Impact of Chemotherapy Dosing Schedule on Ovarian Cancer Tumor Responsiveness
Bibliographic record
Abstract
In Canada, ovarian cancer kills about 67% of diagnosed patients, largely due to difficulties in early diagnosis. Current treatment consists of debulking surgery and intermittent chemotherapy every three weeks. This approach leads to insufficient drug concentrations at disease sites, and long treatment-free intervals cause accelerated tumor proliferation and drug resistance, resulting in a 5-year survival rate of only 25-35%. Drug resistance development is the ultimate cause of the majority of patient deaths. Improvements yielding more effective treatment are fundamental for successful management of this disease. This thesis investigated a continuous chemotherapy strategy devoid of treatment-free intervals for ovarian cancer treatment. A biocompatible, biodegradable polymer-lipid injectable formulation PoLigel, was used for continuous DTX delivery. The formulation was well tolerated; no alterations in body weight, behaviour, histology of peritoneal tissues, or interleukin-6 levels were seen in CD-1 mice treated with the PoLigel. Continuous DTX therapy via the PoLigel was considerably more efficacious than intermittent therapy, resulting in significantly less tumor burden and ascites fluid in models of human and murine ovarian cancer. Continuous therapy resulted in less tumor cell proliferation and angiogenesis, and more tumor cell death than intermittent DTX. The presence and length of treatment-free intervals was shown to contribute to the development of drug resistance. Eliminating these intervals by continuous dosing resulted in superior antitumor efficacy in both chemosensitive and chemoresistant xenograft models of human ovarian cancer, and prevented drug resistance increase after a 21-day treatment period. Survival studies revealed that intermittent dosing led to a mild survival prolongation of 36% and 10% in chemosensitive and chemoresistant models, respectively, whereas continuous DTX prolonged survival by a striking 114% and 95%. Although long-term continuous chemotherapy substantially improved survival, increased drug resistance mechanisms were found at the endpoint. Overall, results presented here encourage the clinical implementation of continuous chemotherapy due to greater achievable therapeutic advantages.
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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) | 0.002 | 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".