5′-AMP-activated Protein Kinase (AMPK) Supports the Growth of Aggressive Experimental Human Breast Cancer Tumors
Bibliographic record
Abstract
Rapid tumor growth can establish metabolically stressed microenvironments that activate 5'-AMP-activated protein kinase (AMPK), a ubiquitous regulator of ATP homeostasis. Previously, we investigated the importance of AMPK for the growth of experimental tumors prepared from HRAS-transformed mouse embryo fibroblasts and for primary brain tumor development in a rat model of neurocarcinogenesis. Here, we used triple-negative human breast cancer cells in which AMPK activity had been knocked down to investigate the contribution of AMPK to experimental tumor growth and core glucose metabolism. We found that AMPK supports the growth of fast-growing orthotopic tumors prepared from MDA-MB-231 and DU4475 breast cancer cells but had no effect on the proliferation or survival of these cells in culture. We used in vitro and in vivo metabolic profiling with [(13)C]glucose tracers to investigate the contribution of AMPK to core glucose metabolism in MDA-MB-231 cells, which have a Warburg metabolic phenotype; these experiments indicated that AMPK supports tumor glucose metabolism in part through positive regulation of glycolysis and the nonoxidative pentose phosphate cycle. We also found that AMPK activity in the MDA-MB-231 tumors could systemically perturb glucose homeostasis in sensitive normal tissues (liver and pancreas). Overall, our findings suggest that the contribution of AMPK to the growth of aggressive experimental tumors has a critical microenvironmental component that involves specific regulation of core glucose metabolism.
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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".