We describe a strategy for the development of a rational approach of neoplastic disease therapy based on the demonstration that scale-free networks are susceptible to specific attacks directed against its connective hubs. This strategy involves the (i) selection of up-regulated hubs of connectivity in the tumors interactome, (ii) drug repurposing of these hubs, (iii) RNA silencing of non-druggable hubs, (iv) in vitro hub validation, (v) tumor-on-a-chip, (vi) in vivo validation, and (vii) clinical trial. Hubs are protein targets that are assessed as targets for rational therapy of cancer in the context of personalized oncology. We confirmed the existence of a negative correlation between malignant cell aggressivity and the target number needed for specific drugs or RNA interference (RNAi) to maximize the benefit to the patient’s overall survival. Interestingly, we found that some additional proteins not generally targeted by drug treatments might justify the addition of inhibitors designed against them in order to improve therapeutic outcomes. However, many proteins are not druggable, or the available pharmacopeia for these targets is limited, which justifies a therapy based on encapsulated RNAi.

Since cancer is a genetic disease, studying gene regulatory networks related to this pathology offers the possibility of obtaining helpful information for therapeutic purposes. However, the complexity expressed by the interconnections between network components grows exponentially with the number of genes in the system. In this report, the Boolean logic’s use for regulating the existing relationships between network components has allowed us to simplify the modeling capable of producing attractors representing the cell phenotypes from breast cancer RNA-seq data. From this point of view, a therapeutic objective is to induce the cell, through appropriate interventions, to leave the current cancer attractor to migrate toward another attractor physiologically different from cancer. In this report, we developed a computational method that identifies network nodes whose inhibition achieves the therapeutic objective of transitioning from one tumor attractor to another associated with apoptosis based on transcriptomic data from cell lines. We used previously published in vitro experiments, in which the knockdown of a few proteins led a breast cancer cell line to death, to validate the model taking into account genes related to apoptosis in cancer cells. The method proposed in this manuscript uses heterogeneous data integration, structural network analysis, and biological knowledge to identify potential targets of interest for breast cancer on a personalized medicine approach.

Triple-negative Breast cancer is a highly heterogeneous tumor subtype lacking universal therapeutic targets, a factor that frequently renders standard pharmacological approaches ineffective. To address the urgent clinical need for precision strategies, we developed a computational framework centered on personalization. Utilizing specific gene expression data, we initially constructed and calibrated 41 individualized regulatory networks, thereby capturing the precise molecular signature of each patient. Then, we applied a hybrid approach to these models—integrating Boolean logical determinism with the exploratory optimization of the combinatorial search space via a Genetic Algorithm—and identified the minimal sets of driver nodes capable of irreversibly driving the tumor cell toward apoptosis. Subsequently, a rigorous optimization process translated these targets into actionable clinical options by mapping them to optimal combinations of oncological drugs under stringent therapeutic constraints. The apoptotic transition was successfully achieved in 92.7% of the samples. Results indicate that effective tumor reprogramming relies on manipulating critical nodes that orchestrate the network's logical dynamics. By minimizing unnecessary perturbations to the overall signaling architecture, this methodology demonstrates the computational feasibility of identifying robust, non-obvious therapeutic targets through Boolean modeling. Ultimately, it offers an adaptive and scalable platform for the rational design of minimally disruptive, patient-specific combinatorial therapies.