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Updated: Nov 1, 2025
Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
Published on: August 16, 2020
Microphysiological systems to study tumor-stroma interactions in brain cancer
Edward R Neves1, Brendan A C Harley1, Sara Pedron1
1Department of Chemical and Biomolecular Engineering, Carl R Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.
Abstract:
Brain tumors still lack effective treatments, and the mechanisms of tumor progression and therapeutic resistance are unclear. Multiple parameters affect cancer prognosis (e.g., type and grade, age, location, size, and genetic mutations) and election of suitable treatments is based on preclinical models and clinical data. However, most candidate drugs fail in human trials due to inefficacy. Cell lines and tissue culture plates do not provide physiologically relevant environments, and animal models are not able to adequately mimic characteristics of disease in humans. Therefore, increasing technological advances are focusing on in vitro and computational modeling to increase the throughput and predicting capabilities of preclinical systems. The extensive use of these therapeutic agents requires a more profound understanding of the tumor-stroma interactions, including neural tissue, extracellular matrix, blood-brain barrier, astrocytes and microglia. Microphysiological brain tumor models offer physiologically relevant vascularized 'minitumors' that can help deciphering disease mechanisms, accelerating the drug discovery and predicting patient's response to anticancer treatments. This article reviews progress in tumor-on-a-chip platforms that are designed to comprehend the particular roles of stromal cells in the brain tumor microenvironment.
Insights
Brain tumor treatments remain ineffective due to unclear progression mechanisms. Microphysiological brain tumor models offer a promising approach to understand tumor-stroma interactions and accelerate drug discovery for better patient outcomes.
Area of Science:
- Neuro-oncology
- Biomedical Engineering
- Drug Discovery
Background:
- Brain tumors lack effective treatments, with unclear progression and resistance mechanisms.
- Current preclinical models (cell lines, animal models) fail to accurately mimic human disease, leading to high drug trial failure rates.
- Understanding tumor-stroma interactions is crucial for developing effective brain tumor therapies.
Purpose of the Study:
- To review advancements in microphysiological brain tumor models, specifically tumor-on-a-chip platforms.
- To highlight the role of these models in understanding the brain tumor microenvironment and stromal cell interactions.
- To assess the potential of these platforms in improving preclinical drug screening and predicting patient response.
Main Methods:
- Review of current literature on microphysiological systems and tumor-on-a-chip technologies for brain tumors.
- Focus on platforms incorporating key components of the brain tumor microenvironment, such as vascularization and stromal cells (astrocytes, microglia).
- Analysis of how these models enhance physiological relevance compared to traditional methods.
Main Results:
- Microphysiological brain tumor models create "minitumors" that are physiologically relevant and vascularized.
- These platforms facilitate the study of complex tumor-stroma interactions within the brain tumor microenvironment.
- Tumor-on-a-chip systems show potential for increased throughput and predictive capability in preclinical research.
Conclusions:
- Microphysiological brain tumor models represent a significant technological advance for studying brain tumors in vitro.
- These models offer a more accurate preclinical system for deciphering disease mechanisms and accelerating drug discovery.
- Tumor-on-a-chip platforms hold promise for predicting patient response to anticancer treatments and improving therapeutic strategies.
