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Record W4417013308 · doi:10.1182/blood-2025-372

Daratumumab monotherapy versus active monitoring in patients with high-risk smoldering multiple myeloma: Aquila outcomes based on mayo 2018/IMWG 2020 risk stratification, IMWG 2020 plus cytogenetic criteria, and age

2025· article· en· W4417013308 on OpenAlexaff
Peter M. Voorhees, Meletios Α. Dimopoulos, Yaël C. Cohen, Fredrik Schjesvold, Vânia Hungria, Irwindeep Sandhu, Jindriska Lindsay, Ross Baker, Kenshi Suzuki, Hiroshi Kosugi, Mark‐David Levin, Meral Beksaç, Keith Stockerl‐Goldstein, Hila Magen, Albert Oriol, Gábor Mikala, Gonzalo Garate, Koen Theunissen, Ivan Špıčka, Anne K. Mylin, Simon Hallam, Katarina Uttervall, Bartosz Puła, Abdullah Khan, Eva Medvedova, J Christine Ye, Andrew J. Cowan, Philippe Moreau, María‐Victoria Mateos, Hartmut Goldschmidt, Diego Vieyra, Linlin Sha, Liang Li, Els Rousseau, Robyn M. Dennis, Robin Carson, S. Vincent Rajkumar

Bibliographic record

VenueBlood · 2025
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsHotel Dieu HospitalAlberta Cancer Foundation
Fundersnot available
KeywordsDaratumumabMultiple myelomaClinical endpointLenalidomideAsymptomaticData monitoring committeeSurrogate endpointProgression-free survivalPomalidomide

Abstract

fetched live from OpenAlex

Abstract Introduction : Current standard of care for smoldering multiple myeloma (SMM), an asymptomatic precursor of multiple myeloma (MM), is observation until progression to active MM. Increasing evidence suggests that patients (pts) with SMM at high risk for progression to active MM may benefit from early treatment (tx). In AQUILA, a phase 3 study in pts with high-risk SMM (NCT03301220), daratumumab (Dara) monotherapy significantly reduced risk of progression to active MM or death with a trend of extending overall survival (OS) vs active monitoring (ActMon), with no new safety concerns. Given the evolution in SMM risk stratification, a post hoc analysis was performed to assess outcomes using the IMWG 2020 and IMWG 2020 plus cytogenetic risk models to assess which AQUILA pts benefited most from Dara monotherapy. Safety and efficacy analysis by age and stem cell collection outcomes were also assessed. Methods: Eligible pts with a confirmed diagnosis (≤5 y) of high-risk SMM per IMWG 2014 criteria, defined as clonal bone marrow plasma cells (BMPC) ≥10% and ≥1 risk factor (IgA SMM, serum protein ≥30 g/dL, serum involved:uninvolved free light chain [I/U FLC] ratio ≥8 and <100, immunoparesis with reduction of 2 uninvolved Ig isotypes, and/or clonal BMPC >50% to <60%) at study entry, were randomized 1:1 to receive subcutaneous Dara or ActMon for 39 cycles, 36 months, or until confirmed disease progression (PD), whichever came first. Primary endpoint was progression-free survival (PFS) assessed by independent review committee, defined as progression to active MM (based on IMWG SLiM-CRAB diagnostic criteria) or death. Secondary endpoints included time to first-line (1L) MM tx and OS. For this post hoc analysis, we assessed outcomes by age, IMWG 2020 high-risk SMM criteria (BMPC >20%, monoclonal spike >2 g/dL, serum FLC ratio >20; ≥2 factors=high risk; also known as the Mayo 2018 or the 20-2-20 criteria), and the IMWG 2020 plus cytogenetic criteria (IMWG 2020 criteria + presence of ≥1 high-risk cytogenetic abnormalities [t(4;14), t(14;16), +1q and/or del13q]; ≥3 factors=high risk). Results : Dara tx showed a PFS benefit across all IMWG 2020 risk subgroups (low: hazard ratio [HR], 0.59; intermediate: HR, 0.70), with the largest benefit in the high-risk subgroup, where the PD/death rate in the ActMon arm was ~1.6-fold that in the Dara arm (62.8% vs 37.5%; HR, 0.36). This benefit of Dara vs ActMon was preserved over time, with 5-year PFS rates of 78.2% vs 71.6%, 56.2% vs 42.9%, and 60.4% vs 23.6% in IMWG 2020 low-, intermediate-, and high-risk groups, respectively. There was a positive trend favoring Dara for time to 1L MM treatment across all IMWG 2020 risk groups (low-risk HR, 0.63; 95% CI, 0.22–1.80; intermediate-risk HR, 0.57; 95% CI, 0.35–0.92; high-risk HR, 0.39; 95% CI, 0.25–0.62). IMWG 2020 plus cytogenetic risk classification results will be available at the time of presentation. When comparing the younger (<65y HR, 0.51; 95% CI, 0.32–0.79) vs the older (≥65y, HR, 0.50; 95% CI, 0.32–0.77) pt population, a PFS benefit was observed regardless of age. Similarity was also observed in TEAE incidence rate when looking into <65, 65 to <75, and ≥75 y subgroups (82.7%, 81.1%, and 87.5% with ActMon; 96.2%, 98.5%, and 95.2% with Dara); however, serious TEAEs were more frequently observed in older ActMon pts (12.2%, 18.9%, and 50.0% with ActMon; 24.8%, 35.8%, and 28.6% with Dara). Across all pts treated/monitored in AQUILA, 23 (11.9%) and 41 (20.9%) pts in the Dara and ActMon arms, respectively, received autologous stem cell transplant as part of their first tx after progressing to active MM, with very limited plerixafor use (Dara, 3 [1.6%] vs ActMon, 9 [4.6%]). Median (range) CD34+ cell yield was 5.0 (2–20)×106 cells/kg body weight among 22 pts in the Dara arm and 5.1 (2–21)×106 cells/kg body weight among 39 pts in the ActMon arm. Conclusions : In this analysis, pts with high-risk SMM from the phase 3 AQUILA study treated with Dara monotherapy experienced long-term PFS benefit across IMWG 2020 subgroups, with the most pronounced benefit in the high-risk subgroup. No notable differences in PFS or safety were observed across age subgroups. Early Dara tx for high-risk SMM did not have a detrimental impact on stem cell yield. Although mature OS analyses are forthcoming, overall, these results further support early intervention with Dara monotherapy vs ActMon among pts with high-risk SMM, regardless of risk stratification criteria used.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.016
GPT teacher head0.288
Teacher spread0.272 · 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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Citations3
Published2025
Admission routes1
Has abstractyes

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