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Enregistrement W4417002349 · doi:10.1182/blood-2025-5820

Functional high-risk Relapsed/Refractory multiple myeloma (RRMM) outcomes with belantamab mafodotin (belamaf): Dreamm-7 and dreamm-8 subgroup analysis

2025· article· en· W4417002349 sur OpenAlexaff
María-Victoria Mateos, Vânia Hungria, Meral Beksaç, Paweł Robak, Kihyun Kım, Sergey Voloshin, Hanlon Sia, Esther González García, Gracia Martınez, Chang‐Ki Min, Marcelo Pitombeira de Lacerda, Anna Sureda Balarí, Ivan Špıčka, Sosana Delimpasi, Marek Hus, Vera Zherebtsova, Christopher Wård, Phoebe Joy Ho, Roman Hájek, Sebastian Grosicki, Al-Ola Abdallah, Jorge Mouro, Zeyad Khalaf, Hena Baig, Ainslee Moore, Chee Paul Lin, Maureen Nichols, Ianire Garrobo-Calleja, Nick Pirooz, Lydia Eccersley, Elisabet E. Manasanch, Luděk Pour, Meletios Α. Dimopoulos

Notice bibliographique

RevueBlood · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueMultiple Myeloma Research and Treatments
Établissements canadiensGlaxoSmithKline (Canada)
Organismes subventionnairesnon disponible
Mots-clésSubgroup analysisClinical endpointHazard ratioDexamethasoneAdverse effectMultiple myelomaClinical trialRandomized controlled trial

Résumé

récupéré en direct d'OpenAlex

Abstract Background: There is a significant need for effective and well-tolerated therapies for patients (pts) with functional high-risk (FHR) MM. In DREAMM-7 (D7; NCT04246047), belamaf, bortezomib, and dexamethasone (BVd) demonstrated significant PFS and OS benefit vs daratumumab-Vd (DVd) in pts with RRMM with ≥1 prior line of therapy (LOT). In DREAMM-8 (D8; NCT04484623), belamaf, pomalidomide, and dexamethasone (BPd) demonstrated a significant PFS benefit vs PVd in pts with RRMM who received ≥1 prior LOT, including lenalidomide. Here, we present a subgroup analysis in pts with FHR MM treated with 1 prior LOT. Methods: Pts treated with ≥1 prior LOT were randomized (1:1) to BVd or DVd in D7 and BPd or PVd in D8. The primary endpoint of both trials was independent review committee–assessed PFS. Key secondary endpoints for both trials were OS, MRD, and DOR. In the D7 and D8 trials, FHR was defined as RRMM that progressed ≤18 mo after the start of ASCT or start of first-line therapy. Descriptive statistics were used for response rates, MRD negativity (10−5) rates, and adverse events (AEs). Hazard ratios (HRs) for PFS, OS, and DOR were estimated with the Cox model. The Kaplan-Meier method estimated median survival times. Results: Baseline disease characteristics were generally balanced between treatment arms in pts with 1 prior LOT, regardless of FHR status. In D7, 125 pts per arm who received 1 prior LOT were treated with BVd or DVd, with 43 pts in each arm having FHR MM. In D8, 82 and 77 pts who received 1 prior LOT were treated with BPd and PVd, respectively, with 28 (BPd) and 31 pts (PVd) having FHR MM. In D7, median follow-up was 28.2 mo for pts who received 1 prior LOT and 39.4 mo for pts with FHR MM. In D8, median follow-up was 21.8 mo for all pts. In D7, median PFS was longer in the BVd vs DVd arm, both in pts with 1 prior LOT (36.6 mo vs 19.1 mo; HR, 0.52; 95% CI, 0.36-0.76), and in those with FHR MM (28.4 mo vs 13.4 mo; HR, 0.65; 95% CI, 0.37-1.14). Similarly, in D8, median PFS favored the BPd vs PVd arm, both in pts with 1 prior LOT (not reached [NR] vs 18.5 mo; HR, 0.50; 95% CI, 0.30-0.85) and in those with FHR MM (NR vs 14.8 mo; HR, 0.66; 95% CI, 0.28-1.54). In pts treated with 1 prior LOT in D7, ORR was similar between BVd and DVd arms (83% vs 82%), with greater depth of response in the BVd arm (≥ CR: 39% vs 24%). In pts treated with 1 prior LOT in D7, ≥ CR MRD negativity rates were 28% (35/125) with BVd vs 14% (18/125) with DVd. In D7 pts with FHR MM, both ORR (86% vs 74%) and depth of response (≥ CR: 33% vs 21%) were higher with BVd vs DVd, respectively. In D7 pts with FHR MM, ≥ CR MRD negativity rates were 21% (9/43) with BVd vs 9% (4/43) with DVd. In pts treated with 1 prior LOT in D8, ORR was higher with PVd (88%) vs BPd (79%); however, response was deeper with BPd (≥ CR: 46% vs 23%). In pts treated with 1 prior LOT in D8, ≥ CR MRD negativity rates were 33% (27/82) with BPd vs 5% (4/77) with PVd. In D8 pts with FHR MM, ORR was comparable between pts in the BPd and PVd arms (82% vs 87%, respectively), with greater depth of response seen with BPd (≥ CR: 50% vs 23%). In pts with FHR MM in D8, ≥ CR MRD negativity rates were higher with BPd (36% [10/28]) than with PVd (7% [2/31]). In both the D7 and D8 trials, mOS was NR in pts with 1 prior LOT, regardless of FHR status. Among D7 pts with FHR MM, 70% in the BVd arm and 58% in the DVd arm were alive. Among pts with FHR MM in D8, 79% in the BPd arm vs 77% in the PVd arm were alive. In D7 pts treated with 1 prior LOT and FHR MM, grade 3/4 treatment-related AEs (TRAEs) related to any study treatment occurred in 91% with BVd and 60% with DVd. In D8 pts treated with 1 prior LOT and FHR MM, grade 3/4 TRAEs related to any study treatment occurred in 78% with BPd and 71% with PVd. Conclusions: In both D7 and D8, BVd and BPd were associated with extended PFS vs standard-of-care (SOC) regimens DVd and PVd, respectively, in patients with FHR MM. Deeper responses with higher rates of ≥ CR MRD negativity were also observed vs SOC regimens in both studies. mOS was NR in both trials at the time of this analysis.Funding:GSK (study numbers: 207503; 207499). Drug linker technology licensed from Seagen Inc; monoclonal antibody produced using POTELLIGENT Technology licensed from BioWa

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,005
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,004
Score d'incertitude au seuil0,022

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,005
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0040,011
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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 Opus0,010
Tête enseignante GPT0,246
Écart entre enseignants0,236 · 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 source (Gemma direct ou Codex distillé), 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

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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