Cribriform neuroepithelial tumor: molecular characterization of a SMARCB1‐deficient non‐rhabdoid tumor with favorable long‐term outcome
Bibliographic record
Abstract
Rhabdoid phenotype and loss of SMARCB1 expression in a brain tumor are characteristic features of atypical teratoid/rhabdoid tumors (ATRT). Rare non-rhabdoid brain tumors showing cribriform growth pattern and SMARCB1 loss have been designated cribriform neuroepithelial tumor (CRINET). Small case series suggest that CRINETs may have a relatively favorable prognosis. However, the long-term outcome is unclear and it remains uncertain whether CRINET represents a distinct entity or a variant of ATRT. Therefore, 10 CRINETs were clinically and molecularly characterized and compared with 10 ATRTs of each of three recently described molecular subgroups (i.e. ATRT-TYR, ATRT-SHH and ATRT-MYC) using Illumina Infinium HumanMethylation450 arrays, FISH, MLPA, and sequencing. Furthermore, outcome was compared to a larger cohort of 27 children with ATRT-TYR. Median age of the 6 boys and 4 girls harboring a CRINET was 20 months. On histopathological examination, all CRINETs demonstrated a cribriform growth pattern and distinct tyrosinase staining. On unsupervised cluster analysis of methylation data, all CRINETs examined exclusively clustered within the ATRT-TYR molecular subgroup. As ATRT-TYR, CRINETs mainly showed large heterozygous 22q deletions (9/10) and SMARCB1 mutations of the other allele. In two patients, SMARCB1 mutations were also present in the germline. Estimated mean overall survival in patients with CRINETs was 125 months (95% confidence interval 100-151 months) as compared to only 53 (33-74) months in patients with ATRTs of the ATRT-TYR subgroup (Log-Rank P < 0.05). In conclusion, CRINET represents a SMARCB1-deficient non-rhabdoid tumor, which shares molecular similarities with the ATRT-TYR subgroup but has distinct histopathological features and favorable long-term outcome.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".