Abstract PR008: The 3D epigenome of rhabdomyosarcoma
Bibliographic record
Abstract
Abstract Rhabdomyosarcoma (RMS) is driven by transcription factors that set up super-enhancers: large clusters of transcriptional machinery accumulated at oncogenes. Super-enhancers are also the key vulnerability of RMS, as we have revealed by CRISPR screening and chemical genomics. However, the identification of super-enhancers is inconsistent, because they are extracted from 1-dimensional ChIP-seq data, but are in fact 3D genomic objects (“super-clusters”) that form biomolecular condensates (phase-separated droplets). We have now applied new 3D genomics approaches to enable proper discovery of these epigenomic features, also yielding proper enhancer-gene connectivity maps. Furthermore, super-clusters are co-built by transcription factors recruiting p300 (acetylation writer) and BRD4 (acetylation reader). We have generated preliminary evidence that directly perturbing/degrading p300 and BRD4 are the most effective and selective means to shut down super-clusters at all oncogenes driving this childhood cancer. We applied Absolute Quantification of Architecture (AQuA) HiChIP, and were able to uncover the drug-induced 3D folding abnormalities that selectively halt RNA-Polymerase 2 at super-cluster driven oncogenes in this sarcoma. Citation Format: Berkley E. Gryder, Issra Osman, Adam Durbin, Jun Qi. The 3D epigenome of rhabdomyosarcoma [abstract]. In: Proceedings of the AACR Special Conference: Sarcomas; 2022 May 9-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2022;28(18_Suppl):Abstract nr PR008.
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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) | 0.004 | 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".