Radiomic Consensus Clustering in Glioblastoma and Association with Gene Expression Profiles.

Simple Summary: Glioblastoma (GBM) is an aggressive primary central nervous system tumor with poor survival outcomes and limited treatment options. In this study, we investigate the use of radiomic features derived from magnetic resonance imaging (MRI) scans to identify unique gene expression profiles in a cohort of patients with GBM. This study grouped patients based on radiomic features using a consensus clustering approach, which iteratively clusters patients to find robust and stable groups. We identified three clusters which yielded unique gene expression profiles. Significant differentially expressed genes previously associated with GBM prognosis and treatment sensitivity were identified in one cluster. In pathway enrichment analyses, genes upregulated in immune-related and DNA metabolism pathways and downregulated protein and histone deacetylation pathways were identified in the same cluster. Together, these findings suggest that consensus clustering of radiomic features may be a promising avenue for non-invasive characterization of molecular heterogeneity of GBM. Background/Objectives: Glioblastoma (GBM) is the most common malignant primary central nervous system tumor with extremely poor prognosis and survival outcomes. Non-invasive methods like radiomic feature extraction, which assess sub-visual imaging features, provide a potentially powerful tool for distinguishing molecular profiles across groups of patients with GBM. Using consensus clustering of MRI-based radiomic features, this study aims to investigate differential gene expression profiles based on radiomic clusters. Methods: Patients from the TCGA and CPTAC datasets (n = 114) were included in this study. Radiomic features including T1, T1 with contrast, T2, and FLAIR MRI sequences were extracted using PyRadiomics. Selected radiomic features were then clustered using ConsensusClusterPlus (k-means base algorithm and Euclidean distance), which iteratively subsamples and clusters 80% of the data to identify stable clusters by calculating the frequency in which each patient is a member of a cluster across iterations. Gene expression data (available for n = 69 patients) was analyzed using differential gene expression (DEG) and gene set enrichment (GSEA) approaches, after batch correction using ComBat-seq. Results: Three distinct clusters were identified based on the relative consensus matrix and cumulative distribution plots (Cluster 1, n = 25; Cluster 2, n = 46; Cluster 3, n = 43). No significant differences in patient demographic characteristics, MGMT methylation status, tumor location, or overall survival were identified across clusters. Differentially expressed genes were identified in Cluster 1, which have been previously associated with GBM prognosis, recurrence, and treatment sensitivity. GSEA of Cluster 1 showed an enrichment of genes upregulated for immune-related and DNA metabolism pathways and genes downregulated in pathways associated with protein and histone deacetylation. Clusters 2 and 3 exhibited fewer DEGs which failed to reach significance after multiple testing corrections. Conclusions: Consensus clustering of radiomic features revealed unique gene expression profiles in the GBM cohort which likely represent subtle differences in tumor biology and radiosensitivity that are not visually discernible, underscoring the potential of radiomics to serve as a non-invasive alternative for identifying GBM molecular heterogeneity. Further investigation is still required to validate these findings and their clinical implications. [ABSTRACT FROM AUTHOR]