Understanding diversity and disparities in a real-world locally advanced/metastatic urothelial carcinoma (la/mUC): Clinical characteristics, genomic landscape, and self-reported social determinants of health (SDOH).
Bibliographic record
Abstract
669 Background: Fibroblast growth factor receptor (FGFR) inhibitor therapy improves overall survival in LA/mUC patients with FGFR 2/3 alterations and prior systemic therapy including immune checkpoint inhibitors (ICI). The current treatment landscape is rapidly evolving with inequities in molecular testing and access to novel therapies. In the real-world, the genomic landscape, treatment patterns, and outcomes in LA/mUC have yet to be established. In this multicenter prospective cohort study, we examined the RW treatment patterns, outcomes, genomic profile, diversity, and SDOH in pts with LA/mUC. Methods: In this LA/mUC cohort, we assessed baseline characteristics, self-reported SDOH, clinical management patterns, and treatment outcomes (PFS, OS). Comprehensive genomic profiling (CGP), including FGFR1-4, of DNA and RNA from archived FFPE tissue were analyzed. Results: 42 pts with LA/mUC were enrolled from Apr – Sept 2024 (79% bladder; 21% upper tract). 93% (39/42) had distant metastases (18% visceral). Median age at diagnosis was 69 yrs and 79% were male. 23 (62%) completed a questionnaire. 96% self-reported as White/Caucasian, 4% as Indigenous. 26% live in a rural setting >1 hour from a cancer centre. 47% report financial stress. 30% report a family history of Lynch Syndrome related malignancies. In 35 pts who underwent CGP, 11 pts (31%) had FGFR alterations, comprised of FGFR1-3 fusions, amplification, or mutations (Table 1). 88% (37/42) of pts received first-line (1L) therapy, primarily CTx (78%), and 1 pt received an FGFR-inhibitor. 62% (23/37) received 2L therapy (83% ICI, of which 68% was maintenance avelumab). 8 pts received 3L (63% (5/8) antibody drug conjugate [ADC]). 2 pts received 4L (n=1 ADC, n=1 CTx); 1 pt received 5L (ICI). Median progression free survival (mPFS) was 7.6 mo (95% CI: 5.1 – 22.5), 6.6 mo [95% CI: 2.5 – NR] and 6.9 mo [2.1 – NR], for 1L, 2L, 3L, respectively, but not reached for 4L and 5L. 88% are alive with a median follow-up of 10.3 months. Overall survival was not yet reached. Conclusions: This RW analysis of pts with LA/mUC provides valuable insights into the genomic landscape, clinical characteristics, and SDOH within this population. The presence of targetable genomic alterations underscores the necessity for an equitable precision medicine approach in diverse LA/mUC pt populations to optimize outcomes. This study demonstrates the feasibility of collection of a comprehensive array of data and samples to guide a management for patients with LA/mUC. NGS results. FGFR Alteration (n=11) Number (%) FGFR1 amp (borderline) 1 (9) FGFR1::TBC1D22A fusion 1 (9) FGFR2::USP11 fusion 1 (9) FGFR3 mutation 3 (27) FGFR3::FGFR1 fusion 2 (18) Insufficient sample 3 (27)
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".