Impact of soluble BCMA and non–T-cell factors on refractoriness to BCMA-targeting T-cell engagers in multiple myeloma
Bibliographic record
Abstract
ABSTRACT: Adoptive T-cell therapy is a promising therapy for multiple myeloma (MM), but its efficacy hinges on understanding the relevant biologic and predictive markers of response. B-cell maturation antigen (BCMA) is a key target antigen in MM with active development of multiple anti-BCMA T-cell engagers (TCEs) and chimeric antigen receptor T-cell therapies. The regulation of surface BCMA expression by MM cells, which leads to shedding of soluble BCMA (sBCMA), has triggered debate about the significance of sBCMA as a predictive marker and its potential impact on treatment outcomes. To address this, we leveraged whole-genome sequencing and in vitro assays to demonstrate that sBCMA may independently predict primary refractoriness to anti-BCMA therapies. In addition to sBCMA, tumor burden and surface BCMA antigen density collectively influenced the anti-BCMA TCE cytotoxic efficacy. Correlative analyses of 163 patients treated with the anti-BCMA TCE teclistamab validated and further underscored the association between elevated baseline sBCMA (>400 ng/mL) and refractoriness. Importantly, increasing the TCE dose, using TCE against alternative targets (eg, GPRC5D), and gamma secretase inhibitors were able to overcome the high sBCMA levels. These findings highlight the importance of taking into account the baseline sBCMA levels, disease burden, and TCE dose intensity when administering anti-BCMA TCEs, thereby offering critical insights for optimizing therapeutic strategies to overcome specific high-risk features and primary anti-BCMA TCE refractoriness.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".