Novel <i>YAP1‐TFE3</i> fusion defines a distinct subset of epithelioid hemangioendothelioma
Bibliographic record
Abstract
Conventional epithelioid hemangioendotheliomas (EHE) have a distinctive morphologic appearance and are characterized by a recurrent t(1;3) translocation, resulting in a WWTR1-CAMTA1 fusion gene. We have recently encountered a fusion-negative subset characterized by a somewhat different morphology, including focally well-formed vasoformative features, which was further investigated for recurrent genetic abnormalities. Based on a case showing strong transcription factor E3 (TFE3) immunoreactivity, fluorescence in situ hybridization (FISH) analysis for TFE3 gene rearrangement was applied to the index case as well as to nine additional cases, selected through negative WWTR1-CAMTA1 screening. A control group, including 18 epithelioid hemangiomas, nine pseudomyogenic HE, and three epithelioid angiosarcomas, was also tested. TFE3 gene rearrangement was identified in 10 patients, with equal gender distribution and a mean age of 30 years old. The lesions were located in somatic soft tissue in six cases, lung in three and one in bone. One case with available frozen tissue was tested by RNA sequencing and FusionSeq data analysis to detect novel fusions. A YAP1-TFE3 fusion was thus detected, which was further validated by FISH and reverse transcription polymerase chain reaction (RT-PCR). YAP1 gene rearrangements were then confirmed in seven of the remaining nine TFE3-rearranged EHEs by FISH. No TFE3 structural abnormalities were detected in any of the controls. The TFE3-rearranged EHEs showed similar morphologic features with at least focally, well-formed vascular channels, in addition to a variably solid architecture. All tumors expressed endothelial markers, as well as strong nuclear TFE3. In summary, we are reporting a novel subset of EHE occurring in young adults, showing a distinct phenotype and YAP1-TFE3 fusions.
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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 | 0.000 | 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) | 0.001 | 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".