A State-Space and Entropy-Based Framework for Understanding Cancer as a Complex Adaptive System

Cancer is increasingly recognised as a complex adaptive system characterised by heterogeneity, phenotypic plasticity, microenvironmental coupling, and continuous interaction with host-level constraints. While these features are well established in contemporary oncology, they are often analysed in isolation rather than within a unified dynamical framework. Here, we present a physics-informed, systems-level interpretation of cancer based on the concept of intermediate-entropy attractors. Building on prior theoretical work in physics, tumour–host dynamics are formulated as trajectories through a high-dimensional state space defined by genetic, phenotypic, spatial, metabolic, and immune variables. Entropy is used strictly in an operational sense, representing measurable heterogeneity and uncertainty rather than a causal force. We propose that tumour robustness and persistence may be associated with bounded intermediate-entropy regimes, where structured variability and stabilising coupling between degrees of freedom permit adaptation without loss of coherence. Low-entropy regimes correspond to constrained, homogeneous states, while high-entropy regimes reflect loss of organisation and viability. Importantly, robustness is argued to depend not only on entropy magnitude, but on the coupling structure between entropy dimensions. This exploratory framework generates testable hypotheses using existing multi-omic and spatial cancer datasets; however, empirical null-controlled analyses presented here do not provide robust support for a simple quadratic intermediate-entropy optimum, underscoring the sensitivity of entropy-based interpretations to modelling assumptions.