Entinostat, a Histone Deacetylase Inhibitor, improves Anti-Tumor Activity of CAR-NK Cells by Sustaining CAR Expression 4143
Bibliographic record
Abstract
Abstract Description Allogeneic natural killer (NK) cell therapy has emerged as a promising approach in cancer immunotherapy. Chimeric antigen receptor (CAR)-engineered NK cells targeting CD138 present a novel therapeutic strategy for treating multiple myeloma (MM). However, maintaining CAR expression during ex vivo expansion remains a critical challenge, limiting therapeutic applications. In this study, primary NK cells were isolated, cryopreserved, and engineered to express anti-CD138 CARs using retroviral transduction. To address CAR expression downmodulation, histone deacetylase inhibitors (HDACi), particularly entinostat (ENT), were employed. Our results demonstrate that ENT treatment significantly restores CAR expression, thereby boosting the cytotoxic potency of CAR-NK cells against CD138-positive MM cells. In a mouse model of MM, ENT-treated CAR-NK cells exhibited superior tumor reduction, emphasizing their therapeutic efficacy. This study is the first to show that HDAC inhibitors can be used to restore CAR expression in CAR-NK cells through a promoter-dependent mechanism, enhancing anti-tumor activity in MM and warranting further clinical exploration. Funding Sources The Canadian Institutes of Health Research (PJT-178197, PJT-518790). Topic Categories Tumor Immunology: Checkpoints, Prevention, and Treatment (TIPT)
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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) | 0.003 | 0.001 |
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".