Konstantinopoulos, PanagiotisCannistra, StephenFountzilas, HelenCulhane, AedinPillay, KamanaRueda, BoCramer, DanielSeiden, MichaelBirrer, Michael J.Coukos, GeorgeZhang, LinQuackenbush, JohnSpentzos, Dimitrios2016-06-162011Konstantinopoulos, Panagiotis A., Stephen A. Cannistra, Helen Fountzilas, Aedin Culhane, Kamana Pillay, Bo Rueda, Daniel Cramer, et al. 2011. “Integrated Analysis of Multiple Microarray Datasets Identifies a Reproducible Survival Predictor in Ovarian Cancer.” Edited by Chad Creighton. PLoS ONE 6 (3) (March 29): e18202. doi:10.1371/journal.pone.0018202.1932-6203http://nrs.harvard.edu/urn-3:HUL.InstRepos:27332639Background 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<0.01), 1st validation set (median OS 32 months versus not-yet-reached, p = 0.026) and 2nd 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 1st validation set. Low and high-risk tumors were enriched for favorable and unfavorable molecular subtypes and pathways, previously defined in the public 2nd 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.en-USJournal Article2016-06-1610.1371/journal.pone.0018202