STEM-18. EPIGENETIC AND MOLECULAR COORDINATION BETWEEN HDAC2 AND SMAD3-SKI IS REQUIRED FOR GROWTH AND STEM CELL CHARACTERISTICS OF BRAIN TUMOUR STEM CELLS
Bibliographic record
Abstract
Abstract Brain tumour stem cell population in glioblastoma (GBM) display key cancer stem cell characteristics of high self-renewal and drug resistance that are maintained by the coordinated functions of epigenetic and molecular regulators. Yet, specific epigenetic mechanisms that, in collaboration with relevant molecular pathways, help maintain a stem-like state in BTSCs remain poorly understood. Here, we identify HDAC2 as a foremost epigenetic regulator in BTSCs that specifically utilizes the transforming growth factor-β (TGF-β) pathway related proteins, SMAD3-SKI, for remodelling BTSC chromatin accessibility and transcriptional programs to facilitate their stemness and tumorigenic potentials. Our initial drug screening revealed that selective inhibition of HDAC1 and 2 with romidepsin was effective in targeting BTSC viability, cell proliferation and self-renewal in vitro. Using CRISPR-cas9 knockout and shRNA knockdown strategies, we further demonstrated that loss of HDAC2 disrupts an epigenetic and molecular coordination between HDAC2 and SMAD-SKI proteins, which negatively impacts BTSC survival, cell proliferation and self-renewal in vitro and improves median survival in orthotopic xenograft mouse models. Loss of HDAC2 showed reduction in the protein abundance of transcriptional regulator, SMAD3 and negative regulator protein, SKI. However, overexpression of SMAD3 in HDAC2 deficient BTSCs could partially rescues their cell functional deficits. These findings suggest that context-specific epigenetic regulations by HDAC2 and its interaction with the critical transcriptional regulators, SMAD3-SKI, maintains the stemness and growth characteristics of BTSCs. Further HDAC2 overexpression increases cell proliferation and self-renewal abilities in normal neural stem cells (NSCs). These findings thus support the role of HDAC2 as a key epigenetic determinant of stemness in normal NSCs and of cancer stem cell characteristics and tumorigenic potential in BTSCs. Collectively, our data raises the potential that disruption of the coordinated mechanisms regulated by HDAC2-SMAD3-SKI axis may be an effective therapeutic approach for targeting GBM BTSCs.
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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) | 0.003 | 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".