Insights into the pharmacologic inhibition of insulin receptor tyrosine kinase in cancer: avoiding severe metabolic toxicity
Bibliographic record
Abstract
In view of accumulating evidence that links hyperinsulinemia to aggressive cancer behavior, the insulin receptor tyrosine kinase has been viewed as a potentially important molecular target for certain cancers. To study the effects of attenuation of insulin signaling on the growth of the mouse 4T1 breast cancer model in vivo, we compared the effects of alloxan‐induced insulin deficiency to those of BMS‐536924, an inhibitor of the insulin and IGF‐I receptor kinases. Both interventions reduced Akt Ser473 phosphorylation in neoplastic tissue and significantly reduced tumor growth. Insulin deficiency led to reduced muscle Akt Ser473 phosphorylation, while BMS‐536924 led to hyperinsulinemia and increased muscle Akt Ser473 phosphorylation, a finding which correlated with significantly lower drug accumulation in muscle than in neoplastic tissue. We measured glucose uptake and utilization in muscle to determine if BMS‐536924 abolished insulin‐dependent glucose uptake. Our data indicate that insulin dependent glucose uptake by muscle remained intact. Thus, tissue‐specific distribution of BMS‐536924 may account for antineoplastic activity without severe metabolic toxicity, indicating that pharmacologic targeting of the insulin receptor in neoplastic disease may be practical. This work is supported by the McGill Integrated Cancer Research Training Program (MICRTP).
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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) | 0.001 | 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".