ArticleJournal of visualized experiments : JoVE2023
Establishing a Physiologic Human Vascularized Micro-Tumor Model for Cancer Research.
Article in Journal of visualized experiments : JoVE, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 9 citations in OpenAlex.
- Three-dimensional spheroid models in breast cancer: tumor microenvironment complexity, cancer stem cell-driven resistance, and translational model integration.Journal of translational medicine · 2026Review
- CDC42-effector interaction inhibitors alter patterns of vessel arborization in skin and tumorsiScience · 2025Article
- Innovative organ-on-a-chip platforms for exploring tumorigenesis and therapy in head and neck cancer.Journal of translational medicine · 2025Review
- Methods for processing and analyzing images of vascularized micro-organ and tumor systems.Frontiers in bioengineering and biotechnology · 2025Article
- Targeting tumor-stromal interactions in triple-negative breast cancer using a human vascularized micro-tumor model.Breast cancer research : BCR · 2024Article
- Microphysiological systems as models for immunologically 'cold' tumors.Frontiers in cell and developmental biology · 2024Review
- Improving tumor microenvironment assessment in chip systems through next-generation technology integration.Frontiers in bioengineering and biotechnology · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
A lack of validated cancer models that recapitulate the tumor microenvironment of solid cancers in vitro remains a significant bottleneck for preclinical cancer research and therapeutic development. To overcome this problem, we have developed the vascularized microtumor (VMT), or tumor chip, a microphysiological system that realistically models the complex human tumor microenvironment. The VMT forms de novo within a microfluidic platform by co-culture of multiple human cell types under dynamic, physiological flow conditions. This tissue-engineered micro-tumor construct incorporates a living perfused vascular network that supports the growing tumor mass just as newly formed vessels do in vivo. Importantly, drugs and immune cells must cross the endothelial layer to reach the tumor, modeling in vivo physiological barriers to therapeutic delivery and efficacy. Since the VMT platform is optically transparent, high-resolution imaging of dynamic processes such as immune cell extravasation and metastasis can be achieved with direct visualization of fluorescently labeled cells within the tissue. Further, the VMT retains in vivo tumor heterogeneity, gene expression signatures, and drug responses. Virtually any tumor type can be adapted to the platform, and primary cells from fresh surgical tissues grow and respond to drug treatment in the VMT, paving the way toward truly personalized medicine. Here, the methods for establishing the VMT and utilizing it for oncology research are outlined. This innovative approach opens new possibilities for studying tumors and drug responses, providing researchers with a powerful tool to advance cancer research.
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What OpenQuestion 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 OpenQuestion graph.