CD38 biallelic loss is a recurrent mechanism of resistance to anti-CD38 antibodies in multiple myeloma
Bibliographic record
Abstract
ABSTRACT Monoclonal antibodies targeting CD38 are a therapeutic mainstay in multiple myeloma (MM). While they have contributed to improved outcomes, most patients still experience disease relapse, and little is known about tumor-intrinsic mechanisms of resistance to these drugs. Antigen escape has been implicated as a mechanism of tumor cell evasion in immunotherapy. Yet, it is unknown whether MM cells can develop permanent resistance to anti-CD38 antibodies by acquiring genomic events leading to biallelic disruption of the CD38 gene locus. Here, by using whole genome and whole exome sequencing data from 701 newly diagnosed patients, 67 patients at relapse with naivety to anti-CD38 antibodies, and 50 patients collected at relapse following anti-CD38 antibodies. We report a loss of CD38 in 20% (10/50) of patients post-CD38 therapy, three of which exhibited a loss of both copies. Two of these cases showed convergent evolution where distinct subclones independently acquired similar advantageous variants. Functional studies on missense mutations involved in biallelic CD38 events revealed that two variants, L153H and C275Y, decreased binding affinity and antibody-dependent cellular cytotoxicity of the commercial antibodies Daratumumab and Isatuximab. However, a third mutation, R140G, conferred selective resistance to Daratumumab, while retaining sensitivity to Isatuximab. Clinically, patients with MM are often rechallenged with CD38 antibodies following disease progression and these data support a role for next generation sequencing to guide treatment selection.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".