Molecular Classification of Epithelial Ovarian Cancer Based on Methylation Profiling: Evidence for Survival Heterogeneity

Abstract

Purpose: Ovarian cancer is a heterogeneous disease that can be divided into multiple subtypes with variable etiology, pathogenesis, and prognosis. We analyzed DNA methylation profiling data to identify biologic subgroups of ovarian cancer and study their relationship with histologic subtypes, copy number variation, RNA expression data, and outcomes.

Experimental design: A total of 162 paraffin-embedded ovarian epithelial tumor tissues, including the five major epithelial ovarian tumor subtypes (high- and low-grade serous, endometrioid, mucinous, and clear cell) and tumors of low malignant potential were selected from two different sources: The Polish Ovarian Cancer study, and the Surveillance, Epidemiology, and End Results Residual Tissue Repository (SEER RTR). Analyses were restricted to Caucasian women. Methylation profiling was conducted using the Illumina 450K methylation array. For 45 tumors array copy number data were available. NanoString gene expression data for 39 genes were available for 61 high-grade serous carcinomas (HGSC).

Results: Consensus nonnegative matrix factorization clustering of the 1,000 most variable CpG sites showed four major clusters among all epithelial ovarian cancers. We observed statistically significant differences in survival (log-rank test, P = 9.1 × 10^-7) and genomic instability across these clusters. Within HGSC, clustering showed three subgroups with survival differences (log-rank test, P = 0.002). Comparing models with and without methylation subgroups in addition to previously identified gene expression subtypes suggested that the methylation subgroups added significant survival information (P = 0.007).

Conclusions: DNA methylation profiling of ovarian cancer identified novel molecular subgroups that had significant survival difference and provided insights into the molecular underpinnings of ovarian cancer.See related commentary by Ishak et al., p. 5729.

Legend Figure 1.

Unsupervised non-negative matrix factorization of the 1,000 most variable probes based on the median absolute deviation (MAD). A. Consensus matrices for rank values from 2 to 7 based on 100 indendendt NMF runs. B. Cophenetic correlation coefficient for rank values from 2 to 7 with 95% confidence intervals computed by bootstrap. C. Methylation profiles of the clusters. D. Survival curves for the methylation derived clusters: C1 (N=51), C2 (N=44), C3 (N=34) and C4 (N=33). E. Survival for the different histologies: clear cell (N=7), endometrioid (N=33), mucinous (N=16), serous (N=76) and serous LMP (N=30).

Legend Figure 2.

A. Genomic profile of the segmented data, ordered by GII. B. GII distribution by clusters indicated by dots: C1 (N=8), C2 (N=7), C3 (N=15) and C4 (N=15); segments indicate the mean values. Wilcoxon rank-test P-value (C1 vs. C3)=7.75×10⁻⁵. Wilcoxon rank-test P-value (C3 vs. C4)=0.0037. Other comparisons were not statistically significant. C. GII distribution by histologies indicated by dots: clear cell (N=3), endometrioid (N=20), mucinous (N=7) and serous (N=15); segments indicate the mean values. Wilcoxon rank-test P-value (serous vs. endometrioid)=8.2×10⁻⁴. Wilcoxon rank-test P-value (endometrioid vs. clear cell)=0.035. Other comparisons were not statistically significant.

Legend Figure 3.

Unsupervised non-negative matrix factorization based on the 1,000 most variable probes based on the median absolute deviation (MAD). A. Consensus matrices for k=2 to k=7. B. Cophenetic correlation coefficient for k=2 to k=7. C. Methylation profiles of the clusters. D. Survival curves for the methylation derived clusters: M1 (N=16), M2 (N=18) and M3 (N=36).

Legend Figure 4.

A. Survival curves for gene expression subtypes (N1: Mesenchymal (N=12); N2: Immunoreactive (N=13); N4: Differenciated (N=17); N5: Proliferative(N=19)). B. Survival curves for methylation clusters among patients with gene expression data: M1 (N=16), M2 (N=14) and M3 (N=31). C. Gene expression heat map showing the relationship between methylation subgroups and gene expression subtypes.