Modeling dependent gene expression

In this paper we propose a Bayesian approach for inference about dependence of high throughput gene expression. Our goals are to use prior knowledge about pathways to anchor inference about dependence among genes; to account for this dependence while making inferences about differences in mean expression across phenotypes; and to explore differences in the dependence itself across phenotypes. Useful features of the proposed approach are a model-based parsimonious representation of expression as an ordinal outcome, a novel and flexible representation of prior information on the nature of dependencies, and the use of a coherent probability model over both the structure and strength of the dependencies of interest. We evaluate our approach through simulations and in the analysis of data on expression of genes in the Complement and Coagulation Cascade pathway in ovarian cancer.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS525 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Nearly 30 Years of Treatment for Recurrent Granulosa Cell Tumor of the Ovary: A Case Report and Review of the Literature

A 30-year-old woman was diagnosed with a stage IA granulosa cell tumor (GCT) of the ovary in 1979. Following removal of the adnexal mass and complete surgical staging, she remained disease-free for 12 years. In 1991 she underwent a resection of a retroperitoneal mass, confirmed to be a recurrent GCT. Despite adjuvant radiation treatment at the time of recurrence, the patient presented five years later with abdominal pain, and was found to have a second recurrence. Over the next 10 years the patient had multiple recurrences and progressive disease despite surgical resection, cytotoxic, hormonal and targeted chemotherapy treatments. In conclusion, there is no standard management for recurrent GCT of the ovary. We review this patient's treatment in the context of the current literature