PGR Gene Fusions Identify a Molecular Subset of Uterine Epithelioid Leiomyosarcoma With Rhabdoid Features
Bibliographic record
Abstract
Genetic aberrations among uterine epithelioid leiomyosarcomas are unknown. Following identification of an index case with NR4A3-PGR fusion demonstrating monomorphic morphologic features, we interrogated additional uterine tumors demonstrating similar histology and sought to describe the morphologic and immunohistochemical characteristics of PGR-rearranged sarcomas. Targeted next-generation RNA sequencing was performed on RNA extracted from formalin-fixed paraffin-embedded tissue of the index case. Fluorescence in situ hybridization using custom probes flanking PGR and NR4A3 genes was applied to 17 epithelioid leiomyosarcomas, 6 endometrial stromal tumors, and 3 perivascular epithelioid cell tumors. NR4A3-PGR fusion (n=4) and PGR rearrangement (n=2) were detected in 6 (35%) epithelioid leiomyosarcomas. Median patient age was 45 years, and all presented with FIGO stage I or II tumors, 2 being alive with disease at 75 and 180 months. All tumors were centered in the cervical stroma or myometrium and consisted of cells with abundant eosinophilic cytoplasm (epithelioid), including many displaying dense intracytoplasmic inclusions (rhabdoid). Myxoid matrix and hydropic change imparted a microcystic growth pattern in 4 tumors. Five also showed a minor spindle cell component which was low-grade in 3, consisting of bland spindle cells with low mitotic activity. High-grade spindle cell morphology was seen in 2 tumors, exhibiting a storiform pattern of atypical spindle cells associated with brisk mitotic activity. Desmin, estrogen receptor, and progesterone receptor were positive in all 6 tumors, while CD10 and HMB45 were negative. PGR rearrangements define a genetic subset of epithelioid leiomyosarcomas with often biphasic morphology consisting of epithelioid and rhabdoid as well as spindle cell components.
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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 | 0.000 | 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".