Abstract B46: DICER1 and FOXL2 mutations correlate with clinicopathologic features of ovarian Sertoli-Leydig cell tumors
Bibliographic record
Abstract
Abstract Sertoli-Leydig cell tumors are rare mixed ovarian sex cord-stromal neoplasms consisting of a mixture of Sertoli cells, Leydig cells, and occasionally heterologous elements or retiform differentiation. The only known recurrent genetic abnormality is mutations in DICER1, with rare mutations reported in FOXL2. We set out to determine the DICER1 and FOXL2 mutation status in a series of 51 SLCTs with the goal of establishing a molecular classifier using mutation status and clinicopathologic features. Five tumors were well differentiated, 39 moderately differentiated, and 7 poorly differentiated. Nine tumors had heterologous elements (8 epithelial, one mesenchymal), and four showed retiform differentiation; all of these were moderately differentiated. DICER1 mutations were identified in 16/45 of successfully genotyped tumors (36%, 15 moderately differentiated, 1 poorly differentiated), including 7 with heterologous elements and 3 of 4 with retiform differentiation. FOXL2 c.402C>G mutation was identified in 10/51 tumors (20%, 8 moderately differentiated, 2 poorly differentiated). DICER1 and FOXL2 mutations were mutually exclusive. Median age for the entire cohort was 44 years (range 15-90). Patients with DICER1 mutations were younger (median 23.5 years, range 15-62) than patients with neither DICER1 nor FOXL2 mutation (median 41.5 years, range 16-74). Tumors with FOXL2 mutation occurred exclusively in postmenopausal patients (median 78 years, range 54-90), whereas 11/13 tumors with either heterologous elements (median 26 years, range 16-62) or retiform differentiation (median 38 years, range 18-57) occurred in premenopausal patients. Our data suggest the existence of at least three molecular subtypes of SLCT: DICER1 mutant (younger, moderately/poorly differentiated, including heterologous elements or retiform differentiation), FOXL2 mutant (postmenopausal, moderately/poorly differentiated, no heterologous elements or retiform differentiation), and DICER1/FOXL2 wild-type (no specific age, no heterologous elements, including all well-differentiated tumors). These findings suggest that well-differentiated and moderately/poorly differentiated SLCTs may be different histomolecular entities with different etiologies. Citation Format: Anthony N. Karnezis, Yemin Wang, Jamie Magrill, Jacqueline Keul, Stefan Kommoss, Basile Tessier-Cloutier, Lily Proctor, Dietmar Schmidt, C Blake Gilks, David G. Huntsman, Friedrich Kommoss. DICER1 and FOXL2 mutations correlate with clinicopathologic features of ovarian Sertoli-Leydig cell tumors. [abstract]. In: Proceedings of the AACR Conference: Addressing Critical Questions in Ovarian Cancer Research and Treatment; Oct 1-4, 2017; Pittsburgh, PA. Philadelphia (PA): AACR; Clin Cancer Res 2018;24(15_Suppl):Abstract nr B46.
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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) | 0.003 | 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".