A Bispecific Single-Domain Antibody Boosts Autologous Vγ9Vδ2-T Cell Responses Toward CD1d in Chronic Lymphocytic Leukemia
Bibliographic record
Abstract
Abstract Purpose: Although considerable progress has been made with autologous T cell–based therapy in B-cell malignancies, application in chronic lymphocytic leukemia (CLL) lags behind due to disappointing response rates as well as substantial toxicity that is of particular concern in the elderly CLL population. Vγ9Vδ2-T cells form a conserved T-cell subset with strong intrinsic immunotherapeutic potential, largely because of their capacity to be triggered by phosphoantigens that can be overproduced by CLL and other malignant cells. Specific activation of Vγ9Vδ2-T cells by a bispecific antibody may improve the efficacy and toxicity of autologous T-cell–based therapy in CLL. Experimental Design: We evaluated CD1d expression in a cohort of 78 untreated patients with CLL and generated and functionally characterized a CD1d-specific Vγ9Vδ2-T cell engager based on single-domain antibodies (VHH). Results: CD1d was expressed by CLL in the majority of patients, particularly in patients with advanced disease. The CD1d-specific Vγ9Vδ2-T cell engager induced robust activation and degranulation of Vγ9Vδ2-T cells, enabling Vγ9Vδ2-T cells from patients with CLL to lyse autologous leukemic cells at low effector-to-target ratios. Expression of CD1d on CLL cells is upregulated by all-trans retinoic acid, and sensitizes the malignant cells to bispecific VHH-induced lysis. Furthermore, we provide evidence that the Vγ9Vδ2-T cell receptor retains responsiveness to phosphoantigens when the bispecific VHH is bound, and aminobisphosphonates can therefore enhance bispecific Vγ9Vδ2-T cell engager–mediated tumor-specific killing. Conclusions: Collectively, our data demonstrate the immunotherapeutic potential of this novel CD1d-specific Vγ9Vδ2-T cell engager in CLL.
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 | 0.000 |
| 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) | 0.001 | 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".