Mathematical models of breast and ovarian cancers.
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, as 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. WIREs Syst Biol Med 2016, 8:337-362. doi: 10.1002/wsbm.1343 For further resources related to this article, please visit the WIREs website.
Department of Mathematics and Center for Scientific Computation and Mathematical Modeling (CSCAMM), University of Maryland, College Park, MD, USA.|Women's Malignancies Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA.
Dana-Adriana
DA
Botesteanu
Women's Malignancies Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA.
Stanley
S
Lipkowitz
Women's Malignancies Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA.
Jung-Min
JM
Lee
Department of Mathematics and Center for Scientific Computation and Mathematical Modeling (CSCAMM), University of Maryland, College Park, MD, USA.
Doron
D
Levy
D001943:Breast Neoplasms / Q000175:diagnosis* / Q000378:metabolism / Q000628:therapy; D019008:Drug Resistance, Neoplasm; D055088:Early Detection of Cancer; D005260:Female; D006801:Humans; D008962:Models, Theoretical; D058990:Molecular Targeted Therapy; D010051:Ovarian Neoplasms / Q000175:diagnosis* / Q000378:metabolism / Q000628:therapy; D012008:Recurrence