Evidence map›Paper›PMID 38023147›Full record

ArticleFrontiers in oncology2023

Use of comprehensive genomic profiling for biomarker discovery for the management of non-small cell lung cancer brain metastases.

Mohammed Abdulhaleem, John C Hunting, Yuezhu Wang, Margaret R Smith, Ralph D' Jr Agostino, Thomas Lycan, Michael K Farris, James Ververs, Hui-Wen Lo, Kounosuke Watabe and 8 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.5field-weighted citation impact, top 28% 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

4 citing papers in PubMed, 2 citations in OpenAlex.

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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

18 authors at 3 institutions in 1 country.

Mohammed AbdulhaleemDepartment of Internal Medicine (Hematology & Oncology), Wake Forest School of Medicine, Winston-Salem, NC, United States.
John C HuntingDepartment of Internal Medicine (Hematology & Oncology), Wake Forest School of Medicine, Winston-Salem, NC, United States.
Yuezhu WangDepartment of Cancer Biology, Wake Forest School of Medicine, Winston-Salem, NC, United States.
Margaret R SmithDepartment of Cancer Biology, Wake Forest School of Medicine, Winston-Salem, NC, United States.
Ralph D' Jr AgostinoDepartment of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, United States.
Thomas LycanDepartment of Internal Medicine (Hematology & Oncology), Wake Forest School of Medicine, Winston-Salem, NC, United States.
Michael K FarrisDepartment of Radiation Oncology, Wake Forest School of Medicine, Winston-Salem, NC, United States.
James VerversDepartment of Radiation Oncology, Wake Forest School of Medicine, Winston-Salem, NC, United States.
Hui-Wen LoDepartment of Cancer Biology, Wake Forest School of Medicine, Winston-Salem, NC, United States.
Kounosuke WatabeDepartment of Cancer Biology, Wake Forest School of Medicine, Winston-Salem, NC, United States.
Umit TopalogluDepartment of Cancer Biology, Wake Forest School of Medicine, Winston-Salem, NC, United States.
Wencheng LiDepartment of Pathology, Wake Forest School of Medicine, Winston-Salem, NC, United States.
Christopher WhitlowDepartment of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, United States.
Jing SuDepartment of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN, United States.
Ge WangDepartment of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, United States.
Michael D ChanDepartment of Radiation Oncology, Wake Forest School of Medicine, Winston-Salem, NC, United States.
Fei XingDepartment of Cancer Biology, Wake Forest School of Medicine, Winston-Salem, NC, United States.
Jimmy RuizDepartment of Internal Medicine (Hematology & Oncology), Wake Forest School of Medicine, Winston-Salem, NC, United States.
Wake Forest University · USIndiana University – Purdue University Indianapolis · USRensselaer Polytechnic Institute · US

Funding

Tumor Microenvironment and Metastasis ProgramP30CA082709 · NCI · INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS · PI Sara K Quinney · 1999 to 2026
$59.3M
NCI NIH HHS P30 CA082709
6 · The paper itself

Abstract

Background: Clinical biomarkers for brain metastases remain elusive. Increased availability of genomic profiling has brought discovery of these biomarkers to the forefront of research interests. Method: In this single institution retrospective series, 130 patients presenting with brain metastasis secondary to Non-Small Cell Lung Cancer (NSCLC) underwent comprehensive genomic profiling conducted using next generation circulating tumor deoxyribonucleic acid (DNA) (Guardant Health, Redwood City, CA). A total of 77 genetic mutation identified and correlated with nine clinical outcomes using appropriate statistical tests (general linear models, Mantel-Haenzel Chi Square test, and Cox proportional hazard regression models). For each outcome, a genetic signature composite score was created by summing the total genes wherein genes predictive of a clinically unfavorable outcome assigned a positive score, and genes with favorable clinical outcome assigned negative score. Results: Seventy-two genes appeared in at least one gene signature including: 14 genes had only unfavorable associations, 36 genes had only favorable associations, and 22 genes had mixed effects. Statistically significant associated signatures were found for the clinical endpoints of brain metastasis velocity, time to distant brain failure, lowest radiosurgery dose, extent of extracranial metastatic disease, concurrent diagnosis of brain metastasis and NSCLC, number of brain metastases at diagnosis as well as distant brain failure. Some genes were solely associated with multiple favorable or unfavorable outcomes. Conclusion: Genetic signatures were derived that showed strong associations with different clinical outcomes in NSCLC brain metastases patients. While these data remain to be validated, they may have prognostic and/or therapeutic impact in the future. Statement of translation relevance: Using Liquid biopsy in NSCLC brain metastases patients, the genetic signatures identified in this series are associated with multiple clinical outcomes particularly these ones that lead to early or more numerous metastases. These findings can be reverse-translated in laboratory studies to determine if they are part of the genetic pathway leading to brain metastasis formation.

Indexed as

biomarkersbrain metastasisgenomic profilingnon-small cell lung canceroutcomes

Identifiers

PMID38023147
PMCPMC10661935
OpenAlexW4388458568

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.