Table3_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】患有全面性发育迟缓（Global Developmental Delay, GDD）等神经发育障碍的个体，同时存在基因型与表型异质性。由于每种个体遗传病因相对罕见，这种异质性阻碍了靶向干预手段的开发。一类新型临床试验方法——篮式试验（basket trials），可针对存在关联的不同疾病使用同一种药物进行治疗，该方法在肿瘤学领域已展现出益处，但尚未应用于GDD研究。然而，目前尚不清楚如何对GDD患者进行聚类分析。本研究评估了两种不同的聚类方法：聚合聚类（agglomerative clustering）与分裂聚类（divisive clustering）。
【研究方法】本研究采用经过系统化表征的最大规模GDD患者队列——发育障碍解析（Deciphering Developmental Disorders, DDD）队列，从6588名GDD患者中提取基因型与表型信息。随后分别采用k均值聚类（k-means clustering，分裂聚类）与层级聚合聚类（hierarchical agglomerative clustering, HAC）对患者进行亚组划分。最后，针对两种方法得到的聚类结果，分别提取基因网络与分子功能相关信息。
【研究结果】基于GDD患者表型的层级聚合聚类（HAC）共得到16个聚类簇，每个聚类簇均存在一种主导表型（覆盖簇内多数患者），并伴随其他次要表型。最常见的表型包括言语发育迟缓、言语缺失与癫痫发作。值得注意的是，每个表型聚类簇在分子层面均包含3~12个基因子网络，这些子网络由功能相关性更高的基因组成，且具备多样的分子功能。k均值聚类同样可对携带上述表型的患者进行划分，但该方法识别到的遗传通路与层级聚合聚类的结果存在差异。
【研究结论】本研究证实，可通过分裂聚类（k均值聚类）与聚合聚类两种方法对GDD患者进行分组，为未来开展篮式试验提供依据。此外，本研究分析结果表明，应将表型聚类簇进一步划分为分子子网络，以提升治疗成功的可能性。最后，若要开发全面的治疗方案，可能需要结合聚合聚类与分裂聚类两种方法。