Selective estrogen receptor modulators as inhibitors of repopulation of human breast cancer cell lines after chemotherapy.
Bibliographic record
Abstract
PURPOSE: Repopulation of surviving tumor cells between courses of chemotherapy might lead to effective drug resistance. Here we study inhibition of repopulation of hormone-responsive human breast cancer cell lines by selective estrogen receptor (ER) modulators (SERMs) during courses of chemotherapy. EXPERIMENTAL DESIGN: Hormone responsive breast cancer cell lines MCF-7 and T47D, and the ER- cell line MDA-231, were treated with either 4-hydroxy tamoxifen (4OHT) or arzoxifene during weekly courses of treatment with 5-fluorouracil (5-FU) or methotrexate (MTX). Clonogenic assays were performed to determine the overall survival of tumor cells after treatment with the SERMs alone, after one to three doses of 5-FU or MTX alone, and after 5-FU or MTX followed by each of the SERMs. RESULTS: Both SERMs inhibited the growth of ER+ cells MCF-7 and T47D but had no effect on the ER-cell line MDA-231. Arzoxifene was more effective than 4OHT. Between courses of treatment with either 5-FU or MTX, repopulation of ER+ cells was specifically inhibited by the SERMs, whereas repopulation of ER- MDA-231 was not affected. CONCLUSIONS: Arzoxifene and 4OHT can inhibit specifically the repopulation of ER+ breast cancer cells between courses of chemotherapy. Scheduling of short-acting SERMs between courses of chemotherapy has the potential to improve therapeutic index.
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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) | 0.001 | 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 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".