Anti-CD19 antibody cotreatment enhances serial killing activity of anti-CD19 CAR-T/-NK cells and reduces trogocytosis
Bibliographic record
Abstract
ABSTRACT: Anti-CD19 chimeric antigen receptor (CAR)-engineered T and natural killer (NK) cell therapies have revolutionized the treatment of B-cell malignancies, but challenges including CD19 antigen loss greatly hinder their full therapeutic potential. Here, we revealed that cotreatment with anti-CD19 monoclonal antibody enhances antitumor activity of anti-CD19 CAR-T and -NK cells. Even though the treated antibody interferes with CD19 antigen binding of CAR, it significantly induces rapid detachment of anti-CD19 CAR effector cells from target cells, facilitating improved serial killing. This reduced interaction between CAR effector cells and target cells also leads to the alleviation of CAR-mediated trogocytosis. Interestingly, cotreatment with anti-CD19 antibody reveals time-dependent effects on the antitumor activity of anti-CD19 CAR-T cells, characterized by a reduction in early T cell activation followed by sustained high activity during prolonged exposure to target cells. This temporal modulation ultimately results in enhanced antitumor potency in vivo. These findings underscore the improved therapeutic efficacy achieved by combining anti-CD19 antibody with anti-CD19 CAR-T or -NK cells against 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) | 0.002 | 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".