Anti-CD38 monoclonal antibodies in multiple myeloma with gain/amplification of chromosome arm 1q: a review of the literature
Bibliographic record
Abstract
INTRODUCTION: Gain/amplification of 1q (+1q) represents one of the most prevalent cytogenetic abnormalities (CAs) observed in multiple myeloma (MM). Historical studies predating the advent of anti-CD38 monoclonal antibodies (moAbs) implicated + 1q in poor prognoses, prompting its integration into novel staging systems. However, with the emergence of daratumumab and isatuximab, two pivotal anti-CD38 moAbs, the landscape of MM therapy has undergone a profound transformation. AREAS COVERED: This review encompasses a comprehensive analysis of diverse study methodologies, including observational investigations, clinical trials, meta-analyses, and real-world database analyses. By synthesizing these data sources, we aim to provide an overview of the current understanding of + 1q in the context of anti-CD38 moAbs therapies. EXPERT OPINION: Despite the paucity of available data, evidence suggests a potential mitigating effect of daratumumab on the adverse prognostic implications of + 1q. However, this benefit seems to diminish in patients harboring ≥ 4 copies or with concurrent high-risk CAs. On the other hand, isatuximab demonstrated promising outcomes in the relapsed-refractory setting for + 1q MM patients. Nevertheless, direct comparison between the two compounds is currently challenging. The current evidence firmly supports the integration of anti-CD38 moAb-based therapies as the standard of care for + 1q patients, pending further elucidation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".