Breast cancer is a heterogeneous and complex disease, a clear manifestation of this is its classification into different molecular subtypes. On the other hand, gene transcriptional networks may exhibit different modular structures that can be related to known biological processes. Thus, modular structures in transcriptional networks may be seen as manifestations of regulatory structures that tightly controls biological processes. In this work, we identify modular structures on gene transcriptional networks previously inferred from microarray data of molecular subtypes of breast cancer: luminal A, luminal B, basal, and HER2-enriched. We analyzed the modules (communities) found in each network to identify particular biological functions (described in the Gene Ontology database) associated to them. We further explored these modules and their associated functions to identify common and unique features that could allow a better level of description of breast cancer, particularly in the basal-like subtype, the most aggressive and poor prognosis manifestation. Our findings related to the immune system and a decrease in cell death-related processes in basal subtype could help to understand it and design strategies for its treatment.

Transcriptional co-expression networks represent the concerted gene regulation programs by means of statistical inference of co-expression patterns. The rich phenomenology of transcriptional processes behind complex phenotypes such as cancer, is often captured (at least partially) in the connectivity structure of transcriptional co-expression networks. By analyzing the community structure of these networks, we may develop a deeper understanding of that phenomenology. We identified the modular structure of a transcriptional co-expression network obtained from breast cancer gene expression as well as a non-cancer adjacent breast tissue network as a control. We then analyzed the biological functions associated to the resulting communities by means of enrichment analysis. We also generated two projected networks for both, tumor and control networks: The first one is a projection to a network in which nodes are communities and edges represent topologically adjacent communities, indicating co-expression patterns between them. For the second projection, a bipartite network was generated containing a layer of modules and a layer of biological processes, with links between modules and the functions in which they are enriched; from this bipartite network, a projection to the community layer was obtained. From the analysis of the communities and projections, we were able to discern distinctive patterns of regulation between tumors and controls. Even though the connectivity structure of transcriptional co-expression networks is quite different, the topology of the projected networks is somehow similar, indicating functional compartmentalization, in both tumor and control conditions. However, the biological functions represented in the corresponding modules resulted notably different, with the tumor network comprising functional modules enriched for well-known hallmarks of cancer.

HER2-enriched breast cancer is a complex disease characterized by the overexpression of the ERBB2 amplicon. While the effects of this genomic aberration on the pathology have been studied, genome-wide deregulation patterns in this subtype of cancer are also observed. A novel approach to the study of this malignant neoplasy is the use of transcriptional networks. These networks generally exhibit modular structures, which in turn may be associated to biological processes. This modular regulation of biological functions may also exhibit a hierarchical structure, with deeper levels of modular organization accounting for more specific functional regulation. In this work, we identified the most probable (maximum likelihood) model of the hierarchical modular structure of the HER2-enriched transcriptional network as reconstructed from gene expression data, and analyzed the statistical associations of modules and submodules to biological functions. We found modular structures, independent from direct ERBB2 amplicon regulation, involved in different biological functions such as signaling, immunity, and cellular morphology. Higher resolution submodules were identified in more specific functions, such as micro-RNA regulation and the activation of viral-like immune response. We propose the approach presented here as one that may help to unveil mechanisms involved in the development of the pathology.