Muscle-invasive bladder cancer: Molecular subtypes and response to neoadjuvant chemotherapy.
Bibliographic record
Abstract
281 Background: Molecular subtypes of muscle-invasive bladder cancers (MIBC) have recently been discovered based on gene expression. We investigated the impact of different subtyping methods on response to neoadjuvant cisplatin-based chemotherapy (NAC) and developed a single sample model for subtyping. Methods: Transcriptome-wide microarray analysis was conducted on pre-NAC transurethral resection (TUR) specimens of 223 patients with MIBC who received NAC followed by cystectomy at 5 centers. The specimens were classified according to four published methods for molecular subtype (UNC, MDA, TCGA, Lund). Overall survival (OS) for each subtype was compared between NAC patients in this study and non-NAC patients from the provisional TCGA. A genomic classifier (GSC) was trained to predict subtype in a single sample model and validated in independent NAC (2 centers) and non-NAC datasets. Results: The models generated subtype calls similar to previously published ratios. Concordance of a given subtype between the different methods was high. Luminal tumors had the best OS independent of NAC. Patients with tumors classified as UNC basal, MDA basal and TCGA cluster III experienced the greatest improvement in OS after NAC compared to surgery alone. Tumors assigned as UNC claudin-low had the worst OS irrespective of treatment regimen (p=0.005). GSC accurately predicted four classes (luminal, luminal-infiltrated, basal, claudin-low) and the differential impact of a basal subtype on patient OS in NAC (3-yr survival of 75.2%; p=0.001) and non-NAC (3-yr survival of 42.4%; p=0.014) cohorts could be validated. Conclusions: The benefit of NAC varies between molecular subtypes. The good prognosis of luminal/cluster I tumors could not be improved with NAC, which suggests these patients may be managed best with surgery alone. The prognosis of patients with basal tumors improved the most when treated with NAC compared to surgery alone. Poor OS of claudin-low tumors even after NAC implies that these tumors are resistant to cisplatin-based chemotherapy, and these patients should be included in protocols investigating alternative treatment options like immunotherapy. Further validation prior to clinical implementation is needed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".