Adipose Tissue from Lean and Obese Mice Induces a Mesenchymal to Epithelial Transition-Like Effect in Triple Negative Breast Cancers Cells Grown in 3-Dimensional Culture
Bibliographic record
Abstract
Breast cancer is the second leading cause of cancer-related mortality among women globally with obesity being one risk factor. Obese breast cancer patients have at least a 30% increased risk of death from breast cancer compared to non-obese breast cancer patients because they present with larger tumors and generally have increased rates of metastasis. Moreover, obese breast cancer patients respond more poorly to treatment compared to non-obese patients, particularly pre-menopausal women diagnosed with triple negative breast cancer (TNBC). To help understand the molecular mechanisms underlying the increased metastasis associated with obesity, we previously established a three-dimensional culture system that permits the co-culture of adipocytes and TNBC cells in a manner that mimics an in vivo milieu. Using this system, we demonstrate that white adipose tissue from both lean and obese mice can induce a partial mesenchymal-to-epithelial transition (MET). Triple negative breast cancer cells adopt an epithelial morphology and have an increased expression of some epithelial markers, but they maintain the expression of mesenchymal markers, furnishing the breast cancer cells with hybrid properties that are associated with more aggressive tumors. Thus, these data suggest that adipose tissue has the potential to promote secondary tumor formation in lean and obese women. Further work is needed to determine if targeting the partial MET induced by adipose tissue could reduce metastasis.
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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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".