One of the current directions of precision medicine is the use of computational methods to aid in the diagnosis, prognosis, and treatment of disease based on data driven approaches. For instance, in oncology, there has been a particular focus on development of algorithms and biomarkers that can be used for pre-clinical and clinical applications. In particular large-scale omics-based models to predict drug sensitivity in in vitro cancer cell line panels have been used to explore the utility and aid in the development of these models as clinical tools. Additionally, a number of web-based interfaces have been constructed for researchers to explore the potential of drug perturbed gene expression as biomarkers including the NCI Transcriptional Pharmacodynamic Workbench. In this paper we explore the influence of drug perturbed gene dynamics of the NCI Transcriptional Pharmacodynamics Workbench in computational models to predict in vitro drug sensitivity for 15 drugs on the NCI60 cell line panel. This work presents three main findings. First, our models show that gene expression profiles that capture changes in gene expression after 24 h of exposure to a high concentration of drug generates the most accurate predictive models compared to the expression profiles under different dosing conditions. Second, signatures of 100 genes are developed for different gene expression profiles; furthermore, when the gene signatures are applied across gene expression profiles model performance is substantially decreased when gene signatures developed using changes in gene expression are applied to non-drugged gene expression. Lastly, we show that the gene interaction networks developed on these signatures show different network topologies and can be used to inform selection of cancer relevant genes. Our models suggest that perturbed gene signatures are predictive of drug response, but cannot be applied to predict drug response using unperturbed gene expression. Furthermore, additional drug perturbed gene expression measurements in in vitro cell lines could generate more predictive models; but, more importantly be used in conjunction with computational methods to discover important drug disease relationships.

中文翻译：

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使用药物扰动的基因动力学预测化学敏感性

精确医学的当前方向之一是使用计算方法，以基于数据驱动的方法来辅助疾病的诊断，预后和治疗。例如，在肿瘤学中，一直特别关注可用于临床前和临床应用的算法和生物标记物的开发。特别是，用于预测体外癌细胞系中药物敏感性的基于大规模组学的模型已用于探索其实用性，并有助于将这些模型开发为临床工具。此外，已经为研究人员构建了许多基于Web的界面，以探索药物干扰基因表达作为生物标记物的潜力，包括NCI转录药效学工作台。在本文中，我们在计算机模型中探讨了NCI转录药效学工作台的药物扰动基因动力学的影响，以预测NCI60细胞系面板上15种药物的体外药物敏感性。这项工作提出了三个主要发现。首先，我们的模型表明，与不同剂量条件下的表达谱相比，在暴露于高浓度药物24小时后捕获基因表达变化的基因表达谱可产生最准确的预测模型。其次，针对不同的基因表达谱开发了100个基因的签名。此外，当跨基因表达谱应用基因签名时，将使用基因表达变化开发的基因签名应用于非药物基因表达时，模型性能会大大降低。最后，我们证明了在这些特征上开发的基因相互作用网络显示出不同的网络拓扑，可用于告知癌症相关基因的选择。我们的模型表明，受干扰的基因签名可预测药物反应，但不能应用于未受干扰的基因表达来预测药物反应。此外，在体外细胞系中进行其他药物扰动基因表达测量可以产生更多的预测模型。但更重要的是与计算方法结合使用，以发现重要的药物疾病关系。但不能用于使用不受干扰的基因表达来预测药物反应。此外，在体外细胞系中进行其他药物扰动基因表达测量可以产生更多的预测模型。但更重要的是与计算方法结合使用，以发现重要的药物疾病关系。但不能用于使用不受干扰的基因表达来预测药物反应。此外，在体外细胞系中进行其他药物扰动基因表达测量可以产生更多的预测模型。但更重要的是与计算方法结合使用，以发现重要的药物疾病关系。