Computational modeling of ovarian cancer dynamics suggests optimal strategies for therapy and screening

High-grade serous tubo-ovarian carcinoma (HGSC) is a major cause of cancer-related death. Treatment is not uniform, with some patients undergoing primary debulking surgery followed by chemotherapy (PDS) and others being treated directly with chemotherapy and only having surgery after three to four cycles (NACT). Which strategy is optimal remains controversial. We developed a mathematical framework that simulates hierarchical or stochastic models of tumor initiation and reproduces the clinical course of HGSC. After estimating parameter values, we infer that most patients harbor chemoresistant HGSC cells at diagnosis and that, if the tumor burden is not too large and complete debulking can be achieved, PDS is superior to NACT due to better depletion of resistant cells. We further predict that earlier diagnosis of primary HGSC, followed by complete debulking, could improve survival, but its benefit in relapsed patients is likely to be limited. These predictions are supported by primary clinical data from multiple cohorts. Our results have clear implications for these key issues in HGSC management.

Review of Mathematical Modelling Techniques in Breast Cancer Dynamics

Cancer is an age related autoimmune disease initiated by genetic mutations leading to increased proliferation rate and tumor formation. Accumulation of various genetic and epigenetic changes, in normal cells gives cancer cells a selective advantage over healthy cells, and lead to cancer initiation and progression. Interactions of tumor cells with other cells within their reach, the microenvironment, is an important component in carcinogenesis. Its treatment is based on our level of understanding of its biology. Breast cancer is a collection of complex and heterogeneous diseases with distinct histopathological features. It arises due to abnormal growths of the cells lining the breast lobules hence men and women can develop it, although very rare in men (less than ). There are different types of breast cancer, and the most common are the invasive and non-invasive cancers. Usually, it is common to study breast cancer tumors as isolated entities, but its biological and molecular structure are indicative of an unstable heterogeneous disease with multiple subtypes coexisting within a tumor. Many studies with different methodologies have been carried out to understand cancer development and progression, however, another approach of mathematical oncology has emerged. This approach uses mathematical and computational techniques to unpack generation, growth and evolution of cancer cells. With this work we would like to review what has been done by others so as to improve our understanding of cancer dynamics