EPEN-26. DRUG SCREENING IN PATIENT-DERIVED MODELS OF POSTERIOR FOSSA A EPENDYMOMA REVEALS NOVEL PHARMACOLOGICAL VULNERABILITIES
Bibliographic record
Abstract
Abstract Ependymoma (EPN) is the third most common malignant intracranial pediatric brain tumor. The most common and aggressive EPN subgroup, posterior fossa ependymoma group A (PFA-EPN), occurs predominantly in younger children and has a 5-year progression free survival of only 33%. Despite progress in the molecular characterization and subtyping of EPN, the standard treatment remains surgery with adjuvant radiation therapy. There is currently no approved role for chemotherapy in PFA-EPN. As such, identifying novel therapies for PFA-EPN is an important unmet medical need. To define therapeutic sensitivities for PFA-EPN, we performed a drug screen on three patient-derived PFA-EPN cell lines. We found that PFA-EPN cell lines were sensitive to several clinically relevant drugs, most notably those targeting epigenetic regulators. This is of a particular interest as a common molecular feature of PFA-EPN is the global reduction of the repressive post-translational histone modification H3K27me3 due to transcriptional EZHIP overexpression or H3 K27M mutations. To further characterize our PFA-EPN cell lines, we performed optical genome mapping analysis, revealing previously undescribed structural variants affecting genes encoding proteins responsible for signaling and transcription. One PFA-EPN sample that showed profound sensitivity to EGFR inhibitors, also had a duplication of a genomic region containing the EGFR ligand encoding genes, AREG, BTC, EREG, and EPGN. We are currently working to validate these results in larger cohorts of PFA-EPN models. Collectively, these findings provide new clues on drugs, signaling and putative therapeutic targets in these rare yet aggressive tumors.
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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".