ArticleNature biotechnology2025
A comprehensive proteogenomic pipeline for neoantigen discovery to advance personalized cancer immunotherapy.
Article in Nature biotechnology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 88 papers.
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Who cites it
88 citing papers in PubMed.
- Warehouse-based, immunopeptidome-guided design of personalised peptide vaccines shows feasibility in clinical trial evaluation in CLL patients.Frontiers in immunology · 2024Trial
- Toward next-generation therapeutic HPV vaccines: A review on expanding the antigen repertoire and an integrated bioinformatics selection framework.Human vaccines & immunotherapeutics · 2026Review
- Colorectal cancer and tumor vaccines (Review).Oncology letters · 2026Review
- Predicting Neoantigen Immunogenicity frombioRxiv : the preprint server for biology · 2026Article
- Personalized mRNA Neoantigen Vaccines in Cancer: Linking Precision Antigen Selection, Immune Remodeling, and Checkpoint Blockade.International journal of molecular sciences · 2026Review
- Therapeutic cancer vaccines: development, challenges, and future perspectives.Acta pharmacologica Sinica · 2026Review
- Current landscape and future directions of neoantigen vaccines: A new era of personalized cancer immunotherapy.Innovation (Cambridge (Mass.)) · 2026Review
- Immunoediting and precision biomarkers in prostate cancer: The emerging role of liquid biopsy, immune contexture and HLA genotype (Review).International journal of oncology · 2026Review
- Beyond the genetic code: orchestrating epigenetic and immune landscapes with multivalent mRNA-exosome vaccines.Precision clinical medicine · 2026Review
- Artificial intelligence for translational personalized neoantigen cancer vaccine development.Journal of biomedical science · 2026Review
- From Universal Aspiration to Precision Immunization: A Hypothesis Framework for Antigen-Defined, HLA-Restricted Cancer Vaccines.Vaccines · 2026Review
- Review
- Technical review of artificial intelligence in TCR-T therapy.Journal of the National Cancer Center · 2026Review
- Innate Immune Cells in Non-Small Cell Lung Cancer: Roles in Tumor Progression and Therapeutic Responses.Cancer innovation · 2026Review
- The Central Role of HLA Class II-Restricted Helper Neoantigen Vaccines in Cancer Immunotherapy.Cancers · 2026Review
- AI-driven neoantigen identification: a comprehensive review from somatic variant calling to T cell recognition.Journal of translational medicine · 2026Review
- The global patent landscape of bispecific antibodies: technological innovations and therapeutic applications.Nature biotechnology · 2026Article
- Advances in delivery technologies-powered cancer vaccines.Bioactive materials · 2026Review
- Cancer Heterogeneity and Cancer Cell Plasticity: Molecular Mechanisms and Precision Therapy.MedComm · 2026Review
- pVACtools v6: A comprehensive suite for neoantigen prediction, visualization, and therapy design.ArXiv · 2026Article
28 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
29 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The accurate identification and prioritization of antigenic peptides is crucial for the development of personalized cancer immunotherapies. Publicly available pipelines to predict clinical neoantigens do not allow direct integration of mass spectrometry immunopeptidomics data, which can uncover antigenic peptides derived from various canonical and noncanonical sources. To address this, we present an end-to-end clinical proteogenomic pipeline, called NeoDisc, that combines state-of-the-art publicly available and in-house software for immunopeptidomics, genomics and transcriptomics with in silico tools for the identification, prediction and prioritization of tumor-specific and immunogenic antigens from multiple sources, including neoantigens, viral antigens, high-confidence tumor-specific antigens and tumor-specific noncanonical antigens. We demonstrate the superiority of NeoDisc in accurately prioritizing immunogenic neoantigens over recent prioritization pipelines. We showcase the various features offered by NeoDisc that enable both rule-based and machine-learning approaches for personalized antigen discovery and neoantigen cancer vaccine design. Additionally, we demonstrate how NeoDisc's multiomics integration identifies defects in the cellular antigen presentation machinery, which influence the heterogeneous tumor antigenic landscape.
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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.