Rebalancing TGFβ1/BMP Signaling Epigenetically Reprograms Fully Exhausted Human CD8 T Cells into a Functional State
Bibliographic record
Abstract
Abstract Epigenetic scarring of exhausted T cells (Tex) remains a major obstacle to achieving durable responses by T cell immunotherapies. While we established that de novo DNA methylation programs are causally linked to T cell’s full exhaustion and poor response to immune checkpoint blockade (ICB), major gaps remain in our current understanding of T cell exhaustion—(1) What are the upstream signals that regulate acquisition of exhaustion-specific epigenetic programs? (2) Can we remodel the epigenetic state of Tex to an ICB-responsive state? To address these questions, we developed a novel in vitro model of human T cell dysfunction. First, we demonstrated that chronic TCR stimulation of CD8 T cells is insufficient to develop T cell exhaustion. Instead, our integrative analyses of epigenetic and transcriptional changes in CD8 T cell subsets during chronic virus infections and cancer revealed TGFβ1 as the most significant upstream regulator linked to full exhaustion in mice and humans. Indeed, we found that post-effector TGFβ1 signaling accelerates full exhaustion in chronically stimulated CD8 T cells through stable epigenetic changes linked to impaired effector function and memory potential. Therapeutic rebalancing of TGFβ1/BMP signals by blocking TGFβ1 while boosting BMP signals not only restored effector function, but also unlocked memory programs in human Tex cells. This new therapeutic approach induced a superior anti-tumor activity of human T cells and synergized the ICB response in a murine model of chronic LCMV infection. Our findings highlight the role of TGFβ1/BMP signals in T cell exhaustion and propose a novel therapeutic strategy to epigenetically reprogram fully exhausted T cells, ultimately enhancing T cell immunotherapies.
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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".