Studying breast cancer lung metastasis using a multi-compartment microfluidic device with a mimetic tumor-stroma interaction model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Understanding the mechanisms underlying the metastasis of breast cancer cells to the lungs is challenging, and appropriate simulation of the tumor microenvironment with mimetic cancer-stroma crosstalk is essential. β4 integrin is known to contribute to triggering a variety of different signaling cues involved in the malignant phenotype of cancer but its role in organ-specific metastasis needs further study. In this work, a multi-compartment microfluidic tumor model was developed to evaluate cancer cell invasion. MATERIALS AND METHODS: To model the primary tumor microenvironment, breast cancer cells (MCF7) and cancer-associated fibroblasts (CAFs) were co-cultured within the tumor compartment of the microfluidic chip while normal lung fibroblasts (NLFs) were seeded in a different compartment, as the secondary tumor site, separated from the tumor compartment via a Matrigel™ layer resembling the extracellular matrix. RESULTS: The cytotoxic effect of β4 integrin blockade on cancer cells gradually increased after 48 and 72 h of co-culture. Invasion of breast cancer cells in both single and coculture models was characterized in response to β4 integrin blockade. The invasion rate and gap closure of MCF7/CAF_NLF was significantly higher than MCF7_NLF (P < 0.0001). β4 integrin inhibition reduced the rate of gap closure and invasion of both (P < 0.0001). CONCLUSIONS: Biomimetic microfluidic-based tumor models hold promise for studying cancer metastasis mechanisms. Precise manipulation, simulation, and analysis of the cancer microenvironment are made possible by microfluidics.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it