Cancer as a Failure of Biological Self-Reference

Abstract Cancer is traditionally understood as a genetic and evolutionary disease caused by somatic mutations that disrupt cell-autonomous regulation. This paper reframes malignant transformation as a systemic breakdown of macro-to-micro biological information integration using the Self-Referential Ignorance (SRI) framework. We model this via the scalar Ψ = M/K, where M denotes the magnitude of cell-internal, mutation-driven oncogenic signaling and K denotes the magnitude of organismal regulatory feedback reaching and processed by the cell. In healthy tissue, tissue-level homeostasis keeps Ψ near zero and the regulatory integrity index R near one. Malignant transformation is mapped as a deterministic trajectory where organismal feedback K collapses faster than oncogenic noise M accumulates. We present a rectified two-variable coupled dynamical system for R(t) and Ψ(t), resolving mathematical oversights in prior drafts regarding the characterization of the malignant regime. Local stability analysis via the system Jacobian proves that the healthy state is a locally stable node, whereas the malignant fixed point functions as an unstable saddle threshold. Crucially, the negative determinant of the Jacobian dictates that crossing the saddle's stable manifold (the separatrix) forces unbounded divergence—explaining why untreated tumors expand progressively rather than settling into an elevated homeostatic plateau. The system is mapped to the four Universal Balance-Feedback Framework (UBFF) laws, and quantitative, falsifiable experimental predictions are detailed for in vitro and in vivo validation.