EPEN-28. HETEROGENEITY WITHIN THE PFB EPENDYMOMA SUBGROUP
Bibliographic record
Abstract
Posterior fossa ependymoma comprise two distinct molecular groups, termed EPN_PFA and EPN_PFB. Clinically they are very disparate and EPN_PFB are currently being explored for de-escalation of therapy. However, to move forward, a risk stratification within EPN_PFB would be highly desirable. To discern the molecular heterogeneity within EPN_PFB, we performed an integrated analysis consisting of DNA methylation profiling, copy number profiling and clinical correlation across a cohort of 217 primary EPN_PFB. DNA methylation data were analyzed using various methods including spectral clustering, unsupervised consensus clustering and t-distributed stochastic neighbor embedding analysis. The integrated analyses revealed four distinct subgroups with distinct age distributions, copy number alterations, and survival rates. 1q gain was strongly enriched for one of the subgroups and is associated with a worse progression free survival. A univariable analysis revealed that 1q gain, incomplete resection and no upfront radiation were significant predictors of poor progression free survival, and in a multivariable cox regression model, 1q gain was a highly predictive marker of 5 and 10 year progression free survival (HR 3.534 95% CI 1.59–7.87). There is significant intertumoral heterogeneity within EPN_PFB, revealing at least four distinct molecular subgroups. Identification of these subgroups may lead to a better understanding what is driving these tumors. The biological heterogeneity must be accounted for in future pre-clinical modeling and personalized therapies. 1q gain is a significant risk factor of poor progression free survival in EPN_PFB and may represent a prognostic marker in future trials of de-escalation of therapy for EPN_PFB.
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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 | 0.001 |
| 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 | 0.001 | 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".