Evidence map›Paper›PMID 30505499›Full record

ArticleJournal of thoracic disease2018

Exploratory analysis of introducing next-generation sequencing-based method to treatment-naive lung cancer patients.

Yufang Feng, Gaohua Feng, Xiaoling Lu, Wenxia Qian, Junyi Ye, Carmen Areses Manrique, Chunping Ma, Yadong Lu, written on behalf of the AME Lung Cancer Collaborative Group

Open access · hybridAbstract read
In one paragraph

Article in Journal of thoracic disease, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
1.0field-weighted citation impact, top 29% 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

15 citing papers in PubMed, 20 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Review
  6. JTO clinical and research reports · 2022
    Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Article
  12. Article
  13. Article
  14. TheJournal of thoracic disease · 2019
    Article
  15. Article
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

9 authors at 2 institutions in 2 countries.

Yufang FengDepartment of Pathology, Zhangjiagang First Peoples Hospital, Suzhou 215000, China.
Gaohua FengDepartment of Respiratory Medicine, Zhangjiagang Hospital of Traditional Chinese Medicine, Suzhou 215000, China.
Xiaoling LuDepartment of Oncology, Zhangjiagang First Peoples Hospital, Suzhou 215000, China.
Wenxia QianDepartment of Respiratory Medicine, Zhangjiagang First Peoples Hospital, Suzhou 215000, China.
Junyi YeBurning Rock Biotech, Guangzhou 510000, China.
Carmen Areses ManriqueComplexo Hospitalario Universitario de Ourense, Calle Ramon Puga Noguerol, Ourense, Spain.
Chunping MaDepartment of Thoracic Surgery, Zhangjiagang First Peoples Hospital, Suzhou 215000, China.
Yadong LuDepartment of Thoracic Surgery, Zhangjiagang First Peoples Hospital, Suzhou 215000, China.
written on behalf of the AME Lung Cancer Collaborative Group
Soochow University · CNBurning Rock Biotech (China) · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe utilization of cancer-linked genetic alterations for categorizing patients against optimal treatment is becoming increasingly popular, especially in non-small cell lung cancer (NSCLC). However, disadvantages of the conventional techniques, such as the low throughput and limited detectable alteration types, lead to the demand of large-scale parallel sequencing for different forms of genetic variants.

methodsWe evaluated the potential of performing next-generation sequencing (NGS)-based methods in a cohort of 61 treatment-naive NSCLC patients to profile their driver mutations, using a panel consisting of 8 well-established driver genes of lung cancer.

resultsOur data revealed that 80% of patients harbored driver mutations. Moreover, our data revealed a few rare mutations, such as

conclusionsOur study highlights the potential of NGS-based methods in treatment-naive patients, thus paving its way for routine clinical use. Investigation of clinical correlation of driver mutations might be helpful for clinicians in cancer diagnosis and has implications for seeking patients with specific gene alteration in clinical studies.

Indexed as

driver mutationsnext-generation sequencing (NGS)Non-small cell lung cancer (NSCLC)targeted therapytreatment-naive

Identifiers

PMID30505499
PMCPMC6236175
OpenAlexW2896307734

What OpenQuestion holds

Textmetadata
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.