Dicer-Mediated Upregulation of BCRP Confers Tamoxifen Resistance in Human Breast Cancer Cells
Bibliographic record
Abstract
PURPOSE: Tamoxifen (Tam) is the most prescribed hormonal agent for treatment of estrogen receptor α (ERα)-positive breast cancer patients. Using microarray analysis, we observed that metastatic breast tumors resistant to Tam therapy had elevated levels of Dicer. EXPERIMENTAL DESIGN: We overexpressed Dicer in ERα-positive MCF-7 human breast cancer cells and observed a concomitant increase in expression of the breast cancer resistance protein (BCRP). We thus hypothesized that Tam resistance associated with Dicer overexpression in ERα-positive breast cancer cells may involve BCRP. We analyzed BCRP function in Dicer-overexpressing cells using growth in soft agar and mammosphere formation and evaluated intracellular Tam efflux. RESULTS: In the presence of Tam, Dicer-overexpressing cells formed resistant colonies in soft agar, and treatment with BCRP inhibitors restored Tam sensitivity. Tumor xenograft studies confirmed that Dicer-overexpressing cells were resistant to Tam in vivo. Tumors and distant metastases could be initiated with as few as five mammosphere cells from both vector and Dicer-overexpressing cells, indicating that the mammosphere assay selected for cells with enhanced tumor-initiating and metastatic capacity. Dicer-overexpressing cells with elevated levels of BCRP effluxed Tam more efficiently than control cells, and BCRP inhibitors were able to inhibit efflux. CONCLUSION: Dicer-overexpressing breast cancer cells enriched for cells with enhanced BCRP function. We hypothesize that it is this population which may be involved in the emergence of Tam-resistant growth. BCRP may be a novel clinical target to restore Tam sensitivity.
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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) | 0.000 | 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) | 0.002 | 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".