Genomic profiling of muscle invasive bladder cancer to predict response to bladder-sparing trimodality therapy.
Bibliographic record
Abstract
513 Background: Trimodality therapy with TURBT followed by chemoradiation is an acceptable alternative to cystectomy for muscle invasive bladder cancer (MIBC). Genomic profiling has demonstrated MIBC can be divided into molecular subtypes with differing responses to chemotherapy. We explored the utility of genomic data to select patients for bladder-sparing trimodality therapy. Methods: Transcriptome wide gene expression profiles were generated for 189 MIBC TURBT samples from patients treated with trimodality therapy at a single institution. Of these, 103 passed microarray QC. Molecular subtype and expression of bladder cancer genes were assessed for association with overall and disease-specific survival. Transcriptome wide differential expression analysis was used to explore gene set enrichment in trimodality therapy response groups. Results: The chemoradiation cohort (n = 103) had a median followup of 6.9 years for alive patients, and was classified into four subtypes: basal (n = 44), basal claudin-low (n = 12), infiltrated luminal (n = 17) and luminal tumors (n = 30). There was no significant difference in overall or disease-specific survival by subtype. However, higher expression of the luminal-associated PPARG was correlated with increased survival after adjusting for subtype and clinical factors (HR = 0.52, p = 0.002). In contrast, a p53 signature predicted worse survival after adjusting for clinical factors (HR = 1.92, p = 0.022). Elevated mRNA expression of the DNA damage repair gene MRE11 was associated with improved survival in the trimodality cohort (HR = 0.69, P = 0.031), consistent with its potential role as a predictive biomarker for radiation response. Gene set enrichment revealed differential regulation of immune pathways in trimodality therapy responders relative to non-responders, including enrichment of interferon gamma signaling (p = 0.01) and CXCL9 (p = 0.031), suggestive of an interplay between tumor immunologic microenvironment and response to chemoradiation. Conclusions: Transcriptional profiling of MIBC revealed gene signatures correlated with response to chemoradiation, suggesting the potential of genomics to guide use of trimodality therapy.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".