BET bromodomain inhibition enhances T cell persistence and function in adoptive immunotherapy models
Bibliographic record
Abstract
Adoptive immunotherapy is a potentially curative therapeutic approach for patients with advanced cancer. However, the in vitro expansion of antitumor T cells prior to infusion inevitably incurs differentiation towards effector T cells and impairs persistence following adoptive transfer. Epigenetic profiles regulate gene expression of key transcription factors over the course of immune cell differentiation, proliferation, and function. Using comprehensive screening of chemical probes with defined epigenetic targets, we found that JQ1, an inhibitor of bromodomain and extra-terminal motif (BET) proteins, maintained CD8+ T cells with functional properties of stem cell-like and central memory T cells. Mechanistically, the BET protein BRD4 directly regulated expression of the transcription factor BATF in CD8+ T cells, which was associated with differentiation of T cells into an effector memory phenotype. JQ1-treated T cells showed enhanced persistence and antitumor effects in murine T cell receptor and chimeric antigen receptor gene therapy models. Furthermore, we found that histone acetyltransferase p300 supported the recruitment of BRD4 to the BATF promoter region, and p300 inhibition similarly augmented antitumor effects of the adoptively transferred T cells. These results demonstrate that targeting the BRD4-p300 signaling cascade supports the generation of superior antitumor T cell grafts for adoptive immunotherapy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.002 |
| 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".