A genomic classifier for identifying a neuroendocrine-like bladder cancer subtype.
Bibliographic record
Abstract
440 Background: Neuroendocrine (NE) carcinoma is a rare and aggressive variant of muscle invasive bladder cancer (MIBC). Molecular subtyping studies found 5-15% of bladder tumors had transcriptome profiles consistent with NE carcinoma but lacked NE histology (Robertson 2017, Sjödahl 2017). Identifying NE variants may have prognostic implications and modify treatment recommendations. In this study, we present a robust genomic classifier trained to identify NE carcinoma. Methods: Transcriptome-wide expression profiles were generated for 576 MIBC patients collected from seven institutions. Model training included profiles generated from TURBT and RC specimens from 320 patients prior to treatment with NAC or chemo-radiation. The validation cohort consisted of 256 RC specimens (no prior systemic treatment). Using 10 MIBC-related gene sets, a GLMNET model was built to predict patients with a NE carcinoma expression profile. Uni- and multi-variable survival analyses were used to characterize outcomes of the predicted NE tumors. Results: In the training set, hierarchical clustering using a panel of 54 genes showed a cluster of 17 patients (5.3%) with a NE carcinoma expression profile. These patients had significantly worse 1 year progression free survival (65% vs 82% for NE vs overall; p = 0.046). In the validation set, 7 tumors were classified (2.7%) as NE with 4 (57%) patients dying from the disease at 1 year after RC. Within 3 years of RC, 100% (7/7) of patients with NE tumors had died. After adjusting for various clinical and pathological factors, patients with predicted NE tumors had a 6.40 increased risk of all-cause mortality (p = 0.001). Conclusions: We have developed a gene expression signature that predicts a particularly high-risk group that may need treatment intensification, alternative chemotherapy or clinical trials. Validation will be required to assess the potential clinical utility of this NE carcinoma classifier.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".