Synergistic inhibition of breast cancer cell lines with a dual inhibitor of EGFR-HER-2/neu and a Bcl-2 inhibitor
Bibliographic record
Abstract
The epidermal growth factor receptor (EGFR) (ErbB1) and HER-2/neu (ErbB2) are members of the ErbB family of receptor tyrosine kinases. These receptors are overexpressed in a variety of human tumors and overexpression generally correlates with poor prognosis and decreased survival. Lapatinib, a reversible inhibitor of both EGFR and HER-2/neu, has shown some success in achieving clinical responses in heavily pretreated advanced cancer patients. GW2974 is a reversible dual inhibitor similar to lapatinib, but GW2974 was not progressed to clinical trials due to pharmacokinetic issues. Bcl-2, an anti-apoptotic protein, is also overexpressed in a number of human tumors. Bcl-2 inhibitors induce apoptosis and sensitize cancer cells to other therapies. The purpose of this study was to assess the effects of combining ErbB and Bcl-2 inhibitors on the growth of human breast cancer cell lines. EGFR/HER-2/neu tyrosine kinase inhibitors (lapatinib and GW2974) were combined with Bcl-2 inhibitors (HA14-1 or GX15-070) and the anti-proliferative effects were determined by the MTT tetrazolium dye assay. Combinations were tested in MCF-7 human breast cancer cells, a HER-2/neu transfected MCF-7 cell line (MCF/18), and a tamoxifen-resistant MCF-7 cell line (MTR-3). A synergistic inhibitory effect was observed with the combination of inhibitors of EGFR-HER-2/neu (lapatinib or GW2974) and Bcl-2 (GX15-070 or HA14-1) on the growth of the MCF-7, MCF/18, and MTR-3 human breast cancer cell lines. This study suggests that simultaneously blocking the ErbB family of receptor tyrosine kinases and Bcl-2 family of proteins may be a benefit to breast cancer patients.
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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 | 0.000 |
| 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".