Endocrine therapy in advanced high-grade ovarian cancer: real-life data from a multicenter study and a review of the literature.

BACKGROUND: In women, ovarian cancer is the eighth most frequent cancer in incidence and mortality. It is often diagnosed at advanced stages; relapses are frequent, with a poor prognosis. When platinum resistant, subsequent lines of chemotherapy are of limited effect and often poorly tolerated, leading to quality of life deterioration. Various studies suggest a hormonal role in ovarian carcinogenesis, with a rationale for endocrine therapy in these cancers. PATIENTS AND METHODS: This multicenter, retrospective study assessed the use of endocrine treatment for high-grade ovarian epithelial carcinomas treated between 2010 and 2020. RESULTS: Eighty-one patients with ovarian cancers were included. The median duration of platinum sensitivity was 29 months. We observed a 35% disease control rate with endocrine therapy, and 10% reported symptom improvement. For 19 patients (23.5%), the disease was stabilized for more than 6 months. Median overall survival from diagnosis was 62.6 months. Regarding endocrine therapy predictive factors of response, in a multivariate analysis, 3 factors were statistically significant in favoring progression-free survival: platinum sensitivity (P = .021), an R0 surgical resection (P = .020), and the indication for hormone therapy being maintenance therapy (P = .002). CONCLUSION: This study shows real-life data on endocrine therapy in ovarian cancer. As it is a low-cost treatment with many advantages such as its oral administration and its safety, it may be an option to consider. A perspective lies in the search for cofactors to aim as future therapeutic targets to improve the effectiveness of hormone treatment by means of combination therapy.

Modeling tumor cell migration: From microscopic to macroscopic models.

It has been shown experimentally that contact interactions may influence the migration of cancer cells. Previous works have modelized this thanks to stochastic, discrete models (cellular automata) at the cell level. However, for the study of the growth of real-size tumors with several million cells, it is best to use a macroscopic model having the form of a partial differential equation (PDE) for the density of cells. The difficulty is to predict the effect, at the macroscopic scale, of contact interactions that take place at the microscopic scale. To address this, we use a multiscale approach: starting from a very simple, yet experimentally validated, microscopic model of migration with contact interactions, we derive a macroscopic model. We show that a diffusion equation arises, as is often postulated in the field of glioma modeling, but it is nonlinear because of the interactions. We give the explicit dependence of diffusivity on the cell density and on a parameter governing cell-cell interactions. We discuss in detail the conditions of validity of the approximations used in the derivation, and we compare analytic results from our PDE to numerical simulations and to some in vitro experiments. We notice that the family of microscopic models we started from includes as special cases some kinetically constrained models that were introduced for the study of the physics of glasses, supercooled liquids, and jamming systems.