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On a mathematical model of tumor-immune system interactions with an oncolytic virus therapy

  • * Corresponding author: Sophia R-J Jang

    * Corresponding author: Sophia R-J Jang 
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  • We investigate a mathematical model of tumor–immune system interactions with oncolytic virus therapy (OVT). Susceptible tumor cells may become infected by viruses that are engineered specifically to kill cancer cells but not healthy cells. Once the infected cancer cells are destroyed by oncolysis, they release new infectious virus particles to help kill surrounding tumor cells. The immune system constructed includes innate and adaptive immunities while the adaptive immunity is further separated into anti-viral or anti-tumor immune cells. The model is first analyzed by studying boundary equilibria and their stability. Numerical bifurcation analysis is performed to investigate the outcomes of the oncolytic virus therapy. The model has a unique tumor remission equilibrium, which is unlikely to be stable based on the parameter values given in the literature. Multiple stable positive equilibria with tumor sizes close to the carrying capacity coexist in the system if the tumor is less antigenic. However, as the viral infection rate increases, the OVT becomes more effective in the sense that the tumor can be dormant for a longer period of time even when the tumor is weakly antigenic.

    Mathematics Subject Classification: Primary: 92D25, 92B05.

    Citation:

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  • Figure 1.  A schematic diagram of model (1), depicting the interactions between different populations is presented

    Figure 3.  One solution of the immune $ ZY_TY_V $ subsystem (8) is plotted to illustrate stabilization of the model, Proposition 3.2(b). The parameter values are given in text

    Figure 2.  The parameter values are $ h_T = 2.7\times 10^4, \ \delta_{YT} = 3.75\times 10^{-4}, \ r_T = 0.0192, \ a_{AT} = 0.0016 $ and $ k_{TA} = 1/24 $ taken from the baseline values in [38] to illustrate oscillations of the $ T_SY_T $ subsystem (5), where the time unit is an hour as given in [38]

    Figure 4.  The plots demonstrate the existence of positive equilibria for system (13), which corresponds to the positive intersections of equations (11) and (12). Dimensionless parameter values in (a) are $ h_T = 0.5, \ \delta_{YT} = 1, \ \hat Z = 0.5, \ a_{TZ} = 0.3 $ and $ a_{AT} = 1.6 $ with $ T_{S_c} = 0.833 $ so that $ g(0)<f(0) $ and $ \delta_{YT}<a_{AT} $. The black vertical line segment is the line $ T_S = T_{S_c} $. In (b), $ h_T = 0.1 $, $ \delta_{YT} = 0.5, \ \hat Z = 0.6, \ a_{TZ} = 0.1 $ and $ a_{AT} = 0.15 $ so that $ g(0)> f(0) $. The curves have two positive intersections

    Figure 5.  (a) Bifurcation diagram using $ \beta $ as the bifurcation parameter, where all other parameter values are the same as in (23). The dashed lines at $ \log(T_S) = -1 $ (blue), $ \log(T_S) = 8.7124 $ (green), and $ \log(T_S) = 3.9172 $ (cyan) represent the unstable equilibria $ E_0 $, $ E_1 $, and $ E_2 $, respectively. The black dashed line between $ E_2 $ and $ E_0 $ represents an unstable positive equilibrium. (b) and (c) are the time series of cell populations for $ \beta = 6\times 10^{-8} $. The other parameter values in (b) are same as those in (a) while $ a_{TZ} = 2.4 $, $ s_{ZR} = 4.8 $, $ \delta_{ZR} = 1.658 $, and $ a_{ZZ} = 4.8 $ in (c). The tick label -1 on the vertical axis represents a population level less than 0.1

    Figure 6.  (a) Bifurcation diagram using $ \beta $ and $ a_{AT} $ as the bifurcation parameters, where all other parameter values are the same as in (23). (b) A closer look at the bifurcation curves for $ \log(\beta)\in [-10.12,-9.97] $

    Figure 7.  (a) Bifurcation diagram using $ a_{AT} = 0.005 $ and $ \beta $ as the bifurcation parameters, where all other parameter values are the same as in (23). The blue dashed curve represents unstable positive equilibria and the red curve represents stable equilibria. The light blue and green curves represent stable limit cycles. (b) A closer look at the rectangle shown in (a)

    Figure 8.  Time series of cell populations for $ a_{AT} = 0.005 $ and (a) $ \beta = 9.4\times 10^{-9} $, (b) $ \beta = 9.4\times 10^{-9} $, and (c) $ \beta = 9.5\times 10^{-9} $. The tick label -1 on the vertical axis represents a population level less than 0.1

    Figure 9.  (a) Bifurcation diagram using $ a_{AT} = 0.008 $, $ a_{AI} = 0.1 $, and $ \beta $ and $ a_{AI} $ as the bifurcation parameters, where all other parameter values are the same as in (23). (b) Bifurcation diagram using $ a_{AT} = 0.008 $, $ a_{AI} = 0.1 $, $ b_{T} = 50 $, and $ \beta $ as the bifurcation parameter. The blue dashed curve represents unstable positive equilibria and the red curve represents stable equilibria. The green curve represents stable limit cycles. (c) Bifurcation diagram using $ \beta $ and $ r_T $ as the bifurcation parameters, where all other parameter values are the same as in (23)

    Table 2.  Equilibria of $ T_SZY_TY_V $ subsystem (10) and their biological interpretations

    Equilibrium Interpretation
    $ E_{30}=(0,0,0,0) $ Extinction of all cell populations
    $ E_{31}=(1, 0,0,0) $ Susceptible tumor only
    $ E_{32}=(\bar T_S, 0, \bar Y_S,0) $ Coexistence of susceptible tumor and anti-tumor immune cells
    $ E_{33}=(0,\hat Z,\hat Y_T,\hat Y_V) $ Immune cells only
    $ E_{34*}=(\tilde T_S, \hat Z, \tilde T_T, \hat Y_V) $ Coexistence of susceptible tumor and immune cells
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    Table 3.  Existence and stability of boundary equilibria of system (4)

    Equilibrium Existence Asymptotic stability
    $ E_{0} $ always unstable
    $ E_{1} $ always $ s_{ZR}a_{ZZ}<\delta_Z\delta_{ZR} $, $ a_{AT}<\delta_{YT}(h_T+1) $, $ b_T<\omega $
    $ E_{2} $ $ a_{AT}>\delta_{YT}(h_T+1) $ $ s_{ZR}a_{ZZ}<\delta_Z\delta_{ZR} $, $ \bar T_S>(1-h_T)/2 $, $ \omega(\delta_T+\bar Y_T/h_I)>h_T\delta_T\bar T_S $
    $ E_{3} $ $ s_{ZR}a_{ZZ}>\delta_Z\delta_{ZR} $ $ \hat Y_T>h_T $
    $ E_{4*} $ $ s_{ZR}a_{ZZ}>\delta_Z\delta_{ZR} $, $ h_T\geq 1 $, $ \hat Y_T<h_T $ $\eta < 0 < \tilde \eta $
    $ s_{ZR}a_{ZZ}>\delta_Z\delta_{ZR} $, $ h_T<1 $, $ \hat Y_T<h_T $, $ a_{AT}>\delta_{YT} $
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    Table 1.  Existence and stability of equilibria of $ T_SZY_TY_V $ subsystem (10)

    Equilibrium Existence Asymptotic stability
    $ E_{30} $ Always Unstable
    $ E_{31} $ Always $ s_{ZR}a_{ZZ}<\delta_{ZR}\delta_Z $ and $ a_{AT}<\delta_{YT}(h_T+1) $
    $ E_{32} $ $ a_{AT}>\delta_{YT}(h_T+1) $ $ s_{ZR}a_{ZZ}<\delta_{ZR}\delta_Z $ and $ \bar T_S>(1-h_T)/2 $
    $ E_{33} $ $ s_{ZR}a_{ZZ}>\delta_{ZR}\delta_Z $ $ \hat Y_T>h_T $ (i.e., $ a_{TZ}\hat Z>\delta_{YT}h_T $)
    $ E_{34*} $ $ s_{ZR}a_{ZZ}>\delta_Z\delta_{ZR} $, $ h_T\geq 1 $, $ \hat Y_T<h_T $ $\eta < 0$
    $ s_{ZR}a_{ZZ}>\delta_Z\delta_{ZR} $, $ h_T<1 $, $ \hat Y_T<h_T $, $ a_{AT}>\delta_{YT} $
    * This is a case for which the positive equilibrium is unique. System (10) may have more than one positive equilibrium.
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    Table 4.  Biological interpretations of boundary equilibria of full system (4)

    Equilibrium Biological meaning
    $ E_0=(0,0, 0, 0,0,0) $ Extinction of tumor, virus and immune cells
    $ E_1=(1, 0,0,0,0,0) $ Susceptible tumor only
    $ E_2=(\bar T_S, 0,0,0,\bar Y_T, 0) $ Coexistence of susceptible tumor and anti-tumor immune cells
    $ E_{3}=(0,0,0,\hat Z, \hat Y_T, \hat Y_V) $ Immune cells only
    $ E_{4}=(\tilde T_S, 0,0,\hat Z, \tilde Y_T, \hat Y_V) $ Coexistence of susceptible tumor and immune cells
     | Show Table
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