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ENGOT-ov65/KEYNOTE-B96: Phase 3, randomized, double-blind study of pembrolizumab versus placebo plus paclitaxel with optional bevacizumab for platinum-resistant recurrent ovarian cancer.

2022· article· en· W4281685079 on OpenAlexaff
Nicoletta Colombo, Robert L. Coleman, Xiaohua Wu, Fatih Köse, Robert M. Wenham, Alexandra Sebastianelli, Kosei Hasegawa, Emese Zsíros, Thibault De La Motte Rouge, Mariusz Bidziński, Iain A. McNeish, Jalid Sehouli, Jacob Korach, Philip R. Debruyne, Jae‐Weon Kim, Andréia Cristina de Melo, Xuan Peng, Agata M. Bogusz, Karin Yamada, Bradley J. Monk

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicOvarian cancer diagnosis and treatment
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMedicineBevacizumabOvarian cancerPembrolizumabInternal medicinePaclitaxelOncologyChemotherapyPlaceboResponse Evaluation Criteria in Solid TumorsSurgeryPhases of clinical researchCancerImmunotherapyPathology

Abstract

fetched live from OpenAlex

TPS5617 Background: Despite therapeutic advances in ovarian cancer, platinum-resistant recurrent ovarian cancer (PROC) remains an area of high unmet clinical need and there is an urgent need for new treatments to further improve clinical outcomes. Addition of bevacizumab to non-platinum-based chemotherapy significantly improved PFS in patients with PROC but did not show a clear OS benefit. Thus far, the combination of paclitaxel and bevacizumab has shown the most promise in treatment of PROC, although the proportion of patients eligible for bevacizumab is limited by treatment-associated toxicities. Combination of the anti-PD-1 antibody pembrolizumab with weekly paclitaxel showed antitumor activity and manageable toxicity in patients with PROC in a single-arm, phase 2 study (Wenham Int J Gynecol Cancer 2018). The current study ENGOT-ov65/KEYNOTE-B96 (NCT05116189) compares the efficacy and safety of the addition of pembrolizumab to standard of care chemotherapy (weekly paclitaxel) with/without bevacizumab vs placebo plus weekly paclitaxel with/without bevacizumab in patients with PROC. Methods: In this randomized, placebo-controlled, double-blind, phase 3 study, eligible patients are aged ≥18 y with histologically confirmed epithelial ovarian, fallopian tube, or primary peritoneal carcinoma with 1-2 prior lines of systemic therapy, including at least 1 prior platinum-based therapy with ≥4 cycles in first line. Patients must have platinum-resistant disease (radiographic evidence of PD ≤6 mo after last platinum-based therapy dose), be eligible for paclitaxel (with/without bevacizumab per investigator discretion), have ECOG PS ≤1, radiographically evaluable disease per RECIST v1.1, and have a tumor sample for central evaluation of PD-L1 status. Approximately 616 patients will be randomized 1:1 to receive pembrolizumab 400 mg IV or placebo Q6W for up to 18 cycles (̃2 y) plus paclitaxel 80 mg/m2 on days 1, 8, and 15 of each Q3W cycle (with/without bevacizumab 10 mg/kg Q2W per investigator discretion) until PD or unacceptable toxicity. Randomization is stratified by planned bevacizumab use (yes vs no), region (US vs Europe vs rest of world), and PD-L1 status (combined positive score [CPS] < 1 vs CPS 1- < 10 vs CPS ≥10). Tumor PD-L1 status is determined using the PD-L1 IHC 22C3 pharmDx (Investigational Use Only) diagnostic kit. Tumor imaging is performed Q9W from randomization to week 54 and Q12W thereafter. The primary endpoint is PFS per RECIST version 1.1 by investigator review in patients with tumor PD-L1 CPS ≥1 and in all patients. Secondary endpoints are OS in patients with tumor PD-L1 CPS ≥1 and in all patients, PFS per RECIST version 1.1 by blinded independent central review in patients with tumor PD-L1 CPS ≥1 and in all patients, safety, and patient-reported outcomes. Enrollment is ongoing. Clinical trial information: NCT05116189.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0100.002

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.

Opus teacher head0.218
GPT teacher head0.504
Teacher spread0.286 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2022
Admission routes1
Has abstractyes

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