High-grade ovarian carcinoma is a histopathological diagnosis, however, at the molecular level, ovarian cancer represents a heterogeneous group of diseases. Studies aimed at identifying molecular genetic subtypes of ovarian cancer are conducted in order to find the answer to the question: can different molecular subgroups influence the choice of treatment? One of the achievements in this trend is the recognition of the dualistic model that categorizes various types of ovarian cancer into two groups designated high-grade (HG) and low-grade (LG) tumors. However, the tumor genome sequencing data suggest the existence of 6 ovarian carcinoma subtypes, including two LG and four HG subtypes. Subtype C1 exhibits a high stromal response and the lowest survival. Subtypes C2 and C4 demonstrate higher number of intratumoral CD3 + cells, lower stromal gene expression and better survival than sybtype C1. Subtype C5 (mesenchymal) is characterized by mesenchymal cells, over-expression of N-cadherin and P-cadherin, low expression of differentiation markers, and lower survival rates than C2 and C4. The use of a consensus algorithm to determine the subtype allows identification of only a minority of ovarian carcinomas (approximately 25%) therefore, the practical importance of this classification requires additional research. There is evidence that it makes sense to randomize tumors into groups with altered expression of angiogenic genes and groups with overexpression of the immune response genes, as in the angiogenic group there is a comparative superiority in terms of survival. The administration of bevacizumab in the angiogenic group improves survival, while the administration of bevacizumab in the immune group even worsens the outcome. Molecular subtypes with worse survival rates (proliferative and mesenchymal) also benefit most from bevacizumab treatment. This review focuses on some of the advances in understanding molecular, cellular, and genetic changes in ovarian carcinomas with the results achieved so far regarding the formulation of molecular subtypes of ovarian cancer, however further studies are needed.
RANSAC algorithm for instantaneous frequency estimation and reconstruction of frequency-modulated undersampled signals
