PNR-39DISTINCT GENE FUSIONS SEGREGATE SUB-CLASSES OF CNS-PNETs
Bibliographic record
Abstract
Primitive neuroectodermal brain tumours (PNETs) have dismal overall 20-40% survival. Clinical diagnosis of PNETs has proven challenging due to histological similarities to other tumours types. Our previous studies indicated at least 3 transcriptional sub-types of CNS-PNETs. To further define molecular features of CNS-PNETs that are distinct from other brain tumours, this study compared genetic and epigenetic profiles of 221 PNETs with that of 15 angiosarcomas, 162 atypical teratoid/rhabdoid tumours, 8 chondroblastomas, 29 liposarcomas, 48 ependymomas, 293 glioblastomas, 42 leiomyosarcomas, 377 low grade gliomas, 91 medulloblastomas, 9 malignant rhabdoid tumours, 34 neuroblastomas, 60 pleomorphic adenomas, 20 pineoblastomas, 10 sarcomas, 14 normal brain, and 27 fetal brain. Analyses included RNA and exome sequencing (n = 40), methylation, CNV (n = 1355), and gene expression profiling (n= 95), as well as targeted sequencing and Nanostring analyses for known alterations and fusions characteristic of other tumour types. To date these analyses reveal further segregation of CNS-PNETs into 4 epigenetic sub-types including Group 1 PNETs with the known C19MC OncomiR cluster. Groups 2 and 3 were respectively defined by gene-fusion events and elevated FOXR2 and BCOR expression, while group 4 was enriched for mesenchymal features. Integrated analyses indicate molecular sub-types of CNS-PNETs have distinct clinical and survival features.
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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) | 0.002 | 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".