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Abstract：
Next Generation Sequencing (NGS) has dramatically improved the flexibility and outcomes of cancer research and clinical trials, providing highly sensitive and accurate high-throughput platforms for large-scale genomic testing. In contrast to whole-genome (WGS) or whole-exome sequencing (WES), targeted genomic sequencing (TS) focuses on a panel of genes or targets known to have strong associations with pathogenesis of disease and/or clinical relevance, offering greater sequencing depth with reduced costs and data burden. This allows targeted sequencing to identify low frequency variants in targeted regions with high confidence, thus suitable for profiling low-quality and fragmented clinical DNA samples. As a result, TS has been widely used in clinical research and trials for patient stratification and the development of targeted therapeutics. However, its transition to routine clinical use has been slow. Many technical and analytical obstacles still remain and need to be discussed and addressed before large-scale and cross-centre implementation. Gold-standard and state-of-the-art procedures and pipelines are urgently needed to accelerate this transition. In this review we first present how TS is conducted in cancer research, including various target enrichment platforms, the construction of target panels, and selected research and clinical studies utilising TS to profile clinical samples. We then present a generalised analytical workflow for TS data discussing important parameters and filters in detail, aiming to provide the best practices of TS usage and analyses.
摘要：
下一代测序（NGS）极大地改善了癌症研究和临床试验的灵活性和结果，为大规模基因组测试提供高度敏感和准确的高通量平台。与全基因组（WGS）或全外显子组测序（WES）相比，靶向基因组测序（TS）专注于一组已知与疾病发病机理和/或临床相关性密切相关的基因或靶标，提供更大的测序深度，降低成本和数据负担。这允许靶向测序以高置信度识别靶向区域中的低频率变体。因此适用于分析低质量和片段化的临床DNA样本。因此,TS已广泛用于临床研究和试验，用于患者分层和靶向疗法的开发。然而,它向常规临床应用的过渡一直很缓慢。许多技术和分析障碍仍然存在，需要在大规模和跨中心实施之前进行讨论和解决。迫切需要黄金标准和最先进的程序和管道来加速这一过渡。在这篇综述中，我们首先介绍了TS是如何在癌症研究中进行的，包括各种目标浓缩平台，目标面板的构建，以及利用TS对临床样本进行分析的选定研究和临床研究。然后，我们为TS数据提供了一个通用的分析工作流程，详细讨论了重要的参数和过滤器，旨在提供TS使用和分析的最佳实践。
