MEDU-11. MOLECULAR CHARACTERIZATION OF ETMRs REVEALS A ROLE FOR R-LOOP MEDIATED CHROMOSOMAL INSTABILITY
Bibliographic record
Abstract
ETMRs are aggressive embryonal brain tumors with a very poor prognosis, having a median overall survival of ~12 months after diagnosis. ETMRs harbour in ~90% of all cases amplification of a miRNA cluster on chromosome 19 (C19MC) that is thought to be the driver of the disease. However, current treatment options are lacking as (a) the mechanisms downstream of C19MC are poorly understood and (b) the drivers in cases lacking the C19MC aberration are unknown. To investigate the genomic landscape of ETMR, we collected 186 ETMR samples and 23 matched relapses. Interestingly, among the 18 tumors without C19MC amplification, we identified five cases with truncating DICER1 germline mutations in one allele and somatic missense mutations in the RNASE III domain in the other allele. In addition, SVs affecting C19MC were found in three other cases and amplification of the miR-17–92 cluster in another two cases. However, despite the presence of different genetic aberrations, based on DNA methylation profiling no molecular subgrouping was observed within our cohort. Whole-genome sequencing revealed an overall low recurrence and conservation of SNVs but strong conservation of SVs from primary tumors to relapses especially surrounding C19MC. SVs detected in ETMRs significantly co-localized with R-loops, structures that form upon a collision of replication and transcription and are associated to increased levels of chromosomal instability, which is frequently observed in ETMRs. Using a DICER1 KO model we found that global deregulation of miRNAs led to increased levels of R-loops and R-loop associated chromosomal instability. Finally, we show that treating ETMR cells with topoisomerase and PARP inhibitors strongly increased the levels of both R-loops and DNA damage and effectively killed the cells. Our results show that genomically instable ETMR cells are vulnerable to further increases in chromosomal instability, knowledge that may lead to new treatment strategies in the future.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".