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Computational modeling of tumor response to vascular-targeting therapies--part I: validation.

Gevertz JL - Comput Math Methods Med (2011)

Bottom Line: Mathematical modeling techniques have been widely employed to understand how cancer grows, and, more recently, such approaches have been used to understand how cancer can be controlled.In this manuscript, a previously validated hybrid cellular automaton model of tumor growth in a vascularized environment is used to study the antitumor activity of several vascular-targeting compounds of known efficacy.The results presented herein suggest that vascular-targeting agents, as currently administered, cannot lead to cancer eradication, although a highly efficacious agent may lead to long-term cancer control.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics and Statistics, The College of New Jersey, Ewing, NJ 08628-0718, USA. gevertz@tcnj.edu

ABSTRACT
Mathematical modeling techniques have been widely employed to understand how cancer grows, and, more recently, such approaches have been used to understand how cancer can be controlled. In this manuscript, a previously validated hybrid cellular automaton model of tumor growth in a vascularized environment is used to study the antitumor activity of several vascular-targeting compounds of known efficacy. In particular, this model is used to test the antitumor activity of a clinically used angiogenesis inhibitor (both in isolation, and with a cytotoxic chemotherapeutic) and a vascular disrupting agent currently undergoing clinical trial testing. I demonstrate that the mathematical model can make predictions in agreement with preclinical/clinical data and can also be used to gain more insight into these treatment protocols. The results presented herein suggest that vascular-targeting agents, as currently administered, cannot lead to cancer eradication, although a highly efficacious agent may lead to long-term cancer control.

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Snapshots of a tumor treated with an AI only. (a) Tumor after two months of growth, before treatment is applied. (b) Tumor after four months of growth, two weeks after treatment is first administered. (c) Tumor after eight months of growth, 19 weeks after treatment is first administered. (d) Tumor after one year of growth, 37 weeks after treatment is first administered.
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fig3: Snapshots of a tumor treated with an AI only. (a) Tumor after two months of growth, before treatment is applied. (b) Tumor after four months of growth, two weeks after treatment is first administered. (c) Tumor after eight months of growth, 19 weeks after treatment is first administered. (d) Tumor after one year of growth, 37 weeks after treatment is first administered.

Mentions: As described in the treatment protocol, the first therapy tested is the administration of an AI such as bevacizumab. In Figure 2, I show the antitumor activity of the AI, as compared to the case where no treatment is utilized. In particular, I show the tumor area as a function of time (Figure 2(a)) and the active tumor area (meaning, the area of the proliferative and hypoxic regions of the tumor—Figure 2(b)), both averaged over 10 simulations. By comparing the growth of the entire tumor mass with AI treatment and without (Figure 2(a)), a clear decrease in the rate of tumor expansion is observed. Further, when looking only at the area of the active tumor region (Figure 2(b)), it is observed that this region essentially stabilizes, with no measurable growth or shrinkage after drug administration. Therefore, despite the extreme decrease in active tumor growth rate, the active tumor region is not eliminated by AI treatment. This observation is confirmed by looking at simulated images of an AI-treated tumor (Figure 3). In this figure, it can be seen that AI administration leaves a number of hypoxic and proliferative cells remaining at the tumor periphery, and this active region leads to slow growth of the tumor mass observed in Figure 2(a). The survival of active tumor cells and the resulting slow growth is largely a consequence of the fact that the tumor grows in a well-vascularized environment, highlighting the important role the microenvironment plays in treatment response.


Computational modeling of tumor response to vascular-targeting therapies--part I: validation.

Gevertz JL - Comput Math Methods Med (2011)

Snapshots of a tumor treated with an AI only. (a) Tumor after two months of growth, before treatment is applied. (b) Tumor after four months of growth, two weeks after treatment is first administered. (c) Tumor after eight months of growth, 19 weeks after treatment is first administered. (d) Tumor after one year of growth, 37 weeks after treatment is first administered.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3065055&req=5

fig3: Snapshots of a tumor treated with an AI only. (a) Tumor after two months of growth, before treatment is applied. (b) Tumor after four months of growth, two weeks after treatment is first administered. (c) Tumor after eight months of growth, 19 weeks after treatment is first administered. (d) Tumor after one year of growth, 37 weeks after treatment is first administered.
Mentions: As described in the treatment protocol, the first therapy tested is the administration of an AI such as bevacizumab. In Figure 2, I show the antitumor activity of the AI, as compared to the case where no treatment is utilized. In particular, I show the tumor area as a function of time (Figure 2(a)) and the active tumor area (meaning, the area of the proliferative and hypoxic regions of the tumor—Figure 2(b)), both averaged over 10 simulations. By comparing the growth of the entire tumor mass with AI treatment and without (Figure 2(a)), a clear decrease in the rate of tumor expansion is observed. Further, when looking only at the area of the active tumor region (Figure 2(b)), it is observed that this region essentially stabilizes, with no measurable growth or shrinkage after drug administration. Therefore, despite the extreme decrease in active tumor growth rate, the active tumor region is not eliminated by AI treatment. This observation is confirmed by looking at simulated images of an AI-treated tumor (Figure 3). In this figure, it can be seen that AI administration leaves a number of hypoxic and proliferative cells remaining at the tumor periphery, and this active region leads to slow growth of the tumor mass observed in Figure 2(a). The survival of active tumor cells and the resulting slow growth is largely a consequence of the fact that the tumor grows in a well-vascularized environment, highlighting the important role the microenvironment plays in treatment response.

Bottom Line: Mathematical modeling techniques have been widely employed to understand how cancer grows, and, more recently, such approaches have been used to understand how cancer can be controlled.In this manuscript, a previously validated hybrid cellular automaton model of tumor growth in a vascularized environment is used to study the antitumor activity of several vascular-targeting compounds of known efficacy.The results presented herein suggest that vascular-targeting agents, as currently administered, cannot lead to cancer eradication, although a highly efficacious agent may lead to long-term cancer control.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics and Statistics, The College of New Jersey, Ewing, NJ 08628-0718, USA. gevertz@tcnj.edu

ABSTRACT
Mathematical modeling techniques have been widely employed to understand how cancer grows, and, more recently, such approaches have been used to understand how cancer can be controlled. In this manuscript, a previously validated hybrid cellular automaton model of tumor growth in a vascularized environment is used to study the antitumor activity of several vascular-targeting compounds of known efficacy. In particular, this model is used to test the antitumor activity of a clinically used angiogenesis inhibitor (both in isolation, and with a cytotoxic chemotherapeutic) and a vascular disrupting agent currently undergoing clinical trial testing. I demonstrate that the mathematical model can make predictions in agreement with preclinical/clinical data and can also be used to gain more insight into these treatment protocols. The results presented herein suggest that vascular-targeting agents, as currently administered, cannot lead to cancer eradication, although a highly efficacious agent may lead to long-term cancer control.

Show MeSH
Related in: MedlinePlus