Table6_Developing a cluster-based approach for deciphering complexity in individuals with neurodevelopmental differences.xlsx

ObjectiveIndividuals with neurodevelopmental disorders such as global developmental delay (GDD) present both genotypic and phenotypic heterogeneity. This diversity has hampered developing of targeted interventions given the relative rarity of each individual genetic etiology. Novel approaches to clinical trials where distinct, but related diseases can be treated by a common drug, known as basket trials, which have shown benefits in oncology but have yet to be used in GDD. Nonetheless, it remains unclear how individuals with GDD could be clustered. Here, we assess two different approaches: agglomerative and divisive clustering.MethodsUsing the largest cohort of individuals with GDD, which is the Deciphering Developmental Disorders (DDD), characterized using a systematic approach, we extracted genotypic and phenotypic information from 6,588 individuals with GDD. We then used a k-means clustering (divisive) and hierarchical agglomerative clustering (HAC) to identify subgroups of individuals. Next, we extracted gene network and molecular function information with regard to the clusters identified by each approach.ResultsHAC based on phenotypes identified in individuals with GDD revealed 16 clusters, each presenting with one dominant phenotype displayed by most individuals in the cluster, along with other minor phenotypes. Among the most common phenotypes reported were delayed speech, absent speech, and seizure. Interestingly, each phenotypic cluster molecularly included several (3–12) gene sub-networks of more closely related genes with diverse molecular function. k-means clustering also segregated individuals harboring those phenotypes, but the genetic pathways identified were different from the ones identified from HAC.ConclusionOur study illustrates how divisive (k-means) and agglomerative clustering can be used in order to group individuals with GDD for future basket trials. Moreover, the result of our analysis suggests that phenotypic clusters should be subdivided into molecular sub-networks for an increased likelihood of successful treatment. Finally, a combination of both agglomerative and divisive clustering may be required for developing of a comprehensive treatment.

研究目标：患有神经发育障碍，如全球发育迟缓（GDD）的个体，表现出基因型和表型的高度异质性。这种多样性阻碍了针对个体遗传病因相对罕见的针对性干预措施的开发。在临床试验中，针对不同但相关的疾病使用同一种药物的篮子试验是一种新颖的方法，这种方法在肿瘤学领域已显示出益处，但尚未在GDD中应用。尽管如此，如何对GDD患者进行分组仍然不明确。在本研究中，我们评估了两种不同的方法：聚合聚类和分裂聚类。方法：利用最大的GDD个体队列——解码发育障碍（DDD），采用系统性的方法进行特征描述，我们从6,588名GDD患者中提取了基因型和表型信息。随后，我们运用k-means聚类（分裂聚类）和层次聚类（HAC）来识别个体亚组。接下来，我们针对每种方法确定的聚类提取了基因网络和分子功能信息。结果：基于GDD个体表型识别的HAC揭示了16个聚类，每个聚类中表现出一种主导表型，大多数个体均表现出此表型，同时还伴随其他次要表型。其中最常见的表型包括语言发育迟缓、无语言能力和癫痫发作。有趣的是，每个表型聚类在分子层面上包含了多个（3-12）与基因紧密相关的基因亚网络，这些基因具有不同的分子功能。k-means聚类同样将具有这些表型的个体进行了分割，但识别出的遗传途径与HAC不同。结论：我们的研究展示了如何利用分裂（k-means）和聚合聚类来对GDD患者进行分组，以供未来的篮子试验使用。此外，我们的分析结果还表明，应将表型聚类进一步细分为分子亚网络，以提高成功治疗的可能性。最后，为了开发全面的治疗方案，可能需要结合使用聚合和分裂聚类。