Establishing guidelines to harmonize tumor mutational burden (TMB): in silico assessment of variation in TMB quantification across diagnostic platforms: phase I of the Friends of Cancer Research TMB Harmonization Project
Establishing guidelines to harmonize tumor mutational burden (TMB): in silico assessment of variation in TMB quantification across diagnostic platforms: phase I of the Friends of Cancer Research TMB Harmonization Project
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DOI：
10.1136/jitc-2019-000147
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发表时间：
2020-01-01
影响因子：
10.9
通讯作者：
Allen, Jeff
中科院分区：
文献类型：
--
作者：
Merino, Diana M.;Mcshane, Lisa M.;Allen, Jeff
肿瘤突变负荷（TMB），定义为每兆碱基询问的基因组序列的体细胞突变数量，证明了预测生物标志物的潜力，用于识别最有可能对免疫检查点抑制剂有反应的癌症患者。TMB最好通过全外显子组测序（WES）计算，但下一代测序靶向面板以具有时间效益和成本效益的方式提供TMB估计。然而，除了潜在的生物信息学管道之外，面板大小和基因覆盖率的差异是实验室之间TMB估计值变异性的已知驱动因素。通过直接比较来自参与实验室的基于小组的TMB估计值，本研究旨在表征基于小组的TMB估计值的理论变异性，并提供关于TMB报告、分析验证要求和参比标准品比对的指南，以保持跨平台TMB估计值的一致性。方法11个实验室使用来自癌症基因组图谱多中心突变调用多癌症（MC3）样本的WES数据，并使用自己的生物信息学管道（面板TMB）从限制于其靶向面板所涵盖基因的外显子组子集计算TMB。使用所有成员一致同意的统一生物信息学管道（WES TMB）从整个外显子组计算参考TMB值。进行线性回归分析以研究所有32种癌症类型组合和单独的WES和面板TMB之间的关系。还使用95%预测限定量了不同WES TMB值下的面板TMB值的变异性。结果研究结果表明，随着WES TMB值的增加，组内和组间TMB值的变异性增加。对于每个样本组，计算了基于线性回归分析（将样本组TMB建模为WES TMB的函数）的预测限，发现其近似捕获了预期的95%的样本组TMB观测值。某些癌症类型，如子宫癌、膀胱癌和结肠癌，与肺癌和头颈癌相比，TMB值的变异性更大。结论：TMB作为临床预测生物标志物的应用越来越多，迫切需要将利益相关者聚集在一起，就基于小组的TMB估计的关键方面达成一致，例如TMB报告的标准化，分析验证研究的标准化以及基于小组的TMB值与参考标准的一致性。这些协调工作应提高小组TMB估计值的一致性和可靠性，并有助于临床决策。
Background Tumor mutational burden (TMB), defined as the number of somatic mutations per megabase of interrogated genomic sequence, demonstrates predictive biomarker potential for the identification of patients with cancer most likely to respond to immune checkpoint inhibitors. TMB is optimally calculated by whole exome sequencing (WES), but next-generation sequencing targeted panels provide TMB estimates in a time-effective and cost-effective manner. However, differences in panel size and gene coverage, in addition to the underlying bioinformatics pipelines, are known drivers of variability in TMB estimates across laboratories. By directly comparing panel-based TMB estimates from participating laboratories, this study aims to characterize the theoretical variability of panel-based TMB estimates, and provides guidelines on TMB reporting, analytic validation requirements and reference standard alignment in order to maintain consistency of TMB estimation across platforms. Methods Eleven laboratories used WES data from The Cancer Genome Atlas Multi-Center Mutation calling in Multiple Cancers (MC3) samples and calculated TMB from the subset of the exome restricted to the genes covered by their targeted panel using their own bioinformatics pipeline (panel TMB). A reference TMB value was calculated from the entire exome using a uniform bioinformatics pipeline all members agreed on (WES TMB). Linear regression analyses were performed to investigate the relationship between WES and panel TMB for all 32 cancer types combined and separately. Variability in panel TMB values at various WES TMB values was also quantified using 95% prediction limits. Results Study results demonstrated that variability within and between panel TMB values increases as the WES TMB values increase. For each panel, prediction limits based on linear regression analyses that modeled panel TMB as a function of WES TMB were calculated and found to approximately capture the intended 95% of observed panel TMB values. Certain cancer types, such as uterine, bladder and colon cancers exhibited greater variability in panel TMB values, compared with lung and head and neck cancers. Conclusions Increasing uptake of TMB as a predictive biomarker in the clinic creates an urgent need to bring stakeholders together to agree on the harmonization of key aspects of panel-based TMB estimation, such as the standardization of TMB reporting, standardization of analytical validation studies and the alignment of panel-based TMB values with a reference standard. These harmonization efforts should improve consistency and reliability of panel TMB estimates and aid in clinical decision-making.