Measurement of tumor mutational burden (TMB) in routine molecular diagnostics: in silico and real-life analysis of three larger gene panels
Measurement of tumor mutational burden (TMB) in routine molecular diagnostics: in silico and real-life analysis of three larger gene panels
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DOI：
10.1002/ijc.32002
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发表时间：
2019-05-01
影响因子：
6.4
通讯作者：
Stenzinger, Albrecht
中科院分区：
文献类型：
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作者：
Endris, Volker;Buchhalter, Ivo;Stenzinger, Albrecht
评估肿瘤突变负荷(TMB)对接受免疫检查点抑制剂治疗的癌症患者的反应分层是一种新的生物标志物。TMB通常被定义为外显子体细胞突变的总数，近似于免疫系统潜在识别的新抗原的数量。虽然整个外显子组测序(WES)是一种公正的方法来量化TMB，但在诊断学中的实施受到组织可用性以及时间和成本限制的阻碍。相反，基于面板的靶向测序如今被广泛应用于常规分子诊断，但关于其性能的数据非常有限，用于TMB估计。在这里，我们评估了三个商业上可用的更大的基因板，其覆盖的基因组区域为0.39兆碱基对(MBP)、0.53MBP和1.7MBP，使用i)对TCGA(癌症基因组图谱)数据的电子分析和ii)湿法实验室测序，总共92个福尔马林固定和石蜡包埋(FFPE)癌症样本分组在三个独立的队列(非小细胞肺癌，NSCLC；结直肠癌，结直肠癌；和混合癌症类型)，可获得匹配的WES数据。我们观察到小组数据与WES突变计数有很强的相关性，特别是对于基因小组>；1MBP。通过湿法实验室实验确定的非小细胞肺癌检查点抑制反应的TMB临界点的灵敏度和特异度很好地反映了计算机数据。此外，我们强调了生物信息学管道中的潜在陷阱，并提供了变异过滤的建议。总之，我们的研究对于免疫肿瘤学领域的研究人员以及计划TMB检测的诊断实验室来说是一个有价值的数据来源。
Assessment of Tumor Mutational Burden (TMB) for response stratification of cancer patients treated with immune checkpoint inhibitors is emerging as a new biomarker. Commonly defined as the total number of exonic somatic mutations, TMB approximates the amount of neoantigens that potentially are recognized by the immune system. While whole exome sequencing (WES) is an unbiased approach to quantify TMB, implementation in diagnostics is hampered by tissue availability as well as time and cost constrains. Conversely, panel-based targeted sequencing is nowadays widely used in routine molecular diagnostics, but only very limited data are available on its performance for TMB estimation. Here, we evaluated three commercially available larger gene panels with covered genomic regions of 0.39 Megabase pairs (Mbp), 0.53 Mbp and 1.7 Mbp using i) in silico analysis of TCGA (The Cancer Genome Atlas) data and ii) wet-lab sequencing of a total of 92 formalin-fixed and paraffin-embedded (FFPE) cancer samples grouped in three independent cohorts (non-small cell lung cancer, NSCLC; colorectal cancer, CRC; and mixed cancer types) for which matching WES data were available. We observed a strong correlation of the panel data with WES mutation counts especially for the gene panel >1Mbp. Sensitivity and specificity related to TMB cutpoints for checkpoint inhibitor response in NSCLC determined by wet-lab experiments well reflected the in silico data. Additionally, we highlight potential pitfalls in bioinformatics pipelines and provide recommendations for variant filtering. In summary, our study is a valuable data source for researchers working in the field of immuno-oncology as well as for diagnostic laboratories planning TMB testing.