ETMR-06. DISSECTING THE MOLECULAR AND DEVELOPMENTAL BASIS OF PINEOBLASTOMA THROUGH GENOMICS
Bibliographic record
Abstract
Abstract Pineoblastoma (PB) is an aggressive embryonal brain tumor comprising 1% of pediatric CNS tumors. The clinico-molecular heterogeneity and developmental origins underlying PB are poorly understood; therefore, we have assembled a molecular cohort of histologically defined PBs (n=43) with corresponding outcome data. Methylation profiling revealed four molecularly and clinically distinct PB subgroups, including two novel entities. Mutational and transcriptional analysis identified characteristic molecular features of each subgroup, such as mutations in the miRNA processing pathway or FOXR2 proto-oncogene overexpression. Furthermore, subgroups exhibited differences in propensity for metastasis, cytogenetics, and clinical outcomes. To dissect PB developmental origins and resolve PB subgroup biology, we have employed a combination of single-cell genomics and genetically engineered mouse modeling. We created a single-cell transcriptional atlas of the developing murine pineal gland across 11 timepoints and are currently integrating these data with single nuclei RNA-seq data of human PB (n=25). Single-cell analysis of the developing pineal gland revealed three distinct populations of pinealocytes, referred to as early, mid and late pinealocytes, which segregate by developmental stage yet lie along a single developmental trajectory. Preliminary results implicate significant associations between PBs and the early pinealocyte population as well as subgroup-specific differences in intratumoral heterogeneity. Furthermore, this knowledge has informed the downstream generation of biologically faithful disease models, including a transgenic mouse model of the PB-RB subgroup. Remarkably, this model shows up-regulation of key markers of PB such as Crx, Asmt and Otx2 and substantiates early pinealocytes as the probable cell-of-origin for this PB subgroup.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".