Evidence map›Paper›PMID 41150755›Full record

ReviewCurrent issues in molecular biology2025

Dendritic Cell Immunotherapy for Solid Tumors: Advances in Translational Research and Clinical Application.

Mi Eun Kim, Jun Sik Lee

Abstract readReview
In one paragraph

Review in Current issues in molecular biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. Review
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

2 authors.

Mi Eun KimDepartment of Biological Science, Immunology Research Lab & BK21-Four Educational Research Group for Age-Associated Disorder Control Technology, Chosun University, Gwangju 61452, Republic of Korea.
Jun Sik LeeDepartment of Biological Science, Immunology Research Lab & BK21-Four Educational Research Group for Age-Associated Disorder Control Technology, Chosun University, Gwangju 61452, Republic of Korea.ORCID 0000-0003-1051-581X

Funding

Chosun University K206212016
6 · The paper itself

Abstract

Dendritic cells (DCs) are critical antigen-presenting cells that orchestrate the interface between innate and adaptive immunity, making them attractive approaches for cancer immunotherapy. Recent advances in the characterization of DC subsets, antigen delivery strategies, and adjuvant design have enabled the enhancement of DC-based vaccines for solid tumors. Clinical studies across melanoma, glioblastoma, prostate cancer, and non-small cell lung cancer have demonstrated safety and immunogenicity, with encouraging signals of clinical efficacy, particularly when DC vaccination is combined with immune checkpoint blockade or personalized neoantigen approaches. However, translational barriers remain, including the immunosuppressive tumor microenvironment, inefficient DC migration, and variability in manufacturing protocols. Developing solutions such as in vivo DC targeting, biomaterials-based delivery systems, high-resolution single-cell analyses, and artificial intelligence-driven epitope prediction are controlled to overcome these challenges. Together, these innovations highlight the evolving role of DC immunotherapy as a foundation of precision oncology, offering the potential to integrate personalized vaccination strategies into standard treatment paradigms for solid tumors. Therefore, in this review, we specifically focus on these advances in dendritic cell immunotherapy for solid tumors and their translational implications.

Indexed as

cancer vaccinesdendritic cell immunotherapysolid tumorstumor microenvironment

Identifiers

PMID41150755
PMCPMC12564614

What OpenQuestion holds

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LicenceCC BY
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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.