ZFTA–RELA Dictates Oncogenic Transcriptional Programs to Drive Aggressive Supratentorial Ependymoma
Bibliographic record
Abstract
Abstract More than 60% of supratentorial ependymomas harbor a ZFTA–RELA (ZRfus) gene fusion (formerly C11orf95–RELA). To study the biology of ZRfus, we developed an autochthonous mouse tumor model using in utero electroporation (IUE) of the embryonic mouse brain. Integrative epigenomic and transcriptomic mapping was performed on IUE-driven ZRfus tumors by CUT&RUN, chromatin immunoprecipitation sequencing, assay for transposase-accessible chromatin sequencing, and RNA sequencing and compared with human ZRfus-driven ependymoma. In addition to direct canonical NFκB pathway activation, ZRfus dictates a neoplastic transcriptional program and binds to thousands of unique sites across the genome that are enriched with PLAGL family transcription factor (TF) motifs. ZRfus activates gene expression programs through recruitment of transcriptional coactivators (Brd4, Ep300, Cbp, Pol2) that are amenable to pharmacologic inhibition. Downstream ZRfus target genes converge on developmental programs marked by PLAGL TF proteins, and activate neoplastic programs enriched in Mapk, focal adhesion, and gene imprinting networks. Significance: Ependymomas are aggressive brain tumors. Although drivers of supratentorial ependymoma (ZFTA- and YAP1-associated gene fusions) have been discovered, their functions remain unclear. Our study investigates the biology of ZFTA–RELA-driven ependymoma, specifically mechanisms of transcriptional deregulation and direct downstream gene networks that may be leveraged for potential therapeutic testing. This article is highlighted in the In This Issue feature, p. 2113
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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) | 0.001 | 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".