January  2018, 23(1): 425-441. doi: 10.3934/dcdsb.2018029

Treatment of glioma with virotherapy and TNF-α inhibitors: Analysis as a dynamical system

1. 

Institute of Mathematics, Lodz University of Technology, 90-924 Lodz, Poland

2. 

Dept. of Mathematics and Statistics, Southern Illinois University Edwardsville, Edwardsville, IL, 62026-1653, USA

3. 

Dept. of Electrical and Systems Engr., Washington University, St. Louis, Mo, 63130, USA

* Corresponding author

Received  August 2016 Published  January 2018

Oncolytic viruses are genetically altered replication-competent vi-ruses which upon death of a cancer cell produce many new viruses that then infect neighboring tumor cells. A mathematical model for virotherapy of glioma is analyzed as a dynamical system for the case of constant viral infusions and TNF-α inhibitors. Aside from a tumor free equilibrium point, the system also has positive equilibrium point solutions. We investigate the number of equilibrium point solutions depending on the burst number, i.e., depending on the number of new viruses that are released from a dead cancer cell and then infect neighboring tumor cells. After a transcritical bifurcation with a positive equilibrium point solution, the tumor free equilibrium point becomes asymptotically stable and if the average viral load in the system lies above a threshold value related to the transcritical bifurcation parameter, the tumor size shrinks to zero exponentially. Other bifurcation events such as saddle-node and Hopf bifurcations are explored numerically.

Citation: Elzbieta Ratajczyk, Urszula Ledzewicz, Maciej Leszczyński, Heinz Schättler. Treatment of glioma with virotherapy and TNF-α inhibitors: Analysis as a dynamical system. Discrete & Continuous Dynamical Systems - B, 2018, 23 (1) : 425-441. doi: 10.3934/dcdsb.2018029
References:
[1] A. Ambrosetti and G. Prodi, A Primer of Nonlinear Analysis, Cambridge University Press, Cambridge, 1993. Google Scholar
[2]

B. AuffingerU. Ahmed and S. Lesniak, Oncolytic virotherapy for malignant glioma: Translating laboratory insights into clinical practice, Front. Oncol., 252 (2008), 109-122. doi: 10.3389/fonc.2013.00032. Google Scholar

[3]

Z. BajzerT. CarrK. JosićS. J. Russell and D. Dingli, Modeling of cancer virotherapy with recombinant viruses, J. Theoretical Biology, 252 (2008), 109-122. doi: 10.1016/j.jtbi.2008.01.016. Google Scholar

[4]

M. BieseckerJ. H. KimnH. LuD. Dingli and Z. Bajzer, Optimization of virotherapy for cancer, Bull. Math. Biology, 72 (2010), 469-489. doi: 10.1007/s11538-009-9456-0. Google Scholar

[5]

E. A. Chiocca, Oncolytic viruses, Nat. Rev. Cancer, 2 (2002), 938-950. Google Scholar

[6]

B. S. Choudhury and B. Nasipuri, Efficient virotherapy of cancer in the presence of immune response, Int. J. Dynamics and Control, 2 (2014), 314-325. doi: 10.1007/s40435-013-0035-8. Google Scholar

[7]

J. J. CrivelliJ. FöldesP. S. Kim and J. Wares, A mathematical model for cell-cycle specific cancer virotherapy, J. of Biological Dynamics, 6 (2012), 104-120. doi: 10.1080/17513758.2011.613486. Google Scholar

[8]

A. El-alami LaaroussiM. El HiaM. RachikE. Benlahmar and Z. Rachik, Analysis of a mathematical model for treatment of cancer with oncolytic virotherapy, Appl. Math. Sci., 8 (2014), 929-940. doi: 10.12988/ams.2014.311663. Google Scholar

[9]

A. FriedmanJ. TianG. FulciE. Chioca and J. Wang, Glioma virotherapy: Effects of innate immune suppression and increased viral replication capacity, Can. Res., 66 (2006), 2314-2319. doi: 10.1158/0008-5472.CAN-05-2661. Google Scholar

[10]

G. FulciL. BreymannD. GianniK. KurozomiS. S. RheeJ. YuB. KaurD. N. LouisR. WeisslederM. A. Caligiuri and E. A. Chiocca, Cyclophosphamide enhances glioma virotherapy by inhibiting innate immune responses, Proc. of the National Academy of Sciences -PNAS, 103 (2006), 12873-12878. doi: 10.1073/pnas.0605496103. Google Scholar

[11] J. Guckenheimer and P. Holmes, Nonlinear Oscillations, Dynamical Systems, and Bifurcations of Vector Fields, Springer Verlag, New York, 1983. doi: 10.1007/978-1-4612-1140-2. Google Scholar
[12]

N. L. Komarova and D. Wodarz, Targeted Cancer Treatment in Silico -Small Molecule inhibitors and Oncolytic Viruses, Birkhäuser, (2014). doi: 10.1007/978-1-4614-8301-4. Google Scholar

[13]

L. R. PaivaC. BinnyS. C. Ferreira jr and M. L. Martins, A multiscale mathematical model for oncolytic virotherapy, Can. Res., 69 (2009), 1205-1211. doi: 10.1158/0008-5472.CAN-08-2173. Google Scholar

[14]

E. RatajczykU. LedzewiczM. Leszczyński and A. Friedman, The role of TNF-α Inhibitor in Glioma virotherapy: A mathematical model, Math. Biosci. and Engr. -MBE, 14 (2017), 305-319. doi: 10.3934/mbe.2017020. Google Scholar

[15]

D. Wodarz, Viruses as antitumor weapons: Defining conditions for tumor remission, Can. Res., 61 (2001), 3501-3507. Google Scholar

show all references

References:
[1] A. Ambrosetti and G. Prodi, A Primer of Nonlinear Analysis, Cambridge University Press, Cambridge, 1993. Google Scholar
[2]

B. AuffingerU. Ahmed and S. Lesniak, Oncolytic virotherapy for malignant glioma: Translating laboratory insights into clinical practice, Front. Oncol., 252 (2008), 109-122. doi: 10.3389/fonc.2013.00032. Google Scholar

[3]

Z. BajzerT. CarrK. JosićS. J. Russell and D. Dingli, Modeling of cancer virotherapy with recombinant viruses, J. Theoretical Biology, 252 (2008), 109-122. doi: 10.1016/j.jtbi.2008.01.016. Google Scholar

[4]

M. BieseckerJ. H. KimnH. LuD. Dingli and Z. Bajzer, Optimization of virotherapy for cancer, Bull. Math. Biology, 72 (2010), 469-489. doi: 10.1007/s11538-009-9456-0. Google Scholar

[5]

E. A. Chiocca, Oncolytic viruses, Nat. Rev. Cancer, 2 (2002), 938-950. Google Scholar

[6]

B. S. Choudhury and B. Nasipuri, Efficient virotherapy of cancer in the presence of immune response, Int. J. Dynamics and Control, 2 (2014), 314-325. doi: 10.1007/s40435-013-0035-8. Google Scholar

[7]

J. J. CrivelliJ. FöldesP. S. Kim and J. Wares, A mathematical model for cell-cycle specific cancer virotherapy, J. of Biological Dynamics, 6 (2012), 104-120. doi: 10.1080/17513758.2011.613486. Google Scholar

[8]

A. El-alami LaaroussiM. El HiaM. RachikE. Benlahmar and Z. Rachik, Analysis of a mathematical model for treatment of cancer with oncolytic virotherapy, Appl. Math. Sci., 8 (2014), 929-940. doi: 10.12988/ams.2014.311663. Google Scholar

[9]

A. FriedmanJ. TianG. FulciE. Chioca and J. Wang, Glioma virotherapy: Effects of innate immune suppression and increased viral replication capacity, Can. Res., 66 (2006), 2314-2319. doi: 10.1158/0008-5472.CAN-05-2661. Google Scholar

[10]

G. FulciL. BreymannD. GianniK. KurozomiS. S. RheeJ. YuB. KaurD. N. LouisR. WeisslederM. A. Caligiuri and E. A. Chiocca, Cyclophosphamide enhances glioma virotherapy by inhibiting innate immune responses, Proc. of the National Academy of Sciences -PNAS, 103 (2006), 12873-12878. doi: 10.1073/pnas.0605496103. Google Scholar

[11] J. Guckenheimer and P. Holmes, Nonlinear Oscillations, Dynamical Systems, and Bifurcations of Vector Fields, Springer Verlag, New York, 1983. doi: 10.1007/978-1-4612-1140-2. Google Scholar
[12]

N. L. Komarova and D. Wodarz, Targeted Cancer Treatment in Silico -Small Molecule inhibitors and Oncolytic Viruses, Birkhäuser, (2014). doi: 10.1007/978-1-4614-8301-4. Google Scholar

[13]

L. R. PaivaC. BinnyS. C. Ferreira jr and M. L. Martins, A multiscale mathematical model for oncolytic virotherapy, Can. Res., 69 (2009), 1205-1211. doi: 10.1158/0008-5472.CAN-08-2173. Google Scholar

[14]

E. RatajczykU. LedzewiczM. Leszczyński and A. Friedman, The role of TNF-α Inhibitor in Glioma virotherapy: A mathematical model, Math. Biosci. and Engr. -MBE, 14 (2017), 305-319. doi: 10.3934/mbe.2017020. Google Scholar

[15]

D. Wodarz, Viruses as antitumor weapons: Defining conditions for tumor remission, Can. Res., 61 (2001), 3501-3507. Google Scholar

Figure 1.  Illustration of Proposition 3 for $c=0$
Figure 2.  'Graph' of polynomial $P$ if $w^0 < 1$ (top) and in the two subcases that arise for $w^0>1$ (bottom)
Figure 3.  Illustration of Propositions 3 and 4
Figure 4.  Transcritical bifurcation
Figure 5.  Values of the tumor free and positive equilibrium points for $B=90$ (left), $B=125$ (middle) and $B=140$ (right) plotted as function of $C$. In each case there is a saddle-node bifurcation which generates an upper ($z_u$) and lower branch $(z_\ell)$ of positive solutions. The upper branch only exists for the short range and terminates as $\bar{y}=0.1=\frac{\delta_M}{s}$ which causes $\bar{M}\rightarrow \infty$. The lower branch exists until $C=2.5$ and terminates as $\bar{y}$ becomes zero
Figure 6.  Periodic orbit for $C=0.3$ and $B=140$
Table 1.  States and parameters of the model
States Description Dimension Num. Value(s)
x density of uninfected cancer stem cells $\frac{g}{{c{m^3}}}$
y density of infected cancer stem cells $\frac{g}{{c{m^3}}}$
M density of macrophages $\frac{g}{{c{m^3}}}$
T density of TNF-α $\frac{g}{{c{m^3}}}$
v density of virus $\frac{g}{{c{m^3}}}$
z z = (x; y; M; T; v#8224;
Parameter Description Dimension Num. Value(s)
α proliferation rate of uninfected tumor cells 1/day 0.2
β infection rate of tumor cells by viruses $\frac{{c{m^3}}}{{g \cdot day}}$ 2·104
ρ rate of loss of viruses during infection $ρ$ 0.04
k effectiveness of the inhibitory action of TNF-α 1/day 0.4
δy infected tumor cell death rate 1/day 0.2
λ TNF-α production rate 1/day 2.86·10-3
δT TNF-α cell degradation rate 1/day 55.45
δM macrophages death rate 1/day 0.015
b burst size of infected cells during apoptosis ×10-6 50 - 150
KT carrying capacity of TNF-α $\frac{g}{{c{m^3}}}$ 5·10-7
κ degradation of TNF-α due to its action on infected cells 1/day 4·10-10
δv virus lysis rate 1/day 0.5
A constant source of macrophages $\frac{g}{{c{m^3} \cdot day}}$ 0.9·10-6
s stimulation rate of macrophages by infected cells $\frac{{c{m^3}}}{{g \cdot day}}$ 0.15
δx death rate of uninfected cancer cells 1/day 0.1
c constant infusion of the virus $\frac{{g \times {{10}^{-6}}}}{{c{m^3} \cdot day}}$ 0 - 150
d constant infusion of the TNF-α inhibitor
States Description Dimension Num. Value(s)
x density of uninfected cancer stem cells $\frac{g}{{c{m^3}}}$
y density of infected cancer stem cells $\frac{g}{{c{m^3}}}$
M density of macrophages $\frac{g}{{c{m^3}}}$
T density of TNF-α $\frac{g}{{c{m^3}}}$
v density of virus $\frac{g}{{c{m^3}}}$
z z = (x; y; M; T; v#8224;
Parameter Description Dimension Num. Value(s)
α proliferation rate of uninfected tumor cells 1/day 0.2
β infection rate of tumor cells by viruses $\frac{{c{m^3}}}{{g \cdot day}}$ 2·104
ρ rate of loss of viruses during infection $ρ$ 0.04
k effectiveness of the inhibitory action of TNF-α 1/day 0.4
δy infected tumor cell death rate 1/day 0.2
λ TNF-α production rate 1/day 2.86·10-3
δT TNF-α cell degradation rate 1/day 55.45
δM macrophages death rate 1/day 0.015
b burst size of infected cells during apoptosis ×10-6 50 - 150
KT carrying capacity of TNF-α $\frac{g}{{c{m^3}}}$ 5·10-7
κ degradation of TNF-α due to its action on infected cells 1/day 4·10-10
δv virus lysis rate 1/day 0.5
A constant source of macrophages $\frac{g}{{c{m^3} \cdot day}}$ 0.9·10-6
s stimulation rate of macrophages by infected cells $\frac{{c{m^3}}}{{g \cdot day}}$ 0.15
δx death rate of uninfected cancer cells 1/day 0.1
c constant infusion of the virus $\frac{{g \times {{10}^{-6}}}}{{c{m^3} \cdot day}}$ 0 - 150
d constant infusion of the TNF-α inhibitor
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