Subgroup delineation in genetic epilepsies and developmental brain disorders

项目总结 在过去的十年里，神经发育的遗传因素呈指数增长。 精神障碍和癫痫。识别了100多个基因，了解表型与特定基因的关系 考虑到发育性大脑疾病的临床复杂性，基因变异是至关重要的。鉴于这种待遇 而预后取决于对基因-表型相关性的理解，迫切需要更好地 评估遗传性癫痫的临床特征。然而，表型分析是一项耗时的手动任务 吞吐量有限。为了克服这一瓶颈，我们开发了一种新的方法，基于人类 表型本体(HPO)来捕获和分析纵向表型数据。在我们的初步数据中 STXBP1和SCN8A相关疾病，我们已经重建了550个患者月，我们确定了 独特的自然病史、结果和对特定治疗策略的独特反应模式。自然历史 儿童癫痫的治疗反应是深深地交织在一起的，往往很难解开。 因此，对遗传性癫痫的综合评估需要考虑两个因素，即亚组 具有共同的临床轨迹以及基因特异性的治疗反应。因此，我们建议的项目 有两个目标。首先，我们计划基于纵向临床数据检测遗传性癫痫的相关亚群。 (目标1)。我们将重建15种最常见的遗传性癫痫的纵向轨迹和结果 每个基因有50-75个个体来描述纵向癫痫负担、癫痫发作类型和发育 里程碑。在此基础上，我们将确定由临床特征和全球临床定义的子组 相似性，以及变异和基因组。此外(目标2)，我们将确定具体的治疗方法 使用标准化表型对遗传性癫痫的反应。我们将结合重建的自然历史 用治疗数据比较癫痫发作频率的减少和维持癫痫自由的效果 跨&>20种治疗策略，目的是在调整 年龄和癫痫发作类型。最后，我们还将比较主要不同类别和不同类别的药物反应 所有的遗传病因加在一起。我们的团队以前曾在 癫痫，为我们解决这些问题提供了独特的定位。我们的分析，绘制纵向临床数据 一种统一的格式将在破译遗传性癫痫的轨迹方面提供前所未有的粒度， 在这些情况下告知临床实践。我们希望这些结果将为分析提供一个模板 对罕见疾病有限的临床数据进行分析，以便最大限度地提供与治疗相关的信息，特别是在 有复杂的纵向病史的情况。

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.