A recent multi-platform analysis by The Cancer Genome Atlas identified four distinct molecular subtypes for glioblastoma (GBM) and demonstrated that the subtypes correlate with clinical phenotypes and treatment responses. In this study, we developed a computational drug repurposing approach to predict GBM drugs based on the molecular subtypes. Our approach leverages the genomic signature for each GBM subtype, and integrates the human cancer genomics with mouse phenotype data to identify the opportunity of reusing the FDA-approved agents to treat specific GBM subtypes. Specifically, we first constructed the phenotype profile for each GBM subtype using their genomic signatures. For each approved drug, we also constructed a phenotype profile using the drug target genes. Then we developed an algorithm to match and prioritize drugs based on their phenotypic similarities to the GBM subtypes. Our approach is highly generalizable for other disorders if provided with a list of disorder-specific genes. We first evaluated the approach in predicting drugs for the whole GBM. For a combined set of approved, potential and off-label GBM drugs, we achieved a median rank of 9.3%, which is significantly higher (p<e-7) than 45.7% for a recent approach that also uses the mouse phenotype data. Then we applied the approach on GBM subtypes. Analysis result shows the variations of enriched pathways, associated phenotypes and prioritized drugs across different subtypes. We ranked the first-line chemotherapy for GBM in different positions for each subtypes, and the rank variation was consistent with the previous finding on different drug responses among subtypes. In summary, this study makes an effort towards translating the molecular stratification into better survival for GBM.
Keywords: Drug discovery; Glioblastoma; Molecular subtypes; Precision medicine.
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