Factors Promoting Tamoxifen Resistance in Breast Cancer via Stimulating Breast Cancer Stem Cell Expansion
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; 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".