The bone marrow NK-cell profile predicts MRD negativity in patients with multiple myeloma treated with daratumumab-based therapy
Bibliographic record
Abstract
ABSTRACT: Natural killer (NK) cells are important effector cells in antibody-based immune therapies for multiple myeloma (MM) through antibody-dependent cellular cytotoxicity. Here, we used single-cell transcriptomics, flow cytometry, and functional assays to investigate the bone marrow NK-cell compartment of patients with MM at diagnosis and during treatment. We show reduced proportion of CD16+ cytotoxic NK cells in a subset of patients at diagnosis, which correlated with decreased cytokine production and NK-cell degranulation against MM cells in the presence of the anti-CD38 antibody daratumumab. In line with these findings, a low proportion of CD16+ bone marrow NK cells at diagnosis was associated with a reduced likelihood of achieving measurable (or minimal) residual disease (MRD) negativity after consolidation in patients treated with daratumumab, bortezomib, thalidomide, and dexamethasone in conjunction with autologous stem cell transplantation in the CASSIOPEIA trial. In contrast, NK-cell distribution did not predict MRD negativity in patients treated in the control arm without daratumumab. These findings highlight the impact of the bone marrow NK-cell compartment on therapeutic outcomes in patients with MM receiving immunotherapy with CD38-targeting antibodies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".