Inhibition of insulin-like growth factor-1 receptor signaling enhances growth-inhibitory and proapoptotic effects of gefitinib (Iressa) in human breast cancer cells
Bibliographic record
Abstract
INTRODUCTION: Gefitinib (Iressa, ZD 1839, AstraZeneca) blocks the tyrosine kinase activity of the epidermal growth factor receptor (EGFR) and inhibits proliferation of several human cancer cell types including breast cancer. Phase II clinical trials with gefitinib monotherapy showed an objective response of 9 to 19% in non-small-cell lung cancer patients and less than 10% for breast cancer, and phase III results have indicated no benefit of gefitinib in combination with chemotherapy over chemotherapy alone. In order to improve the antineoplastic activity of gefitinib, we investigated the effects of blocking the signalling of the insulin-like growth factor 1 receptor (IGF-1R), a tyrosine kinase with a crucial role in malignancy that is coexpressed with EGFR in most human primary breast carcinomas. METHODS: AG1024 (an inhibitor of IGF-1R) was used with gefitinib for treatment of MDA468, MDA231, SK-BR-3, and MCF-7 breast cancer lines, which express similar levels of IGF-1R but varying levels of EGFR. Proliferation assays, apoptosis induction studies, and Western blot analyses were conducted with cells treated with AG1024 and gefitinib as single agents and in combination. RESULTS: Gefitinib and AG1024 reduced proliferation in all lines when used as single agents, and when used in combination revealed an additive-to-synergistic effect on cell growth inhibition. Flow cytometry measurements of cells stained with annexin V-propidium iodide and cells stained for caspase-3 activation indicated that adding an IGF-1R-targeting strategy to gefitinib results in higher levels of apoptosis than are achieved with gefitinib alone. Gefitinib either reduced or completely inhibited p42/p44 Erk kinase phosphorylation, depending on the cell line, while Akt phosphorylation was reduced by a combination of the two agents. Overexpression of IGF-1R in SK-BR-3 cells was sufficient to cause a marked enhancement in gefitinib resistance. CONCLUSION: These results indicate that IGF-1R signaling reduces the antiproliferative effects of gefitinib in several breast cancer cell lines, and that the addition of an anti-IGF-1R strategy to gefitinib treatment may be more effective than a single-agent approach.
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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) | 0.001 | 0.001 |
| 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) | 0.002 | 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".