Immunological Effects of Epigenetic Modifiers
Bibliographic record
Abstract
Epigenetic alterations are associated with major pathologies including cancer. Epigenetic dysregulation, such as aberrant histone acetylation, altered DNA methylation, or modified chromatin organization, contribute to oncogenesis by inactivating tumor suppressor genes and activating oncogenic pathways. Targeting epigenetic cancer hallmarks can be harnessed as an immunotherapeutic strategy, exemplified by the use of pharmacological inhibitors of DNA methyltransferases (DNMT) and histone deacetylases (HDAC) that can result in the release from the tumor of danger-associated molecular patterns (DAMPs) on one hand and can (re-)activate the expression of tumor-associated antigens on the other hand. This finding suggests that epigenetic modifiers and more specifically the DNA methylation status may change the interaction of chromatin with chaperon proteins including HMGB1, thereby contributing to the antitumor immune response. In this review, we detail how epigenetic modifiers can be used for stimulating therapeutically relevant anticancer immunity when used as stand-alone treatments or in combination with established immunotherapies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".