PO.BCS01.10 · 生物信息与计算
整合式元聚类揭示儿童低级别胶质瘤的亚型特异性转录组异质性
Integrated meta-clustering reveals subtype-specific transcriptomic heterogeneity of pediatric low-grade glioma
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
儿童低级别胶质瘤（pLGG）是儿童最常见的脑肿瘤类型，约占儿童所有中枢神经系统肿瘤的30%。pLGG具有多种分子亚型，它们在疾病进展、复发模式和治疗反应方面各不相同。用于pLGG表征的传统湿实验方法（包括分子谱分析和组织病理学研究）耗时、昂贵且费力。近期，基于人工智能（AI）或机器学习（ML）的方法已被广泛用于pLGG分子分类，但大多数方法只能识别两到三种pLGG亚型。为更全面地表征pLGG的分子亚型及其潜在的生物学和治疗意义，我们开发了一种整合式元聚类方法，以探索pLGG的高分辨率分子亚型及其转录异质性。具体而言，我们首先执行多轮随机投影（RP），从pLGG转录组学数据生成降维特征向量，每个向量随后由一些传统聚类算法（包括层次聚类、K-means和自组织映射）作为基聚类方法进行聚类。然后，为获得稳健的聚类性能，我们通过采用加权元聚类（wMetaC）方法整合这些基于RP的各个聚类算法的聚类结果。基于543名pLGG患者的结果表明，与传统方法相比，我们提出的方法在更高分辨率的pLGG亚型划分上展现出更优的稳定性和判别能力。基于共识矩阵分析，我们识别出两大pLGG亚型，其中一个进一步细分为三个亚组，另一个细分为两个。随后，我们执行了簇特异性差异基因表达分析、分子通路分析以及基因-药物-疾病关联分析。结果显示，所识别的五个亚组表现出显著的亚型特异性转录组异质性。总之，我们的元聚类方法在识别pLGG的更高分辨率分子亚型方面展现出更高的准确性和稳健性，揭示了pLGG内部的分子异质性，并有望为更精确的分子亚型划分和精准治疗提供新的洞见。
查看英文原文 English abstract
Pediatric low-grade glioma (pLGG) is the most common type of brain tumor in children, accounting for approximately 30% of all central nervous system tumors in children. pLGG has multiple molecular subtypes that differ in disease progression, recurrence patterns, and treatment responses. Conventional wet-lab approaches including molecular profiling and histopathological studies for pLGG characterization are time-consuming, costly, and laborious. Recently, methods based on artificial intelligence (AI) or machine learning (ML) have been widely used for pLGG molecular categorization, but most of them can only identify two or three pLGG subtypes. To more comprehensively characterize the molecular subtypes of pLGG and their potential biological and therapeutic significance, we develop an integrated meta-clustering approach to explore high-resolution molecular subtypes and their transcriptional heterogeneity for pLGG. Specifically, we first performed multiple rounds of random projection (RP) to generate dimension-reduced feature vectors from pLGG transcriptomics data, each of which was subsequently clustered by some conventional clustering algorithms including hierarchical clustering, K-means, and self-organizing maps, as base clustering methods. Then, to yield robust clustering performance, we integrated the clustering results of these RP-based individual clustering algorithms by adopting a weighted meta-clustering (wMetaC) approach. Results based on 543 pLGG patients suggested that our proposed approach demonstrated superior stability and discriminative powers for higher-resolution pLGG subtyping compared to conventional approaches. Based on consensus matrix analysis, we identified two major pLGG subtypes, with one further subdivided into three subgroups and the other into two. Then, we performed cluster-specific differential gene expression analysis, molecular pathway analysis, and gene-drug-disease association analysis. The results showed that the identified five subgroups exhibited significant subtype-specific transcriptomic heterogeneity. In summary, our meta-clustering approach demonstrates higher accuracy and robustness in identifying higher-resolution molecular subtypes of pLGG, revealing the molecular heterogeneity within pLGG and potentially providing new insights for more precise molecular subtyping and precision therapy.
利益披露 Disclosure
B. Tuerhanbayi, None..
J. Wang, None..
S. Wan, None.