Studying breast cancer lung metastasis using a multi-compartment microfluidic device with a mimetic tumor-stroma interaction model
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".