ATRT-10. SINGLE-CELL TRANSCRIPTOME ANALYSIS REVEALS CELLULAR HIERARCHIES AND THERAPEUTIC VULNERABILITIES OF ETMR
Bibliographic record
Abstract
Abstract Embryonal tumors with multilayered rosettes (ETMR) are malignant brain tumors that occur predominantly in infants and young children. 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 to identify novel therapeutic targets, we set out to study the cellular heterogeneity of ETMR using single-cell RNA sequencing. Analyses conducted on >4,000 high-quality cells collected from eleven primary and relapse specimens revealed a common cellular hierarchy across all tumors: A highly proliferative neural stem cell-like population (SOX2+) that gives rise to intermediate progenitors (NEUROD1/NEUROG1+) and more differentiated neuron-like cells (STMN2/4+). These malignant cell populations closely overlap with histological patterns of ETMRs, as confirmed by multiplexed immunofluorescence microscopy on patients’ tumors. Comparison to single-cell datasets from human fetuses indicated high resemblance to normal cortical neurogenesis but also revealed key tumor-specific differences. These include expression of the chromosome 19 miRNA cluster (C19MC, the presumed driver in ~90% of ETMRs), which was restricted to the malignant stem cell-like population. Investigating oncogenic mechanisms of C19MC (comprising 46 miRNA genes) through transcriptome-wide RNA immunoprecipitation analysis, we identified extensive target gene regulation for most C19MC members, including distinct regulators of cell cycle, pluripotency, and neuronal differentiation. Silencing of C19MC families with antisense oligonucleotides resulted in pronounced reduction of ETMR cell line growth, indicating potential avenues for therapeutic targeting in the future. To identify more immediately actionable targets, we investigated inter-cellular signaling between malignant cell populations of ETMRs. Interestingly, we identified marked FGFR and NOTCH receptor-ligand interactions common to all tumors. An in vitro screen of experimental and approved small molecule inhibitors designed to target these interactions nominated several promising candidates for clinical evaluation. Our unpublished results provide much needed insight into targeting ETMR cellular states using multiple modes of action.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".