Process analysis of carcinogenesis: concept derivation of the tissue function "preservation of a homogeneous gene expression"

Modeling Tumor Disease and Sepsis by Networks of Adaptively Coupled Phase Oscillators

In this study, we provide a dynamical systems perspective to the modelling of pathological states induced by tumors or infection. A unified disease model is established using the innate immune system as the reference point. We propose a two-layer network model for carcinogenesis and sepsis based upon the interaction of parenchymal cells and immune cells via cytokines, and the co-evolutionary dynamics of parenchymal, immune cells, and cytokines. Our aim is to show that the complex cellular cooperation between parenchyma and stroma (immune layer) in the physiological and pathological case can be qualitatively and functionally described by a simple paradigmatic model of phase oscillators. By this, we explain carcinogenesis, tumor progression, and sepsis by destabilization of the healthy homeostatic state (frequency synchronized), and emergence of a pathological state (desynchronized or multifrequency cluster). The coupled dynamics of parenchymal cells (metabolism) and nonspecific immune cells (reaction of innate immune system) are represented by nodes of a duplex layer. The cytokine interaction is modeled by adaptive coupling weights between the nodes representing the immune cells (with fast adaptation time scale) and the parenchymal cells (slow adaptation time scale) and between the pairs of parenchymal and immune cells in the duplex network (fixed bidirectional coupling). Thereby, carcinogenesis, organ dysfunction in sepsis, and recurrence risk can be described in a correct functional context.

Aspects of tumor progression

A model is introduced here that for the first time describes carcinogenesis in the context of and interacting with associated inflammatory processes. Central to the model are the control of cytokine production by the innate immune system and its disturbance by additional uncontrolled cytokine sources. The model aims to answer the following questions: Why don't tumors form more often? What drives tumor recurrence after an R0 surgery even in UICC I cases, and what causes tumor progression? Which are the host-tumor-host interactions that ultimately lead to lethal outcome in the disease? The model describes the innate immune system under normal conditions as in a dynamic equilibrium, which is shifted toward pro-inflammation when a tumor forms. That in turn causes tumor-associated symptoms, metastasis, and tumor relapse. The recurrence of the tumor from R0/N0/M0-conditions results from the activation of a memory function of the innate immune system, which is conditioned during the initial tumor growth and survives the tumor removal. If activated, this memory function reestablishes, often irreversibly, the shift of the innate immune system away from dynamic equilibrium toward a pro-inflammatory state characterized by nonspecific symptoms originating from the tumor and by activation of dissemination of tumor cells. Once disseminated, these cells can proliferate and form new metastatic structures. Although elements of the memory function are unclear, some properties can be derived from the relapse behavior of tumors. A therapeutic path to influence the innate immune system could be an element in oncologic therapy: Reducing the deviation from the dynamic equilibrium would diminish the clinical effects of such a disturbance and decouple the presence of tumor cells from the influence they have on the organism, and thus build a resilience to tumor growth. The model presented here could also influence sepsis and SIRS therapy and possibly other diseases for which the innate immune system is disturbed.