Subtyping muscle-invasive bladder cancer to assess clinical response to trimodality therapy.
Bibliographic record
Abstract
287 Background: Trimodality therapy with TURBT followed by chemoradiation is an acceptable alternative to cystectomy for muscle invasive bladder cancer (MIBC). Recently, genomic profiling has demonstrated MIBC can be divided into three or more subtypes with differing responses to chemotherapy, suggesting genomic subtype may impact therapeutic response. Here, we explore the utility of genomic information to better select patients for bladder-sparing trimodality therapy. Methods: Transcriptome-wide gene expression profiles were generated for 189 MIBC TURBT samples from patients undergoing trimodality therapy at the Massachusetts General Hospital. Of these, 108 passed microarray QC and 100 had complete clinical information. The patient tumors were classified as basal, basal claudin-low, infilrated luminal or luminal subtype. The subtype and the expression of a number of bladder cancer genes were assessed for their association with need for salvage cystectomy and for overall survival. Finally, transcriptome-wide differential expression analysis was used to explore gene set enrichment in trimodality therapy response groups. Results: Our chemoradiation cohort (n = 108) was classified into the four subtypes: basal (n = 45), basal claudin low (n = 13), infiltrated luminal (n = 17) and luminal tumors (n = 33). Survival analysis (n = 100) showed that patients of the luminal subtype trended to better overall survival, but did not reach significance (HR = 0.63, p = 0.1). Fewer patients with infiltrated luminal tumors (12%) required a salvage cystectomy compared to all other subtypes (34+/-1.5%, p = 0.08). We found high expression of BLACAT1 and NORAD (a lncRNA with a role in genome stability) correlated with worse (p = 0.01) and better prognosis (p = 0.008), respectively. Likewise, patients with high levels of the luminal-associated PPARG showed a significant increase in overall survival (p = 0.0002). Gene set enrichment revealed differential regulation of immune pathways in the trimodality therapy responders relative to the non-responders (p < 0.05). Conclusions: Preliminary data exploring MIBC subtyping suggests the possibility of using genomics to predict response to trimodality bladder-sparing 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".