Genes with bimodal expression are robust diagnostic targets that define distinct subtypes of epithelial ovarian cancer with different overall survival.

In some cancer types, certain genes behave as molecular switches, with on and off expression states. These genes tend to define tumor subtypes associated with different treatments and different patient survival. We hypothesized that clinically relevant molecular switch genes exist in epithelial ovarian cancer. To test this hypothesis, we applied a bimodal discovery algorithm to a publicly available ovarian cancer expression microarray data set, GSE9891 [285 tumors: 246 malignant serous (MS), 20 endometrioid (EM), and 18 low malignant potential (LMP) ovarian carcinomas]. Genes with robust bimodal expression patterns were identified across all ovarian tumor types and also within selected subtypes: 73 bimodal genes demonstrated differential expression between LMP versus MS and EM; 22 bimodal genes distinguished MS from EM; and 14 genes had significant association with survival among MS tumors. When these genes were combined into a single survival score, the median survival for patients with a favorable versus unfavorable score was 65 versus 29 months (P < 0.0001, hazard ratio = 0.4221). Two independent data sets [high-grade, advanced-stage serous (n = 53) and advanced-stage (n = 119) ovarian tumors] validated the survival score performance. We conclude that genes with bimodal expression patterns not only define clinically relevant molecular subtypes of ovarian carcinoma but also provide ideal targets for translation into the clinical laboratory.