Mosaic RASopathies concept: different skin lesions, same systemic manifestations?

BACKGROUND: Cutaneous epidermal nevi are genotypically diverse mosaic disorders. Pathogenic hotspot variants in HRAS, KRAS, and less frequently, NRAS and BRAF may cause isolated keratinocytic epidermal nevi and sebaceous nevi or several different syndromes when associated with extracutaneous anomalies. Therefore, some authors suggest the concept of mosaic RASopathies to group these different disorders. METHODS: In this paper, we describe three new cases of syndromic epidermal nevi caused by mosaic HRAS variants: one associating an extensive keratinocytic epidermal nevus with hypomastia, another with extensive mucosal involvement and a third combining a small sebaceous nevus with seizures and intellectual deficiency. Moreover, we performed extensive literature of all cases of syndromic epidermal nevi and related disorders with confirmed pathogenic postzygotic variants in HRAS, KRAS, NRAS or BRAF. RESULTS: Most patients presented with bone, ophthalmological or neurological anomalies. Rhabdomyosarcoma, urothelial cell carcinoma and pubertas praecox are also repeatedly reported. KRAS pathogenic variants are involved in 50% of the cases, especially in sebaceous nevi, oculoectodermal syndrome and encephalocraniocutaneous lipomatosis. They are frequently associated with eye and brain anomalies. Pathogenic variants in HRAS are rather present in syndromic keratinocytic epidermal nevi and phacomatosis pigmentokeratotica. CONCLUSION: This review delineates genotype/phenotype correlations of syndromic epidermal nevi with somatic RAS and BRAF pathogenic variants and may help improve their follow-up.

Next generation phenotyping for diagnosis and phenotype-genotype correlations in Kabuki syndrome.

The field of dysmorphology has been changed by the use Artificial Intelligence (AI) and the development of Next Generation Phenotyping (NGP). The aim of this study was to propose a new NGP model for predicting KS (Kabuki Syndrome) on 2D facial photographs and distinguish KS1 (KS type 1, KMT2D-related) from KS2 (KS type 2, KDM6A-related). We included retrospectively and prospectively, from 1998 to 2023, all frontal and lateral pictures of patients with a molecular confirmation of KS. After automatic preprocessing, we extracted geometric and textural features. After incorporation of age, gender, and ethnicity, we used XGboost (eXtreme Gradient Boosting), a supervised machine learning classifier. The model was tested on an independent validation set. Finally, we compared the performances of our model with DeepGestalt (Face2Gene). The study included 1448 frontal and lateral facial photographs from 6 centers, corresponding to 634 patients (527 controls, 107 KS); 82 (78%) of KS patients had a variation in the KMT2D gene (KS1) and 23 (22%) in the KDM6A gene (KS2). We were able to distinguish KS from controls in the independent validation group with an accuracy of 95.8% (78.9-99.9%, p < 0.001) and distinguish KS1 from KS2 with an empirical Area Under the Curve (AUC) of 0.805 (0.729-0.880, p < 0.001). We report an automatic detection model for KS with high performances (AUC 0.993 and accuracy 95.8%). We were able to distinguish patients with KS1 from KS2, with an AUC of 0.805. These results outperform the current commercial AI-based solutions and expert clinicians.