Integrated genomic analysis of triple-negative breast cancers reveals novel microRNAs associated with clinical and molecular phenotypes and sheds light on the pathways they control

Abstract

BackgroundThis study focuses on the analysis of miRNAs expression data in a cohort of 181 well characterised breast cancer samples composed primarily of triple-negative (ER/PR/HER2-negative) tumours with associated genome-wide DNA and mRNA data, extensive patient follow-up and pathological information.ResultsWe identified 7 miRNAs associated with prognosis in the triple-negative tumours and an additional 7 when the analysis was extended to the set of all ER-negative cases. miRNAs linked to an unfavourable prognosis were associated with a broad spectrum of motility mechanisms involved in the invasion of stromal tissues, such as cell-adhesion, growth factor-mediated signalling pathways, interaction with the extracellular matrix and cytoskeleton remodelling. When we compared different intrinsic molecular subtypes we found 46 miRNAs that were specifically expressed in one or more intrinsic subtypes. Integrated genomic analyses indicated these miRNAs to be influenced by DNA genomic aberrations and to have an overall influence on the expression levels of their predicted targets. Among others, our analyses highlighted the role of miR-17-92 and miR-106b-25, two polycistronic miRNA clusters with known oncogenic functions. We showed that their basal-like subtype specific up-regulation is influenced by increased DNA copy number and contributes to the transcriptional phenotype as well as the activation of oncogenic pathways in basal-like tumours.ConclusionsThis study analyses previously unreported miRNA, mRNA and DNA data and integrates these with pathological and clinical information, from a well-annotated cohort of breast cancers enriched for triple-negative subtypes. It provides a conceptual framework, as well as integrative methods and system-level results and contributes to elucidate the role of miRNAs as biomarkers and modulators of oncogenic processes in these types of tumours.

Recent trends in microRNA research into breast cancer with particular focus on the associations between microRNAs and intrinsic subtypes

“…As a result, they detected different miRNA expression levels between the basal and luminal subtypes. In addition, De Rinaldis et al 32 identified a 46-miRNA signature that could be used to differentiate between breast cancer subtypes. Dvinge et al 33 also obtained similar findings in their research.…”

Noncoding RNAs in breast cancer

“…Patients with basal-like breast cancers frequently have poor prognosis, and are difficult to treat owing to the frequent lack of hormone receptors (a triple-negative phenotype with loss of ER and PR, and no HER2 amplification) that can be therapeutically targeted [138][139][140]. Six of these 16 basal-related miRNAs have been reported to be associated with basal-like/triple-negative breast cancer, including miR-9, miR-18 a, miR-19 a, miR-93, miR-106 b and miR-126 [141][142][143][144][145][146]. Noticeably, three miRNAs (miR-126, miR-143, miR-210) implicated in the DCIS-to-IDC transition are specifically dysregulated in basal-like IDC, suggesting that their deregulation plays a critical role in driving the progression of DCIS lesions to malignant basal-like IDC tumors (Table 2) Among luminal subtypes, luminal-B is a more aggressive subtype than luminal-A owing to low/negative expression of PR and/or HER2 overexpression, and high proliferation indicated by high Ki-67 [148].…”

A probabilistic approach for automated discovery of perturbed genes using expression data from microarray or RNA-Seq

“…Gene expression array platforms, such as microarray and RNA-Seq, have been routinely used in the study of complex diseases [9][10][11][12]. These platforms have been useful in assessing the collective behavior of cellular systems in response to disease perturbations by identifying a subset of significantly changing genes, which are likely to play a role in the disease [11,13]. Expression platforms produce high-dimensional data by simultaneously screening thousands of genes [13][14][15].…”

“…These platforms have been useful in assessing the collective behavior of cellular systems in response to disease perturbations by identifying a subset of significantly changing genes, which are likely to play a role in the disease [11,13]. Expression platforms produce high-dimensional data by simultaneously screening thousands of genes [13][14][15]. To reduce the dimensionality of gene expression data and identify the differentially expressed genes, 'frequentist' approaches employ fold change cutoffs and statistical significance levels [16,17].…”