Sheikh Khalifa Bin Zayed Al Nahyan Institute for Personalized Cancer Therapy.
Department of Systems Biology, University of Texas, MD Anderson Cancer Center, Houston, Texas 77030; email:
Annu Rev Med. 2017 Jan 14;68:113-125. doi: 10.1146/annurev-med-102115-021556. Epub 2016 Nov 2.
The tools of next-generation sequencing (NGS) technology, such as targeted sequencing of candidate cancer genes and whole-exome and -genome sequencing, coupled with encouraging clinical results based on the use of targeted therapeutics and biomarker-guided clinical trials, are fueling further technological advancements of NGS technology. However, NGS data interpretation is associated with challenges that must be overcome to promote the techniques' effective integration into clinical oncology practice. Specifically, sequencing of a patient's tumor often yields 30-65 somatic variants, but most of these variants are "passenger" mutations that are phenotypically neutral and thus not targetable. Therefore, NGS data must be interpreted by multidisciplinary decision-support teams to determine mutation actionability and identify potential "drivers," so that the treating physician can prioritize what clinical decisions can be pursued in order to provide cancer therapy that is personalized to the patient and his or her unique genome.
下一代测序（NGS）技术的工具，如靶向候选癌症基因的测序、外显子组和基因组测序，以及基于靶向治疗和生物标志物指导的临床试验的令人鼓舞的临床结果，正在推动 NGS 技术的进一步技术进步。然而，NGS 数据解释与必须克服的挑战相关联，以促进这些技术有效整合到临床肿瘤学实践中。具体来说，对患者肿瘤的测序通常会产生 30-65 个体细胞变异，但其中大多数变异是表型中性的“乘客”突变，因此无法靶向。因此，必须由多学科决策支持团队来解释 NGS 数据，以确定突变的可操作性，并确定潜在的“驱动因素”，以便治疗医生能够确定可以优先考虑哪些临床决策，以便为患者及其独特的基因组提供个性化的癌症治疗。
