Abstract
Tissues can span a large structural phase space, but only occupy a small set of configurations as interfacial tension-driven self-organization counters entropically-favored disorganization. Historical self-organization models often do not address structural variability and transitions commonly observed during morphogenesis and disease. In breast cancer, structural breakdown of the bilayered mammary epithelium, comprised of inner luminal (LEP) layer surrounded by an outer myoepithelial (MEP) layer, is directly linked increased patient risk upon invasion. Organotypic cultures of patient-derived human mammary epithelial cells self-organize in vitro, largely driven by differences in favorability of LEP and MEP interface with the extracellular matrix. The observed distribution of organoid structures closely aligns with Boltzmann statistics - a function of the underlying interfacial energies (enthalpy), geometric constraints (entropy), and mechanical fluctuations (activity) of the tissue. We predict that transformations which increase the probability of LEP occupancy in the basal compartment can destabilize tissue structure and promote invasion, consistent with observations in murine organoid models.To test these predictions experimentally, we examined the ability of 15 cancer-associated genetic changes to alter interfacial tensions of LEP and disrupt self-organization in reconstituted human mammary organoids. While most perturbations only minimally impacted self-organization, PIK3CA activation in LEP uniquely reduced their ECM interfacial energy and disrupted tissue structure. Modeling predicts that normalization of PIK3CA-LEP interfacial energy or decreasing overall tissue activity can correct tissue structure, which we confirm experimentally. Consistently, upregulation of basal adhesion (enthalpy) is observed during progression from in situ to invasive human cancers. Additionally changes in tissue composition (entropy) and remodeling (activity) are linked to changes in cancer risk post-pregnancy. Collectively, this statistical mechanical framework presents a new strategy for understanding and targeting cancer progression, emphasizing the importance of structural probability distributions rather than average structures.
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