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Enregistrement W4380080925 · doi:10.1002/hon.3163_81

ZANUBRUTINIB PLUS OBINUTUZUMAB VERSUS OBINUTUZUMAB IN PATIENTS WITH RELAPSED/REFRACTORY FOLLICULAR LYMPHOMA: UPDATED ANALYSIS OF THE ROSEWOOD STUDY

2023· article· en· W4380080925 sur OpenAlexaff
Pier Luigi Zinzani, Jiřı́ Mayer, Judith Trotman, Fontanet Bijou, Ana Carla Oliveira, Yuqin Song, Q. Zhang, Michele Merli, Krimo Bouabdallah, Peter Ganly, H. Zhang, Rod Johnson, Marek Trněný, Sam Yuen, Sarit Assouline, Rebecca Auer, E. Ivanoa, P. Kim, Alissa Greenbaum, Shanshan Huang, Richard Delarue, Christopher R. Flowers

Notice bibliographique

RevueHematological Oncology · 2023
Typearticle
Langueen
DomaineMedicine
ThématiqueChronic Lymphocytic Leukemia Research
Établissements canadiensJewish General Hospital
Organismes subventionnairesSeoul National UniversitySeoul National University HospitalInstitut National de la Santé et de la Recherche MédicaleRegeneron PharmaceuticalsCleveland Clinic
Mots-clésMedicineObinutuzumabInternal medicineFollicular lymphomaRituximabRefractory (planetary science)OncologyInternational Prognostic IndexClinical endpointProgression-free survivalNeutropeniaSalvage therapyGastroenterologySurgeryChemotherapyLymphomaRandomized controlled trial

Résumé

récupéré en direct d'OpenAlex

Introduction: In an early-phase study, the combination of zanubrutinib plus obinutuzumab (ZO) was well tolerated and associated with an early signal of efficacy in patients (pts) with follicular lymphoma (FL) (Tam et al. Blood Adv, 2020). ROSEWOOD (NCT03332017) is a phase 2, randomized study designed to assess efficacy and safety of ZO versus obinutuzumab (O) in patients with relapsed/refractory (R/R) FL. Here, we present an updated analysis with a median follow-up of 20.2 months. Methods: Pts with R/R FL (grade 1–3a) who received ≥2 lines of therapy including an anti-CD20 antibody and alkylating agent were randomized 2:1 to receive ZO or O. Zanubrutinib was given at 160 mg twice daily until progression or unacceptable toxicity. The primary endpoint was overall response rate (ORR) by independent central review. Secondary endpoints included duration of response (DOR), progression-free survival (PFS), time to next treatment (TTNT), overall survival (OS), and safety. Results: A total of 217 patients were randomized (145 for ZO; 72 for O). Median age was 64 years. Of the 217 pts, 114 (52.5%) had a high Follicular Lymphoma International Prognostic Index (FLIPI) score at screening and 123 (56.7%) pts had high tumor burden according to Groupe d'Etude des Lymphomes Folliculaires (GELF) criteria. Median number of prior lines of therapy was 3 (range, 2–11). A total of 114 (52.5%) pts were refractory to rituximab; 214 (98.6%) patients received prior immunochemotherapy. Prior exposure to anticancer drugs included anthracyclines (80.6%), cyclophosphamide (94.0%), and bendamustine (54.8%). ORR was 69.0% (ZO) versus 45.8% (O) (p = 0.0012). Complete response rate was 39.3% (ZO) versus 19.4% (O); 18-month DOR rate was 69.3% (ZO) versus 41.9% (O); median PFS was 28.0 months (ZO) versus 10.4 months (O) (hazard ratio [HR], 0.50 [95% CI: 0.33, 0.75]; p = 0.0007). Median TTNT was not evaluable for ZO and 12.2 months for O (HR, 0.34 [95% CI: 0.22, 0.52]; p < 0.0001). Estimated OS rate at 24 months was 77.3% (ZO) and 71.4% (O), with median OS not reached (ZO) and 34.6 months (O). Nonhematologic treatment-emergent adverse events of any grade that occurred more frequently for ZO versus O (>5% difference) were petechiae (6.3% vs. 0%) and herpes zoster infection (6.3% vs. 0%); in contrast, pyrexia (13.3% vs. 19.7%) and infusion-related reaction (2.8% vs. 9.9%) occurred more frequently in patients on O. When adjusted for duration of treatment exposure, incidences of infection and cytopenia were similar, and incidence of all grades of hemorrhage was 2.4 (ZO) versus 1.3 (O) persons per 100 person-months. Two patients in each treatment group reported major hemorrhage. Incidences of atrial fibrillation and hypertension were low and similar in both treatment arms. Conclusions: ZO demonstrated meaningful activity and a manageable safety profile in patients with heavily pretreated R/R FL, representing a potential novel therapy. Encore Abstract—previously submitted to ASCO 2023 and EHA 2023 The research was funded by: BeiGene Keywords: combination therapies, indolent non-Hodgkin lymphoma, molecular targeted therapies Conflicts of interests pertinent to the abstract P. L. Zinzani Consultant or advisory role: Secura Bio, Celltrion, Gilead, Janssen-Cilag, BMS, Servier, Sandoz, MSD, AstraZeneca, Takeda, Roche, Eusa Pharma, Kyowa Kirin, Novartis, ADC Therapeutics, Incyte, BeiGene Other remuneration: Speakers Bureau: Celltrion, Gilead, Janssen-Cilag, BMS, Servier, MSD, AstraZeneca, Takeda, Roche, Eusa Pharma, Kyowa Kirin, Novartis, Incyte, BeiGene J. Mayer Research funding: BeiGene J. Trotman Research funding: BeiGene, Janssen, PCYC, Roche, Celgene BMS, Selectar A. C. de Oliveira Consultant or advisory role: Janssen, Alexion Educational grants: Janssen K. Bouabdallah Consultant or advisory role: Roche, Takeda, Kite/Gilead Honoraria: Roche, Takeda Science Foundation, AbbVie, Kite/Gilead, Sandoz-Novartis, BeiGene Educational grants: Roche, Takeda, Kite/Gilead R. Johnson Consultant or advisory role: Kite/Gilead Honoraria: Kite/Gilead, Novartis, Takeda Other remuneration: Speakers Bureau: Kite/Gilead, Novartis A. M. Garcia-Sancho Consultant or advisory role: Roche, BMS, Kyowa Kirin, Clinigen, Eusa Pharma, Novartis, Gilead/Kite, Incyte, Lilly, Takeda, ADC Therapeutics America, Miltenyi, Ideogen, AbbVie Honoraria: BMS, Janssen, Gilead/Kite, Takeda, Eusa Pharma, Novartis Research funding: Janssen Educational grants: Gilead/Kite, Janssen, Roche, BMS M. Provencio Pulla Consultant or advisory role: BMS, Takeda, MSD, Roche Honoraria: BMS, AstraZeneca, MSD, Takeda, Janssen, Roche Research funding: BMS, Roche Educational grants: BMS, AstraZeneca, Roche, MSD M. Trněný Employment or leadership position: First Faculty of Medicine, Charles University General Hospital in Prague Consultant or advisory role: Janssen, Gilead Sciences, Takeda, Bristol-Myers Squibb, Amgen, AbbVie, Roche, MorphoSys, Incyte, Novartis, Portolla Honoraria: Janssen, Gilead Sciences, Bristol-Myers Squibb, Amgen, AbbVie, Roche, AstraZeneca, MorphoSys, Incyte, Portolla, Takeda, Novartis Educational grants: Gilead, Takeda, Bristol-Myers Squibb, Roche, Janssen, AbbVie S. E. Assouline Consultant or advisory role: Genentech-Roche, Novartis, Janssen, Amgen, BMS, Palladin labs, Pfizer Research funding: Novartis Other remuneration: Speakers Bureau: Novartis, Pfizer R. Auer Consultant or advisory role: BeiGene, Eli-Lilly Research funding: Janssen Other remuneration: Speakers Bureau: BeiGene E. Ivanoa Employment or leadership position: BeiGene Stock ownership: BeiGene P. Kim Employment or leadership position: BeiGene Stock ownership: BeiGene A. Greenbaum Employment or leadership position: BeiGene, ICON Stock ownership: BeiGene S. Huang Employment or leadership position: BeiGene Stock ownership: BeiGene R. Delarue Employment or leadership position: BeiGene Stock ownership: BeiGene C. R. Flowers Consultant or advisory role: Bayer, Gilead Sciences, Spectrum Pharmaceuticals, AbbVie, Celgene, Denovo Biopharma, BeiGene, Karyopharm Therapeutics, Pharmacyclics/Janssen, Genentech/Roche, Epizyme, Genmab, Seattle Genentics, Foresight Diagnostics, Bristol-Myers Squibb/Celgene, Curio Science, AstraZeneca, MorphoSys Stock ownership: Foresight Diagnostics, N Power Research funding: Acerta Pharma, Janssen Oncology, Gilead Sciences, Celgene, TG Therapeutics, Genentech/Roche, Pharmacyclics, AbbVie, Millennium, Alimera Sciences, Xencor, 4D Pharma, Adaptimmune, Amgen, Bayer, Cellectis, EMD Serono, Guardant Health, Iovance Biotherapeutics, Kite/Gilead, MorphoSys, Nektar, Novartis, Pfizer, Sanofi, Takeda, Ziopharm Oncology

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,133
Score d'incertitude au seuil0,994

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0020,000
Bibliométrie0,0010,006
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,001
Intégrité de la recherche0,0010,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,033
Tête enseignante GPT0,328
Écart entre enseignants0,295 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations3
Publié2023
Routes d'admission1
Résumé présentoui

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