the tumour samples to simulate the presentation process of peptides in-vivo through an integrated calculation model. Validation on the benchmark dataset of TESLA and clinically validated neoantigens illustrated that neo-intline could outperform current state-of-the-art tools on both sample level and melanoma level. Furthermore, by taking the mouse melanoma model as an example, we verified the effectiveness of 20 neoantigens, including 10 MHC-I and 10 MHC-II peptides. The in-vitro and in-vivo experiments showed that both peptides predicted by Neo-intline could recruit corresponding CD4
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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.