This study presented a glioma growth model that accounts for drug-sensitive and drug-resistant cells in response to chemotherapy and anti-angiogenic therapy. Chemotherapy induces mutations in drug-sensitive cells, leading to the emergence of drug-resistant cells and highlighting the benefits of combined therapy. Anti-angiogenic therapy can mitigate mutations by inducing angiogenic dormancy. We have identified two reproduction numbers associated with the non-cell and disease-free states. Numerical sensitivity analysis has highlighted influential parameters that control glioma growth dynamics, emphasizing the interactions between drug-sensitive and drug-resistant cells. To reduce glioma endemicity among sensitive cases, it was recommended to decrease chemotherapy expenditure, increase angiogenic dormancy, and adjust chemotherapy infusion rates. In addition, to combat resistance to glioma endemicity, enhancing angiogenic dormancy is crucial.
Citation: Latifah Hanum, Dwi Ertiningsih, Nanang Susyanto. Sensitivity analysis unveils the interplay of drug-sensitive and drug-resistant Glioma cells: Implications of chemotherapy and anti-angiogenic therapy[J]. Electronic Research Archive, 2024, 32(1): 72-89. doi: 10.3934/era.2024004
This study presented a glioma growth model that accounts for drug-sensitive and drug-resistant cells in response to chemotherapy and anti-angiogenic therapy. Chemotherapy induces mutations in drug-sensitive cells, leading to the emergence of drug-resistant cells and highlighting the benefits of combined therapy. Anti-angiogenic therapy can mitigate mutations by inducing angiogenic dormancy. We have identified two reproduction numbers associated with the non-cell and disease-free states. Numerical sensitivity analysis has highlighted influential parameters that control glioma growth dynamics, emphasizing the interactions between drug-sensitive and drug-resistant cells. To reduce glioma endemicity among sensitive cases, it was recommended to decrease chemotherapy expenditure, increase angiogenic dormancy, and adjust chemotherapy infusion rates. In addition, to combat resistance to glioma endemicity, enhancing angiogenic dormancy is crucial.
[1] | D. N. Louis, A. Perry, P. Wesseling, D. J. Brat, I. A Cree, D. Figarella-Branger, et al, WHO classification of tumors of the central nervoussystem, Int. Agency Res. Cancer, 4 (2007). https://doi.org/10.1093%2Fneuonc%2Fnoab106 |
[2] | M. Weller, W. Wick, K. Aldape, M. Brada, M. Berger, S. M, Pfister, et al, Glioma, Nat. Rev. Dis. Primers, 1 (2015), 15017. https://doi.org/10.1038/nrdp.2015.17 doi: 10.1038/nrdp.2015.17 |
[3] | B. Mansoori, A. Mohammadi, S. Davudian, S. Shirjang, B. Baradaran, The different mechanisms of cancer drug resistance: A brief review, Adv. Pharm. Bull., 7 (2017), 339–348. https://doi.org/10.15171%2Fapb.2017.041 |
[4] | P. Shamshiripour, F. Hajiahmadi, S. Lotfi, N. R. Esmaeili, A. Zare, M. Akbarpour, et al., Next-generation anti-angiogenic therapies as a future prospect for glioma immunotherapy; from bench to bedside, Front. Immunol., 2022. https://doi.org/10.3389/fimmu.2022.859633 doi: 10.3389/fimmu.2022.859633 |
[5] | O. Nave, A mathematical model for treatment using chemo-immunotherapy, Heliyon, 8 (2022), e09288. https://doi.org/10.1016/j.heliyon.2022.e09288 doi: 10.1016/j.heliyon.2022.e09288 |
[6] | F. L. Coelho, F. Martins, S. A. Pereira, J. Serpa, Anti-angiogenic therapy: Current challenges and future perspectives, Int. J. Mol. Sci., 3765 (2021), 22. https://doi.org/10.3390/ijms22073765 doi: 10.3390/ijms22073765 |
[7] | L. Holmgren, M. S. O'Reilly, J. Folkman, Dormancy of micrometastases: Balanced proliferation and apoptosis in the presence of angiogenesis suppression, Nat. Med., 1 (1995), 149–153. https://doi.org/10.1038/nm0295-149 doi: 10.1038/nm0295-149 |
[8] | S. Giuriato, S. Ryeom, A. C. Fan, A. C. Fan, P. Bachireddy, R. C. Lynch, et al., Sustained regression of tumors upon MYC inactivation requires p53 or thrombospondin reverse the angiogenic switch, Proc. Natl. Acad. Sci. U. S. A., 103 (2006), 16266–16271. https://doi.org/10.1073/pnas.0608017103 doi: 10.1073/pnas.0608017103 |
[9] | S. Indraccolo, L. Stievano, S. Minuzzo, V. Tosello, G. Esposito, E. Piovan, et al., Interruption of tumor dormancy by a transient angiogenic burst within the tumor microenvironment, Proc. Natl. Acad. Sci. U. S. A., 103 (2006), 4216–4221. https://doi.org/10.1073/pnas.0506200103 doi: 10.1073/pnas.0506200103 |
[10] | T. Ogawa, K. Ogawa, K. Shiga, T. Furukawa, H. Nagase, S. Hashimoto, et al., Upregulation of IGF2 is associated with an acquired resistance for cis- diamminedichloroplatinum in human head and neck squamous cell carcinoma, Eur. Arch. Oto-Rhino-Laryngol., 267 (2010), 1599–1606. https://doi.org/10.1007/s00405-010-1257-4 doi: 10.1007/s00405-010-1257-4 |
[11] | T. G. Phan, P. I. Croucher, The dormant cancer cell life cycle, Nat. Rev. Cancer, 20 (2020), 398–411. https://doi.org/10.1038/s41568-020-0263-0 doi: 10.1038/s41568-020-0263-0 |
[12] | L. Hanum, N. Susyanto, D. Ertiningsih, Mathematical model of the impact of chemotherapy and anti-angiogenic therapy on drug resistance in glioma growth, preprint, arXiv: 2308.11212v1. https://doi.org/10.48550/arXiv.2308.11212 |
[13] | L. M. Childs, N. N. Abuelezam, C. Dye, S. Gupta, M. B. Murray, B. G Williams, et al., Modelling challenges in context: Lessons from malaria, HIV, and tuberculosis, Epidemics, 10 (2015), 102–107. https://doi.org/10.1016/j.epidem.2015.02.002 doi: 10.1016/j.epidem.2015.02.002 |
[14] | O. Diekmann, J. Heesterbeek, M. G. Roberts, The construction of next-generation matrices for compartmental epidemic models, J. R. Soc. Interface, 7 (2009), 873–85. https://doi.org/10.1098/rsif.2009.0386 doi: 10.1098/rsif.2009.0386 |
[15] | P. V. D. Driessche, Reproduction numbers of infectious disease models, Infect. Dis. Modell., 2 (2017), 288–303. https://doi.org/10.1016%2Fj.idm.2017.06.002 |
[16] | M. T. Meehan, D. G. Cocks, J. M. Trauer, E. S. McBryde, Coupled, multi-strain epidemic models of mutating pathogens, Math. Biosci., 296 (2018), 82–92. https://doi.org/10.1016/j.mbs.2017.12.006 doi: 10.1016/j.mbs.2017.12.006 |
[17] | Y. B. Jia, Roots of Polynomials, Com S, In press, 477/577. |
[18] | N. Chitnis, J. M. Hyman, J. M. Cushing, Determining Important Parameters in the Spread of Malaria Through the Sensitivity Analysis of a Mathematical Model, Bull. Math. Biol., 70 (2008), 1272–1296. https://doi.org/10.1007/s11538-008-9299-0 doi: 10.1007/s11538-008-9299-0 |
[19] | S. T. R. Pinho, A mathematical model for the effect of anti-angiogenic therapy in the treatment of cancer tumours by chemotherapy, Nonlinear Anal.: Real World Appl., 14 (2012), 815–828. https://doi.org/10.1016/j.nonrwa.2012.07.034 doi: 10.1016/j.nonrwa.2012.07.034 |
[20] | T. Würdinger, B. A. Tannous, Glioma angiogenesis, Cell Adhesi. Migrat., 3 (2009), 230–235. https://doi.org/10.4161%2Fcam.3.2.7910 |
[21] | J. Trobia, K. Tian, A. M. Batista, C. Grebogi, H. P. Ren, M. S. Santos, et al., Mathematical model of brain tumour growth with drug resistance, Commun. Nonlinear Sci. Numer. Simulat. 103 (2020), 1007–5704. https://doi.org/10.1016/j.cnsns.2021.106013 doi: 10.1016/j.cnsns.2021.106013 |
[22] | B. Alberts, A. Johnson, J. Lewis, M. Raff, K. Roberts, P. Walter, Molecular Biology of the Cell, 4th edition, New York: Garland Science, 2002. |
[23] | R. K. Sachs, L. R. Hlatky, P. Hahnfeldt, Simple ODE models of tumor growth and anti-angiogenic or radiation treatment, Math. Comput. Modell., 33 (2001), 1297–1305. https://doi.org/10.1016/S0895-7177(00)00316-2 doi: 10.1016/S0895-7177(00)00316-2 |
[24] | D. Hanahan, J. Folkman, Patterns and emerging mechanisms of the angiogenic switch during tumorigenesis, Cell, 86 (1996), 353–364. https://doi.org/10.1016/s0092-8674(00)80108-7 doi: 10.1016/s0092-8674(00)80108-7 |
[25] | M. A. Böttcher, J. Held-Feindt, M. Synowitz, R. Lucius, A. Traulsen, K. Hattermann, Modeling treatment-dependent glioma growth including a dormant tumor cell subpopulation, BMC Cancer, 18 (2018), 1–12. |
[26] | R. T. Silver, R. D. Lauper, I. Charles, A Synopsis of Cancer Chemotherapy, 2nd edition, New York, N.Y. : Yorke Medical Books, c1987. |
[27] | T. Browder, C. E. Butterfield, B. M. Kraling, B. Shi, B. Marshall, M. S. O'reilly, et al., Antiangiogenic scheduling of chemotherapy improves efficacy against experimental drug-resistant cancer, Cancer Res., 60 (2001), 1878–1886. |
[28] | R. Said, M. Abdel-Rehim, B. Sadeghi, S. Al-Hashemi, Z. Hassan, M. Hassan, Cyclophosphamide pharmacokinetics in mice: A comparison between retro orbital sampling versus serial tail vein bleeding, Open Pharmacol. J., 1 (2007), 30–35. http://dx.doi.org/10.2174/1874143600701010030 doi: 10.2174/1874143600701010030 |
[29] | S. Shusterman, S. A. Grupp, R. Barr, D. Carpentieri, H. Zhao, J. M. Maris, The angiogenesis inhibitor TNP-470 effectively inhibits human neuroblastoma xenograft growth, especially in the setting of subclinical disease, Clin. Cancer Res., 7 (2001), 977–984. |
[30] | I. Dattner, C. A. J. Klaassen, Optimal rate of direct estimators in systems of ordinary differential equations linear in functions of the parameters, Electron. J. Stat., 9 (2013), 1939–1973. http://dx.doi.org/10.1214/15-EJS1053 doi: 10.1214/15-EJS1053 |
[31] | P. Hurtik, V. Molek, J. Hula, Data preprocessing technique for neural networks based on image represented by a fuzzy function, IEEE Trans. Fuzzy Syst., 28 (2019), 1195–1204. https://doi.org/10.1109/TFUZZ.2019.2911494 doi: 10.1109/TFUZZ.2019.2911494 |
[32] | M. Versaci, G. Angiulli, P. D Barba, F. C Morabito, Joint use of eddy current imaging and fuzzy similarities to assess the integrity of steel plates, Open Phys., 18 (2020), 230–240. https://doi.org/10.1515/phys-2020-0159 doi: 10.1515/phys-2020-0159 |
[33] | J. B. Liu, N. Salamat, M. Kamran, S. Ashraf, R. H. Khan, Single-valued neutrosophic eutrosophic set with quaternion information: A promising approach to assess image quality, Fractals, 31 (2023), 2340074. https://doi.org/10.3390/math8030439 doi: 10.3390/math8030439 |