Evidence map›Paper›PMID 34047470›Full record

ArticleClinical and translational medicine2021

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

Wenxin Zhang, Ruixia Wang, Huan Fang, Xiangyuan Ma, Dan Li, Tao Liu, Zhenxi Chen, Ke Wang, Shiguang Hao, Zicheng Yu and 14 more

Open access · goldAbstract read
In one paragraph

Article in Clinical and translational medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
0.9field-weighted citation impact, top 32% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 11 citations in OpenAlex.

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  7. [The companion diagnostics of targeted therapy and immunotherapy for hepatocellular carcinoma].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2022
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

24 authors at 8 institutions in 2 countries.

Wenxin ZhangDepartment of In Vitro Diagnostic Reagent, National Institutes for Food And Drug Control (NIFDC), Beijing, China.
Ruixia WangDepartment of In Vitro Diagnostic Reagent, Beijing Institute of Medical Device Testing, Beijing, China.
Huan FangGeneplus-Beijing, Beijing, China.ORCID 0000-0003-1306-1358
Xiangyuan MaNanjing Geneseeq Technology Inc., Nanjing, China.
Dan LiGeneplus-Beijing, Beijing, China.
Tao LiuGeneplus-Beijing, Beijing, China.
Zhenxi ChenGeneplus-Beijing, Beijing, China.
Ke WangGeneplus-Beijing, Beijing, China.
Shiguang HaoGeneplus-Beijing, Beijing, China.
Zicheng YuGeneplus-Beijing, Beijing, China.
Zhili ChangNanjing Geneseeq Technology Inc., Nanjing, China.
Chenglong NaNanjing Geneseeq Technology Inc., Nanjing, China.
Yin WangBerry Oncology Corporation, Beijing, China.
Jian BaiBerry Oncology Corporation, Beijing, China.ORCID 0000-0001-5667-0618
Yanyan ZhangMGI, BGI-Shenzhen, Shenzhen, China.
Fang ChenMGI, BGI-Shenzhen, Shenzhen, China.
Miao LiYuceBio Technology Co., Ltd., Shenzhen, China.
Chao ChenYuceBio Technology Co., Ltd., Shenzhen, China.
Liangshen WeiGeneWell Biotech Ltd., Shenzhen, China.
Jinghua LiGeneWell Biotech Ltd., Shenzhen, China.
Xiaoyan ChangDepartment of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tsinghua University, Beijing, China.
Shoufang QuDepartment of In Vitro Diagnostic Reagent, National Institutes for Food And Drug Control (NIFDC), Beijing, China.
Ling YangGeneplus-Beijing, Beijing, China.
Jie HuangDepartment of In Vitro Diagnostic Reagent, National Institutes for Food And Drug Control (NIFDC), Beijing, China.
Annoroad Gene Technology (China) · CNNational Institutes for Food and Drug Control · CNBerry Oncology (China) · CNBGI Group (China) · CNLifetech Scientific (China) · CNYebio Bioengineering (China) · CNChinese Academy of Medical Sciences & Peking Union Medical College · CNThe Medical Device (United Kingdom) · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTumor 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.

methodWe 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.

resultsLinear 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.

conclusionsWe 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.

Indexed as

MutationCell Line, TumorExome SequencingHigh-Throughput Nucleotide SequencingHumansLiquid BiopsyNeoplasmsTumor Burdenbiomarkertargeted panel sequencingtumor mutational burdenwhole-exome sequencing

Identifiers

PMID34047470
PMCPMC8102856
OpenAlexW3160215292

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.