Bio-algorithms for the modeling and simulation of cancer cells and the immune response

• Autorzy: Idrees Muhammad , Sohail Ayesha

Identyfikatory

DOI

10.1515/bams-2020-0054

• Języki publikacji: EN

Abstrakty

EN

There have been significant developments in clinical, experimental, and theoretical approaches to understand the biomechanics of tumor cells and immune cells. Cytotoxic T lymphocytes (CTLs) are regarded as a major antitumor mechanism of immune cells. Mathematical modeling of tumor growth is an important and useful tool to observe and understand clinical phenomena analytically. This work develops a novel two-variable mathematical model to describe the interaction of tumor cells and CTLs. The designed model is providing an integrated framework to investigate the complexity of tumor progression and answer clinical questions that cannot always be reached with experimental tools. The parameters of the model are estimated from experimental study and stability analysis of the model is performed through nullclines. A global sensitivity analysis is also performed to check the uncertainty of the parameters. The results of numerical simulations of the model support the importance of the CTLs and demonstrate that CTLs can eliminate small tumors. The proposed model provides efficacious information to study and demonstrate the complex dynamics of breast cancer.

Słowa kluczowe

EN

breast cancer; cytotoxic T lymphocytes; immune response; mathematical modeling; numerical simulations

• Wydawca: Uniwersytet Jagielloński - Collegium Medicum ; Index Copernicus Sp. z o.o.
• Czasopismo: Bio-Algorithms and Med-Systems
• Rocznik: 2021
• Tom: Vol. 17, no. 1
• Strony: 55--63
• Opis fizyczny: Bibliogr. 45 poz., rys., tab.

Twórcy

autor

Idrees Muhammad

• Department of Mathematics, COMSATS University Islamabad, Lahore, Pakistan

autor

Sohail Ayesha

• Department of Mathematics, COMSATS University Islamabad, Lahore 54000, Pakistan

Bibliografia

• 1. Zgura A, Galesa L, Bratila E, Anghel R. Relationship between tumor infiltrating lymphocytes and progression in breast cancer. Maedica 2018;13:317.
• 2. Katkuri S, Gorantla M. Awareness about breast cancer among women aged 15 years and above in urban slums: a cross sectional study. Int J Community Med Public Health 2018;5:929-32.
• 3. Ji P, Gong Y, Jin ML, Hu X, Di GH, Shao ZM. The burden and trends of breast cancer from 1990 to 2017 at the global, regional, and national levels: results from the global burden of disease study 2017. Front Oncol 2020:10. https://doi.org/10.3389/fonc.2020. 00650.
• 4. Li CI, Uribe DJ, Daling JR. Clinical characteristics of different histologic types of breast cancer. Br J Canc 2005;93:1046-52.
• 5. Woodhams R, Matsunaga K, Kan S, Hata H, Ozaki M, Iwabuchi K, et al. ADC mapping of benign and malignant breast tumors. Magn Reson Med Sci 2005;4:35-42.
• 6. Cristofanilli M, Budd GT, Ellis MJ, Stopeck A, Matera J, Miller MC, et al. Circulating tumor cells, disease progression, and survival in metastatic breast cancer. N Engl J Med 2004;351:781-91.
• 7. Gao Q, Qiu SJ, Fan J, Zhou J, Wang XY, Xiao YS, et al. Intratumoral balance of regulatory and cytotoxic T cells is associated with prognosis of hepatocellular carcinoma after resection. J Clin Oncol 2007;25:2586-93.
• 8. Hamann D, Roos MT, van Lier RA. Faces and phases of human CD8+ T-cell development. Immunol Today 1999;20:177-80.
• 9. Ronchetti A, Rovere P, Iezzi G, Galati G, Heltai S, Protti MP, et al. Immunogenicity of apoptotic cells in vivo: role of antigen load, antigen-presenting cells, and cytokines. J Immunol 1999;163: 130-6.
• 10. Kupfer A, Swain SL, Janeway CA, Singer SJ. The specific direct interaction of helper T cells and antigen-presenting B cells. Proc Natl Acad Sci USA 1986;83:6080-3.
• 11. Todd JA, Acha-Orbea H, Bell JI, Chao N, Fronek Z, Jacob CO, et al. A molecular basis for MHC class II – associated autoimmunity. Science 1988;240:1003-9.
• 12. Stenger S, Rosat JP, Bloom BR, Krensky AM, Modlin RL. Granulysin: a lethal weapon of cytolytic T cells. Immunol Today 1999;20:390-4.
• 13. Fahmy MA. Boundary element modeling and simulation of biothermomechanical behavior in anisotropic laser-induced tissue hyperthermia. Eng Anal Bound Elem 2019;101:156-64.
• 14. Fahmy MA. A new LRBFCM-GBEM modeling algorithm for general solution of time fractional-order dual phase lag bioheat transfer problems in functionally graded tissues. Numer Heat Trans Part A Appl 2019;75:616-26.
• 15. Fahmy MA. Boundary element algorithm for modeling and simulation of dual-phase lag bioheat transfer and biomechanics of anisotropic soft tissues. Int J Appl Mech 2018; 10:1850108.
• 16. Fahmy MA. A new computerized boundary element algorithm for cancer modeling of cardiac anisotropy on the ECG simulation. Asian J Res Comput Sci 2018;2:1-10.
• 17. de Vladar HP, González JA. Dynamic response of cancer under the influence of immunological activity and therapy. J Theor Biol 2004;227:335-48.
• 18. Forys U, Waniewski J, Zhivkov P. Anti-tumor immunity and tumor anti-immunity in a mathematical model of tumor immunotherapy. J Biol Syst 2006;14:13-30.
• 19. Cappuccio A, Elishmereni M, Agur Z. Cancer immunotherapy by interleukin-21: potential treatment strategies evaluated in a mathematical model. Canc Res 2006;66:7293-300.
• 20. Jarrett AM, Bloom MJ, Godfrey W, Syed AK, Ekrut DA, Ehrlich LI, et al. Mathematical modelling of trastuzumab-induced immune response in an in vivo murine model of HER2+ breast cancer. Math Med Biol 2019;36:381-410.
• 21. Annan K, Nagel M, Brock HA. A mathematical model of breast cancer and mediated immune system interactions. J Math Syst Sci 2012;2:430-46.
• 22. Roe-Dale R, Isaacson D, Kupferschmid M. A mathematical model of breast cancer treatment with CMF and doxorubicin. Bull Math Biol 2011;73:585-608.
• 23. Eftimie R, Bramson JL, Earn DJ. Interactions between the immune system and cancer: a brief review of non-spatial mathematical models. Bull Math Biol 2011;73:2-32.
• 24. Gruber I, Landenberger N, Staebler A, Hahn M, Wallwiener D, Fehm T. Relationship between circulating tumor cells and peripheral T-cells in patients with primary breast cancer. Anticancer Res 2013;33:2233-8.
• 25. Nawata H, Chong MT, Bronzert D, Lippman ME. Estradiolindependent growth of a subline of MCF-7 human breast cancer cells in culture. J Biol Chem 1981;256:6895-902.
• 26. Doubilet P, Begg CB, Weinstein MC, Braun P, McNeil BJ. Probabilistic sensitivity analysis using Monte Carlo simulation: a practical approach. Med Decis Making 1985;5:157-77.
• 27. Britton NF. Essential mathematical biology. Springer Science & Business Media. London: Springer-Verlag; 2012.
• 28. Kawarada Y, Ganss R, Garbi N, Sacher T, Arnold B, Hämmerling GJ. NK-and CD8+ T cell-mediated eradication of established tumors by peritumoral injection of CpG-containing oligodeoxynucleotides. J Immunol 2001;167:5247-53.
• 29. Dudley ME, Wunderlich JR, Robbins PF, Yang JC, Hwu P, Schwartzentruber DJ, et al. Cancer regression and autoimmunity in patients after clonal repopulation with antitumor lymphocytes. Science 2002;298:850-4.
• 30. Adam JA, Bellomo N. A survey of models for tumor-immune system dynamics. Springer Science & Business Media. Basel: Birkhäuser; 2012.
• 31. de Pillis LG, Gu W, Radunskaya AE. Mixed immunotherapy and chemotherapy of tumors: modeling, applications and biological interpretations. J Theor Biol 2006;238:841-62.
• 32. Lopez AG, Seoane JM, Sanjuan MA. A validated mathematical model of tumor growth including tumor-host interaction, cellmediated immune response and chemotherapy. Bull Math Biol 2014;76:2884-906.
• 33. Fernandez M, Zhou M, Soto-Ortiz L. A computational assessment of the robustness of cancer treatments with respect to immune response strength, tumor size and resistance. Int J Tumor Ther 2018;7:1-9.
• 34. Müller MR, Grünebach F, Nencioni A, Brossart P. Transfection of dendritic cells with RNA induces CD4-and CD8-mediated T cell immunity against breast carcinomas and reveals the immunodominance of presented T cell epitopes. J Immunol 2003; 170:5892–6.
• 35. Kuznetsov VA, Makalkin IA, Taylor MA, Perelson AS. Nonlinear dynamics of immunogenic tumors: parameter estimation and global bifurcation analysis. Bull Math Biol 1994;56:295-321.
• 36. Wiggins S. Introduction to applied nonlinear dynamical systems and chaos. Springer Science & Business Media. New York: Springer-Verlag; 2003.
• 37. Vacca P, Munari E, Tumino N, Moretta F, Pietra G, Vitale M, et al. Human natural killer cells and other innate lymphoid cells in cancer: friends or foes? Immunol Lett 2018;201:14-9.
• 38. Fidler IJ. Metastasis: quantitative analysis of distribution and fate of tumor emboli labeled with 125I-5-iodo-2-deoxyuridine. J Natl Cancer Inst 1970;45:773-82.
• 39. Wei HC. Mathematical modeling of tumor growth: the MCF-7 breast cancer cell line. Math Biosci Eng 2019;16:6512-35.
• 40. Folkman J, Kalluri R. Cancer without disease. Nature 2004;427: 787.
• 41. Fehm T, Mueller V, Marches R, Klein G, Gueckel B, Neubauer H, et al. Tumor cell dormancy: implications for the biology and treatment of breast cancer. Apmis 2008;116:742-53.
• 42. Franco OE, Shaw AK, Strand DW, Hayward SW Cancer associated fibroblasts in cancer pathogenesis. In: Seminars in cell & developmental biology. Academic Press; 2010, vol. 21:33-9pp.
• 43. Liu G, Fan X, Cai Y, Fu Z, Gao F, Dong J, et al. Efficacy of dendritic cell-based immunotherapy produced from cord blood in vitro and in a humanized NSG mouse cancer model. Immunotherapy 2019; 11:599-616.
• 44. Schnekenburger M, Dicato M, Diederich MF. Anticancer potential of naturally occurring immunoepigenetic modulators: a promising avenue? Cancer 2019;125:1612-28.
• 45. Feng XY, Lu L, Wang KF, Zhu BY, Wen XZ, Peng RQ, et al. Low expression of CD80 predicts for poor prognosis in patients with gastric adenocarcinoma. Future Oncol 2019;15:473-83.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).