×

Mathematical modeling of gastro-intestinal metastasis resistance to tyrosine kinase inhibitors. (English) Zbl 1497.92110

Suzuki, Takashi (ed.) et al., Methods of mathematical oncology. Fusion of mathematics and biology. Selected papers based on the presentations at the symposium, Osaka, Japan, October 26–28, 2020. Singapore: Springer. Springer Proc. Math. Stat. 370, 15-49 (2021).
Summary: In this paper, we study a continuum mechanics model of gastrointestinal stroma tumor (GIST) evolution under the action of two specific treatments. The first-line treatment is a specific tyrosine kinase inhibitor (TKI), with a cytotoxic effect, that induces direct cell death. The second-line treatment is a multi-targeted TKI, with both cytotoxic and anti-angiogenic effect. The model is a coupled hyperbolic/elliptic system based on mass balance equations on cell densities coupled with a diffusion equation on the nutrients and oxygen concentrations. The tumor model involves 3 proliferating cell densities and a necrotic phase. Each proliferating cell density responds differently to the treatments: \(P_1\)-cells are killed by both treatments, \(P_2\)-cells are affected by the second-line treatment only, and \(P_3\) cells are resistant to both therapies. The necrotic cells are eliminated at a rate \(1/\tau\). We first prove the well-posedness of the model for any non-negative \(\tau\). Then we study the asymptotic behavior of the solution as \(\tau\) goes to zero. In particular, we proved that the limit problem correspond to a tumor growth model without necrosis. This is of great interest regarding the modeling, since it proves the continuity with respect to \(\tau\) of the family of \(\tau\)-dependent, ensuring the consistency of the modeling.
For the entire collection see [Zbl 1480.92010].

MSC:

92C50 Medical applications (general)
35B40 Asymptotic behavior of solutions to PDEs
35Q92 PDEs in connection with biology, chemistry and other natural sciences
92C10 Biomechanics
92-10 Mathematical modeling or simulation for problems pertaining to biology
Full Text: DOI

References:

[1] Alinhac, S., Gérard, P.: Pseudo-Differential Operators and the Nash-Moser Theorem, vol. 82. American Mathematical Society (2007) · Zbl 1121.47033
[2] Ambrosi, D.; Preziosi, L., On the closure of mass balance models for tumor growth, Math. Models Methods Appl. Sci., 12, 737-754 (2002) · Zbl 1016.92016 · doi:10.1142/S0218202502001878
[3] Anderson, AR; Chaplain, M., Continuous and discrete mathematical models of tumor-induced angiogenesis, Bull. Math. Biol., 60, 857-899 (1998) · Zbl 0923.92011 · doi:10.1006/bulm.1998.0042
[4] Billy, F., A pharmacologically based multiscale mathematical model of angiogenesis and its use in investigating the efficacy of a new cancer treatment strategy, J. Theor. Biol., 260, 545-562 (2009) · Zbl 1402.92050 · doi:10.1016/j.jtbi.2009.06.026
[5] Bresch, D.; Colin, T.; Grenier, E.; Ribba, B.; Saut, O., A viscoelastic model for avascular tumor growth, Discret. Continuous Dyn. Syst. Suppl., 2009, 101-108 (2009) · Zbl 1185.92059
[6] Brezis, H., Functional Analysis, Sobolev Spaces and Partial Differential Equations (2010), Heidelberg: Springer, Heidelberg · Zbl 1220.46002 · doi:10.1007/978-0-387-70914-7
[7] Byrne, HM; Chaplain, M., Growth of necrotic tumors in the presence and absence of inhibitors, Math. Biosci., 135, 187-216 (1996) · Zbl 0856.92010 · doi:10.1016/0025-5564(96)00023-5
[8] Byrne, H.; Preziosi, L., Modelling solid tumour growth using the theory of mixtures, Math. Med. Biol., 20, 341-366 (2003) · Zbl 1046.92023 · doi:10.1093/imammb/20.4.341
[9] Chen, X.; Cui, S.; Friedman, A., A hyperbolic free boundary problem modeling tumor growth: asymptotic behavior, Trans. Am. Math. Soc., 357, 4771-4804 (2005) · Zbl 1082.35166 · doi:10.1090/S0002-9947-05-03784-0
[10] Colin, T.; Cornelis, F.; Jouganous, J.; Palussière, J.; Saut, O., Patient-specific simulation of tumor growth, response to the treatment, and relapse of a lung metastasis: a clinical case, J. Comput. Surgery, 2, 1, 1-17 (2015) · doi:10.1186/s40244-014-0014-1
[11] Cui, S.; Friedman, A., Analysis of a mathematical model of the growth of necrotic tumors, J. Math. Anal. Appl., 255, 636-677 (2001) · Zbl 0984.35169 · doi:10.1006/jmaa.2000.7306
[12] Drasdo, D.; Höhme, S., Individual-based approaches to birth and death in avascular tumors, Math. Comput. Model., 37, 1163-1175 (2003) · Zbl 1047.92023 · doi:10.1016/S0895-7177(03)00128-6
[13] Folkman, J., Tumor angiogenesis: therapeutic implications, New Engl. J. Med., 285, 1182-1186 (1971) · doi:10.1056/NEJM197108122850711
[14] Friedman, A., A hierarchy of cancer models and their mathematical challenges, Discret. Continuous Dyn. Syst. Ser. B, 4, 147-160 (2004) · Zbl 1052.35094 · doi:10.3934/dcdsb.2004.4.147
[15] Friedman, A.; Hu, B., Bifurcation from stability to instability for a free boundary problem arising in a tumor model, Arch. Ration. Mech. Anal., 180, 293-330 (2006) · Zbl 1087.92039 · doi:10.1007/s00205-005-0408-z
[16] Gatenby, RA; Gawlinski, ET, A reaction-diffusion model of cancer invasion, Can. Res., 56, 5745-5753 (1996)
[17] Hillen, T.; Painter, KJ; Winkler, M., Convergence of a cancer invasion model to a logistic chemotaxis model, Math. Models Methods Appl. Sci., 23, 165-198 (2013) · Zbl 1263.35204 · doi:10.1142/S0218202512500480
[18] Lefebvre, G., Cornelis, F., Cumsille, P., Colin, T., Poignard, C., Saut, O.: Spatial modelling of tumour drug resistance: the case of GIST liver metastases. Math. Med. Biol. http://imammb.oxfordjournals.org/content/early/2016/03/30/imammb.dqw002.abstract · Zbl 1400.92268
[19] Lorz, A., Lorenzi, T., Clairambault, J., Escargueil, A., Perthame, B.: Effects of space structure and combination therapies on phenotypic heterogeneity and drug resistance in solid tumors, arXiv preprint arXiv:1312.6237 · Zbl 1334.92204
[20] Perthame, B., Vauchelet, N.: Incompressible limit of mechanical model of tumor growth with viscosity, arXiv preprint arXiv:1409.6007 · Zbl 1353.35294
[21] Preziosi, L.; Tosin, A., Multiphase modelling of tumour growth and extracellular matrix interaction: mathematical tools and applications, J. Math. Biol., 58, 625-656 (2009) · Zbl 1311.92029 · doi:10.1007/s00285-008-0218-7
[22] Saitou, T.; Rouzimaimaiti, M.; Koshikawa, N.; Seiki, M.; Ichikawa, K.; Suzuki, T., Mathematical modeling of invadopodia formation, J. Theor. Biol., 298, 138-146 (2012) · Zbl 1397.92361 · doi:10.1016/j.jtbi.2011.12.018
[23] Swanson, KR; Bridge, C.; Murray, J.; Alvord, EC, Virtual and real brain tumors: using mathematical modeling to quantify glioma growth and invasion, J. Neurol. Sci., 216, 1-10 (2003) · doi:10.1016/j.jns.2003.06.001
[24] Tosin, A., Initial/boundary-value problems of tumor growth within a host tissue, J. Math. Biol., 66, 163-202 (2013) · Zbl 1259.35212 · doi:10.1007/s00285-012-0505-1
[25] Ward, JP; King, JR, Mathematical modelling of drug transport in tumour multicell spheroids and monolayer cultures, Math. Biosci., 181, 177-207 (2003) · Zbl 1014.92021 · doi:10.1016/S0025-5564(02)00148-7
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.