Obtaining accurate drug response data in large cohorts of cancer patients is very challenging; thus, most cancer pharmacogenomics discovery is conducted in preclinical studies, typically using cell lines and mouse models. However, these platforms suffer from serious limitations, including small sample sizes. Here, we have developed a novel computational method that allows us to impute drug response in very large clinical cancer genomics data sets, such as The Cancer Genome Atlas (TCGA). The approach works by creating statistical models relating gene expression to drug response in large panels of cancer cell lines and applying these models to tumor gene expression data in the clinical data sets (e.g., TCGA). This yields an imputed drug response for every drug in each patient. These imputed drug response data are then associated with somatic genetic variants measured in the clinical cohort, such as copy number changes or mutations in protein coding genes. These analyses recapitulated drug associations for known clinically actionable somatic genetic alterations and identified new predictive biomarkers for existing drugs.

在大量癌症患者中获得准确的药物反应数据非常具有挑战性；因此，大多数癌症药物基因组学发现都是在临床前研究中进行的，通常使用细胞系和小鼠模型。然而，这些平台受到严重限制，包括样本量小。在这里，我们开发了一种新颖的计算方法，使我们能够在非常大的临床癌症基因组数据集（例如癌症基因组图谱（TCGA））中估算药物反应。该方法的工作原理是在大量癌细胞系中创建将基因表达与药物反应相关的统计模型，并将这些模型应用于临床数据集（例如TCGA）中的肿瘤基因表达数据。这会产生每位患者每种药物的估算药物反应。然后将这些估算的药物反应数据与临床队列中测量的体细胞遗传变异相关联，例如蛋白质编码基因的拷贝数变化或突变。这些分析概括了已知临床上可操作的体细胞遗传改变的药物关联，并确定了现有药物的新预测生物标志物。