Gene co-expression networks in Basal-Like Breast Cancer allowed to identify E2F3 and TFDP1 transcription factors as central hubs regulating aggressiveness features

Intrinsic features of tumor cells determining their aggressiveness, such as survival, proliferation and invasion, are the result of the orchestrated activity of many components interacting with each other, inducing specific gene regulation events. Gene co-expression networks are considered useful tools to define prognostic gene signatures and identify centrally connected genes as potential therapeutic targets. Preliminary data collected in the laboratory, obtained by reconstructing breast cancer (BC) transcriptional co-expression networks from the METABRIC dataset, allowed to identify groups of genes (modules) significantly correlated with survival and tumor grade. Subtype-specific BC gene co-expression networks were then generated, allowing to identify a module (named b_E2F_targets) where genes are more tightly connected in the highly aggressive basal-like breast cancer (BLBC) subtype. The expression levels of the module strongly correlate with clinical features and poor prognosis, supporting its relevant biological functions. In order to identify central module’s regulators to be used as module’s switches, we focused on transcription factors (TFs), ranking them on the bases of network centrality. Five of the most central TF hubs, namely E2F3, TFDP1, TEAD4, PTTG1 and CEBPG, were chosen as potential regulators of the entire module. In vitro validations showed that targeting the selected TFs via RNA silencing significantly reduced cell proliferation. To further investigate the role of the identified TF hubs, we carried out CRISPR/Cas12a-mediated KO of two of them, E2F3 and TFDP1, and demonstrated that both TFs are required to support proliferation and anchorage-independent growth of several BLBC cell lines, which exhibit cell type-specific requirement for these factors. In conclusion this work demonstrating the relevance of gene co-expression networks, allowed to identify a pool of potential master regulators of aggressiveness features and to validate E2F3 and TFDP1 as useful therapeutic targets to disrupt the entire module. Further investigation will expand the scope to the other TF hubs identified, reconstructing more precisely the regulation of BLBC expression networks and improving our weapons against this aggressive BC type. The same approach could then be extended to other BC subtypes, and to all tumors for which gene expression data are increasingly available, allowing for the identification of druggable targets.