Factors Promoting Tamoxifen Resistance in Breast Cancer via Stimulating Breast Cancer Stem Cell Expansion
Why this work is in the frame
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Bibliographic record
Abstract
Estrogen receptor-alpha positive (ER(+)) breast cancer constitutes 70-75% of the disease incidence. Tamoxifen has been the basis of endocrine therapy for patients with ER(+) breast cancer for more than three decades. The treatment reduces the annual mortality rate of breast cancer by 31%, and remains the most effective targeted cancer therapy. However, approximately one-third of patients treated with adjuvant tamoxifen suffer from aggressive recurrent disease. Resistance to tamoxifen, thus, remains a major challenge in providing effective treatments for these patients. In an effort to overcome the resistance, intensive research has been conducted to understand the underlying mechanisms; this has resulted in the identification of complex factors/pathways contributing to tamoxifen resistance, including modulations of the ERsignaling, upregulation of a set of growth factor receptor networks (HER2, EGFR, FGFR, and IGF1R), alterations of the PI3K-PTEN/AKT/mTOR pathway, and an elevation of the NF-κB signaling. Despite these advances, our understanding of the acquired resistance remains fragmented and there is a lack of a platform to integrate these diversified molecular factors/ pathways into a cohesive mechanistic model. Nonetheless, at the cellular level, it is becoming increasingly recongnized that cancer stem cells (CSCs) are key in driving cancer metastasis and therapy resistance. Likewise, evidence is emerging for the critical contributions of breast cancer stem cells (BCSCs) to tamoxifen resistance. In this review, we will discuss these recent developments of BCSC-mediated resistance to tamoxifen and the contributions of those demonstrated molecular factors/pathways to BCSC expansion during the emergency of tamoxifen resistance.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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