Evidence map›Paper›PMID 42564263›Full record

ReviewFrontiers in cell and developmental biology2026

Tumor organoid-immune cell co-culture systems for precision oncology.

Lei Cai, Yunfeng Xiao, Feihong Xie, Zongke Yang

Abstract readReview
In one paragraph

Review in Frontiers in cell and developmental biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

4 authors.

Lei Cai *Department of Breast Surgery, The Fourth Affiliated Hospital of China Medical University, Shenyang, China.
Yunfeng Xiao *Department of Pediatric Surgery, Mianyang Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Mianyang, China.
Feihong XieDepartment of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Zongke YangDepartment of Urology, Dianjiang People's Hospital of Chongqing, Chongqing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Three-dimensional tumor organoids, particularly patient-derived organoids (PDOs), recapitulate key morphological, genetic, and functional features of original tumors. Co-culture with immune cells enables studies of the tumor immune microenvironment (TIME) and holds promise for personalized immunotherapy. In this review, we critically evaluate established methodologies for tumor organoid-immune cell co-culture, including reductionist, holistic (tumor slice culture and air-liquid interface), and organoid-on-a-chip approaches. We provide quantitative benchmarking of success rates, immune cell persistence, and predictive accuracy, and discuss contradictory findings, reproducibility challenges, and technical barriers that limit clinical translation. We also analyze how these systems reveal mechanisms of antitumor immunity and immune escape, and assess their applications in immune checkpoint blockade screening, adoptive cell therapy (CAR-T, CAR-NK, γδ T cells), and emerging "organoid+" technologies including spatial transcriptomics, AI-assisted imaging, and machine learning. Finally, we address ethical, regulatory, and standardization issues. Despite substantial progress, current systems face major limitations-including batch variability, loss of native heterogeneity, insufficient vascularization, and lack of systemic immune modeling-that must be overcome before clinical adoption.

Indexed as

immune cell co-cultureimmunotherapypatient-derived organoidsprecision oncologytumor immune microenvironmenttumor organoids

Identifiers

PMID42564263
PMCPMC13442663

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.