Effect of the Selective Estrogen Receptor Modulator Arzoxifene on Repopulation of Hormone-Responsive Breast Cancer Xenografts between Courses of Chemotherapy
Bibliographic record
Abstract
Selective inhibition of repopulation of clonogenic tumor cells between courses of chemotherapy has potential to improve the effectiveness of treatment. Here we study arzoxifene, a short-acting selective estrogen receptor modulator, for its potential to inhibit repopulation in estrogen-dependent human breast cancer MCF-7 xenografts between courses of chemotherapy. Proliferation of tumor cells was evaluated by cyclin D1 expression and uptake of 5-bromo-2'-deoxyuridine. Arzoxifene decreased cell proliferation in xenografts. To model adjuvant treatment of human breast cancer, MCF-7 cells were injected s.c. into nude mice and four groups of mice received the following treatments beginning after implantation: (a) control (vehicle solution); (b) arzoxifene alone, 5 days per week by oral gavage for 3 weeks; (c) 5-fluorouracil (5-FU) or paclitaxel i.p. weekly, for 3 doses; and (d) arzoxifene following each cycle of chemotherapy. The incidence of tumors with volume > or =50 mm(3) was determined as a function of time. MCF-7 xenografts developed in 100% of control mice by 4 weeks after implantation. Paclitaxel or 5-FU alone had minor effects to delay the appearance of xenografts whereas arzoxifene alone caused longer delay. Combined treatment with arzoxifene given between cycles of 5-FU or paclitaxel had substantial effects, with approximately 50% tumor incidence by 5 weeks. Our results indicate that arzoxifene can inhibit repopulation of hormone-responsive MCF-7 breast cancer xenografts when given between courses of chemotherapy. The scheduling of short-acting hormonal agents between courses of adjuvant chemotherapy for human breast cancer has potential to improve the outcome of treatment.
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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".