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Daratumumab + bortezomib, lenalidomide, and dexamethasone (DVRd) vs VRd in transplant-ineligible (TIE)/transplant-deferred (TD) newly diagnosed multiple myeloma (NDMM): Phase 3 CEPHEUS trial cytogenetic subgroup analysis.

2025· article· en· W4410809877 on OpenAlexaff
Nizar J. Bahlis, Saad Z. Usmani, Thierry Façon, Sonja Zweegman, Christopher P. Venner, Marc Braunstein, Luděk Pour, Josep Martí, Supratik Basu, Yaël C. Cohen, Morio Matsumoto, Kenshi Suzuki, Cyrille Hulin, Sebastian Grosicki, Wojciech Legieć, Ângelo Maiolino, Mai Ngo, Maria Krevvata, Melissa Rowe, Vânia Hungria

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsInstitute of Cancer ResearchUniversity of AlbertaUniversity of Calgary
FundersJohnson and Johnson
KeywordsMedicineLenalidomideDaratumumabBortezomibMultiple myelomaDexamethasoneOncologyInternal medicine

Abstract

fetched live from OpenAlex

7529 Background: In CEPHEUS, DVRd significantly improved overall MRD negativity (MRD neg + ≥CR) and sustained MRD neg rates and PFS in patients (pts) with TIE/TD NDMM. In this post hoc analysis, we report outcomes in cytogenetic risk subgroups. Methods: Pts with TIE/TD NDMM were randomized 1:1 to DVRd or VRd. High-risk (HiR) cytogenetic abnormalities (HRCAs) were assessed by FISH. HiR was ≥1 of: del(17p); t(4;14); t(14;16). Revised HiR (R-HiR) was ≥1 of above or gain (3 copies) or amp(1q) (≥4 copies). Standard risk (SR) was 0 HRCAs; revised SR (R-SR) was 0 revised HRCAs. Additional risk groups included: gain or amp(1q) + other HRCAs; 1 and ≥2 revised HRCAs. We assessed overall MRD neg rate, sustained MRD neg, ≥CR rate, and PFS. We reported all MRD neg rates at 10 -5 unless noted. Results: Of 395 randomized pts (DVRd, n=197; VRd, n=198), 298 had SR (DVRd, n=149; VRd, n=149) and 52 HiR (DVRd, n=25; VRd, n=27). 184 pts had R-SR (DVRd, n=94; VRd, n=90) and 167 R-HiR (DVRd, n=83; VRd, n=84). At median 58.7-month (mo) follow-up, overall MRD neg rate was higher with DVRd vs VRd in SR (64% vs 38%; P <0.0001) and R-SR pts (68% vs 38%; P <0.0001). Rates by treatment (tx) arm in HiR (48% vs 56%; P =0.7816) and R-HiR pts (55% vs 45%; P =0.2169) were comparable. DVRd improved ≥1-year (y) sustained MRD neg rate vs VRd in SR (51% vs 26%; P <0.0001) and R-SR pts (54% vs 24%; P <0.0001). Sustained MRD neg rates by tx arm were comparable in HiR (40% vs 37%; P =1.0000) and R-HiR pts (43% vs 30%; P =0.0782). PFS was improved with DVRd vs VRd in SR and R-SR pts and was comparable by tx arm in HiR and R-HiR pts (Table), including in MRD neg pts (R-SR: hazard ratio [HR]=0.63 [95% CI, 0.26–1.52]; P =0.3003; R-HiR: HR=0.71 [95% CI, 0.32–1.58]; P =0.3995). Remaining outcomes, including rates of ≥CR, ≥2-y sustained MRD neg, and overall and ≥1-y sustained MRD neg at 10 -6 , were improved with DVRd in SR and R-SR pts and comparable by tx arm in HiR and R-HiR pts. Conclusions: In CEPHEUS, DVRd consistently improved the key response outcomes of MRD neg and PFS in (R-)SR pts. In HiR pts, MRD and PFS outcomes trended lower in both tx arms vs those in SR pts. Here, DVRd mostly improved PFS outcomes vs VRd; however, pt numbers were small, with the study underpowered for HiR pts. These data support use of DVRd for TIE/TD NDMM regardless of cytogenetic risk status. Clinical trial information: NCT03652064 . DVRd VRd n mPFS, mo n mPFS, mo HR (95% CI); P -value HiR a 25 39.8 27 31.7 0.88 (0.42–1.84); 0.7387 R-HiR 83 NE 84 45.6 0.73 (0.46–1.15); 0.1739 SR a 149 NE 149 60.6 0.61 (0.41–0.91); 0.0136 R-SR 94 NE 90 60.6 0.54 (0.32–0.91); 0.0189 Gain(1q) + other HRCAs 43 60.3 48 42.2 0.80 (0.45–1.43); 0.4496 Amp(1q) + other HRCAs 31 NE 20 NE 0.97 (0.38–2.47); 0.9525 1 revised HRCA 66 NE 72 47.2 0.63 (0.37–1.09); 0.0938 ≥2 revised HRCA 17 22.7 12 29.7 1.01 (0.42–2.44); 0.9868 a Unknown cytogenetic risk: DVRd, n=23; VRd, n=22. mPFS, median PFS; NE, not estimable.

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)0.001
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: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.103
GPT teacher head0.468
Teacher spread0.365 · 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 designRandomized trial
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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Citations1
Published2025
Admission routes1
Has abstractyes

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