Cellular hierarchies of embryonal tumors with multilayered rosettes are shaped by oncogenic microRNAs and receptor–ligand interactions
Bibliographic record
Abstract
Embryonal tumor with multilayered rosettes (ETMR) is a pediatric brain tumor with dismal prognosis. Characteristic alterations of the chromosome 19 microRNA cluster (C19MC) are observed in most ETMR; however, the ramifications of C19MC activation and the complex cellular architecture of ETMR remain understudied. Here we analyze 11 ETMR samples from patients using single-cell transcriptomics and multiplexed spatial imaging. We reveal a spatially distinct cellular hierarchy that spans highly proliferative neural stem-like cells and more differentiated neuron-like cells. C19MC is predominantly expressed in stem-like cells and controls a transcriptional network governing stemness and lineage commitment, as resolved by genome-wide analysis of microRNA-mRNA binding. Systematic analysis of receptor–ligand interactions between malignant cell types reveals fibroblast growth factor receptor and Notch signaling as oncogenic pathways that can be successfully targeted in preclinical models and in one patient with ETMR. Our study provides fundamental insights into ETMR pathobiology and a powerful rationale for more effective targeted therapies. Beck et al. conducted single-cell and spatial profiling of embryonal tumors with multilayered rosettes, finding that malignant cellular hierarchies are driven by developmental programs and specific members of the chromosome 19 microRNA cluster.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".