NF-κB epigenetic attractor landscape drives breast cancer heterogeneity

Heterogeneity in breast cancer (BC) subtypes within a tumor contributes to therapy resistance and cancer recurrence. Subtype heterogeneity in tumors arises through a combination of stochastic genetic/epigenetic changes, phenotypic plasticity, and microenvironment-driven selection as the tumor evolves over time. Here, we sought to characterize how NF-κB epigenetic variability contributes to the progression of the HER2⁺ BC subtype. Initially, we used RNA to determine the expression levels of NF-κB, TWIST1, SIP1, and SLUG in two breast cancer (BC) cell lines, HCC-1954 and MDA-MB-231, classified as HER2⁺ and triple-negative breast cancer (TNBC) subtypes, respectively. Then, we built and calibrated a gene regulatory network (GRN) model that reproduces the transcriptional interactions between these genes. The model epigenetic landscape exhibits two attractor basins that reproduces the observed expression profiles of both HER2⁺ and TNBC subtypes, separated by an unstable stationary state. For validation, we used DHMEQ-treated cells, along with published patient data and in vitro results. Stochastic fluctuations in the NF-κB levels induce spontaneous irreversible transition from HER2⁺ to TNBC attractor basins at different times, contributing to subtype heterogeneity. The unstable state mediates this transition by providing a slow route between subtypes in the phase space that is susceptible to dynamic fluctuations. Mutations or drugs that change the availability of NF-κB alters the size of the subtype basins, changing the transition probabilities. Together, our findings enhance the established attractor landscape formulation and deepen understanding of BC heterogeneity, leading to more precise classification, prognosis, and targeted strategies for BC progression.