ReviewNature reviews. Cancer2025
PDX models for functional precision oncology and discovery science.
Review in Nature reviews. Cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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
31 citing papers in PubMed.
- Cancer drug response and resistance: molecular mechanisms and combating strategies.Signal transduction and targeted therapy · 2026Review
- Preclinical and Virtual Models of Mucosal Melanoma: Bridging Translational Gaps in a Rare and Lethal Cancer.Pigment cell & melanoma research · 2026Review
- Macrophage-Centric Phenotypic Screening Identifies Tetrazolone-Based HDAC6 Inhibitors That Reprogram the Tumor Immune Microenvironment and Improve Immune Checkpoint Blockade.Journal of medicinal chemistry · 2026Article
- Organoids, organ-on-a-chip, and microtumors: Biomimetic 3D tumor models advancing drug development and precision medicine.Acta pharmaceutica Sinica. B · 2026Review
- A Roadmap to Transform Lung Cancer Outcomes: Priorities in Biology, Therapeutic Innovation, Early Detection, Prevention, and Interception.Cancer discovery · 2026Review
- Orthotopic Esophageal Cancer Xenograft Model in Immunosuppressed Microminipigs for Near-Infrared Fluorescence Endoscopy.Cancer science · 2026Article
- Overcoming ADC resistance in advanced colorectal cancer by dual targeting of TROP2 and PERK to suppress Wnt/β-catenin signaling.Cell reports. Medicine · 2026Article
- DSTYK predicts Chemoresistance in Triple-Negative Breast Cancer Patient-Derived Xenograft Models.Research square · 2026Article
- Decoding cancer across scales with metabolomics.Nature reviews. Cancer · 2026Review
- Patient-derived organoid xenografts reveal the multifaceted role of the lncRNAbioRxiv : the preprint server for biology · 2026Article
- Animal models and pathogenesis of gastric cancer: from premalignant conditions-to-metastasis.Cancer biology & medicine · 2026Review
- Pharmaco-genomic characterization of pancreatic and biliary tract cancer tumoroids for drug response.iScience · 2026Article
- Clinical applications and future perspectives of circulating tumor cells in solid tumors.Discover oncology · 2026Review
- Harnessing PDX and PDX 2.0: the next-generation paradigm for precision oncology and translational breakthroughs.Molecular cancer · 2026Review
- Establishment of a Large-Scale PDX Library of Head and Neck Cancers for Functional Precision Oncology.Cancer medicine · 2026Article
- In Vivo Prostate Cancer Modelling: From the Pre-Clinical to the Clinical Setting.Life (Basel, Switzerland) · 2026Review
- Nanomedicine-Based Therapeutic Approaches in Colorectal Cancer Using Patient-Derived Xenograft Models: Prospects and Challenges.International journal of nanomedicine · 2026Review
- Informing development of brain cancer therapies within "preclinical trials" using ex vivo patient tumors.Advanced drug delivery reviews · 2026Review
- USP20, a Super-enhancer Regulated Gene, Promotes Acute Myeloid Leukemia Progression through CTNNB1 Deubiquitination.International journal of biological sciences · 2026Article
- TRANSPIRE-DRP: a deep learning framework for translating patient-derived xenograft drug response to clinical patients via domain adaptation.Journal of translational medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Precision oncology relies on detailed molecular analysis of how diverse tumours respond to various therapies, with the aim to optimize treatment outcomes for individual patients. Patient-derived xenograft (PDX) models have been key to preclinical validation of precision oncology approaches, enabling the analysis of each tumour's unique genomic landscape and testing therapies that are predicted to be effective based on specific mutations, gene expression patterns or signalling abnormalities. To extend these standard precision oncology approaches, the field has strived to complement the otherwise static and often descriptive measurements with functional assays, termed functional precision oncology (FPO). By utilizing diverse PDX and PDX-derived models, FPO has gained traction as an effective preclinical and clinical tool to more precisely recapitulate patient biology using in vivo and ex vivo functional assays. Here, we explore advances and limitations of PDX and PDX-derived models for precision oncology and FPO. We also examine the future of PDX models for precision oncology in the age of artificial intelligence. Integrating these two disciplines could be the key to fast, accurate and cost-effective treatment prediction, revolutionizing oncology and providing patients with cancer with the most effective, personalized treatments.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.