EP-03 * MOLECULAR CLASSIFICATION OF EPENDYMAL TUMORS ACROSS ALL CNS COMPARTMENTS, HISTOPATHOLOGICAL GRADES AND AGE GROUPS
Bibliographic record
Abstract
Ependymal tumors across age groups are currently classified and graded solely by histopathology. It is, however, commonly accepted that this classification scheme has limited clinical utility based on its lack of reproducibility in predicting patients' outcome. DNA methylation patterns in tumors have been shown to represent a very stable molecular memory of the respective cell of origin throughout disease course, making them particularly suitable for tumor classification purposes. To attempt a uniform molecular classification for ependymal tumors across all age groups, histopathological grades and all anatomical CNS compartments – spine (SP), posterior fossa (PF), supratentorial region (ST) - we generated genome-wide DNA methylation profiles for 500 ependymal tumors using the Illumina 450k methylation array. Unsupervised hierarchical clustering of the DNA methylation data identified nine distinct molecular subgroups of ependymal tumors, three within each CNS compartment. One of the subgroups within each compartment was enriched with grade I subependymomas (SE) which were labeled as SP-SE, PF-SE and ST-SE. Within the spinal compartment the other molecular subgroups showed a relatively good concordance with the histopathological subtypes myxopapillary ependymoma (SP-MPE) and ependymoma (SP-EPN). The remaining molecular subgroups within the posterior fossa were previously described as posterior fossa Group A and B ependymomas and are now labeled as PF-EPN-A and PF-EPN-B. Two supratentorial subgroups are characterized by prototypic fusion genes involving RELA(ST-EPN-RELA) and YAP1 (ST-EPN-YAP1), respectively. Notably, the vast majority of high-risk patients (mostly children), for whom novel therapeutic concepts are desperately needed, were restricted to just two of the nine molecular subgroups identified, namely PF-EPN-A and ST-EPN-RELA. Regarding clinical associations, the molecular classification proposed herein outperforms the current histopathological classification and thus might serve as a basis for the next WHO classification of CNS 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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".