The impact of competition between cancer cells and healthy cells on optimal drug delivery
Mathematical modelling of natural phenomena, Tome 15 (2020), article no. 42.

Voir la notice de l'article provenant de la source EDP Sciences

Cell competition is recognized to be instrumental to the dynamics and structure of the tumor-host interface in invasive cancers. In mild competition scenarios, the healthy tissue and cancer cells can coexist. When the competition is aggressive, competitive cells, the so called super-competitors, expand by killing other cells. Novel chemotherapy drugs and molecularly targeted drugs are commonly administered as part of cancer therapy. Both types of drugs are susceptible to various mechanisms of drug resistance, obstructing or preventing a successful outcome. In this paper, we develop a cancer growth model that accounts for the competition between cancer cells and healthy cells. The model incorporates resistance to both chemotherapy and targeted drugs. In both cases, the level of drug resistance is assumed to be a continuous variable ranging from fully-sensitive to fully-resistant. Using our model we demonstrate that when the competition is moderate, therapies using both drugs are more effective compared with single drug therapies. However, when cancer cells are highly competitive, targeted drugs become more effective. The results of the study stress the importance of adjusting the therapy to the pre-treatment resistance levels. We conclude with a study of the spatiotemporal propagation of drug resistance in a competitive setting, verifying that the same conclusions hold in the spatially heterogeneous case.
DOI : 10.1051/mmnp/2019043

Heyrim Cho 1 ; Doron Levy 2, 3

1 Department of Mathematics, University of California, Riverside, CA 92521, USA.
2 Department of Mathematics, University of Maryland, College Park, MD 20742, USA.
3 Center for Scientific Computation and Mathematical Modeling, University of Maryland, College Park, MD 20742, USA.
@article{MMNP_2020_15_a40,
     author = {Heyrim Cho and Doron Levy},
     title = {The impact of competition between cancer cells and healthy cells on optimal drug delivery},
     journal = {Mathematical modelling of natural phenomena},
     eid = {42},
     publisher = {mathdoc},
     volume = {15},
     year = {2020},
     doi = {10.1051/mmnp/2019043},
     language = {en},
     url = {https://geodesic-test.mathdoc.fr/articles/10.1051/mmnp/2019043/}
}
TY  - JOUR
AU  - Heyrim Cho
AU  - Doron Levy
TI  - The impact of competition between cancer cells and healthy cells on optimal drug delivery
JO  - Mathematical modelling of natural phenomena
PY  - 2020
VL  - 15
PB  - mathdoc
UR  - https://geodesic-test.mathdoc.fr/articles/10.1051/mmnp/2019043/
DO  - 10.1051/mmnp/2019043
LA  - en
ID  - MMNP_2020_15_a40
ER  - 
%0 Journal Article
%A Heyrim Cho
%A Doron Levy
%T The impact of competition between cancer cells and healthy cells on optimal drug delivery
%J Mathematical modelling of natural phenomena
%D 2020
%V 15
%I mathdoc
%U https://geodesic-test.mathdoc.fr/articles/10.1051/mmnp/2019043/
%R 10.1051/mmnp/2019043
%G en
%F MMNP_2020_15_a40
Heyrim Cho; Doron Levy. The impact of competition between cancer cells and healthy cells on optimal drug delivery. Mathematical modelling of natural phenomena, Tome 15 (2020), article  no. 42. doi : 10.1051/mmnp/2019043. https://geodesic-test.mathdoc.fr/articles/10.1051/mmnp/2019043/

[1] R.A. Anderson, M. Chaplain Continuous and discrete mathematical models of tumor-induced angiogenesis. Bull. Math. Biol. 1998 857 899

[2] E.-A. D. Amir, L.K. Davis, D.M. Tadmor, F.E. Simonds, H. Levlne, Jacob, C.S. Bendall, K.D. Shenfeld, S. Krishnaswamy, P.G. Nolan, D. Pe’Er viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia Nat. Biotechnol 2013 545 552

[3] K. Bacevic, R. Noble, A. Soffar, O. Wael Ammar, B. Boszonyik, S. Prieto, C. Vincent, E.M. Hochberg, L. Krasinska, D. Fisher Spatial competition constrains resistance to targeted cancer therapy Nat. Commun 2017 1 15

[4] I. Bozic, T. Antal, H. Ohtsuki, H. Carter, D. Kim, S. Chen Accumulation of driver and passenger mutations during tumor progression PNAS 2010 18545 18550

[5] G.B. Birkhead, M.E. Rakin, S. Gallivan, L. Dones, D.R. Rubens A mathematical model of the development of drug resistance to cancer chemotherapy Eur. J. Cancer Clin. Oncol 1987 1421 1427

[6] R. Biswas, S. Gao, M.C. Cultraro, K.T. Maity, A. Venugopalan, Z. Abdullaev, K.A. Shaytan, A.C. Carter, A. Thomas, A. Rajan, Y. Song Genomic profiling of multiple sequentially acquired tumor metastatic sites from an “exceptional responder” lung adenocarcinoma patient reveals extensive genomic heterogeneity and novel somatic variants driving treatment response Cold Spring Harbor molecular case studies 2016 1 26

[7] D. Bray, Cell Movements: From Molecules to Motility, Garland Science, 2 edition (2000).

[8] A. Brock, H. Chang, S. Huang Non-genetic heterogeneity – a mutation-independent driving force for the somatic evolution of tumours Nat. Rev. Genet 2009 336 342

[9] A.H. Burris, S.H. Rugo, J.S. Vukelja, L.C. Vogel, A.R. Borson, S. Limentani, E. Tan-Chiu, E.I. Krop, A.R. Michaelson Phase II study of the antibody drug conjugate trastuzumab-DM1 for the treatment of human epidermal growth factor receptor 2 (HER2)-positive breast cancer after prior HER2-directed therapy J. Clin. Oncol 2011 398 405

[10] A.L. Byers, L. Diao, J. Wang, P. Saintigny, L. Girard, M. Peyton, L. Shen, Y. Fan, U. Giri, K.P. Tumula, B.M. Nilsson, J. Gudikote An epithelial-mesenchymal transition gene signature predicts resistance to EGFR and PI3K inhibitors and identifies Axl as a therapeutic target for overcoming EGFR inhibitor resistance Clin. Cancer Res 2013 279 290

[11] C. Carrère Optimization of an in vitro chemotherapy to avoid resistant tumours J. Theor. Biol 2017 24 33

[12] H.H. Chang, Y.P. Oh, E.D. Ingber, S. Huang Multistable and multistep dynamics in neutrophil differentiation BMC Cell Biol 2006 1 12

[13] H. Cho, D. Levy Modeling the dynamics of heterogeneity of solid tumors in response to chemotherapy Bull. Math. Biol 2017 2986 3012

[14] H. Cho, D. Levy Modeling the chemotherapy-induced selection of drug-resistant traits during tumor growth J. Theor. Biol 2018 120 134

[15] H. Cho, D. Levy Modeling continuous levels of resistance to multidrug therapy in cancer Appl. Math. Model 2018 733 751

[16] H. Cho, D. Venturi, E.G. Karniadakis Numerical methods for high-dimensional probability density function equations J. Comput. Phys 2016 817 837

[17] H.R. Chisholm, T. Lorenzi, A. Lorz, K.A. Larsen, N.D.L. Almeida, A. Escargueil, J. Clairambault Emergence of Drug Tolerance in Cancer Cell Populations: An Evolutionary Outcome of Selection, Nongenetic Instability, and Stress-Induced Adaptation Cancer Res 2015 930 940

[18] K. Dorris, C. Liu, D. Li, R.T. Hummel, X. Wang, J. Perentesis, O.M. Kim, M. Fouladi A comparison of safety and efficacy of cytotoxicversus molecularly targeted drugs in pediatric phase I solid tumor oncology trials Pediatr. Blood Cancer 2017 1 11

[19] T. Eichenlaub, S.M. Cohen, H. Herranz Cell competition drives the formation of metastatic tumors in a Drosophila model of epithelial tumor formation. Curr. Biol. 2016 419 427

[20] V. Fodal, M. Pierobon, L. Liotta, E. Petricoin Mechanisms of cell adaptation: when and how do cancer cells develop chemoresistance? Cancer J. 2011 89 95

[21] J. Foo, F. Michor Evolution of acquired resistance to anti-cancer therapy J. Theor. Biol 2014 10 20

[22] K. Fosgerau, T. Hoffmann Peptide therapeutics: current status and future directions Drug Discov. Today 2015 122 128

[23] K. Furugaki, I. Toshiki, S. Masatoshi, K. Kumiko, M. Yoichiro, M. Kazushige Schedule–dependent antitumor activity of the combination with erlotinib and docetaxel in human non-small cell lung cancer cells with EGFR mutation , KRAS mutation or both wild-type EGFR and KRAS Oncol. Rep 2010 1141 1146

[24] A.R. Gatenby, T.E. Gawlinski A reaction-diffusion model of cancer invasion Cancer Res 1996 5745 5753

[25] A.R. Gatenby, S.A. Silva, J.R. Gillies, R.B. Frieden Adaptive therapy Cancer Res 2009 4894 4903

[26] R. Glasspool, M.J. Teodoridis, R. Brown Epigenetics as a mechanism driving polygenic clinical drug resistance Br. J. Cancer 2006 1087 1092

[27] B.P. Gupta, M.C. Fillmore, G. Jiang, D.S. Shapira, K. Tao, C. Kuperwasser, S.E. Lander Stochastic state transitions give rise to phenotypic equilibrium in populations of cancer cells Cell 2011 633 644

[28] M.C. Garvey, E. Spiller, D. Lindsay, C.-T. Chiang, C.N. Choi, B.D. Agus, P. Mallick, J. Foo, M.S. Mumenthaler A high-content image-based method for quantitatively studying context-dependent cell population dynamics Sci Rep 2016 1 12

[29] R. Gatenby, R. Gillies A microenvironmental model of carcinogenesis Nat. Rev. Cancer 2008 56 61

[30] J. Gil, T. Rodriguez Cancer: the transforming power of cell competition Curr. Biol. 2016 R164 R166

[31] J.-P. Gillet, M.M. Gottesman Mechanisms of multidrug resistance in cancer Methods Mol. Biol 2010 47 76

[32] B.S. Goldberg, R.G. Oxnard, S. Digumarthy, A. Muzikansky, M.D. Jackman, T.I. Lennes, V.L. Sequist Chemotherapy with Erlotinib or chemotherapy alone in advanced non-small cell lung cancer with acquired resistance to EGFR tyrosine kinase inhibitors Oncologist 2013 1214 1220

[33] M.M. Gottesman Mechanisms of cancer drug resistance. Annu. Rev. Med. 2002 615 627

[34] M.M. Gottesman, T. Fojo, S.E. Bates Multidrug resistance in cancer: role of ATP-dependent transporters Nat. Rev. Cancer 2002 48 58

[35] L. Grasedyck, D. Kressner, C. Tobler A literature survey of low-rank tensor approximation techniques GAMM Mitteilungen 2013 53 78

[36] J. Greene, O. Lavi, M.M. Gottesman, D. Levy The impact of cell density and mutations in a model of multidrug resistance in solid tumors Bull. Math. Biol 2014 627 653

[37] D. Hanahan, R.A. Weinberg Hallmarks of cancer: the next generation Cell 2011 646 674

[38] G. Housman, S. Byler, S. Heerboth, K. Lapinska, M. Longacre, N. Snyder, S. Sarkar Drug resistance in cancer : An Overview Cancers 2014 1769 1792

[39] T. Hillen, M. Lewis, Managing Complexity, 2016 Reducing Perplexity – Modeling biological systems. Springer 13–25.

[40] Y. Iwasa, A.M. Nowak, F. Michor Evolution of resistance during clonal expansion Genetics 2006 2557 2566

[41] S. Jones, W. Chen, G. Parmigiani, F. Diehl, N. Beerenwinkel, T. Antal Comparative lesion sequencing provides insights into tumor evolution PNAS 2008 4283 4288

[42] Y. Jiang, Q. Yuan, Q. Fang Schedule-dependent synergistic interaction between docetaxel and gefitinib in NSCLC cell lines regardless of the mutation status of EGFR and KRAS and its molecular mechanisms J. Cancer Res. Clin. Oncol 2014 1087 1095

[43] R. Levayer Cell competition: How to take over the space left by your neighbours Curr. Biol 2018 R741 R744

[44] D.V. Jonsson, M.C. Blakely, L. Lin, S. Asthana, N. Matni, V. Olivas, E. Pazarentzos, A.M. Gubens, C.B. Bastian, S.B. Taylor, C.J. Doyle, G.T. Bivona Novel computational method for predicting polytherapy switching strategies to overcome tumor heterogeneity and evolution Sci. Rep 2017 1 14

[45] G. Kalemkerian, W. Akerley, P. Bogner, H. Borghaei, L. Chow, R. Downey, L. Gandhi, A. Ganti, R. Govindan Non-small cell lung cancer J. Natl. Comprehensive Cancer Netw 2012 1236 1271

[46] N. Komarova Stochastic modeling of drug resistance in cancer Theor. Popul. Biol 2006 351 366

[47] M. Kimmel, A. Swierniaka, A. Polanski Infinite-dimensional model of evolution of drug resistance of cancer cells J. Math. Syst. Estim. Control 1998 1 16

[48] O. Lavi, M.M. Gottesman, D. Levy The dynamics of drug resistance: a mathematical perspective Drug Resist. Updates 2012 90 97

[49] T. Lorenzi, H.R. Chisholm, J. Clairambault Tracking the evolution of cancer cell populations through the mathematical lens of phenotype-structured equations Biol. Dir 2016 1 17

[50] T. Lorenzi, H.R. Chisholm, L. Desvillettes, D.B. Hughes Dissecting the dynamics of epigenetic changes in phenotype-structured populations exposed to fluctuating environments J. Theor. Biol 2015 166 176

[51] A. Lorz, T. Lorenzi, E.M. Hochberg, J. Clairambault, B. Perthame Populational adaptive evolution, chemotherapeutic resistance and multiple anti-cancer therapies ESAIM: M2AN 2013 377 399

[52] A. Lorz, T. Lorenzi, J. Clairambault, A. Escargueil, B. Perthame Modeling the effects of space structure and combination therapies on phenotypic heterogeneity and drug resistance in solid tumors Bull. Math. Biol 2015 1 22

[53] C.C. Maley, A. Aktipis, A.T. Graham, A. Sottoriva, M.A. Boddy, M. Janiszewska, S.A. Silva, M. Gerlinger, Y. Yuan, J.K. Pienta Classifying the evolutionary and ecological features of neoplasms Nat. Rev. Cancer 2017 605 619

[54] A. Marusyk, V. Almendro, K. Polyak Intra-tumour heterogeneity: a looking glass for cancer? Nat. Rev. Cancer 2012 323 334

[55] L. Merlo, J. Pepper, B. Reid, C. Maley Cancer as an evolutionary and ecological process Nat. Rev. Cancer 2006 924 935

[56] K. Masui, B. Gini, J. Wykosky, C. Zanca, P.S. Mischel, F.B. Furnari, W.K. Cavenee A tale of two approaches: Complementary mechanisms of cytotoxic and targeted therapy resistance may inform next-generation cancer treatments Carcinogenesis 2013 725 738

[57] J.P. Medema Cancer stem cells: the challenges ahead Nat. Cell Biol 2013 338 344

[58] F. Michor, A.M. Nowak, Y. Iwasa Evolution of Resistance to Cancer Therapy Curr. Pharm. Des 2006 261 271

[59] S. Misale, I. Bozic, J. Tong, A. Peraza-Penton, A. Lallo, F. Baldi, K. Lin, M. Truini, L. Trusolino, A. Bertotti, F. Di Nicolantonio, M. Nowak, L. Zhang, K. Wood, A. Bardelli Vertical suppression of the EGFR pathway prevents onset of resistance in colorectal cancers Nat. Commun 2015 1 9

[60] S.T. Mok, Y.-L. Wu, C.-J. Yu, C. Zhou, Y.-M. Chen, L. Zhang, J. Ignacio, M. Liao, V. Srimuninnimit Randomized, placebo-controlled, phase II study of sequential Erlotinib and chemotherapy as first-line treatment for advanced non-small-cell lung cancer J. Clin. Oncol 2009 5080 5087

[61] E. Moreno Iscell competition relevant to cancer? Nat. Rev. Cancer 2008 141 147

[62] E. Moreno, K. Basler, G. Morata Cells compete for decapentaplegic survival factor to prevent apoptosis in Drosophila wing development Nature 2002 755 759

[63] M.S. Mumenthaler, J. Foo, C.N. Choi, N. Heise, K. Leder, B.D. Agus, W. Pao, F. Michor, P. Mallick The impact of microenvironmental heterogeneity on the evolution of drug resistance in cancer cells Cancer Inf 2015 19 31

[64] J. Murray, Mathematical Biology. Springer-Verlag (2002).

[65] B. Perthame, G. Barles Dirac concentrations in Lotka-Volterra parabolic PDEs Indiana Univ. Math. J 2008 3275 3301

[66] E. Piretto, M. Delitala, M. Ferraro Combination therapies and intra-tumoral competition: Insights from mathematical modeling J. Theor. Biol 2018 149 159

[67] O.A. Pisco, A. Brock, J. Zhou, A. Moor, M. Mojtahedi, D. Jackson, S. Huang Non-darwinian dynamics in therapy-induced cancer drug resistance Nat. Commun 2013 2467

[68] C. Pouchol, E. Trélat Global stability with selection in integro-differential Lotka-Volterra systems modelling trait-structured populations J. Biol. Dyn 2018 872 893

[69] C. Pouchol, J. Clairambault, A. Lorz, E. Trélat Asymptotic analysis and optimal control of an integro-differential system modelling healthy and cancer cells exposed to chemotherapy J. Math. Pures Appl 2018 268 308

[70] B. Perthame, F. Quirós, L.J. Vázquez The Hele-Shaw asymptotics for mechanical models of tumor growth Arch. Rational Mech. Anal 2014 93 127

[71] L. Peng, D. Trucu, P. Lin, A. Thompson, A.J.M. Chaplain A multiscale mathematical model of tumour invasive growth Bull. Math. Biol 2016 389 429

[72] J.M. Rowe, B. Löwenberg Gemtuzumab ozogamicin in acute myeloid leukemia: a remarkable saga about an active drug Blood 121 2013 4838 4841

[73] T.J. Ribeiro, T.L. Macedo, G. Curigliano, L. Fumagalli, M. Locatelli, M. Dalton, A. Quintela, B.J. Carvalheira, S. Manunta, L. Mazzarella, J. Brollo, A. Goldhirsch Cytotoxic drugs for patients with breast cancer in the era of targeted treatment: Back to the future? Ann. Oncol. 2012 547 555

[74] T. Roose, S.J. Chapman, P.K. Maini Mathematical models of avascular tumor growth SIAM Rev 2007 179 208

[75] V.S. Sharma, Y.D. Lee, B. Li, P.M. Quinlan, F. Takahashi, S. Maheswaran, U. Mcdermott, N. Azizian, L. Zou, M.A. Fischbach A chromatin-mediated reversible drug-tolerant state in cancer cell subpopulations Cell 2010 69 80

[76] S. Singh, K.N. Tank, P. Dwiwedi, J. Charan, R. Kaur, P. Sidhu, K.V. Chugh Monoclonal antibodies: a review. Curr. Clin. Pharmacol 2017 85 99

[77] P. Simpson Parameters of cell competition in the compartments of the wing disc of Drosophila Dev. Biol 1979 182 193

[78] M. Slingerland, H. Guchelaar, H. Gelderblom Liposomal drug formulations in cancer therapy: 15 years along the road Drug Discov. Today 2012 160 166

[79] S. Suijkerbuijk, G. Kolahgar, I. Kucinski, E. Piddini Cell competition drives the growth of intestinal adenomas in Drosophila Curr. Biol 2016 428 438

[80] A. Swierniak, M. Kimmel, J. Smieja Mathematical modeling as a tool for planning anticancer therapy Eur. J. Pharmacol 2009 108 121

[81] B.A. Teicher, Cancer Drug Resistance. Humana Press, Totowa, N.J. (2006).

[82] A. Tsuboi, S. Ohsawa, D. Umetsu, Y. Sando, E. Kuranaga, T. Igaki, K. Fujimoto Competition for space is controlled by apoptosis-induced change of local epithelial topology Curr. Biol 2018 2115 2128

[83] O. Trédan, M.C. Galmarini, K. Patel, I.F. Tannock Drug resistance and the solid tumor microenvironment J. Natl. Cancer Inst 2007 1441 1454

[84] S. Vivarelli, L. Wagstaff, E. Piddini Cell wars: regulation of cell survival and proliferation by cell competition Essays Biochem 2012 69 82

[85] L. Wagstaff, G. Kolahgar, E. Piddini Competitive cell interactions in cancer: a cellular tug of war Trends Cell Biol 2013 160 167

[86] K. Wosikowski, A.J. Silverman, P. Bishop, J. Mendelsohn, E.S. Bates Reduced growth rate accompanied by aberrant epidermal growth factor signaling in drug resistant human breast cancer cells Biochim. Biophys. Acta 2000 215 226

[87] N. Yoon, R. Velde, A. Marusyk, J. Scott Optimal therapy scheduling based on a pair of collaterally sensitive drugs Bull. Math. Biol 2018 1 34

[88] Z. Zhang, C.J. Lee, L. Lin, V. Olivas, V. Au, M. Abdel-Rahman, X. Wang, D.A. Levine, J. Kyung, J.Y. Choi, C.-M. Choi, S.-W. Kim, J.S. Jang, S.Y. Park, S.W. Kim, H.D. Lee, J.-S. Lee, V. Miller, M. Arcila Activation of the AXL kinase causes resistance to EGFR-targeted therapy in lung cancer Nat. Genet 2012 852 860

Cité par Sources :