Insulin-Mediated Acceleration of Breast Cancer Development and Progression in a Nonobese Model of Type 2 Diabetes
Bibliographic record
Abstract
Epidemiologic studies suggest that type 2 diabetes (T2D) increases breast cancer risk and mortality, but there is limited experimental evidence supporting this association. Moreover, there has not been any definition of a pathophysiological pathway that diabetes may use to promote tumorigenesis. In the present study, we used the MKR mouse model of T2D to investigate molecular mechanisms that link T2D to breast cancer development and progression. MKR mice harbor a transgene encoding a dominant-negative, kinase-dead human insulin-like growth factor-I receptor (IGF-IR) that is expressed exclusively in skeletal muscle, where it acts to inactivate endogenous insulin receptor (IR) and IGF-IR. Although lean female MKR mice are insulin resistant and glucose intolerant, displaying accelerated mammary gland development and enhanced phosphorylation of IR/IGF-IR and Akt in mammary tissue, in the context of three different mouse models of breast cancer, these metabolic abnormalities were found to accelerate the development of hyperplastic precancerous lesions. Normal or malignant mammary tissue isolated from these mice exhibited increased phosphorylation of IR/IGF-IR and Akt, whereas extracellular signal-regulated kinase 1/2 phosphorylation was largely unaffected. Tumor-promoting effects of T2D in the models were reversed by pharmacological blockade of IR/IGF-IR signaling by the small-molecule tyrosine kinase inhibitor BMS-536924. Our findings offer compelling experimental evidence that T2D accelerates mammary gland development and carcinogenesis,and that the IR and/or the IGF-IR are major mediators of these effects.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".