Identification of Subtype-Specific Vulnerabilities in Resistant Glioblastoma: A Computational Pipeline for Biomarkers and Drug Discovery

Therapeutic resistance in glioblastoma (GBM) remains a critical clinical challenge. To better understand its molecular basis, we performed a multistage computational analysis on a resistant GBM data set derived from the Ivy Glioblastoma Atlas Project, filtered for unmethylated MGMT and EGFR amplification. A subtype-specificity analysis identified distinct expression patterns for established biomarkers, including PDGFRA, FAP, and CD163, providing refined context for their roles in specific GBM subtypes. Kaplan–Meier analysis confirmed that high PDGFRA expression correlates with poor survival in this resistant population. To explore its therapeutic tractability, we developed a Quantitative Structure–Activity Relationship (QSAR) model targeting PDGFRA. This led to the in silico identification of a novel lead compound with a distinct thiazolopyridine-based scaffold and high predicted potency. Our findings demonstrate how integrated bioinformatic pipelines can dissect the complex landscape of GBM resistance, contextualize the roles of key oncogenes, and guide the rational design of potential new inhibitors.