Squamous-cell carcinoma variant histology (SCC-VH) in muscle-invasive bladder cancer (MIBC): A comprehensive clinical, genomic, and therapeutic assessment from multiple datasets.
Bibliographic record
Abstract
4535 Background: Pure or predominant SCC-VH is not uncommon in MIBC. Nevertheless, very few data are available about the efficacy of neoadjuvant chemotherapy (NAC). Here, we examined the outcomes after NAC, explored novel therapeutic targets, and propose new results in these patients (pts) by integrating multiple datasets. Methods: Within RISC and San Raffaele databases (1990-2018), we identified 2858 MIBC pts with urothelial cancer (UC, N = 2229) or VH (N = 629) who received RC +/- NAC. Kaplan-Meier and Cox regression analyses compared cancer-specific survival (CSS) between SCC and UC with NAC stratification. Logistic regression models tested the odds of clinical-to-pathological downstaging (cT > pT). Foundation Medicine (FMI) dataset was queried for SCC-VH. 97 pts were assayed with hybrid-capture based comprehensive genomic profiling (CGP). Finally, we looked at the results from the PURE-01 study, that is now amended and enrolling pts with VH (NCT02736266). Results: Overall, 127 (4.4%) had predominant SCC-VH, 157 (5.5%) UC+SCC. Among the NAC-treated pts, SCC was the only VH (N = 44) significantly associated with worse CSS, (p < 0.001) and higher mortality (HR 2.10, p = 0.003) vs. UC. After NAC adjustment, SCC-VH showed lower rate of downstaging (3.7 vs 9.3%, OR 0.4, p = 0.028) vs. UC. Similar negative trends were confirmed in pN0 pts, where SCC exhibited worse CSS (p = 0.006) and higher mortality (HR 5.15, p = 0.002). In the FMI cohort, the median tumor mutational burden (TMB) of SCC was 6.25 mut/mb (vs 6.9 mut/mb of 1984 UC), 27% of pts having > 10 mut/mb and 14% > 20 mut/mb. Clinically relevant alterations occurred in PIK3CA (42%), CCND1 (15%), PTEN (9.3%), FGFR3 (9.3%), and ERBB2 (6.2%). In the PURE-01 study, 13/84 (15.5%) SCC-VH pts received pembrolizumab before RC. PD-L1 combined positive score was ≥10 in 11/13 pts; results yielded 4 pT0 (30.8%), 10 pT≤1 (76.9%), and no deaths (median FUP: 10.4 mo). Conclusions: We present a comprehensive assessment of SCC-VH in MIBC. SCC represents the VH with the lowest activity of NAC. While CGP revealed multiple opportunities for targeted therapy, the efficacy of neoadjuvant pembrolizumab in SCC is encouraging.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".