The Oncogenic FOXL2 C134W Mutation Is a Key Driver of Granulosa Cell Tumors

Role of ERβ in the ovary and ovary related diseases

Estrogen and estrogen receptors (ERα and ERβ) regulate a multitude of complicated physiological and pathological processes. Jan-Ake Gustafsson's group discovered ERβ in 1996, this crucial finding gives us new insights into the understanding of estrogen signaling. ERβ is highly expressed in the ovary and particularly exists in granulosa cells (GCs). ERβ is a key transcription factor in the maintenance of ovarian granulosa cell growth, differentiation, and homeostasis, and the ovulation function of ovarian follicles and oocytes. Additionally, ERβ can modulate the steroidogenic transcriptional program through phosphorylation and regulate both gonadotropin response and FOXL2 expression within the ovary. In this review, we focus on the role of ERβ in regulating ovarian granulosa cell development and homeostasis, particularly its significance in ovarian cancer (OC), premature ovarian failure (POF), and polycystic ovary syndrome (PCOS). It also highlights the prospects of small molecule compounds targeting ERβ, providing a new strategy for the treatment of ovarian-related diseases.

Deep-Learning Model for Tumor-Type Prediction Using Targeted Clinical Genomic Sequencing Data

Tumor type guides clinical treatment decisions in cancer, but histology-based diagnosis remains challenging. Genomic alterations are highly diagnostic of tumor type, and tumor-type classifiers trained on genomic features have been explored, but the most accurate methods are not clinically feasible, relying on features derived from whole-genome sequencing (WGS), or predicting across limited cancer types. We use genomic features from a data set of 39,787 solid tumors sequenced using a clinically targeted cancer gene panel to develop Genome-Derived-Diagnosis Ensemble (GDD-ENS): a hyperparameter ensemble for classifying tumor type using deep neural networks. GDD-ENS achieves 93% accuracy for high-confidence predictions across 38 cancer types, rivaling the performance of WGS-based methods. GDD-ENS can also guide diagnoses of rare type and cancers of unknown primary and incorporate patient-specific clinical information for improved predictions. Overall, integrating GDD-ENS into prospective clinical sequencing workflows could provide clinically relevant tumor-type predictions to guide treatment decisions in real time.

SIGNIFICANCE

We describe a highly accurate tumor-type prediction model, designed specifically for clinical implementation. Our model relies only on widely used cancer gene panel sequencing data, predicts across 38 distinct cancer types, and supports integration of patient-specific nongenomic information for enhanced decision support in challenging diagnostic situations. See related commentary by Garg, p. 906. This article is featured in Selected Articles from This Issue, p. 897.

A cellular model provides insights into the pathogenicity of the oncogenic FOXL2 somatic variant p.Cys134Trp

BACKGROUND

FOXL2 is a transcription factor expressed in ovarian granulosa cells. A somatic variant of FOXL2 (c.402 C > G, p.Cys134Trp) is the hallmark of adult-type granulosa cell tumours.

METHODS

We generated KGN cell clones either heterozygous for this variant (MUT) or homozygous for the wild-type (WT) allele by CRISPR/Cas9 editing. They underwent RNA-Seq and bioinformatics analyses to uncover pathways impacted by deregulated genes. Cell morphology and migration were studied.

RESULTS

The differentially expressed genes (DEGs) between WT/MUT and WT/WT KGN cells (DEGs-WT/MUT), pointed to several dysregulated pathways, like TGF-beta pathway, cell adhesion and migration. Consistently, WT/MUT cells were rounder than WT/WT cells and displayed a different distribution of stress fibres and paxillin staining. A comparison of the DEGs-WT/MUT with those found when FOXL2 was knocked down (KD) in WT/WT KGN cells showed that most DEGs-WT/MUT cells were not so in the KD experiment, supporting a gain-of-function (GOF) scenario. MUT-FOXL2 also displayed a stronger interaction with SMAD3.

CONCLUSIONS

Our work, aiming at better understanding the GOF scenario, shows that the dysregulated genes and pathways are consistent with this idea. Besides, we propose that GOF might result from an enhanced interaction with SMAD3 that could underlie an ectopic capacity of mutated FOXL2 to bind SMAD4.

Relevance of Molecular Pathology for the Diagnosis of Sex Cord-Stromal Tumors of the Ovary: A Narrative Review

Ovarian sex cord-stromal tumors (SCSTs) account for 8% of all primary ovarian neo-plasms. Accurate diagnosis is crucial since each subtype has a specific prognostic and treatment. Apart from fibrosarcomas, stromal tumors are benign while sex cord tumors may recur, sometimes with a significant time to relapse. Although the diagnosis based on morphology is straightforward, in some cases the distinction between stromal tumors and sex cord tumors may be tricky. Indeed, the immunophenotype is usually nonspecific between stromal tumors and sex cord tumors. Therefore, molecular pathology plays an important role in the diagnosis of such entities, with pathognomonic or recurrent alterations, such as FOXL2 variants in adult granulosa cell tumors. In addition, these neoplasms may be associated with genetic syndromes, such as Peutz-Jeghers syndrome for sex cord tumors with annular tubules, and DICER1 syndrome for Sertoli-Leydig cell tumors (SLCTs), for which the pathologist may be in the front line of syndromic suspicion. Molecular pathology of SCST is also relevant for patient prognosis and management. For instance, the DICER1 variant is associated with moderately to poorly differentiated SLCTS and a poorer prognosis. The present review summarizes the histomolecular criteria useful for the diagnosis of SCST, using recent molecular data from the literature.

Sertoli Cell-Specific Activation of Transforming Growth Factor Beta Receptor 1 Leads to Testicular Granulosa Cell Tumor Formation

The transforming growth factor β (TGFβ) superfamily, consisting of protein ligands, receptors, and intracellular SMAD transducers, regulates fundamental biological processes and cancer development. Our previous study has shown that sustained activation of TGFβ receptor 1 (TGFBR1) driven by anti-Mullerian hormone receptor type 2 (Amhr2)-Cre in the mouse testis induces the formation of testicular granulosa cell tumors (TGCTs). As Amhr2-Cre is expressed in both Sertoli cells and Leydig cells, it remains unclear whether the activation of TGFBR1 in Sertoli cells alone is sufficient to induce TGCT formation. Therefore, the objective of this study was to determine whether Sertoli cell-activation of TGFBR1 drives oncogenesis in the testis. Our hypothesis was that overactivation of TGFBR1 in Sertoli cells would promote their transdifferentiation into granulosa-like cells and the formation of TGCTs. To test this hypothesis, we generated mice harboring constitutive activation of TGFBR1 in Sertoli cells using anti-Mullerian hormone (Amh)-Cre. Disorganized seminiferous tubules and tumor nodules were found in TGFBR1CA; Amh-Cre mice. A histological analysis showed that Sertoli cell-specific activation of TGFBR1 led to the development of neoplasms resembling granulosa cell tumors, which derailed spermatogenesis. Moreover, TGCTs expressed granulosa cell markers including FOXL2, FOXO1, and INHA. Using a dual fluorescence reporter line, the membrane-targeted tdTomato (mT)/membrane-targeted EGFP (mG) mouse, we provided evidence that Sertoli cells transdifferentiated toward a granulosa cell fate during tumorigenesis. Thus, our findings indicate that Sertoli cell-specific activation of TGFBR1 leads to the formation of TGCTs, supporting a key contribution of Sertoli cell reprogramming to the development of this testicular malignancy in our model.

Developments in Genetics: Better Management of Ovarian Cancer Patients

The purpose of this article is to highlight the new advancements in molecular and diagnostic genetic testing and to properly classify all ovarian cancers. In this article, we address statistics, histopathological classification, molecular pathways implicated in ovarian cancer, genetic screening panels, details about the genes, and also candidate genes. We hope to bring new information to the medical field so as to better prevent and diagnose ovarian cancer.