DNA Methylation Profiles of Ovarian Clear Cell Carcinoma

Abstract

Background: Ovarian clear cell carcinoma (OCCC) is a rare ovarian cancer histotype that tends to be resistant to standard platinum-based chemotherapeutics. We sought to better understand the role of DNA methylation in clinical and biological subclassification of OCCC.

Methods: We interrogated genome-wide methylation using DNA from fresh frozen tumors from 271 cases, applied nonsmooth nonnegative matrix factorization (nsNMF) clustering, and evaluated clinical associations and biological pathways.

Results: Two approximately equally sized clusters that associated with several clinical features were identified. Compared with Cluster 2 (N = 137), Cluster 1 cases (N = 134) presented at a more advanced stage, were less likely to be of Asian ancestry, and tended to have poorer outcomes including macroscopic residual disease following primary debulking surgery (P < 0.10). Subset analyses of targeted tumor sequencing and IHC data revealed that Cluster 1 tumors showed TP53 mutation and abnormal p53 expression, and Cluster 2 tumors showed aneuploidy and ARID1A/PIK3CA mutation (P < 0.05). Cluster-defining CpGs included 1,388 CpGs residing within 200 bp of the transcription start sites of 977 genes; 38% of these genes (N = 369 genes) were differentially expressed across cluster in transcriptomic subset analysis (P < 10^-4). Differentially expressed genes were enriched for six immune-related pathways, including IFNα and IFNγ responses (P < 10^-6).

Conclusions: DNA methylation clusters in OCCC correlate with disease features and gene expression patterns among immune pathways.

Impact: This work serves as a foundation for integrative analyses that better understand the complex biology of OCCC in an effort to improve potential for development of targeted therapeutics.

Figure 1.. nsNMF DNA methylation Clustering Based on 271 OCCC Tumors.

(A) The estimated cophenetic correlation coefficient is plotted against the number of clusters (Rank), for k=2 through k=7, along with the corresponding 95% confidence interval that is empiricially calculated from 2,000 bootstrap samples. (B) For k=2 through k=7, the plot of the consensus matrix displays the agreement in cluster group across 100 clustering runs (red indicates 1=agreement, blue indicates 0=disagreement).

Figure 2.. Alignment of Tumor and Clinical Features by DNA Methylation Clusters in 271 OCCC Tumors

Columns represent samples with those on the left indicating (A) Methylation Cluster 1 tumors and those on the right Methylation Cluster 2 tumors, (B) mutation group (see methods), (C) ARID1A/PIK3CA mutations (yes/yes, yes/no, no/yes, no/no, missing), (D) PIK3CA mutation (yes, no, missing), (E) ARID1A mutation (yes, no, missing), (F) TP53 mutation (yes, no, missing), (G) WGD, whole-genome duplication (yes, no, missing), (H) total aneuploidy (none, 1–10, 11–20, 21–29, missing), (I) stage (early, advanced, missing), (J) residual disease (macroscopic, no macroscopic, missing), (K) race (White non-Hispanic, Asian, Black, missing), (L) Illumina Infinium Beadchip (HumanMethylation450, Methylation EPIC), (M) continent (North America, Europe, Australia). Samples are ordered based on hierarchical clustering which used nsNMF two-cluster analysis, although the dendrogram is suppressed.

Figure 3.. Overview of Characteristics of DNA Methylation Clusters.

Methylated genes with decreased gene expression are shown, along with molecular and clinical characteristics of OCCC samples within each methylation-based cluster.