B cell maturation antigen is a novel target for immunotherapy of acute myeloid leukemia
Bibliographic record
Abstract
Abstract B cell maturation antigen (BCMA) has emerged as a prominent immunotherapeutic target in multiple myeloma (MM) due to its restricted expression on MM cells, plasma cells and mature B cells, with minimal presence in other normal tissues. In this study, we demonstrate through RNA sequencing and flow cytometry analyses of acute myeloid leukemia (AML) cell lines and primary patient samples that BCMA is also a relevant AML-associated antigen. Its robust surface expression on AML cells positions it as a promising candidate for targeted immunotherapy. Functionally, our findings indicate that BCMA in AML operates similarly to its role in MM – engaging the NF-kB pathway upon ligand binding, thereby activating gene expression programs that support leukemia cell survival and proliferation. We assessed several BCMA-targeted immunotherapeutic strategies, including bispecific T-cell engagers (TCE) and chimeric antigen receptor (CAR) transduced T-cells, NK-cells, and macrophages. We found that TCE treatment and BCMA CAR engineering markedly improved effector cell mediated cytotoxicity against AML cells, underscoring BCMA’s potential as a viable therapeutic target in AML. Furthermore, BCMA- directed TCE therapy significantly augmented the anti-leukemic activity of adoptively transferred CD8 + T-cells in a human AML xenograft model. Taken together, these findings support BCMA as a novel immunotherapeutic target in AML. Leveraging existing BCMA-directed treatments developed for MM could enable rapid clinical translation and broaden immunotherapy options for patients with AML.
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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 | 0.000 | 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 | 0.001 |
| 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".