How mixtures of immune cells associate with cancer cell phenotype and affect pathogenesis is still unclear. In 15 breast cancer gene expression datasets, we invariably identify three clusters of patients with gradual levels of immune infiltration. The intermediate immune infiltration cluster (Cluster B) is associated with a worse prognosis independently of known clinicopathological features. Furthermore, immune clusters are associated with response to neoadjuvant chemotherapy. In silico dissection of the immune contexture of the clusters identified Cluster A as immune cold, Cluster C as immune hot while Cluster B has a pro-tumorigenic immune infiltration. Through phenotypical analysis, we find epithelial mesenchymal transition and proliferation associated with the immune clusters and mutually exclusive in breast cancers. Here, we describe immune clusters which improve the prognostic accuracy of immune contexture in breast cancer. Our discovery of a novel independent prognostic factor in breast cancer highlights a correlation between tumor phenotype and immune contexture.

MicroRNAs (miRNAs) are non-coding RNAs regulating gene expression post-transcriptionally. We have characterized the role of miRNAs in regulating the human epidermal growth factor receptor 2 (HER2)-pathway in breast cancer. We performed miRNA gain-of-function assays by screening two HER2 amplified cell lines (KPL-4 and JIMT-1) with a miRNA mimic library consisting of 810 human miRNAs. The levels of HER2, phospho-AKT, phospho-ERK1/2, cell proliferation (Ki67) and apoptosis (cPARP) were analyzed with reverse-phase protein arrays. Rank product analyses identified 38 miRNAs (q < 0.05) as inhibitors of HER2 signaling and cell growth, the most effective being miR-491-5p, miR-634, miR-637 and miR-342-5p. We also characterized miRNAs directly targeting HER2 and identified seven novel miRNAs (miR-552, miR-541, miR-193a-5p, miR-453, miR-134, miR-498, and miR-331-3p) as direct regulators of the HER2 3'UTR. We demonstrated the clinical relevance of the miRNAs and identified miR-342-5p and miR-744* as significantly down-regulated in HER2-positive breast tumors as compared to HER2-negative tumors from two cohorts of breast cancer patients (101 and 1302 cases). miR-342-5p specifically inhibited HER2-positive cell growth, as it had no effect on the growth of HER2-negative control cells in vitro. Furthermore, higher expression of miR-342-5p was associated with better survival in both breast cancer patient cohorts. In conclusion, we have identified miRNAs which are efficient negative regulators of the HER2 pathway that may play a role in vivo during breast cancer progression. These results give mechanistic insights in HER2 regulation which may open potential new strategies towards prevention and therapeutic inhibition of HER2-positive breast cancer.

BackgroundBreast cancer is a heterogeneous disease at the clinical and molecular level. In this study we integrate classifications extracted from five different molecular levels in order to identify integrated subtypes.MethodsTumor tissue from 425 patients with primary breast cancer from the Oslo2 study was cut and blended, and divided into fractions for DNA, RNA and protein isolation and metabolomics, allowing the acquisition of representative and comparable molecular data. Patients were stratified into groups based on their tumor characteristics from five different molecular levels, using various clustering methods. Finally, all previously identified and newly determined subgroups were combined in a multilevel classification using a “cluster-of-clusters” approach with consensus clustering.ResultsBased on DNA copy number data, tumors were categorized into three groups according to the complex arm aberration index. mRNA expression profiles divided tumors into five molecular subgroups according to PAM50 subtyping, and clustering based on microRNA expression revealed four subgroups. Reverse-phase protein array data divided tumors into five subgroups. Hierarchical clustering of tumor metabolic profiles revealed three clusters. Combining DNA copy number and mRNA expression classified tumors into seven clusters based on pathway activity levels, and tumors were classified into ten subtypes using integrative clustering. The final consensus clustering that incorporated all aforementioned subtypes revealed six major groups. Five corresponded well with the mRNA subtypes, while a sixth group resulted from a split of the luminal A subtype; these tumors belonged to distinct microRNA clusters. Gain-of-function studies using MCF-7 cells showed that microRNAs differentially expressed between the luminal A clusters were important for cancer cell survival. These microRNAs were used to validate the split in luminal A tumors in four independent breast cancer cohorts. In two cohorts the microRNAs divided tumors into subgroups with significantly different outcomes, and in another a trend was observed.ConclusionsThe six integrated subtypes identified confirm the heterogeneity of breast cancer and show that finer subdivisions of subtypes are evident. Increasing knowledge of the heterogeneity of the luminal A subtype may add pivotal information to guide therapeutic choices, evidently bringing us closer to improved treatment for this largest subgroup of breast cancer.Electronic supplementary materialThe online version of this article (doi:10.1186/s13058-017-0812-y) contains supplementary material, which is available to authorized users.