Sustained minimal residual disease negativity in newly diagnosed multiple myeloma and the impact of daratumumab in MAIA and ALCYONE
Bibliographic record
Abstract
In patients with transplant-ineligible newly diagnosed multiple myeloma (NDMM), daratumumab reduced the risk of disease progression or death by 44% in MAIA (daratumumab/lenalidomide/dexamethasone [D-Rd]) and 58% in ALCYONE (daratumumab/bortezomib/melphalan/prednisone [D-VMP]). Minimal residual disease (MRD) is a sensitive measure of disease and response to therapy. MRD-negativity status and durability were assessed in MAIA and ALCYONE. MRD assessments using next-generation sequencing (10-5) occurred for patients achieving complete response (CR) or better and after at least CR at 12, 18, 24, and 30 months from the first dose. Progression-free survival (PFS) by MRD status and sustained MRD negativity lasting ≥6 and ≥12 months were analyzed in the intent-to-treat population and among patients achieving at least CR. In MAIA (D-Rd, n = 368; lenalidomide and dexamethasone [Rd], n = 369) and ALCYONE (D-VMP, n = 350; bortezomib/melphalan/prednisone [VMP], n = 356), the median duration of follow-up was 36.4 and 40.1 months, respectively. MRD-negative status and sustained MRD negativity lasting ≥6 and ≥12 months were associated with improved PFS, regardless of treatment group. However, daratumumab-based therapy improved rates of MRD negativity lasting ≥6 months (D-Rd, 14.9% vs Rd, 4.3%; D-VMP, 15.7% vs VMP, 4.5%) and ≥12 months (D-Rd, 10.9% vs Rd, 2.4%; D-VMP, 14.0% vs VMP, 2.8%), both of which translated to improved PFS vs control groups. In a pooled analysis, patients who were MRD negative had improved PFS vs patients who were MRD positive. Patients with NDMM who achieved MRD-negative status or sustained MRD negativity had deep remission and improved clinical outcomes. These trials were registered at www.clinicaltrials.gov as #NCT02252172 (MAIA) and #NCT02195479 (ALCYONE).
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".