EPCO-27. SINGLE-CELL ANALYSIS OF ETMR PATIENT SAMPLES LINKS TRANSCRIPTIONAL PHENOTYPES TO GENETIC DRIVER ALTERATIONS AND INFORMS NOVEL THERAPEUTIC STRATEGIES
Bibliographic record
Abstract
Abstract Embryonal tumor with multilayered rosettes (ETMR) is a malignant brain tumor that typically occurs in children under the age of three. Most patients die within two years of diagnosis, and more effective, targeted therapies are urgently needed. To better characterize the oncogenic mechanisms of key driver alterations and identify novel therapeutic targets, we studied the cellular heterogeneity of ETMR using single-cell RNA sequencing. Analyses conducted on >3,000 high-quality cells collected from ten primary and relapse specimens revealed a common cellular hierarchy across all tumors: A highly proliferative neural stem cell-like population (SOX2+) gives rise to intermediate progenitors (ASCL1+) and more differentiated neuron-like cells (STMN2/4+). These malignant populations closely match histological patterns of ETMR (i.e. rosettes, neuropil), as observed by immunofluorescence microscopy. Comparison to single-cell datasets from human embryos indicates resemblance to cell populations of the developing brain, but also reveals key ETMR-specific differences, including expression of the chromosome 19 miRNA cluster (C19MC, the presumed genetic driver of most ETMRs), which is restricted to the stem cell-like population. We next investigated if targeting C19MC is a viable strategy to disrupt the cellular hierarchy of ETMR. Silencing with antisense oligonucleotides shows pronounced reduction of cell line growth for a specific subset of the 46 members of C19MC. These miRNAs share seed sequences with evolutionary conserved miRNAs that have been shown to regulate pluripotency and self-renewal of embryonic stem cells. We hypothesize that select C19MC members play similar roles in ETMR and represent bona fide targets for therapeutic targeting using antisense technology.
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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) | 0.005 | 0.002 |
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".