EZH2 inhibition enhances the activity of platinum chemotherapy in aggressive-variant prostate cancer
Bibliographic record
Abstract
ABSTRACT Background EZH2 promotes aggressive-variant prostate cancer (AVPC) progression via histone H3-Lysine-27 tri-methylation (H3K27me3). We hypothesize that epigenetic reprogramming via EZH2 inhibitors (EZH2i) improves the efficacy of chemotherapy in AVPC. Methods We studied the expression of EZH2 in clinical prostate cancer cohorts (bioinformatics). We determined the effect of EZH2i on both cellular- and cell-free-H3K27me3 levels. We measured effects of carboplatin with/without EZH2i on AVPC cell viability (IC 50 ). We studied how EZH2i modulate gene expression (RNA Seq). Results EZH2 was significantly up-regulated in AVPC vs other prostate cancer types. EZH2i reduced both cellular and cell free-H3K27me3 levels. EZH2i significantly reduced carboplatin IC 50 . EZH2i reduced the expression of DNA repair and increased the expression of pro-apoptotic genes. Article Highlights Polycomb-mediated gene silencing promotes prostate cancer progression Aggressive-variant prostate cancers (AVPCs) are characterized by increased activity of the Polycomb-Repressive Complex 2 (PRC2) Here we show that PRC2 inhibitors are scarcely effective as monotherapy in ACPC cells However the combination of PRC2 inhibitors and carboplatin is highly synergistic RNA Seq studies revealed that PRC2 inhibitors enhance carboplatin activity by modulating several key pathways, including DNA repair and apoptosis.
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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".