The impact of histone deacetylase inhibitors on immune cells and implications for cancer therapy
Bibliographic record
Abstract
Many cancers possess the ability to suppress the immune response to malignant cells, thus facilitating tumour growth and invasion, and this has fuelled research to reverse these mechanisms and re-activate the immune system with consequent important therapeutic benefit. One such approach is to use histone deacetylase inhibitors (HDACi), a novel class of targeted therapies, which manipulate the immune response to cancer through epigenetic modification. Four HDACi have recently been approved for clinical use in malignancies including multiple myeloma and T-cell lymphoma. Most research in this context has focussed on HDACi and tumour cells, however, little is known about their impact on the cells of the immune system. Additionally, HDACi have been shown to impact the mechanisms by which other anti-cancer therapies exert their effects by, for example, increasing accessibility to exposed DNA through chromatin relaxation, impairing DNA damage repair pathways and increasing immune checkpoint receptor expression. This review details the effects of HDACi on immune cells, highlights the variability in these effects depending on experimental design, and provides an overview of clinical trials investigating the combination of HDACi with chemotherapy, radiotherapy, immunotherapy and multimodal regimens.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".