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

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 challeng...

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Autores principales: Zhang, Wenxin, Wang, Ruixia, Fang, Huan, Ma, Xiangyuan, Li, Dan, Liu, Tao, Chen, Zhenxi, Wang, Ke, Hao, Shiguang, Yu, Zicheng, Chang, Zhili, Na, Chenglong, Wang, Yin, Bai, Jian, Zhang, Yanyan, Chen, Fang, Li, Miao, Chen, Chao, Wei, Liangshen, Li, Jinghua, Chang, Xiaoyan, Qu, Shoufang, Yang, Ling, Huang, Jie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8102856/
https://www.ncbi.nlm.nih.gov/pubmed/34047470
http://dx.doi.org/10.1002/ctm2.415
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author Zhang, Wenxin
Wang, Ruixia
Fang, Huan
Ma, Xiangyuan
Li, Dan
Liu, Tao
Chen, Zhenxi
Wang, Ke
Hao, Shiguang
Yu, Zicheng
Chang, Zhili
Na, Chenglong
Wang, Yin
Bai, Jian
Zhang, Yanyan
Chen, Fang
Li, Miao
Chen, Chao
Wei, Liangshen
Li, Jinghua
Chang, Xiaoyan
Qu, Shoufang
Yang, Ling
Huang, Jie
author_facet Zhang, Wenxin
Wang, Ruixia
Fang, Huan
Ma, Xiangyuan
Li, Dan
Liu, Tao
Chen, Zhenxi
Wang, Ke
Hao, Shiguang
Yu, Zicheng
Chang, Zhili
Na, Chenglong
Wang, Yin
Bai, Jian
Zhang, Yanyan
Chen, Fang
Li, Miao
Chen, Chao
Wei, Liangshen
Li, Jinghua
Chang, Xiaoyan
Qu, Shoufang
Yang, Ling
Huang, Jie
author_sort Zhang, Wenxin
collection PubMed
description 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.
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spelling pubmed-81028562021-05-10 Influence of low tumor content on tumor mutational burden estimation by whole‐exome sequencing and targeted panel sequencing Zhang, Wenxin Wang, Ruixia Fang, Huan Ma, Xiangyuan Li, Dan Liu, Tao Chen, Zhenxi Wang, Ke Hao, Shiguang Yu, Zicheng Chang, Zhili Na, Chenglong Wang, Yin Bai, Jian Zhang, Yanyan Chen, Fang Li, Miao Chen, Chao Wei, Liangshen Li, Jinghua Chang, Xiaoyan Qu, Shoufang Yang, Ling Huang, Jie Clin Transl Med Research Articles 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. John Wiley and Sons Inc. 2021-05-06 /pmc/articles/PMC8102856/ /pubmed/34047470 http://dx.doi.org/10.1002/ctm2.415 Text en © 2021 The Authors. Clinical and Translational Medicine published by John Wiley & Sons Australia, Ltd on behalf of Shanghai Institute of Clinical Bioinformatics https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Articles
Zhang, Wenxin
Wang, Ruixia
Fang, Huan
Ma, Xiangyuan
Li, Dan
Liu, Tao
Chen, Zhenxi
Wang, Ke
Hao, Shiguang
Yu, Zicheng
Chang, Zhili
Na, Chenglong
Wang, Yin
Bai, Jian
Zhang, Yanyan
Chen, Fang
Li, Miao
Chen, Chao
Wei, Liangshen
Li, Jinghua
Chang, Xiaoyan
Qu, Shoufang
Yang, Ling
Huang, Jie
Influence of low tumor content on tumor mutational burden estimation by whole‐exome sequencing and targeted panel sequencing
title Influence of low tumor content on tumor mutational burden estimation by whole‐exome sequencing and targeted panel sequencing
title_full Influence of low tumor content on tumor mutational burden estimation by whole‐exome sequencing and targeted panel sequencing
title_fullStr Influence of low tumor content on tumor mutational burden estimation by whole‐exome sequencing and targeted panel sequencing
title_full_unstemmed Influence of low tumor content on tumor mutational burden estimation by whole‐exome sequencing and targeted panel sequencing
title_short Influence of low tumor content on tumor mutational burden estimation by whole‐exome sequencing and targeted panel sequencing
title_sort influence of low tumor content on tumor mutational burden estimation by whole‐exome sequencing and targeted panel sequencing
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8102856/
https://www.ncbi.nlm.nih.gov/pubmed/34047470
http://dx.doi.org/10.1002/ctm2.415
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