ENGOT-OV44/FIRST study: a randomized, double-blind, adaptive, phase III study of standard of care (SOC) platinum-based therapy ± dostarlimab followed by niraparib ± dostarlimab maintenance as first-line (1L) treatment of stage 3 or 4 ovarian cancer (OC).
Bibliographic record
Abstract
TPS6101 Background: Despite surgery and CT (paclitaxel + carboplatin ± bevacizumab [bev]), 5-year survival rates remain low for patients (pts) with FIGO stage 3 or 4 OC. Niraparib is a poly (ADP-ribose) polymerase (PARP) inhibitor that has recently demonstrated efficacy in 1L therapy. Dostarlimab (TSR-042) is an anti-programmed death (PD)-1 humanized monoclonal antibody that has shown clinical activity as monotherapy in early phase trials. The currently enrolling ENGOT-OV44/FIRST study will compare efficacy and safety of CT + dostarlimab + niraparib ± bev (Arm 3) vs CT + niraparib ± bev (Arm 2). Methods: Eligible pts are ≥18 years of age, with FIGO stage 3 or 4 non-mucinous epithelial OC, ECOG performance status < 2, and tumor tissue available for PD-1 ligand (PD-L1) testing. After cycle 1 of CT, pts are stratified by concurrent bev use, BRCA mutation/homologous recombination repair status, and disease burden, then randomized 1:2 into trial Arms 2 and 3 (Table). Dostarlimab is administered at 500 mg IV Q3W during the CT period, then 1000 mg IV Q6W during the maintenance period. Niraparib dosing is 200 mg PO QD for pts with baseline bodyweight (BW) < 77 kg and/or platelet count (PC) < 150,000/µL, or 300 mg QD for pts with baseline BW ≥77 kg and PC ≥150,000/µL. The dual primary endpoints are PFS, based on investigator assessment per RECIST v1.1, in both PD-L1+ and all patients. Initially the study enrolled pts to Arm 1. This arm was discontinued following positive results from the PRIMA/ENGOT-OV26/GOG-3012 and PAOLA-1/ENGOT-OV25 studies. This allows investigators to offer the current standard of care to all patients. Clinical trial information: NCT03602859, EUDRACT 2018-000413-20. [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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".