Herceptin Sensitizes ErbB2–Overexpressing Cells to Apoptosis by Reducing Antiapoptotic Mcl-1 Expression
Bibliographic record
Abstract
PURPOSE: Monoclonal antibodies, such as herceptin and trastuzumab, against the epidermal growth factor receptor ErbB2 (also known as HER2/neu) are an effective therapy for breast cancer patients with overexpression of ErbB2. Herceptin, in combination with standard chemotherapy, such as taxol or etoposide, gives a synergistically apoptotic response in breast tumors. EXPERIMENTAL DESIGN: The mechanism underlying this synergy between chemotherapy and herceptin treatment is not well understood. Herein, we have determined that addition of herceptin, sensitized breast cancer cell lines MDA-MB-231 and MCF-7 to etoposide- or taxol-induced apoptosis. RESULTS: This treatment resulted in reduced expression of ErbB2 and the antiapoptotic Bcl-2 family member Mcl-1 in MDA-MB-231 cells. Using antisense oligonucleotides against Mcl-1, MDA-MB-231 cells were rendered sensitive to etoposide-induced apoptosis similar to herceptin, but combined treatment of antisense against Mcl-1 and herceptin failed to give a significant increase in apoptosis. In 29 human breast tumors immunostained for ErbB2 and Mcl-1, we found that when ErbB2 was overexpressed, there was a corresponding increase in Mcl-1 expression. DISCUSSION: Using murine fibroblasts that express human ErbB2, but no other ErbB family member (NE2), these cells showed resistance to both taxol- and etoposide-induced apoptosis compared with parental cells. In addition, NE2 cells preferentially express the antiapoptotic Bcl-2 family member Mcl-1 compared with parental cells, and treatment with herceptin reduces Mcl-1 expression. Taken together, these results suggest that herceptin sensitizes ErbB2-overexpressing cells to apoptosis by reducing antiapoptotic Mcl-1 protein levels.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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; both teacher heads agree on what is shown here.
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".