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An online tool for the validation of survival-predicting biomarkers in non small-cell lung cancer using microarray data of 1,329 1,715 patients. - PowerPoint PPT Presentation
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1.DATABASE construction n=1,715
Median OS=40.0 months, age: 64+/-10 yrs
Histology (adeno/squamous/large): 50% / 45% / 5%
Stage 1/2/3/4: 63% / 27% / 10% / 1%
2.META-ANALYSIS of biomarker candidates Biomarker candidates identified in Pubmed n=22
For each gene the exact conditions in which it was identified have been retrieved, and these have been used as filtering when selecting the patients for the survival analysis.
Of the 22, the best performing genes are:
n(1): number of patients in original study, n(2): number of patients in the KM-plotter, HR: hazard ratio, ADE: adenocarcinoma, NSCLC: all non-small-cell lung cancer patients
3.Selected KAPLAN-MEIER plots (table: *)
Background and ObjectiveBackground and Objective
Materials & MethodsMaterials & Methods
ResultsResults
Web addressesWeb addresses
SummarySummary
Grant support: OTKA PD 83154, PREDICT 259303 (EU Health.2010.2.4.1.-8), KTIA EU_BONUS_12-1-2013-0003, Alexander von Humboldt-Foundation
Grant support: OTKA PD 83154, PREDICT 259303 (EU Health.2010.2.4.1.-8), KTIA EU_BONUS_12-1-2013-0003, Alexander von Humboldt-Foundation
Balázs Győrffy and András LánczkyResearch Laboratory for Pediatrics and Nephrology, Hungarian Academy of
Sciences and Semmelweis University 1st Dept. of Pediatrics, Budapest, Hungary
Symbol Reference n(1) Cohort n(2) HR p value
VEGF Zhan et al 2009 5386 NSCLC 1404 1.9 3.3e-10
Cyclin E Huang et al 2012 2606 NSCLC 1404 1.59 2.3e-09*
ADE 486 2.44 4.8e-08
CDK1 Zhang et al 2012a 2731 NSCLC 1404 2.56 <1e-16*
CADM1 Botling et al 2012 617 ADE 486 0.38 7e-12*
CDKN2A Jin et al 2001 106 NSCLC 1404 1.65 1.8e-09
CD24 Lee et al 2010 267 ADE 486 2.45 3.6e-10
ERCC1 Simon et al 2005 51 NSCLC 1404 1.65 1.4e-10
1.DATABASE construction Repositories: GEO, TCGA, ArrayExpress, caBIG Platforms: Affymetrix HGU133A, plus2 & A 2.0
arrays at least 30 patients with survival information MAS5 normalization + quality control
2.SURVIVAL analysis Kaplan-Meier plot „survival” Bioconductor package Cox univariate + multivariate analysis
3.ONLINE platform Apache web server on Debian Linux script developed in PHP Open access at: www.kmplot.com/lung
4.META-analysis Pubmed search of published biomarkers Best cutoff selection: each percentile (of expression)
between the lower and upper quartiles are computed and the best performing threshold is used as the final cutoff in the Cox regression analysis.
1.optimized treatment for non-small cell lung cancer had lead to improved prognosis, but the overall survival is still very short
2.by identifying biomarkers related to survival we can further understand the molecular basis of the disease
OBJECTIVE: we present the development of an online available tool suitable for the real-time meta-analysis of published lung
cancer microarray datasets to identify biomarkers related to survival
we performed a meta-analysis of survival-associated genes
an integrated database and an online tool for future in silico validation of new candidates has been established
Online access: http://www.kmplot.com/lung Group homepage:
http://gyer1-6.sote.hu/gyorffyContact: [email protected]
Cyclin E1 CDK1 CADM1
