Glioblastoma (GBM) is a highly heterogeneous and treatment-refractory tumor, where the tumor microenvironment (TME) plays a central role in shaping progression and therapeutic response. However, the molecular and cellular basis of TME-linked heterogeneity, and how it can be captured through noninvasive imaging, remains poorly understood. This study aims to establish a noninvasive framework for characterizing GBM heterogeneity by bridging radiomic features (RFs) with TME architecture, and to identify novel druggable vulnerabilities for personalized treatment. We analyzed magnetic resonance imaging (MRI)-derived RFs to identify prognostic RFs characterizing tumor heterogeneity. These RFs were correlated with TME composition through integrated analysis of single-cell transcriptomic data and functional enrichment. We utilized a ranking-based computational approach to evaluate gene set activity at single-cell resolution, assessing the enrichment of critical gene subsets within individual cells’ expressed genes. Drug sensitivity was assessed by matching RF-associated gene signatures with pharmacogenomic perturbation profiles. Leveraging noninvasive MRI, we identified 31 prognostic RFs that effectively stratified patients into distinct risk groups (C-index = 0.84; HR = 2.16, p < 0.001). These RFs showed significant associations with key dimensions of TME heterogeneity, showing significant associations with specific cellular states—including neural progenitor cell-like (NPC-like)/oligodendrocyte progenitor cell-like (OPC-like) tumor subclasses, macrophages, and myeloid-derived suppressor cells (MDSCs)—as revealed by single-cell RNA-sequencing (scRNA-seq) analysis. Computational drug screening based on these associations identified targeted agents capable of reversing high-risk expression patterns linked to specific RFs, thereby suggesting potential therapeutic strategies aligned with individual TME profiles. Our findings indicate that combining imaging-derived RFs with transcriptomic profiling of the TME offers a promising approach to decode GBM heterogeneity and uncover therapeutic opportunities. This multimodal strategy enables noninvasive stratification and may aid in the design of personalized treatment approaches in GBM. A preoperative MRI-based radiomic model was developed to predict survival in glioblastoma patients with high accuracy. Radiogenomic analysis linked imaging features to underlying gene expression patterns, suggesting their biological basis. Radiomic features were associated with tumor microenvironment components, including neoplastic and immune cell subpopulations. Specific RFs correlated with key biological processes such as DNA repair, cell cycle, stemness, and inflammation. Targetable cellular states linked to RFs suggest potential therapeutic strategies, including nocodazole and parthenolide.
Zhao et al. (Tue,) studied this question.