Table8_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）患者，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靶向干预措施的开发。在肿瘤学领域已显示出益处的篮式临床试验，即通过一种共同药物治疗不同但相关的疾病，尚未在GDD中得到应用。尽管如此，如何对GDD患者进行聚类仍不明确。在本研究中，我们评估了两种不同的方法：凝聚聚类和分裂聚类。方法：利用最大的GDD患者队列——解码发育障碍（DDD），采用系统化方法对其进行分析，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）和层次凝聚聚类（HAC）来识别个体亚组。接着，我们针对每种方法识别出的聚类提取了基因网络和分子功能信息。结果：基于GDD患者中识别出的表型进行的HAC分析揭示了16个聚类，每个聚类呈现出一种由大多数患者表现出的主要表型，以及其他次要表型。最常见的主要表型包括语言迟缓、无语言和癫痫发作。有趣的是，每个表型聚类在分子层面包括了几种（3–12）紧密相关基因的具有多样分子功能的基因子网络。k-means聚类也分离了具有这些表型的个体，但识别出的遗传通路与HAC分析中识别出的不同。结论：我们的研究说明了如何利用分裂（k-means）和凝聚聚类来对GDD患者进行分组，以供未来的篮式临床试验使用。此外，我们的分析结果建议，应将表型聚类进一步细分为分子子网络，以提高治疗成功的可能性。最后，可能需要结合凝聚和分裂聚类以开发全面的治疗方案。