<scp>SWI</scp>/<scp>SNF</scp>‐deficient undifferentiated malignancies: where to draw the line<sup>†</sup>
Bibliographic record
Abstract
Alterations in chromatin remodelling genes are increasingly recognised as drivers of undifferentiated malignancies. In atypical teratoid/rhabdoid tumours (ATRTs) and extracranial rhabdoid tumours (ECRTs), inactivation of SMARCB1 underlies >95% of cases. In the remainder, the culprit is another SWI/SNF family member, SMARCA4. By contrast, in small cell carcinoma of the ovary hypercalcaemic type (SCCOHT), SMARCA4 deficiency is by far the most common driver mechanism, while SMARCB1 alterations are rarely seen. It is unclear why alterations are so heavily weighted towards one or another subunit based on site alone, but both have become essential markers for the diagnosis and management of these undifferentiated lesions. Core SMARCA4-deficient undifferentiated malignancies share an aggressive clinical course and show an overlapping morphologic phenotype. In their study, Andrianteranagna, Cyrta and colleagues used DNA methylation and gene expression profiling to compare two subsets of SMARCA4-deficient malignancies diagnosed as SCCOHT and ECRT. Their work gives further insight into the subtle molecular spectrum of SMARCA4-deficient tumours, and their distinction from ATRT and ECRT with SMARCB1 inactivation. The characterisation of these molecular features is likely to play an important role in the future as we try to establish a clinically meaningful framework for the diagnosis and management of these lesions. © 2021 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".