Evidence map›Paper›PMID 40266219›Full record

ArticleVaccines2025

Targeted Delivery of Personalized Cancer Vaccines Based on Antibody-Antigen Complexes.

Yaling Zhang, Lingling Yan, He Sun, Ziyi Zhang, Fengyun Shen, Lele Sun

Abstract read
In one paragraph

Article in Vaccines, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Yaling ZhangSchool of Life Sciences, Shanghai University, Shanghai 200444, China.
Lingling YanSchool of Life Sciences, Shanghai University, Shanghai 200444, China.
He SunSchool of Life Sciences, Shanghai University, Shanghai 200444, China.
Ziyi ZhangSchool of Life Sciences, Shanghai University, Shanghai 200444, China.
Fengyun ShenInstitute of Materiobiology, Department of Chemistry, College of Science, Shanghai University, Shanghai 200444, China.
Lele SunInstitute of Materiobiology, Department of Chemistry, College of Science, Shanghai University, Shanghai 200444, China.

Funding

National Key Research and Development Program of China 2022YFB3808200National Natural Science Foundation of China 22277071National Natural Science Foundation of China 22307078
6 · The paper itself

Abstract

backgroundPersonalized cancer vaccines based on tumor neoantigens show great potential in cancer immunotherapy due to their high safety and specificity. However, it is inherently difficult to realize the efficiently targeted delivery of personalized cancer vaccines to antigen-presenting cells (APCs).

methodsThis study aimed to address these challenges by developing and evaluating a personalized cancer vaccine based on antibody-antigen complexes, which was designed to enhance antitumor effects by increasing the utilization of tumor neoantigens by APCs. Mice were immunized with a carrier protein, keyhole limpet hemocyanin (KLH), to induce the production of antibodies against KLH. Subsequently, mice were immunized with KLH loaded with tumor neoantigens and the immunoadjuvant CpG ODN and underwent immunological analysis to evaluate the immune and antitumor effects.

resultsThe results showed that preimmunization with KLH could promote the uptake of the personalized KLH-based tumor vaccine, which was enhanced by dendritic cells (DCs) and macrophages (Mφs), by strengthening the T-cell immune responses to tumors.

conclusionsCollectively, this work provides a new idea for the targeted delivery of personalized cancer vaccines.

Indexed as

antibody–antigen complexescancer vaccineimmunotherapyneoantigen

Identifiers

PMID40266219
PMCPMC11946472

What OpenQuestion holds

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Registered trials

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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.