Investigating Intra-group Molecular Heterogeneity in IDH-mut Lower-Grade Gliomas Using Integrative Multi-Omics Clustering

Abstract

This study investigated intra-group molecular heterogeneity in IDH-mutant lower-grade gliomas (LGG) by implementing integrative multi-omics clustering, to provide further evidence of the LGG subtype classification and the molecular heterogeneity within groups. This research collects clinical, mutation, copy number alteration (CNA) and messenger RNA (mRNA) data from the TCGA Lower Grade Glioma dataset via the cBioPortal platform. Hence, two dimensionality reduction methods Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP) were used to process the raw data. Then, combining K-means clustering algorithm, samples were grouped. The results showed that, among the overall LGG cohort, 2 molecular types are classified, with significant differences in the proportion of IDH-mutated samples and overall survival. Further clustering of the IDH-mutated group yielded 2 subtypes, with significant differences on CNA burden, mRNA expression and gene markers. Although the overall survival rate did not show significant difference with the 2 subclusters, treatment stratification can be done according to the risk level, involving de-escalation/surveillance-leaning therapy and standard combined therapy respectively. This study demonstrated that, integrative multi-omics clustering methods could effectively reveal the intra-group molecular heterogeneity within the IDH-mutated LGG. Moreover, CNA variation is a more important source of heterogeneity than transcriptional variation. These results provided new guidance in precise classification of gliomas and personalized clinical treatments.