Synergistic inhibition of breast cancer cell lines with the combination of a dual inhibitor of EGFR/HER-2/neu and a Bcl-2 inhibitor
Bibliographic record
Abstract
13100 Background: The epidermal growth factor receptor (EGFR; ErbB1) and HER-2/neu (ErbB2), members of the ErbB family of receptor tyrosine kinases, are overexpressed in a variety of human tumors and overexpression generally correlates with poor prognosis and decreased survival. Use of inhibitors of these receptors as monotherapies, e.g., trastuzumab, Iressa, and erlotinib, has led to advances in treatment, but many patients do not respond or develop resistance. The anti-apoptotic protein, Bcl-2, is also overexpressed in a number of human tumors. Inhibitors of Bcl-2 induce apoptosis and sensitize cancer cells to other therapies. This study assesses the effects of a combination of a reversible inhibitor of both EGFR and HER-2/neu that is similar to lapatinib (GW2974) and a pan inhibitor of the Bcl-2 family (GX15–070: Gemin X Biotechnologies, Inc.) on the growth of human breast cancer cells. Methods: The MCF-7 human breast cancer cell line transfected with a control vector, MCF/neo, and the HER-2/neu transfected MCF-7 cell line, MCF/18, were treated with various concentrations of GW2974 (0.25–10 μM) and/or the GX15–070 pan Bcl-2 inhibitor (50–500 nM). After a 3 day exposure, cell number was determined using the colorimetric MTT tetrazolium dye assay. Percent of control was normalized to corresponding concentrations of the solvent for both agents (DMSO). Results: Treatment with the GW2974 dual inhibitor or the GX15–070 pan Bcl-2 inhibitor resulted in dose-dependent growth inhibition in both the control and HER-2/neu transfected MCF-7 cell lines. The combination of both agents produced synergistic growth inhibition in both cell lines as confirmed by isobologram analysis. Conclusions: This study has demonstrated synergy with the combination of a dual inhibitor of EGFR and HER-2/neu and an inhibitor of Bcl-2 in control and HER-2/neu overexpressing MCF-7 human breast cancer cells. This finding warrants an evaluation of this combination in clinical trials for the treatment of patients with metastatic breast cancer. [Table: see text]
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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) | 0.002 | 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".