Cuppens Tania, Kaur Manpreet, Kumar Ajay A, Shatto Julie, Ng Andy Cheuk-Him, Leclercq Mickael, Reformat Marek Z, Droit Arnaud, Dunham Ian, Bolduc François V
Département de Médecine Moléculaire de L'Université Laval, Centre de Recherche du CHU de Québec-Université Laval, Québec, QC, Canada.
Department of Pediatric Neurology, University of Alberta, Edmonton, AB, Canada.
Front Pediatr. 2023 Sep 18;11:1171920. doi: 10.3389/fped.2023.1171920. eCollection 2023.
Individuals 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.
Using 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.
HAC 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.
Our 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.
患有神经发育障碍（如全面发育迟缓，GDD）的个体存在基因型和表型的异质性。鉴于每种个体遗传病因相对罕见，这种多样性阻碍了靶向干预措施的开发。一种新的临床试验方法，即篮子试验，可用于治疗不同但相关的疾病，这种方法在肿瘤学中已显示出益处，但尚未用于GDD。然而，目前尚不清楚如何对GDD患者进行聚类。在此，我们评估两种不同的方法：凝聚式聚类和分裂式聚类。
利用最大的GDD患者队列，即发育障碍解析（DDD）队列，通过系统方法进行特征分析，我们从6588名GDD患者中提取了基因型和表型信息。然后，我们使用k均值聚类（分裂式）和层次凝聚式聚类（HAC）来识别个体亚组。接下来，我们提取了每种方法所识别聚类的基因网络和分子功能信息。
基于GDD患者所识别的表型进行HAC分析，发现了16个聚类，每个聚类都呈现出一种大多数个体表现出的主要表型，以及其他次要表型。报告的最常见表型包括言语发育迟缓、无言语能力和癫痫发作。有趣的是，每个表型聚类在分子水平上都包含几个（3 - 12个）由功能多样但关系更密切的基因组成的基因子网。k均值聚类也将具有这些表型的个体进行了分类，但所识别的遗传途径与HAC所识别的不同。
我们的研究说明了如何使用分裂式（k均值）聚类和凝聚式聚类来对GDD患者进行分组，以便未来进行篮子试验。此外，我们的分析结果表明，应将表型聚类细分为分子子网，以提高成功治疗的可能性。最后，可能需要结合凝聚式聚类和分裂式聚类来制定全面的治疗方案。
