Breast 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. Tumor 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. Based 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. The 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.

Long non-coding RNAs (lncRNAs) are involved in breast cancer pathogenesis through chromatin remodeling, transcriptional and post-transcriptional gene regulation. We report robust associations between lncRNA expression and breast cancer clinicopathological features in two population-based cohorts: SCAN-B and TCGA. Using co-expression analysis of lncRNAs with protein coding genes, we discovered three distinct clusters of lncRNAs. In silico cell type deconvolution coupled with single-cell RNA-seq analyses revealed that these three clusters were driven by cell type specific expression of lncRNAs. In one cluster lncRNAs were expressed by cancer cells and were mostly associated with the estrogen signaling pathways. In the two other clusters, lncRNAs were expressed either by immune cells or fibroblasts of the tumor microenvironment. To further investigate the cis-regulatory regions driving lncRNA expression in breast cancer, we identified subtype-specific transcription factor (TF) occupancy at lncRNA promoters. We also integrated lncRNA expression with DNA methylation data to identify long-range regulatory regions for lncRNA which were validated using ChiA-Pet-Pol2 loops. lncRNAs play an important role in shaping the gene regulatory landscape in breast cancer. We provide a detailed subtype and cell type-specific expression of lncRNA, which improves the understanding of underlying transcriptional regulation in breast cancer.

Introduction: Hypercoagulability in malignancy increases the risk of thrombosis, but is also involved in cancer progression. Experimental studies suggest that tissue factor (TF) and tissue factor pathway inhibitor (TFPI) are involved in cancer biology as a tumor- promoter and suppressor, respectively, but the clinical significance is less clear. Here, we aimed to investigate the clinical relevance of TF and TFPI genetic and phenotypic diversity in breast cancer.Methods: The relationship between tumor messenger RNA (mRNA) expression and plasma levels of TF and TFPI (α and β), tagging single nucleotide polymorphisms (tagSNPs) inF3(TF) (n = 6) andTFPI(n = 18), and clinicopathological characteristics and molecular tumor subtypes were explored in 152 treatment naive breast cancer patients. The effect of tumor expressed TF and TFPIα and TFPIβ on survival was investigated in a merged breast cancer dataset of 1881 patients.Results: Progesterone receptor negative patients had higher mRNA expression of total TFPI (α + β) (P = 0.021) and TFPIβ (P = 0.014) in tumors. TF mRNA expression was decreased in grade 3 tumors (P = 0.003). In plasma, total TFPI levels were decreased in patients with larger tumors (P = 0.013). SNP haplotypes ofTFPI, but notTF, were associated with specific clinicopathological characteristics like tumor size (odds ratio (OR) 3.14,P = 0.004), triple negativity (OR 2.4,P = 0.004), lymph node spread (OR 3.34,P = 0.006), and basal-like (OR 2.3,P = 0.011) and luminal B (OR 3.5,P = 0.005) molecular tumor subtypes. Increased expression levels of TFPIα and TFPIβ in breast tumors were associated with better outcome in all tumor subtypes combined (P = 0.007 andP = 0.005) and in multiple subgroups, including lymph node positive subjects (P = 0.006 andP = 0.034).Conclusions: This study indicates that genetic and phenotypic variation of both TFPIα and TFPIβ, more than TF, are markers of cancer progression. Together with the previously demonstrated tumor suppressor effects of TFPI, the beneficial effect of tumor expressed TFPI on survival, renders TFPI as a potential anticancer agent, and the clinical significance of TFPI in cancer deserves further investigation.

This case describes an infiltrating breast tumour with thyroid transcription factor-1 (TTF-1) positive staining and ductal differentiation in a 72-year-old woman. The presence of ductal carcinoma in situ with positive TTF-1 is a strong indication that this is a primary tumour and not a metastasis from lung.