Cancer as robust intrinsic state shaped by evolution: a key issues review

Area of Science:

• Systems Biology
• Cancer Research
• Theoretical Physics

Background:

• Cancer complexity defies understanding through isolated genes or pathways.
• Large 'omics' data necessitates a global, mechanistic framework for cancer research.
• Existing linear-additive models inadequately capture cancer's intricate nature.

Purpose of the Study:

• To propose a unifying, quantitative theory of cancer based on endogenous molecular-cellular networks.
• To explore cancer as a robust, emergent state within a hierarchical biological system.
• To provide a nonlinear dynamical model for understanding cancer phenotypes.

Main Methods:

• Development of a unifying theory for cancer based on endogenous molecular-cellular networks.
• Construction of a decision network from experimental knowledge, reflecting hierarchical biological structure.
• Examination of nonlinear stochastic dynamics within the network to identify emergent robust states.

Main Results:

• The proposed theory conceptualizes cancer as a robust state of the endogenous molecular-cellular network.
• A hierarchical structure within molecular biology systems is suggested by the theory.
• Nonlinear dynamical modeling naturally yields robust states corresponding to normal and pathological phenotypes, including cancer.
• Initial successful applications of the theory to prostate, hepatocellular, gastric cancers, and acute promyelocytic leukemia.

Conclusions:

• Cancer's complexity is better understood through a systems-level, nonlinear dynamical approach.
• The theory offers a more encompassing framework than traditional linear-additive thinking in cancer research.
• This physics-inspired approach may reveal general rules governing biological and medical systems.