Cancer is a genomic disease involving various intertwined pathways with complex cross-communication links. Conceptually, this complex interconnected system forms a network, which allows one to model the dynamic behavior of the elements that characterize it to describe the entire system’s development in its various evolutionary stages of carcinogenesis. Knowing the activation or inhibition status of the genes that make up the network during its temporal evolution is necessary for the rational intervention on the critical factors for controlling the system’s dynamic evolution. In this report, we proposed a methodology for building data-driven boolean networks that model breast cancer tumors. We defined the network components and topology based on gene expression data from RNA-seq of breast cancer cell lines. We used a Boolean logic formalism to describe the network dynamics. The combination of single-cell RNA-seq and interactome data enabled us to study the dynamics of malignant subnetworks of up-regulated genes. First, we used the same Boolean function construction scheme for each network node, based on canalyzing functions. Using single-cell breast cancer datasets from The Cancer Genome Atlas, we applied a binarization algorithm. The binarized version of scRNA-seq data allowed identifying attractors specific to patients and critical genes related to each breast cancer subtype. The model proposed in this report may serve as a basis for a methodology to detect critical genes involved in malignant attractor stability, whose inhibition could have potential applications in cancer theranostics.
No abstract
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 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.
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