Daratumumab Improves Depth of Response and Progression-free Survival in Transplant-ineligible, High-risk, Newly Diagnosed Multiple Myeloma
Bibliographic record
Abstract
BACKGROUND: Patients with high-risk, newly diagnosed multiple myeloma (HR-NDMM) who are ineligible for autologous stem cell transplant (ASCT) have limited first-line treatment options. Recent meta-analyses evaluating the impact of incorporating daratumumab in the backbone regimen on progression-free survival (PFS) have found mixed results in these patients. MATERIALS AND METHODS: A pooled analysis of patient-level data for ASCT-ineligible patients with HR-NDMM [ie, del(17p), t(4;14), t(14;16)] from the MAIA and ALCYONE trials; stratified by study identifier and adjusting for cytogenetic abnormality subtype, baseline performance status, International Staging System stage, myeloma type, and renal impairment; was conducted. Impact of daratumumab on PFS and rates of complete response or better (≥CR), minimal residual disease (MRD)-negative CR, very good partial response or better (≥VGPR), and overall response (ORR) was compared to control. RESULTS: Among 101 patients in the daratumumab and 89 patients in the control cohort, median follow-up was 43.7 months. Daratumumab reduced the risk of progression or death by 41% (adjusted hazard ratio for PFS [95% confidence interval (CI)] = 0.59 [0.41-0.85]) versus control. At 36 months, the estimated proportion of patients who did not progress and were still alive was 41.3% in the daratumumab and 19.9% in the control cohort. Rates of ≥CR (41.6% vs. 22.5%), MRD-negative CR (24.8% vs. 5.6%), ≥VGPR (75.2% vs. 46.1%), and ORR (92.1% vs. 74.2%) were higher for daratumumab versus control. CONCLUSION: These findings demonstrate that incorporation of daratumumab in frontline treatment regimens reduced the risk of progression or death and improved response rates among ASCT-ineligible HR-NDMM patients.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| 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) | 0.001 | 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".