Combining mTOR Inhibition with Radiation Improves Antitumor Activity in Bladder Cancer Cells In Vitro and In Vivo: A Novel Strategy for Treatment
Bibliographic record
Abstract
PURPOSE: Radiation therapy for invasive bladder cancer allows for organ preservation but toxicity and local control remain problematic. As such, improving efficacy of treatment requires radiosensitization of tumor cells. The aim of study is to investigate if the mammalian Target of Rapamycin (mTOR), a downstream kinase of the phosphatidylinositol 3-kinase (PI3K)/AKT survival pathway, may be a target for radiation sensitization. EXPERIMENTAL DESIGN: Clonogenic assays were performed using 6 bladder cancer cell lines (UM-UC3, UM-UC5, UM-UC6, KU7, 253J-BV, and 253-JP) in order to examine the effects of ionizing radiation (IR) alone and in combination with RAD001, an mTOR inhibitor. Cell cycle analysis was performed using flow cytometry. In vivo, athymic mice were subcutaneously injected with 2 bladder cancer cell lines. Treatment response with RAD001 (1.5 mg/kg, daily), fractionated IR (total 9Gy = 3Gy×3), and combination of RAD001 and IR was followed over 4 weeks. Tumor weight was measured at experimental endpoint. RESULTS: Clonogenic assays revealed that in all bladder cell lines tested, an additive effect was observed in the combined treatment when compared to either treatment alone. Our data indicates that this effect is due to arrest in both G1 and G2 phases of cell cycle when treatments are combined. Furthermore, our data show that this arrest is primarily regulated by changes in levels of cyclin D1, p27 and p21 following treatments. In vivo, a significant decrease in tumor weight was observed in the combined treatment compared to either treatment alone or control. CONCLUSIONS: Altering cell cycle by inhibiting the mTOR signaling pathway in combination with radiation have favorable outcomes and is a promising therapeutic modality for bladder 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".