title: Stem cell-like gene expression in ovarian cancer predicts type II subtype and prognosis.
abstract: Although ovarian cancer is often initially chemotherapy-sensitive, the vast majority of tumors eventually relapse and patients die of increasingly aggressive disease. Cancer stem cells are believed to have properties that allow them to survive therapy and may drive recurrent tumor growth. Cancer stem cells or cancer-initiating cells are a rare cell population and difficult to isolate experimentally. Genes that are expressed by stem cells may characterize a subset of less differentiated tumors and aid in prognostic classification of ovarian cancer. The purpose of this study was the genomic identification and characterization of a subtype of ovarian cancer that has stem cell-like gene expression. Using human and mouse gene signatures of embryonic, adult, or cancer stem cells, we performed an unsupervised bipartition class discovery on expression profiles from 145 serous ovarian tumors to identify a stem-like and more differentiated subgroup. Subtypes were reproducible and were further characterized in four independent, heterogeneous ovarian cancer datasets. We identified a stem-like subtype characterized by a 51-gene signature, which is significantly enriched in tumors with properties of Type II ovarian cancer; high grade, serous tumors, and poor survival. Conversely, the differentiated tumors share properties with Type I, including lower grade and mixed histological subtypes. The stem cell-like signature was prognostic within high-stage serous ovarian cancer, classifying a small subset of high-stage tumors with better prognosis, in the differentiated subtype. In multivariate models that adjusted for common clinical factors (including grade, stage, age), the subtype classification was still a significant predictor of relapse. The prognostic stem-like gene signature yields new insights into prognostic differences in ovarian cancer, provides a genomic context for defining Type I/II subtypes, and potential gene targets which following further validation may be valuable in the clinical management or treatment of ovarian cancer.
authors.affiliation: Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America.
authors.forename: Matthew
authors.initials: M
authors.lastname: Schwede
authors.forename: Dimitrios
authors.initials: D
authors.lastname: Spentzos
authors.forename: Stefan
authors.initials: S
authors.lastname: Bentink
authors.forename: Oliver
authors.initials: O
authors.lastname: Hofmann
authors.forename: Benjamin
authors.initials: B
authors.lastname: Haibe-Kains
authors.forename: David
authors.initials: D
authors.lastname: Harrington
authors.forename: John
authors.initials: J
authors.lastname: Quackenbush
authors.forename: Aedín C
authors.initials: AC
authors.lastname: Culhane
mesh_terms: D000328:Adult; D000368:Aged; D001943:Breast Neoplasms / Q000235:genetics / Q000473:pathology; D016000:Cluster Analysis; D005260:Female; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D008875:Middle Aged; D060787:Neoplasm Grading; D009367:Neoplasm Staging; D018295:Neoplasms, Basal Cell / Q000235:genetics / Q000473:pathology; D014411:Neoplastic Stem Cells / Q000378:metabolism* / Q000473:pathology; D010051:Ovarian Neoplasms / Q000175:diagnosis* / Q000235:genetics* / Q000401:mortality; D010641:Phenotype; D011379:Prognosis; D015203:Reproducibility of Results
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