Genomic characterization and identification of actionable variants in patients with locally advanced or metastatic urothelial carcinoma (mUC).
Bibliographic record
Abstract
562 Background: Next generation sequencing (NGS)-based molecular profiling has been widely used across various tumor types including genitourinary malignancies to better understand and potentially predict tumor behavior. The goal of this study was to determine the feasibility of using NGS to characterize mUC and to identify potentially actionable variants. Methods: Patients with locally advanced or metastatic urothelial carcinoma (mUC) treated at the Princess Margaret Cancer Center were prospectively recruited for the OCTANE study, which collects archival tumor samples to provide genomic characterization through NGS with a custom hybridization capture DNA-based panel (555 genes) or a targeted DNA/RNA amplicon panel (Oncomine Comprehensive Assay v3, 161 driver gene panel). Variants were annotated using a somatic variant scheme [PMID: 25880439] or the oncoKB database. Comprehensive clinical information including patient, disease and treatment characteristics was collected using electronic patient records. Kaplan-Meier and cox regression were used for survival analysis. Results: Of the 73 patients, median age was 65 (23-83), majority were men (69%), most had lower tract disease (80%) and 41% had de novo metastatic disease. NGS was deemed feasible, based on availability of tumor samples, and performed in 67 (92%) patients. Overall, 61 (91%) had at least one oncogenic variant, including 34 (56%) with a potentially "druggable" target. The most frequent genomic alterations found were TP53 (44%), FGFR (18%), TERT (18%), ARID1A (18%) and PIK3CA (16%), all classified as "pathogenic/likely pathogenic". Only 2 patients received targeted treatment, one as part of clinical trial and one as standard of care. mOS was numerically longer among patients with genomic mutations compared to the ones without: 55 vs 31 months (HR: 0.87, CI 95% 0.3-2.4; p:0.7). No specific mutation was shown to significantly impact survival (table). Conclusions: Use of NGS technology in characterizing the genomic profile of patients with locally advanced or metastatic UC was feasible in most cases. Oncogenic variants were detected in the majority of patients, and more than half of them harbored a potentially "druggable" target, which may lead to future therapeutic advances.[Table: see text]
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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 | 0.000 |
| 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".