DICER1 and FOXL2 Mutation Status Correlates With Clinicopathologic Features in Ovarian Sertoli-Leydig Cell Tumors
Bibliographic record
Abstract
Sertoli-Leydig cell tumors (SLCTs) are rare ovarian sex cord-stromal neoplasms. The only known recurrent genetic abnormality is DICER1 mutation, with rare mutations reported in FOXL2. We set out to establish a molecular classifier using DICER1 and FOXL2 somatic mutation status and clinicopathologic features in 42 SLCTs. Five tumors (12%) were well differentiated, 31 (74%) moderately differentiated, and 6 (14%) poorly differentiated. Eight (19%) had heterologous elements, and 2 (5%) showed retiform differentiation; all 10 were moderately differentiated. DICER1 RNase IIIb domain mutations were identified in 18/41 (44%; 17 moderately, 1 poorly differentiated), including all cases with retiform or heterologous elements. FOXL2 c.402C>G (p.C134W) mutation was identified in 8/42 (19%) tumors (5 moderately, 3 poorly differentiated). DICER1 and FOXL2 mutations were mutually exclusive. Median age for the cohort was 47 years (range, 15 to 90 y). Patients with DICER1 mutations were younger (median, 24.5 y; range, 15 to 62 y) than patients with FOXL2 mutation (median, 79.5 y; range, 51 to 90 y) (P<0.0001). Nine of 10 tumors with retiform or heterologous elements occurred in premenopausal patients (median, 26.5 y; range, 15 to 57 y). Patients with tumors that were wild type for DICER1 and FOXL2 (15/42, 37%) had an intermediate age (median, 51 y; range, 17 to 74 y). All tumors were FOXL2 positive by immunohistochemistry. Patients with FOXL2 mutation trended toward presenting more often with abnormal bleeding (P=0.13); DICER1-mutant patients trended toward having more androgenic symptoms (P=0.22). Our data suggest at least 3 molecular subtypes of SLCT with distinct clinicopathologic features: DICER1 mutant (younger, more androgenic symptoms, moderately/poorly differentiated, retiform or heterologous elements), FOXL2 mutant (postmenopausal, abnormal bleeding, moderately/poorly differentiated, no retiform or heterologous elements), and DICER1/FOXL2 wild type (intermediate age, no retiform or heterologous elements, including all well-differentiated tumors).
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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 | 0.001 | 0.002 |
| 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".