Not only Dravet Syndrome – How broad the phenotypic spectrum of SCN1A mutations may be?

Abstract

Objective: Mutations in the SCN1A gene are the well-recognised cause of the early infantile epileptic encephalopathy – Dravet Syndrome. They are also identified in GEFS+ and rarely in other syndromes. The aim of our analysis was characterization of the epileptic syndromes caused by SCN1A mutations identified among Polish patients, to whom analysis of this gene was performed. Methods: The group under analysis consisted of 89 probands, 61% of patients with identified mutation in SCN1A gene, to whom detailed clinical data were obtained. Sanger sequencing of all SCN1A exons and exon/intron boundaries as well the target NGS, with custom designed panel of 49 EIEEs genes were used. The SCN1A deletion analysis was performed with MLPA method in patients without identified pathogenic mutation or mutation of ambiguous pathogenicity. Syndromes characterization/nomenclature was according to the guidelines of Commission on Classification and Terminology of ILAE. Results: Variants, of the SCN1A gene, identified in probands encompass all type of mutations; truncation (nonsense, frameshift), splicing, missense and gene deletions. The patients presented broad phenotypic spectrum. Identified phenotypes included Dravet Syndrome (61) and Dravet Syndrome–Borderline (14) but also GEFS+ syndrome (8), Panayotopoulous Syndrome (2), Lennox-Gastaut Syndrome (1), Migrating Partial Seizures of Infancy (1). In one case neonatal seizures evolved in DS. In two cases identified as atypical West Syndrome and Epilepsy-intellectual disability in females, the SCN1A variants were identified as additional factors to mutations in ARX and PCDH19 genes modifying the disease course, and as a cause of migraine in probands' families (3). Conclusion: Phenotypic and genetic heterogeneity among EIEEs has been recognized since the first “epilepsy genes” identification. Because of that we can expect involvement more then one gene in phenotype expression, so the broad EIEEs genes panels analysis in correlation with phenotypic picture may be crucial in interpretation of all those data and final diagnosis.