Variant <i>WWTR1</i> gene fusions in epithelioid hemangioendothelioma—A genetic subset associated with cardiac involvement
Bibliographic record
Abstract
The genetic hallmark of epithelioid hemangioendothelioma (EHE) is a recurrent WWTR1-CAMTA1 fusion, which is present in most cases bearing a conventional histology. A subset of cases is characterized by a distinct morphology and harbors instead of YAP1-TFE3 fusion. Nevertheless, isolated cases lack these canonical fusions and remain difficult to classify. Triggered by an index case of a left atrial mass in a 76-year-old female with morphologic features typical of EHE, but which showed a WWTR1-MAML2 fusion by targeted RNA sequencing, we searched our files for similar cases displaying alternative WWTR1 fusions. A total of 6 EHE cases were identified with variant WWTR1 fusions, four of them presenting within the heart. There were three females and three males, with a wide age range at diagnosis (21-76 years, mean 62, median 69). The four cardiac cases occurred in older adults (mean age of 72, equal gender distribution); three involved the left atrium and one the right ventricle. One case presented in the vertebral bone and one in pelvic soft tissue. Microscopically, all tumors had morphologic features within the spectrum of classic EHE; two of the cases appeared overtly malignant. All cases were tested by FISH and four were investigated by targeted RNA sequencing. Two tumors harbored WWTR1-MAML2 fusions, one WWTR1-ACTL6A, and in three cases, no WWTR1 partner was identified. Of the four patients with follow-up, two died of disease, one was alive with lung metastases, and the only patient free of disease was s/p resection of a T11 vertebral mass. Our findings report on additional genetic variants involving WWTR1 rearrangements, with WWTR1-MAML2 being a recurrent event, in a small subset of EHE, which appears to have predilection for the heart.
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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".