Tumors, especially malignant tumors, are diseases of uncontrolled proliferation. The most fundamental properties of malignant tumors, including nongenetic heterogeneity, the necessity of supporting tissues, rarity of occurrence, and inevitable therapy resistant relapses, remain unexplained by mutation-centric models. Here, we provide a multiscale tumor model rooted in first principles. The central premise of the model is that globally arbitrary accesses of chromatins in a cell unleash a repertoire of stochastic gene expression modules, including proliferation, death, stemness, EMT, angiogenesis, neurogenesis, fibrogenesis, and inflammation, that compete for finite transcriptional resources and are antagonized by the differentiation program that drive the tumor cell along its differentiation pathway from what it originates. A tumor can only become clinically diseases when it successfully self-organizes a complete supporting stroma of blood vessels, nerves, connective tissues, and an inflammation-driven clearance cycle, thereby locking into a self-provisioning, noise-driven, evolvable dissipative structure. The model visualizes the probability of tumor formation across pediatric and adult contexts, the therapeutic responses, the phases of tumorigenesis and the nature of tumors and displays testable predictions, such as prognostically favorable and poor tumor cell subpopulations. By providing a unified physical theory of tumors, the model activates new interdisciplinary research areas aimed at understanding and controlling tumors.
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莫显明 et al. (2026) studied this question.
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