Cancer Genesis and Progression as Dynamics in Functional Landscape of Endogenous Molecular-Cellular Network

TL;DR

This paper proposes a nonlinear stochastic dynamical systems model of cellular networks to understand cancer as a stable state within a landscape of cellular attractors, offering new insights into cancer development and treatment strategies.

Contribution

It introduces a novel dynamical systems framework for modeling cancer genesis and progression as stochastic transitions between network attractors, integrating experimental data.

Findings

Cancer viewed as a stable attractor in cellular network landscape

Progression involves stochastic transitions between attractors

Implications for new prevention and treatment strategies

Abstract

An endogenous molecular-cellular network for both normal and abnormal functions is assumed to exist. This endogenous network forms a nonlinear stochastic dynamical system, with many stable attractors in its functional landscape. Normal or abnormal robust states can be decided by this network in a manner similar to the neural network. In this context cancer is hypothesized as one of its robust intrinsic states. This hypothesis implies that a nonlinear stochastic mathematical cancer model is constructible based on available experimental data and its quantitative prediction is directly testable. Within such model the genesis and progression of cancer may be viewed as stochastic transitions between different attractors. Thus it further suggests that progressions are not arbitrary. Other important issues on cancer, such as genetic vs epigenetics, double-edge effect, dormancy, are discussed in the light of present hypothesis. A different set of strategies for cancer prevention, cure, and care, is therefore suggested.