Atypical teratoid/rhabdoid tumors (ATRTs) with SMARCA4 mutation are molecularly distinct from SMARCB1-deficient cases
Bibliographic record
Abstract
Atypical teratoid/rhabdoid tumors (ATRTs) are very aggressive childhood malignancies of the central nervous system. The underlying genetic cause are inactivating bi-allelic mutations in SMARCB1 or (rarely) in SMARCA4. ATRT-SMARCA4 have been associated with a higher frequency of germline mutations, younger age, and an inferior prognosis in comparison to SMARCB1 mutated cases. Based on their DNA methylation profiles and transcriptomics, SMARCB1 mutated ATRTs have been divided into three distinct molecular subgroups: ATRT-TYR, ATRT-SHH, and ATRT-MYC. These subgroups differ in terms of age at diagnosis, tumor location, type of SMARCB1 alterations, and overall survival. ATRT-SMARCA4 are, however, less well understood, and it remains unknown, whether they belong to one of the described ATRT subgroups. Here, we examined 14 ATRT-SMARCA4 by global DNA methylation analyses. We show that they form a separate group segregating from SMARCB1 mutated ATRTs and from other SMARCA4-deficient tumors like small cell carcinoma of the ovary, hypercalcemic type (SCCOHT) or SMARCA4 mutated extra-cranial malignant rhabdoid tumors. In contrast, medulloblastoma (MB) samples with heterozygous SMARCA4 mutations do not group separately, but with established MB subgroups. RNA sequencing of ATRT-SMARCA4 confirmed the clustering results based on DNA methylation profiling and displayed an absence of typical signature genes upregulated in SMARCB1 deleted ATRT. In summary, our results suggest that, in line with previous clinical observations, ATRT-SMARCA4 should be regarded as a distinct molecular subgroup.
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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) | 0.000 | 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) | 0.000 | 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".