Breast cancer heterogeneity is evident at the clinical, histological and molecular level. High throughput technologies allowed the identification of intrinsic subtypes that capture transcriptional differences among tumors. A remaining question is whether said differences are associated to a particular transcriptional program which involves different connections between the same molecules. In other words, whether particular transcriptional network architectures can be linked to specific phenotypes. In this work we infer, construct and analyze transcriptional networks from whole-genome gene expression microarrays, by using an information theory approach. We use 493 samples of primary breast cancer tissue classified in four molecular subtypes: Luminal A, Luminal B, Basal and HER2-enriched. For comparison, a network for non-tumoral mammary tissue (61 samples) is also inferred and analyzed. Transcriptional networks present particular architectures in each breast cancer subtype as well as in the non-tumor breast tissue. We find substantial differences between the non-tumor network and those networks inferred from cancer samples, in both structure and gene composition. More importantly, we find specific network architectural features associated to each breast cancer subtype. Based on breast cancer networks' centrality, we identify genes previously associated to the disease, either, generally (i.e., CNR2) or to a particular subtype (such as LCK). Similarly, we identify LUZP4, a gene barely explored in breast cancer, playing a role in transcriptional networks with subtype-specific relevance. With this approach we observe architectural differences between cancer and non-cancer at network level, as well as differences between cancer subtype networks which might be associated with breast cancer heterogeneity. The centrality measures of these networks allow us to identify genes with potential biomedical implications to breast cancer.

Gene regulatory networks are useful to understand the activity behind the complex mechanisms in transcriptional regulation. A main goal in contemporary biology is using such networks to understand the systemic regulation of gene expression. In this work, we carried out a systematic study of a transcriptional regulatory network derived from a comprehensive selection of all potential transcription factor interactions downstream from MEF2C, a human transcription factor master regulator. By analyzing the connectivity structure of such network, we were able to find different biologically functional processes and specific biochemical pathways statistically enriched in communities of genes into the network, such processes are related to cell signaling, cell cycle and metabolism. In this way we further support the hypothesis that structural properties of biological networks encode an important part of their functional behavior in eukaryotic cells.

Analysis of gene regulatory networks allows the identification of master transcriptional factors that control specific groups of genes. In this work, we inferred a gene regulatory network from a large dataset of breast cancer samples to identify the master transcriptional factors that control the genes within signal transduction pathways. The focus in a particular subset of relevant genes constitutes an extension of the original Master Regulator Inference Algorithm (MARINa) analysis. This modified version of MARINa utilizes a restricted molecular signature containing genes from the 25 human pathways in KEGG's signal transduction category. Our breast cancer RNAseq expression dataset consists of 881 samples comprising tumors and normal mammary gland tissue. The top 10 master transcriptional factors found to regulate signal transduction pathways in breast cancer we identified are: TSHZ2, HOXA2, MEIS2, HOXA3, HAND2, HOXA5, TBX18, PEG3, GLI2, and CLOCK. The functional enrichment of the regulons of these master transcriptional factors showed an important proportion of processes related to morphogenesis. Our results suggest that, as part of the aberrant regulation of signaling pathways in breast cancer, pathways similar to the regulation of cell differentiation, cardiovascular system development, and vasculature development may be dysregulated and co-opted in favor of tumor development through the action of these transcription factors.

Inflammation has been recognized as an important driver in the development and growth of malignancies. Inflammatory signaling in cancer emerges from the combinatorial interaction of several deregulated pathways. Pathway deregulation is often driven by changes in the underlying gene regulatory networks. Confronted with such complex scenario, it can be argued that a closer analysis of the structure of such regulatory networks will shed some light on how gene deregulation led to sustained inflammation in cancer. Here, we inferred an inflammation-associated gene regulatory network from 641 breast cancer and 78 healthy samples. A modular structure analysis of the regulatory network was carried out, revealing a hierarchical modular structure. Modules show significant overrepresentation score p-values for biological processes unveiling a definite association between inflammatory processes and adaptive immunity. Other modules are enriched for T-cell activation, differentiation of CD8+ lymphocytes and immune cell migration, thus reinforcing the aforementioned association. These analyses suggest that in breast cancer tumors, the balance between antitumor response and immune tolerance involving CD8+ T cells is tipped in favor of the tumor. One possible mechanism is the induction of tolerance and anergization of these cells by persistent antigen exposure.