Genes with Bimodal Expression Are Robust Diagnostic Targets that Define Distinct Subtypes of Epithelial Ovarian Cancer with Different Overall Survival

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Genes with Bimodal Expression Are Robust Diagnostic Targets that Define Distinct Subtypes of Epithelial Ovarian Cancer with Different Overall Survival Dawn N. Kernagis, Allison H.S. Hall, Michael B. Datto The Journal of Molecular Diagnostics Volume 14, Issue 3, Pages 214-222 (May 2012) DOI: 10.1016/j.jmoldx.2012.01.007 Copyright © 2012 American Society for Investigative Pathology and the Association for Molecular Pathology Terms and Conditions

Figure 1 Bimodal gene expression in ovarian tumors (GSE9891). A: Distribution of bimodal index scores across all genes. Genes with a BI of >1.9 were selected for further evaluation. Genes demonstrating robust bimodal expression distribution among all samples include HLA-DQB1 (B), GSTT1 (C), and NUMA1 (D). Cutoff values distinguishing low, high, and indeterminate gene expression of NUMA1 are shown. The Journal of Molecular Diagnostics 2012 14, 214-222DOI: (10.1016/j.jmoldx.2012.01.007) Copyright © 2012 American Society for Investigative Pathology and the Association for Molecular Pathology Terms and Conditions

Figure 2 Hierarchical cluster analysis was conducted for all 285 samples (GSE9891) using BI > 1.9 probe sets after filtering for those associated with HLA or large copy number variants. Genes are clustered on the left; arrays are clustered along the top. Sample types are denoted by color below the dendrogram: blue, low malignant potential serous; green, endometrioid; red, malignant serous. The Journal of Molecular Diagnostics 2012 14, 214-222DOI: (10.1016/j.jmoldx.2012.01.007) Copyright © 2012 American Society for Investigative Pathology and the Association for Molecular Pathology Terms and Conditions

Figure 3 A: RMA and bimodal analysis was performed on MS tumors (GSE9891), and hierarchical cluster analysis was conducted for the 246 MS samples using BI > 1.9 probe sets after filtering for those associated with HLA or population copy number variants. B: Kaplan-Meier survival analysis identified a survival difference between patients in the two major branches of the hierarchical clustering dendrogram. The Journal of Molecular Diagnostics 2012 14, 214-222DOI: (10.1016/j.jmoldx.2012.01.007) Copyright © 2012 American Society for Investigative Pathology and the Association for Molecular Pathology Terms and Conditions

Figure 4 Survival scores were generated in the training set (GSE9891; n = 242). A: Score cutoffs for low, high, and indeterminate were established. B: Kaplan-Meier analysis was conducted between all groups. Training set-based cutoffs were applied to validate survival significance in the GSE18520 data set (U133 Plus 2.0 data set, ovarian tumor epithelial tumors; n = 53) (C) and in the data set of Bild et al9 (U133A data set, advanced-stage ovarian tumors; n = 119) (D). The Journal of Molecular Diagnostics 2012 14, 214-222DOI: (10.1016/j.jmoldx.2012.01.007) Copyright © 2012 American Society for Investigative Pathology and the Association for Molecular Pathology Terms and Conditions