INMEGEN (Mexico)
Breast cancer is a complex heterogeneous disease. It can be classified into four molecular subtypes: Luminal A, Luminal B, HER2-Enriched and Basal-like. These subtypes have been studied at the molecular, histopathological and clinical level. However, their associated genome regulatory program has not been fully understood. In these terms, network theory has proven to be a robust tool to analyze and understand the context of global genetic regulation in cancer.
Previously, by constructing normal tissue and breast cancer gene regulatory networks, we have observed that gene co- expression in breast cancer occurs mostly between neighbor genes located in the same chromosome. On the contrast, in non-cancerous tissue, appears along the whole genome. Here, we extend the approach, in order to observe into what extent the loss of trans- regulation occurs in the different intrinsic breast cancer subtypes.
A collection of 1,300 whole-genome breast cancer samples were classified into molecular subtypes. Gene regulatory networks were inferred for each of the four subtypes and the healthy tissue, using mutual information (MI) values associated with each gene-gene interaction.
We found that loss of trans- regulation is a common feature of breast cancer molecular subtypes. In the healthy network, the largest component (LC) comprises almost all genes and interactions occur between genes from different chromosomes, contrary to any tumor subtype network, where the size of the LC is clearly smaller and components comprise interactions mostly from genes located on the same chromosome. Furthermore, components in cancer networks are comprised for intra- chromosome genes, but that they belong to the same cytoband (the smallest scale to define a chromosome region). This effect is more evident in the most aggressive subtypes, observing a direct correlation between loss of trans- regulation and bad prognosis.
With this kind of approach, we have been able to elaborate a more comprehensive landscape of cancer genomics. This effort may be complemented with other types of genomic data such as copy number alterations, micro-RNAs or Hi-C data with the aim of providing a multi-omics-based framework to elaborate more specific questions in the era of personalized medicine.