Epigenetic Enhancement of Antigen Processing and Presentation Promotes Immune Recognition of Tumors
Bibliographic record
Abstract
Histone deacetylase inhibitors (HDACi) have been hailed as a powerful new class of anticancer drugs. The HDACi, trichostatin A (TSA), is thought to interfere with epigenetic control of cell cycle progression in G1 and G2-M phase, resulting in growth arrest, differentiation, or apoptosis. Here, we describe a novel mechanism of action of HDACis in promoting immune responses against tumors. We report that treatment of carcinoma cells with TSA increases the expression of many components of the antigen processing machinery, including TAP-1, TAP-2, LMP-2, and Tapasin. Consistent with this result, we found that treatment of metastatic carcinoma cells with TSA also results in an increase in MHC class I expression on the cell surface that functionally translates into an enhanced susceptibility to killing by antigen-specific CTLs. Finally, we observed that TSA treatment suppresses tumor growth and increases tap-1 promoter activity in TAP-deficient tumor cells in vivo. Intriguingly, this in vivo anti-tumoral effect of TSA is entirely mediated by an increase in immunogenicity of the tumor cells, as it does not occur in immunodeficient mice. These novel insights into the molecular mechanisms controlling tumor immune escape may help revise immunotherapeutic modalities for eradicating cancers.
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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) | 0.002 | 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".