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Record 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 on 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

Bibliographic record

VenueBlood · 2025
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsGlaxoSmithKline (Canada)
Fundersnot available
KeywordsSubgroup analysisClinical endpointHazard ratioDexamethasoneAdverse effectMultiple myelomaClinical trialRandomized controlled trial

Abstract

fetched live from 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

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)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.011
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.246
Teacher spread0.236 · 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 designObservational
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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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