ETMR-19. SINGLE CELL ANALYSES OF ETMRs REVEAL THAT C19MC+ POPULATION DRIVES CELL CYCLE PROGRESSION AND STEM CELL MAINTENANCE
Bibliographic record
Abstract
Abstract Embryonal tumors with multilayered rosettes (ETMRs) are highly fatal diseases characterized by recurrent amplification of C19MC, an oncogenic miRNA cluster. While C19MC was discovered as a major driver of ETMRs, its direct role in ETMRs remains unknown. As ETMRs exhibit significant heterogeneity in C19MC expression, we employed single cell transcriptomics to investigate features of C19MC+ population. We conducted single-nuclei RNAseq of 23,269 cells from 6 primary and 2 matched recurrent ETMRs. We also conducted single-cell RNAseq of human neural stem cells (hNSC-5miR) and ETMR cell line (A664-5miR) with stable expression of 5 C19MC miRNAs. Bulk RNAseq (n=27), H3K27Ac ChiP-seq (n=5) and ATAC-seq (n=5) corroborated scRNAseq data and identified core transcription factors (TFs) of C19MC+ population. C19MC+ population (24%) mapped to neuro-epithelial cells and exhibited signatures of cell cycle and stem cell maintenance, consistent with bulk-RNAseq data. The C19MC+ population overlaps with MKI67+ cycling (57%) and PROM1+ stem cell population (56%). Interestingly, interrogation of hNSC-5mir and A664-5miR showed a larger MKI67+/PROM1+ population compared to controls. Likewise, hNSC-5miR/A664-5miR in vitro and in vivo experiments showed increased proliferation/stemness. C19MC+ population is characterized by SHH, WNT, mTOR, Hippo and IGF-signalling and driven by MEIS1, SOX11, ZNF521, RFX4 and NR2F2 TFs. Recurrent ETMRs exhibit a persistent but smaller C19MC+ population. Intriguingly, recurrent tumors were more quiescent with a smaller proliferative population. C19MC is directly involved in driving cell cycle and stemness in ETMRs. Cellular and molecular features of primary and recurrent ETMRs were remarkably different, suggesting that C19MC plays a different role upon recurrence.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".