Evidence mapPaperPMID 40335487Full record

ReviewCell death discovery2025

Breakthroughs and challenges of organoid models for assessing cancer immunotherapy: a cutting-edge tool for advancing personalised treatments.

Qian Wang, Fangwei Yuan, Xianglin Zuo, Ming Li

Abstract readReview
In one paragraph

Review in Cell death discovery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.

0numbers the graph read from it
0cells of the map it votes in
43citing 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

43 citing papers in PubMed.

  1. Review
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  8. Why in vivo models of disease remain indispensable.Disease models & mechanisms · 2026
    Article
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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.

Qian Wang *Department of Thoracic Surgery, The Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing, 210009, Jiangsu, PR China.
Fangwei Yuan *Department of Thoracic Surgery, Lian Shui County People's Hospital, Huaian, 223400, Jiangsu, PR China.
Xianglin ZuoBiobank of Jiangsu Cancer Hospital (Jiangsu Institute of Cancer Research & The Affiliated Cancer Hospital of Nanjing Medical University), Nanjing, 210000, Jiangsu, PR China. zuoxianglin@njmu.edu.cn.ORCID http://orcid.org/0009-0005-7447-7877
Ming LiDepartment of Thoracic Surgery, The Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing, 210009, Jiangsu, PR China. mingli@njmu.edu.cn.

Funding

National Natural Science Foundation of China (National Science Foundation of China) 82103169National Natural Science Foundation of China (National Science Foundation of China) 82200426Natural Science Foundation of Jiangsu Province (Jiangsu Provincial Natural Science Foundation) BK20241992Postdoctoral Research Foundation of China (China Postdoctoral Research Foundation) 2021M691339
6 · The paper itself

Abstract

Organoid models are powerful tools for evaluating cancer immunotherapy that provide a more accurate representation of the tumour microenvironment (TME) and immune responses than traditional models. This review focuses on the latest advancements in organoid technologies, including immune cell co-culture, 3D bioprinting, and microfluidic systems, which enhance the modelling of TME and facilitate the assessment of immune therapies such as immune checkpoint inhibitors (ICIs), CAR-T therapies, and oncolytic viruses. Although these models have great potential in personalised cancer treatment, challenges persist in immune cell diversity, long-term culture stability, and reproducibility. Future developments integrating artificial intelligence (AI), multi-omics, and high-throughput platforms are expected to improve the predictive power of organoid models and accelerate the clinical translation of immunotherapy.

Identifiers

PMID40335487
PMCPMC12059183

What Socratic holds

Textmetadata
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 Socratic graph.