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将来自大型癌症检测板的肿瘤突变负荷评估的生物信息学方法与全外显子组测序进行基准测试。

Benchmarking bioinformatics approaches for tumour mutational burden evaluation from a large cancer panel against whole-exome sequencing.

作者信息

Pang Jiuhong, Xia Hongai, Mi Shijun, Zhang Wen, Pendrick Danielle, Freeman Christopher, Fernandes Helen, Mansukhani Mahesh, Hsiao Susan J

机构信息

Pathology and Cell Biology, Columbia University Irving Medical Center, New York City, New York, USA.

Pathology and Cell Biology, Columbia University Irving Medical Center, New York City, New York, USA

出版信息

J Clin Pathol. 2023 Apr;76(4):276-280. doi: 10.1136/jcp-2022-208385. Epub 2022 Jul 29.

Abstract

Tumour mutational burden (TMB) is used to predict response to immunotherapies. Although several groups have proposed calculation methods for TMB, a clear consensus has not yet emerged. In this study, we explored TMB calculation approaches with a 586-gene cancer panel (1.75 Mb) benchmarked to TMB measured by whole-exome sequencing (WES), using 30 samples across a range of tumour types. We explored variant allelic fraction (VAF) cut-offs of 5% and 10%, population database filtering at 0.001, 0.0001 and 0.000025, as well as different combinations of synonymous, insertion/deletion and intronic (splice site) variants, as well as exclusion of hotspot mutations, and examined the effect on TMB correlation. Good correlation (Spearman, range 0.66-0.78) between WES and panel TMB was seen across all methods evaluated. Each method of TMB calculation evaluated showed good positive per cent agreement and negative per cent agreement using 10 mutations/Mb as a cut-off, suggesting that multiple TMB calculation approaches may yield comparable results.

摘要

肿瘤突变负荷(TMB)用于预测免疫治疗的疗效。尽管有几个研究小组提出了TMB的计算方法,但尚未形成明确的共识。在本研究中,我们使用涵盖一系列肿瘤类型的30个样本,探索了基于586个基因的癌症基因检测板(1.75 Mb)的TMB计算方法,并将其与通过全外显子组测序(WES)测量的TMB进行基准比较。我们探讨了5%和10%的变异等位基因频率(VAF)截止值、0.001、0.0001和0.000025的人群数据库过滤,以及同义、插入/缺失和内含子(剪接位点)变异的不同组合,以及排除热点突变,并研究了其对TMB相关性的影响。在所有评估方法中,WES与检测板TMB之间均呈现出良好的相关性(Spearman相关性,范围为0.66 - 0.78)。使用10个突变/Mb作为截止值,所评估的每种TMB计算方法均显示出良好的阳性百分比一致性和阴性百分比一致性,这表明多种TMB计算方法可能会产生可比的结果。

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