Mathematical models of breast and ovarian cancers

Abstract

Women constitute the majority of the aging United States (US) population, and this has substantial implications on cancer population patterns and management practices. Breast cancer is the most common women's malignancy, while ovarian cancer is the most fatal gynecological malignancy in the US. In this review we focus on these subsets of women's cancers, seen more commonly in postmenopausal and elderly women. In order to systematically investigate the complexity of cancer progression and response to treatment in breast and ovarian malignancies, we assert that integrated mathematical modeling frameworks viewed from a systems biology perspective are needed. Such integrated frameworks could offer innovative contributions to the clinical women's cancers community, since answers to clinical questions cannot always be reached with contemporary clinical and experimental tools. Here, we recapitulate clinically known data regarding the progression and treatment of the breast and ovarian cancers. We compare and contrast the two malignancies whenever possible, in order to emphasize areas where substantial contributions could be made by clinically inspired and validated mathematical modeling. We show how current paradigms in the mathematical oncology community focusing on the two malignancies do not make comprehensive use of, nor substantially reflect existing clinical data, and we highlight the modeling areas in most critical need of clinical data integration. We emphasize that the primary goal of any mathematical study of women's cancers should be to address clinically relevant questions.

Experimentally-driven mathematical modeling to improve combination targeted and cytotoxic therapy for HER2+ breast cancer

“…(4):where P f is the amount of free paclitaxel with an uptake rate of per the number of tumor cells ( T ). With internalized paclitaxel ( P i > 0), the function h ( P i , t ) becomes non-zero with the carrying capacity reduction effect —similar to other modeling studies that modified the carrying capacity due to paclitaxel treatment 16 . The expression represents the immediate toxic effect due to paclitaxel treatment, where is the rate of decay of this toxicity effect, and is the time at which the toxic effect becomes less than one and decreases to zero as time progresses.…”

Crosstalk between HER2 and PD-1/PD-L1 in Breast Cancer: From Clinical Applications to Mathematical Models

“…Specific mathematical models of tumor-immune interaction with respect to the adopted mode of treatment such as chemotherapy [158], radiotherapy [159], immunotherapy [160], hormone therapy [161], anti-angiogenesis [162], nanomedicine-based cancer therapy [33], gene therapy and/or oncovirotherapy [163], and combination therapies [164] have been widely discussed recently. Many recent reviews summarize the history of mathematical models in various treatment areas related to cancer [33,40,156,[165][166][167][168]. As mentioned earlier, one of the focusses of this paper is to highlight the progress achieved in the mathematical modeling of BC with specific emphasis on anti-HER2 treatments and immune checkpoint inhibitors.…”

Prodigiosin/PU-H71 as a novel potential combined therapy for triple negative breast cancer (TNBC): preclinical insights

“…Breast cancers represent a collection of malignancies that arise either in the breast tissue made up of glands for milk production, called lobules or in the ducts connecting the lobules to the nipple 1 , 2 . More than 60% of breast cancers are either estrogen receptor (ER) or progesterone receptor (PR) positive.…”

“…More than 60% of breast cancers are either estrogen receptor (ER) or progesterone receptor (PR) positive. Other defined subtypes are characterised by human epidermal growth factor receptor 2 (HER2) protein overexpression or HER2 gene amplification, and triple negative breast cancer (TNBC), where there is neither ER/PR expression nor HER2 overexpression 2 , 3 . Up to 20% of breast cancers are TNBCs, occurring often in women ˂ 50 years of age 4 .…”