Boolean network modeling: The first step toward a new understanding of breast cancer heterogeneity.

e15100 Background: Intratumoral heterogeneity, driven by phenotypic plasticity, contributes to disease progression and therapy failures. However, a thorough understanding of its underlying mechanisms is still lacking. Breast cancer exemplifies this with its histological and molecular diversity; estrogen receptor-positive (ER+), progesterone receptor-positive (PR+), and HER2- luminal A subtypes, such as the MCF7 cell line, are not the exception. While single-cell RNA sequencing (scRNA-seq) and three-dimensional multicellular tumor spheroids (MCTS) represent initial approaches to understanding heterogeneity, integrating these tools with computational strategies offers new paths to uncover the regulatory groundwork of tumor behavior. When modeled via Boolean frameworks, transcriptional regulatory networks (TRNs) provide a dynamic platform for studying gene interactions, identifying attractors (stable cellular states corresponding to distinct phenotypes), and simulating cellular transitions. This study represents the first step in constructing such a framework for ER+ breast cancer to advance precision medicine and improve therapeutic strategies. Methods: Regulatory elements relevant to the MCF7 cell line were identified through an extensive review of NCBI literature. Interactions were strictly curated based on evidence quality (ranging from robust experimental validation to predictive hypotheses), and evaluated for type and significance, such as feedback loops, activation, coactivation, and inhibition. Logical operators (AND, OR, NOT) were used to translate regulatory connections into Boolean functions to simulate binary gene expression dynamics. The final TRN was visualized using Cytoscape (v3.10.3) with a hierarchical layout. Results: The TRN consists of 54 nodes and 81 interactions, representing the regulatory landscape of ER+, PR+, and HER2- breast cancer. Boolean equations offer a framework for simulating the evolution of binary states to attractors, enabling the observation of how perturbations, such as gene overexpression or silencing, influence heterogeneity. While simulation and attractor analyses are ongoing, the network provides a foundation for elucidating the regulatory mechanisms underlying breast cancer plasticity. Conclusions: This study highlights the potential of integrating experimental data, TRN reconstruction, and Boolean modeling to understand intratumoral heterogeneity. Our rigorously curated model establishes a foundational framework for precision oncology and the integration of experimental and computational methods.