A CDK4/6-Dependent Epigenetic Mechanism Protects Cancer Cells from PML-induced Senescence
Bibliographic record
Abstract
Promyelocytic leukemia (PML) plays a tumor suppressive role by inducing cellular senescence in response to oncogenic stress. However, tumor cell lines fail to engage in complete senescence upon PML activation. In this study, we investigated the mechanisms underlying resistance to PML-induced senescence. Here, we report that activation of the cyclin-dependent kinases CDK4 and CDK6 are essential and sufficient to impair senescence induced by PML expression. Disrupting CDK function by RNA interference or pharmacological inhibition restored senescence in tumor cells and diminished their tumorigenic potential in mouse xenograft models. Complete senescence correlated with an increase in autophagy, repression of E2F target genes, and an gene expression signature of blocked DNA methylation. Accordingly, treatment of tumor cells with inhibitors of DNA methylation reversed resistance to PML-induced senescence. Further, CDK inhibition with palbociclib promoted autophagy-dependent degradation of the DNA methyltransferase DNMT1. Lastly, we found that CDK4 interacted with and phosphorylated DNMT1 in vitro, suggesting that CDK activity is required for its stabilization. Taken together, our findings highlight a potentially valuable feature of CDK4/6 inhibitors as epigenetic modulators to facilitate activation of senescence programs in tumor cells. Cancer Res; 76(11); 3252-64. ©2016 AACR.
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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 | 0.001 |
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".