Radiomics-based gradient boosting model on contrast-enhanced MRI for non-invasive prediction of epidermal growth factor receptor expression and therapeutic response to EGFR-targeted antibody-drug conjugates in high-grade glioma organoid models.
Authors
Affiliations (8)
Affiliations (8)
- Department of Neurosurgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Department of Radiology Department, The First Affiliated Hospital to Dalian Medical University, Dalian, Liaoning, China.
- Department of Oncology, The First Affiliated Hospital to Dalian Medical University, Dalian, Liaoning, China.
- Department of Neurosurgery, The First Affiliated Hospital to Dalian Medical University, Dalian, Liaoning, China.
- Department of Endocrinology, Shanghai Geriatric Medical Center, Shanghai, China.
- Department of Neurosurgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. [email protected].
- Department of Oncology, The First Affiliated Hospital to Dalian Medical University, Dalian, Liaoning, China. [email protected].
- Department of Oncology, The First Affiliated Hospital to Dalian Medical University, Dalian, Liaoning, China. [email protected].
Abstract
Epidermal growth factor (EGF) and its receptor EGF(EGFR) play crucial roles in glioblastoma (GBM) prognosis. However, non-invasive assessment of their expression remains challenging. This study aimed to determine whether radiomics features extracted from contrast-enhanced MRI could predict EGFR expression in high-grade gliomas (HGG) and to explore their associations with immune infiltration and therapeutic response of EGFR-Targeted antibody drug conjugates(EGFR-ADCs). We extracted radiomic features from contrast-enhanced MRI of 298 GBM patients from The Cancer Imaging Archive (TCIA) and matched them with RNA-seq data from The Cancer Genome Atlas (TCGA). Feature selection was performed using minimum redundancy maximum relevance (mRMR) and recursive feature elimination (RFE). Machine learning models were built to predict EGF/EGFR expression. Radiogenomic associations were validated by immune infiltration analysis. Patient-Derived Tumor-Like Cell Clusters (PTC) were used to compare the antitumor efficacy of EGFR- ADCs and temozolomide. Elevated EGF/EGFR expression correlated with poor prognosis and increased infiltration of M2 macrophages, regulatory T cells, and CD4⁺ memory T cells. Pathway analysis demonstrated significant enrichment of the mechanistic target of rapamycin (mTOR) and Mitogen-Activated Protein Kinase (MAPK) signaling cascades. Radiomics-based prediction models achieved robust performance (AUC > 0.85) in stratifying EGFR expression status. In EGFR-positive tumor tissues, EGFR-ADCs exerted antitumor efficacy similar to that of temozolomide. EGF/EGFR expression is associated with immunosuppressive microenvironments and adverse outcomes in HGG. Radiomics may provide a non-invasive approach for estimating EGFR expression, although model performance requires external validation and EGFR-ADCs showed partial inhibitory activity within the tested range, though potency remains to be defined.These findings suggest a framework into radiogenomic stratification and targeted therapy in GBM.