Evaluation of Sustained Minimal Residual Disease Negativity With Daratumumab-Combination Regimens in Relapsed and/or Refractory Multiple Myeloma: Analysis of POLLUX and CASTOR
Bibliographic record
Abstract
PURPOSE In relapsed and/or refractory multiple myeloma, daratumumab reduced the risk of progression or death by > 60% in POLLUX (daratumumab/lenalidomide/dexamethasone [D-Rd]) and CASTOR (daratumumab/bortezomib/dexamethasone [D-Vd]). Minimal residual disease (MRD) is a sensitive measure of disease control. Sustained MRD negativity and outcomes were evaluated in these studies. METHODS MRD was assessed via next-generation sequencing (10 −5 ) at suspected complete response (CR), 3 and 6 months following confirmed CR (POLLUX), 6 and 12 months following the first dose (CASTOR), and every 12 months post-CR in both studies. Sustained MRD negativity (≥ 6 or ≥ 12 months) was evaluated in the intention-to-treat (ITT) and ≥ CR populations. RESULTS The median follow-up was 54.8 months in POLLUX and 50.2 months in CASTOR. In the ITT population, MRD-negativity rates were 32.5% versus 6.7% for D-Rd versus lenalidomide and dexamethasone (Rd) and 15.1% versus 1.6% for D-Vd versus bortezomib and dexamethasone (Vd; both P < .0001). Higher MRD negativity rates were achieved in ≥ CR patients in POLLUX (D-Rd, 57.4%; Rd, 29.2%; P = .0001) and CASTOR (D-Vd, 52.8%; Vd, 17.4%; P = .0035). More patients in the ITT population achieved sustained MRD negativity ≥ 6 months with D-Rd versus Rd (20.3% v 2.1%; P < .0001) and D-Vd versus Vd (10.4% v 1.2%; P < .0001), and ≥ 12 months with D-Rd versus Rd (16.1% v 1.4%; P < .0001) and D-Vd versus Vd (6.8% v 0%). Similar results for sustained MRD negativity were observed among ≥ CR patients. More patients in the daratumumab-containing arms achieved MRD negativity and sustained MRD negativity, which were associated with prolonged progression-free survival. CONCLUSION Daratumumab-based combinations induce higher rates of sustained MRD negativity versus standard of care, which are associated with durable remissions and prolonged clinical outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".