Chemotherapy Dosing Schedule Influences Drug Resistance Development in Ovarian Cancer
Bibliographic record
Abstract
Drug resistance leads to chemotherapy failure and is responsible for the death of a great majority of patients with metastatic, late-stage ovarian cancer. The present study addressed whether changes in the chemotherapy dosing schedule affect the development, further worsening, or circumvention of drug resistance in chemosensitive and chemoresistant ovarian cancer. Severe combined immunodeficient mice bearing HeyA8 and HeyA8-MDR xenografts were treated with docetaxel intermittently (1×/wk or 3×/wk) or continuously for 21 days. Tumor mRNA expression of genes implicated in docetaxel resistance was measured by quantitative real-time-PCR. Analyzed genes included those encoding for the drug efflux transporters mdr1 and mrp7 and for molecules that interfere with or overcome the effects of docetaxel, including β-tubulinIII, actinin4, stathmin1, bcl2, rpn2, thoredoxin, and akt2. In both models, continuous docetaxel resulted in greater antitumor efficacy than 1×/wk or 3×/wk dosing and did not induce upregulation of any analyzed genes. Once weekly dosing caused upregulation of various drug resistance-related genes, especially in chemoresistant xenografts. More frequent, 3×/wk dosing diminished this effect, although levels of various genes were higher than for continuous chemotherapy. Drug efflux transporter expression was further examined by Western blotting, confirming that intermittent, but not continuous, docetaxel induced significant upregulation. Overall, our results show that the presence and length of treatment-free intervals contribute to the development of drug resistance. Elimination of these intervals by continuous dosing resulted in superior antitumor efficacy and prevented drug resistance induction in chemosensitive and chemoresistant disease. These results encourage the clinical implementation of continuous chemotherapy to overcome and/or prevent drug resistance in newly diagnosed and recurrent, refractory ovarian cancer.
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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 | 0.001 |
| 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".