ReviewCancers2023
Challenges and Prospects of Patient-Derived Xenografts for Cancer Research.
Review in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
69 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Oncogenic Role of SRPK2 in Different Types of Cancer: A Systematic Review.Journal of cellular and molecular medicine · 2026Pooled it
- Review
- The Application of 3D Cell Culture in Thyroid Cancer.Annals of biomedical engineering · 2026Review
- Poly (lactic-co-glycolic acid) nanoplatforms for triple-negative breast cancer: current progress, advances, and future outlook.Daru : journal of Faculty of Pharmacy, Tehran University of Medical Sciences · 2026Review
- Immune, molecular and genetic profiles of gastric signet ring cell carcinoma: Recent progress and future challenges.International journal of cancer · 2026Review
- Gastric Cancer Organoids: Mechanistic Insights, Drug Discovery, and Translational Advances in Precision Medicine.Journal of gastric cancer · 2026Review
- Forming a Precise and Reproducible Hypoxic Tumor Microenvironment in Glioblastoma by High Cell Density Bioprinting.Advanced healthcare materials · 2026Article
- Homomultimeric FAP Inhibitor-Based Radioligands for Cancer Theranostics: Design Principles, Structure-Function Relationships, and Preclinical Performance.Molecules (Basel, Switzerland) · 2026Review
- The evolving global landscape of first-in-class oncology drug innovation.Signal transduction and targeted therapy · 2026Review
- Comparison of Cell Line-Derived and Patient-Derived Xenograft Technologies in Oncology (Review).Bulletin of experimental biology and medicine · 2026Review
- CRISPR/Cas9 Genome Editing in Oncology: Mechanisms, Therapeutic Platforms and Translational Challenges.Molecular biotechnology · 2026Review
- Evolving therapeutics in colorectal cancer peritoneal metastases.Cancer metastasis reviews · 2026Review
- Organoid-based systems for biomedical innovation: advances in disease modeling, drug screening, and precision medicine.Naunyn-Schmiedeberg's archives of pharmacology · 2026Review
- Introduction to animal modelling: factors and tools for choosing the optimal model for sarcoma research-a comprehensive literature review.Translational cancer research · 2026Review
- Human Fecal Transplantation Modifies the Gut Microbiota but Not Metabolites in Colon Cancer Patient-Derived Xenografts.International journal of molecular sciences · 2026Article
- Mechano-Organ-on-Chip for Cancer Research.International journal of molecular sciences · 2026Review
- Review
- Computational Innovations in Cancer Research and How Computing is Transforming Drug Discovery and Development: A Review.Mini reviews in medicinal chemistry · 2026Review
- A clinically concordant vulvar cancer patient-derived xenograft model retaining primary molecular signature for preclinical drug sensitivity assessment.Open medicine (Warsaw, Poland) · 2026Article
- Aquatic-derived antimicrobial peptides and their strategically modified analogues as prospective anticancer therapeutics: a comprehensive systematic review of enhancement methodologies and mechanistic insights.Frontiers in chemistry · 2026Review
9 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
We discuss the importance of the in vivo models in elucidating cancer biology, focusing on the patient-derived xenograft (PDX) models, which are classic and standard functional in vivo platforms for preclinical evaluation. We provide an overview of the most representative models, including cell-derived xenografts (CDX), tumor and metastatic cell-derived xenografts, and PDX models utilizing humanized mice (HM). The orthotopic models, which could reproduce the cancer environment and its progression, similar to human tumors, are particularly common. The standard procedures and rationales of gastric adenocarcinoma (GAC) orthotopic models are addressed. Despite the significant advantages of the PDX models, such as recapitulating key features of human tumors and enabling drug testing in the in vivo context, some challenges must be acknowledged, including loss of heterogeneity, selection bias, clonal evolution, stroma replacement, tumor micro-environment (TME) changes, host cell carryover and contaminations, human-to-host cell oncogenic transformation, human and host viral infections, as well as limitations for immunologic research. To compensate for these limitations, other mouse models, such as syngeneic and humanized mouse models, are currently utilized. Overall, the PDX models represent a powerful tool in cancer research, providing critical insights into tumor biology and potential therapeutic targets, but their limitations and challenges must be carefully considered for their effective use. Lastly, we present an intronic quantitative PCR (qPCR) method to authenticate, detect, and quantify human/murine cells in cell lines and PDX samples.
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Registered trials
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.