Abstract 3952: New advances in regulation of senescence by PML and the PML nuclear bodies
Bibliographic record
Abstract
Abstract Senescence is a cellular defense mechanism activated by short telomeres, DNA damage or expression of oncogenes. The program includes the expression of high levels of the promyelocytic leukemia protein PML that form nuclear spherical bodies, known as PML-nuclear bodies (PML-NB). The expression of PML in normal fibroblasts is sufficient to induce senescence while genetic inactivation of PML inhibits the process. These results suggest that PML is a critical component of the senescence tumor suppressor mechanism. Accordingly PML is poorly expressed in malignant human tumors but highly expressed in benign tumors. We recently discovered a new mechanism of regulation of RAS-induced senescence by PML, which implicates the recruitment of the RB/E2F complex to the PML-NB via RB-PML interaction. This leads to inhibition of cell cycle and DNA repair genes, DNA damage, p53 activation and ultimately to senescence. We show now that the cyclin dependent kinase CDK4 blocks the ability of PML to regulate E2F gene expression and senescence and that CDK inhibitors potentiate the ability of PML to restore the senescence program in tumor cells. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3952. doi:1538-7445.AM2012-3952
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".