Everolimus decreases [U-13C]glucose utilization by pyruvate carboxylase in breast cancer cells in vitro and in vivo
Bibliographic record
Abstract
Reprogrammed metabolism is a hallmark of cancer, but notoriously difficult to target due to metabolic plasticity, especially in response to single metabolic interventions. Combining mTOR inhibitor everolimus and mitochondrial complex 1 inhibitor metformin results in metabolic synergy in in vitro models of triple-negative breast cancer. Here, we investigated whether the effect of this drug combination on tumor size is reflected in changes in tumor metabolism using [U-13C]glucose labeling in an MDA-MB-231 triple negative breast cancer xenograft model. The in vitro effects of everolimus and metformin treatment on oxidative phosphorylation and glycolysis reflected changes in 13C-labeling of metabolites in MDA-MB-231 cells. Treatment of MDA-MB-231 xenografts in SCID/Beige mice with everolimus resulted in slower tumor growth and reduced tumor size and tumor viability by 35%. Metformin treatment moderately inhibited tumor growth but did not enhance everolimus-induced effects. High serum levels of everolimus were reached, whereas levels of metformin were relatively low. Everolimus decreased TCA cycle metabolite labeling and inhibited pyruvate carboxylase activity. Metformin only caused a mild reduction in glycolytic metabolite labeling and did not affect pyruvate carboxylase activity or TCA cycle metabolite labeling. In conclusion, treatment with everolimus, but not metformin, decreased tumor size and viability. Furthermore, the efficacy of everolimus was reflected in reduced 13C-labeling of TCA cycle intermediates and reduced pyruvate carboxylase activity. By using in-depth analysis of drug-induced changes in glucose metabolism in combination with measurement of drug levels in tumor and plasma, effects of metabolically targeted drugs can be explained, and novel targets can be identified.
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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) | 0.001 | 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".