Epithelial ovarian cancer (EOC) remains one of the most challenging areas of cancer research as it is a highly heterogeneous disease from both molecular and etiological points of view. Furthermore, EOC is the fifth leading cause of cancer-related deaths among women, and the leading cause of death from gynecological cancer. Early detection is paramount to increase survival, but only 25% of all EOC are found at an early stage; furthermore, tumors that appear similar based on traditional clinical and histopathologic features may respond very differently to therapy. At the biological level, the most relevant need is for a new molecular classification of EOC that would enable identification of targetable pathways and predict outcome of disease; at the clinical level, the open issues are early detection of disease and early identification of patients with drug-resistant cancers so that alternative therapeutic modalities can be offered. Microarray-based technologies are powerful tools that may potentially help in understanding the relationship between clinical features of cancers and their underlying biological alterations by measuring the simultaneous structural alteration/expression of thousands of genes. The genomic landscape in EOC, herein described, refers to genomic, functional genomic, and epigenomic studies published in the last 10 years. On the basis of this genomic landscape, the following can be affirmed: (i) all approaches have contributed to the identification of tumor subtypes, but none of the proposed genetic signatures has been sufficiently confirmed or validated; (ii) the clinical question of early identification remains unanswered. In fact, even if there are promising data from epigenetic-based analysis of blood samples from EOC patients, their predictive power is still too low for population-based screening; (iii) genomic and methylation analyses have only recently been carried out on a genome-wide level, and accordingly only a limited number of promising prognostic signatures and predictors have emerged; (iv) gene and miRNA expression analyses, based on more mature technologies, have provided a larger number of promising prognostic signatures and predictors. In the case of early detection, improvement in terms of accuracy and further confirmation of reliability as specific markers in adequately-sized prospective studies are needed; in the case of prognosis and prediction, it is imperative to confirm potential genetic signatures in large, well annotated independent sets of patient samples coming from multicenter randomized phase III clinical trials. The use of these type of sample sets, combined with the introduction of new high throughput technologies and the integration of data raised by different genome-wide approaches, will hopefully enable a global view of the DNA-RNA relationships and ultimately lead to identification of clinically useful biomarkers.
|Title of host publication||Cancer Genomics: Molecular Classification, Prognosis and Response Prediction|
|Number of pages||54|
|ISBN (Print)||9789400758421, 9400758413, 9789400758414|
|Publication status||Published - Sep 1 2014|
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