Everolimus and pazopanib (E/P) benefit genomically selected patients with metastatic urothelial carcinoma
Bibliographic record
Abstract
BACKGROUND: Metastatic urothelial carcinoma (mUC) is a genomically diverse disease with known alterations in the mTOR pathway and tyrosine kinases including FGFR. We investigated the efficacy and safety of combination treatment with everolimus and pazopanib (E/P) in genomically profiled patients with mUC. METHODS: mUC patients enrolled on a Phase I dose escalation study and an expansion cohort treated with E/P were included. The primary end point was objective response rate (ORR); secondary end points were safety, duration of response (DOR), progression-free survival (PFS) and overall survival (OS). Patients were assessed for mutations and copy number alterations in 300 relevant cancer-associated genes using next-generation sequencing and findings were correlated with outcomes. Time-to-event data were estimated with Kaplan-Meier methods. RESULTS: Of the 23 patients enrolled overall, 19 had mUC. ORR was 21% (one complete response (CR), three partial responses (PR), eight with stable disease (SD). DOR, PFS and OS were 6.5, 3.6, and 9.1 months, respectively. Four patients with clinical benefit (one CR, two PR, one SD) had mutations in TSC1/TSC2 or mTOR and a 5th patient with PR had a FGFR3-TACC3 fusion. CONCLUSIONS: Combination therapy with E/P is safe in mUC and select patients with alterations in mTOR or FGFR pathways derive significant clinical benefit.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".