A Bispecific Antibody Antagonizes Prosurvival CD40 Signaling and Promotes Vγ9Vδ2 T cell–Mediated Antitumor Responses in Human B-cell Malignancies
Bibliographic record
Abstract
Abstract Novel T cell–based therapies for the treatment of B-cell malignancies, such as chronic lymphocytic leukemia (CLL) and multiple myeloma (MM), are thought to have strong potential. Progress, however, has been hampered by low efficacy and high toxicity. Tumor targeting by Vγ9Vδ2 T cells, a conserved T-cell subset with potent intrinsic antitumor properties, mediated by a bispecific antibody represents a novel approach promising high efficacy with limited toxicity. Here, we describe the generation of a bispecific Vγ9Vδ2 T-cell engager directed against CD40, which, due to its overexpression and biological footprint in malignant B cells, represents an attractive target. The CD40-targeting moiety of the bispecific antibody was selected because it can prevent CD40L-induced prosurvival signaling and reduce CD40-mediated resistance of CLL cells to venetoclax. Selective activation of Vγ9Vδ2 T cells in the presence of CD40+ tumor cells induced potent Vγ9Vδ2 T-cell degranulation, cytotoxicity against CLL and MM cells in vitro, and in vivo control of MM in a xenograft model. The CD40-bispecific γδ T-cell engager demonstrated lysis of leukemic cells by autologous Vγ9Vδ2 T cells present in patient-derived samples. Taken together, our CD40 bispecific γδ T-cell engager increased the sensitivity of leukemic cells to apoptosis and induced a potent Vγ9Vδ2 T cell–dependent antileukemic response. It may, therefore, represent a potential candidate for the development of novel treatments for B-cell malignancies.
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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".