Abstract
BackgroundPublic 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 FindingsFour 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/SignificanceIntegration 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.
Highlights
Epithelial ovarian cancer (EOC) presents an example of the promise and challenges of using microarray analysis for prognostic biomarker research
We subsequently used the pool of the 650 marker genes in order to generate multi-gene prognostic classifiers in the combined training set
When we included chemotherapy response (i.e. achievement of complete clinical response (CCR) after first line chemotherapy versus no achievement of CCR) in the multivariate analysis for the 1st validation set, the 19-gene profile maintained its independent prognostic significance (HR = 3.96, 95% C.I. 1.56–10.1;p = 0.004)
Summary
Epithelial ovarian cancer (EOC) presents an example of the promise and challenges of using microarray analysis for prognostic biomarker research. None produced a gene expression signature that has been appropriate for clinical use This is largely due to, among other reasons, variable or small sample size, lack of adequate validation, or inclusion of subtypes (clear cell, mucinous, papillary EOCs), which constitute distinct molecular entities [11]. While collectively these studies may be sufficient to identify useful signatures, combining data or the analytical results is difficult for many reasons, including the use of a variety of array platforms, different data normalization and analysis approaches, and variability in experimental protocols and patient selection. We integrated several ovarian cancer datasets to identify a reproducible predictor of survival
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