ETMR-17. SINGLE-CELL TRANSCRIPTOME ANALYSIS OF ETMR PATIENT SAMPLES
Bibliographic record
Abstract
Abstract Brain tumors are comprised of cells with heterogeneous genetic and transcriptional states, resulting in substantial phenotypic diversity. This diversity is particularly evident in embryonal tumor with multilayered rosettes (ETMR), which shows a striking bi-phasic pattern for which it is named. A better understanding of its underlying molecular makeup is urgently needed to develop more effective therapeutic strategies that eliminate all malignant cell types underlying ETMR initiation, maintenance, progression, and relapse. Furthermore, the cellular origin of ETMR is currently poorly understood. We used plate-based single-cell RNA sequencing to assess the intratumoral heterogeneity in 6 fresh and 4 snap-frozen surgical biopsies, following a workflow that we have previously established to study pediatric high grade gliomas, medulloblastomas, and ependymomas. Computational analyses conducted on >4,000 single cells identified cellular hierarchies ranging from a proliferative, undifferentiated cell population to more differentiated, predominantly neural-like progeny in all samples. Patient-derived cell line and xenograft models partially recapitulated this hierarchy. We further integrated transcriptional programs identified in single cells with available datasets of the developing normal brain, as well as with programs identified in other pediatric brain tumor entities, to inform both putative cellular origins and ETMR-specific oncogenic pathways. These timely results provide unparalleled insights into the molecular underpinnings of the phenotypic heterogeneity observed in ETMR. Analyses aimed at further integrating malignant cell type abundances with genetic alterations and clinical annotations, and therapeutical targeting of malignant cell populations using in-vitro models are currently ongoing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".