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）是成人中最致命、最具异质性且最常见的脑癌。当前不仅迫切需要寻找有效的治疗方法，还需要将这些疗法与能够帮助针对适宜患者群体进行个体化治疗的生物标志物相结合。我们通过整合患者肿瘤转录组数据与高通量细胞系药物筛选数据，并利用贝叶斯网络推断患者基因表达与药物反应之间的关系，构建了患者药物反应模型。通过这些发现流程，我们确定了在五个独立患者队列及小鼠替身模型中均对GBM有效的候选药物，其中包括多种MEK抑制剂。同时，我们预测磷酸甘油酸脱氢酶（PHGDH）基因表达水平与MEK抑制剂疗效存在因果关联——该基因的敲低可增强肿瘤对MEK抑制剂的敏感性，而过表达则导致耐药性。总体而言，我们的研究展示了整合计算方法的应用价值。通过这种方法，我们快速筛选出多种具有不同已知作用机制且能有效靶向GBM的药物。通过同步识别这些药物的生物标志物，我们还为后续评估中筛选适宜患者群体提供了有效工具。
肿瘤(癌症)患者之家