Targeting Epigenetic Mechanisms: A Boon for Cancer Immunotherapy
Bibliographic record
Abstract
Immunotherapy is rapidly emerging as a promising approach against cancer. In the last decade, various immunological mechanisms have been targeted to induce an increase in the immune response against cancer cells. However, despite promising results, many patients show partial response, resistance, or serious toxicities. A promising way to overcome this is the use of immunotherapeutic approaches, in combination with other potential therapeutic approaches. Aberrant epigenetic modifications play an important role in carcinogenesis and its progression, as well as in the functioning of immune cells. Thus, therapeutic approaches targeting aberrant epigenetic mechanisms and the immune response might provide an effective antitumor effect. Further, the recent development of potent epigenetic drugs and immunomodulators gives hope to this combinatorial approach. In this review, we summarize the synergy mechanism between epigenetic therapies and immunotherapy for the treatment of cancer, and discuss recent advancements in the translation of this approach.
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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 | 0.001 | 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 | 0.001 | 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".