ATRT-40. IMPACT OF MOLECULAR SUBTYPES ON TREATMENT OUTCOMES IN RHABDOID TUMORS - A REPORT FROM THE RARE TUMOR CONSORTIUM
Bibliographic record
Abstract
Rhabdoid Tumors (RTs) are rare heritable cancers with bi-allelic SMARCB1/A4 alterations, arising in the brain (Atypical Teratoid/Rhabdoid Tumors/ATRTs) and in various extra-cranial locations (Malignant Rhabdoid Tumors/MRTs). Recent studies uncovered molecular sub-types of ATRTs and MRTs, however, the molecular relationship and clinico-pathologic implications of MRTs and ATRTs sub-types remain unclear. To inform clinical understanding of ATRTs and MRTs, 450/850K methylation array profiles generated from 202 ATRTs and 57 MRTs were used to define molecular categories of ATRTs/MRTs and examine their clinical significance. ATRTs segregated into 3 known molecular subgroups, group 1/SHH (n=87), group 2A/TYR (n=69), group 2B/MYC (n= 46); 13 and 44 MRTs respectively segregated with group 2A/TYR and 2B/MYC ATRTs. Median patient age was 19.1 months for group 1/SHH, 12.3 months for group 2A/TYR and 22.3 months for group 2B/MYC Rhabdoid tumors. Complete clinical information and treatment information available for 162 patients (n=125 ATRTs and n=37 MRTs), indicated only 65 patients received a combined chemo-radiation therapy. 24-month PFS was 30%,38% and 28% and the 24-month OS was 43%,48% and 43% for subgroups 1/SHH, 2A/TYR and 2B/MYC tumors respectively. MRT and ATRT comprise a biological spectrum with overlapping molecular features. Clinicopathologic analysis suggest ATRT/MRT molecular subtypes have different therapeutic response to chemo-radiotherapeutic regimens.
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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".