August  2019, 24(8): 4629-4663. doi: 10.3934/dcdsb.2019157

SDE-driven modeling of phenotypically heterogeneous tumors: The influence of cancer cell stemness

1. 

Basque Center for Applied Mathematics, Alameda Mazarredo 14, 48009 Bilbao, Spain

2. 

Technische Universität Darmstadt, Schlossgartenstr. 7, 64289 Darmstadt, Germany

3. 

Technische Universität Kaiserslautern, Paul-Ehrlich-Str. 31, 67663 Kaiserslautern, Germany

4. 

German Breast Group Forschungs GmbH, 63263 Neu-Isenburg, Germany

* Corresponding author: Christina Surulescu

Received  October 2018 Revised  May 2019 Published  August 2019 Early access  June 2019

Fund Project: This work is dedicated to Peter Kloeden on the occasion of his 70th birthday.

We deduce cell population models describing the evolution of a tumor (possibly interacting with its environment of healthy cells) with the aid of differential equations. Thereby, different subpopulations of cancer cells allow accounting for the tumor heterogeneity. In our settings these include cancer stem cells known to be less sensitive to treatment and differentiated cancer cells having a higher sensitivity towards chemo- and radiotherapy. Our approach relies on stochastic differential equations in order to account for randomness in the system, arising e.g., due to the therapy-induced decreasing number of clonogens, which renders a pure deterministic model arguable. The equations are deduced relying on transition probabilities characterizing innovations of the two cancer cell subpopulations, and similarly extended to also account for the evolution of normal tissue. Several therapy approaches are introduced and compared by way of tumor control probability (TCP) and uncomplicated tumor control probability (UTCP). A PDE approach allows to assess the evolution of tumor and normal tissue with respect to time and to cell population densities which can vary continuously in a given set of states. Analytical approximations of solutions to the obtained PDE system are provided as well.

Citation: Julia M. Kroos, Christian Stinner, Christina Surulescu, Nico Surulescu. SDE-driven modeling of phenotypically heterogeneous tumors: The influence of cancer cell stemness. Discrete and Continuous Dynamical Systems - B, 2019, 24 (8) : 4629-4663. doi: 10.3934/dcdsb.2019157
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show all references

References:
[1]

A. AgrenA. Brahme and I. Turesson, Optimization of uncomplicated control for head and neck tumors, Int. J. Radiology Oncology Biol. Phys., 19 (1990), 1077-1085. 

[2]

E. Allen, Modeling with Itô Stochastic Differential Equations, Mathematical Modelling: Theory and Applications, Springer, 2007.

[3]

S. Bao, Q. Wu, R. E. McLendon and Y. Hao, et al., Glioma stem cells promote radioresistance by preferential activation of the DNA damage response, Nature, 444 (2006), 756-760. doi: 10.1038/nature05236.

[4]

A. Barrett, J. Dobbs, S. Morris and T. Roques, Practical Radiotherapy Planning, Hodder Arnold, 2009, 4th edition.

[5]

E. BerettaV. Capasso and N. Morozova, Mathematical modeling of cancer stem cells population behavior, Mat. Model. Nat. Phenom., 7 (2012), 279-305.  doi: 10.1051/mmnp/20127113.

[6]

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D.-Y. ChoS.-Z. LinW.-K. YangH.-C. LeeD.-M. HsuH.-L. LinC.-C. ChenC.-L. LiuW.-Y. Lee and L.-H. Ho, Targeting cancer stem cells for treatment of glioblastoma multiforme, Cell Transplantation, 22 (2013), 731-739.  doi: 10.3727/096368912X655136.

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J. Cresson and S. Sonner, A note on a derivation method for sde models: Applications in biology and viability criteria, Stoch. Anal. Appl., 36 (2018), 224-239.  doi: 10.1080/07362994.2017.1386571.

[11]

A. Dawson and T. Hillen, Derivation of the tumor control probability (tcp) from a cell cycle model, Computational and Mathematical Methods in Medicine, 7 (2006), 121-141.  doi: 10.1080/10273660600968937.

[12]

J. E. Dick, Stem cell concepts renew cancer research, Blood, 112 (2008), 4793-4807.  doi: 10.1182/blood-2008-08-077941.

[13]

D. Dingli and F. Michor, Successful therapy must eradicate cancer stem cells, Stem Cells, 24 (2006), 2603-2610.  doi: 10.1634/stemcells.2006-0136.

[14]

J. Douglas, Alternating direction methods for three space variables, Numer. Math., 4 (1962), 41-63.  doi: 10.1007/BF01386295.

[15]

T. EckschlagerJ. PlchM. Stiborova and J. Hrabeta, Histone deacetylase inhibitors as anticancer drugs, Int. J. Molec. Sci., 18 (2017), 1414.  doi: 10.3390/ijms18071414.

[16]

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H. FakirL. HlatkyH. Li and R. Sachs, Repopulation of interacting tumor cells during fractionated radiotherapy: Stochastic modeling of the tumor control probability, Medical Physics, 40 (2013), 121716.  doi: 10.1118/1.4829495.

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A. A. Forastiere, H Goepfert and M. Maor, et al., Concurrent chemotherapy and radiotherapy for organ preservation in advanced laryngeal cancer, N. Engl. J. Med., 349 (2003), 2091-2098. doi: 10.1056/NEJMoa031317.

[20]

R. Ganguli and I. K. Puri, Mathematical model for the cancer stem cell hypothesis, Cell Proliferation, 39 (2006), 3-14.  doi: 10.1111/j.1365-2184.2006.00369.x.

[21]

J. Gong, Tumor Control Probability Models, PhD thesis, University of Alberta, Canada, 2011.

[22]

J. GongM. M. dos SantosC. Finlay and T. Hillen, Are more complicated tumour control probability models better?, Math. Med. Biol., 30 (2013), 1-19.  doi: 10.1093/imammb/dqr023.

[23]

P. B. GuptaC. M. FillmoreG. JiangS. D. ShapiraK. TaoC. Kuperwasser and E. S. Lander, Stochastic state transitions give rise to phenotypic equilibrium in populations of cancer cells, Cell, 146 (2011), 633-644. 

[24]

D. Hanahan and R. A. Weinberg, The hallmarks of cancer, Cell, 100 (2000), 57-70.  doi: 10.1016/S0092-8674(00)81683-9.

[25]

T. Hillen and J. W. N. Bachmann, Mathematical optimization of the combination of radiation and differentiation therapies for cancer, Frontiers in Oncology, 3 (2013). 

[26]

T. HillenH. Enderling and P. Hahnfeldt, The tumor growth paradox and immune system-mediated selection for cancer stem cells, Bulletin of Mathematical Biology, 75 (2013), 161-184.  doi: 10.1007/s11538-012-9798-x.

[27]

S. HiremathS. SonnerC. Surulescu and A. Zhigun, On a coupled SDE-PDE system modeling acid-mediated tumor invasion, Discr. Cont. Dyn. Syst. B, 23 (2018), 2339-2369.  doi: 10.3934/dcdsb.2018071.

[28]

S. Hiremath and C. Surulescu, A stochastic multiscale model for acid mediated cancer invasion, Nonlinear Analysis: Real World Applications, 22 (2015), 176-205.  doi: 10.1016/j.nonrwa.2014.08.008.

[29]

S. Hiremath and C. Surulescu, A stochastic model featuring acid-induced gaps during tumor progression, Nonlinearity, 29 (2016), 851-914.  doi: 10.1088/0951-7715/29/3/851.

[30]

Y. IwasaM. A. Nowak and F. Michor, Evolution of resistance during clonal expansion, Genetics, 172 (2006), 2557-2566.  doi: 10.1534/genetics.105.049791.

[31]

A. JacksonG. J. Kutscher and E. D. Yorke, Probability of radiation-induced complications for normal tissues with parallel architecture subject to non-uniform irradiation, Medical Physiscs, 20 (1993), 613-625.  doi: 10.1118/1.597056.

[32]

M. JacksonF. Hassiotou and A. Nowak, Glioblastoma stem-like cells: At the root of tumor recurrence and a therapeutic target, Carcinogenesis, 36 (2015), 177-185.  doi: 10.1093/carcin/bgu243.

[33]

P. KällmanB. K. Lind and A. Brahme, An algorithm for maximizing the probability of complication-free tumour control in radiation therapy, Phys. Med. Biol., 37 (1992), 871-890. 

[34]

I. Karatzas and S. Shreve, Brownian Motion and Stochastic Calculus, Springer, 1991. doi: 10.1007/978-1-4612-0949-2.

[35]

W. K. KellyO. A. O'ConnorL. M. KrugJ. H. ChiaoM. HeaneyT. CurleyB. MacGregore-CortelliW. TongJ. P. SecristL. SchwartzS. RichardsonE. ChuS. OlgacP. A. MarksH. Scher and V. M. Richon, Phase 1 study of an oral histone deacetylase inhibitor, suberoylanilide hydroxamic acid, in patients with advanced cancer, Journal of Clinical Oncology, 23 (2005), 3923-3931.  doi: 10.1200/JCO.2005.14.167.

[36]

Y. KimK. M. JooJ. Jin and D. H. Nam, Cancer stem cells and their mechanism of chemo-radiation resistance, Int. J. Stem Cells, 2 (2009), 109-114.  doi: 10.15283/ijsc.2009.2.2.109.

[37]

P. KloedenS. Sonner and C. Surulescu, A nonlocal sample dependence SDE-PDE system modeling proton dynamics in a tumor, Discr. Cont. Dyn. Syst. B, 21 (2016), 2233-2254.  doi: 10.3934/dcdsb.2016045.

[38]

N. Komarova and D. Wodarz, Effect of cellular quiescence on the success of targeted CML therapy, PLoS ONE, 2 (2007), e990.1-e990.9. 

[39]

J. Kroos, An SDE approach to cancer therapy including stem cells, Master thesis, University of Münster, 2014.

[40]

G. J. Kutscher and C. Burman, Calculation of complication probability factors for non-uniform normal tissue irradiation: The effective volume method, Int. J. Rad. Oncol. Biol. Phys., 16 (1989), 1623-1630. 

[41]

K. LederE. C. Holland and F. Michor, The therapeutic implications of plasticity of the cancer stem cell phenotype, PLoS ONE, 5 (2010), e14366.  doi: 10.1371/journal.pone.0014366.

[42]

J. T. Lymann, Complication probability as assessed from dose-volume histograms, Radiation Research, 104 (1985), 13-19. 

[43]

C. MaenhautJ. E. DumontP. P. Roger and W. C. G. van Staveren, Cancer stem cells: A reality, a myth, a fuzzy concept or a misnomer? an analysis, Carcinogenesis, 31 (2010), 149-158.  doi: 10.1093/carcin/bgp259.

[44]

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Figure 1.  Interaction of differentiated cancer cells $ c $ and cancer stem cells $ s $
Figure 2.  Several trajectories of the solution components of system (1) and averages (black lines) of $ 10^3 $ trajectories solved with the Euler-Maruyama algorithm and using parameter values as in Table 2. Initial condition: $ (c,s)^t = (0.9,0.1)^t $
Figure 3.  Different trajectories of the SDE system (1) and their averages (thick black lines) for $ 10^3 $ trajectories when applying chemotherapy. Parameter values as in Table 2, dose-dependent probability $ a_3 $ of symmetric splitting of a cancer stem cell into differentiated cells as given in (3). Upper row: $ \tilde \alpha = 0.5 $, lower row: $ \tilde \alpha = 10 $
Figure 4.  Different trajectories of the solutions components of system (1) with the application of radiation therapy and their averages (thick black lines) over $ 10^3 $ trajectories. Parameter values as in Tables 2 and 4, follow up to $ 70 $ days
Figure 5.  Trajectories of the SDE model (1) with the simultaneous application of radio- and chemotherapy (differentiation promoter) and their averages (thick black lines) over $ 10^3 $ simulated trajectories. Parameter values as in Tables 2 and 4, with $ \tilde{\alpha} = 10 $, follow up to $ 70 $ days
Figure 6.  Different trajectories of the SDE model (1) with the alternating application of chemotherapy and radiation therapy and their mean values (thick black lines) for $ 10^3 $ simulated trajectories. Parameter values as in Tables 2 and 4 with $ \tilde{\alpha} = 10 $, follow up to $ 70 $ days
Figure 7.  Trajectories of (1) and their averages (thick black lines) over $ 10^3 $ simulated trajectories with a time-discrete radiotherapy solved with the Euler-Maruyama scheme, parameter values as in Table 2. Subfigures 7(a) and 7(b) show the results of the simulation for the two subpopulations of cancer cells and 7(c) shows the processes $ L_t^{(s)} $, $ L_t^{(c)} $
Figure 8.  TCP for the different therapy strategies based on (1), with $ 10^3 $ simulations
Figure 9.  Trajectories of (12) for normal tissue density and of CSCs and DCs as solutions to (1). Their averages over $ 10^3 $ trajectories are shown as thick black lines. Parameter values are given in Table 5. The dotted grey line represents the threshold value $ \gamma $ for the normal cell density
Figure 10.  Trajectories of (12) for normal tissue density and of CSCs and DCs as solutions to (1) for the differentiation therapy approach in Section 3.1, with $ \widetilde \alpha = 10 $. Their averages over $ 10^3 $ trajectories are shown as thick black lines. Parameter values are given in Table 5. The dotted grey line represents the threshold value $ \gamma $ for the normal cell density
Figure 11.  Trajectories of the SDE (12) and of system (1) and their averages (black lines) over $ 10^3 $ trajectories. Parameters as in Tables 2, 4, 5 and 6. Plots 11(a)-11(c): effect of radiation therapy only. Plots 11(d)-11(f): concurrent chemo and radiotherapy. Plots 11(g)-11(i): alternating chemo and radiotherapy
Figure 12.  Trajectories of the SDE (12) and of processes $ c(t) $, $ s(t) $ obtained by applying time discrete therapy: radiotherapy alone (plots 12(a)-12(c)); in a concurrent (plots 12(d)- 12(f)) or an alternating way (plots 12(g)-12(i)). Averages over $ 10^3 $ trajectories are shown in thick black lines. Parameters as in Tables 2, 4, 5 and 6
Figure 13.  NTCP for the different therapy strategies based on SDEs, $ 10^3 $ simulations
Figure 14.  UTCP for the different treatment strategies based on $ 10^3 $ simulations of (1) and (12)
Figure 15.  Solution components of (27) and (29) at various time moments, no therapy applied. Left column: expected total tumor burden, middle: expected density of cancer stem cells, right: expected deviations of normal cells from critical threshold for which $ \gamma = 0.7 $. Plots 15(a)-15(c): initial state, 15(d)-15(f): 10 days, 15(g)-15(i): 30 days, 15(j)-15(l): 70 days
Figure 16.  Solution of (29) at different times, with no therapy applied. Shown are expected deviations of normal cells from the critical threshold for which $ \gamma = 0.85 $. Plot 16(a): 10 days, 16(b): 30 days, 16(c): 70 days
Table 1.  Transition probabilities for $ {\bf X} = (c,s)^t $, according to the scheme in Figure 1
Changes in $ {\bf X} $ Probability
$ \Delta X^{(1)} = (1,0)^t $ $ p_1=a_2(c) b_ss \Delta t $
$ \Delta X^{(2)} = (-1,0)^t $ $ p_2=d_c c \Delta t $
$ \Delta X^{(3)} = (0,1)^t $ $ p_3= a_1(c)b_s s \Delta t $
$ \Delta X^{(4)} = (0,-1)^t $ $ p_4= d_s s\Delta t $
$ \Delta X^{(5)} = (2,-1)^t $ $ p_5= a_3(c)b_s s \Delta t $
$ \Delta X^{(6)} = (-1,1)^t $ $ p_6=d_cca_1(c)b_ss\Delta t $
$ \Delta X^{(7)} = (2,0)^t $ $ p_7=0 $
$ \Delta X^{(8)} = (-1,-1)^t $ $ p_8=d_cc d_ss\Delta t $
$ \Delta X^{(9)} = (1,-1)^t $ $ p_9=b_cc d_ss\Delta t=0 $
$ \Delta X^{(10)} = (1,1)^t $ $ p_{10}=b_cc a_1(c)b_ss\Delta t=0 $
$ \Delta X^{(11)} = (3,-1)^t $ $ p_{11}=0 $
$ \Delta X^{(12)} = (0,0)^t $ $ p_{12}=1-\sum_{i=1}^{11} p_i $
Changes in $ {\bf X} $ Probability
$ \Delta X^{(1)} = (1,0)^t $ $ p_1=a_2(c) b_ss \Delta t $
$ \Delta X^{(2)} = (-1,0)^t $ $ p_2=d_c c \Delta t $
$ \Delta X^{(3)} = (0,1)^t $ $ p_3= a_1(c)b_s s \Delta t $
$ \Delta X^{(4)} = (0,-1)^t $ $ p_4= d_s s\Delta t $
$ \Delta X^{(5)} = (2,-1)^t $ $ p_5= a_3(c)b_s s \Delta t $
$ \Delta X^{(6)} = (-1,1)^t $ $ p_6=d_cca_1(c)b_ss\Delta t $
$ \Delta X^{(7)} = (2,0)^t $ $ p_7=0 $
$ \Delta X^{(8)} = (-1,-1)^t $ $ p_8=d_cc d_ss\Delta t $
$ \Delta X^{(9)} = (1,-1)^t $ $ p_9=b_cc d_ss\Delta t=0 $
$ \Delta X^{(10)} = (1,1)^t $ $ p_{10}=b_cc a_1(c)b_ss\Delta t=0 $
$ \Delta X^{(11)} = (3,-1)^t $ $ p_{11}=0 $
$ \Delta X^{(12)} = (0,0)^t $ $ p_{12}=1-\sum_{i=1}^{11} p_i $
Table 2.  Summary of the model parameters for cancer cells based on [61]
Parameter $ b_s $ $ d_c $ $ d_s $ $ \widetilde{a_1} $ $ \widetilde{a_2} $ $ \widetilde{a_3} $
Value $ \log 2/55 $ $ \log 2/60 $ $ \log 2/200 $ $ 0.5 $ $ 0.05 $ $ 0.1 $
Parameter $ b_s $ $ d_c $ $ d_s $ $ \widetilde{a_1} $ $ \widetilde{a_2} $ $ \widetilde{a_3} $
Value $ \log 2/55 $ $ \log 2/60 $ $ \log 2/200 $ $ 0.5 $ $ 0.05 $ $ 0.1 $
Table 3.  Persistence times for the whole tumor $ c+s $, averaged over 1000 trajectories, for different compositions $ (c,s) $ of the initial tumor
Initial values $ (c_0,s_0)^t $ Persistence time (in days)
$ (0.01, 0.01) $ 16.313
$ (0.1,0.1) $ 56.052
$ (0.99,0.01) $ 69.463
$ (0.8, 0.2) $ 69.790
$ (0.95,0.05) $ 69.791
$ (0.7, 0.3) $ 69.799
$ (0.4,0.6) $ 69.804
$ (0.3,0.7) $ 69.807
$ (0.6, 0.4) $ 69.871
$ (0.9,0.1) $ 69.892
$ (0.5,0.5) $ 69.909
$ (1, 1) $ 70
Initial values $ (c_0,s_0)^t $ Persistence time (in days)
$ (0.01, 0.01) $ 16.313
$ (0.1,0.1) $ 56.052
$ (0.99,0.01) $ 69.463
$ (0.8, 0.2) $ 69.790
$ (0.95,0.05) $ 69.791
$ (0.7, 0.3) $ 69.799
$ (0.4,0.6) $ 69.804
$ (0.3,0.7) $ 69.807
$ (0.6, 0.4) $ 69.871
$ (0.9,0.1) $ 69.892
$ (0.5,0.5) $ 69.909
$ (1, 1) $ 70
Table 4.  Model parameters for radiation therapy based on [53,66], where no difference was made between various phenotypes of tumor cells. The parameter $ \beta _s $ was chosen here slightly larger than $ \beta _c $ to account for the reduced sensitivity of CSCs towards therapy
Parameter $ \alpha\ (\text{in Gy}^{-1}) $ $ \beta_c \ (\text{in Gy}^{-2}) $ $ \beta_s \ (\text{in Gy}^{-2}) $ $ d $ (in Gy) $ \nu $ (in days)
Value $ 0.0906 $ $ 0.006 $ $ 0.00605 $ $ 2 $ $ 25 $
Parameter $ \alpha\ (\text{in Gy}^{-1}) $ $ \beta_c \ (\text{in Gy}^{-2}) $ $ \beta_s \ (\text{in Gy}^{-2}) $ $ d $ (in Gy) $ \nu $ (in days)
Value $ 0.0906 $ $ 0.006 $ $ 0.00605 $ $ 2 $ $ 25 $
Table 5.  Model parameters for normal tissue (without therapy)
Parameter Description Value
$ \mu_n $ constant maximum birth rate (in $ \text{day}^{-1} $) $ 0.01 $
$ \delta_n $ interaction factor $ 0.002 $
$ \gamma $ threshold of functionality $ 0.7 $
$ n_0 $ initial density of normal tissue $ 1 $
$ M $ actual carrying capacity (normalized) $ 1 $
Parameter Description Value
$ \mu_n $ constant maximum birth rate (in $ \text{day}^{-1} $) $ 0.01 $
$ \delta_n $ interaction factor $ 0.002 $
$ \gamma $ threshold of functionality $ 0.7 $
$ n_0 $ initial density of normal tissue $ 1 $
$ M $ actual carrying capacity (normalized) $ 1 $
Table 6.  Model parameters for radiotherapy affecting normal tissue, based on [21]
Parameter Description Value
$ \alpha _n $ sensitivity parameter (in Gy$ ^{-1} $) $ 0.0025 $
$ \beta _n $ sensitivity parameter (in Gy$ ^{-2} $) $ 0.003 $
Parameter Description Value
$ \alpha _n $ sensitivity parameter (in Gy$ ^{-1} $) $ 0.0025 $
$ \beta _n $ sensitivity parameter (in Gy$ ^{-2} $) $ 0.003 $
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