Departments of Epidemiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
University of Texas School of Public Health, Houston, TX, USA.
BMC Bioinformatics. 2018 Jan 4;19(1):5. doi: 10.1186/s12859-017-1991-3.
'Next-generation' (NGS) sequencing has wide application in medical genetics, including the detection of somatic variation in cancer. The Ion Torrent-based (IONT) platform is among NGS technologies employed in clinical, research and diagnostic settings. However, identifying mutations from IONT deep sequencing with high confidence has remained a challenge. We compared various computational variant-calling methods to derive a variant identification pipeline that may improve the molecular diagnostic and research utility of IONT.
Using IONT, we surveyed variants from the 409-gene Comprehensive Cancer Panel in whole-section tumors, intra-tumoral biopsies and matched normal samples obtained from frozen tissues and blood from four early-stage non-small cell lung cancer (NSCLC) patients. We used MuTect, Varscan2, IONT's proprietary Ion Reporter, and a simple subtraction we called "Poor Man's Caller." Together these produced calls at 637 loci across all samples. Visual validation of 434 called variants was performed, and performance of the methods assessed individually and in combination. Of the subset of inspected putative variant calls (n=223) in genomic regions that were not intronic or intergenic, 68 variants (30%) were deemed valid after visual inspection. Among the individual methods, the Ion Reporter method offered perhaps the most reasonable tradeoffs. Ion Reporter captured 83% of all discovered variants; 50% of its variants were visually validated. Aggregating results from multiple packages offered varied improvements in performance.
Overall, Ion Reporter offered the most attractive performance among the individual callers. This study suggests combined strategies to maximize sensitivity and positive predictive value in variant calling using IONT deep sequencing.
“下一代”（NGS）测序在医学遗传学中具有广泛的应用，包括检测癌症中的体细胞变异。基于 Ion Torrent（IONT）的平台是应用于临床、研究和诊断环境中的 NGS 技术之一。然而，从 IONT 深度测序中高置信度地识别突变仍然是一个挑战。我们比较了各种计算变异调用方法，以得出一种可能提高 IONT 的分子诊断和研究效用的变异识别管道。
我们使用 IONT 对来自四个早期非小细胞肺癌（NSCLC）患者的冰冻组织和血液中的全切片肿瘤、肿瘤内活检以及匹配的正常样本中的 409 个基因综合癌症面板进行了变异检测。我们使用 MuTect、Varscan2、IONT 的专有的 Ion Reporter 以及我们称之为“Poor Man's Caller”的简单减法方法。这些方法一起在所有样本中产生了 637 个位点的变异调用。我们对 434 个调用的变异进行了可视化验证，并评估了各个方法以及组合的性能。在经过视觉检查的非内含子或非基因间的基因组区域的被检查的假定变异调用子集（n=223）中，有 68 个变异（30%）被认为是有效的。在单个方法中，Ion Reporter 方法提供了最合理的权衡。Ion Reporter 捕获了所有发现的变异的 83%；其中 50%的变异经过了视觉验证。聚合来自多个软件包的结果可以在变异调用中提高性能。
总体而言，Ion Reporter 在单个调用器中提供了最有吸引力的性能。本研究提出了使用 IONT 深度测序的组合策略，以最大化变异调用的敏感性和阳性预测值。
