oru.sePublikationer
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Biomarker Discovery in Non-Small Cell Lung Cancer: Integrating Gene Expression Profiling, Meta-analysis, and Tissue Microarray Validation
Departments of Immunology, Genetics and Pathology, Uppsala, Sweden.
Departments of Immunology, Genetics and Pathology, Uppsala, Sweden.
Department of Statistics, TU Dortmund University, Dortmund, Germany.
Department of Statistics, TU Dortmund University, Dortmund, Germany.
Show others and affiliations
2013 (English)In: Clinical Cancer Research, ISSN 1078-0432, E-ISSN 1557-3265, Vol. 19, no 1, 194-204 p.Article in journal (Refereed) Published
Abstract [en]

Purpose: Global gene expression profiling has been widely used in lung cancer research to identify clinically relevant molecular subtypes as well as to predict prognosis and therapy response. So far, the value of these multigene signatures in clinical practice is unclear, and the biologic importance of individual genes is difficult to assess, as the published signatures virtually do not overlap.

Experimental Design: Here, we describe a novel single institute cohort, including 196 non-small lung cancers (NSCLC) with clinical information and long-term follow-up. Gene expression array data were used as a training set to screen for single genes with prognostic impact. The top 450 probe sets identified using a univariate Cox regression model (significance level P < 0.01) were tested in a meta-analysis including five publicly available independent lung cancer cohorts (n = 860).

Results: The meta-analysis revealed 14 genes that were significantly associated with survival (P < 0.001) with a false discovery rate < 1%. The prognostic impact of one of these genes, the cell adhesion molecule 1 (CADM1), was confirmed by use of immunohistochemistry on tissue microarrays from 2 independent NSCLC cohorts, altogether including 617 NSCLC samples. Low CADM1 protein expression was significantly associated with shorter survival, with particular influence in the adenocarcinoma patient subgroup.

Conclusions: Using a novel NSCLC cohort together with a meta-analysis validation approach, we have identified a set of single genes with independent prognostic impact. One of these genes, CADM1, was further established as an immunohistochemical marker with a potential application in clinical diagnostics. Clin Cancer Res; 19(1); 194-204. (c) 2012 AACR.

Place, publisher, year, edition, pages
2013. Vol. 19, no 1, 194-204 p.
National Category
Cancer and Oncology
Research subject
Oncology
Identifiers
URN: urn:nbn:se:oru:diva-38714DOI: 10.1158/1078-0432.CCR-12-1139ISI: 000313051100021PubMedID: 23032747Scopus ID: 2-s2.0-84871959921OAI: oai:DiVA.org:oru-38714DiVA: diva2:764078
Note

Funding agencies are:

Swedish Cancer Society  

Lions Cancer Foundation, Uppsala, Sweden  

German Research Foundation (DFG) RA 870/4-1, RA 870/5-1 

Astra Zeneca  

Knut and Alice Wallenberg Foundation 

Available from: 2014-11-18 Created: 2014-11-18 Last updated: 2017-10-18Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full textPubMedScopus

Search in DiVA

By author/editor
Holmberg, LarsLambe, Mats G.Karlsson, MatsHelenius, GiselaKarlsson, Christina
By organisation
Örebro University HospitalSchool of Health and Medical Sciences, Örebro University, Sweden
In the same journal
Clinical Cancer Research
Cancer and Oncology

Search outside of DiVA

GoogleGoogle Scholar

Altmetric score

Total: 385 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf