The application of next‐generation sequencing‐based molecular diagnostics in endometrial stromal sarcoma
Bibliographic record
Abstract
AIMS: Endometrial stromal sarcomas (ESSs) are divided into low-grade and high-grade subtypes, with the latter showing more aggressive clinical behaviour. Although histology and immunophenotype can aid in the diagnosis of these tumours, genetic studies can provide additional diagnostic insights, as low-grade ESSs frequently harbour fusions involving JAZF1/SUZ12 and/or JAZF1/PHF1, whereas high-grade ESSs are defined by YWHAE-NUTM2A/B fusions. The aim of this study was to evaluate the utility of a next-generation sequencing (NGS)-based assay in identifying ESS fusions in archival formalin-fixed paraffin-embedded tumour samples. METHODS AND RESULTS: We applied an NGS-based fusion transcript detection assay (Archer FusionPlex Sarcoma Panel) that targets YWHAE and JAZF1 fusions in a series of low-grade ESSs (n = 11) and high-grade ESSs (n = 5) that were previously confirmed to harbour genetic rearrangements by fluorescence in-situ hybridization (FISH) and/or reverse transcription polymerase chain reaction (RT-PCR) analyses. The fusion assay identified junctional fusion transcript sequences that corresponded to the known FISH/RT-PCR results in all cases. Four low-grade ESSs harboured JAZF1-PHF1 fusions with different junctional sequences, and all were correctly identified because of the open-ended nature of the assay design, using anchored multiplex polymerase chain reaction. Seven non-ESS sarcomas were also included as negative controls, and no strong ESS fusion candidates were identified in these cases. CONCLUSIONS: Our findings demonstrate good sensitivity and specificity of an NGS-based gene fusion assay in the detection of ESS fusion transcripts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".