Rucaparib for maintenance treatment of platinum-sensitive, recurrent ovarian carcinoma: Final results of the phase 3, randomized, placebo-controlled ARIEL3 trial
Bibliographic record
Abstract
BACKGROUND: In ARIEL3, rucaparib maintenance significantly improved progression-free survival (PFS; primary endpoint) and long-term follow-up (LTFU) outcomes (including PFS2: time to disease progression on subsequent therapy or death) versus placebo in patients with recurrent, platinum-sensitive ovarian cancer. Here we report the final analysis of overall survival (OS; key secondary endpoint), LTFU outcomes, and safety. METHODS: OS and updated LTFU efficacy outcomes were analyzed (data cutoff date: April 4, 2022) across three nested populations (BRCA-mutated, homologous recombination deficient [HRD], and intention to treat [ITT]). RESULTS: Patients were randomized 2:1 to rucaparib (600 mg BID; n = 375) or placebo (n = 189). Median follow-up was 77.0 months. 168 patients in the placebo arm received subsequent treatment; of these, 77 (46 %) received a poly(ADP-ribose) polymerase inhibitor-containing treatment. Median OS from randomization post chemotherapy for rucaparib vs placebo was 45.9 vs 47.8 months (HR 0.83, 95 % CI 0.58-1.19) for the BRCA-mutated population; no OS benefit was found with rucaparib in the HRD and ITT populations. Median PFS2 for rucaparib vs placebo was 26.1 vs 18.4 months (HR 0.67, 95 % CI 0.48-0.94) for the BRCA-mutated population. Rucaparib numerically improved PFS2 and other LTFU outcomes versus placebo in the HRD and ITT populations. Safety was consistent with prior reports; myelodysplastic syndrome and/or acute myeloid leukemia occurred in 4 % and 3 % of patients in the rucaparib and placebo arms, respectively. CONCLUSIONS: OS was similar between treatment arms. PFS benefit with rucaparib was maintained through the subsequent therapy line. These data support rucaparib as maintenance treatment for recurrent ovarian carcinoma.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".