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.

在大批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是在临床前研究中进行的，通常使用细胞系和小鼠模型进行。但是，这些平台存在严重的局限性，包括样本量小。在这里，我们开发了一种新颖的计算方法，该方法使我们能够在非常大的临床癌症基因组学数据集（例如The Cancer Genome Atlas（TCGA））中估算药物反应。该方法通过创建将基因表达与大量癌细胞系中的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而起作用。对于每个患者中的每种药物，都会产生推定的药物反应。然后，将这些估算的药物反应数据与在临床队列中测得的体细胞遗传变异相关联，例如拷贝数变化或蛋白质编码基因的突变。这些分析概述了已知临床上可行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。