Abstract 2300: Insulin receptor kinase inhibition is better tolerated than hypoinsulinemia and more effective than metformin in treating breast cancer
Bibliographic record
Abstract
Abstract Epidemiologic and experimental evidence suggest that a subset of breast cancer is insulin-responsive, but it is unclear if safe and effective therapies that target the insulin receptor can be developed. We demonstrate that both insulin receptor family tyrosine kinase inhibition and insulin deficiency have anti-neoplastic activity in a model of insulin-responsive breast cancer in mice metabolically normal at baseline. In contrast to insulin deficiency, insulin receptor inhibition does not lead to hyperglycemia and is well-tolerated. We show that pharmacokinetic factors explain the safety of receptor inhibition relative to ligand deficiency, as BMS-536924 does not accumulate in muscle at levels sufficient to block insulin-stimulated glucose uptake. Metformin, which lowers the elevated insulin levels present in settings of insulin resistance, had minimal activity in this model. The findings highlight the importance of tissue-specific drug accumulation as a determinant of efficacy and toxicity of tyrosine kinase inhibitors, and suggest that therapeutic targeting of the insulin receptor family for cancer treatment is practical. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 2300. doi:10.1158/1538-7445.AM2011-2300
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".