Epithelial ovarian cancer: the molecular genetics of epithelial ovarian cancer

Abstract

Background: Epithelial ovarian cancer (EOC) remains one of the leading causes of cancer-related deaths among women worldwide, despite gains in diagnostics and treatments made over the last three decades. Existing markers of ovarian cancer possess very limited clinical relevance highlighting the emerging need for identification of novel prognostic biomarkers as well as better predictive factors that might allow the stratification of patients who could benefit from a more targeted approach.

Patients and methods: A summary of molecular genetics of EOC.

Results: Large-scale high-throughput genomic technologies appear to be powerful tools for investigations into the genetic abnormalities in ovarian tumors, including studies on dysregulated genes and aberrantly activated signaling pathways. Such technologies can complement well-established clinical histopathology analysis and tumor grading and will hope to result in better, more tailored treatments in the future. Genomic signatures obtained by gene expression profiling of EOC may be able to predict survival outcomes and other important clinical outcomes, such as the success of surgical treatment. Finally, genomic analyses may allow for the identification of novel predictive biomarkers for purposes of treatment planning. These data combined suggest a pathway to progress in the treatment of advanced ovarian cancer and the promise of fulfilling the objective of providing personalized medicine to women with ovarian cancer.

Conclusions: The understanding of basic molecular events in the tumorigenesis and chemoresistance of EOC together with discovery of potential biomarkers may be greatly enhanced through large-scale genomic studies. In order to maximize the impact of these technologies, however, extensive validation studies are required.

Keywords: biomarkers; clinical trials; genomics; ovarian cancer.

Figure 1.

Graphic depiction of principle component analysis of ovarian and endometrial cancers according to histology. Analysis of tumors with serous (A) and endometrioid (B) histology showed two non-overlapping regions separating endometrial (top) from ovarian (bottom) specimens, whereas the analysis of tumors with clear-cell histology (C) showed overlapping elliptical regions. (D) Analysis of tumors according to organ of origin shows three overlapping elliptical regions among ovarian, endometrial, and renal clear cell specimens. Dendrograms produced by unsupervised hierarchical clustering according to (E) serous histology, (F) endometrioid histology, and (G) clear cell histology. O, ovarian samples; E, endometrial samples; R, renal samples. Adapted from Zorn et al. [26].

Figure 2.

Expression profiles of low malignant potential/low-grade serous and high-grade carcinomas. Unsupervised hierarchical clustering revealing an extensive molecular segregation between low malignant potential/low-grade serous and high-grade serous tumors. Adapted from Bonome et al. [19].

Figure 3.

Meta-analysis of 1525 late-stage ovarian cancer samples. (A) Flowchart of the study outlining the steps for training and validating the prognostic models presented in this meta-analysis study. (B) Validation of POSTN, pSmad2/3, and CXCL14 in an independent cohort by immunohistochemistry and Validation of selected genes associated with debulking status by quantitative reverse-transcription–polymerase chain reaction (qRT–PCR) in the Bonome et al. validation data. (C) Pathway analysis of the debulking signature, using the Pathway Studio 7.1 (Ariadne Genomics) software and a novel signature of 200 debulking-associated genes with suboptimal debulking surgery. Genes are labeled in red when overexpressed in tumors that were subsequently suboptimally debulked. Conversely, genes overexpressed in tumors with optimal cytoreduction are labeled in blue. Genes with predictive power toward poor prognosis based on the meta-analysis are highlighted with pink borders. Red broken arrows indicate direct stimulatory modification. Green arrows indicate EGR-1-based transcriptional regulations. Orange arrows indicate TGF-β/Smad-based transcriptional regulations. Blue solid arrows indicate other direct regulations. Blue broken arrows indicate other indirect regulations. Purple sticks indicate binding. Adapted from Riester et al. [38].

Figure 4.

Identification of FGF18 as a gene with high prognostic capacities in high-grade advanced-stage papillary serous ovarian tumors. (A) The Kaplan–Meier analysis of FGF18 expression in patients in three independent sets of serous ovarian cancer samples (Mok 2009, Spentzos 2004, and TCGA Project 2011) linking FGF18 with poor prognosis. (B) Oncogenic effects of FGF18 on ovarian cancer cells. (B) In vitro FGF18 overexpression stimulates migration and invasion in OVCA429 and A224 cells. (C) In vivo with ectopic FGF18 overexpression promoting tumorigenicity of ovarian cancer cells s.c. inoculated in SCID mice (five mice in each group; triangles, RFP-overexpressing cells; circles, FGF18-overexpressing cells). Adapted from Wei et al. [40].