A luminal non‐coding RNA‐based genomic classifier confirms favourable outcomes in patients with clinically organ‐confined bladder cancer treated with radical cystectomy
Bibliographic record
Abstract
OBJECTIVE: To further evaluate a genomic classifier (GC) in a cohort of patients undergoing radical cystectomy (RC), as long non-coding RNA (lncRNA)-based genomic profiling has suggested utility in identifying a distinct tumour subgroup corresponding to a favourable prognosis in patients with bladder cancer. PATIENTS AND METHODS: Transcriptome-wide expression profiling using Decipher Bladder was performed on transurethral resection of bladder tumour samples from a cohort of patients with high-grade, clinically organ-confined (cTa-T2N0M0) urothelial carcinoma (UC) who subsequently underwent RC without any neoadjuvant therapy (n = 226). The lncRNA-based luminal favourable status was determined using a previously developed GC. The primary endpoint was overall survival (OS) after RC. Secondary endpoints included cancer-specific mortality and upstaging at RC. RESULTS: In the study, 134 patients were clinical non-muscle-invasive bladder cancer (cTa/Tis/T1) and 92 patients were cT2. We identified 60 patients with luminal favourable subtype, all of which showed robust gene expression patterns associated with less aggressive bladder cancer biology. On multivariate analysis, patients with the luminal favourable subtype (vs without) were significantly associated with lower odds of upstaging to pathological (p)T3+ disease (odds ratio [OR] 0.32, 95% confidence interval [CI] 0.12-0.82; P = 0.02), any upstaging (OR 0.41, 95% CI 0.20-0.83; P = 0.01), and any upstaging and/or pN+ (OR 0.50, 95% CI 0.25-1.00; P = 0.05). Luminal favourable bladder cancer was significantly associated with better OS (hazard ratio 0.33, 95% CI 0.15-0.74; P = 0.007). CONCLUSIONS: This study validates the performance of the GC for identifying UCs with a luminal favourable subtype, harbouring less aggressive tumour biology.
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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) | 0.001 | 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".