Mammary adipocytes stimulate breast cancer invasion through metabolic remodeling of tumor cells
Bibliographic record
Abstract
In breast cancer, a key feature of peritumoral adipocytes is their loss of lipid content observed both in vitro and in human tumors. The free fatty acids (FFAs), released by adipocytes after lipolysis induced by tumor secretions, are transferred and stored in tumor cells as triglycerides in lipid droplets. In tumor cell lines, we demonstrate that FFAs can be released over time from lipid droplets through an adipose triglyceride lipase-dependent (ATGL-dependent) lipolytic pathway. In vivo, ATGL is expressed in human tumors where its expression correlates with tumor aggressiveness and is upregulated by contact with adipocytes. The released FFAs are then used for fatty acid β-oxidation (FAO), an active process in cancer but not normal breast epithelial cells, and regulated by coculture with adipocytes. However, in cocultivated cells, FAO is uncoupled from ATP production, leading to AMPK/acetyl-CoA carboxylase activation, a circle that maintains this state of metabolic remodeling. The increased invasive capacities of tumor cells induced by coculture are completely abrogated by inhibition of the coupled ATGL-dependent lipolysis/FAO pathways. These results show a complex metabolic symbiosis between tumor-surrounding adipocytes and cancer cells that stimulate their invasiveness, highlighting ATGL as a potential therapeutic target to impede breast cancer progression.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".