Evidence map›Paper›PMID 35017147›Full record

ArticleJournal for immunotherapy of cancer2022

Bedside formulation of a personalized multi-neoantigen vaccine against mammary carcinoma.

Mona O Mohsen, Daniel E Speiser, Justine Michaux, HuiSong Pak, Brian J Stevenson, Monique Vogel, Varghese Philipose Inchakalody, Simone de Brot, Said Dermime, Georges Coukos and 2 more

Open access · goldAbstract read
In one paragraph

Article in Journal for immunotherapy of cancer, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
2.4field-weighted citation impact, top 10% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 29 citations in OpenAlex.

  1. Review
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  3. Review
  4. Article
  5. Article
  6. Review
  7. Review
  8. Review
  9. Article
  10. Virus-like Particle (VLP) Vaccines for Cancer Immunotherapy.International journal of molecular sciences · 2023
    Review
  11. Review
  12. Virus-like particle vaccinology, from bench to bedside.Cellular & molecular immunology · 2022
    Review
  13. Article
  14. Finding the right help in the tumor microenvironment.The Journal of clinical investigation · 2022
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 7 institutions in 3 countries.

Mona O MohsenDepartment of Medical Oncology, Hamad Medical Corporation, Doha, Qatar mona.mohsen@dbmr.unibe.ch Michal.Bassani@chuv.ch.ORCID 0000-0003-3510-9148
Daniel E SpeiserDepartment of Oncology UNIL CHUV, University of Lausanne, Epalinges, Switzerland.
Justine MichauxDepartment of Oncology, University Hospital of Lausanne, Lausanne, Switzerland.
HuiSong PakDepartment of Oncology, University Hospital of Lausanne, Lausanne, Switzerland.
Brian J StevensonSwiss Institute of Bioinformatics, Lausanne, Switzerland.
Monique VogelDepartment of BioMedical Research, University of Bern, Bern, Switzerland.
Varghese Philipose InchakalodyDepartment of Medical Oncology, Hamad Medical Corporation, Doha, Qatar.
Simone de BrotUniversity of Bern, Bern, Switzerland.
Said DermimeDepartment of Medical Oncology, National Center for Cancer Care and Research, Doha, Qatar.ORCID 0000-0002-5526-7496
Georges CoukosLudwig Institute for Cancer Research, Lausanne Branch, Lausanne, Switzerland.
Michal Bassani-SternbergDepartment of Oncology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland mona.mohsen@dbmr.unibe.ch Michal.Bassani@chuv.ch.
Martin F BachmannDepartment of BioMedical Research, University of Bern, Bern, Switzerland.
University of Bern · CHUniversity Hospital of Lausanne · CHHamad Medical Corporation · QALudwig Cancer Research · CHNational Center for Cancer Care and Research · QASIB Swiss Institute of Bioinformatics · CHUniversity of Lausanne · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHarnessing the immune system to purposely recognize and destroy tumors represents a significant breakthrough in clinical oncology. Non-synonymous mutations (neoantigenic peptides) were identified as powerful cancer targets. This knowledge can be exploited for further improvements of active immunotherapies, including cancer vaccines, as T cells specific for neoantigens are not attenuated by immune tolerance mechanism and do not harm healthy tissues. The current study aimed at developing an optimized multitarget vaccine using short or long neoantigenic peptides utilizing virus-like particles (VLPs) as an efficient vaccine platform.

methodsMutations of murine mammary carcinoma cells were identified by integrating mass spectrometry-based immunopeptidomics and whole exome sequencing. Neoantigenic peptides were synthesized and covalently linked to virus-like nanoparticles using a Cu-free click chemistry method for easy preparation of vaccines against mouse mammary carcinoma.

resultsAs compared with short peptides, vaccination with long peptides was superior in the generation of neoantigen-specific CD4

conclusionIntegrating mass spectrometry-based immunopeptidomics and whole exome sequencing is an efficient approach for identifying neoantigenic peptides. Our multitarget VLP-based vaccine shows a promising anti-tumor effect in an aggressive murine mammary carcinoma model. Future clinical application using this strategy is readily feasible and practical, as click chemistry coupling of personalized synthetic peptides to the nanoparticles can be done at the bedside directly before injection.

Indexed as

AnimalsAntigens, NeoplasmBreast NeoplasmsCancer VaccinesCell Line, TumorFemaleHumansImmunotherapyMicePrecision MedicineAntigens, NeoplasmCancer Vaccinesbreast neoplasmsimmunotherapyvaccination

Identifiers

PMID35017147
PMCPMC8753436
OpenAlexW4225708665

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.