Deep phenotyping of monogenic epilepsies towards the identification of targeted treatments

Epilepsy is a common neurological disorder with a prevalence of about 0.5-1% individuals. The underlying causes of epilepsy are heterogeneous. However, a genetic basis to epilepsy has long been recognized as a major possible cause. Over the past decades, an increasing number of new genes has been related with Developmental and epileptic encephalopathies (DEEs). 

The discovery of new “epilepsy genes” begets necessity of a proper characterization of the corresponding phenotypic features and description of the natural evolution over time of the diseases.

Typically, different pathogenic variants in the same “epilepsy gene” give rise to a wide spectrum of clinical features including epilepsy, ranging from self-limited epilepsy to severe DEEs, and neurological regression, intellectual disability and behavioral disturbances. A better knowledge of the diseases allows to identify endpoints and outcome measures, useful for the development of clinical trials with the aim to find specific targeted treatments. To reach these objectives, it is important a proper multimodal data collection. Specific diseaseregistries are the gold standard tools to collect data for rare diseases and should be standardized for retrospective and prospective studies. 

My Ph.D. project was focused on bridging the gaps in the electroclinical characterization of some of the most frequent monogenic epilepsies, with particular focus on SCN8A and STXBP1, to describe the clinical spectrum of symptoms and to identify genotype-phenotype correlations, possibly leading to specific targeted treatments. 

To phenotypically characterize the disorders, I created disease specific registries for data collection of phenotypic and genotypic features. Based on this experience, we found some requirements for their construction and suitability, that helped us to improve and use them as a model for data collection in large cohorts of patients with DEEs (such as SCN8A and STXBP1) as well as in smaller cohorts of more rare diseases (such as ANK3), allowing specific and collaborative studies.

From the SCN8A-registry, we observed that sleep disturbances are often reported in these patients. Even if sleep disturbances are often observed and invalidating on patients and caregivers quality of life, there are no studies on this topic. So we collected data from 47 patients with pathogenic SCN8A variants and found that the majority of them (82%) had sleep disturbances, mainly consisting in difficulty in initiating and maintaining sleep (64%), followed by sleep breathing disorder (43%), sleep–wake transition disorder (34%), and daytime sleepiness (34%). Sleep disturbances were more frequent in patients with severe DEE (96%) and ongoing seizures (93%) and were more severe in patients with sleep-related seizures. 

From the experience of the SCN8A registry, I created a Scandinavian STXBP1-registry where I collected phenotypic and genotypic, retrospective and prospective, data about 40 patients. This registry has been used as a model for the construction of the European STXBP1-registry, which is the base for the European STXBP1 NHS. 

From the STXBP1-Scandinavian and international registries a relevant topic concerning early mortality in patients with STXBP1-related disorders emerged. I extracted the data about the circumstances of death in 40 patients with STXBP1 pathogenic variants. We found a higher incidence of mortality compared to general epilepsy population (3/1000 versus 1.2- 1.3/1000 person-year), a mortality rate comparable to other DEEs (3.2%), an early median age of death (13 years), and SUDEP as well as pulmonary infections as the main causes of death (36% and 28% respectively).

The registry structure used for SCN8A and STXBP1 has been applied also to a smaller registry for the phenotypic-genotypic characterization of ANK3-related disorders. We collected data of 27 patients with mono and bi allelic ANK3 variants. Our results corroborated the involvement of ANK3 in neurodevelopmental disorder, mainly characterized by language delay (92%), behavioral/psychiatric features (100%), intellectual disability (78%), motor delay (68%), hypotonia (65%), sleep disturbances (50%), and epilepsy (35%). Patients with biallelic variants had more clinical features compared to those with monoallelic variants. These results could help for a better clinical management of the patients.

In conclusion, my Ph.D. researches focused on the phenotypic and genotypic characterization of genetic DEEs through dedicated disease specific registries giving a better understanding and knowledge of the diseases, and finding some genotype-phenotype correlations that can be useful for patients and families counselling and management.