Co-targeting HER2/ErbB2 and insulin-like growth factor-1 receptors causes synergistic inhibition of growth in HER2-overexpressing breast cancer cells.
Bibliographic record
Abstract
BACKGROUND: The humanized anti-HER2 monoclonal antibody trastuzumab (Herceptin) is useful in the treatment of ErbB2-overexpressing breast cancers, but its efficiency is limited because development of resistance is common. In order to study the possibility of improving the efficacy of therapies directed against HER2/erbB2, we investigated the effects of co-targeting this receptor and the insulin-like growth factor 1 receptor (IGF-1R), a widely-expressed protein tyrosine kinase with important roles in suppression of apoptosis and stimulation of proliferation. MATERIAL/METHODS: The experimental strategy involved combining trastuzumab treatment and reduction of IGF-1R signaling through incremental heat-induced expression of the dominant-negative IGF-1 receptor 486/STOP under the control of the heat-sensitive Drosophila HSP70 promoter, in HER2/erbB2-overexpressing MCF7her18 breast cancer cells. RESULTS: Isobologram analysis of combinatorial treatment data revealed a strong synergistic interaction between trastuzumab treatment and the induction of the dominant-negative IGF-1R expression, resulting in potentiation of growth inhibition in transfected cancer cells. CONCLUSIONS: These observations support the concept that simultaneously co-targeting tyrosine kinase receptors may be therapeutically useful, and provide a specific rationale for combining IGF-1R and HER2/erbB2 targeting strategies in anti-neoplastic approaches.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".