Concurrent and Sequential Administration of Chemotherapy and the Mammalian Target of Rapamycin Inhibitor Temsirolimus in Human Cancer Cells and Xenografts
Bibliographic record
Abstract
PURPOSE: Optimal scheduling of cycle-active chemotherapy with (initially cytostatic) molecular-targeted agents is important to maximize clinical benefit. Concurrent scheduling might allow up-regulation of cell death pathways at the time of chemotherapy, whereas sequential treatments might maximize inhibition of repopulation and avoid putting tumor cells out of cycle when administering cycle-active chemotherapy. We compared the effects of concurrent and sequential administration of chemotherapy and the mammalian target of rapamycin (mTOR) inhibitor temsirolimus (CCI-779) on tumor cells and xenografts. EXPERIMENTAL DESIGN: Human prostate cancer PC-3 and LnCaP, and human breast cancer MDA-468 cells and xenografts were treated with chemotherapy (docetaxel and 5-fluorouracil, respectively) and temsirolimus, using concurrent and sequential treatment schedules. Cell killing and repopulation were evaluated by clonogenic assays. Cell cycle analysis was done using flow cytometry. Effects on xenografts were assessed by tumor growth delay. RESULTS: The proliferation of all cell lines was inhibited by temsirolimus in a dose-dependent manner; PTEN negative PC-3 and mutant LnCaP cells were more sensitive than PTEN-negative MDA-468 cells. Temsirolimus inhibited cell cycle progression from G(1) to S phase in all cell lines. Combined treatment had greater effects than temsirolimus or chemotherapy alone: for PC-3 and LnCaP xenografts, concurrent treatment seemed superior to sequential scheduling, whereas MDA-468 cells and xenograft tumors did not show schedule dependence. CONCLUSIONS: Combined treatment with temsirolimus and chemotherapy had a greater therapeutic effect than monotherapy; concurrent scheduling was more effective for PC-3 and LnCaP cells and xenografts that were sensitive to temsirolimus.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".