Decoding EGFR A289 Mutation in Glioblastoma: A Predictive Biomarker Framework and Targeted Therapeutic Insights.

Abstract

Glioblastoma (GBM) is the most common primary malignant intracranial tumor, accounting for over 50% of central nervous system tumors. Among EGFR mutations in GBM, A289 is a prevalent point mutation associated with poor prognosis, yet its unique characteristics and role in malignant progression remain unclear. To address this, we analyzed EGFR A289 mutation frequency by integrating tumor mutation data from TCGA and CGGA databases as well as Beijing Tiantan Hospital's Neuropathology Center. We established U87-MG cell lines carrying EGFR A289T/V/D mutations via lentiviral transduction and performed transcriptome sequencing. Differential gene expression analysis was assessed by integrating the cell lines' and TCGA tumor tissues' transcriptomic data. Followed by gene correlation analysis, univariate logistic regression, and LASSO regression, the key differential expressed genes were identified, leading to the development of a multivariable logistic regression model to decode the EGFR A289 mutation. Our study identified EGFR A289 as the most frequent EGFR missense mutation in GBM. The prediction model and nomogram, based on EGFR, CLEC18B, and PDK1 expression, exhibited strong predictive performance. Additionally, drug sensitivity analysis and in vitro validation demonstrated that gefitinib and XAV939 hold therapeutic potential for GBM with EGFR A289 mutations, showing significant synergistic effects. These findings provide critical insights into the role of EGFR A289 mutation in GBM, enabling precise diagnosis and offering targeted therapeutic strategies to overcome chemoresistance.