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Daratumumab (DARA) in combination with bortezomib plus dexamethasone (D-Vd) or lenalidomide plus dexamethasone (D-Rd) in relapsed or refractory multiple myeloma (RRMM): Subgroup analysis of the phase 3 CASTOR and POLLUX studies in patients (pts) with early or late relapse after initial therapy.

2022· article· en· W4286298426 on OpenAlexaff
Andrew Spencer, Philippe Moreau, María‐Victoria Mateos, Hartmut Goldschmidt, Kenshi Suzuki, Mark‐David Levin, Pieter Sonneveld, Sung-Soo Yoon, Katja Weisel, Donna Reece, Tahamtan Ahmadi, Huiling Pei, Wendy Garvin Mayo, Xue Gai, Jodi Carey, Robin Carson, Meletios Α. Dimopoulos

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicineInternal medicineLenalidomideDaratumumabClinical endpointOncologyDexamethasoneSubgroup analysisClinical trialConfidence interval

Abstract

fetched live from OpenAlex

8052 Background: High-risk multiple myeloma (MM) is often defined based on cytogenetic abnormalities (ie, t[4;14], t[14;16], and/or del17p); however, pts who relapse early (12-18 months) after initial therapy are considered a functional high-risk group that is also associated with poor prognosis. DARA, a human IgGκ monoclonal antibody targeting CD38, is approved in combination with standard-of-care regimens for MM. In the phase 3 CASTOR and POLLUX studies, D-Vd and D-Rd significantly improved progression-free survival (PFS), regardless of cytogenetic risk, and achieved higher rates of complete response or better (≥CR) and minimal residual disease (MRD)–negativity vs Vd or Rd alone in pts with RRMM. Post hoc analyses of CASTOR and POLLUX evaluated D-Vd vs Vd and D-Rd vs Rd in pt subgroups with 1 prior line of therapy based on timing of relapse (early or late) after initiation of the first line of therapy. Methods: In CASTOR and POLLUX, pts with RRMM and ≥1 prior line of therapy were randomized to D-Vd/Vd or D-Rd/Rd, respectively. The primary endpoint was PFS. In this analysis, the early relapse subgroup included pts with 1 prior line of therapy who relapsed <18 months after initiating their first line of therapy; pts with 1 prior line of therapy who relapsed ≥18 months after initiating their first line of therapy were included in the late relapse subgroup. Results: 49 and 186 pts from CASTOR and 99 and 196 pts from POLLUX were included in the early relapse and late relapse subgroups, respectively. Median follow-up was 72.6 months (CASTOR) and 79.7 months (POLLUX). PFS consistently favored the DARA-containing regimens across subgroups (Table). In CASTOR, ≥CR rates were higher with D-Vd vs Vd in the early relapse (21% vs 17%; P = 0.7360) and late relapse (51% vs 14%; P <0.0001) subgroups. In POLLUX, ≥CR rates were higher with D-Rd vs Rd in the early relapse (53% vs 12%; P <0.0001) and late relapse (62% vs 38%; P = 0.0012) subgroups. MRD-negativity rates (10–5) were higher with D-Vd/D-Rd vs Vd/Rd regardless of relapse timing (CASTOR: early, 13% vs 0%; P = 0.1476; late, 23% vs 3%; P <0.0001; POLLUX: early, 30% vs 4%; P = 0.0006; late, 34% vs 14%; P = 0.0009). Conclusions: These post hoc analyses of CASTOR and POLLUX showed PFS and depth of response benefits of DARA-containing regimens in patients with 1 prior line of therapy, regardless of relapse timing (early or late). Our results support the use of D-Vd and D-Rd in RRMM, including in pts who are considered functional high risk. Clinical trial information: NCT02136134 and NCT02076009. [Table: see text]

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)0.003
metaresearch head score (Gemma)0.002
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 score0.003
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
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 head0.151
GPT teacher head0.458
Teacher spread0.306 · 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".

Quick stats

Citations5
Published2022
Admission routes1
Has abstractyes

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Same venueJournal of Clinical Oncology→Same topicMultiple Myeloma Research and Treatments→French-language works237,207→