Targeting EZH2 Increases Therapeutic Efficacy of Check-Point Blockade in Models of Prostate Cancer
Bibliographic record
Abstract
Abstract Prostate cancers are considered immunologically ‘cold’ tumors given the very few patients who respond to checkpoint inhibitor therapy (CPI). Recently, enrichment of interferon (IFN) response genes predicts a favorable response to CPI across various disease sites. The enhancer of zeste homolog-2 (EZH2) is over-expressed in prostate cancer and is known to negatively regulate IFN response genes. Here, we demonstrate that inhibition of EZH2 catalytic activity in prostate cancer models derepresses expression of double-strand RNA (dsRNA), associated with upregulation of genes involved in antigen presentation, Th-1 chemokine signaling, and interferon (IFN) response, including PD-L1. Similarly, application of a novel EZH2 derived gene signature to human prostate sample analysis indicated an inverse correlation between tumor EZH2 activity/expression with T-cell inflamed and IFN gene signatures and PD-L1 expression. EZH2 inhibition combined with PD-1 CPI significantly enhances antitumor response that is dependent on up-regulation of tumor PD-L1 expression. Further, combination therapy significantly increases intratumoral trafficking of activated CD8+ T-cells and M1 tumor associated macrophages (TAMs) with concurrent loss of M2 TAMs. Our study identifies EZH2 as a potent inhibitor of antitumor immunity and responsiveness to CPI. This data suggests EZH2 inhibition as a novel therapeutic direction to enhance prostate cancer response to PD-1 CPI.
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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) | 0.002 | 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".