Evidence map›Paper›PMID 31222846›Full record

ArticleCancer science2019

Small lung tumor biopsy samples are feasible for high quality targeted next generation sequencing.

Hidenori Kage, Shinji Kohsaka, Aya Shinozaki-Ushiku, Yoshihisa Hiraishi, Jiro Sato, Kazuhiro Nagayama, Tetsuo Ushiku, Daiya Takai, Jun Nakajima, Kiyoshi Miyagawa and 3 more

Open access · goldAbstract read
In one paragraph

Article in Cancer science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed
2.3field-weighted citation impact, top 12% 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

36 citing papers in PubMed, 48 citations in OpenAlex.

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  13. Problems in the Pathologic Diagnosis of Suspected Lung Cancer.Tuberculosis and respiratory diseases · 2023
    Article
  14. Small samples, big problems: lipoid pneumonia mimicking lung adenocarcinoma.Jornal brasileiro de pneumologia : publicacao oficial da Sociedade Brasileira de Pneumologia e Tisilogia · 2023
    Article
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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

13 authors at 1 institution in 1 country.

Hidenori KageDepartment of Respiratory Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.ORCID https://orcid.org/0000-0003-3658-4442
Shinji KohsakaDivision of Cellular Signaling, National Cancer Center Research Institute, Tokyo, Japan.ORCID https://orcid.org/0000-0001-8651-6136
Aya Shinozaki-UshikuDepartment of Pathology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Yoshihisa HiraishiDepartment of Respiratory Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Jiro SatoDepartment of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Kazuhiro NagayamaDepartment of Thoracic Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Tetsuo UshikuDepartment of Pathology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Daiya TakaiDepartment of Clinical Laboratory, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Jun NakajimaDepartment of Thoracic Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Kiyoshi MiyagawaLaboratory of Molecular Radiology, Center for Disease Biology and Integrative Medicine, The University of Tokyo, Tokyo, Japan.
Hiroyuki AburataniGenome Science Laboratory, Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan.
Hiroyuki ManoDivision of Cellular Signaling, National Cancer Center Research Institute, Tokyo, Japan.ORCID https://orcid.org/0000-0003-4645-0181
Takahide NagaseDepartment of Respiratory Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
The University of Tokyo · JP

Funding

Japan Agency for Medical Research and DevelopmentSysmex Corporation
6 · The paper itself

Abstract

Next-generation sequencing (NGS) has been implemented in clinical oncology to analyze multiple genes and to guide therapy. In patients with advanced lung cancer, small biopsies such as computed tomography-guided needle biopsy (CTNB), endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) and transbronchial biopsy (TBB) are less invasive and are preferable to resection to make a pathological diagnosis. However, the quality of DNA/RNA and NGS from small lung tumor biopsy samples is unknown. Between April 2017 and March 2018, 107 consecutive samples were obtained from thoracic tumors or metastatic sites for targeted NGS analysis. Fifteen samples were obtained through CTNB, 11 through EBUS-TBNA, 11 through TBB and 70 through surgical resection. All samples were formalin-fixed and paraffin-embedded. DNA and RNA quality was measured using the ddCq method and the percentage of RNA fragments above 200 nucleotides (DV200), respectively. Our custommade probes were designed to capture exon sequences of 464 cancer-related genes and transcripts of 463 genes. DNA and RNA yield from the 3 biopsy methods were similar, and less than the yield obtained from resected samples. The quality of DNA and RNA was similar across all methods. Overall, 12 of 15 CTNB samples (80%), all 11 EBUS-TBNA samples, and 9 of 11 TBB samples (82%) underwent successful NGS assays from DNA. NGS analysis from RNA was successful in all 12 CTNB samples, 9 of 11 EBUS-TBNA samples (82%), and 8 of 11 TBB samples (73%). CTNB, EBUS-TBNA and TBB mostly resulted in adequate DNA and RNA quality and enabled high-quality targeted NGS analysis.

Indexed as

BiopsyDNAHigh-Throughput Nucleotide SequencingHumansLung NeoplasmsRNADNARNAbiopsybronchoscopyCT-guided needle biopsylung cancertargeted next generation sequencing

Identifiers

PMID31222846
PMCPMC6676108
OpenAlexW2949097131

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.