Evidence map›Paper›PMID 39830019›Full record

ReviewMedComm2025

Patient-derived xenograft model in cancer: establishment and applications.

Ao Gu, Jiatong Li, Meng-Yao Li, Yingbin Liu

Abstract readReview
In one paragraph

Review in MedComm, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.

0numbers the graph read from it
0cells of the map it votes in
47citing 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

47 citing papers in PubMed.

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  15. Unleashing the biological power and chemical profile ofBiochemistry and biophysics reports · 2026
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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.

Ao GuDepartment of Biliary-Pancreatic Surgery Renji Hospital Shanghai Jiao Tong University School of Medicine Shanghai China.
Jiatong LiDepartment of Biliary-Pancreatic Surgery Renji Hospital Shanghai Jiao Tong University School of Medicine Shanghai China.
Meng-Yao LiDepartment of Biliary-Pancreatic Surgery Renji Hospital Shanghai Jiao Tong University School of Medicine Shanghai China.ORCID https://orcid.org/0000-0002-7054-448X
Yingbin LiuDepartment of Biliary-Pancreatic Surgery Renji Hospital Shanghai Jiao Tong University School of Medicine Shanghai China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The patient-derived xenograft (PDX) model is a crucial in vivo model extensively employed in cancer research that has been shown to maintain the genomic characteristics and pathological structure of patients across various subtypes, metastatic, and diverse treatment histories. Various treatment strategies utilized in PDX models can offer valuable insights into the mechanisms of tumor progression, drug resistance, and the development of novel therapies. This review provides a comprehensive overview of the establishment and applications of PDX models. We present an overview of the history and current status of PDX models, elucidate the diverse construction methodologies employed for different tumors, and conduct a comparative analysis to highlight the distinct advantages and limitations of this model in relation to other in vivo models. The applications are elucidated in the domain of comprehending the mechanisms underlying tumor development and cancer therapy, which highlights broad applications in the fields of chemotherapy, targeted therapy, delivery systems, combination therapy, antibody-drug conjugates and radiotherapy. Furthermore, the combination of the PDX model with multiomics and single-cell analyses for cancer research has also been emphasized. The application of the PDX model in clinical treatment and personalized medicine is additionally emphasized.

Indexed as

cancermultiomicspatient‐derived xenograft modeltherapytumor progress

Identifiers

PMID39830019
PMCPMC11742426

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.