Background
Breast cancer is one of the most common types of cancer among American women - about 12 % of women in the USA will develop invasive breast cancer during their lifetime - and it is a leading cause of cancer death. It is a heterogeneous disease, with substantial genotypic and phenotypic diversity [
1]. Depending on the expression status of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2), it can be classified into four molecular subtypes: luminal A, luminal B, HER2-positive, and basal-like (or triple-negative) breast cancer. In a widely used mouse model of breast cancer, mammary gland-specific expression of the polyoma middle T (PyMT) oncoprotein under the control of the MMTV promoter/enhancer in transgenic mice (MMTV-PyMT) results in widespread transformation of the mammary epithelium and subsequent development of multifocal mammary adenocarcinomas and metastatic lesions in the lymph nodes and in the lungs [
2]. Breast cancer in PyMT mice is particularly noted by its short latency, high penetrance, and a high incidence of lung metastasis [
3]. Tumor formation and progression in these mice can be divided into four stages: hyperplasia, adenoma/mammary intra-epithelial neoplasia, early carcinoma, and late carcinoma [
4] (we refer to all these four stages as tumor in this study). Whole-genome array profiling indicates that PyMT tumors most closely resemble the luminal B subtype of human breast cancer [
5], although end-stage PyMT tumors are ER-negative and PR-negative [
4]. Most genetically engineered mouse models that target oncogenes such as PyMT do not fully recapitulate several aspects of the development of human breast cancer. Not only are transgenes expressed at a level different from that of the same oncogenes in human breast cancer, but also throughout the ductal tree [
6] they do not target the cell types that are the cells of origin in human breast cancer. Despite these limitations, the use of the PyMT mouse model and other similarly engineered models has been instrumental in elucidating the genetics and biology of breast cancer.
In addition to numerous protein-coding genes, many microRNAs (miRNAs) also play important roles in breast cancer. Since their discovery over two decades ago, miRNAs have been recognized as important regulators of many key cellular processes including development [
7], cell cycle progression [
8], differentiation [
9], and apoptosis [
10]. Their dysregulation occurs in various types of cancer [
11] and is associated with different stages and aspects such as tumor initiation, drug resistance, and metastatic spread of the disease [
12]. While some miRNAs have similar expression patterns across all cancer types, others are cell-type-specific and thus could potentially serve as cancer biomarkers [
13]. A recent study using microarray and machine learning data analysis identified a small number of miRNAs differentially expressed in luminal B breast cancer [
14]. Another study of miRNA expression in different breast cancer subtypes also identified positive and negative miRNA signatures for ER-positive, PR-positive, and Her2-positive luminal B tumors [
15]. From carcinogenesis to metastasis, like any other types of cancer, the development of breast cancer is a multistage process. MiRNAs have been associated with different developmental stages including epithelial to mesenchymal transition (EMT), migration, invasion, and angiogenesis of breast cancer [
16].
Affecting approximately 20 % of all patients with breast cancer, luminal B is the second most common but the least studied subtype of breast cancer. Only a few studies have examined the expression profiles of miRNAs in luminal B breast cancer [
17‐
21], and the regulatory roles of miRNAs in the progression of the disease have yet to be investigated. In this study, using a luminal B breast cancer mouse model we profiled miRNA expression at four time points that represent different key stages of cancer progression and identified miRNAs differentially expressed between tumor and normal mammary gland cells. We considered the expression of both miRNAs and messenger RNAs (mRNAs) at multiple time points to improve the identification of potential targets of miRNAs. By combining gene functional and pathway annotation with miRNA-mRNA interactions, we created a PyMT-specific tripartite miRNA-mRNA-pathway network and identified novel functional regulatory programs (FRPs), that is miRNA-mRNA regulatory modules relevant to the development of luminal B breast cancer. The identification of novel cancer-related miRNAs and the FRPs shed new light on the disease mechanisms behind breast cancers, and will help the development of new biomarkers for early cancer detection and drug targets for plausible treatments.
Conclusions
In this study we profiled the expression of miRNAs during breast cancer progression in the PyMT mouse model. This model closely resembles ER-positive luminal B breast cancer, a subtype of breast cancer that traditionally is poorly studied in humans. Instead of a particular cancer state, we considered the whole disease progression, sampling four time points to represent hyperplasia, adenoma, early carcinoma, and late carcinoma phases. By integrating miRNA and mRNA expression profiles, we identified 151 differentially expressed miRNAs and their target genes involved in the regulatory activities of cancer biology, with a strict dual nature of either upregulation or downregulation during the whole disease progression. From the comparison with miRCancer as an assessment of concordance with other miRNA studies as a whole, we observed high-level agreement in miRNA expression between breast cancer in PyMT mice and human cancers reported in previous studies. The expression profiles of several key miRNAs from PyMT mice in our study matched those from previously obtained ER-positive cancers, confirming the luminal B subtype status of the PyMT mouse model, while different expression profiles of signature miRNAs for the triple-negative subtype indicated their divergence. Using this model, we identified 82 novel breast cancer-related miRNAs, 35 of which can potentially regulate 271 protein-coding genes based on sequence complementarity and expression profiles. After a comprehensive study, we found that a subset of 26 novel miRNAs potentially regulate 69 genes that are implicated in biological processes related to cancer biology, including cell and focal adhesion functions, cell migration, and angiogenesis. During breast cancer progression in PyMT mice, genes regulated by significantly over-expressed miRNAs participate in cell adhesion or cell differentiation, biological processes related to the cancer progression and metastasis. Collated with cancer hallmarks, our study showed that genes targeted by miRNAs with perturbed expression in breast cancer in PyMT mice are involved in activating invasion and metastasis, sustaining proliferative signaling, and evading growth suppressors during the transition from adenoma to carcinoma. Finally, applying a novel clustering method to an annotated miRNA-mRNA regulatory network, we identified 84 FRPs, all of which are involved in cancer-related biological processes, including metabolism, endocytosis, transmembrane transport, and the cellular immune response.
Abbreviations
ER, estrogen receptor; FRP, functional regulatory program; HER2, human epidermal growth factor receptor 2; KEGG, Kyoto Encyclopedia of Genes and Genomes; MB, maximal biclique; miRNA, microRNA; mRNA, messenger RNA; PR, progesterone receptor; PyMT, polyoma middle T antigen; RNA-seq, RNA sequencing; TCGA, The Cancer Genome Atlas