Abstract 3094: Epigenetic classification of ependymal brain tumors across age groups
Bibliographic record
Abstract
Abstract Since it has become evident that histopathological grading of ependymoma according to the WHO classification of CNS tumors is not capable of accurately classifying patients into meaningful strata, a broadly accepted molecular classification scheme with prognostic significance is desperately needed. In recent years, ependymomas were classified into molecular subgroups based on transcriptomic alterations. In tumors localized within the posterior fossa, two distinct biological entities of ependymoma were delineated by several studies (designated posterior fossa A and posterior fossa B), which show striking differences in genetic characteristics and clinical outcome. A similar consensus for supratentorial and spinal ependymoma is lacking. We studied genome-wide DNA methylation (Illumina HumanMethylation450 (450k) array) in 180 primary ependymal tumors (80 with corresponding gene expression profiling data generated by Affymetrix 133plus2.0 arrays), including ependymomas (posterior fossa, supratentorial, spinal), subependymomas (SE), myxopapillary ependymoma (MPE), pineal parenchymal tumors of intermediate differentiation (PPTID), and papillary tumors of the pineal region (PTPR). We performed hierarchical clustering to identify robust molecular subgroups. Independent gene expression profiling datasets from previously published ependymoma studies (Johnson et al.; Wani et al.; Witt et al.) were used as validation cohorts. DNA methylation data showed that ependymal brain tumors can be classified into eight molecular subgroups. Notably, MPE, SE, PPTID and PTPR tumors formed robust distinct clusters, as did posterior fossa Group A and Group B ependymomas. Supratentorial ependymomas can be classified into two principle molecular subgroups, one of which displays a dismal prognosis, and comprises predominantly children and infants, and is associated with highly recurrent gene fusion. Notably, a significant number of ependymomas previously classified by histology as WHO Grade II/III look like SE by methylation, and also have extremely good survival. In summary, using genome-wide DNA methylation and transcriptome analysis we could decipher robust molecular subgroups of ependymal brain tumors including supratentorial ependymoma. Diagnoses of tumors with challenging histopathological features can now be supported by this technology. Hence, this approach offers the possibility to replace the unambiguous histological grading system that is currently in use with a robust molecular classification that readily distinguishes biologically, genetically, and clinically meaningful subgroups of ependymal brain tumors. Citation Format: Hendrik Witt, Martin Sill, Khalida Wani, Steve Mack, David Capper, Stephanie Heim, Pascal Johann, Sally Lambert, Marina Rhyzova, Volker Hovestadt, Theophilos Tzaridis, Kristian Pajtler, Sebastian Bender, Till Milde, Paul A. Northcott, Andreas E. Kulozik, Olaf Witt, Peter Lichter, V Peter Collins, Andreas von Deimling, Marcel Kool, Michael D. Taylor, Martin Hasselblatt, David TW Jones, Andrey Korshunov, Ken Aldape, Stefan Pfister. Epigenetic classification of ependymal brain tumors across age groups. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 3094. doi:10.1158/1538-7445.AM2014-3094
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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 | 0.001 |
| 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) | 0.003 | 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".