Madhukar Neel S, Elemento Olivier
Department of Physiology and Biophysics, Institute for Precision Medicine, Institute for Computational Biomedicine, Weill Cornell Medical College, 1305 York Avenue, New York, NY, 10021, USA.
Methods Mol Biol. 2018;1711:277-296. doi: 10.1007/978-1-4939-7493-1_14.
Fulfilling the promises of precision medicine will depend on our ability to create patient-specific treatment regimens. Therefore, being able to translate genomic sequencing into predicting how a patient will respond to a given drug is critical. In this chapter, we review common bioinformatics approaches that aim to use sequencing data to predict sample-specific drug susceptibility. First, we explain the importance of customized drug regimens to the future of medical care. Second, we discuss the different public databases and community efforts that can be leveraged to develop new methods for identifying new predictive biomarkers. Third, we cover the basic methods that are currently used to identify markers or signatures of drug response, without any prior knowledge of the drug's mechanism of action. We further discuss how one can integrate knowledge about drug targets, mechanisms, and predictive markers to better estimate drug response in a diverse set of samples. We begin this section with a primer on popular methods to identify targets and mechanism of action for new small molecules. This discussion also includes a set of computational methods that incorporate other drug features, which do not relate to drug-induced genetic changes or sequencing data such as drug structures, side-effects, and efficacy profiles. Those additional drug properties can aid in gaining higher accuracy for the identification of drug target and mechanism of action. We then progress to discuss using these targets in combination with disease-specific expression patterns, known pathways, and genetic interaction networks to aid drug choice. Finally, we conclude this chapter with a general overview of machine learning methods that can integrate multiple pieces of sequencing data along with prior drug or biological knowledge to drastically improve response prediction.
实现精准医学的承诺将取决于我们创建针对患者的治疗方案的能力。因此，能够将基因组测序转化为预测患者对特定药物的反应至关重要。在本章中，我们回顾了旨在利用测序数据预测样本特异性药物敏感性的常见生物信息学方法。首先，我们解释定制药物方案对未来医疗保健的重要性。其次，我们讨论了不同的公共数据库和社区努力，这些可用于开发识别新的预测生物标志物的新方法。第三，我们介绍了目前用于识别药物反应标志物或特征的基本方法，而无需事先了解药物的作用机制。我们进一步讨论了如何整合有关药物靶点、作用机制和预测标志物的知识，以更好地估计不同样本中的药物反应。我们以介绍识别新小分子靶点和作用机制的常用方法作为本节的开篇。该讨论还包括一组纳入其他药物特征的计算方法，这些特征与药物诱导的基因变化或测序数据无关，如药物结构、副作用和疗效概况。这些额外的药物特性有助于提高识别药物靶点和作用机制的准确性。然后，我们继续讨论如何将这些靶点与疾病特异性表达模式、已知途径和基因相互作用网络结合起来辅助药物选择。最后，我们以机器学习方法的概述作为本章的结尾，这些方法可以整合多条测序数据以及先前的药物或生物学知识，以大幅提高反应预测能力。
