Table9_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.

目标：神经发育障碍患者，如全球发育迟缓（GDD），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情况发展针对性干预措施。篮式试验作为一种新颖的临床试验方法，通过使用一种药物来治疗不同的但相关的疾病，已在肿瘤学领域显示出益处，但尚未应用于GDD。尽管如此，如何对GDD患者进行聚类仍不明确。本研究评估了两种不同的方法：聚合和分裂聚类。方法：利用最大的GDD患者队列——解码发育障碍（DDD），采用系统性的方法，我们从6,588名GDD患者中提取了基因型和表型信息。随后，我们使用k-means聚类（分裂）和层次聚合聚类（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患者表型识别的HAC揭示了16个聚类，每个聚类中大多数个体表现出一种主导表型，以及其他次要表型。最常见的表型包括语言延迟、无语言和癫痫发作。有趣的是，每个表型聚类分子上包括几个（3-12个）与更密切相关基因具有多样分子功能的基因子网络。k-means聚类也将携带这些表型的个体分离出来，但识别出的遗传途径与HAC识别的途径不同。结论：本研究展示了如何利用分裂（k-means）和聚合聚类对GDD患者进行分组，以期为未来的篮式试验提供参考。此外，我们的分析结果提示，表型聚类应进一步细分为分子子网络，以提高治疗成功的可能性。最后，为了开发全面的治疗方案，可能需要结合聚合和分裂聚类。