Antitumor Efficacy of Intravesical BCG, Gemcitabine, Interferon-α and Interleukin-2 as Mono- or Combination-Therapy for Bladder Cancer in an Orthotopic Tumor Model
Bibliographic record
Abstract
OBJECTIVE: To reduce adverse effects and improve efficacy of intravesical BCG for bladder cancer, alternative treatment options were investigated in an orthotopic rat tumor model. METHODS: Superficial bladder cancer was established in syngeneic female rat bladders by instillation of AY-27 cells. Animals were randomly assigned to treatment groups including dose escalation of intravesical BCG with or without interferon-α (IFN-α) or interleukin-2 (IL-2); or graded doses of gemcitabine alone; or BCG plus gemcitabine. Treatments were given twice weekly for 3 weeks. Rats in control groups received saline instillations. Treatment response was monitored by animals' well-being, survival days, tumor growth inhibition, and histological examination at necropsy. RESULTS: Rats receiving monotherapy with intravesical BCG, gemcitabine, or IFN-α, attained significantly better survival and tumor reduction compared with control (P = 0.002; 0.001; 0.002, respectively, Log-rank Test). A dose-dependent treatment response was observed in animals with established bladder tumor receiving escalated BCG instillations. Only high-dose BCG significantly improved animal survival. Although high-dose BCG plus gemcitabine or IFN-α did not increase benefit over monotherapies, low-dose BCG plus IL-2 did show improved efficacy (P = 0.01). CONCLUSION: Intravesical monotherapies with gemcitabine and IFN-α were as effective as BCG for treatment of early non-muscle-invasive urothelial bladder cancer in this immune competent rat model. Combining these agents with high-dose BCG did not further increase efficacy. However, combining low-dose BCG with IL-2 enhanced BCG effectiveness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.001 |
| 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 teacher head, 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".