HDACi-dependent Microenvironmental Normalization Overcomes Tumor Burden–induced T-cell Exhaustion
Bibliographic record
Abstract
PURPOSE: T-cell exhaustion limits immunotherapy for the treatment of solid tumors. Although immune checkpoint blockade and adoptive T-cell therapy (ACT) can mediate tumor regression, their potency is often determined by tumor burden. Here, we identified tumor burden-related pathway changes that are conducive to T-cell exhaustion. We then determined whether microenvironmental reprogramming via epigenetic modulation could reverse T-cell exhaustion and improve immunotherapeutic responsiveness. EXPERIMENTAL DESIGN: We developed a murine syngeneic tumor model wherein an increased burden ablated therapeutic responsiveness to ACT, which corresponded with systemic induction of T-cell exhaustion. Transcriptome analysis of these large tumors allowed us to characterize changes to immunosuppressive pathway expression during class I histone deacetylase inhibitor MS-275 treatment. We then measured the therapeutic impact of MS-275 during ACT and assessed T-cell exhaustion by transcriptome/phenotypic analysis. RESULTS: ACT durably regressed small tumors but failed to control large tumors, which were associated with systemic T-cell exhaustion and ablation of T-cell responses. Large tumors were defined by an immunosuppressive pathway signature. MS-275 reversed this pathway signature and promoted durable regression of large tumors during ACT. Prototypical exhaustion marker Tim-3 was selectively upregulated in transferred T cells despite displaying a reduced exhaustion signature. Instead, we observed enhanced activation-dependent signaling correlating with enrichment of the IL2-STAT5 signaling axis. Activated CD8+ T-cell responses were predominantly skewed toward terminal effector cell-like CD44+ Tim-3hi TCF1- CD127- KLRG1+ differentiation. CONCLUSIONS: Tumor burden-induced pathway changes can be reversed through epigenetic reprogramming, enabling the conversion from T-cell exhaustion to effector lineage differentiation.
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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 | 0.001 |
| 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".