Integration of Computational Pipeline to Streamline Efficacious Drug Nomination and Biomarker Discovery in Glioblastoma

Danielle Maeser, Robert F. Gruener, Robert Galvin, Adam Lee, Tomoyuki Koga, Florina Nicoleta Grigore, Yuta Suzuki, Frank B. Furnari, Clark Chen, R. Stephanie Huang

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Article number1723
JournalCancers
Volume16
Issue number9
DOIs
StatePublished - May 2024

Bibliographical note

Publisher Copyright:
© 2024 by the authors.

Keywords

  • biomarker discovery
  • drug discovery
  • drug response prediction
  • glioblastoma multiforme
  • precision medicine

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