EZH2 inhibition enhances the activity of Carboplatin in aggressive-variant prostate cancer cell lines
Bibliographic record
Abstract
Background Aggressive Variant Prostate Cancers (AVPCs) are incurable malignancies. Platinum-based chemotherapies are used for the palliative treatment of AVPC. The Polycomb Repressive Complex 2 (PRC2) promotes prostate cancer progression via histone H3 Lysine 27 tri-methylation (H3K27me3). EZH2 encodes the catalytic subunit of PRC2. A recently developed nucleosome capture technology (Nu.QⓇ).measures H3K27me3 levels in biological fluids. EZH2 inhibitors (EZH2i) are being tested in clinical trials. We hypothesize that epigenetic reprogramming via EZH2i improves the efficacy of Carboplatin in AVPC and that EZH2i activity can be measured via both cellular- and cell-free nucleosomal H3K27me3 (cf-H3K27me3) levels.Methods We studied the expression of PRC2 genes in clinical prostate cancer cohorts (bioinformatics). We determined the effect of EZH2i on cellular- and cf-H3K27me3 levels. We measured dose-dependent effects of Carboplatin with/without EZH2i on AVPC cell viability (IC50). We used RNA-Seq to study how EZH2i modulates gene expression in AVPC cells.Results PRC2 genes were significantly up-regulated in AVPC vs other prostate cancer types. EZH2i reduced both cellular and cf-H3K27me3 levels. EZH2i significantly reduced Carboplatin IC50. EZH2i reduced the expression of DNA repair genes and increased the expression of p53-dependent pro-apoptotic factors.Conclusions EZH2i plus Carboplatin is a promising combination treatment for AVPC.
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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".