Germline stem cells that produce oocytes in vitro and fertilization-competent eggs in vivo have been identified in and isolated from adult mouse ovaries. Here we describe and validate a FACS-based protocol that can be used with adult mouse ovaries and human ovarian cortical tissue to purify rare mitotically-active cells that exhibit a gene expression profile consistent with primitive germ cells. Once established in vitro, these cells can be expanded for months and spontaneously generate 35–50 µm oocytes, as determined by morphology, gene expression and attainment of haploid (1n) status. Injection of the human germline cells, engineered to stably express GFP, into human ovarian cortical biopsies leads to formation of follicles containing GFP-positive oocytes 1–2 weeks after xenotransplantation into immunodeficient female mice. Thus, ovaries of reproductive-age women, like adult mice, possess rare mitotically-active germ cells that can be propagated in vitro as well as generate oocytes in vitro and in vivo.

Purpose: High-grade serous ovarian cancers are heterogeneous not only in terms of clinical outcome but also at the molecular level. Our aim was to establish a novel risk classification system based on a gene expression signature for predicting overall survival, leading to suggesting novel therapeutic strategies for high-risk patients.Experimental Design: In this large-scale cross-platform study of six microarray data sets consisting of 1,054 ovarian cancer patients, we developed a gene expression signature for predicting overall survival by applying elastic net and 10-fold cross-validation to a Japanese data set A (n ¼ 260) and evaluated the signature in five other data sets. Subsequently, we investigated differences in the biological characteristics between high-and low-risk ovarian cancer groups.Results: An elastic net analysis identified a 126-gene expression signature for predicting overall survival in patients with ovarian cancer using the Japanese data set A (multivariate analysis, P ¼ 4 Â 10 À20 ). We validated its predictive ability with five other data sets using multivariate analysis (Tothill's data set, P ¼ 1 Â 10 À5 ; Bonome's data set, P ¼ 0.0033; Dressman's data set, P ¼ 0.0016; TCGA data set, P ¼ 0.0027; Japanese data set B, P ¼ 0.021). Through gene ontology and pathway analyses, we identified a significant reduction in expression of immune-response-related genes, especially on the antigen presentation pathway, in high-risk ovarian cancer patients.Conclusions: This risk classification based on the 126-gene expression signature is an accurate predictor of clinical outcome in patients with advanced stage high-grade serous ovarian cancer and has the potential to develop new therapeutic strategies for high-grade serous ovarian cancer patients.