Targeting of cancer stem cells by inhibitors of DNA and histone methylation
Bibliographic record
Abstract
INTRODUCTION: Curative chemotherapy should target cancer stem cells (CSCs). The key characteristics of CSCs are a block in differentiation and an epigenetic signature similar to embryonic stem cells (ESCs). Differentiation by ESCs and CSCs is suppressed by gene silencing through the polycomb repressive complex 2 (PRC2) and/or DNA methylation. PRC2 contains the EZH2 subunit, which catalyzes the trimethylation of histone 3 lysine 27, a gene silencing marker. It is possible to reverse this 'double lock' mechanism using a combination of inhibitors of EZH2 and DNA methylation (5-aza-2'-deoxycytidine), which exhibits remarkable synergistic antineoplastic activity in preclinical studies. AREAS COVERED: The authors discuss several specific EZH2 inhibitors that have been synthesized with antineoplastic activity. One such inhibitor, EPZ-6438 (E7438), has been shown to be effective against lymphoma in a Phase I study. The indirect EZH2 inhibitor, 3-deazaneplanocin-A (DZNep), also exhibits remarkable anticancer activity due to its inhibition of methionine metabolism. EXPERT OPINION: Agents that target EZH2 warrant Phase I trials. Due to its positive pharmacodynamics, DZNep merits a high priority for clinical investigation. Agents that show positive results in Phase I studies should be advanced to clinical trials for use in combination with 5-aza-2'-deoxycytidine due to the interesting potential of this epigenetic therapy to target CSCs.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".