Targeting epigenetic crosstalk that restrains the responses of exhausted T cells to immune checkpoint blockade therapy
Bibliographic record
Abstract
Abstract Epigenetic scarring of exhausted CD8 T (TEX) cells during chronic virus infections or cancer remains a major cell-intrinsic barrier to T cell immunotherapies, including immune checkpoint blockade (ICB). For successful epigenetic reprogramming of TEX cells, it is crucial to identify and target the molecular mechanisms underlying terminal exhaustion. Previous work revealed that de novo DNA methylation enforces silencing of T cell function and restrains their responses to ICB therapy. Yet, it remains largely unknown whether post-translational histone modifications crosstalk with de novo DNA methylation during the progression to terminal exhaustion. To better understand the interplay between these epigenetic mechanisms in chronically stimulated CD8 T cells, we employed our novel in vitro model of human T cell dysfunction, as well as preclinical models of T cell exhaustion including chronic LCMV infection. We found a significant relationship between dynamic histone changes and de novo DNA methylation in TEX cells. Importantly, targeting a key histone-modifying enzyme in dysfunctional human T cells improved their effector function and cytotoxicity in vitro. In addition, during chronic LCMV infection, combined treatment by a selective inhibitor and anti-PD-L1 significantly enhanced the ICB responses of stem-like and cytolytic subsets of TEX cells. We further explored the impact of targeting histone modifications on DNA methylation programming in CD8 T cells. These findings provide important mechanistic insights for developing novel therapeutic approaches to epigenetically reprogram TEX cells and enhance T cell immunotherapies. Supported by the Ohio State University College of Medicine and the OSU Comprehensive Cancer Center.
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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".