Breast cancer mammospheres secrete Adrenomedullin to induce lipolysis and browning of adjacent adipocytes
Bibliographic record
Abstract
BACKGROUND: Cancer cells cooperate with cells that compose their environment to promote tumor growth and invasion. Among them, adipocytes provide lipids used as a source of energy by cancer cells and adipokines that contribute to tumor expansion. Mechanisms supporting the dynamic interactions between cancer cells and stromal adipocytes, however, remain unclear. METHODS: We set-up a co-culture model with breast cancer cells grown in 3D as mammospheres and human adipocytes to accurately recapitulate intrinsic features of tumors, such as hypoxia and cancer cell-adipocytes interactions. RESULTS: Herein, we observed that the lipid droplets' size was reduced in adipocytes adjacent to the mammospheres, mimicking adipocyte morphology on histological sections. We showed that the uncoupling protein UCP1 was expressed in adipocytes close to tumor cells on breast cancer histological sections as well as in adipocytes in contact with the mammospheres. Mammospheres produced adrenomedullin (ADM), a multifactorial hypoxia-inducible peptide while ADM receptors were detected in adipocytes. Stimulation of adipocytes with ADM promoted UCP1 expression and increased HSL phosphorylation, which activated lipolysis. Invalidation of ADM in breast cancer cells dramatically reduced UCP1 expression in adipocytes. CONCLUSIONS: Breast tumor cells secreted ADM that modified cancer-associated adipocytes through paracrine signaling, leading to metabolic changes and delipidation. Hence, ADM appears to be crucial in controlling the interactions between cancer cells and adipocytes and represents an excellent target to hinder them.
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How this classification was reachedexpand
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) | 0.002 | 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 teacher head, 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".