Recurrent YAP1 and KMT2A Gene Rearrangements in a Subset of MUC4-negative Sclerosing Epithelioid Fibrosarcoma
Bibliographic record
Abstract
Sclerosing epithelioid fibrosarcoma (SEF) is an aggressive soft tissue sarcoma, characterized by a distinctive epithelioid phenotype in a densely sclerotic collagenous stroma, that shows frequent MUC4 immunoreactivity and recurrent gene fusions, often involving EWSR1 gene. A pathogenetic link with low-grade fibromyxoid sarcoma (LGFMS) has been suggested, due to cases with hybrid morphology as well as overlapping genetic signature. However, a small subset of SEF is negative for MUC4 and lacks the canonical EWSR1/FUS gene rearrangements. Triggered by the identification of recurrent YAP1-KMT2A gene fusions by RNA sequencing in 3 index cases of MUC4-negative, EWSR1/FUS fusion-negative SEF, we further investigated a cohort of 14 similar SEF cases (MUC4-negative, EWSR1/FUS fusion-negative) by fluorescence in situ hybridization (FISH), reverse transcription-polymerase chain reaction, and/or DNA-based massively parallel sequencing (MSK-IMPACT) for abnormalities in these genes. Three additional SEFs with KMT2A gene rearrangements and one additional case with YAP1 gene rearrangements were identified by FISH. In addition, one case with YAP1-KMT2A and one with KMT2A-YAP1 fusion were detected by reverse transcription-polymerase chain reaction and MSK-IMPACT, respectively. As a control group, 24 fibromyxoid spindle cell tumors, diagnosed or suspected as fusion-negative LGFMS, were also tested for YAP1 and KMT2A abnormalities by FISH, but none were positive. The YAP1/KMT2A-rearranged SEF group affected patients ranging from 10 to 86 years old (average and median: 45) of both sexes (4 females, 5 males). The tumors involved somatic soft tissues with a wide distribution, including extremities, trunk, neck, and dura. Histologically, the tumors showed variable cellularity, with monotonous ovoid to epithelioid tumor cells and hyalinized collagenous background typical of SEF. More than half of the cases showed infiltrative borders, within fat or skeletal muscle. No LGFMS component was identified. All tumors were negative for MUC4 and had an otherwise nonspecific immunophenotype. Of the 6 cases with available follow-up information, 2 had local recurrences, and 2 developed soft tissue and/or bone metastases, including 1 of them died of the disease.
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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".