
Citation: Sonia Shastri, Ravichandra Vemuri, Nuri Gueven, Madhur D. Shastri, Rajaraman Eri. Molecular mechanisms of intestinal inflammation leading to colorectal cancer[J]. AIMS Biophysics, 2017, 4(1): 152-177. doi: 10.3934/biophy.2017.1.152
[1] | Zizi Wang, Zhiming Guo, Hal Smith . A mathematical model of oncolytic virotherapy with time delay. Mathematical Biosciences and Engineering, 2019, 16(4): 1836-1860. doi: 10.3934/mbe.2019089 |
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[5] | Jianjun Paul Tian . The replicability of oncolytic virus: Defining conditions in tumor virotherapy. Mathematical Biosciences and Engineering, 2011, 8(3): 841-860. doi: 10.3934/mbe.2011.8.841 |
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[9] | Prathibha Ambegoda, Hsiu-Chuan Wei, Sophia R-J Jang . The role of immune cells in resistance to oncolytic viral therapy. Mathematical Biosciences and Engineering, 2024, 21(5): 5900-5946. doi: 10.3934/mbe.2024261 |
[10] | Lu Gao, Yuanshun Tan, Jin Yang, Changcheng Xiang . Dynamic analysis of an age structure model for oncolytic virus therapy. Mathematical Biosciences and Engineering, 2023, 20(2): 3301-3323. doi: 10.3934/mbe.2023155 |
Oncolytic viruses are genetically engineered viruses that preferentially target and destroy cancer cells [1]. This emerging anticancer strategy exploits the lytic nature of viral replication to enhance the killing of malignant cells. Over the past decade, many oncolytic viruses have been tested in clinical trials [2], with one of these approved by the FDA for treatment of metastatic melanoma [3]. With the advancement of a growing number of oncolytic viruses to clinical development, treatment protocol is becoming a crucial factor for achieving optimal therapeutic efficacy.
Host immunity and the tumor microenvironment contribute significantly to inefficient virus delivery [4]. To overcome these obstacles, a range of novel mechanisms have been developed: gel-based mediums, nanoparticles, immunomodulatory agents and molecules that can manipulate the tumor microenvironment [4]. Gel-based mediums have been used successfully to improve virotherapy, chemotherapy and immunotherapy [5,6,7,8,9,10,11]. In these therapies, therapeutic agents are either loaded onto gels or coated to provide sustained therapy release and therapeutic efficacy. Nanoparticles have been investigated as a viral DNA and RNA delivery system as they can be engineered to have a decreased immune response [12], and their physical properties can be used to provide controlled viral release and diminish infectivity, maintaining an elevated local concentration [4,13,14]. Alternatively, polymer-based nanomaterials, such as polyethyleneglycol (PEG), have been shown to be effective at shielding particles from the extracellular environment and preventing clearance [12].
Each of these mechanisms looks to extend the treatment activity by controlling delivery and modulating immune system involvement. Using these therapeutic devices it could be possible to control viral diffusion through the tumour bulk prior to initial infection and avoid activation of immune clearance. Previously, Jenner et al. [15] developed a Voronoi cell-based model that predicted that modifying viruses to delay their initial infection of tumour cells and avoid immune clearance could improve virotherapy. They showed that the length of the delay before initial infection has an impact on the efficacy of this modified treatment. While insightful, due to the complexity and computation time of their model they did not investigate what proportion of delayed and non-delayed virus would be optimal. Additionally, they were not able to consider whether a distribution of delayed-infection times throughout the injected virus population could improve treatment. In this work, we develop a deterministic formulation for their Voronoi Cell-Based model to be able to analyse in-depth their treatment predictions using an optimal control approach.
Mathematical modelling has been used to improve the understanding of oncolytic viruses. Systems of ordinary differential equations (ODEs) that consider the basic interaction between uninfected cells, infected cells and viruses have been used to suggest significant improvements to treatment protocols [16,17,18,19]. More complex systems of ODEs that consider the heterogeneity in the cancer cell cycle have also been used to determine effective dosage protocols using in silico clinical trials [20]. Partial differential equations (PDEs) have also been successful at understanding treatment pitfalls and suggesting improved therapies by considering the spatial aspect of this therapy [21,22,23].
Optimal control theory is a useful approach used to understand optimal viral characteristics and dosage protocols [17,18,20,24,25]. Finite-horizon optimal control provides a useful tool to determine ways of minimising a variable in an ODE system. Zurakowski and Wodarz [25] used this method to find the constant concentration of drug that minimised the lowest excursion of the total tumour size during the optimisation horizon. Genetic algorithms are heuristic global optimization routines frequently employed to estimate parameters in computational biology models. Cassidy and Craig [20] used this method to generate personalised optimal schedules for patients in an in silico virotherapy trial.
Coating an oncolytic virus to delay infection and evade the immune system using either gel-based mediums or nanoparticles could be instrumental in improving oncolytic therapy. Drawing on previous modelling of the delayed-infection and immune-evading coated oncolytic virus by Jenner et al. [15], we derive a PDE system for this therapy and investigate its potential. Using optimal control theory, we determine a dosage combination for the modified (coated) virus that can minimise the tumour size. Simultaneously, we investigate the impact of the viral clearance on the outcome of therapy by investigating the optimal dosage regime for different viral-clearance models.
As previously demonstrated in the work by Jenner et al. [15], the efficacy of oncolytic virotherapy can be improved by altering the virus's delivery mechanism to delay infection and avoid immune clearance. To investigate the applicability of this suggested treatment improvement and to optimise the protocol, we have developed a system of PDEs based on their model, that incorporates the interaction between an oncolytic virus and a population of tumour cells. Virotherapy is predominantly administered intratumourally or intravenously, resulting in individual infection sites and a non-homogeneous distribution of the virus throughout the tumour. We simplify this aspect to consider a single injection of virus that initially coats the tumour periphery uniformly, and consider the dynamics of the virus-tumour interaction are radially symmetric. To consider the impact of clearance on the virus's effectiveness, we develop four different modelling assumptions. Parameters for the model were then taken from previous model optimisations to data.
Consider a density of susceptible tumour cells S(x,t), growing under a logistic growth rate with a normalised carrying capacity, where x is the radial distance from the centre of a circular tumour. Two types of viruses are considered in the model: coated virus particles VC(x,t) and uncoated virus particles VN(x,t). We assume that the coating inhibits the virus from infecting susceptible cancer cells. Uncoated virus infects susceptible cells with rate constant β, creating a density of infected tumour cells I(x,t). Infected cells die due to lysis at rate dI and produce α uncoated viruses. Infected cells grow at the same logistic rate as the susceptible tumour cells. In the model developed by Jenner et al. [15] they assumed virus-infected cells do not replicate; however, there is evidence that this can occur for different modified viruses [26,27]. Both susceptible and infected tumour cells have a diffusion coefficient DT.
The coating on the coated virus particles degrades at rate dC. We assume coated viruses are not detectable by the immune system and as such are removed from the system, through loss into the tissue, lymphatic system or vasculature at a slow rate δC. Uncoated virus, however, is detectable by the immune system and as such is cleared more rapidly at rate Imm(S,I,VN). This function represents the immune-driven clearance of the virus and will be described in the following subsection. Realistically, virus diffusion would be influenced by the fluid flow through the tumour and obstructions in the microenvironment, such as densely packed cells and vasculatures [15]. Since, we do not model additional factors such as these, we assume one-dimensional radially-symmetric diffusion is able to account for the average dynamics of virus diffusion. Coated and uncoated viruses, therefore, diffuse through a tumour with the same diffusion coefficient DV.
To model the decay of coating from the virus in more detail, we consider K levels of viral coating. The Kth level representing fully coated virus, which degrades until there is no coating (i.e., to an uncoated virus). We model coated virus as having discrete levels of coating, and the density of virus particles with an ith level of coating, 1≤i≤K, is given by VC,i(x,t).
A summary of the dynamics for coated and uncoated virus treatments of a growing tumour is presented in Figure 1. The corresponding partial differential equation (PDE) system is provided in Equation 2.1–Equation 2.7, with corresponding boundary conditions and initial conditions in Equation 2.8.
∂VC,K∂t=DV∂2VC,K∂x2−dCVC,K−δCVC,K, | (2.1) |
∂VC,K−1∂t=DV∂2VC,K−1∂x2+dCVC,K−dCVC,K−1−δCVC,N−1, | (2.2) |
...∂VC,i∂t=DV∂2VC,i∂x2+dCVC,i+1−dCVC,i−δCVC,i, | (2.3) |
...∂VC,1∂t=DV∂2VC,1∂x2+dCVC,2−dCVC,1−δVC,1, | (2.4) |
∂VN∂t=DV∂2VN∂x2+dCVC,1−Imm(S,I,VN)−β(S+I)VN+αdII, | (2.5) |
∂S∂t=DT∂2S∂x2+rS(1−(S+I))−βSVN, | (2.6) |
∂I∂t=DT∂2I∂x2+rI(1−(S+I))+βSVN−dII, | (2.7) |
{∂xVC,i(0,t)=0,∂xVN(0,t)=0,∂xS(0,t)=0,∂xI(0,t)=0,VC,i(L,t)=0,VN(L,t)=0,∂xS(L,t)=0,∂xI(L,t)=0,VC,i(x,0)=piVT(H(x−(P−ϵ))−H(x−(P+ϵ))),VN(x,0)=(1−∑pi)VT(H(x−(P−ϵ))−H(x−(P+ϵ))),S(x,0)=H(x)−H(x−P),I(x,0)=0. | (2.8) |
In this system, t represents days and x is the radial distance from the tumour centre in μm. The tumour grows within the fixed domain [0,L] and the initial radius of the tumour, P, must satisfy P<<L. We model a single injection of virus at the periphery P of the tumour and consider an initial combination of both uncoated and coated virus with different levels of coating. The initial injection of virus is administered at the periphery of a circular tumour and, as such, the area of the injection is AI=π[(P+ϵ)2−(P−ϵ)2], and as a result the total virus injected at the periphery of the one-dimensional problem considered is VT=V0AI. The Heaviside function H(x) is used to model the initial cell densities and virus injection. The proportion of the initial injection of virus, V0, that contains each level of coated virus is given by pi, where i is the coating level. Note that 0≤pi≤1 and ∑Ki=1pi≤1.
No-flux boundary conditions are used for the tumour populations at x=L to simulate that tumour cells do not leave the edge of the domain. Since virus particles are much smaller than cells, we assume they can diffuse past x=L and be lost, whether into the surrounding tissue or the vasculature system, and we impose that VC,i(L,t)=VN(L,t)=0. As we are modelling a radially symmetric tumour, no-flux boundary conditions are also applied for cells and virus at x=0.
The biological mechanisms modelled in Equation 2.1–Equation 2.7 are further described as follows:
● In Equation 2.1, virus particles at the maximum coating level VC,K diffuse through the tumour with coefficient DV. The coating decays at rate dC, and as such, viruses leave the Kth coating level at rate dCVC,K and enter the subsequent lower-coating-level population VC,K−1. Coated viruses are cleared at rate δC.
● In Equation 2.2, virus particles at the next maximum coating level VC,K−1 diffuse through the tumour with coefficient DV. Viruses from the Kth coating level join the (K−1)th coating level at rate dCVC,K, and viruses leave the (K−1)th coating level at rate dCVC,K−1 and enter the subsequent lower coating level VC,K−2. Coated viruses are cleared at rate δC.
● Equation 2.3 is the general formulation for coated viruses. Virus particles at the ith coating level diffuse through the tumour with coefficient DV. The coating decays at rate dC, meaning viruses from the coating levels above enter at rate dCVC,i+1, and viruses at the currect coating level leave at rate dCVC,i. Coated viruses are cleared at rate δC.
● In Equation 2.4, virus particles at the last coating level diffuse and are cleared at the same rates as the preceding coating levels. As this is the last coating level, virus lose the remaining coating and enter the uncoated virus population VN at rate dCVC,1.
● In Equation 2.5, uncoated virus, VN, diffuses through the tumour with the same diffusion coefficient DV. Viruses are cleared by an immune response described by the function Imm(S,I,VN), which can be dependent on the susceptible and infected tumour cells, S and I. Uncoated viruses infect susceptible and infected tumour cells at rate β(S+I)VN, and new uncoated viruses are created through cell lysis which occurs at rate dII and produces α new viruses.
● In Equation 2.6, susceptible tumour cells diffuse with coefficient DT. They undergo proliferation at a logistic growth rate r, normalised to a carrying capacity of 1. Susceptible cells are infected by uncoated-virus at rate βSVN.
● In Equation 2.7, infected tumour cells also diffuse with coefficient DT. They similarly undergo logistic growth. Infected cells arise through infection of the susceptible tumour cell population at rate βSVN. These cells undergo lysis at rate dII.
The immune response to an oncolytic virus is complex and depends heavily on the tumour type, level of heterogeneity and the genetic modification of the virus. In this section, we develop four hierarchically-related formulations for the clearance rate of uncoated virus by the immune system Imm(S,I,VN). Each of the models considers a different aspect of the immune response to virotherapy. Our primary goal is not to prove which model is correct, but to investigate how different assumptions play a role in the outcome of therapy.
Virus particles that infect tumour cells can instigate both an anti-viral and anti-cancer immune response. In general, an anti-viral response is one that targets either extracellular virus particles or infected cells and an anti-cancer response is one that elicits an immune response that can apoptose both infected and susceptible tumour cells. The clearance of viruses by the immune system has been shown to be a major hindrance to oncolytic virotherapy [28]. In this work, we choose to model the clearance of extracellular virus particles by the immune system with varying degrees of dependency on the uncoated virus density, susceptible tumour cell density and infected tumour cell density.
The simplest way to model clearance of the uncoated virus is at a rate proportional to the number of particles:
Imm(S,I,VN)=δNVN, | (2.9) |
where δN is the clearance rate constant. This model is regularly used in deterministic models [16]. It assumes that the immune system activation is not dependent on the activities of the virus or the presence of the tumour, but on the density of the virus population.
In reality, the anti-viral immune response would be proportional to the number of infected cells as the immune system becomes stimulated by the presentation of viral antigen on the surface of infected cells [32]. As the number of infected cells increases and more immune cells are activated, the strength of the immune response will also increase. As such, we also consider the clearance of virus particles as proportional to the number of infected tumour cells:
Imm(S,I,VN)=δNVN∫L0I(˜x,t)d˜x. | (2.10) |
In this model, we assume that the proportion of the tumour that is uninfected does not play a role in the activation of an immune response.
In addition, the presence of tumour cells can elicit an immune response resulting in an influx of immune cells at the tumour site. These cells can then become aware of the virus particles and contribute to viral clearance. Assuming, therefore, that the immune system can be stimulated by the presence of both susceptible and infected tumour cells provides a third viral clearance model:
Imm(S,I,VN)=δNVN∫L0(S+I)(˜x,t)d˜x, | (2.11) |
where the activation of clearance is equally proportional to both types of tumour cells.
In Equation 2.11 we assume that the rate at which susceptible and infected cells stimulate the clearance of virus particles by immune cells is equivalent; however, this may not be the case. In the adaptive immune system, cells such as dendritic cells and macrophages are stimulated by the presentation of antigen [32]. Susceptible cells will only present tumour antigen (as they do not contain any viruses) whereas infected cells will present virus antigen (as they have been infected by viruses) and tumour antigen. As such there may be two different immune responses: an anti-cancer response and an anti-viral response. These will affect the clearance of the virus in different ways. As expected the virus immune response will rapidly clear virus particles, as this immune response is specific to viruses. The tumour immune response will increase immune cell presence at the tumour site, which we assume increases the likelihood of virus immune stimulation. As such, the viral clearance can be modelled with different dependencies on the tumour cell populations:
Imm(S,I,VN)=VN(δNS∫L0S(˜x,t)d˜x+δNI∫L0I(˜x,t)d˜x), | (2.12) |
where δNS and δNI are the respective clearance rates of the anti-tumour and anti-viral responses.
In this work, we investigate the dynamics of Equation 2.1–Equation 2.7 with each of the different models of the immune response to virotherapy. To date, no one has investigated how different viral-clearance models may impact the efficacy of therapy, and we aim to provide motivation for this discussion.
The majority of the parameter values used in the following analysis were taken from Jenner et al.'s investigation of a PEG-modified adenovirus conjugated with herceptin [30]. In this work, a reduced ODE system similar to the PDE system in this work was used to optimise the infectivity rate, replication rate, lysis burst rate and size (see Table 1). These parameter values were also similar to the ones used in the VCBM model that conducted the preliminary investigation into the efficacy of this viral coating [15].
Parameter | Units | Description | Value | Source |
Virus diffusion coefficient | 0.001 | - | ||
Tumour diffusion coefficient | 0.0001 | - | ||
day |
Decay rate of coating from virus | 0.1 | - | |
day |
Decay rate of coated virus | 0.001 | - | |
day |
Decay rate of uncoated virus | 1.38 | [30] | |
day |
Infected cell lysis rate | 0.1 | [30] | |
day |
Virus infection rate | 0.862 | [30] | |
day |
Tumour cell replication rate | 0.037 | [30] | |
Virus ( |
Initial injection | 2 | [30] | |
Virus | Lysis burst size | 3500 | [30] |
The remaining parameters were then estimated at values relative to our biological understanding of the system. For the diffusion coefficients, we assumed the virus would diffuse faster than the tumour, as such we chose DV=0.001 and DT=0.0001. Additionally, to simulate the immune evasion of the coated virus we chose δC<<δN, and as such set δC=0.0001.
Lastly, we wanted a reasonable estimate for the degradation rate of viral coating. Oh et al. [9] reported that gelatin-hydroxyphenyl propionic acid based hydrogel loaded with oncolytic adenovirus degraded after 6 days. Alternatively, Croyle et al. [6] found that administration of polyethylene glycol-coated viral vectors extended viral-gene expression by 38 days. Degradation of viral coating may also be a function of particular molecular concentrations, with Tseng et al. [14] designed a hypoxia-responsive vector carrier that oxidises with lactate oxidase. As we are not modelling a specific coating device, but instead the theoretical concept of a coating that instigates a delay in viral infection, we chose dC=0.1, which is equivalent to the rate of cell lysis and a half-life for the coating of approximately 7 days. In this work, we examine a range of different initial proportions and levels of coating, which implicitly determines the impact of varying the coating's degradation rate.
The domain was fixed to [0,2000], i.e., the maximum radial growth of the tumour was L=2000μm. The initial radius of the tumour was P=1000μm and the width of the initial virus injection rim was 2ϵ=0.1μm. To demonstrate the effectiveness of the coated therapy, model simulations for different levels of coating are plotted in Figure 2. Three coating situations are considered: no coated virus, one level of coated virus K=1, and five levels of coated virus K=5. It is clear that treatment does significantly better by 50 and 100 days when coated virus is administered. Additionally, when the coating level is increased to K=5, it is clear the treatment is significantly more effective. This is most likely a result of the coating providing further intratumoural diffusion before infection and clearance can commence. All parameter values in the simulations were taken from Table 1 and the initial proportion of coated virus at all coating levels was equal. The clearance model for the uncoated virus was Equation 2.9.
As a few of the parameters were estimated, we conducted a sensitivity analysis to determine the variability in our model. Fixing all parameters to the values in Table 1 and using the clearance model in Equation 2.9 and initial proportion of uncoated virus p=0.5, we measured the change in the total tumour cells at 100 days from perturbations of ±10% for the parameters noted in Figure 3. The lysis burst size α most significantly affected the tumour size at 100 days. This is not surprising, as increasing or decreasing the amount of virus created through lysis would have a significant effect on the infection and subsequent death of uninfected tumour cells. The next highest variability in the tumour volume was exhibited from perturbations in the clearance rate of coated virus, δC, and the degradation rate of the coating, dC. This suggests, that the dynamics of the coating mechanism will influence the tumour volume reached under treatment. Overall, since the magnitude of the change in tumour volume is not large, fixing these parameters and investigating an optimised scheduling for a delayed-infection coating can provide a reliable approximation to the potential of this treatment.
To develop an optimised therapeutic protocol for the delayed-infection and immune-evading viral treatment we first investigated whether coating the virus was optimal when compared to uncoated virus. From this, we determined for the simple viral-clearance model Equation 2.9 the optimal proportion of coated to uncoated virus in the initial injection. Furthermore, we then investigated whether there is an optimal maximum level of coating K for each of the viral-clearance models in Equation 2.9–Equation 2.12.
We consider two mathematical formulations to measure the efficacy of treatment. The first aims to minimise the susceptible tumour density at a final assessment time Tf days after the initial treatment injection. In this metric, we ignore the infected cell population as once a cell becomes infected it will eventually die through lysis. Additionally, we wish to suppress tumour growth throughout treatment, as a treatment that allows for an excessive amount of tumour growth, irrespective of the size at a particular point in time, would be unrealistic. These two metrics of treatment efficacy result in the following mathematical formalisms:
minp∈[0,1]KJf(p,Tf)=‖S(x,p,Tf)‖L1x=∫L0|S(x,p,Tf)|dx, | (3.1) |
minp∈[0,1]KJ(p)=‖S(x,p,t)‖L1x,t=∫Tf0∫L0|S(x,p,t)|dxdt, | (3.2) |
where S(x,p,t) is the density of susceptible tumour cells S(x,t) defined in Equation 2.1–Equation 2.7 for a given vector p, which is the initial proportion of coated virus in each coating level.
Initially, we investigated the effects of a single coating level (i.e., K=1), allowing for the proportion of initial coated virus p=p1 to vary. In Figure 4(a), we plot the value of the objective function Equation 3.1 with respect to Tf and p. In Figure 4(b), the optimal proportion for p for the constraint in Equation 3.1 is plotted as a function of Tf. After initial injection, there is a short period of days where the treatment is optimal when there is no coated virus. As the final assessment time Tf increases, we see that this optimal proportion reaches p=1. This is crucial for defining Tf in the objective functions above (Equation 3.1–Equation 3.2), as we need to set Tf so that a non-trivial amount of coated virus is necessary to minimise the amount of susceptible cancer cells. Interestingly, there appears to be a critical threshold at 5 days, before which coated virus treatment is ineffective.
After 5 days, there is a non-zero proportion (p>0) of initial coated virus that is optimal. From this we can deduce that coating the virus improves the efficacy of treatment. In this simulation, the viral clearance was modelled using the simple model in Equation 2.9. To investigate whether the viral-clearance model affects the effectiveness of the coated virus, we now investigate the optimal maximum number of coating levels, K, for each viral-clearance model presented in Section 2.2.
As in Figure 4, in Figure 5 and Figure 6, we plot our metric (Equation 3.1) of tumour growth under treatment with varying maximum coating levels K≥1. To simplify the study, we set p by fixing the virus's initial condition so that 50% of the virus is uncoated and the remaining 50% is equally distributed between the K levels of coating, i.e., pi=0.5/K for i=1,…,K. We model the efficacy of this therapy with each of the viral-clearance models defined in Equation 2.9–Equation 2.12.
For the viral-clearance model in Equation 2.9, dynamics of the metric (Equation 3.1 with p set as above) are plotted in Figure 5(a) and (b). This case considers the viral clearance to depend only on the density of virus. We observe that the model eventually reaches a steady state irrespective of the maximum coating level. Following the line in Figure 5(a) or (b), we see that the optimal number of coating levels increases with time Tf, similar to what was already observed in Figure 4. Assuming, instead, that viral clearance is proportional to the amount of infected cancer cells (Equation 2.10) gives the optimal coating levels over time Tf plotted in Figure 5(c) and (d). For smaller time horizons (Tf<100), a faster-acting virus is more optimal, so the optimal coating level remains low, but as Tf increased beyond 200 days, the optimal coating level increases to around K=20.
We wished to then examine whether modelling viral clearance as dependent on the susceptible tumour population would result in similar dynamics to Figure 5. Modelling the viral-clearance rate to be δNVN∫N0S(˜x,t)d˜x, results in almost complete tumour eradication, irrespective of the maximum coating, see Figure 6(a). This may be a result of the rapid infection of susceptible cancer cells reducing the viral clearance rate. In comparison, when both susceptible and infected cells contribute at different rates to the viral clearance term (Equation 2.12), we see the observed dynamics in Figure 6(b). Under this assumption, the tumour size over time depends more significantly on the maximum coating level. When the density of susceptible and infected tumour cells contribute equally to the clearance rate (Equation 2.11), we obtain the dynamics in Figure 6(c) and (d).
It is clear from Figure 5 and Figure 6 that the different viral-clearance models result in significantly different treatment efficacy. Additionally, the resulting impact on the optimal maximum coating level is noticeable. Based on this, we chose to determine the optimal proportion of virus in each coating level using the Equation 2.11. The reason for this is that Figure 6(d) illustrated a change in the optimal maximum coating as time horizon Tf increased. Additionally, we feel that allowing both susceptible and infected cells to elicit and immune response that results in direct (or indirect) viral clearance is more biologically reasonable. To set our final time Tf and maximum number of coats K to consider, we observe the minimum point of the objective function in Figure 6(c). It is clear that the cancer regrows to its initial size after about 300 to 400 days and achieves its minimum point for K≤30 and T≤300, so we fix these as our final time Tf and maximum coating level K in the following optimisation.
Based on the observations in the previous section, we define the following optimal control problems:
minp∈[0,1]30Jf(p)=∫L0|S(x,p,365)|dx subject to Equation 2.1–Equation 2.7 and Equation 2.11 | (3.3) |
and
minp∈[0,1]30J(p)=∫3650∫L0|S(x,p,t)|dxdt subject to Equation 2.1–Equation 2.7 and Equation 2.11 | (3.4) |
where Tf=365 and K=30 have been chosen large enough so that a reasonable optimal point may be obtained. Both Equation 3.3 and Equation 3.4 are based on our original optimisation formulations in Equation 3.1 and Equation 3.2. Each objective function aims to optimise the proportion p of coated virus in each coating level, where there is a maximum of K=30 coating levels. The solution to Equation 3.3 optimises p at the final time Tf=365, while the solution to Equation 3.4 optimises p for the total cancer cell density over 365 days. The objective given by Equation 3.4 does not guarantee a minimum tumour size at the final time; therefore, the resulting optimal p may be different.
As demonstrated in the previous section, there is a non-trivial optimal maximum viral coating that reduces the total tumour size, see Figure 6(c) and (d). To investigate whether the treatment efficacy could be improved, we relaxed the condition on the initial proportion of virus in each coating level. Below we describe the method undertaken to obtain the optimal proportion of initial virus in each coating level. We considered the objective function set-up described in Section 3.2.
Fixing the optimal control problems to be defined by Equation 3.3 and Equation 3.4, we first generated a vector for the initial proportion in each coating layer p=[p1,p2,....pK] where values for pi were generated from the uniform distribution sets constraining ∑ipi≤1 and pi≥0. This was done several times, and the fmincon optimisation method in MATLAB was then applied. This optimisation method uses the interior-point algorithm [29] for solving the optimal control problem. We investigated different objective final times Tf=91,182,274 and 365 for Equation 3.3 and observed the changes of optimal vector p as the objective time increased. The results for the optimisation of for Equation 3.3 and Equation 3.4 are presented in Figure 7 and Figure 8 respectively.
The results of the optimisation of both Equation 3.3 and Equation 3.4 imply that a mixture of high-coating levels and low-coating levels are necessary to achieve a minimum tumour size. If we consider the physical meaning of high and low-coating levels, this may correspond to thick and thin coating. As such, there is some non-trivial dependence of the treatment efficacy on the decay rate from the coated to uncoated virus states.
Moreover, the final-time optimisation results in Figure 7 suggest that the maximum coating increases as time increases. When we classify classes of coating levels as either thin or thick (i.e., Vthin and Vthick) the optimal initial proportion of virus can be summarised as
argminp∈[0,1]30Jf(p,91)=[VN,Vthin,Vthick]≃[3%,97%,0%]argminp∈[0,1]30Jf(p,182)=[VN,Vthin,Vthick]≃[7%,52%,41%]argminp∈[0,1]30Jf(p,274)=[VN,Vthin,Vthick]≃[8%,49%,42%]argminp∈[0,1]30Jf(p,365)=[VN,Vthin,Vthick]≃[9%,48%,42%]argminp∈[0,1]30J(p)=[VN,Vthin,Vthick]≃[10%,62%,28%], | (4.1) |
which allows us to clearly stratify how the proportion of thickly versus thinly coated virus changes with time. As time increases, the optimal initial injection requires thicker coated virus. Overall, the optimal ratio of uncoated to thin and thick virus is similar irrespective of the final time considered, with p0:pthin:pthick≃1:5:4. This does not include the case where Tf=91, the shortest objective final time. On the other hand, the results for optimisation of the integrated tumour size, Figure 8, is slightly different with the ratio of uncoated to thin and thick coated virus as 1:6:3.
Oncolytic virotherapy is a promising cancer treatment due to its ability to induce tumour-specific cell lysis and as a delivery vector for other tumour-targeting agents. One major pitfall of these viruses is their inhomogeneous diffusion throughout the tumour, a result of immune clearance [1] or high multiplicity of infection of a single tumour cell. In this work, we look at overcoming these obstacles by simulating a delayed-infection and immune-evading coated virus. We optimised the initial dosage of this virus and demonstrated that modifying a virus in this manner could improve treatment.
Experimentalists have developed ways to avoid the immune clearance of viruses through modifications with gel-based mediums, coating with polymers such as polyethylene glycol (PEG) [31], immunogenic-engineered viral capsid and additional manipulations of the tumour microenvironment with hormones such as relaxin. In previous work, Jenner et al. modelled a PEG-coated oncolytic adenovirus with a system of ODEs and analysed the virus's ability to avoid immune detection. In other previous work by Jenner et al. [15], a Voronoi cell-based model was developed to show the effectiveness of a virus that was able to avoid immune detection and delay its infection. Extending both of the modelling frameworks in [30] and [15], we developed a system of PDEs that considered the spatial interaction of an oncolytic virus with a growing population of susceptible tumour cells. We investigated the efficacy of an undefined coating mechanism that delayed the infection of cells and avoided immune clearance. We also developed four viral-clearance models (Equation 2.9–Equation 2.12), that modelled different aspects of the clearance process.
Coating the virus to delay infection and avoid immune clearance improves the efficacy of virotherapy (see Figure 2). Simulating the model with and without coated viruses we found that coated virus diffused farther intratumourally before the onset of infection. This resulted in a significant increase in the infected tumour cell number and reduced the overall tumour size (see Figure 2 last column). To investigate the efficacy of the coated virus treatment, we considered one level of coated virus and varying proportions of initial virus that was coated (Figure 4). For the first 5 days, the optimal proportion of initially coated virus was negligible and the coated therapy was not as effective. But as time increased, the optimal proportion of coated virus in the initial injection increased until all virus needed to be coated to achieve a tumour minimum. This suggests that coating the virus improves virotherapy in a time-dependent manner.
As the coating mechanism we are modelling in this work is theoretical, we decided to extend the model to consider K levels of discrete coating. We then determined the optimal maximum coating level that would minimise the tumour size, given equal proportions of coated virus in the initial injection (Figure 5 and Figure 6). The optimal maximum coating was simulated for a range of different viral-clearance models. The goal was to quantify how the efficacy of treatment depended on both the maximum coating level and the viral-clearance assumptions.
Initially, we considered viral clearance to be proportional to the amount of virus (see Figure 4(a) and (b), and Figure 5(a) and (b)). Simulating Equation 3.1, we found that only a small maximum coating level (K≤5) was necessary for the first 250 days, after which the optimal maximum coating increased linearly with time (Figure 5(b)). This suggests that the effectiveness of the coating is limited by the initial rapid clearance of the uncoated virus. As such, the coated virus is needed to instigate the infection and lysis of tumour cells. As time goes on, we see a tipping point at 250 days, after which the ability of the coated virus to diffuse is sufficient to warrant a larger maximum coating.
When the rate of clearance depends on the number of either susceptible or infected cells, the maximum coating for the virus appears to approximately stabilise for 20<K<25 (Figure 5(d) and Figure 6(d))). In addition, when the viral clearance is equally proportional to the number of infected and susceptible tumour cells, the tumour is able to evade elimination and reaches a steady state value (see Figure 6(c)). The dynamics with this model are similar to those where the clearance is proportional to the number of virus particles (Figure 5(b)); however, the optimal maximum coating is completely different. This difference in the optimal maximum coating trace is due to a larger initial clearance in the case of Figure 6(c). This requires a larger delay in the initial viral infection and therefore a larger maximum coating of the virus.
Realistically, the clearance of viral particles will not be equally proportional to the susceptible and infected cells (Equation 2.12), see Figure 6(b). Simulating this viral-clearance model results in a linear relationship between the optimal number of coats and time (figure not included). As time increases the number of coats required to achieve a minimum tumour burden increased with a gradient of 0.25. In contrast, when the clearance of viruses is equally proportional to the number of susceptible and infected cells, the optimal number of coats roughly stabilises after 300 days. In other words, to reduce the tumour volume over a long period of time approximately the same number of coats is needed when we consider immune clearance to be equally proportional to both susceptible and infected tumour cells. Whereas, if the clearance is not equally proportional to both cell types, the relationship between the number of optimal coating levels and time is linear. Overall, this suggests that the type of immune response exhibited in response to virotherapy can be crucial in determining, long-term, how many coating levels is optimal.
The different viral-clearance models considered results in significantly polarised dynamics: tumour eradication or tumour stabilisation. For example, the treatments' effectiveness is qualitatively similar when the virus is cleared at a rate proportional to the density of infected or susceptible tumour cells (Figure 5(c) and Figure 6(a)). This is possibly explained by the fact that the uncoated virus would rapidly infect susceptible cells resulting in an approximately equivalent rate of viral clearance that is slow enough to allow for tumour eradication. In comparison, when viral clearance is proportional to both susceptible and infected cells, the tumour manages to stabilise (see Figure 6(c)). This suggests that there is a rapid switching between effective and less effective treatment based on the viral clearance rate.
In summary, modelling clearance as dependent on either the total susceptible or infected population results in quasi-eradication of the tumour, irrespective of the number of coating levels (see Figure 5(c) and Figure 6(a)). In comparison, assuming that the virus is cleared at a rate equally proportional to both susceptible and infected tumour cells results in tumour stabilisation (see Figure 6(c) and Equation 2.11). Interestingly, modulating the clearance rate's dependence on this population provides a significantly non-uniform long-term result to the coating level of the virus (see Figure 6(b)). This suggests that the clearance rate is significant and should be considered more carefully in future when modelling virotherapy.
Following these simulations, we wished to investigate the effects of relaxing the condition on the initial proportion of coated virus at each coating level. From the preliminary simulations (Figure 4–Figure 6), it was possible to define the final objective time and maximum number of coatings (Tf=365 and K=30), so that all observable possible behaviours would occur. We then defined two objective functions (Equation 3.3 and Equation 3.4): the first was to minimise the tumour size at a specific point in time Tf days and the second was to minimise the overall tumour size throughout the entire time period.
If we consider then the optimal initial composition for specific times (Tf=91,182,274 and 365), we see that the proportion of thicker coated virus (K>10) increases as the final time of the experiment increases. Overall, solutions for Tf>91 have very similar ratios of uncoated, thinly coated (0<i<10) and thickly coated (i≥10) virus, see Equation 4.1. This suggests that the thicker coated virus takes a longer time to become effective, which is why in the 91 day window we do not see any thicker coated virus (i>10) being necessary for achieving an optimal tumour minimum.
Overall, for a long time period, a significant proportion of the virus needed to have a thicker coating around i≈13, see Figure 7(d) and Figure 8. Minimising the tumour size on day 365 requires a different initial composition than minimising the tumour concentration over the entire 365 day period. This is not surprising as minimising the tumour volume over a period of time requires a different dynamical approach, as opposed to simply minimising the tumour volume at 365 days. What is surprising is the similarity between the two optimal initial conditions. Both require a significant amount of thinly coated virus (i∈(0,5]), demonstrating that for long-term treatment effectiveness, a short-term delayed-infected immune-evading treatment is key.
A future simplification of our model would be to consider a continuous coating of the virus, as opposed to the discrete coating levels we have modelled. To achieve this, the PDE system could be transformed into an age-structured model where the level of coating would become a continuous variable. We decided not to model it in this way as experimentally it would be easier to create a discretely coated virus that has specific characteristics.
Normally, oncolytic virotherapy is administered in multiple dosages (typically three dosages every second day). This work does not consider the effects of additional dosages on the tumour size. Future work could investigate whether there are different optimal injection configurations for subsequent injections. Alternatively, we could consider injections of solely uncoated or coated virus. For the current preliminary investigation we consider a single dosage to illustrate the potential of this therapy and the existence of an optimal solution.
Lastly, because we did not prove the uniqueness of the optimal solution, our solution does not guarantee the global minimum. In other words, it is possible for another solution to attain a smaller value of the objective function. As we optimised by choosing randomized p several times, we cannot establish whether our solution is a global minimum or not. Therefore, proving uniqueness and existence is another open problem.
A natural extension to this model, would be to consider the dynamics in a non-radially symmetric, heterogeneous tumour microenvironment as non-uniform spatial interactions between virus particles and tumour cells have been shown to significantly impact the outcome of virotherapy [15,21]. Additionally, since we have demonstrated the importance of immune clearance, it would be worth explicitly modelling the immune cells in a spatial environment with restrictions on their point of entry to the tumour, to determine whether this may impact the optimisation results.
The next stage of this work would be to investigate experimentally whether designing an injection with proportions of uncoated and coated virus defined by Figure 7 result in a reduced tumour size. From this, it would be interesting to then investigate why this combination of uncoated, thinly coated and thickly coated virus produces an optimal reduction in the tumour by investigating intratumoural dissemination as a function of time.
Overall, our results suggest that it is possible to improve virotherapy by creating a delayed-infection and immune-evading virus through coating or another mechanism. We have demonstrated that this treatment can be further optimised by considering multiple coating levels. We have determined the composition of the initial injection that should be coated at thin and thick levels. We also simulated our system under different viral clearance assumptions and found that the rate of viral clearance is significant in determining whether treatment results in tumour eradication or tumour stabilisation. While exciting, it is still a theoretical principle and would need significant further experimental investigation to verify its promise as an improvement on the current treatment approach.
TL, ALJ, PSK and JL gratefully acknowledge the support for this work through the University of Sydney and Yonsei University Joint Research Funding Scheme. Furthermore, ALJ and PSK gratefully acknowledge support from the Australian Research Council (DP180101512). The work of JL was also supported by NRF-2015R1A5A1009350 and NRF-2016R1A2B4014178. The work of ALJ was also supported by the Australian Mathematical Society Lift-Off Fellowship and Australian Federation of Graduate Women Tempe Mann Travelling Scholarship.
The authors acknowledge that there were no conflicts of interest in the development of this work.
[1] |
Cosnes J, Gower-Rousseau C, Seksik P, et al. (2011) Epidemiology and natural history of inflammatory bowel diseases. Gastroenterol 140: 1785–1794. doi: 10.1053/j.gastro.2011.01.055
![]() |
[2] |
Shanahan F, Bernstein CN (2009) The evolving epidemiology of inflammatory bowel disease. Curr Opin Gastroenterol 25: 301–305. doi: 10.1097/MOG.0b013e32832b12ef
![]() |
[3] | Economou M, Pappas G (2008) New global map of Crohn's disease: Genetic, environmental, and socioeconomic correlations. Inflamm Bowel Dis 4: 709–720. |
[4] |
Mulder DJ, Noble AJ, Justinich CJ, et al. (2014) A tale of two diseases: the history of inflammatory bowel disease. J Crohns Colitis 8: 341–348. doi: 10.1016/j.crohns.2013.09.009
![]() |
[5] | Hanauer SB (2006) Inflammatory bowel disease: epidemiology, pathogenesis, and therapeutic opportunities. Inflamm Bowel Dis 12: S3–S9. |
[6] |
Dubinsky M (2008) Special issues in pediatric inflammatory bowel disease. World J Gastroenterol 14: 413–420. doi: 10.3748/wjg.14.413
![]() |
[7] |
Diefenbach KA, Breuer CK (2006) Pediatric inflammatory bowel disease. World J Gastroenterol 12: 3204–3212. doi: 10.3748/wjg.v12.i20.3204
![]() |
[8] |
Ekbom A, Helmick C, Zack M, et al. (1991) The epidemiology of inflammatory bowel disease: a large, population-based study in Sweden. Gastroenterol 100: 350–358. doi: 10.1016/0016-5085(91)90202-V
![]() |
[9] |
CROHN BB, Rosenberg H (1925) The sigmoidoscopic picture of chronic ulcerative colitis (non-specific). Am J Med Sci 170: 220–227. doi: 10.1097/00000441-192508010-00006
![]() |
[10] |
Rutter MD, Saunders BP, Wilkinson KH, et al. (2006) Thirty-year analysis of a colonoscopic surveillance program for neoplasia in ulcerative colitis. Gastroenterol 130: 1030–1038. doi: 10.1053/j.gastro.2005.12.035
![]() |
[11] |
Beaugerie L, Svrcek M, Seksik P, et al. (2013) Risk of colorectal high-grade dysplasia and cancer in a prospective observational cohort of patients with inflammatory bowel disease. Gastroenterol 145: 166–175. doi: 10.1053/j.gastro.2013.03.044
![]() |
[12] |
Eaden J, Abrams K, Mayberry J (2001) The risk of colorectal cancer in ulcerative colitis: a meta-analysis. Gut 48: 526–535. doi: 10.1136/gut.48.4.526
![]() |
[13] |
Canavan C, Abrams K, Mayberry J (2006) Meta-analysis: colorectal and small bowel cancer risk in patients with Crohn's disease. Aliment Pharmacol Ther 23: 1097–1104. doi: 10.1111/j.1365-2036.2006.02854.x
![]() |
[14] | Itzkowitz SH, Yio X (2004) Inflammation and cancer IV. Colorectal cancer in inflammatory bowel disease: the role of inflammation. Am J Physiol Gastrointest Liver Physiol 287: G7–G17. |
[15] | Kornfeld D, Ekbom A, Ihre T, et al. (1997) Is there an excess risk for colorectal cancer in patients with ulcerative colitis and concomitant primary sclerosing cholangitis? A population based study. Gut 41: 522–525. |
[16] |
Matula S, Croog V, Itzkowitz S, et al. (2005) Chemoprevention of colorectal neoplasia in ulcerative colitis: the effect of 6-mercaptopurine. Clin Gastroenterol Hepatol 3: 1015–1021. doi: 10.1016/S1542-3565(05)00738-X
![]() |
[17] |
Terdiman JP, Steinbuch M, Blumentals WA, et al. (2007) 5-Aminosalicylic acid therapy and the risk of colorectal cancer among patients with inflammatory bowel disease. Inflamm Bowel Dis 13: 367–371. doi: 10.1002/ibd.20074
![]() |
[18] |
Kiesslich R, Goetz M, Lammersdorf K, et al. (2007) Chromoscopy-guided endomicroscopy increases the diagnostic yield of intraepithelial neoplasia in ulcerative colitis. Gastroenterol 132: 874–882. doi: 10.1053/j.gastro.2007.01.048
![]() |
[19] |
Schneider MR, Hoeflich A, Fischer JR, et al. (2000) Interleukin-6 stimulates clonogenic growth of primary and metastatic human colon carcinoma cells. Cancer Lett 151: 31–38. doi: 10.1016/S0304-3835(99)00401-2
![]() |
[20] |
Sakamoto K, Maeda S, Hikiba Y, et al. (2009) Constitutive NF-κB activation in colorectal carcinoma plays a key role in angiogenesis, promoting tumor growth. Clin Cancer Res 15: 2248–2258. doi: 10.1158/1078-0432.CCR-08-1383
![]() |
[21] |
Kang KA, Zhang R, Kim GY, et al. (2012) Epigenetic changes induced by oxidative stress in colorectal cancer cells: methylation of tumor suppressor RUNX3. Tumor Biol 33: 403–412. doi: 10.1007/s13277-012-0322-6
![]() |
[22] |
Ning Y, Manegold PC, Hong YK, et al. (2011) Interleukin-8 is associated with proliferation, migration, angiogenesis and chemosensitivity in vitro and in vivo in colon cancer cell line models. Int J Cancer 128: 2038–2049. doi: 10.1002/ijc.25562
![]() |
[23] |
Huch M, Koo BK (2015) Modeling mouse and human development using organoid cultures. Development 142: 3113–3125. doi: 10.1242/dev.118570
![]() |
[24] | Van Limbergen J, Geddes K, Henderson P, et al. (2013) Paneth cell marker CD24 in NOD2 knockout organoids and in inflammatory bowel disease (IBD). Gut:gutjnl-2013-305077. |
[25] |
van de Wetering M, Francies HE, Francis JM, et al. (2015) Prospective derivation of a living organoid biobank of colorectal cancer patients. Cell 161: 933–945. doi: 10.1016/j.cell.2015.03.053
![]() |
[26] |
Fatehullah A, Tan SH, Barker N (2016) Organoids as an in vitro model of human development and disease. Nat Cell Biol 18: 246–254. doi: 10.1038/ncb3312
![]() |
[27] |
Dharmani P, Leung P, Chadee K (2011) Tumor necrosis factor-α and Muc2 mucin play major roles in disease onset and progression in dextran sodium sulphate-induced colitis. PLoS One 6: e25058. doi: 10.1371/journal.pone.0025058
![]() |
[28] |
Heazlewood CK, Cook MC, Eri R, et al. (2008) Aberrant mucin assembly in mice causes endoplasmic reticulum stress and spontaneous inflammation resembling ulcerative colitis. PLoS Med 5: e54. doi: 10.1371/journal.pmed.0050054
![]() |
[29] |
Half E, Bercovich D, Rozen P (2009) Familial adenomatous polyposis. Orphanet J Rare Dis 4: 22. doi: 10.1186/1750-1172-4-22
![]() |
[30] |
Fodde R, Smits R (2001) Disease model: familial adenomatous polyposis. Trends Mol Med 7: 369–373. doi: 10.1016/S1471-4914(01)02050-0
![]() |
[31] |
Quesada CF, Kimata H, Mori M, et al. (1998) Piroxicam and acarbose as chemopreventive agents for spontaneous intestinal adenomas in APC gene 1309 knockout mice. JPN J Cancer Res 89: 392–396. doi: 10.1111/j.1349-7006.1998.tb00576.x
![]() |
[32] | Corpet DE, Pierre F (2003) Point: From animal models to prevention of colon cancer. Systematic review of chemoprevention in min mice and choice of the model system. Cancer Epidemiol Biomarkers Prev 12: 391–400. |
[33] |
Aoki K, Tamai Y, Horiike S, et al. (2003) Colonic polyposis caused by mTOR-mediated chromosomal instability in Apc+/Δ716 Cdx2+/− compound mutant mice. Nat Genet 35: 323–330. doi: 10.1038/ng1265
![]() |
[34] |
Heyer J, Yang K, Lipkin M, et al. (1999) Mouse models for colorectal cancer. Oncogene 18: 5325–5333. doi: 10.1038/sj.onc.1203036
![]() |
[35] |
Velcich A, Yang W, Heyer J, et al. (2002) Colorectal cancer in mice genetically deficient in the mucin Muc2. Science 295: 1726–1729. doi: 10.1126/science.1069094
![]() |
[36] |
Van der Sluis M, De Koning BA, De Bruijn AC, et al. (2006) Muc2-deficient mice spontaneously develop colitis, indicating that MUC2 is critical for colonic protection. Gastroenterol 131: 117–129. doi: 10.1053/j.gastro.2006.04.020
![]() |
[37] |
Zhu Y, Richardson JA, Parada LF, et al. (1998) Smad3 mutant mice develop metastatic colorectal cancer. Cell 94: 703–714. doi: 10.1016/S0092-8674(00)81730-4
![]() |
[38] |
Yang X, Letterio JJ, Lechleider RJ, et al. (1999) Targeted disruption of SMAD3 results in impaired mucosal immunity and diminished T cell responsiveness to TGF-β. EMBO J 18: 1280–1291. doi: 10.1093/emboj/18.5.1280
![]() |
[39] | Perše M, Cerar A (2012) Dextran sodium sulphate colitis mouse model: traps and tricks. Biomed Res Int 2012. |
[40] |
Delker DA, McKnight SJ, Rosenberg DW (1998) The role of alcohol dehydrogenase in the metabolism of the colon carcinogen methylazoxymethanol. Toxicol Sci 45: 66–71. doi: 10.1093/toxsci/45.1.66
![]() |
[41] | Haase P, Cowen D, Knowles J (1973) Histogenesis of colonic tumours in mice induced by dimethyl hydrazine. J Pathol 109: Px. |
[42] |
Neufert C, Becker C, Neurath MF (2007) An inducible mouse model of colon carcinogenesis for the analysis of sporadic and inflammation-driven tumor progression. Nat Protoc 2: 1998–2004. doi: 10.1038/nprot.2007.279
![]() |
[43] |
Tanaka T, Kohno H, Suzuki R, et al. (2003) A novel inflammation-related mouse colon carcinogenesis model induced by azoxymethane and dextran sodium sulfate. Cancer Sci 94: 965–973. doi: 10.1111/j.1349-7006.2003.tb01386.x
![]() |
[44] |
De Robertis M, Massi E, Poeta ML, et al. (2011) The AOM/DSS murine model for the study of colon carcinogenesis: From pathways to diagnosis and therapy studies. J Carcinog 10: 9. doi: 10.4103/1477-3163.78279
![]() |
[45] | Reddy BS, Ohmori T (1981) Effect of intestinal microflora and dietary fat on 3, 2'-dimethyl-4-aminobiphenyl-induced colon carcinogenesis in F344 rats. Cancer Res 41: 1363–1367. |
[46] |
Hasegawa R, Sano M, Tamano S, et al. (1993) Dose-dependence of 2-amino-1-methy1-6-phen-ylimidazo [4, 5-b]-pyridine (PhIP) carcinogenicity in rats. Carcinogenesis 14: 2553–2557. doi: 10.1093/carcin/14.12.2553
![]() |
[47] |
Wanibuchi H, Salim EI, Morimura K, et al. (2005) Lack of large intestinal carcinogenicity of 2-amino-1-methyl-6-phenylimidazo [4, 5-b] pyridine at low doses in rats initiated with azoxymethane. Int J Cancer 115: 870–878. doi: 10.1002/ijc.20960
![]() |
[48] | Kobaek-Larsen M, Thorup I, Diederichsen A, et al. (2000) Review of colorectal cancer and its metastases in rodent models: comparative aspects with those in humans. Comp Med 50: 16–26. |
[49] |
Narisawa T, Magadia NE, Weisburger JH, et al. (1974) Promoting effect of bile acids on colon carcinogenesis after intrarectal instillation of N-Methyl-N' nitro-N-nitrosoguanidine in Rats. J Natl Cancer Inst 53: 1093–1097. doi: 10.1093/jnci/53.4.1093
![]() |
[50] |
Einerhand AW, Renes IB, Makkink MK, et al. (2002) Role of mucins in inflammatory bowel disease: important lessons from experimental models. Eur J GastroenterolHepatol 14: 757–765. doi: 10.1097/00042737-200207000-00008
![]() |
[51] |
Randall-Demllo S, Fernando R, Brain T, et al. (2016) Characterisation of colonic dysplasia-like epithelial atypia in murine colitis. World J Gastroenterol 22: 8334–8348. doi: 10.3748/wjg.v22.i37.8334
![]() |
[52] |
Gorrini C, Harris IS, Mak TW (2013) Modulation of oxidative stress as an anticancer strategy. Nat Rev Drug Discov 12: 931–947. doi: 10.1038/nrd4002
![]() |
[53] |
Jackson AL, Loeb LA (2001) The contribution of endogenous sources of DNA damage to the multiple mutations in cancer. Mut Res Fund Mol Mech Mutagen 477: 7–21. doi: 10.1016/S0027-5107(01)00091-4
![]() |
[54] | Kawanishi S, Hiraku Y, Pinlaor S, et al. (2006) Oxidative and nitrative DNA damage in animals and patients with inflammatory diseases in relation to inflammation-related carcinogenesis. Biol Chem 387: 365–372. |
[55] |
Tüzün A, Erdil A, İnal V, et al. (2002) Oxidative stress and antioxidant capacity in patients with inflammatory bowel disease. Clin Biochem 35: 569–572. doi: 10.1016/S0009-9120(02)00361-2
![]() |
[56] |
Nair J, Gansauge F, Beger H, et al. (2006) Increased etheno-DNA adducts in affected tissues of patients suffering from Crohn's disease, ulcerative colitis, and chronic pancreatitis. Antioxid Redox Signal 8: 1003–1010. doi: 10.1089/ars.2006.8.1003
![]() |
[57] |
Vong LB, Yoshitomi T, Matsui H, et al. (2015) Development of an oral nanotherapeutics using redox nanoparticles for treatment of colitis-associated colon cancer. Biomaterials 55: 54–63. doi: 10.1016/j.biomaterials.2015.03.037
![]() |
[58] | Solomon H, Brosh R, Buganim Y, et al. (2010) Inactivation of the p53 tumor suppressor gene and activation of the Ras oncogene: cooperative events in tumorigenesis. Discov Med 9: 448–454. |
[59] | Huang H, Wang H, Lloyd RS, et al. (2008) Conformational interconversion of the trans-4-hydroxynonenal-derived (6S, 8R, 11S) 1, N 2-deoxyguanosine adduct when mismatched with deoxyadenosine in DNA. Chem Res Toxicol 22: 187–200. |
[60] |
Barrett CW, Ning W, Chen X, et al. (2013) Tumor suppressor function of the plasma glutathione peroxidase gpx3 in colitis-associated carcinoma. Cancer Res 73: 1245–1255. doi: 10.1158/0008-5472.CAN-12-3150
![]() |
[61] |
Curtin NJ (2012) DNA repair dysregulation from cancer driver to therapeutic target. Nat Rev Cancer 12: 801–817. doi: 10.1038/nrc3399
![]() |
[62] |
Khor TO, Huang MT, Prawan A, et al. (2008) Increased susceptibility of Nrf2 knockout mice to colitis-associated colorectal cancer. Cancer Prev Res 1: 187–191. doi: 10.1158/1940-6207.CAPR-08-0028
![]() |
[63] | Meira LB, Bugni JM, Green SL, et al. (2008) DNA damage induced by chronic inflammation contributes to colon carcinogenesis in mice. J Clin Invest 118: 2516–2525. |
[64] |
Sohn JJ, Schetter AJ, Yfantis HG, et al. (2012) Macrophages, nitric oxide and microRNAs are associated with DNA damage response pathway and senescence in inflammatory bowel disease. PLoS One 7: e44156. doi: 10.1371/journal.pone.0044156
![]() |
[65] | Kohonen-Corish MR, Daniel JJ, te Riele H, et al. (2002) Susceptibility of Msh2-deficient mice to inflammation-associated colorectal tumors. Cancer Res 62: 2092–2097. |
[66] | Fleisher AS, Esteller M, Harpaz N, et al. (2000) Microsatellite instability in inflammatory bowel disease-associated neoplastic lesions is associated with hypermethylation and diminished expression of the DNA mismatch repair gene, hMLH1. Cancer Res 60: 4864–4868. |
[67] |
Redston MS, Papadopoulos N, Caldas C, et al. (1995) Common occurrence of APC and K-ras gene mutations in the spectrum of colitis-associated neoplasias. Gastroenterol 108: 383–392. doi: 10.1016/0016-5085(95)90064-0
![]() |
[68] |
Burmer GC, Rabinovitch PS, Haggitt RC, et al. (1992) Neoplastic progression in ulcerative colitis: histology, DNA content, and loss of a p53 allele. Gastroenterol 103: 1602–1610. doi: 10.1016/0016-5085(92)91184-6
![]() |
[69] |
Yashiro M (2015) Molecular alterations of colorectal cancer with inflammatory bowel disease. Dig Dis Sci 60: 2251–2263. doi: 10.1007/s10620-015-3646-4
![]() |
[70] |
Mikami T, Yoshida T, Numata Y, et al. (2007) Low frequency of promoter methylation of O6-Methylguanine DNA methyltransferase and hMLH1 in ulcerative colitis-associated tumors. Am J Clin Pathol 127: 366–373. doi: 10.1309/RFETXN6387KLQ1LD
![]() |
[71] |
Foran E, Garrity-Park MM, Mureau C, et al. (2010) Upregulation of DNA methyltransferase-mediated gene silencing, anchorage-independent growth, and migration of colon cancer cells by interleukin-6. Mol Cancer Res 8: 471–481. doi: 10.1158/1541-7786.MCR-09-0496
![]() |
[72] | Hartnett L, Egan LJ (2012) Inflammation, DNA methylation and colitis-associated cancer. Carcinogenesis bgs006. |
[73] |
Nakazawa T, Kondo T, Ma D, et al. (2012) Global histone modification of histone H3 in colorectal cancer and its precursor lesions. Hum Pathol 43: 834–842. doi: 10.1016/j.humpath.2011.07.009
![]() |
[74] |
Li Q, Chen H (2012) Silencing of Wnt5a during colon cancer metastasis involves histone modifications. Epigenetics 7: 551–558. doi: 10.4161/epi.20050
![]() |
[75] |
Binder H, Steiner L, Przybilla J, et al. (2013) Transcriptional regulation by histone modifications: towards a theory of chromatin re-organization during stem cell differentiation. Phys Biol 10: 026006. doi: 10.1088/1478-3975/10/2/026006
![]() |
[76] |
Bardhan K, Liu K (2013) Epigenetics and colorectal cancer pathogenesis. Cancers 5: 676–713. doi: 10.3390/cancers5020676
![]() |
[77] |
Klose RJ, Zhang Y (2007) Regulation of histone methylation by demethylimination and demethylation. Nat Rev Mol Cell Biol 8: 307–318. doi: 10.1038/nrm2143
![]() |
[78] |
Wong JJL, Hawkins NJ, Ward RL (2007) Colorectal cancer: a model for epigenetic tumorigenesis. Gut 56: 140–148. doi: 10.1136/gut.2005.088799
![]() |
[79] |
Portela A, Esteller M (2010) Epigenetic modifications and human disease. Nat Biotechnol 28: 1057–1068. doi: 10.1038/nbt.1685
![]() |
[80] |
Glauben R, Batra A, Fedke I, et al. (2006) Histone hyperacetylation is associated with amelioration of experimental colitis in mice. J Immunol 176: 5015–5022. doi: 10.4049/jimmunol.176.8.5015
![]() |
[81] |
Glauben R, Batra A, Stroh T, et al. (2008) Histone deacetylases: novel targets for prevention of colitis-associated cancer in mice. Gut 57: 613–622. doi: 10.1136/gut.2007.134650
![]() |
[82] |
Griffiths-Jones S, Grocock RJ, Van Dongen S, et al. (2006) miRBase: microRNA sequences, targets and gene nomenclature. Nucleic Acids Res 34: D140–D144. doi: 10.1093/nar/gkj112
![]() |
[83] |
Wu F, Zikusoka M, Trindade A, et al. (2008) MicroRNAs are differentially expressed in ulcerative colitis and alter expression of macrophage inflammatory peptide-2α. Gastroenterol 135: 1624–1635. doi: 10.1053/j.gastro.2008.07.068
![]() |
[84] |
Olaru AV, Selaru FM, Mori Y, et al. (2011) Dynamic changes in the expression of MicroRNA-31 during inflammatory bowel disease-associated neoplastic transformation. Inflamm Bowel Dis 17: 221–231. doi: 10.1002/ibd.21359
![]() |
[85] | Shi C, Yang Y, Xia Y, et al. (2015) Novel evidence for an oncogenic role of microRNA-21 in colitis-associated colorectal cancer. Gut 308–455. |
[86] | Svrcek M, El-Murr N, Wanherdrick K, et al. (2013) Overexpression of microRNAs-155 and 21 targeting mismatch repair proteins in inflammatory bowel diseases. Carcinogenesis bgs408. |
[87] |
Polytarchou C, Hommes DW, Palumbo T, et al. (2015) MicroRNA214 is associated with progression of ulcerative colitis, and inhibition reduces development of colitis and colitis-associated cancer in mice. Gastroenterol 149: 981–992. doi: 10.1053/j.gastro.2015.05.057
![]() |
[88] | Cristóbal I, Manso R, Gónzález-Alonso P, et al. (2015) Clinical value of miR-26b discriminating ulcerative colitis-associated colorectal cancer in the subgroup of patients with metastatic disease. Inflamm Bowel Dis 21: E24–E25. |
[89] |
Ludwig K, Fassan M, Mescoli C, et al. (2013) PDCD4/miR-21 dysregulation in inflammatory bowel disease-associated carcinogenesis. Virchows Arch 462: 57–63. doi: 10.1007/s00428-012-1345-5
![]() |
[90] |
Yang L, Belaguli N, Berger DH (2009) MicroRNA and colorectal cancer. World J Surg 33: 638–646. doi: 10.1007/s00268-008-9865-5
![]() |
[91] |
Kanaan Z, Rai SN, Eichenberger MR, et al. (2012) Differential MicroRNA expression tracks neoplastic progression in inflammatory bowel disease-associated colorectal cancer. Hum Mutat 33: 551–560. doi: 10.1002/humu.22021
![]() |
[92] |
Feng R, Chen X, Yu Y, et al. (2010) miR-126 functions as a tumour suppressor in human gastric cancer. Cancer Lett 298: 50–63. doi: 10.1016/j.canlet.2010.06.004
![]() |
[93] |
Fasseu M, Tréton X, Guichard C, et al. (2010) Identification of restricted subsets of mature microRNA abnormally expressed in inactive colonic mucosa of patients with inflammatory bowel disease. PloS One 5: e13160. doi: 10.1371/journal.pone.0013160
![]() |
[94] | Wang W, Li X, Zheng D, et al. (2015) Dynamic changes and functions of macrophages and M1/M2 subpopulations during ulcerative colitis-associated carcinogenesis in an AOM/DSS mouse model. Mol Med Rep 11: 2397–2406. |
[95] |
Francescone R, Hou V, Grivennikov SI (2015) Cytokines, IBD, and colitis-associated cancer. Inflamm Bowel Dis 21: 409–418. doi: 10.1097/MIB.0000000000000236
![]() |
[96] |
Sarra M, Pallone F, MacDonald TT, et al. (2010) IL-23/IL-17 axis in IBD. Inflamm Bowel Dis 16: 1808–1813. doi: 10.1002/ibd.21248
![]() |
[97] | Reinecker HC, Steffen M, Witthoeft T, et al. (1993) Enhand secretion of tumour necrosis factor-alpha, IL-6, and IL-1β by isolated lamina ropria monouclear cells from patients with ulcretive cilitis and Crohn's disease. Clin Exp Immunol 94: 174–181. |
[98] | Banks C, Bateman A, Payne R, et al. (2003) Chemokine expression in IBD. Mucosal chemokine expression is unselectively increased in both ulcerative colitis and Crohn's disease. J Pathol 199: 28–35. |
[99] |
Wiercinska-Drapalo A, Flisiak R, Jaroszewicz J, et al. (2005) Plasma interleukin-18 reflects severity of ulcerative colitis. World J Gastroenterol 11: 605–608. doi: 10.3748/wjg.v11.i4.605
![]() |
[100] |
Bisping G, Lügering N, Lütke-Brintrup S, et al. (2001) Patients with inflammatory bowel disease (IBD) reveal increased induction capacity of intracellular interferon-gamma (IFN-γ) in peripheral CD8+ lymphocytes co-cultured with intestinal epithelial cells. Clin Exp Immunol 123: 15–22. doi: 10.1046/j.1365-2249.2001.01443.x
![]() |
[101] | Hyun YS, Han DS, Lee AR, et al. (2012) Role of IL-17A in the development of colitis-associated cancer. Carcinogenesis bgs106. |
[102] |
Onizawa M, Nagaishi T, Kanai T, et al. (2009) Signaling pathway via TNF-α/NF-κB in intestinal epithelial cells may be directly involved in colitis-associated carcinogenesis. Am J Physiol Gastrointest Liver Physiol 296: G850–G859. doi: 10.1152/ajpgi.00071.2008
![]() |
[103] |
Matsumoto S, Hara T, Mitsuyama K, et al. (2010) Essential roles of IL-6 trans-signaling in colonic epithelial cells, induced by the IL-6/soluble–IL-6 receptor derived from lamina propria macrophages, on the development of colitis-associated premalignant cancer in a murine model. J Immunol 184: 1543–1551. doi: 10.4049/jimmunol.0801217
![]() |
[104] |
Atreya R, Mudter J, Finotto S, et al. (2000) Blockade of interleukin 6 trans signaling suppresses T-cell resistance against apoptosis in chronic intestinal inflammation: evidence in crohn disease and experimental colitis in vivo. Nat Med 6: 583–588. doi: 10.1038/75068
![]() |
[105] | Popivanova BK, Kitamura K, Wu Y, et al. (2008) Blocking TNF-α in mice reduces colorectal carcinogenesis associated with chronic colitis. J Clin Invest 118: 560–570. |
[106] |
Fukata M, Chen A, Vamadevan AS, et al. (2007) Toll-like receptor-4 promotes the development of colitis-associated colorectal tumors. Gastroenterol 133: 1869–1869. doi: 10.1053/j.gastro.2007.09.008
![]() |
[107] |
Garrett WS, Punit S, Gallini CA, et al. (2009) Colitis-associated colorectal cancer driven by T-bet deficiency in dendritic cells. Cancer Cell 16: 208–219. doi: 10.1016/j.ccr.2009.07.015
![]() |
[108] |
Allen IC, TeKippe EM, Woodford RMT, et al. (2010) The NLRP3 inflammasome functions as a negative regulator of tumorigenesis during colitis-associated cancer. J Exp Med 207: 1045–1056. doi: 10.1084/jem.20100050
![]() |
[109] |
Allen IC, Wilson JE, Schneider M, et al. (2012) NLRP12 suppresses colon inflammation and tumorigenesis through the negative regulation of noncanonical NF-κB signaling. Immunity 36: 742–754. doi: 10.1016/j.immuni.2012.03.012
![]() |
[110] |
Chen GY, Liu M, Wang F, et al. (2011) A functional role for Nlrp6 in intestinal inflammation and tumorigenesis. J Immunol 186: 7187–7194. doi: 10.4049/jimmunol.1100412
![]() |
[111] | Eckmann L, Greten T (2004) IKKbeta links inflam. mation and tumorigenesis in a mouse model of colitis-associated cancer. Cell 118: 285–296. |
[112] |
Cooks T, Pateras IS, Tarcic O, et al. (2013) Mutant p53 prolongs NF-κB activation and promotes chronic inflammation and inflammation-associated colorectal cancer. Cancer Cell 23: 634–646. doi: 10.1016/j.ccr.2013.03.022
![]() |
[113] |
Grivennikov S, Karin E, Terzic J, et al. (2009) IL-6 and Stat3 are required for survival of intestinal epithelial cells and development of colitis-associated cancer. Cancer Cell 15: 103–113. doi: 10.1016/j.ccr.2009.01.001
![]() |
[114] |
Bollrath J, Phesse TJ, von Burstin VA, et al. (2009) gp130-mediated Stat3 activation in enterocytes regulates cell survival and cell-cycle progression during colitis-associated tumorigenesis. Cancer Cell 15: 91–102. doi: 10.1016/j.ccr.2009.01.002
![]() |
[115] |
Ghosh S, Karin M (2002) Missing pieces in the NF-κB puzzle. Cell 109: S81–S96. doi: 10.1016/S0092-8674(02)00703-1
![]() |
[116] |
Karin M (2006) Nuclear factor-κB in cancer development and progression. Nature 441: 431–436. doi: 10.1038/nature04870
![]() |
[117] |
Greten FR, Eckmann L, Greten TF, et al. (2004) IKKβ links inflammation and tumorigenesis in a mouse model of colitis-associated cancer. Cell 118: 285–296. doi: 10.1016/j.cell.2004.07.013
![]() |
[118] |
Rose-John S (2012) IL-6 trans-signaling via the soluble IL-6 receptor: importance for the pro-inflammatory activities of IL-6. Int J Biol Sci 8: 1237–1247. doi: 10.7150/ijbs.4989
![]() |
[119] |
Yu H, Pardoll D, Jove R (2009) STATs in cancer inflammation and immunity: a leading role for STAT3. Nat Rev Cancer 9: 798–809. doi: 10.1038/nrc2734
![]() |
[120] |
Pickert G, Neufert C, Leppkes M, et al. (2009) STAT3 links IL-22 signaling in intestinal epithelial cells to mucosal wound healing. J Exp Med 206: 1465–1472. doi: 10.1084/jem.20082683
![]() |
[121] |
Putoczki TL, Thiem S, Loving A, et al. (2013) Interleukin-11 is the dominant IL-6 family cytokine during gastrointestinal tumorigenesis and can be targeted therapeutically. Cancer Cell 24: 257–271. doi: 10.1016/j.ccr.2013.06.017
![]() |
[122] | Chichlowski M, Sharp JM, Vanderford DA, et al. (2008) Helicobacter typhlonius and Helicobacter rodentium differentially affect the severity of colon inflammation and inflammation-associated neoplasia in IL10-deficient mice. Comp Med 58: 534–541. |
[123] |
Uronis JM, Mühlbauer M, Herfarth HH, et al. (2009) Modulation of the intestinal microbiota alters colitis-associated colorectal cancer susceptibility. PloS One 4: e6026. doi: 10.1371/journal.pone.0006026
![]() |
[124] |
O'mahony L, Feeney M, O'halloran S, et al. (2001) Probiotic impact on microbial flora, inflammation and tumour development in IL-10 knockout mice. Aliment Pharmacol Ther 15: 1219–1225. doi: 10.1046/j.1365-2036.2001.01027.x
![]() |
[125] |
Tözün N, Vardareli E (2016) Gut microbiome and gastrointestinal cancer: les liaisons dangereuses. J Clin Gastroenterol 50: S191–S196. doi: 10.1097/MCG.0000000000000714
![]() |
[126] | Yamamoto M, Matsumoto S (2016) Gut microbiota and colorectal cancer. Genes and Environ 38: 1–7. |
[127] |
Abreu MT (2010) Toll-like receptor signalling in the intestinal epithelium: how bacterial recognition shapes intestinal function. Nat Rev Immunol 10: 131–144. doi: 10.1038/nri2707
![]() |
[128] | Grivennikov SI (2013) Inflammation and colorectal cancer: colitis-associated neoplasia, In: Seminars in immunopathology, Springer-Verlag, 229–244. |
[129] |
Lowe EL, Crother TR, Rabizadeh S, et al. (2010) Toll-like receptor 2 signaling protects mice from tumor development in a mouse model of colitis-induced cancer. PloS One 5: e13027. doi: 10.1371/journal.pone.0013027
![]() |
[130] |
Fukata M, Chen A, Vamadevan AS, et al. (2007) Toll-like receptor-4 promotes the development of colitis-associated colorectal tumors. Gastroenterol 133: 1869–1869. doi: 10.1053/j.gastro.2007.09.008
![]() |
[131] | Araki T, Toiyama Y, Okita Y, et al. (2016) Surgical treatment for ulcerative colitis-associated cancer or dysplasia, In: Colitis-associated cancer, Springer-Verlag, 109–130. |
[132] |
Nio K, Higashi D, Kumagai H, et al. (2016) Efficacy and safety analysis of chemotherapy for advanced colitis-associated colorectal cancer in Japan. Anticancer Drugs 27: 457–463. doi: 10.1097/CAD.0000000000000338
![]() |
[133] |
Impellizzeri D, Esposito E, Mazzon E, et al. (2011) Oleuropein aglycone, an olive oil compound, ameliorates development of arthritis caused by injection of collagen type II in mice. J Pharmacol Exp Ther 339: 859–869. doi: 10.1124/jpet.111.182808
![]() |
[134] |
Giner E, Recio MC, Ríos JL, et al. (2013) Oleuropein protects against dextran sodium sulfate-induced chronic colitis in mice. J Nat Prod 76: 1113–1120. doi: 10.1021/np400175b
![]() |
[135] | Acquaviva R, Di Giacomo C, Sorrenti V, et al. (2012) Antiproliferative effect of oleuropein in prostate cell lines. Int J Oncol 41: 31. |
[136] |
Elamin MH, Daghestani MH, Omer SA, et al. (2013) Olive oil oleuropein has anti-breast cancer properties with higher efficiency on ER-negative cells. Food Chem Toxicol 53: 310–316. doi: 10.1016/j.fct.2012.12.009
![]() |
[137] |
Giner E, Recio MC, Ríos JL, et al. (2016) Chemopreventive effect of oleuropein in colitis-associated colorectal cancer in c57bl/6 mice. Mol Nutr Food Res 60: 242–255. doi: 10.1002/mnfr.201500605
![]() |
[138] |
Zhang M, Viennois E, Prasad M, et al. (2016) Edible ginger-derived nanoparticles: a novel therapeutic approach for the prevention and treatment of inflammatory bowel disease and colitis-associated cancer. Biomaterials 101: 321–340. doi: 10.1016/j.biomaterials.2016.06.018
![]() |
[139] | Lin L, Sun Y, Wang D, et al. (2015) Celastrol ameliorates ulcerative colitis-related colorectal cancer in mice via suppressing inflammatory responses and epithelial-mesenchymal transition. Front Pharmacol 6. |
[140] |
Shaker ME, Ashamallah SA, Houssen ME (2014) Celastrol ameliorates murine colitis via modulating oxidative stress, inflammatory cytokines and intestinal homeostasis. Chem Biol Interact 210: 26–33. doi: 10.1016/j.cbi.2013.12.007
![]() |
[141] |
Fung KY, Cosgrove L, Lockett T, et al. (2012) A review of the potential mechanisms for the lowering of colorectal oncogenesis by butyrate. Br J Nutr 108: 820–831. doi: 10.1017/S0007114512001948
![]() |
[142] |
Hu Y, Le Leu RK, Christophersen CT, et al. (2016) Manipulation of the gut microbiota using resistant starch is associated with protection against colitis-associated colorectal cancer in rats. Carcinogenesis 37: 366–375. doi: 10.1093/carcin/bgw019
![]() |
[143] | Kong ZL, Kao NJ, Hu JY, et al. (2016) Fucoxanthin-rich brown algae extract decreases inflammation and attenuates colitis-associated colon cancer in mice. J Food Nutr Res 4: 137–147. |
[144] |
Pandurangan AK, Saadatdoust Z, Hamzah H, et al. (2015) Dietary cocoa protects against colitis-associated cancer by activating the Nrf2/Keap1 pathway. Biofactors 41: 1–14. doi: 10.1002/biof.1195
![]() |
[145] | Wu WT, Tsai YT, Chen HL (2016) Konjac glucomannan and inulin oligosaccharide attenuated the progression of colitic-associated colon carcinogenesis and modulated immune response in mice. FASEB J 30: 1174. |
[146] | Periasamy S, Liu CT, Wu WH, et al. (2015) Dietary Ziziphus jujuba fruit influence on aberrant crypt formation and blood cells in colitis-associated colorectal cancer in mice. Asian Pac J Cancer Prev:16: 7561–7566. |
[147] |
Viennois E, Xiao B, Ayyadurai S, et al. (2014) Micheliolide, a new sesquiterpene lactone that inhibits intestinal inflammation and colitis-associated cancer. Lab Invest 94: 950–965. doi: 10.1038/labinvest.2014.89
![]() |
[148] | Kunchari Kalaimathi S, Sudhandiran G (2016) Fisetin ameolirates the azoxymethane and dextran sodium sulfate induced colitis associated colorectal cancer. Int J Pharm Clin Res 8: 551–560. |
[149] |
Yasui Y, Hosokawa M, Mikami N, et al. (2011) Dietary astaxanthin inhibits colitis and colitis-associated colon carcinogenesis in mice via modulation of the inflammatory cytokines. Chem Biol Interact 193: 79–87. doi: 10.1016/j.cbi.2011.05.006
![]() |
[150] | Yang X, Zhang F, Wang Y, et al. (2013) Oroxylin A inhibits colitis-associated carcinogenesis through modulating the IL-6/STAT3 signaling pathway. Inflamm Bowel Dis 19: 1990–2000. |
[151] |
Kannengiesser K, Maaser C, Heidemann J, et al. (2008) Melanocortin-derived tripeptide KPV has anti-inflammatory potential in murine models of inflammatory bowel disease. Inflamm Bowel Dis 14: 324–331. doi: 10.1002/ibd.20334
![]() |
[152] |
Viennois E, Ingersoll SA, Ayyadurai S, et al. (2016) Critical role of PepT1 in promoting colitis-associated cancer and therapeutic benefits of the anti-inflammatory PepT1-mediated tripeptide KPV in a murine model. CMGH Cell Mol Gastroenterol Hepatol 2: 340–357. doi: 10.1016/j.jcmgh.2016.01.006
![]() |
[153] |
Seraj MJ, Umemoto A, Kajikawa A, et al. (1997) Effects of dietary bile acids on formation of azoxymethane-induced aberrant crypt foci in F344 rats. Cancer Lett 115: 97–103. doi: 10.1016/S0304-3835(97)04719-8
![]() |
[154] |
Tung BY, Emond MJ, Haggitt RC, et al. (2001) Ursodiol use is associated with lower prevalence of colonic neoplasia in patients with ulcerative colitis and primary sclerosing cholangitis. Ann Intern Med 134: 89–95. doi: 10.7326/0003-4819-134-2-200101160-00008
![]() |
[155] |
Xie L, Jiang FC, Zhang LM, et al. (2016) Targeting of MyD88 homodimerization by novel synthetic inhibitor TJ-M2010-5 in preventing colitis-associated colorectal cancer. J Natl Cancer Inst 108: djv364. doi: 10.1093/jnci/djv364
![]() |
[156] |
Amini-Khoei H, Momeny M, Abdollahi A, et al. (2016) Tropisetron suppresses colitis-associated cancer in a mouse model in the remission stage. Int Immunopharmacol 36: 9–16. doi: 10.1016/j.intimp.2016.04.014
![]() |
[157] |
Drechsler S, Bruntsch U, Eggert J, et al. (1997) Comparison of three tropisetron-containing antiemetic regimens in the prophylaxis of acute and delayed chemotherapy-induced emesis and nausea. Support Care Cancer 5: 387–395. doi: 10.1007/s005200050097
![]() |
[158] |
Koh SJ, Kim JM, Kim I-K, et al. (2011) Fluoxetine inhibits NF-κB signaling in intestinal epithelial cells and ameliorates experimental colitis and colitis-associated colon cancer in mice. Am J Physiol Gastrointest Liver Physiol 301: G9–G19. doi: 10.1152/ajpgi.00267.2010
![]() |
[159] |
Tanaka T, Kochi T, Shirakami Y, et al. (2016) Cimetidine and clobenpropit attenuate inflammation-associated colorectal carcinogenesis in male ICR mice. Cancers 8: 25. doi: 10.3390/cancers8020025
![]() |
[160] |
Masini E, Fabbroni V, Giannini L, et al. (2005) Histamine and histidine decarboxylase up-regulation in colorectal cancer: correlation with tumor stage. Inflamm Res 54: S80–S81. doi: 10.1007/s00011-004-0437-3
![]() |
[161] |
Miyamoto S, Epifano F, Curini M, et al. (2008) A novel prodrug of 4'-geranyloxy-ferulic acid suppresses colitis-related colon carcinogenesis in mice. Nutr Cancer 60: 675–684. doi: 10.1080/01635580802008286
![]() |
[162] |
Yao J, Xie J, Xie B, et al. (2016) Therapeutic effect of hydroxychloroquine on colorectal carcinogenesis in experimental murine colitis. Biochem Pharmacol 115: 51–63. doi: 10.1016/j.bcp.2016.06.004
![]() |
[163] |
Dai Y, Jiao H, Teng G, et al. (2014) Embelin reduces colitis-associated tumorigenesis through limiting IL-6/STAT3 signaling. Mol Cancer Ther 13: 1206–1216. doi: 10.1158/1535-7163.MCT-13-0378
![]() |
[164] |
Liang J, Nagahashi M, Kim EY, et al. (2013) Sphingosine-1-phosphate links persistent STAT3 activation, chronic intestinal inflammation, and development of colitis-associated cancer. Cancer Cell 23: 107–120. doi: 10.1016/j.ccr.2012.11.013
![]() |
[165] | Kawamori T, Kaneshiro T, Okumura M, et al. (2009) Role for sphingosine kinase 1 in colon carcinogenesis. FASEB J 23: 405–414. |
[166] |
Snider AJ, Kawamori T, Bradshaw SG, et al. (2009) A role for sphingosine kinase 1 in dextran sulfate sodium-induced colitis. FASEB J 23: 143–152. doi: 10.1096/fj.08-118109
![]() |
[167] |
Wang D, DuBois RN (2010) The role of COX-2 in intestinal inflammation and colorectal cancer. Oncogene 29: 781–788. doi: 10.1038/onc.2009.421
![]() |
[168] |
Kohno H, Suzuki R, Sugie S, et al. (2005) Suppression of colitis-related mouse colon carcinogenesis by a COX-2 inhibitor and PPAR ligands. BMC Cancer 5: 1. doi: 10.1186/1471-2407-5-1
![]() |
[169] |
Setia S, Nehru B, Sanyal SN (2014) The PI3K/Akt pathway in colitis associated colon cancer and its chemoprevention with celecoxib, a Cox-2 selective inhibitor. Biomed Pharmacother 68: 721–727. doi: 10.1016/j.biopha.2014.07.006
![]() |
[170] |
Glauben R, Sonnenberg E, Zeitz M, et al. (2009) HDAC inhibitors in models of inflammation-related tumorigenesis. Cancer Lett 280: 154–159. doi: 10.1016/j.canlet.2008.11.019
![]() |
[171] | Wei TT, Lin YT, Tseng RY, et al. (2016) Prevention of colitis and colitis-associated colorectal cancer by a novel polypharmacological Histone deacetylase inhibitor. Am Assoc Cancer Res 22: 4158–4169. |
[172] |
Reinhard A, Bressenot A, Dassonneville R, et al. (2015) Photodynamic therapy relieves colitis and prevents colitis-associated carcinogenesis in mice. Inflamm Bowel Dis 21: 985–995. doi: 10.1097/MIB.0000000000000354
![]() |
[173] |
Zhang D, Mi M, Jiang F, et al. (2015) Apple polysaccharide reduces NF-kb mediated colitis-associated colon carcinogenesis. Nutr Cancer 67: 177–190. doi: 10.1080/01635581.2015.965336
![]() |
[174] |
Yang Y, Cai X, Yang J, et al. (2014) Chemoprevention of dietary digitoflavone on colitis-associated colon tumorigenesis through inducing Nrf2 signaling pathway and inhibition of inflammation. Mol Cancer 13: 48. doi: 10.1186/1476-4598-13-48
![]() |
[175] | Tian Y, Wang K, Wang Z, et al. (2013) Chemopreventive effect of dietary glutamine on colitis-associated colon tumorigenesis in mice. Carcinogenesis bgt088. |
[176] |
Tian Y, Wang K, Fan Y, et al. (2016) Chemopreventive effect of dietary glutamineon colitis-associated colorectal cancer is associated with modulation of the DEPTOR/mTOR signaling pathway. Nutrients 8: 261. doi: 10.3390/nu8050261
![]() |
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9. | Ela Guo, Hana M. Dobrovolny, Mathematical Modeling of Oncolytic Virus Therapy Reveals Role of the Immune Response, 2023, 15, 1999-4915, 1812, 10.3390/v15091812 | |
10. | Darshak K. Bhatt, Thijs Janzen, Toos Daemen, Franz J. Weissing, Effects of virus-induced immunogenic cues on oncolytic virotherapy, 2024, 14, 2045-2322, 10.1038/s41598-024-80542-8 |
Parameter | Units | Description | Value | Source |
Virus diffusion coefficient | 0.001 | - | ||
Tumour diffusion coefficient | 0.0001 | - | ||
day |
Decay rate of coating from virus | 0.1 | - | |
day |
Decay rate of coated virus | 0.001 | - | |
day |
Decay rate of uncoated virus | 1.38 | [30] | |
day |
Infected cell lysis rate | 0.1 | [30] | |
day |
Virus infection rate | 0.862 | [30] | |
day |
Tumour cell replication rate | 0.037 | [30] | |
Virus ( |
Initial injection | 2 | [30] | |
Virus | Lysis burst size | 3500 | [30] |
Parameter | Units | Description | Value | Source |
Virus diffusion coefficient | 0.001 | - | ||
Tumour diffusion coefficient | 0.0001 | - | ||
day |
Decay rate of coating from virus | 0.1 | - | |
day |
Decay rate of coated virus | 0.001 | - | |
day |
Decay rate of uncoated virus | 1.38 | [30] | |
day |
Infected cell lysis rate | 0.1 | [30] | |
day |
Virus infection rate | 0.862 | [30] | |
day |
Tumour cell replication rate | 0.037 | [30] | |
Virus ( |
Initial injection | 2 | [30] | |
Virus | Lysis burst size | 3500 | [30] |