Suppr超能文献

低肿瘤含量对全外显子组测序和靶向 panel 测序检测肿瘤突变负荷的影响。

Influence of low tumor content on tumor mutational burden estimation by whole-exome sequencing and targeted panel sequencing.

机构信息

Department of In Vitro Diagnostic Reagent, National Institutes for Food And Drug Control (NIFDC), Beijing, China.

Department of In Vitro Diagnostic Reagent, Beijing Institute of Medical Device Testing, Beijing, China.

出版信息

Clin Transl Med. 2021 May;11(5):e415. doi: 10.1002/ctm2.415.

Abstract

BACKGROUND

Tumor mutational burden (TMB) is a promising biomarker for stratifying patient subpopulation who would benefit from immune checkpoint blockade (ICB) therapies. Although great efforts have been made for standardizing TMB measurement, mutation calling and TMB quantification can be challenging in samples with low tumor content including liquid biopsies. The effect of varying tumor content on TMB estimation by different assay methods has never been systematically investigated.

METHOD

We established a series of reference standard DNA samples derived from 11 pairs of tumor-normal matched human cell lines across different cancer types. Each tumor cell line was mixed with its matched normal at 0% (control), 1%, 2%, 5%, and 10% mass-to-mass ratio to mimic the clinical samples with low tumor content. TMB of these reference standards was evaluated by both ∼1000× whole-exome sequencing (wesTMB) and targeted panel sequencing (psTMB) at four different vendors. Both regression and classification analyses of TMB were performed for theoretical investigation and clinical practice purposes.

RESULTS

Linear regression model was established that demonstrated in silico psTMB determined by regions of interest (ROI) as a great representative of wesTMB based on TCGA dataset. It was also true in our reference standard samples as the predicted psTMB interval based on the observed wesTMB captured the intended 90% of the in silico psTMB values. Although ∼1000× deep WES was applied, reference standard samples with less than 5% of tumor proportions are below the assay limit of detection (LoD) of wesTMB quantification. However, predicted wesTMB based on observed psTMB accurately classify (>0.97 AUC) for TMB high and low patient stratification even in samples with 2% of tumor content, which is more clinically relevant, as TMB determination should be a qualitative assay for TMB high and low patient classification. One targeted panel sequencing vendor using an optimized blood psTMB pipeline can further classify TMB status accurately (>0.82 AUC) in samples with only 1% of tumor content.

CONCLUSIONS

We developed a linear model to establish the quantitative correlation between wesTMB and psTMB. A set of DNA reference standards was produced in aid to standardize TMB measurements in samples with low tumor content across different targeted sequencing panels. This study is a significant contribution aiming to harmonize TMB estimation and extend its future application in clinical samples with low tumor content including liquid biopsy.

摘要

背景

肿瘤突变负荷(TMB)是一种有前途的生物标志物,可用于对可能从免疫检查点阻断(ICB)治疗中获益的患者亚群进行分层。尽管为标准化 TMB 测量做出了巨大努力,但在包括液体活检在内的肿瘤含量低的样本中,突变调用和 TMB 定量可能具有挑战性。不同检测方法的肿瘤含量变化对 TMB 估计的影响从未被系统地研究过。

方法

我们建立了一系列源自 11 对不同癌症类型的肿瘤-正常配对人细胞系的参考标准 DNA 样本。每个肿瘤细胞系均以 0%(对照)、1%、2%、5%和 10%的质量比与匹配的正常细胞混合,以模拟肿瘤含量低的临床样本。来自四个不同供应商的 ∼1000×全外显子组测序(wesTMB)和靶向面板测序(psTMB)对这些参考标准的 TMB 进行了评估。为了理论研究和临床实践目的,对 TMB 进行了回归和分类分析。

结果

建立了线性回归模型,该模型表明基于 TCGA 数据集,通过感兴趣区域(ROI)确定的虚拟 psTMB 是基于 TCGA 数据集的 wesTMB 的良好代表。在我们的参考标准样本中也是如此,因为基于观察到的 wesTMB 预测的 psTMB 间隔捕获了预期的 90%虚拟 psTMB 值。尽管应用了 ∼1000×深 WES,但肿瘤比例小于 5%的参考标准样本低于 wesTMB 定量的检测限(LoD)。然而,基于观察到的 psTMB 预测的 wesTMB 可以准确地对 TMB 高和低患者分层进行分类(>0.97 AUC),即使在肿瘤含量为 2%的样本中也是如此,这更具临床意义,因为 TMB 测定应该是 TMB 高和低患者分类的定性测定。使用优化的血液 psTMB 流水线的一个靶向面板测序供应商可以进一步准确地对仅含有 1%肿瘤含量的样本进行 TMB 状态分类(>0.82 AUC)。

结论

我们开发了一种线性模型来建立 wesTMB 和 psTMB 之间的定量相关性。一组 DNA 参考标准被制作出来,以帮助在不同的靶向测序面板中标准化肿瘤含量低的样本中的 TMB 测量。这项研究是一项重要贡献,旨在协调 TMB 估计,并将其扩展到包括液体活检在内的肿瘤含量低的临床样本中的未来应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f5c9/8102856/09dcd0378970/CTM2-11-e415-g004.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验