ArticleEuropean urology oncology2018
Patient-derived Models Reveal Impact of the Tumor Microenvironment on Therapeutic Response.
Article in European urology oncology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
36 citing papers in PubMed, 1 synthesis or guideline pooled it, 52 citations in OpenAlex.
- Relevance of humanized three-dimensional tumor tissue models: a descriptive systematic literature review.Cellular and molecular life sciences : CMLS · 2020Pooled it
- Pulsed Low-Dose-Rate Chemoradiation Induces Stromal Reprogramming in Pancreatic CAF-Generated ECM: Quantification by the HOST-Factor.Gastro hep advances · 2026Article
- A Modular Biomimetic Preclinical Platform to Elucidate the Interaction Between Cancer Cells and the Bone Metastatic Niche.Journal of functional biomaterials · 2025Article
- Article
- AR coactivators, CBP/p300, are critical mediators of DNA repair in prostate cancer.bioRxiv : the preprint server for biology · 2024Article
- Defining the challenges and opportunities for using patient-derived models in prostate cancer research.The Prostate · 2024Review
- Tumour organoids and assembloids: Patient-derived cancer avatars for immunotherapy.Clinical and translational medicine · 2024Review
- Patient-Derived Microphysiological Systems for Precision Medicine.Advanced healthcare materials · 2024Review
- Empowering personalized medicine: unleashing the potential of patient-derived explants in clinical practice.EXCLI journal · 2024Article
- Preclinical models of prostate cancer - modelling androgen dependency and castration resistance in vitro, ex vivo and in vivo.Nature reviews. Urology · 2023Review
- Making In Vitro Tumor Models Whole Again.Advanced healthcare materials · 2023Review
- Functional precision oncology using patient-derived assays: bridging genotype and phenotype.Nature reviews. Clinical oncology · 2023Review
- Review
- Prevalence of tumour-infiltrating CD103British journal of cancer · 2023Article
- Let's Go 3D! New Generation of Models for Evaluating Drug Response and Resistance in Prostate Cancer.International journal of molecular sciences · 2023Review
- Key aspects for conception and construction of co-culture models of tumor-stroma interactions.Frontiers in bioengineering and biotechnology · 2023Review
- Role of the tumor microenvironment in malignant melanoma organoids during the development and metastasis of tumors.Frontiers in cell and developmental biology · 2023Review
- Identification and validation of a poor clinical outcome subtype of primary prostate cancer with Midkine abundance.Cancer science · 2022Article
- Targeting ESR1 mutation-induced transcriptional addiction in breast cancer with BET inhibition.JCI insight · 2022Article
- A Novel Role for DNA-PK in Metabolism by Regulating Glycolysis in Castration-Resistant Prostate Cancer.Clinical cancer research : an official journal of the American Association for Cancer Research · 2022Article
Corrections and comments
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Authors and funding
16 authors at 6 institutions in 2 countries.
Funding
Abstract
backgroundAndrogen deprivation therapy is a first-line treatment for disseminated prostate cancer (PCa). However, virtually all tumors become resistant and recur as castration-resistant PCa, which has no durable cure. One major hurdle in the development of more effective therapies is the lack of preclinical models that adequately recapitulate the heterogeneity of PCa, significantly hindering the ability to accurately predict therapeutic response.
objectiveTo leverage the ex vivo culture method termed DESIGN SETTING AND
participantsFresh PCa tissue from patients who underwent radical prostatectomy was cultured as PDEs to examine therapeutic response. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The impact of genomic and chemical perturbations in PDEs was assessed using various parameters (eg, AR levels, Ki67 staining, and desmoplastic indices). RESULTS AND LIMITATIONS: PDE maintained the integrity of the native tumor microenvironment (TME), tumor tissue morphology, viability, and endogenous hormone signaling. Tumor cells in this model system exhibited de novo proliferative capacity. Examination of the native TME in the PDE revealed a first-in-field insight into patient-specific desmoplastic stromal indices and predicted responsiveness to AR-directed therapeutics.
conclusionsThe PDE model allows for a comprehensive evaluation of individual tumors in their native TME to ultimately develop more effective therapeutic regimens tailored to individuals. Discernment of novel stromal markers may provide a basis for applying precision medicine in treating advanced PCa, which would have a transformative effect on patient outcomes. PATIENT SUMMARY: In this study, an innovative model system was used to more effectively mimic human disease. The patient-derived explant (PDE) system can be used to predict therapeutic response and identify novel targets in advanced disease. Thus, the PDE will be an asset for the development of novel metrics for the implementation of precision medicine in prostate cancer.The patient-derived explant (PDE) model allows for a comprehensive evaluation of individual human tumors in their native tumor microenvironment (TME). TME analysis revealed first-in-field insight into predicted tumor responsiveness to AR-directed therapeutics through evaluation of patient-specific desmoplastic stromal indices.
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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.