ArticleEBioMedicine2018
Organotypic microfluidic breast cancer model reveals starvation-induced spatial-temporal metabolic adaptations.
Article in EBioMedicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers.
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Who cites it
58 citing papers in PubMed, 84 citations in OpenAlex.
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- Dissecting Cancer Metabolism and Therapeutic Resistance Using In Vitro Platforms.Advances in experimental medicine and biology · 2026Review
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- Engineering the bone metastatic prostate cancer niche through a microphysiological system to report patient-specific treatment response.Communications biology · 2025Article
- Quantifying and interpreting biologically meaningful spatial signatures within tumor microenvironments.NPJ precision oncology · 2025Review
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- Cancer 3D Models: Essential Tools for Understanding and Overcoming Drug Resistance.Oncology research · 2025Review
- Deciphering fibroblast-induced drug resistance in non-small cell lung carcinoma through patient-derived organoids in agarose microwells.Lab on a chip · 2024Article
- Endothelial cells metabolically regulate breast cancer invasion toward a microvessel.APL bioengineering · 2023Article
- Breast cancer brain metastasis: from etiology to state-of-the-art modeling.Journal of biological engineering · 2023Review
- Oxidative phosphorylation inhibitors inhibit proliferation of endometriosis cells.Reproduction (Cambridge, England) · 2023Article
- Recent advances in lab-on-a-chip systems for breast cancer metastasis research.Nanoscale advances · 2023Review
- The Applications and Challenges of the Development ofCellular and molecular bioengineering · 2023Review
- AnFrontiers in bioengineering and biotechnology · 2023Article
- Single cell metabolic imaging of tumor and immune cellsFrontiers in oncology · 2023Article
- iDMET: network-based approach for integrating differential analysis of cancer metabolomics.BMC bioinformatics · 2022Article
- A role for microfluidic systems in precision medicine.Nature communications · 2022Review
Corrections and comments
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Authors and funding
11 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundDuctal carcinoma in situ (DCIS) is the earliest stage of breast cancer. During DCIS, tumor cells remain inside the mammary duct, growing under a microenvironment characterized by hypoxia, nutrient starvation, and waste product accumulation; this harsh microenvironment promotes genomic instability and eventually cell invasion. However, there is a lack of biomarkers to predict what patients will transition to a more invasive tumor or how DCIS cells manage to survive in this harsh microenvironment.
methodsIn this work, we have developed a microfluidic model that recapitulates the DCIS microenvironment. In the microdevice, a DCIS model cell line was grown inside a luminal mammary duct model, embedded in a 3D hydrogel with mammary fibroblasts. Cell behavior was monitored by confocal microscopy and optical metabolic imaging. Additionally, metabolite profile was studied by NMR whereas gene expression was analyzed by RT-qPCR.
findingsDCIS cell metabolism led to hypoxia and nutrient starvation; revealing an altered metabolism focused on glycolysis and other hypoxia-associated pathways. In response to this starvation and hypoxia, DCIS cells modified the expression of multiple genes, and a gradient of different metabolic phenotypes was observed across the mammary duct model. These genetic changes observed in the model were in good agreement with patient genomic profiles; identifying multiple compounds targeting the affected pathways. In this context, the hypoxia-activated prodrug tirapazamine selectively destroyed hypoxic DCIS cells.
interpretationThe results showed the capacity of the microfluidic model to mimic the DCIS structure, identifying multiple cellular adaptations to endure the hypoxia and nutrient starvation generated within the mammary duct. These findings may suggest new potential therapeutic directions to treat DCIS. In summary, given the lack of in vitro models to study DCIS, this microfluidic device holds great potential to find new DCIS predictors and therapies and translate them to the clinic.
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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.