Department of Oncology, Addenbrooke's Hospital, Cambridge University Hospitals National Health Service (NHS) Foundation Trust, Cambridge CB2 0QQ, United Kingdom; email:
Medical Research Council (MRC) Cancer Unit, University of Cambridge, Cambridge CB2 0XZ, United Kingdom.
Annu Rev Biochem. 2019 Jun 20;88:247-280. doi: 10.1146/annurev-biochem-062917-011840. Epub 2019 Mar 22.
The complexity of human cancer underlies its devastating clinical consequences. Drugs designed to target the genetic alterations that drive cancer have improved the outcome for many patients, but not the majority of them. Here, we review the genomic landscape of cancer, how genomic data can provide much more than a sum of its parts, and the approaches developed to identify and validate genomic alterations with potential therapeutic value. We highlight notable successes and pitfalls in predicting the value of potential therapeutic targets and discuss the use of multi-omic data to better understand cancer dependencies and drug sensitivity. We discuss how integrated approaches to collecting, curating, and sharing these large data sets might improve the identification and prioritization of cancer vulnerabilities as well as patient stratification within clinical trials. Finally, we outline how future approaches might improve the efficiency and speed of translating genomic data into clinically effective therapies and how the use of unbiased genome-wide information can identify novel predictive biomarkers that can be either simple or complex.
人类癌症的复杂性是其具有破坏性临床后果的基础。旨在针对驱动癌症的遗传改变的药物已经改善了许多患者的预后,但不是大多数患者的预后。在这里,我们回顾了癌症的基因组景观,以及基因组数据如何提供的不仅仅是其各个部分的总和,以及开发的用于识别和验证具有潜在治疗价值的基因组改变的方法。我们强调了在预测潜在治疗靶点的价值方面的显著成功和陷阱,并讨论了使用多组学数据来更好地了解癌症依赖性和药物敏感性。我们讨论了如何通过综合方法来收集、管理和共享这些大型数据集,以提高对癌症脆弱性的识别和优先级划分,并在临床试验中对患者进行分层。最后,我们概述了未来的方法如何提高将基因组数据转化为临床有效治疗方法的效率和速度,以及使用无偏基因组信息如何识别新的预测性生物标志物,这些标志物可以是简单的也可以是复杂的。