The Role of Copy Number Variants in Gene Co-Expression Patterns for Luminal B Breast Tumors

Abstract

Gene co-expression networks have become a usual approach to integrate the vast amounts of information coming from gene expression studies in cancer cohorts. The reprogramming of the gene regulatory control and the molecular pathways depending on such control are central to the characterization of the disease, aiming to unveil the consequences for cancer prognosis and therapeutics. There is, however, a multitude of factors which have been associated with anomalous control of gene expression in cancer. In the particular case of co-expression patterns, we have previously documented a phenomenon of loss of long distance co-expression in several cancer types, including breast cancer. Of the many potential factors that may contribute to this phenomenology, copy number variants (CNVs) have been often discussed. However, no systematic assessment of the role that CNVs may play in shaping gene co-expression patterns in breast cancer has been performed to date. For this reason we have decided to develop such analysis. In this study, we focus on using probabilistic modeling techniques to evaluate to what extent CNVs affect the phenomenon of long/short range co-expression in Luminal B breast tumors. We analyzed the co-expression patterns in chromosome 8, since it is known to be affected by amplifications/deletions during cancer development. We found that the CNVs pattern in chromosome 8 of Luminal B network does not alter the co-expression patterns significantly, which means that the co-expression program in this cancer phenotype is not determined by CNV structure. Additionally, we found that region 8q24.3 is highly dense in interactions, as well as region p21.3. The most connected genes in this network belong to those cytobands and are associated with several manifestations of cancer in different tissues. Interestingly, among the most connected genes, we found MAF1 and POLR3D, which may constitute an axis of regulation of gene transcription, in particular for non-coding RNA species. We believe that by advancing on our knowledge of the molecular mechanisms behind gene regulation in cancer, we will be better equipped, not only to understand tumor biology, but also to broaden the scope of diagnostic, prognostic and therapeutic interventions to ultimately benefit oncologic patients.

Loss of long-range co-expression is a common trait in cancer

“…The assessment of the contribution of different regulatory elements such as enhancers, transcription factor binding sites, CTCF binding sites, and Hi-C contacts, to the local gene co-expression in normal tissues done by (21) indicated that no feature can uniquely explain the co-expression patterns observed, but they all contribute in a complementary fashion. We have shown, for Luminal A and B breast cancer subtypes, that intra-chromosomal communities are not bound by CTCF binding sites and that copy number alterations do not influence co-expressed network communities with a similar differential expression trend (27) nor do they affect intra-cytoband co-expression patterns (63). Our group has also analyzed epigenetic mechanisms in the context of cancer gene co-expression, in particular the role of miRNA regulation, showing that specific miRNA families alter coexpression in genes related to the epithelial-mesenchymal transition (64, 65), they also promote subtype-specific pathways in breast cancer (66), and are associated with progression stage in clear cell renal carcinoma (67).…”

Oncogenic Role of miR-217 During Clear Cell Renal Carcinoma Progression

“…However, the mechanism for which this phenomenon emerges in cancer, but not in control, networks remains elusive. We have investigated the role of other biomolecular processes such as those in transcription factor binding sites, CTCF binding sites (30), or copy number alterations (75). It is worth noticing that none of them has shown to be significantly related to the loss of inter-chromosomal interactions.…”

The Breast Cancer Protein Co-Expression Landscape

“…This result shows that there are remarkable differences between the cancer regulation programs at the transcription and the translation levels, indicating the importance of multiomics analysis to understand the general biomolecular expression regulatory program. Efforts in this line may include (but are not limited to) the analysis of micro-RNA-gene co-expression analysis [ 47 , 48 ], copy number alteration’s role in the co-expression landscape [ 31 , 49 ], epigenetic regulation [ 50 ], among others.…”