Targeted RNA Sequencing Highlights a Diverse Genomic and Morphologic Landscape in Low-grade Endometrial Stromal Sarcoma, Including Novel Fusion Genes
Bibliographic record
Abstract
Low-grade endometrial stromal sarcoma (LGESS) represents a morphologically and genetically heterogenous mesenchymal neoplasm. Previous work has shown that approximately half of LGESS are characterized by JAZF1::SUZ12 gene fusions, while a smaller proportion involves rearrangement of other genes. However, a subset of cases has no known genetic abnormalities. To better characterize the genomic landscape of LGESS, we interrogated a cohort with targeted RNA sequencing (RNA-Seq). Cases previously diagnosed as low-grade endometrial stromal neoplasia (n=51) were identified and re-reviewed for morphology and subjected to RNA-Seq, of which 47 were successfully sequenced. The median patient age was 49 years (range: 19 to 85). The most commonly detected fusions were JAZF1::SUZ12 (n=26, 55%) and BRD8::PHF1 (n=3, 6%). In addition to the usual/typical LGESS morphology, some JAZF1::SUZ12 fusion tumors showed other morphologies, including fibrous, smooth muscle, sex-cord differentiation, and myxoid change. Novel translocations were identified in 2 cases: MEAF6::PTGR2 and HCFC1::PHF1 . Ten tumors (21%) had no identifiable fusion, despite a similar morphology and immunophenotype to fusion-positive cases. This suggests that a subset of cases may be attributable to fusion products among genes that are not covered by the assay, or perhaps altogether different molecular mechanisms. In all, these findings confirm that RNA-Seq is a potentially useful ancillary test in the diagnosis of endometrial stromal neoplasms and highlight their diverse morphology.
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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".