title: Integrated analysis of multiple microarray datasets identifies a reproducible survival predictor in ovarian cancer.
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.
  
  
  RESULTS
  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
  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.
authors.affiliation: Division of Hematology/Oncology, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, United States of America.
authors.forename: Panagiotis A
authors.initials: PA
authors.lastname: Konstantinopoulos
authors.forename: Stephen A
authors.initials: SA
authors.lastname: Cannistra
authors.forename: Helen
authors.initials: H
authors.lastname: Fountzilas
authors.forename: Aedin
authors.initials: A
authors.lastname: Culhane
authors.forename: Kamana
authors.initials: K
authors.lastname: Pillay
authors.forename: Bo
authors.initials: B
authors.lastname: Rueda
authors.forename: Daniel
authors.initials: D
authors.lastname: Cramer
authors.forename: Michael
authors.initials: M
authors.lastname: Seiden
authors.forename: Michael
authors.initials: M
authors.lastname: Birrer
authors.forename: George
authors.initials: G
authors.lastname: Coukos
authors.forename: Lin
authors.initials: L
authors.lastname: Zhang
authors.forename: John
authors.initials: J
authors.lastname: Quackenbush
authors.forename: Dimitrios
authors.initials: D
authors.lastname: Spentzos
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D030541:Databases, Genetic; D005260:Female; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D052138:Genes, Neoplasm / Q000235:genetics; D015894:Genome, Human / Q000235:genetics; D006801:Humans; D008875:Middle Aged; D008957:Models, Genetic; D015999:Multivariate Analysis; D020411:Oligonucleotide Array Sequence Analysis; D010051:Ovarian Neoplasms / Q000235:genetics* / Q000473:pathology; D011379:Prognosis; D015203:Reproducibility of Results; D012307:Risk Factors; D015398:Signal Transduction / Q000235:genetics; D016019:Survival Analysis