Maeser Danielle, Gruener Robert F, Galvin Robert, Lee Adam, Koga Tomoyuki, Grigore Florina-Nicoleta, Suzuki Yuta, Furnari Frank B, Chen Clark, Huang R Stephanie
Department of Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN 55455, USA.
Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455, USA.
Cancers (Basel). 2024 Apr 28;16(9):1723. doi: 10.3390/cancers16091723.
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.
多形性胶质母细胞瘤（GBM）是成年人中最致命、最具异质性且最常见的脑癌。不仅迫切需要确定有效的治疗方法，还非常需要将这些治疗方法与生物标志物配对，以帮助为合适的患者群体量身定制治疗方案。我们通过整合患者肿瘤转录组数据、高通量细胞系药物筛选数据以及贝叶斯网络来构建患者药物反应模型，以推断患者基因表达与药物反应之间的关系。通过这些发现流程，我们确定了对GBM有效的感兴趣药物，这些药物在五个独立的患者队列和一个小鼠模型中均有效：其中包括多种MEK抑制剂（MEKis）。我们还预测磷酸甘油酸脱氢酶（PHGDH）基因表达水平与MEKi疗效存在因果关系，该基因的敲低会增加肿瘤对MEKi的敏感性，而过表达则导致对MEKi耐药。总体而言，我们的工作展示了整合计算方法的强大力量。通过这样做，我们迅速提名了几种具有不同已知作用机制的药物，这些药物可以有效靶向GBM。通过同时识别这些药物的生物标志物，我们还提供了工具来选择合适的患者群体进行后续评估。
