Epithelial Ovarian Carcinoma (EOC) is the sixth most common malignancy in women  and has a poor overall survival rate (20 to 30% at 5 years). High-grade serous carcinoma is the most frequent type of ovarian cancer. The poor survival rate is due mainly to a large tumor burden and frequent, extensive peritoneal metastatic lesions at diagnosis. This results in difficulty to achieve complete optimal resection, despite advances in surgical practice. Indeed the importance of the metastatic process in ovarian cancer has been clearly demonstrated in the clinical setting by the fact that peritoneal residual disease at the end of the surgical treatment (even below 5 mm) impacts prognosis [2, 3].
Despite initial chemosensitivity and ultra-radical debulking surgery most patients will present with diffuse peritoneal recurrences rather than distant metastasis. Therefore, understanding the molecular mechanisms of progression from primary to metastases is critical for the development of effective therapies. Most studies of EOC have focused on the primary tumor including mutational and gene expression analysis. Many transcriptomic studies have been performed demonstrating different gene expression signatures depending on the histologic subtypes, the grade and stage [4–6]. Others have defined several prognosis signatures however there is only little overlap between these studies [7–9].
Recent results from the TCGA group’s exome sequencing of 316 epithelial ovarian cancer primary tumors with matched controls revealed a very narrow spectrum for somatic mutations in EOC. Specifically, TP53 was mutated in approximately 96% of EOC primary tumors. The next most frequently affected genes were mutated in less than 6% of tumors. This dramatic prevalence of TP53 mutations suggests an early and central role of TP53 mutations in EOC.
In our recent studies of copy number variations and gene expression differences between primary and metastatic lesions [10, 11] we observed clear targeting of pathways, rather than specific genes in EOC. Indeed our findings showed that analysis of pathways reduced the overall heterogeneity in comparisons and this pathway-based approach may also be important in studying the mutational spectrum of ovarian cancer as well. The importance of pathway-based analysis over single gene analysis is due to the fact that similar downstream effects can be obtained by mutation of different genes within the same pathway. The TCGA study included some discovery-based pathway analysis on copy number and gene expression data using the PARADIGM approach. Analysis of mutations, however, was mainly restricted to certain known cancer pathways. The HOTNET approach overlaid the mutational data on protein interaction networks but no further discovery-based pathway analyses of gene mutations were reported .
We therefore hypothesized that analyzing the recent mutational data in EOC  using a broader functional pathway approach such as the Gene Ontologies or KEGG could reveal consistent targeting of pathways other than known cancer pathways, and could reduce the observed heterogeneity when only individual genes are considered. Furthermore, analysis of low frequency mutated genes within frequently mutated pathways, may offer insight into the metastatic process that only a few clones within the primary tumor undertake, but for which many patients are susceptible to.