DICER1 Mutations Are Consistently Present in Moderately and Poorly Differentiated Sertoli-Leydig Cell Tumors
Bibliographic record
Abstract
Ovarian Sertoli-Leydig cell tumors (SLCTs) are uncommon sex cord-stromal tumors associated with both germ-line and somatic DICER1 mutations, the frequency of which has varied widely in different studies (0% to 62.5%). The current World Health Organization Classification includes 3 histologic types of SLCTs (well-differentiated, moderately differentiated, and poorly differentiated); heterologous elements and/or retiform patterns may be present in moderately and poorly differentiated neoplasms. We investigated the frequency of DICER1 mutations in a series of 38 ovarian tumors initially diagnosed as SLCTs, and explored whether identified mutations were associated with specific morphologic features. Specialist pathology review performed blinded to molecular results confirmed 34 tumors to be SLCTs (22 moderately differentiated, 8 poorly differentiated; 4 well-differentiated), while the remaining 4 neoplasms were considered not to represent SLCTs. Of the 34 cases diagnosed as SLCTs, 30 (88%) harbored ≥1 DICER1 mutation. All 30 moderately differentiated/poorly differentiated SLCTs contained mutations, but we did not find deleterious DICER1 mutations in the 4 well-differentiated SLCTs. Our study reports the highest DICER1 mutation frequency to date in SLCTs, with 100% of moderately differentiated and poorly differentiated tumors being DICER1-mutated. This suggests that DICER1 mutation may be a defining feature of these neoplasms. Although the number of cases is limited, well-differentiated SLCTs appear to be DICER1-independent. Moderately differentiated and poorly differentiated SLCT components often coexist with each other and form part of a spectrum, while well-differentiated SLCTs usually occur in pure form, suggesting that fundamentally, these represent 2 separate and independent tumor types with a different pathogenesis. We suggest that all patients with ovarian SLCTs undergo germ-line DICER1 mutation testing.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".