Thursday, May 11, 2017

Extinction Times In Tumor Public Goods Games

Extinction Times In Tumor Public Goods Games

Philip GerleePhilipp M. Altrock

Abstract

Cancer evolution and progression are shaped by Darwinian selection and cell-to-cell interactions. Evolutionary game theory incorporates both of these principles, and has been recently as a framework to describe tumor cell population dynamics. A cornerstone of evolutionary dynamics is the replicator equation, which describes changes in the relative abundance of different cell types, and is able to predict evolutionary equilibria. Typically, the replicator equation focuses on differences in relative fitness. We here show that this framework might not be sufficient under all circumstances, as it neglects important aspects of population growth. Standard replicator dynamics might miss critical differences in the time it takes to reach an equilibrium, as this time also depends on cellular birth and death rates in growing but bounded populations. As the system reaches a stable manifold, the time to reach equilibrium depends on cellular death and birth rates. These rates shape evolutionary timescales, in particular in competitive co-evolutionary dynamics of growth factor producers and free-riders. Replicator dynamics might be an appropriate framework only when birth and death rates are of comparable magnitude. Otherwise, population growth effects cannot be neglected when predicting the time to reach an equilibrium, and cellular events have to be accounted for explicitly.

http://biorxiv.org/content/early/2017/05/04/134361

Wednesday, May 10, 2017

Mechanistic Modeling Quantifies The Influence Of Tumor Growth Kinetics On The Response To Anti-Angiogenic Treatment

Mechanistic Modeling Quantifies The Influence Of Tumor Growth Kinetics On The Response To Anti-Angiogenic Treatment

Thomas D. Gaddy, Stacey D. Finley

Abstract

Tumors exploit angiogenesis, the formation of new blood vessels from pre-existing vasculature, in order to obtain nutrients required for continued growth and proliferation. Targeting factors that regulate angiogenesis, including the potent promoter vascular endothelial growth factor (VEGF), is therefore an attractive strategy for inhibiting tumor growth. Systems biology modeling enables us to identify tumor-specific properties that influence the response to those anti-angiogenic strategies. Here, we build on our previous systems biology model of VEGF transport and kinetics in tumor-bearing mice to include a tumor compartment whose volume depends on the “angiogenic signal” produced when VEGF binds to its receptors on tumor endothelial cells. We trained and validated the model using in vivo measurements of xenograft tumor volume to produce a model that accurately predicts the tumor's response to anti-angiogenic treatment. We applied the model to investigate how tumor growth kinetics influence the response to anti-angiogenic treatment targeting VEGF. Based on multivariate regression analysis, we found that certain intrinsic kinetic parameters that characterize the growth of tumors could successfully predict response to anti-VEGF treatment. This model is a useful tool for predicting which tumors will respond to anti-VEGF treatment, complementing pre-clinical in vivo studies.