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Background <p>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.</p> Objective <p>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.</p> Methods <p>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.</p> Results <p>Leveraging noninvasive MRI, we identified 31 prognostic RFs that effectively stratified patients into distinct risk groups (C-index = 0.84; HR = 2.16, <i>p</i> &lt; 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.</p> Conclusion <p>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.</p>

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Deciphering the glioblastoma microenvironmental landscape with multi-modal radiogenomics to guide prognosis and personalized therapy

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月5日

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  • Hongying Zhao,
  • Kailai Liu,
  • Marui Guan,
  • Xun Tang,
  • Yangxinyue Zheng,
  • Hongzheng Yu,
  • Shangwei Ning,
  • Li Wang

摘要

Background

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.

Objective

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.

Methods

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.

Results

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.

Conclusion

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.