Yuri Sheikine, Beth Israel Deaconess Medical Center, Harvard Medical School; and Frank C. Kuo and Neal I. Lindeman, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.
J Clin Oncol. 2017 Mar 20;35(9):929-933. doi: 10.1200/JCO.2016.70.7539. Epub 2017 Feb 13.
The emergence of precision medicine has been predicated on significant recent advances in diagnostic technology, particularly the advent of next-generation sequencing (NGS). Although the chemical technology underlying NGS is complex, and the computational biology expertise required to build systems to facilely interpret the results is highly specialized, the variables involved in designing and deploying a genomic testing program for cancer can be readily understood and applied by understanding several basic considerations. In this review, we present key strategic decisions required to optimize a genomic testing program and summarize the technical aspects of different technologies that render those methods more or less suitable for different types of programs.
精准医学的出现是基于诊断技术的重大近期进展，尤其是下一代测序（NGS）的出现。尽管 NGS 所基于的化学技术很复杂，并且构建系统以轻松解释结果所需的计算生物学专业知识也非常专业化，但通过了解几个基本的考虑因素，可以很容易地理解和应用设计和部署癌症基因组测试程序所涉及的变量。在这篇综述中，我们提出了优化基因组测试程序所需的关键战略决策，并总结了不同技术的技术方面，这些技术使得这些方法或多或少适合不同类型的程序。
