Optimizing therapeutic targets for breast cancer using boolean network models.

Studying gene regulatory networks associated with cancer provides valuable insights for therapeutic purposes, given that cancer is fundamentally a genetic disease. However, as the number of genes in the system increases, the complexity arising from the interconnections between network components grows exponentially. In this study, using Boolean logic to adjust the existing relationships between network components has facilitated simplifying the modeling process, enabling the generation of attractors that represent cell phenotypes based on breast cancer RNA-seq data. A key therapeutic objective is to guide cells, through targeted interventions, to transition from the current cancer attractor to a physiologically distinct attractor unrelated to cancer. To achieve this, we developed a computational method that identifies network nodes whose inhibition can facilitate the desired transition from one tumor attractor to another associated with apoptosis, leveraging transcriptomic data from cell lines. To validate the model, we utilized previously published in vitro experiments where the downregulation of specific proteins resulted in cell growth arrest and death of a breast cancer cell line. The method proposed in this manuscript combines diverse data sources, conducts structural network analysis, and incorporates relevant biological knowledge on apoptosis in cancer cells. This comprehensive approach aims to identify potential targets of significance for personalized medicine.