Mitochondrial dysfunction in breast cancer cells prevents tumor growth
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
Metformin is a well-established diabetes drug that prevents the onset of most types of human cancers in diabetic patients, especially by targeting cancer stem cells. Metformin exerts its protective effects by functioning as a weak "mitochondrial poison," as it acts as a complex I inhibitor and prevents oxidative mitochondrial metabolism (OXPHOS). Thus, mitochondrial metabolism must play an essential role in promoting tumor growth. To determine the functional role of "mitochondrial health" in breast cancer pathogenesis, here we used mitochondrial uncoupling proteins (UCPs) to genetically induce mitochondrial dysfunction in either human breast cancer cells (MDA-MB-231) or cancer-associated fibroblasts (hTERT-BJ1 cells). Our results directly show that all three UCP family members (UCP-1/2/3) induce autophagy and mitochondrial dysfunction in human breast cancer cells, which results in significant reductions in tumor growth. Conversely, induction of mitochondrial dysfunction in cancer-associated fibroblasts has just the opposite effect. More specifically, overexpression of UCP-1 in stromal fibroblasts increases β-oxidation, ketone body production and the release of ATP-rich vesicles, which "fuels" tumor growth by providing high-energy nutrients in a paracrine fashion to epithelial cancer cells. Hence, the effects of mitochondrial dysfunction are truly compartment-specific. Thus, we conclude that the beneficial anticancer effects of mitochondrial inhibitors (such as metformin) may be attributed to the induction of mitochondrial dysfunction in the epithelial cancer cell compartment. Our studies identify cancer cell mitochondria as a clear target for drug discovery and for novel therapeutic interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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