<i>EWSR1‐ATF1</i> fusion is a novel and consistent finding in hyalinizing clear‐cell carcinoma of salivary gland
Bibliographic record
Abstract
Hyalinizing clear-cell carcinoma (HCCC) is a rare, low-grade salivary gland tumor with distinctive clear-cell morphology and pattern of hyalinization as well as focal mucinous differentiation. However, histological overlap exists with other salivary gland tumors, such as epithelial-myoepithelial carcinoma (EMCa), salivary myoepithelial carcinoma, and mucoepidermoid carcinoma (MEC). The potential relationship between HCCC and its morphological mimics has not been yet investigated at the genetic level. In this study, we conducted a molecular analysis for the presence of rearrangements in MAML2, commonly seen in MECs, and EWSR1, involved in "soft tissue myoepithelial tumors" (SMET) by fusion with POU5F1, PBX1, or ZNF444. Fluorescence in situ hybridization (FISH) was performed on 23 HCCC cases for abnormalities in MAML2, EWSR1, FUS, POU5F1, PBX1, and ZNF444. FISH for MAML2 was negative in all cases (0 of 14), including those with mucinous differentiation (0 of 7). An EWSR1 rearrangement was identified in 18 of 22 HCCCs (82%), while no break-apart signals were seen in FUS, POU5F1, PBX1, or ZNF444. 3'RACE on an EWSR1 rearranged HCCC identified an EWSR1-ATF1 fusion, which was confirmed by RT-PCR. ATF1 involvement was further confirmed by FISH analysis in 13 of 14 EWSR1-rearranged HCCC cases (93%). In contrast, all control cases tested, including among others 5 EMCa and 3 MEC with clear cells, were negative for EWSR1 and ATF1 rearrangements. The presence of EWSR1-ATF1 fusion in most HCCCs reliably separates these tumors from its histological mimics. The distinction from MEC is particularly important, as conventional MEC grading schemes overgrade these indolent HCCCs, potentially impacting on treatment.
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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 | 0.000 |
| 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 | 0.001 |
| 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".