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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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Sensitivity analysis of the VDA treatment parameter, T3. The average area of the active tumor region predicted by the algorithm is shown for each parameter value.
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fig8: Sensitivity analysis of the VDA treatment parameter, T3. The average area of the active tumor region predicted by the algorithm is shown for each parameter value.

Mentions: It is natural to ask whether the VDA being used in the simulations is not destroying enough angiogenic blood vessels (with T3 = 0.6), and if this is partially responsible for the low antitumor activity of the simulated VDA. Therefore, a sensitivity analysis was performed on T3, the VDA treatment parameter (Figure 8). Surprisingly, I find that increasing the VDA parameter beyond T3 = 0.3 has no measurable impact on the active tumor area. While this may seem improbable, it can be explained by understanding how vessel regression works in the algorithm. Each edge of the lattice that contains an angiogenic vessel is checked to see if it regresses. If regression occurs, not only does that “edge” of the vessel get destroyed, but any lattice edges that are upstream of that edge also get destroyed. In other words, if you kill the source of blood, any vessels that only received blood from that source are also effectively destroyed. This creates the relative insensitivity to changes in the VDA parameter T3. It is worth noting that if the VDA parameter is made sufficiently small (T3 = 0.1), the tumors grow at a noticeably faster rate than for T3 ≥ 0.3.


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

Gevertz JL - Comput Math Methods Med (2011)

Sensitivity analysis of the VDA treatment parameter, T3. The average area of the active tumor region predicted by the algorithm is shown for each parameter value.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig8: Sensitivity analysis of the VDA treatment parameter, T3. The average area of the active tumor region predicted by the algorithm is shown for each parameter value.
Mentions: It is natural to ask whether the VDA being used in the simulations is not destroying enough angiogenic blood vessels (with T3 = 0.6), and if this is partially responsible for the low antitumor activity of the simulated VDA. Therefore, a sensitivity analysis was performed on T3, the VDA treatment parameter (Figure 8). Surprisingly, I find that increasing the VDA parameter beyond T3 = 0.3 has no measurable impact on the active tumor area. While this may seem improbable, it can be explained by understanding how vessel regression works in the algorithm. Each edge of the lattice that contains an angiogenic vessel is checked to see if it regresses. If regression occurs, not only does that “edge” of the vessel get destroyed, but any lattice edges that are upstream of that edge also get destroyed. In other words, if you kill the source of blood, any vessels that only received blood from that source are also effectively destroyed. This creates the relative insensitivity to changes in the VDA parameter T3. It is worth noting that if the VDA parameter is made sufficiently small (T3 = 0.1), the tumors grow at a noticeably faster rate than for T3 ≥ 0.3.

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