Lineage specific transcription factor waves reprogram neuroblastoma from self-renewal to differentiation
Bibliographic record
Abstract
Abstract Temporal regulation of super-enhancer (SE) driven lineage specific transcription factors (TFs) underlies normal developmental programs. Neuroblastoma (NB) arises from an inability of sympathoadrenal progenitors to exit a self-renewal program and terminally differentiate. To identify critical SEs driving TF regulators of NB, we utilized NB cells in which all-trans retinoic acid (ATRA) induces growth arrest and differentiation. H3K27ac ChIP-seq paired with RNA-seq over a time course of ATRA treatment revealed SEs moving in a coordinated manner with four distinct temporal patterns (clusters). SEs that decreased with ATRA linked to 24 TFs involved in stem cell development/specialization ( MYCN, GATA3, SOX11) along with LMO1 , a transcriptional coregulator and oncogene identified via a genome-wide association study (GWAS) of NB. H3K27ac levels and GATA3 binding at the NB-associated rs2168101 site of the LMO1 SE were reduced with ATRA treatment, resulting in 1.46 fold decreased LMO1 expression. The SOX11 SE was lost coincident with a 50% decrease in mRNA after 8 days of ATRA treatment. CRISPR-Cas9 screening and siRNA inhibition showed a dependency on SOX11 for cell growth in NB cell lines. Silencing of the SOX11 SE using dCAS9-KRAB targeted guides caused a 40% decrease in SOX11 mRNA and inhibited cell growth. Three other TF SE clusters had sequential waves of activation at 2, 4 and 8 days of ATRA treatment and involved TFs regulating neural development ( GATA2 and SOX4 ). Silencing of the gained SOX4 SE using dCAS9-KRAB targeting, caused a 50% decrease in SOX4 expression and attenuated expression of ATRA-induced differentiation genes. Our study has identified candidate oncogenic lineage drivers of NB self-renewal and TFs critical for implementing a differentiation program.
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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 | 0.001 |
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".