ATRT-09. INTEGRATIVE ANALYSES OF GENE REGULATORY LANDSCAPES REVEAL RHABDOID TUMOR SUBGROUPS WITH POSSIBLE IMMUNE MODULATION THROUGH EPIGENETIC DYSREGULATION
Bibliographic record
Abstract
Rhabdoid tumors (RTs) are frequent pediatric malignancies that are classified based on tumor localization: In the central nervous system, they are referred to as atypical teratoid/rhabdoid tumors (ATRTs) as compared to extra-cranial malignant RTs (MRTs). The common genetic hallmark of these tumors is the loss of SMARCB1 in 95% of all cases. Molecularly, RTs are heterogeneous. Previous studies reported molecular subgroups within RTs of the same anatomic compartment. However, biological similarities among RT subgroups of different sites, and molecular characteristics shared among them remained unknown. Investigating the similarity of RTs from different anatomic sites at a molecular level, we compared DNA methylation, gene expression, and H3K27ac profiles of 150 MRTs and 161 ATRTs generated by WGBS, 450K/850K-arrays, RNA-, and ChIP-Seq. Clustering of methylation data showed that a subset of MRTs (called Group1) clustered with the MYC-subgroup of ATRTs but not with SHH-and TYR-subgroups of ATRTs. Over-expression of MYC-ATRT representative genes, such as c-MYC and the non-coding regulatory RNA gene HOTAIR, was also observed in these MRTs. Transcription factor binding enrichment analyses displayed a high enrichment of transcription factors implicated in immune cell regulation such as GMEMB1/2, IRF5/8/9, and STAT1 in both ATRT-MYC and MRT. We therefore predicted the immune cell infiltration of RTs using the CIBERSORT and validated these in-silico analyses by immunohistochemistry (IHC) for CD3,CD8, CD68 and PD-L1. In line with the computational results, IHC demonstrated a high number of CD3+ and CD8+ in both MRT and ATRT-MYC, suggestive of abundant cytotoxic T-cell infiltration. Overall our analyses reveal a high degree of similarity between ATRT-MYC and (Group 1)- MRT not only at the epigenetic level, but also in the immune cell composition and underline the suitability of these tumors for a potential immunotherapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".