Evidence map›Paper›PMID 25549342›Full record

ArticlePloS one2014

Kinomic profiling of electromagnetic navigational bronchoscopy specimens: a new approach for personalized medicine.

Joshua C Anderson, Douglas J Minnich, M Christian Dobelbower, Alexander J Denton, Alex M Dussaq, Ashley N Gilbert, Timothy D Rohrbach, Waleed Arafat, Karim Welaya, James A Bonner and 1 more

Erratum issuedAbstract read
In one paragraph

Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–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

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

11 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Oncotarget · 2017
    Article
  7. Article
  8. Article
  9. Mechanistic Parameterization of the Kinomic Signal in Peptide Arrays.Journal of proteomics & bioinformatics · 2016
    Article
  10. KINOMIC ALTERATIONS IN ATYPICAL MENINGIOMA.Medical research archives · 2015
    Article
  11. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Joshua C AndersonThe Department of Radiation Oncology, The University of Alabama at Birmingham, Birmingham, Alabama, United States of America.
Douglas J MinnichThe Department of Surgery, Division of Cardiothoracic Surgery, The University of Alabama at Birmingham, Birmingham, Alabama, United States of America.
M Christian DobelbowerThe Department of Radiation Oncology, The University of Alabama at Birmingham, Birmingham, Alabama, United States of America.
Alexander J DentonThe Department of Radiation Oncology, The University of Alabama at Birmingham, Birmingham, Alabama, United States of America.
Alex M DussaqThe Department of Pathology, The University of Alabama at Birmingham, Birmingham, Alabama, United States of America.
Ashley N GilbertThe Department of Radiation Oncology, The University of Alabama at Birmingham, Birmingham, Alabama, United States of America.
Timothy D RohrbachThe Department of Radiation Oncology, The University of Alabama at Birmingham, Birmingham, Alabama, United States of America.
Waleed ArafatThe Department of Pathology, The University of Alabama at Birmingham, Birmingham, Alabama, United States of America.
Karim WelayaThe University of Alexandria, Alexandria, Egypt.
James A BonnerThe Department of Radiation Oncology, The University of Alabama at Birmingham, Birmingham, Alabama, United States of America.
Christopher D WilleyThe Department of Radiation Oncology, The University of Alabama at Birmingham, Birmingham, Alabama, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeResearchers are currently seeking relevant lung cancer biomarkers in order to make informed decisions regarding therapeutic selection for patients in so-called "precision medicine." However, there are challenges to obtaining adequate lung cancer tissue for molecular analyses. Furthermore, current molecular testing of tumors at the genomic or transcriptomic level are very indirect measures of biological response to a drug, particularly for small molecule inhibitors that target kinases. Kinase activity profiling is therefore theorized to be more reflective of in vivo biology than many current molecular analysis techniques. As a result, this study seeks to prove the feasibility of combining a novel minimally invasive biopsy technique that expands the number of lesions amenable for biopsy with subsequent ex vivo kinase activity analysis.

methodsEight patients with lung lesions of varying location and size were biopsied using the novel electromagnetic navigational bronchoscopy (ENB) technique. Basal kinase activity (kinomic) profiles and ex vivo interrogation of samples in combination with tyrosine kinase inhibitors erlotinib, crizotinib, and lapatinib were performed by PamStation 12 microarray analysis.

resultsKinomic profiling qualitatively identified patient specific kinase activity profiles as well as patient and drug specific changes in kinase activity profiles following exposure to inhibitor. Thus, the study has verified the feasibility of ENB as a method for obtaining tissue in adequate quantities for kinomic analysis and has demonstrated the possible use of this tissue acquisition and analysis technique as a method for future study of lung cancer biomarkers.

conclusionsWe demonstrate the feasibility of using ENB-derived biopsies to perform kinase activity assessment in lung cancer patients.

Indexed as

AgedAged, 80 and overBronchoscopyCrizotinibElectromagnetic PhenomenaErlotinib HydrochlorideFemaleGene Expression ProfilingHumansLapatinibLung NeoplasmsMaleMiddle AgedPhosphotransferasesPrecision MedicineProtein Kinase InhibitorsCrizotinibErlotinib HydrochlorideLapatinibPhosphotransferasesProtein Kinase InhibitorsPyrazolesPyridinesQuinazolines

Identifiers

PMID25549342
PMCPMC4280210

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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.