Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany; Translational Lung Research Center (TLRC) Heidelberg, German Center for Lung Research (DZL), Heidelberg, Germany.
Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany.
J Thorac Oncol. 2020 Jul;15(7):1177-1189. doi: 10.1016/j.jtho.2020.01.023. Epub 2020 Feb 29.
Tumor mutational burden (TMB) is a quantitative assessment of the number of somatic mutations within a tumor genome. Immunotherapy benefit has been associated with TMB assessed by whole-exome sequencing (wesTMB) and gene panel sequencing (psTMB). The initiatives of Quality in Pathology (QuIP) and Friends of Cancer Research have jointly addressed the need for harmonization among TMB testing options in tissues. This QuIP study identifies critical sources of variation in psTMB assessment.
A total of 20 samples from three tumor types (lung adenocarcinoma, head and neck squamous cell carcinoma, and colon adenocarcinoma) with available WES data were analyzed for psTMB using six panels across 15 testing centers. Interlaboratory and interplatform variation, including agreement on variant calling and TMB classification, were investigated. Bridging factors to transform psTMB to wesTMB values were empirically derived. The impact of germline filtering was evaluated.
Sixteen samples had low interlaboratory and interpanel psTMB variation, with 87.7% of pairwise comparisons revealing a Spearman's ρ greater than 0.6. A wesTMB cut point of 199 missense mutations projected to psTMB cut points between 7.8 and 12.6 mutations per megabase pair; the corresponding psTMB and wesTMB classifications agreed in 74.9% of cases. For three-tier classification with cut points of 100 and 300 mutations, agreement was observed in 76.7%, weak misclassification in 21.8%, and strong misclassification in 1.5% of cases. Confounders of psTMB estimation included fixation artifacts, DNA input, sequencing depth, genome coverage, and variant allele frequency cut points.
This study provides real-world evidence that all evaluated panels can be used to estimate TMB in a routine diagnostic setting and identifies important parameters for reliable tissue TMB assessment that require careful control. As complex or composite biomarkers beyond TMB are likely playing an increasing role in therapy prediction, the efforts by QuIP and Friends of Cancer Research also delineate a general framework and blueprint for the evaluation of such assays.
肿瘤突变负担(TMB)是对肿瘤基因组中体细胞突变数量的定量评估。免疫疗法的益处与全外显子组测序(wesTMB)和基因panel 测序(psTMB)评估的 TMB 相关。病理学质量倡议(QuIP)和癌症研究之友的联合倡议解决了组织中 TMB 检测选项协调的必要性。本 QuIP 研究确定了 psTMB 评估中关键的变异来源。
对三种肿瘤类型(肺腺癌、头颈部鳞状细胞癌和结肠腺癌)的 20 个样本进行分析,这些样本具有可用的 WES 数据,使用 15 个检测中心的六个 panel 进行 psTMB 分析。研究了实验室间和平台间的变异,包括变异调用和 TMB 分类的一致性。通过经验推导了将 psTMB 转化为 wesTMB 值的桥梁因素。评估了种系过滤的影响。
16 个样本的实验室间和面板间 psTMB 变异较小,87.7%的成对比较显示 Spearman's ρ 值大于 0.6。wesTMB 截断值 199 个错义突变预测 psTMB 截断值为 7.8 至 12.6 个突变/兆碱基对;74.9%的情况下,相应的 psTMB 和 wesTMB 分类一致。对于 100 和 300 个突变的三级分类,观察到 76.7%的情况一致,21.8%的情况弱分类错误,1.5%的情况强分类错误。psTMB 估计的混杂因素包括固定假象、DNA 输入、测序深度、基因组覆盖和变异等位基因频率截断值。
本研究提供了真实世界的证据,表明所有评估的 panel 都可以用于常规诊断环境中的 TMB 估计,并确定了可靠的组织 TMB 评估所需的重要参数。由于复杂或复合生物标志物(除了 TMB 之外)可能在治疗预测中发挥越来越重要的作用,QuIP 和癌症研究之友的努力还为此类检测的评估勾勒出了一个通用框架和蓝图。