Yin Liangying, Lin Yuping, Qiu Jinghong, Xiang Yong, Li Ming, Xiao Xiao, Lui Simon Sai-Yu, So Hon-Cheong
School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong.
KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research of Common Diseases, Kunming Institute of Zoology and The Chinese University of Hong Kong, Hong Kong SAR, China.
Psychol Med. 2025 May 22;55:e158. doi: 10.1017/S0033291725001096.
Precise stratification of patients into homogeneous disease subgroups could address the heterogeneity of phenotypes and enhance understanding of the pathophysiology underlying specific subtypes. Existing literature on subtyping patients with major depressive disorder (MDD) mainly utilized clinical features only. Genomic and imaging data may improve subtyping, but advanced methods are required due to the high dimensionality of features.
We propose a novel disease subtyping framework for MDD by integrating brain structural features, genotype-predicted expression levels in brain tissues, and clinical features. Using a multi-view biclustering approach, we classify patients into clinically and biologically homogeneous subgroups. Additionally, we propose approaches to identify causally relevant genes for clustering.
We verified the reliability of the subtyping model by internal and external validation. High prediction strengths (PS) (average PS: 0.896, minimum: 0.854), a measure of generalizability of the derived clusters in independent datasets, support the validity of our approach. External validation using patient outcome variables (treatment response and hospitalization risks) confirmed the clinical relevance of the identified subgroups. Furthermore, subtype-defining genes overlapped with known susceptibility genes for MDD and were involved in relevant biological pathways. In addition, drug repositioning analysis based on these genes prioritized promising candidates for subtype-specific treatments.
Our approach successfully stratified MDD patients into subgroups with distinct clinical prognoses. The identification of biologically and clinically meaningful subtypes may enable more personalized treatment strategies. This study also provides a framework for disease subtyping that can be extended to other complex disorders.
将患者精确分层为同质性疾病亚组可以解决表型的异质性问题，并增进对特定亚型潜在病理生理学的理解。现有关于重度抑郁症（MDD）患者亚型分类的文献主要仅利用临床特征。基因组和影像学数据可能会改善亚型分类，但由于特征的高维度性，需要先进的方法。
我们通过整合脑结构特征、脑组织中基因型预测的表达水平和临床特征，提出了一种用于MDD的新型疾病亚型分类框架。使用多视图双聚类方法，我们将患者分类为临床和生物学上的同质性亚组。此外，我们提出了识别与聚类有因果关系的基因的方法。
我们通过内部和外部验证验证了亚型分类模型的可靠性。高预测强度（PS）（平均PS：0.896，最小值：0.854），这是一种衡量独立数据集中派生聚类可推广性的指标，支持了我们方法的有效性。使用患者结局变量（治疗反应和住院风险）进行的外部验证证实了所识别亚组的临床相关性。此外，定义亚型的基因与已知的MDD易感基因重叠，并参与相关的生物学途径。此外，基于这些基因的药物重新定位分析确定了有前景的亚型特异性治疗候选药物。
我们的方法成功地将MDD患者分层为具有不同临床预后的亚组。识别生物学和临床上有意义的亚型可能会实现更个性化的治疗策略。本研究还提供了一个可扩展到其他复杂疾病的疾病亚型分类框架。
