AT-21INTEGRATED (EPI)GENOMIC ANALYSES IDENTIFY SUB-GROUP SPECIFIC THERAPEUTIC TARGETS IN CNS RHABDOID TUMORS
Bibliographic record
Abstract
Atypical Teratoid Rhabdoid Tumors (ATRTs) are the most common malignant embryonal brain tumors arising in younger children that are distinctly lethal cancers for which effective therapies are lacking. In this study, we integrated whole genome/exome/transcriptome sequencing as well as methylation and nucleosomal profiling analyses to comprehensively define the genomic and epigenomic landscape of ATRT sub-groups and identify sub-group specific therapeutic targets. Integration of multiplatform genomic analyses revealed novel recurrent genetic alterations in upto 20% of ATRTs in genes with functions in neural development and epigenetic regulation. Global methylation and gene expression analyses of primary tumors indicated segregation of ATRTs into three epigenetic sub-groups (group 1, 2A and 2B) that correlated with distinct lineage and clinical features. Group 1 ATRT exhibited enrichment of neurogenic/NOTCH signaling loci and were predominantly supra-tentorial tumors with a median age of 24 months. Group 2A tumors were predominantly infra-tentorial locations in the youngest patients, while group 2B tumors were characteristically spinal in location. BMP signaling and mesenchymal differentiation genes were commonly enriched in group 2A and 2B tumors. ATAC-seq analyses revealed a chromatin landscape associated with each ATRT sub-group, that correlated with sub-group specific therapeutic response in ATRT cell lines to a panel of signaling (NOTCH, BMP, Dasatinib) and epigenetic (EZH2, G9a, BRD4) inhibitors. Significantly, we discovered that differential methylation of a novel, PDGFRβ associated enhancer element confers robust sensitivity to tyrosine kinase inhibitors Dasatinib and Nilotinib in group 2 ATRTs, and suggest these as novel agents for this highly lethal ATRT sub-type.
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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) | 0.002 | 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".