Predicting a risk of tumor evolution considering regulatory mechanisms of the body and angiogenesis

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UDC: 
51-76
DOI: 
10.21668/health.risk/2023.4.13.eng
Authors: 

P.V. Trusov1,2, N.V. Zaitseva1, V.М. Chigvintsev1,2

Organization: 

1Federal Scientific Center for Medical and Preventive Health Risk Management Technologies, 82 Monastyrskaya St., Perm, 614045, Russian Federation
2Perm National Research Polytechnic University, 29 Komsomolskii Av., Perm, 614990, Russian Federation

Abstract: 

Adverse environmental and lifestyle factors produce considerable effects on occurrence of cancerous tumors, both directly and indirectly through impaired functionality of the body protection mechanisms. Investigation of these effects has practical significance for risk assessment and development of effective cancer preventive strategies. Mathematical modeling is an eligible method for considering complex multicomponent interactions between elements of various systems involved in tumor growth.

This article presents an approach to assessing risks of cancerous tumors by using a created predictive model that describes dynamics of abnormal cells considering regulatory mechanisms and angiogenesis. An evolution approach is applied to estimate accumulated functional disorders of the immune system due to natural ageing and chemical environmental exposures. The Monte Carlo simulation is employed to estimate a likely outcome of cancerous tumor evolution given different possible properties of abnormal cells.

The article provides the results of accomplished computation experiments aimed at describing dynamics of changes in cell population properties in an analyzed organ tissue. Development of a vessel system is described considering different effects of the most significant factors. Computation results are analyzed within various scenarios that describe cancerous tumor growth in dynamics considering how angiogenesis develops under different parameters of the immune system dysfunction and different properties of abnormal cells. Risks of tumor development are assessed considering parameters that determine the overall state of the body (the immune system) and properties of abnormal cells.

This approach makes it possible to develop a system of preventive and sanitary-hygienic activities in areas where envi-ronmental conditions are unfavorable in order to reduce cancer incidence.

Keywords: 
mathematical modeling, evolution of functional disorders, angiogenesis, immune system, neuroendocrine regulation, tumor development, risk factors, Monte Carlo simulation
Trusov P.V., Zaitseva N.V., Chigvintsev V.М. Predicting a risk of tumor evolution considering regulatory mechanisms of the body and angiogenesis. Health Risk Analysis, 2023, no. 4, pp. 134–145. DOI: 10.21668/health.risk/2023.4.13.eng
References: 
  1. Ferlay J., Colombet M., Soerjomataram I., Parkin D.M., Piñeros M., Znaor A., Bray F. Cancer statistics for the year 2020: An overview. Int. J. Cancer, 2021, vol. 149, no. 4, pp. 778–789. DOI: 10.1002/ijc.33588
  2. Soerjomataram I., Bray F. Planning for tomorrow: global cancer incidence and the role of prevention 2020–2070. Nat. Rev. Clin. Oncol., 2021, vol. 18, no. 10, pp. 663–672. DOI: 10.1038/s41571-021-00514-z
  3. Bonfiglio R., Sisto R., Casciardi S., Palumbo V., Scioli M.P., Giacobbi E., Servadei F., Melino G. [et al.]. Aluminium bioaccumulation in colon cancer, impinging on epithelial-mesenchymal-transition and cell death. Sci. Total Environ., 2023, vol. 908, pp. 168335. DOI: 10.1016/j.scitotenv.2023.168335
  4. Allam M.F. Breast cancer and deodorants/antiperspirants: a systematic review. Cent. Eur. J. Public Health, 2016, vol. 24, no. 3, pp. 245–247. DOI: 10.21101/cejph.a4475
  5. Madia F., Worth A., Whelan M., Corvi R. Carcinogenicity assessment: Addressing the challenges of cancer and chemicals in the environment. Environ. Int., 2019, vol. 128, pp. 417–429. DOI: 10.1016/j.envint.2019.04.067
  6. Glencross D.A., Ho T.-R., Camina N., Hawrylowicz C.M., Pfeffer P.E. Air pollution and its effects on the immune system. Free Radic. Biol. Med., 2020, vol. 151, pp. 56–68. DOI: 10.1016/j.freeradbiomed.2020.01.179
  7. Krainyukov I.P., Ivashinenko F.M., Evsienko R.R., Velibekov R.T. Vliyanie zagryazneniya vozdukha na rasprostranennost' allergopatologii u zhitelei ekologicheski neblagopriyatnykh raionov Rossii: obzor [The influence of air pollution on the prevalence of allergopathology in residents of environmentally unfavorable regions of Russia: a review]. Obshchestvo, obrazovanie, nauka v sovremennykh paradigmakh razvitiya: sb. trudov po materialam Natsional'noi nauchno-prakticheskoi konferentsii. In: E.P. Masyutkin ed., 2020, pp. 227–230 (in Russian).
  8. Kozhin A.A., Zhukov V.V., Popova V.A. Neuroendocrine disorders in human ontogenesis of ecological etiology and their restorative treatment (literature review). Vestnik novykh med-itsinskikh tekhnologii. Elektronnoe izdanie, 2021, no. 1, pp. 83–91. DOI: 10.24412/2075-4094-2021-1-3-1 (in Russian).
  9. Wang J., Li D., Cang H., Guo B. Crosstalk between cancer and immune cells: Role of tumor-associated macrophages in the tumor microenvironment. Cancer Med., 2019, vol. 8, no. 10, pp. 4709–4721. DOI: 10.1002/cam4.2327
  10. Loginova E.N., Lyalyukova E.A., Nadey E.V., Semenova E.V. Basics of immunooncology and immunotherapy in oncology. Eksperimental'naya i klinicheskaya gastroenterologiya, 2022, no. 9 (205), pp. 129–139. DOI: 10.31146/1682-8658-ecg-205-9-190-195 (in Russian).
  11. Mezentseva L.V., Pertsov S.S. Mathematical modeling in biomedicine. Vestnik novykh meditsinskikh tekhnologii, 2013, vol. 20, no. 1, pp. 11–13 (in Russian).
  12. Walpole J., Papin J.A., Peirce S.M. Multiscale computational models of complex biological systems. Annu. Rev. Biomed. Eng., 2013, vol. 15, pp. 137–154. DOI: 10.1146/annurev-bioeng-071811-150104
  13. Trusov P.V., Zaitseva N.V., Chigvintsev V.M., Lanin D.V. Regulation of organism's antiviral immune response: mathematical model, qualitative analysis, results. Matematicheskaya biologiya i bioinformatika, 2018, vol. 13, no. 2, pp. 402–425. DOI: 10.17537/2018.13.402 (in Russian).
  14. Zaitseva N.V., Kiryanov D.A., Lanin D.V., Chigvintsev V.M. A mathematical model of the immune and neuroendocrine systems mutual regulation under the technogenic chemical factors impact. Comput. Math. Methods Med., 2014, vol. 2014, pp. 492489. DOI: 10.1155/2014/492489
  15. Boersma B., Jiskoot W., Lowe P., Bourquin C. The interleukin-1 cytokine family members: Role in cancer pathogenesis and potential therapeutic applications in cancer immunotherapy. Cytokine Growth Factor Rev., 2021, vol. 62, pp. 1–14. DOI: 10.1016/j.cytogfr.2021.09.004
  16. Bairagi N., Chatterjee S., Chattopadhyay J. Variability in the secretion of corticotropin-releasing hormone, adrenocorticotropic hormone and cortisol and understandability of the hypothalamic-pituitary-adrenal axis dynamics – a mathematical study based on clinical evidence. Math. Med. Biol., 2008, vol. 25, no. 1, pp. 37–63. DOI: 10.1093/imammb/dqn003
  17. Kerdiles Y., Ugolini S., Vivier E. T cell regulation of natural killer cells. J. Exp. Med., 2013, vol. 210, no. 6, pp. 1065–1068. DOI: 10.1084/jem.20130960
  18. Balkhi M.Y., Ma Q., Ahmad S., Junghans R.P. T cell exhaustion and Interleukin 2 downregulation. Cytokine, 2015, vol. 71, no. 2, pp. 339–347. DOI: 10.1016/j.cyto.2014.11.024
  19. Boyman O., Sprent J. The role of interleukin-2 during homeostasis and activation of the immune system. Nat. Rev. Immunol., 2012, vol. 12, no. 3, pp. 180–190. DOI: 10.1038/nri3156
  20. Pol J.G., Caudana P., Paillet J., Piaggio E., Kroemer G. Effects of interleukin-2 in im-munostimulation and immunosuppression. J. Exp. Med., 2020, vol. 217, no. 1, pp. e20191247. DOI: 10.1084/jem.20191247
  21. Demas G.E., Adamo S.A., French S.S. Neuroendocrine-immune crosstalk in vertebrates and invertebrates: Implications for host defence. Functional Ecology, 2011, vol. 25, pp. 29–39. DOI: 10.1111/j.1365-2435.2010.01738.x
  22. Haus E., Smolensky M.H. Biologic rhythms in the immune system. Chronobiol. Int., 1999, vol. 16, no. 5, pp. 581–622. DOI: 10.3109/07420529908998730
  23. Marchuk G.I., Petrov R.V., Romanyukha A.A., Bocharov G.A. Mathematical model of antiviral immune response. I. Data analysis, generalized picture construction and parameters evaluation for hepatitis B. J. Theor. Biol., 1991, vol. 151, no. 1, pp. 1–40. DOI: 10.1016/S0022-5193(05)80142-0
  24. Farhood B., Najafi M., Mortezaee K. CD8+ cytotoxic T lymphocytes in cancer immunotherapy: A review. J. Cell. Physiol., 2019, vol. 234, no. 6, pp. 8509–8521. DOI: 10.1002/jcp.27782
  25. Sharonov G.V., Serebrovskaya E.O., Yuzhakova D.V., Britanova O.V., Chudakov D.M. B cells, plasma cells and antibody repertoires in the tumour microenvironment. Nat. Rev. Immunol., 2020, vol. 20, no. 5, pp. 294–307. DOI: 10.1038/s41577-019-0257-x
  26. Oh P.J., Jang E. Effects of psychosocial interventions on cortisol and immune parameters in patients with cancer: A meta-analysis. J. Korean Acad. Nurs., 2014, vol. 44, no. 4, pp. 446–457. DOI: 10.4040/jkan.2014.44.4.446 (in Korean).
  27. Mukherjee A., Madamsetty V.S., Paul M.K., Mukherjee S. Recent advancements of nanomedicine towards antiangiogenic therapy in cancer. Int. J. Mol. Sci., 2020, vol. 21, no. 2, pp. 455. DOI: 10.3390/ijms21020455
  28. Lopes-Coelho F., Martins F., Pereira S.A., Serpa J. Anti-angiogenic therapy: current challenges and future perspectives. Int. J. Mol. Sci., 2021, vol. 22, no. 7, pp. 3765. DOI: 10.3390/ijms22073765
  29. Jiang X., Wang J., Deng X., Xiong F., Zhang S., Gong Z., Li X., Cao K. [et al.]. The role of microenvironment in tumor angiogenesis. J. Exp. Clin. Cancer Res., 2020, vol. 39, no. 1, pp. 204. DOI: 10.1186/s13046-020-01709-5
  30. Melincovici C.S., Boşca A.B., Şuşman S., Mărginean M., Mihu C., Istrate M., Moldovan I.M., Roman A.L., Mihu C.M. Vascular endothelial growth factor (VEGF) – key factor in normal and pathological angiogenesis. Rom. J. Morphol. Embryol., 2018, vol. 59, no. 2, pp. 455–467.
  31. Bates D.O. Vascular endothelial growth factors and vascular permeability. Cardiovasc. Res., 2010, vol. 87, no. 2, pp. 262–271. DOI: 10.1093/cvr/cvq105
  32. Chigvintsev V., Trusov P., Zaitseva N. Mathematical model of antitumor immune response of organism regulation. AIP Conf. Proc.: 29th Russian Conference on Mathematical Modeling in Natural Sciences, 2021, vol. 2371, pp. 060001. DOI: 10.1063/5.0059532
  33. Buyan M.I., Andrianova N.V., Popkov V.A., Zorova L.D., Pevzner I.B., Silachev D.N., Zorov D.B., Plotnikov E.Y. Age-Associated Loss in Renal Nestin-Positive Progenitor Cells. Int. J. Mol. Sci., 2022, vol. 23, no. 19, pp. 11015. DOI: 10.3390/ijms231911015
  34. Vollmers H.P., Brändlein S. Natural antibodies and cancer. N. Biotechnol., 2009, vol. 25, no. 5, pp. 294–298. DOI: 10.1016/j.nbt.2009.03.016
  35. Perl A.J., Schuh M.P., Kopan R. Regulation of nephron progenitor cell lifespan and nephron endowment. Nat. Rev. Nephrol., 2022, vol. 18, no. 11, pp. 683–695. DOI: 10.1038/s41581-022-00620-w
  36. Mahasa K.J., Ouifki R., Eladdadi A., de Pillis L. Mathematical model of tumor-immune surveillance. J. Theor. Biol., 2016, vol. 404, pp. 312–330. DOI: 10.1016/j.jtbi.2016.06.012
  37. Mendoza-Juez B., Martínez-González A., Calvo G.F., Pérez-García V.M. A mathematical model for the glucose-lactate metabolism of in vitro cancer cells. Bull. Math. Biol., 2012, vol. 74, no. 5, pp. 1125–1142. DOI: 10.1007/s11538-011-9711-z
  38. Plank M.J., Sleeman B.D., Jones P.F. A mathematical model of tumour angiogenesis, regulated by vascular endothelial growth factor and the angiopoietins. J. Theor. Biol., 2004, vol. 229, no. 4, pp. 435–454. DOI: 10.1016/j.jtbi.2004.04.012
  39. Vilanova G., Colominas I., Gomez H. A mathematical model of tumour angiogenesis: growth, regression and regrowth. J. R. Soc. Interface, 2017, vol. 14, no. 126, pp. 20160918. DOI: 10.1098/rsif.2016.0918
  40. Kamaltdinov M.R., Kiryanov D.A. The application of recurrent relations for integrated health risk assessment. Zdorov'e sem'i – 21 vek, 2011, no. 3, pp. 6 (in Russian).
  41. Trusov P.V., Zaitseva N.V., Chigvintsev V.M. Assessing risks of adverse clinical course and outcome of an infectious dis-ease with mathematical modeling of exposure to environmental factors on the example of aluminum oxide. Health Risk Analysis, 2019, no. 1, pp. 17–29. DOI: 10.21668/health.risk/2019.1.02.eng
Received: 
30.09.2023
Approved: 
06.12.2023
Accepted for publication: 
20.12.2023

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