Effects of the obesity on optimal control schedules of chemotherapy on a cancerous tumor

Obesity as a risk factor has been found in different types of cancers such as breast cancer and colorectal cancer among others. This challenges us to study the cancer-obesity relationship and the tumor response to chemotherapy. In this work, we study and analyze optimal control protocols for chemotherapy treatments for a mathematical model of cancerous growing tumor that is interacting with the healthy cells, the immune system cells and the stored fat in the organism. This model considers different cell populations using a population dynamics approach. Our main interest is to provide insights about the qualitative and quantitative possible affects of a low/high caloric diet on the chemotherapy protocols when different immune system responses are considered. According to our model the immune system response and the diet are important factors and their inclusion could lead to improved chemotherapy protocols.

[1]  R. Vidal Dynamic optimization: The calculus of variations and optimal control in economics and management: Morton I. KAMIEN and Nancy L. SCHWARTZ Volume 4 in: Dynamic Economics: Theory and Applications, North-Holland, New York, 1981, xi + 331 pages, Dfl.90.00 , 1982 .

[2]  Urszula Ledzewicz,et al.  Optimal controls for a model with pharmacokinetics maximizing bone marrow in cancer chemotherapy. , 2007, Mathematical biosciences.

[3]  Junghyo Jo,et al.  Quantitative dynamics of adipose cells , 2012, Adipocyte.

[4]  L. Wilkinson Immunity , 1891, The Lancet.

[5]  M. Petit Dynamic optimization. The calculus of variations and optimal control in economics and management : by Morton I. Kamien and Nancy L. Schwartz. Second Edition. North-Holland (Advanced Textbooks in Economics), Amsterdam and New York, 1991. Pp. xvii+377. ISBN0-444- 01609-0 , 1994 .

[6]  Robert A. Smith,et al.  An Estrogen Model: The Relationship between Body Mass Index, Menopausal Status, Estrogen Replacement Therapy, and Breast Cancer Risk , 2012, Comput. Math. Methods Medicine.

[7]  B. Leimkuhler,et al.  Optimal control for mathematical models of cancer therapies : an application of geometric methods , 2015 .

[8]  H. Rubin Promotion and selection by serum growth factors drive field cancerization, which is anticipated in vivo by type 2 diabetes and obesity , 2013, Proceedings of the National Academy of Sciences.

[9]  A. Friedman,et al.  Mathematical modeling of preadipocyte fate determination. , 2010, Journal of theoretical biology.

[10]  A. Sjölander,et al.  Body mass index and weight change in men with prostate cancer: progression and mortality , 2014, Cancer Causes & Control.

[11]  M. Thun,et al.  Obesity and cancer , 2004, Oncogene.

[12]  Gary An,et al.  An agent-based modeling framework linking inflammation and cancer using evolutionary principles: description of a generative hierarchy for the hallmarks of cancer and developing a bridge between mechanism and epidemiological data. , 2015, Mathematical biosciences.

[13]  M. Karin,et al.  Immunity, Inflammation, and Cancer , 2010, Cell.

[14]  M. Schwab,et al.  Encyclopedia of Cancer , 2017, Springer Berlin Heidelberg.

[15]  Robert J Gillies,et al.  Glycolysis in cancer: a potential target for therapy. , 2007, The international journal of biochemistry & cell biology.

[16]  S. Hursting Minireview: the year in obesity and cancer. , 2012, Molecular endocrinology.

[17]  J. Sargeant,et al.  Obesity and cancer , 1988, Veterinary Record.

[18]  Roberto A. Ku-Carrillo,et al.  A mathematical model for the effect of obesity on cancer growth and on the immune system response , 2016 .

[19]  J. M. Murray,et al.  The optimal scheduling of two drugs with simple resistance for a problem in cancer chemotherapy. , 1997, IMA journal of mathematics applied in medicine and biology.

[20]  O. Brawley,et al.  Obesity-driven inflammation and cancer risk: role of myeloid derived suppressor cells and alternately activated macrophages. , 2013, American journal of cancer research.

[21]  Gary Taubes,et al.  Cancer research. Unraveling the obesity-cancer connection. , 2012, Science.

[22]  S. Sager,et al.  Optimal control for selected cancer chemotherapy ODE models: a view on the potential of optimal schedules and choice of objective function. , 2011, Mathematical biosciences.

[23]  Ami Radunskaya,et al.  A mathematical tumor model with immune resistance and drug therapy: an optimal control approach , 2001 .

[24]  F. Clavel-Chapelon,et al.  Several anthropometric measurements and breast cancer risk: results of the E3N cohort study , 2006, International Journal of Obesity.

[25]  Ami Radunskaya,et al.  The dynamics of an optimally controlled tumor model: A case study , 2003 .

[26]  P. Katulanda,et al.  Relationship between Body mass index (BMI) and body fat percentage, estimated by bioelectrical impedance, in a group of Sri Lankan adults: a cross sectional study , 2013, BMC Public Health.

[27]  S. Mittelman,et al.  Adipocytes cause leukemia cell resistance to L-asparaginase via release of glutamine. , 2013, Cancer research.