Expression Profiling of Ovarian Cancer: markers and targets for therapy

Abstract

Ovarian cancer is the leading cause of death from gynecological cancer in the Western world. The initial response of the primary tumor to taxane and platinum-based chemotherapy is high, however 20% of patients never achieve a clinical response and the majority of the patients will relapse and eventually die of drug-resistant disease. Chapter 1 includes a general overview of ovarian cancer, its epidemiology, histology, typing and the different therapies. The major drawback in the treatment of ovarian cancer is late detection and therapy failure due to intrinsic and acquired chemotherapy resistance and several mechanisms involved in the platinum-based chemotherapy resistance are described. Furthermore, the importance of expression profiling (mRNA or protein) in the search for tumor markers suitable for early detection of ovarian cancer, response prediction, progression monitoring and identification of targets for therapy is discussed. Chapter 2A The expression profiling of 24 ovarian carcinomas led to the discovery of a discriminating 69-gene signature from which a predictive nine-gene set was extracted. The nine-gene set predicted the resistance in an independent validation set (n=72) with a sensitivity of 89% (95% CI: 0.68-1.09) and a specificity of 59% (95% CI: 0.47-0.71)(OR=0.09, p=0.026). The predictive nine-gene set consists of the following genes, FN1, TOP2A, LBR, ASS, COL3A1, STK6, SGPP1, ITGAE and PCNA. Interestingly, three of these nine genes are already direct or indirect targets for therapy, i.e. topoisomerase 2A (TOP2A), serine/threonine kinase 6 (STK6) and argininosuccinate synthetase (ASS). The predictive power of the nine-gene set needs to be further validated in larger independent multicenter study before this model can be implemented in the clinical practice. Chapter 2B In their â?~letter to the editorâ?T, Gevaert et al. suggest that in clinical practice, a higher specificity would have been more successful assuming that patients predicted not to respond are given a different treatment not containing platinum drugs. We agree that the predictive gene signature needs further validation before implementation in the clinical practice can be advised. However, it is was not our intention to withhold platinum treatment from patients predicted not to respond, but to tailor the treatment based on the expression profile. An overexpression of TOP2A indicates that adding a TOP2A inhibitor, like etoposide, to the conventional platinum treatment, might proof to be beneficial for the patient. Chapter 2C Underexpression of one of the nine genes from the predictive gene set, i.e. Argininosuccinate synthetase (ASS) was associated with platinum-based chemotherapy resistance. To determine if this observed association was functional, ASS was downregulated with siRNA in three ovarian cancer cell lines that were relatively sensitive to cisplatin. For all three cell lines, this did not result in a reduced response to cisplatin measured with an MTT assay. However, due to differences between cell lines and carcinomas, we cannot exclude that ASS might still play a role in platinum-based chemotherapy resistance in ovarian cancer patients. Chapter 3 One of the nine genes of the predictive gene set i.e. proliferating cell nuclear antigen (PCNA), is involved in the DNA mismatch repair (MMR). In vitro, a relationship between MMR deficiency and platinum-drug resistance was suThe full text of this item cannot yet be made available, due to a publisher's embarg