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Immune response to a variable pathogen: a stochastic model with two interlocked Darwinian entities.

ABSTRACT: This paper presents the modeling of a host immune system, more precisely the immune effector cell and immune memory cell population, and its interaction with an invading pathogen population. It will tackle two issues of interest; on the one hand, in defining a stochastic model accounting for the inherent nature of organisms in population dynamics, namely multiplication with mutation and selection; on the other hand, in providing a description of pathogens that may vary their antigens through mutations during infection of the host. Unlike most of the literature, which models the dynamics with first-order differential equations, this paper proposes a Galton-Watson type branching process to describe stochastically by whole distributions the population dynamics of pathogens and immune cells. In the first model case, the pathogen of a given type is either eradicated or shows oscillatory chronic response. In the second model case, the pathogen shows variational behavior changing its antigen resulting in a prolonged immune reaction.

SUBMITTER: Kuhn C

PROVIDER: S-EPMC3574756 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Immune response to a variable pathogen: a stochastic model with two interlocked Darwinian entities.

Kuhn Christoph C

Computational and mathematical methods in medicine 20121202

This paper presents the modeling of a host immune system, more precisely the immune effector cell and immune memory cell population, and its interaction with an invading pathogen population. It will tackle two issues of interest; on the one hand, in defining a stochastic model accounting for the inherent nature of organisms in population dynamics, namely multiplication with mutation and selection; on the other hand, in providing a description of pathogens that may vary their antigens through mut ...[more]

PMID: 23424603

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Project description:Spatial heterogeneity in tumors is generally thought to result from branching clonal evolution driven by random mutations that accumulate during tumor development. However, this concept rests on the implicit assumption that cancer cells never evolve to a fitness maximum because they can always acquire mutations that increase proliferative capacity. In this study, we investigated the validity of this assumption. Using evolutionary game theory, we demonstrate that local cancer cell populations will rapidly converge to the fittest phenotype given a stable environment. In such settings, cellular spatial heterogeneity in a tumor will be largely governed by regional variations in environmental conditions, for example, alterations in blood flow. Model simulations specifically predict a common spatial pattern in which cancer cells at the tumor-host interface exhibit invasion-promoting, rapidly proliferating phenotypic properties, whereas cells in the tumor core maximize their population density by promoting supportive tissue infrastructures, for example, to promote angiogenesis. We tested model predictions through detailed quantitative image analysis of phenotypic spatial distribution in histologic sections of 10 patients with stage 2 invasive breast cancers. CAIX, GLUT1, and Ki67 were upregulated in the tumor edge, consistent with an acid-producing invasive, proliferative phenotype. Cells in the tumor core were 20% denser than the edge, exhibiting upregulation of CAXII, HIF-1Î±, and cleaved caspase-3, consistent with a more static and less proliferative phenotype. Similarly, vascularity was consistently lower in the tumor center compared with the tumor edges. Lymphocytic immune responses to tumor antigens also trended to higher level in the tumor edge, although this effect did not reach statistical significance. Like invasive species in nature, cancer cells at the leading edge of the tumor possess a different phenotype from cells in the tumor core. Our results suggest that at least some of the molecular heterogeneity in cancer cells in tumors is governed by predictable regional variations in environmental selection forces, arguing against the assumption that cancer cells can evolve toward a local fitness maximum by random accumulation of mutations. Cancer Res; 76(11); 3136-44. ©2016 AACR.

Dataset Information

Immune response to a variable pathogen: a stochastic model with two interlocked Darwinian entities.

Publications

Immune response to a variable pathogen: a stochastic model with two interlocked Darwinian entities.

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