Cross-Species Genomics Reveals Oncogenic Dependencies in ZFTA/C11orf95 Fusion–Positive Supratentorial Ependymomas
Bibliographic record
Abstract
Abstract Molecular groups of supratentorial ependymomas comprise tumors with ZFTA–RELA or YAP1-involving fusions and fusion-negative subependymoma. However, occasionally supratentorial ependymomas cannot be readily assigned to any of these groups due to lack of detection of a typical fusion and/or ambiguous DNA methylation–based classification. An unbiased approach with a cohort of unprecedented size revealed distinct methylation clusters composed of tumors with ependymal but also various other histologic features containing alternative translocations that shared ZFTA as a partner gene. Somatic overexpression of ZFTA-associated fusion genes in the developing cerebral cortex is capable of inducing tumor formation in vivo, and cross-species comparative analyses identified GLI2 as a key downstream regulator of tumorigenesis in all tumors. Targeting GLI2 with arsenic trioxide caused extended survival of tumor-bearing animals, indicating a potential therapeutic vulnerability in ZFTA fusion–positive tumors. Significance: ZFTA–RELA fusions are a hallmark feature of supratentorial ependymoma. We find that ZFTA acts as a partner for alternative transcriptional activators in oncogenic fusions of supratentorial tumors with various histologic characteristics. Establishing representative mouse models, we identify potential therapeutic targets shared by ZFTA fusion–positive tumors, such as GLI2. 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 | 0.001 | 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) | 0.002 | 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".