Gene Expression Profiling of Muscle-Invasive Bladder Cancer With Secondary Variant Histology
Bibliographic record
Abstract
OBJECTIVES: To determine the potential impact of the presence of secondary variant histology on the gene expression profiles of muscle-invasive bladder cancer (MIBC) tumors. METHODS: For six tumors, revised samples were collected from urothelial and secondary variant components (cohort A). The commercial cohort (cohort B) consisted of the anonymized gene expression profiles of 173 patients with MIBC. Samples were obtained from the clinical use of the Decipher Bladder test that were available as part of the Decipher GRID prospective registry (NCT02609269). Secondary variant presence in cohort B was abstracted from institutional pathology reports. For the commercial cohort, only the urothelial carcinoma component was profiled. RESULTS: Molecular subtyping of both urothelial and variant components found micropapillary and nested cases were classified as a luminal subtype. Conversely, the sarcomatoid and small cell cases were classified as basal/squamous or neuroendocrine-like, respectively. For cohort B, 50 (29%) of 173 cases had reported secondary variant histology. Cases with squamous variant had basal profiles, small cell cases expressed neuronal markers, and micropapillary cases were classified as luminal. Sarcomatoid tumors had robust epithelial-mesenchymal transition marker expression. CONCLUSIONS: Our data suggest that in MIBC with secondary variant, the urothelial component can demonstrate an expression profile that closely resembles the variant component.
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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 | 0.001 | 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".