Molecular mechanisms underlying IGF‐I‐induced attenuation of the growth‐inhibitory activity of trastuzumab (Herceptin) on SKBR3 breast cancer cells
Bibliographic record
Abstract
The clinical usefulness of trastuzumab (Herceptin; Genentech, San Francisco, CA) in breast cancer treatment is limited by the rapid development of resistance. We previously reported that IGF-I signaling confers resistance to the growth-inhibitory actions of trastuzumab in a model system, but the underlying molecular mechanism remains unknown. We used SKBR3/neo cells (expressing few IGF-I receptors) and SKBR3/IGF-IR cells (overexpressing IGF-I receptor) as our experimental model. IGF-I antagonized the trastuzumab-induced increase in the level of the Cdk inhibitor p27(Kip1). This resulted in decreased association of p27(Kip1) with Cdk2, restoration of Cdk2 activity and attenuation of cell-cycle arrest in G(1) phase, all of which had been induced by trastuzumab treatment in SKBR3/IGF-IR cells. We also found that the decrease in p27(Kip1) induced by IGF-I was accompanied by an increase in expression of Skp2, which is a ubiquitin ligase for p27(Kip1), and by increased Skp2 association with p27(Kip1). A specific proteasome inhibitor (LLnL) completely blocked the ability of IGF-I to reduce the p27(Kip1) protein level, while IGF-I increased p27(Kip1) ubiquitination. This suggests that the action of IGF-I in conferring resistance to trastuzumab involves targeting of p27(Kip1) to the ubiquitin/proteasome degradation machinery. Finally, specific inhibitors of MAPK and PI3K suggest that the IGF-I-mediated reduction in p27(Kip1) protein level by increased degradation predominantly involves the PI3K pathway. Our results provide an example of resistance to an antineoplastic therapy that targets one tyrosine kinase receptor by increased signal transduction through an alternative pathway in a complex regulatory network.
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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) | 0.000 | 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 | 0.001 |
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".