title: Gene-expression signatures in ovarian cancer: Promise and challenges for patient stratification.
abstract: Microarray-based gene expression studies demonstrate that ovarian cancer is both a clinically diverse and molecularly heterogeneous disease compromising subtypes with distinct gene expression patterns that are each associated with statistically significant different clinical outcomes. The information provided by gene expression based assays is promising and deserves incorporation into clinical decision-making. Further studies are needed to determine which subtype signatures are most appropriate to select patients for a given therapy. This process will require the development of standardized molecular diagnostic assays that can be used for retrospective correlative studies and prospective validations of their clinical utility. Recent advances in assay development for FFPE tissues will facilitate accurate and cost-effective classification of ovarian cancer and help move the evolving molecular classification to clinic. The current review will summarize the development of gene expression based assays in ovarian cancer and will describe how the results of studies to date have expanded our appreciation of the heterogeneity of ovarian cancer. We discuss difficulties in the development and validation of molecular classifications in ovarian cancer and we provide future directions how we may be able to soon classify the disease in a manner that might have greater clinical utility.
authors.affiliation: Division of Hematology-Oncology, Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA. Electronic address: gkonecny@mednet.ucla.edu.
authors.forename: Gottfried E
authors.initials: GE
authors.lastname: Konecny
authors.affiliation: Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, University of Minnesota, Minneapolis, MN, USA.
authors.forename: Boris
authors.initials: B
authors.lastname: Winterhoff
authors.affiliation: Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
authors.forename: Chen
authors.initials: C
authors.lastname: Wang
mesh_terms: D005260:Female; D020869:Gene Expression Profiling; D006801:Humans; D010051:Ovarian Neoplasms / Q000145:classification* / Q000235:genetics*; D011379:Prognosis