SUNMO: PHASE III TRIAL OF MOSUNETUZUMAB PLUS POLATUZUMAB VEDOTIN VERSUS RITUXIMAB PLUS GEMCITABINE AND OXALIPLATIN IN RELAPSED/REFRACTORY AGGRESSIVE NON‐HODGKIN LYMPHOMA
Notice bibliographique
Résumé
Introduction: Aggressive non-Hodgkin lymphomas (aNHL) are a diverse group of neoplasms, of which diffuse large B-cell lymphoma (DLBCL) is the most common subtype (Thandra, 2021). Patients (pts) with relapsed/refractory (R/R) DLBCL after one prior therapy who are unable to receive, or relapsed after, an autologous stem cell transplant (ASCT) and/or chimeric antigen receptor T-cell therapy have a poor prognosis (Salles, 2019; Di Blasi, 2022). Mosunetuzumab (Mosun) is an off-the-shelf CD20xCD3 T-cell engaging bispecific antibody that redirects T cells to eliminate malignant B cells (Sun, 2015), with promising efficacy and safety as a single agent, as shown in a Phase I trial in pts with B-cell NHL, including aNHL (Budde, 2022). Mosun has also shown promising safety and efficacy in combination with polatuzumab vedotin (Pola), a CD79b targeted antibody-drug conjugate that delivers the microtubule-disrupting agent monomethyl auristatin E directly to B cells (Dornan, 2009), in a Phase Ib/II trial in pts with R/R aNHL (Budde, ASH 2021). Encore Abstract—previously submitted to ASCO 2023 The research was funded by: SUNMO (NCT05171647) is sponsored by F. Hoffmann-La Roche Ltd. Third-party medical writing assistance, under the direction of all authors, was provided by Martha Warren MSci of Ashfield MedComms, an Inizio company, and was funded by F. Hoffmann-La Roche Ltd. Keywords: aggressive B-cell non-Hodgkin lymphoma, immunotherapy Conflicts of interests pertinent to the abstract J. Westin Consultant or advisory role Novartis, Kite/Gilead, Janssen, ADC Therapeutics, Iksuda Therapeutics, BMS/Celgene/Juno, AstraZeneca, Genentech/Roche, Abbvie, Merck, Monte Rosa Therapeutics, Morphosys/Incyte, Seattle Genetics Research funding: Janssen, Genentech, Novartis, Kite/Gilead, BMS, AstraZeneca, Morphosys/Incyte, ADC Therapeutics A. J. Olszewski Employment or leadership position: Brown Physicians, LLC Consultant or advisory role Genmab, Schrodinger Research funding: Genentech, Adaptive Biotech, Precision Bio, Kymera Therapeutics, Schrodinger W. S. Kim Research funding: Sanofi, Beigene, Boryong, Roche, Kyowa-kirin, Donga H. Shin Employment or leadership position: Pusan National University Hospital D. Leão Employment or leadership position: Legal Person (Beneficencia Portuguesa De São Paulo) Other remuneration: Travel, accommodation, expenses—Janssen, Novartis, Takeda, AMGEN, Roche, Libbs, ABBVIE, AstraZeneca, Zodiac, Kite/Gilead; Leadership—Chronic lymphoproliferative diseases clinic at Beneficencia Portuguesa de Sao Paulo L. Norasetthada Employment or leadership position: Chiang Mai University Research funding: MSD, Astra Zeneca, Roche E. Rego Employment or leadership position: Rede D ́Or/University of São Paulo Consultant or advisory role Astellas, Abbvie Honoraria: Astellas, Abbvie, Pfizer, Novartis Research funding: Astellas Other remuneration: Travel, accommodation, expenses—Astellas, Abbvie, Pfizer, Novartis H. Wu Employment or leadership position: Genentech; Ended employment in past 24 months—Amgen S. Yin Employment or leadership position: Genentech Inc. Stock ownership: Genentech Inc. Other remuneration: Patents, royalties, other intellectual property—Genentech Inc.; Travel, accommodation, expenses—Genentech Inc. C. L. Batlevi Employment or leadership position: Roche/Genentech Stock ownership: Roche/Genentech S. Pham Employment or leadership position: Roche Canada E. Penuel Employment or leadership position: Genentech Stock ownership: Genentech J. Jing Employment or leadership position: Genentech M. C. Wei Employment or leadership position: Genentech Stock ownership: Roche Other remuneration: Travel, accommodation, expenses—Genentech/Roche L. E. Budde Consultant or advisory role Roche/Genentech, Kite/Gilead, Novartis, BeiGene Research funding: Merck, Amgen, MustangBio, AstraZeneca Other remuneration: Patents, royalties, other intellectual property—CCR4 CAR T cells for treatment of patients with CCR4 positive cancer, CD33CAR for treatment of patients with CD33+ acute myeloid leukemia; Travel, accommodation, expenses—Roche/Genentech, Kite/Gilead
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».