Objectives

Retrospective data including subgroup analyses in clinical studies have sparked strong interest in developing tumor mutational burden (TMB) as a predictive biomarker for immune checkpoint blockade. While individual factors influencing panel sequencing based measurement of TMB (psTMB) have been discussed in recent studies, an integrative study quantifying, comparing and combining all potential confounders is still missing.

Material and methods

We separated different potential confounders of psTMB measurement including “panel size”, “germline mutation filtering”, “biological variance” and “technical variance” and developed a specific error model for each of these factors. Published experimental psTMB data were fitted to the error models to quantify the contribution of each of the confounders. The total psTMB variance was obtained as sum over the different variance contributions.

Results

Using a typical large panel (size 1 to 1.5 Mbp) total errors of 57%, 42%, 34% and 28% were observed for tumors with psTMB of 5, 10, 20 and 40 muts/Mbp. Even for large panels, the stochastic error connected to the panel size represented the largest of all contributions to the total psTMB variance, especially for tumors with TMB up to 20 muts/Mbp. Other sources of psTMB variability could be kept under control, but rigorous quality control, best practice laboratory workflows and optimized bioinformatics pipelines are essential.

Conclusion

A statistical framework for the analysis of complex, quantitative biomarkers was developed and applied to the analysis of psTMB variability. The methods developed here can support the analysis of other quantitative biomarkers and their implementation in clinical practice.

中文翻译：

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量化基于面板的肿瘤突变负担（TMB）测量的潜在混杂因素。

目标

回顾性数据包括临床研究中的亚组分析引起了人们对于发展肿瘤突变负担（TMB）作为免疫检查点封锁的预测生物标志物的浓厚兴趣。尽管在最近的研究中已经讨论了影响基于面板测序的TMB（psTMB）测量的各个因素，但仍缺少量化，比较和组合所有潜在混杂因素的综合研究。

材料与方法

我们分离了psTMB测量的不同潜在混杂因素，包括“面板大小”，“种系突变过滤”，“生物方差”和“技术方差”，并针对每个因素开发了特定的误差模型。将已发布的实验psTMB数据拟合到误差模型中，以量化每个混杂因素的贡献。psTMB总方差是不同方差贡献的总和。

结果

使用典型的大型面板（大小为1至1.5 Mbp），对于psTMB为5、10、20和40 muts / Mbp的肿瘤，总误差为57％，42％，34％和28％。即使对于大型面板，与面板大小相关的随机误差也代表了对总psTMB方差的最大贡献，尤其是对于TMB高达20 muts / Mbp的肿瘤。psTMB变异性的其他来源可以得到控制，但严格的质量控制，最佳实践实验室工作流程和优化的生物信息学流程至关重要。

结论

开发了用于分析复杂的定量生物标志物的统计框架，并将其应用于psTMB变异性分析。此处开发的方法可以支持其他定量生物标志物的分析及其在临床实践中的实施。