Mammalian target of rapamycin inhibition abrogates insulin-mediated mammary tumor progression in type 2 diabetes
Bibliographic record
Abstract
Type 2 diabetes increases breast cancer risk and mortality, and hyperinsulinemia is a major mediator of this effect. The mammalian target of rapamycin (mTOR) is activated by insulin and is a key regulator of mammary tumor progression. Pharmacological mTOR inhibition suppresses tumor growth in numerous mammary tumor models in the non-diabetic setting. However, the role of the mTOR pathway in type 2 diabetes-induced tumor growth remains elusive. Herein, we investigated whether the mTOR pathway is implicated in insulin-induced mammary tumor progression in a transgenic mouse model of type 2 diabetes (MKR mice) and evaluated the impact of mTOR inhibition on the diabetic state. Mammary tumor progression was studied in the double transgenic MMTV-Polyoma Virus middle T antigen (PyVmT)/MKR mice and by orthotopic inoculation of PyVmT- and Neu/ErbB2-driven mammary tumor cells (Met-1 and MCNeuA cells respectively). mTOR inhibition by rapamycin markedly suppressed tumor growth in both wild-type and MKR mice. In diabetic animals, however, the promoting action of insulin on tumor growth was completely blunted by rapamycin, despite a worsening of the carbohydrate and lipid metabolism. Taken together, pharmacological mTOR blockade is sufficient to abrogate mammary tumor progression in the setting of hyperinsulinemia, and thus mTOR inhibitors may be an attractive therapeutic modality for breast cancer patients with type 2 diabetes. Careful monitoring of the metabolic state, however, is important as dose adaptations of glucose- and/or lipid-lowering therapy might be necessary.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".