Molecular subgrouping of atypical teratoid/rhabdoid tumors—a reinvestigation and current consensus
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Atypical teratoid/rhabdoid tumors (ATRTs) are known to exhibit molecular and clinical heterogeneity even though SMARCB1 inactivation is the sole recurrent genetic event present in nearly all cases. Indeed, recent studies demonstrated 3 molecular subgroups of ATRTs that are genetically, epigenetically, and clinically distinct. As these studies included different numbers of tumors, various subgrouping techniques, and naming, an international working group sought to align previous findings and to reach a consensus on nomenclature and clinicopathological significance of ATRT subgroups. METHODS: We integrated various methods to perform a meta-analysis on published and unpublished DNA methylation and gene expression datasets of ATRTs and associated clinicopathological data. RESULTS: In concordance with previous studies, the analyses identified 3 main molecular subgroups of ATRTs, for which a consensus was reached to name them ATRT-TYR, ATRT-SHH, and ATRT-MYC. The ATRT-SHH subgroup exhibited further heterogeneity, segregating further into 2 subtypes associated with a predominant supratentorial (ATRT-SHH-1) or infratentorial (ATRT-SHH-2) location. For each ATRT subgroup we provide an overview of its main molecular and clinical characteristics, including SMARCB1 alterations and pathway activation. CONCLUSIONS: The introduction of a common classification, characterization, and nomenclature of ATRT subgroups will facilitate future research and serve as a common ground for subgrouping patient samples and ATRT models, which will aid in refining subgroup-based therapies for ATRT patients.
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.
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 | 0.001 | 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 it