Bladder Preservation Therapy for Muscle-Invading Bladder Cancers on Radiation Therapy Oncology Group Trials 8802, 8903, 9506, and 9706: Vascular Endothelial Growth Factor B Overexpression Predicts for Increased Distant Metastasis and Shorter Survival
Bibliographic record
Abstract
BACKGROUND: From 1988 to 1999, the Radiation Therapy Oncology Group (RTOG) conducted four prospective studies (8802, 8903, 9506, 9706) of patients with clinical stage T2-4a muscle-invasive bladder cancer. Treatment was selective bladder preservation using transurethral surgery (TURBT) plus cisplatin-based induction and consolidation chemoradiation regimens, reserving radical cystectomy for invasive tumor recurrence. We investigated vascular endothelial growth factor (VEGF) pathway biomarkers in this unique clinical dataset (median follow-up of 3.1 years). METHODS: A total of 43 patients with tissue available from the entry TURBT were included in this analysis. Expression of VEGF ligands and receptors were quantified and scored by the AQUA platform (HistoRX, now Genoptix, Carlsbad, CA) and analyzed after median split. RESULTS: VEGF expression levels were not associated with increased rates of complete response to induction chemoradiation. Higher levels of cytoplasmic VEGF-B, VEGF-C, and VEGF-R2 were associated with decreased overall survival rates. The 3-year overall survival estimates for high and low expressers were 43.7% and 75% for VEGF-B cytoplasm (p = .01), 40.2% and 86.7% for VEGF-C cytoplasm (p = .01), and 49.7% and 66.7% for VEGF-R2 cytoplasm (p = .02). Higher expression levels of cytoplasm VEGF-B were associated with higher rates of distant failure (p = .01). CONCLUSIONS: Although VEGF ligands and receptors do not appear to be associated with complete response to induction chemoradiation for muscle-invasive bladder cancer, we report significant associations with overall survival and distant failure for certain VEGF family members.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".