Abstract 3089: (Epi)genetic profiling enables molecular re-classification of CNS-primitive neuroectodermal tumors
Bibliographic record
Abstract
Abstract According to the current WHO classification of CNS tumors, childhood CNS primitive neuro-ectodermal tumors (CNS-PNETs; WHO °IV) are poorly differentiated embryonal tumors with early onset and aggressive clinical behavior. Histological diagnosis can be complicated by morphological heterogeneity and divergent differentiation. Recent studies suggest the existence of molecular subgroups of CNS-PNETs sharing biological characteristics with other childhood CNS tumors. Here, we aimed at a comprehensive molecular characterization of CNS-PNETs and compared our results to profiles of other brain tumor classes in order to define the biological nature of tumors diagnosed as CNS-PNETs. A collective of 197 fresh-frozen or paraffin-embedded tumor samples with an institutional diagnosis “CNS-PNET” was profiled for genome-wide DNA methylation patterns and copy-number alterations, complemented by transcriptomic profiling of a subset (n=63). (Epi-)genetic profiles of CNS-PNETs were compared to those of >1.000 other childhood brain tumors including embryonal, astrocytic, and ependymal entities, and their respective molecular subgroups. We screened selected groups of tumors for recurrent mutations and expression of established molecular markers. Five experienced neuropathologists independently revisited the histology of 48 CNS-PNETs. Bioinformatic analysis of DNA methylation and gene expression profiles using clustering methods and class prediction algorithms resulted in a clear segregation of pediatric brain tumors by histological entities and molecular subgroups. Exceptionally, profiles of tumors classified as “CNS-PNET” suggested significant overlap with various well-defined entities, including AT/RT, ETMR, high-grade glioma, medulloblastoma, and ependymoma. When screening CNS-PNETs with DNA methylation profiles highly resembling other entities, hallmark genetic alterations of these, such as amplification of 19q13.42, mutations in IDH1 or H3F3A, or mutations/deletions of the SMARCB1 locus, were frequently detected. Molecularly distinct tumor subsets were associated with differential protein expression patterns of previously established subgroup markers INI-1, LIN28A, and OLIG2. Blinded histopathological evaluation resulted in a large proportion of CNS-PNETs being re-classified in line with affiliations suggested by molecular diagnostic tools. The correct classification of CNS-PNET remains challenging. Based on the detection of recurrent genetic aberrations, many cases can be reliably re-classified, indicating that a significant proportion of CNS-PNETs may comprise a variety of other tumor subtypes. These findings suggest that the use of established and novel subgroups markers is needed in order to assist the histopathological evaluation of these tumors. The molecular biology underlying distinct subsets of CNS-PNETs only remains to be elucidated. Citation Format: Dominik Sturm, Paul A. Northcott, David T.W. Jones, Andrey Korshunov, Daniel Picard, Peter Lichter, Annie Huang, Stefan M. Pfister, Marcel Kool. (Epi)genetic profiling enables molecular re-classification of CNS-primitive neuroectodermal tumors. [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 3089. doi:10.1158/1538-7445.AM2014-3089
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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) | 0.001 | 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".