Subgroup delineation in genetic epilepsies and developmental brain disorders

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PROJECT SUMMARY Over the last decade, there has been an exponential increase in identified genetic causes of neurodevelopmental disorders and epilepsy. With more than 100 genes identified, understanding how phenotypes relate to specific genetic variants is critical given the clinical complexity of developmental brain disorders. Given that treatment and prognosis is dependent on understanding genotype-phenotype correlations, there is a critical need to better assess clinical features in genetic epilepsies. However, phenotyping is a time-consuming, manual task with limited throughput. To overcome this bottleneck, we have developed a novel approach, based on the Human Phenotype Ontology (HPO) to capture and analyze longitudinal phenotypic data. In our preliminary data for STXBP1- and SCN8A-related disorders, which we have reconstructed for >550 patient months, we identified unique natural histories, outcomes, and distinct response patterns to specific treatment strategies. Natural history and treatment response in pediatric epilepsies are deeply intertwined and often difficult to disentangle. Accordingly, a comprehensive assessment of genetic epilepsies needs to account for two factors, subgroups with common clinical trajectories as well as gene-specific treatment responses. Our suggested project therefore has two aims. First, we plan to detect relevant subgroups in genetic epilepsies based on longitudinal clinical data (Aim #1). We will reconstruct longitudinal trajectories and outcomes in the 15 most common genetic epilepsies with 50-75 individuals per gene to delineate longitudinal seizure burden, seizures types, and developmental milestones. Based on this, we will then identify subgroups defined by clinical features and global clinical resemblance, as well as variant and gene groups. In addition (Aim #2), we will identify specific treatment responses in genetic epilepsies using standardized phenotypes. We will combine reconstructed natural history with treatment data to compare reduction in seizure frequencies and effect on maintaining seizure freedom across >20 treatment strategies with the goal to identify the most effective treatment strategy when adjusting for age and seizure type. Finally, we will also compare medication response across major variant classes and across all genetic etiologies combined. Our team has previously pioneered computational phenotype analysis in the epilepsies, positioning us uniquely to address these questions. Our analysis, mapping longitudinal clinical data to a harmonized format will provide unprecedented granularity in deciphering the trajectory of genetic epilepsies, informing clinical practice in these conditions. We hope that these results will provide a template for the analysis of the limited clinical data in rare diseases in order to maximize treatment-relevant information, especially in conditions with complex, longitudinal disease histories.