Tamoxifen Resistance in Breast Tumors Is Driven by Growth Factor Receptor Signaling with Repression of Classic Estrogen Receptor Genomic Function
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Bibliographic record
Abstract
Not all breast cancers respond to tamoxifen, and many develop resistance despite initial benefit. We used an in vivo model of estrogen receptor (ER)-positive breast cancer (MCF-7 xenografts) to investigate mechanisms of this resistance and develop strategies to circumvent it. Epidermal growth factor receptor (EGFR) and HER2, which were barely detected in control estrogen-treated tumors, increased slightly with tamoxifen and were markedly increased when tumors became resistant. Gefitinib, which inhibits EGFR/HER2, improved the antitumor effect of tamoxifen and delayed acquired resistance, but had no effect on estrogen-stimulated growth. Phosphorylated levels of p42/44 and p38 mitogen-activated protein kinases (both downstream of EGFR/HER2) were increased in the tamoxifen-resistant tumors and were suppressed by gefitinib. There was no apparent increase in phosphorylated AKT (also downstream of EGFR/HER2) in resistant tumors, but it was nonetheless suppressed by gefitinib. Phosphorylated insulin-like growth factor-IR (IGF-IR), which can interact with both EGFR and membrane ER, was elevated in the tamoxifen-resistant tumors compared with the sensitive group. However, ER-regulated gene products, including total IGF-IR itself and progesterone receptor, remained suppressed even at the time of acquired resistance. Tamoxifen's antagonism of classic ER genomic function was retained in these resistant tumors and even in tumors that overexpress HER2 (MCF-7 HER2/18) and are de novo tamoxifen-resistant. In conclusion, EGFR/HER2 may mediate tamoxifen resistance in ER-positive breast cancer despite continued suppression of ER genomic function by tamoxifen. IGF-IR expression remains dependent on ER but is activated in the tamoxifen-resistant tumors. This study provides a rationale to combine HER inhibitors with tamoxifen in clinical studies, even in tumors that do not initially overexpress EGFR/HER2.
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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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it