ReviewGenome medicine2019
Best practices for bioinformatic characterization of neoantigens for clinical utility.
Review in Genome medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 139 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
139 citing papers in PubMed, 2 syntheses or guidelines pooled it, 204 citations in OpenAlex.
- Challenges targeting cancer neoantigens in 2021: a systematic literature review.Expert review of vaccines · 2021Pooled it
- Neoantigen prediction and computational perspectives towards clinical benefit: recommendations from the ESMO Precision Medicine Working Group.Annals of oncology : official journal of the European Society for Medical Oncology · 2020Pooled it
- Developing neoantigen cancer vaccines: where are we now?Nature communications · 2026Review
- TrambaHLApan: A Transformer and Mamba-based Neoantigen Prediction Method Considering both Antigen Presentation and Immunogenicity.Interdisciplinary sciences, computational life sciences · 2026Article
- Take Five: harmonization in personalized cancer vaccines for cancer immunotherapy.Experimental & molecular medicine · 2026Review
- Cancer epitope prediction tools and analysis pipelines in CEDAR.Nucleic acids research · 2026Article
- AI-driven neoantigen identification: a comprehensive review from somatic variant calling to T cell recognition.Journal of translational medicine · 2026Review
- The critical role of artificial intelligence and bioinformatics in accelerating peptide-based vaccine discovery for tackling global infectious diseases.Briefings in bioinformatics · 2026Review
- Human Organoids as a Novel Tool for Vaccine Study.Advanced healthcare materials · 2026Review
- Adjuvant personalized multivalent neoantigen DNA vaccination for MGMT unmethylated glioblastoma: a phase 1 trial.Nature cancer · 2026Article
- Molecular origin, discovery, validation and application of neoantigens.Asian journal of pharmaceutical sciences · 2026Review
- HERVOminer: a sequence similarity-based approach for recognizing endogenous retrovirus origin of the peptidome.NPJ precision oncology · 2026Article
- Neoantigen-Encoded Oncolytic Viruses as Personalized Cancer Vaccines.Pharmaceuticals (Basel, Switzerland) · 2026Review
- A novel simulation tool for low-coverage whole-genome sequencing using multivariate Gaussian mixture models.BMC bioinformatics · 2026Article
- Predominant mutated non-canonical tumor-specific antigens identified by proteogenomics demonstrate immunogenicity and tumor suppression in CRC.Cell genomics · 2026Article
- ImmunoPepper: extracting personalized peptides from complex splicing graphs.Bioinformatics (Oxford, England) · 2026Article
- TAPINTO: A Novel Algorithm for Tumor-Associated Antigen Prediction Based on Information about Target Overexpression.Computational and structural biotechnology journal · 2026Article
- ImmunoNX: a robust bioinformatics workflow to support personalized neoantigen vaccine trials.ArXiv · 2025Article
- Computational neoantigen prediction for cancer immunotherapy.Genes and immunity · 2025Review
- Article
79 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
Neoantigens are newly formed peptides created from somatic mutations that are capable of inducing tumor-specific T cell recognition. Recently, researchers and clinicians have leveraged next generation sequencing technologies to identify neoantigens and to create personalized immunotherapies for cancer treatment. To create a personalized cancer vaccine, neoantigens must be computationally predicted from matched tumor-normal sequencing data, and then ranked according to their predicted capability in stimulating a T cell response. This candidate neoantigen prediction process involves multiple steps, including somatic mutation identification, HLA typing, peptide processing, and peptide-MHC binding prediction. The general workflow has been utilized for many preclinical and clinical trials, but there is no current consensus approach and few established best practices. In this article, we review recent discoveries, summarize the available computational tools, and provide analysis considerations for each step, including neoantigen prediction, prioritization, delivery, and validation methods. In addition to reviewing the current state of neoantigen analysis, we provide practical guidance, specific recommendations, and extensive discussion of critical concepts and points of confusion in the practice of neoantigen characterization for clinical use. Finally, we outline necessary areas of development, including the need to improve HLA class II typing accuracy, to expand software support for diverse neoantigen sources, and to incorporate clinical response data to improve neoantigen prediction algorithms. The ultimate goal of neoantigen characterization workflows is to create personalized vaccines that improve patient outcomes in diverse cancer types.
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What OpenQuestion holds
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.