National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, P. R. China; Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, P. R. China; Beijing Engineering Research Center of Laboratory Medicine, Beijing Hospital, Beijing, P. R. China.
National Center for Clinical Laboratories, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology, P. R. China; Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, P. R. China; Beijing Engineering Research Center of Laboratory Medicine, Beijing Hospital, Beijing, P. R. China.
J Adv Res. 2023 Feb;44:161-172. doi: 10.1016/j.jare.2022.03.016. Epub 2022 Mar 31.
Clinical precision oncology increasingly relies on accurate genome-wide profiling using large panel next generation sequencing; however, difficulties in accurate and consistent detection of somatic mutation from individual platforms and pipelines remain an open question.
To obtain paired tumor-normal reference materials that can be effectively constructed and interchangeable with clinical samples, and evaluate the performance of 56 panels under routine testing conditions based on the reference samples.
Genes involved in mismatch repair and DNA proofreading were knocked down using the CRISPR-Cas9 technology to accumulate somatic mutations in a defined GM12878 cell line. They were used as reference materials to comprehensively evaluate the reproducibility and accuracy of detection results of oncopanels and explore the potential influencing factors.
In total, 14 paired tumor-normal reference DNA samples from engineered cell lines were prepared, and a reference dataset comprising 168 somatic mutations in a high-confidence region of 1.8 Mb were generated. For mutations with an allele frequency (AF) of more than 5% in reference samples, 56 panels collectively reported 1306 errors, including 729 false negatives (FNs), 179 false positives (FPs) and 398 reproducibility errors. The performance metric varied among panels with precision and recall ranging from 0.773 to 1 and 0.683 to 1, respectively. Incorrect and inadequate filtering accounted for a large proportion of false discovery (including FNs and FPs), while low-quality detection, cross-contamination and other sequencing errors during the wet bench process were other sources of FNs and FPs. In addition, low AF (<5%) considerably influenced the reproducibility and comparability among panels.
This study provided an integrated practice for developing reference standard to assess oncopanels in detecting somatic mutations and quantitatively revealed the source of detection errors. It will promote optimization, validation, and quality control among laboratories with potential applicability in clinical use.
临床精准肿瘤学越来越依赖于使用大型面板下一代测序进行准确的全基因组分析；然而，从个体平台和管道中准确且一致地检测体细胞突变仍然是一个悬而未决的问题。
获得可与临床样本有效构建和互换的配对肿瘤-正常参考材料，并基于参考样本评估 56 个面板在常规测试条件下的性能。
使用 CRISPR-Cas9 技术敲除涉及错配修复和 DNA 校对的基因，在已定义的 GM12878 细胞系中积累体细胞突变。将其用作参考材料，全面评估肿瘤 panel 的重现性和检测结果的准确性，并探索潜在的影响因素。
总共制备了 14 对来自工程细胞系的肿瘤-正常参考 DNA 样本，并生成了一个包含 1.8Mb 高置信区 168 个体细胞突变的参考数据集。对于参考样本中等位基因频率（AF）大于 5%的突变，56 个面板总共报告了 1306 个错误，包括 729 个假阴性（FN）、179 个假阳性（FP）和 398 个重现性错误。性能指标因面板而异，精度和召回率分别在 0.773 到 1 和 0.683 到 1 之间。错误和不充分的过滤导致假发现（包括 FN 和 FP）的比例很大，而湿实验过程中的低质量检测、交叉污染和其他测序错误是 FN 和 FP 的其他来源。此外，低 AF（<5%）极大地影响了面板之间的重现性和可比性。
本研究为评估肿瘤 panel 检测体细胞突变的参考标准的制定提供了综合实践，并定量揭示了检测错误的来源。它将促进实验室之间的优化、验证和质量控制，具有潜在的临床应用适用性。
