Avelumab plus sacituzumab govitecan versus avelumab monotherapy as first-line maintenance treatment in patients with advanced urothelial carcinoma: JAVELIN Bladder Medley interim analysis
Bibliographic record
Abstract
BACKGROUND: Avelumab first-line maintenance is a recommended treatment option for patients with locally advanced or metastatic urothelial carcinoma (la/mUC) without progression following platinum-based chemotherapy (PBC). The JAVELIN Bladder Medley phase II trial is investigating the efficacy and safety of maintenance treatment with avelumab combined with other antitumor agents versus avelumab monotherapy. We report an interim analysis of avelumab plus sacituzumab govitecan (SG) versus avelumab monotherapy. PATIENTS AND METHODS: Patients with la/mUC without progression after first-line PBC were randomized 2 : 1 to receive avelumab (800 mg every 2 weeks) plus SG (10 mg/kg on days 1 and 8 of 21-day cycles) or avelumab monotherapy (800 mg every 2 weeks). Primary endpoints are investigator-assessed progression-free survival (PFS) and safety. For PFS and overall survival (OS), data in the avelumab monotherapy arm were extended per protocol using propensity score-weighted JAVELIN Bladder 100 data. RESULTS: At data cut-off (16 September 2024), 38/74 patients (51.4%) in the avelumab plus SG arm and 10/37 patients (27.0%) in the avelumab monotherapy arm were still receiving study treatment. Median PFS with avelumab plus SG versus avelumab monotherapy was 11.17 versus 3.75 months, respectively [hazard ratio (HR) 0.49, 95% confidence interval (CI) 0.31-0.76; prespecified efficacy boundary: HR ≤ 0.60]. OS data were immature; median OS was not reached versus 23.75 months, respectively (HR 0.79, 95% CI 0.42-1.50). In patients treated with avelumab plus SG or avelumab monotherapy, any-grade treatment-related adverse events (TRAEs) occurred in 97.3% versus 63.9% (grade ≥3 in 69.9% versus 0%), respectively. CONCLUSION: In patients with la/mUC without progression after first-line PBC, PFS was prolonged with avelumab plus SG versus avelumab monotherapy as maintenance treatment. TRAEs were more frequent with the combination and were consistent with known safety profiles of SG and avelumab. Combining avelumab with anti-Trop-2 antibody-drug conjugates may be a promising strategy to improve patient outcomes in la/mUC.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".