analisi funzionali di potenziali target terapeutici in basal-like breast cancer

Breast cancer is one of the most common cancer types and leading cause of cancer-associated death in women. It could be classified in 5 subtypes according to immunohistochemical features: luminal A, luminal B, Her2-positive, normal-like and basal-like. Basal-like breast cancer represents the most aggressive subtypes, it’s characterized by a limited response to currently available therapies and by a high mortality rate. Thanks to the availability of transcriptomic data and gene co-expression networks analysis, it was possible for us to better analyze the biology of basal-like tumors. The study of this network allowed us to identify a sub-network (module) in which all the genes are more inter-connected and over-expressed in basal-like tumors. Analyzing the intra-module connectivity, 5 centrally located genes coding for transcriptional factors (TF hubs), were chosen as potential regulators of the entire module: PTTG1, TEAD4, E2F3, TFDP1 e CEBPγ. The aim of this study was to evaluate the functional role of these TF hubs, investigating how they could regulate the entire module they belong and thus influence the aggressiveness of breast cancer. Silencing TF hubs in basal-like breast cancer cell lines leads to a reduced proliferation index, while their migratory capability is preserved. By screening the ChEMBL database a number of compounds annotated to target genes significantly enriched in the basal-like specific module were also identified, representing an alternative approach for interfering with the module activity. Moreover, the inhibition of E2F3 activity reflected in a significant downregulation of the genes of the module it belongs to. Validating the intra-module connectivity as a good parameter to identify the central regulator genes in the module, and demonstrating that these genes represent relevant targets to disrupt tumor gene expression patterns, strongly affecting viability or other relevant biological functions, will allow to extend this approach to other tumors for which transcriptome data are available