ArticleBlood advances2024
Neoantigen landscape supports feasibility of personalized cancer vaccine for follicular lymphoma.
Article in Blood advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03121677 (Pilot Study of a Personalized Tumor Vaccine Strategy and PD-1 Blockade in Patients With Follicular Lymphoma), which is not on this map. Cited by 8 papers.
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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.
Pilot Study of a Personalized Tumor Vaccine Strategy and PD-1 Blockade in Patients With Follicular Lymphoma
Who cites it
8 citing papers in PubMed.
- Automating neoantigen selection for personalized cancer vaccine design.medRxiv : the preprint server for health sciences · 2026Article
- The promises and challenges of neoantigen cancer vaccines.Nature biotechnology · 2026Review
- Soluble multi-epitope protein vaccination leverages antigen availability to drive CD8Frontiers in immunology · 2026Article
- ImmunoNX: a robust bioinformatics workflow to support personalized neoantigen vaccine trials.ArXiv · 2025Article
- mRNA vaccine platforms: linking infectious disease prevention and cancer immunotherapy.Frontiers in bioengineering and biotechnology · 2025Review
- Neoantigen-based immunotherapy: advancing precision medicine in cancer and glioblastoma treatment through discovery and innovation.Exploration of targeted anti-tumor therapy · 2025Review
- Advances in Tumor Antigen Vaccines: A New Frontier in Cancer Immunotherapy.International journal of medical sciences · 2025Review
- Advancements and challenges in personalized neoantigen-based cancer vaccines.Oncology reviews · 2025Review
Corrections and comments
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Authors and funding
33 authors.
Funding
Abstract
abstractPersonalized cancer vaccines designed to target neoantigens represent a promising new treatment paradigm in oncology. In contrast to classical idiotype vaccines, we hypothesized that "polyvalent" vaccines could be engineered for the personalized treatment of follicular lymphoma (FL) using neoantigen discovery by combined whole-exome sequencing (WES) and RNA sequencing (RNA-seq). Fifty-eight tumor samples from 57 patients with FL underwent WES and RNA-seq. Somatic and B-cell clonotype neoantigens were predicted and filtered to identify high-quality neoantigens. B-cell clonality was determined by the alignment of B-cell receptor (BCR) CDR3 regions from RNA-seq data, grouping at the protein level, and comparison with the BCR repertoire from healthy individuals using RNA-seq data. An average of 52 somatic mutations per patient (range, 2-172) were identified, and ≥2 (median, 15) high-quality neoantigens were predicted for 56 of 58 FL samples. The predicted neoantigen peptides were composed of missense mutations (77%), indels (9%), gene fusions (3%), and BCR sequences (11%). Building off of these preclinical analyses, we initiated a pilot clinical trial using personalized neoantigen vaccination combined with PD-1 blockade in patients with relapsed or refractory FL (#NCT03121677). Synthetic long peptide vaccines targeting predicted high-quality neoantigens were successfully synthesized for and administered to all 4 patients enrolled. Initial results demonstrate feasibility, safety, and potential immunologic and clinical responses. Our study suggests that a genomics-driven personalized cancer vaccine strategy is feasible for patients with FL, and this may overcome prior challenges in the field. This trial was registered at www.ClinicalTrials.gov as #NCT03121677.
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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.