Ketone bodies and two-compartment tumor metabolism: Stromal ketone production fuels mitochondrial biogenesis in epithelial cancer cells
Bibliographic record
Abstract
We have previously suggested that ketone body metabolism is critical for tumor progression and metastasis. Here, using a co-culture system employing human breast cancer cells (MCF7) and hTERT-immortalized fibroblasts, we provide new evidence to directly support this hypothesis. More specifically, we show that the enzymes required for ketone body production are highly upregulated within cancer-associated fibroblasts. This appears to be mechanistically controlled by the stromal expression of caveolin-1 (Cav-1) and/or serum starvation. In addition, treatment with ketone bodies (such as 3-hydroxy-butyrate, and/or butanediol) is sufficient to drive mitochondrial biogenesis in human breast cancer cells. This observation was also validated by unbiased proteomic analysis. Interestingly, an MCT1 inhibitor was sufficient to block the onset of mitochondrial biogenesis in human breast cancer cells, suggesting a possible avenue for anticancer therapy. Finally, using human breast cancer tumor samples, we directly confirmed that the enzymes associated with ketone body production (HMGCS2, HMGCL and BDH1) were preferentially expressed in the tumor stroma. Conversely, enzymes associated with ketone re-utilization (ACAT1) and mitochondrial biogenesis (HSP60) were selectively associated with the epithelial tumor cell compartment. Our current findings are consistent with the "two-compartment tumor metabolism" model. Furthermore, they suggest that we should target ketone body metabolism as a new area for drug discovery, for the prevention and treatment of human cancers.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".