EPEN-18. Oncogenic 3D genome conformations identify novel therapeutic targets in ependymoma
Bibliographic record
Abstract
Abstract Ependymoma (EPN) is an aggressive pediatric tumor that occurs throughout the central nervous system. The two most aggressive molecular subgroups of EPN are the supratentorial ZFTA-fusion associated group (ST-EPN-ZFTA) and the posterior fossa group A (PF-EPN-A). Although the molecular characteristics underlying the tumorigenesis of these subgroups have been extensively studied, these tumors remain difficult to treat. Hence, innovative therapeutic approaches are urgently needed. Here, we used genome-wide chromosome conformation capture (Hi-C), complemented with CTCF (insulators) and H3K27ac (active enhancers) ChIP-seq, as well as gene expression and whole-genome DNA methylation profiling in primary and relapsed EPN tumors and cell lines, to identify chromosomal rearrangements and regulatory mechanisms underlying aberrant expression of genes that are essential for EPN tumorigenesis. By integrating these heterogenous data types, we have observed the formation of new topologically associated domains (‘neo-TADs’) caused by intra- and inter-chromosomal structural variants in both tumors. In addition, we observed 3D chromatin complexes of regulatory elements, and the replacement of CTCF insulators by DNA hyper-methylation in PF-EPN-A tumors. These tumor-specific 3D genome conformations can be associated with the transcriptional upregulation of nearby genes. Through inhibition experiments we validated that these newly identified genes, including RCOR2, ITGA6, LAMC1, and ARL4C, are highly essential for the survival of patient-derived EPN cell lines in a disease subgroup-specific manner. Thus, our study identifies novel potential therapeutic vulnerabilities in EPN and extends our ability to reveal tumor-dependency genes and pathways by oncogenic 3D genome conformations even in tumors that lack known genetic alterations.
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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 | 0.000 |
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".