Tumor burden quantified by soluble B-cell maturation antigen and metabolic tumor volume determines myeloma CAR-T outcomes
Bibliographic record
Abstract
ABSTRACT: Chimeric antigen receptor T-cell (CAR-T) therapy has emerged as a breakthrough treatment for relapsed and refractory multiple myeloma (RRMM). However, these products are complex to deliver, and alternative options are now available. Identifying biomarkers that can predict therapeutic outcomes is crucial for optimizing patient selection. There is a paucity of data evaluating the utility of both serum soluble B-cell maturation antigen (sBCMA) levels and metabolic tumor volume (MTV) at baseline in patients with RRMM undergoing CAR-T therapy. We identified a cohort of 183 patients with available serum to measure sBCMA and/or pretreatment MTV, derived from positron emission tomography-computed tomography scans obtained per standard of care. Expectedly, high pretreatment levels of sBCMA correlated with other established markers of tumor burden (eg, bone marrow plasma cells and β2 microglobulin) and inflammation and were highly prognostic for CAR-T-related toxicities and inferior progression-free survival (PFS). High MTV values were also associated with shorter PFS and inferior overall survival. The poor correlation observed between these 2 measures prompted evaluation of those with discordant results, identifying that those with low sBCMA and high MTV frequently had low/absent BCMA expression on plasma cells and suboptimal response. Our findings highlight the potential utility of sBCMA and MTV to facilitate more personalized treatment strategies in the management of RRMM eligible for BCMA-directed CAR-T.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".