부산대학교 도서관

Simple Summary: The major impact from this work is two-fold, first in providing a list of reproducible candidate drugs nominated against multiple glioblastoma (GBM) datasets, and second in providing drug–gene biomarkers of interest for GBM as well as reproducible computational pipelines for identifying drug–biomarker leads that could be extended to other cancer types beyond GBM. We believe the research community will be interested and can utilize our results here for the further development of biomarker-specific drug therapies in both GBM as well as other disease types. Glioblastoma multiforme (GBM) is the deadliest, most heterogeneous, and most common brain cancer in adults. Not only is there an urgent need to identify efficacious therapeutics, but there is also a great need to pair these therapeutics with biomarkers that can help tailor treatment to the right patient populations. We built patient drug response models by integrating patient tumor transcriptome data with high-throughput cell line drug screening data as well as Bayesian networks to infer relationships between patient gene expression and drug response. Through these discovery pipelines, we identified agents of interest for GBM to be effective across five independent patient cohorts and in a mouse avatar model: among them are a number of MEK inhibitors (MEKis). We also predicted phosphoglycerate dehydrogenase enzyme (PHGDH) gene expression levels to be causally associated with MEKi efficacy, where knockdown of this gene increased tumor sensitivity to MEKi and overexpression led to MEKi resistance. Overall, our work demonstrated the power of integrating computational approaches. In doing so, we quickly nominated several drugs with varying known mechanisms of action that can efficaciously target GBM. By simultaneously identifying biomarkers with these drugs, we also provide tools to select the right patient populations for subsequent evaluation. [ABSTRACT FROM AUTHOR]