Department of Pathology and Translational Genomics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
Samsung Genome Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
Cancer Res Treat. 2022 Jan;54(1):75-83. doi: 10.4143/crt.2021.107. Epub 2021 May 3.
Detection of telomerase reverse transcriptase (TERT) promoter mutations is a crucial process in the integrated diagnosis of glioblastomas. However, the TERT promoter region is difficult to amplify because of its high guanine-cytosine (GC) content (> 80%). This study aimed to analyze the capturing of TERT mutations by targeted next-generation sequencing (NGS) using formalin-fixed paraffin-embedded tissues.
We compared the detection rate of TERT mutations between targeted NGS and Sanger sequencing in 25 cases of isocitrate dehydrgenase (IDH)-wildtype glioblastomas and 10 cases of non-neoplastic gastric tissues. Our customized panel consisted of 232 essential glioma-associated genes.
Sanger sequencing detected TERT mutations in 17 out of 25 glioblastomas, but all TERT mutations were missed by targeted NGS. After the manual visualization of the NGS data using an integrative genomics viewer, 16 cases showed a TERT mutation with a very low read depth (mean, 21.59; median, 25), which revealed false-negative results using auto-filtering. We optimized our customized panel by extending the length of oligonucleotide baits and increasing the number of baits spanning the coverage of the TERT promoter, which did not amplify well due to the high GC content.
Our study confirmed that it is crucial to consider the recognition of molecular bias and to carefully interpret NGS data.
端粒酶逆转录酶(TERT)启动子突变的检测是胶质母细胞瘤综合诊断的关键过程。然而,由于其高鸟嘌呤-胞嘧啶(GC)含量(>80%),TERT 启动子区域难以扩增。本研究旨在分析使用福尔马林固定石蜡包埋组织进行靶向下一代测序(NGS)对 TERT 突变的捕获。
我们比较了靶向 NGS 和 Sanger 测序在 25 例异柠檬酸脱氢酶(IDH)野生型胶质母细胞瘤和 10 例非肿瘤性胃组织中检测 TERT 突变的检出率。我们的定制面板包含 232 个必需的神经胶质瘤相关基因。
Sanger 测序在 25 例胶质母细胞瘤中有 17 例检测到 TERT 突变,但靶向 NGS 均未检测到所有 TERT 突变。在用综合基因组浏览器手动可视化 NGS 数据后,16 例显示 TERT 突变的读取深度非常低(平均值为 21.59;中位数为 25),这表明使用自动过滤会出现假阴性结果。我们通过延长寡核苷酸诱饵的长度并增加跨越 TERT 启动子覆盖范围的诱饵数量来优化我们的定制面板,由于 GC 含量高,这些区域的扩增效果不佳。
我们的研究证实,考虑分子偏倚的识别并仔细解释 NGS 数据至关重要。