Transcriptional Network Architecture of Breast Cancer Molecular Subtypes

Abstract

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 co-expression is distance-dependent in breast cancer

“…Transcriptional profiles are the basis of the classification into the main four molecular subtypes: Luminal A, Luminal B, Her2-enriched and Basal. Our group has shown that co-expression networks of breast cancer molecular subtypes are different in terms of their modularity [34] and network architecture [35]. Here, it is shown that the cis-/trans-proportion imbalance is another feature that characterizes each network (Table I, Figure 3).…”

Network Modularity in Breast Cancer Molecular Subtypes

“…Phenotypic variations among breast cancer subtypes arise due to differences in their underlying regulatory programs (de Anda-Jáuregui et al, 2016 ). Said differences in the global structure of subtype-specific gene regulatory networks may indeed reflect differences in lower scales of regulation, in particular in the presence of underlying functional modules, as we will discuss further in this work.…”

Highly-connected, non-redundant microRNAs functional control in breast cancer molecular subtypes

“…For this, we used mutual information (MI) as a measure of miR-gene co-expression. Mutual information has been widely used for the reconstruction of co-expression networks [4,28,29,42,15,7]. In the previous work by our group, we have successfully reconstructed miR-gene co-expression networks using this approach [16,14].…”

“…The transcriptional patterns of breast cancer have been explored in previous works. Our group has found that representing the transcriptional program of breast cancer molecular subtypes as co-expression networks, it is possible by to capture the differences found between each cancer manifestation [15]. We have also shown how genes with coordinated expression patterns are found associated to each cancer subtype, and through these, it is possible to identify and associate functional perturbations to molecular subtypes [1,2].…”