Evidence map›Paper›PMID 34430361›Full record

ArticleTranslational lung cancer research2021

Serum tumor markers for the prediction of concordance between genomic profiles from liquid and tissue biopsy in patients with advanced lung adenocarcinoma.

Xiao-Dong Jiao, Li-Ren Ding, Chuan-Tao Zhang, Bao-Dong Qin, Ke Liu, Lian-Ping Jiang, Xi Wang, Li-Ting Lv, Hao Ding, Dao-Ming Li and 13 more

Abstract read
In one paragraph

Article in Translational lung cancer research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 2 pooled it
–field-weighted citation impact
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

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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

8 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Diagnostic accuracy of next-generation sequencing (NGS) for identifying actionable mutations in advanced non-small cell lung cancer: Systematic Review and Meta-Analysis.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026
    Pooled it
  2. Detection ofFrontiers in oncology · 2023
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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

23 authors.

Xiao-Dong JiaoDepartment of Medical Oncology, Changzheng Hospital, Naval Medical University, Shanghai, China.
Li-Ren DingDepartment of Respiratory Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine at Bingjiang, Hangzhou, China.
Chuan-Tao ZhangDepartment of Medical Oncology, The Affiliated Hospital of Qingdao University, Qingdao, China.
Bao-Dong QinDepartment of Medical Oncology, Changzheng Hospital, Naval Medical University, Shanghai, China.
Ke LiuDepartment of Medical Oncology, Changzheng Hospital, Naval Medical University, Shanghai, China.
Lian-Ping JiangDepartment of Chemotherapy, Minhang Branch, Fudan University, Shanghai Cancer Center, Shanghai, China.
Xi WangDepartment of Oncology, The 903rd Hospital of PLA, Hangzhou, China.
Li-Ting LvDepartment of Oncology, Affiliated Hospital of Nantong University, Nantong, China.
Hao DingDivision of Respiratory Disease, Affiliated People's Hospital of Jiangsu University, Zhenjiang, China.
Dao-Ming LiDepartment of Oncology, The First Affiliated Hospital of Soochow University, Suzhou, China.
Hui YangDepartment of Medical Oncology, Suzhou Hospital Affiliated to Nanjing Medical University, Suzhou, China.
Xue-Qin ChenDepartment of Oncology, Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Wen-Yu ZhuDepartment of Oncology, Changzhou No. 2 People's Hospital Cancer Center, Changzhou, China.
Ying WuDepartment of Medical Oncology, Changzheng Hospital, Naval Medical University, Shanghai, China.
Yan LingDepartment of Medical Oncology, Changzheng Hospital, Naval Medical University, Shanghai, China.
Xi HeDepartment of Medical Oncology, Changzheng Hospital, Naval Medical University, Shanghai, China.
Jun LiuDepartment of Medical Oncology, Changzheng Hospital, Naval Medical University, Shanghai, China.
Lin ShaoDepartment of Data Science, Burning Rock Biotech, Guangzhou, China.
Hao-Zhe WangDepartment of Data Science, Burning Rock Biotech, Guangzhou, China.
Yan ChenDepartment of Medicine, Burning Rock Biotech, Guangzhou, China.
Jing-Jing ZhengDepartment of Medicine, Burning Rock Biotech, Guangzhou, China.
Naoki InuiSecond Division, Department of Internal Medicine, Hamamatsu University School of Medicine, Handayama, Hamamatsu, Japan.
Yuan-Sheng ZangDepartment of Medical Oncology, Changzheng Hospital, Naval Medical University, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe concordance between mutations detected from plasma and tissue is critical for treatment choices of patients with advanced lung adenocarcinoma.

methodsWe prospectively analyzed the association of the serum tumor markers with the concordance between blood and tissue genomic profiles from 185 patients with advanced lung adenocarcinoma. The concordance was defined according to 3 criteria. Class 1 included all targetable driver mutations in 8 genes; class 2 included class 1 mutations plus mutations in

resultsCollectively, 150 out of 185 patients had mutations in both tissue and plasma samples, while one patient was mutation-negative for both, resulting a concordance of 81.6%. The concordance rate for class 1 mutations was 80%, and 65% and 69% for class 2 and class 3, respectively. Carbohydrate antigen 19-9 (CA19-9) or cytokeratin 19 (CYFRA21-1) levels higher than the normal upper limit predicted the concordance of tissue and blood results in class 1 (P=0.005, P=0.011), class 2 (P=0.011, P<0.001), and class 3 (P=0.001, P=0.014). In class 1, the cutoff values of CA19-9 were 30, 36, and 284 U/mL to reach the concordance thresholds of 90%, 95%, and 100%, respectively (P=0.032, P=0.003, P=0.043). For CYFRA21-1, the cutoff values were 6, 18, and 52 µg/L (P=0.005, P=0.051, P=0.354). In class 2, the cutoff values for CYFRA21-1 were 18, 22, and 52 µg/L (P=0.001, P=0.001, P=0.052). In class 3, the cutoff values for CA19-9 were 36, 39, and 85 U/mL (P=0.003, P=0.001, P=0.008). For CYFRA21-1, the cutoff values were 22, 52, and 52 µg/L (P=0.900, P>0.99, P>0.99). When the sum score for 4 serum tumor markers was greater than 35, both class 1, class 2, and class 3 reached a predictive threshold of 90%.

conclusionsSerum tumor markers can be used as easy and practical clinical predictors of concordance in mutation profiles between blood and tissue samples from patients with advanced lung adenocarcinoma.

Indexed as

liquid biopsynext generation sequencing (NGS)non-small cell lung cancer (NSCLC)tissue biopsyTumor markers

Identifiers

PMID34430361
PMCPMC8350084

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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.