Entinostat, a histone deacetylase inhibitor, enhances CAR-NK cell anti-tumor activity by sustaining CAR expression
Bibliographic record
Abstract
Allogeneic natural killer (NK) cell therapy has demonstrated significant potential in cancer immunotherapy by harnessing NK cells to target malignancies. CD138-targeting chimeric antigen receptor (CAR)-engineered NK cells offer a promising therapeutic option for multiple myeloma (MM). However, sustaining CAR expression on CAR-NK cells during ex vivo expansion poses a challenge to developing effective immunotherapies. In this study, primary NK cells were isolated, cryopreserved, and modified to express anti-CD138 CARs through retroviral transduction. Histone deacetylase inhibitors (HDACi), particularly entinostat (ENT), were applied to enhance CAR expression stability in CAR-NK cells. Our findings indicate that ENT treatment significantly improves and maintains CAR expression, thereby enhancing the cytotoxic activity of CAR-NK cells against CD138-positive multiple myeloma cells. ENT-treated CAR-NK cells exhibited prolonged persistence and more significant tumor reduction in an MM tumor-bearing mouse model, highlighting the therapeutic potential of HDACi-treated CAR-NK cells. This study provides the first evidence that HDAC inhibitors can sustain CAR expression in CAR-NK cells in a promoter-dependent manner, potentially enhancing anti-tumor efficacy in multiple myeloma and underscoring the possible need for further clinical evaluation.
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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 | 0.000 |
| 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".