Integrated Analysis of Multiple Microarray Datasets Identifies a Reproducible Survival Predictor in Ovarian Cancer

Panagiotis A Konstantinopoulos, Stephen A Cannistra, Helen Fountzilas, Aedin Culhane, Kamana Pillay, Bo Rueda, Daniel Cramer, Michael Seiden, Michael Birrer, George Coukos, Lin Zhang, John Quackenbush and Dimitrios Spentzos

PLOS ONE, 2011, vol. 6, issue 3, 1-12

Abstract: 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 2nd 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

Date: 2011
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0018202

DOI: 10.1371/journal.pone.0018202

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