Molecular Characterization of Residual Muscle-Invasive Bladder Cancer Identifies a Scar-Like Transcriptomic Profile with Favorable Prognosis after Neoadjuvant Therapy
Bibliographic record
Abstract
PURPOSE: Many patients have residual disease after neoadjuvant therapy, but their prognosis and need for adjuvant therapy are unclear. This study evaluates patient prognosis based on the molecular profiling of residual disease at cystectomy. EXPERIMENTAL DESIGN: RNA sequencing data from TURBT samples were available from N = 84 ABACUS patients, of whom N = 64 had matched radical cystectomy (RC) samples, including 10 patients with pathologic complete response (ypT0N0). Pre- and post-atezolizumab tumor gene expression data were classified into molecular subtypes using the consensus subtyping model as a benchmark. Unsupervised consensus clustering was performed to categorize RC samples de novo, and each cluster was characterized using gene expression signatures. Two RC cohorts (neoadjuvant chemotherapy, N = 133 and University of Texas Southwestern, N = 94) known to harbor a scar-like biologic cluster were used for training and testing of a single-sample transcriptomic classifier that was validated in two independent (PURE-01, N = 26 ad ABACUS, N = 64) RC cohorts after neoadjuvant immunotherapy. RESULTS: Unsupervised consensus clustering revealed four distinct post-atezolizumab clusters (scar-like, basal, luminal-stromal, and luminal). The scar-like cluster was present in 25% (16/64) of the post-atezolizumab samples and expressed genes associated with wound healing/scarring. A transcriptomic classifier trained to identify a favorable scar-like transcriptomic profile in residual bladder tumors showed robust performance in two validation cohorts, indicating a patient subgroup with favorable prognosis among neoadjuvant chemotherapy-treated, pembrolizumab-treated, and atezolizumab-treated patients with residual bladder cancer. CONCLUSIONS: This study contributes to the framework for defining molecular subtypes at RC. Residual bladder cancer with a scar-like transcriptomic profile may predict favorable patient prognosis after neoadjuvant chemotherapy and immunotherapy, identifying potential candidates for treatment deintensification.
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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".