Integrated analysis of multiple microarray datasets identifies a reproducible survival predictor in ovarian cancer.

Détails

ID Serval
serval:BIB_970BE579D201
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Integrated analysis of multiple microarray datasets identifies a reproducible survival predictor in ovarian cancer.
Périodique
Plos One
Auteur⸱e⸱s
Konstantinopoulos P.A., Cannistra S.A., Fountzilas H., Culhane A., Pillay K., Rueda B., Cramer D., Seiden M., Birrer M., Coukos G., Zhang L., Quackenbush J., Spentzos D.
ISSN
1932-6203 (Electronic)
ISSN-L
1932-6203
Statut éditorial
Publié
Date de publication
2011
Volume
6
Numéro
3
Pages
e18202
Langue
anglais
Notes
Publication types: Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov'tPublication Status: epublish
Résumé
BACKGROUND: Public data integration may help overcome challenges in clinical implementation of microarray profiles. We integrated several ovarian cancer datasets to identify a reproducible predictor of survival.
METHODOLOGY/PRINCIPAL FINDINGS: Four microarray datasets from different institutions comprising 265 advanced stage tumors were uniformly reprocessed into a single training dataset, also adjusting for inter-laboratory variation ("batch-effect"). Supervised principal component survival analysis was employed to identify prognostic models. Models were independently validated in a 61-patient cohort using a custom array genechip and a publicly available 229-array dataset. Molecular correspondence of high- and low-risk outcome groups between training and validation datasets was demonstrated using Subclass Mapping. Previously established molecular phenotypes in the 2(nd) validation set were correlated with high and low-risk outcome groups. Functional representational and pathway analysis was used to explore gene networks associated with high and low risk phenotypes. A 19-gene model showed optimal performance in the training set (median OS 31 and 78 months, p < 0.01), 1(st) validation set (median OS 32 months versus not-yet-reached, p = 0.026) and 2(nd) validation set (median OS 43 versus 61 months, p = 0.013) maintaining independent prognostic power in multivariate analysis. There was strong molecular correspondence of the respective high- and low-risk tumors between training and 1(st) validation set. Low and high-risk tumors were enriched for favorable and unfavorable molecular subtypes and pathways, previously defined in the public 2(nd) validation set.
CONCLUSIONS/SIGNIFICANCE: Integration of previously generated cancer microarray datasets may lead to robust and widely applicable survival predictors. These predictors are not simply a compilation of prognostic genes but appear to track true molecular phenotypes of good- and poor-outcome.
Mots-clé
Adult, Aged, Aged, 80 and over, Databases, Genetic, Female, Gene Expression Profiling, Gene Expression Regulation, Neoplastic, Genes, Neoplasm/genetics, Genome, Human/genetics, Humans, Middle Aged, Models, Genetic, Multivariate Analysis, Oligonucleotide Array Sequence Analysis, Ovarian Neoplasms/genetics, Ovarian Neoplasms/pathology, Prognosis, Reproducibility of Results, Risk Factors, Signal Transduction/genetics, Survival Analysis
Pubmed
Web of science
Open Access
Oui
Création de la notice
14/10/2014 12:42
Dernière modification de la notice
20/08/2019 15:59
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