Integrative clustering reveals a novel split in the luminal A subtype of breast cancer with impact on outcome.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

High-resolution analyses of copy number changes in disseminated tumor cells of patients with breast cancer.

The presence of disseminated tumor cells (DTCs) in bone marrow (BM) identifies breast cancer patients with less favorable outcome. Furthermore, molecular characterization is required to investigate the malignant potential of these cells. This study presents a single-cell array comparative genomic hybridization (SCaCGH) method providing molecular analysis of immunomorphologically detected DTCs. The resolution limit of the method was estimated using the cancer cell line SK-BR-3 on 44 and 244k arrays. The technique was further tested on 28 circulating tumor cells and four hematopoietic cells (HCs) from peripheral blood (n = 8 patients). The SCaCGH method was finally applied to 24 DTCs, three immunopositive cells morphologically classified as probable HCs from breast cancer patients and five HC controls from BM (n = 7 patients plus n = 1 healthy donor). The frequency of copy number changes of the DTCs revealed similarities with primary breast tumor samples. Three of the patients had available profiles for DTCs and the corresponding tumor tissue from primary surgery. More than two-third of the analyzed DTCs disclosed equivalent changes, both to each other and to the corresponding primary disease, whereas the rest of the cells showed balanced profiles. The probable HCs revealed either balanced profiles (n = 2) or changes comparable to the tumor tissue and DTCs (n = 1), indicating morphological overlap between HCs and DTCs. Similar aberration patterns were visible in DTCs collected at diagnosis and at 3 years relapse-free follow-up. SCaCGH may be a powerful tool for the molecular characterization of DTCs.

Metabolic clusters of breast cancer in relation to gene- and protein expression subtypes.

BACKGROUND: The heterogeneous biology of breast cancer leads to high diversity in prognosis and response to treatment, even for patients with similar clinical diagnosis, histology, and stage of disease. Identifying mechanisms contributing to this heterogeneity may reveal new cancer targets or clinically relevant subgroups for treatment stratification. In this study, we have merged metabolite, protein, and gene expression data from breast cancer patients to examine the heterogeneity at a molecular level. METHODS: The study included primary tumor samples from 228 non-treated breast cancer patients. High-resolution magic-angle spinning magnetic resonance spectroscopy (HR MAS MRS) was performed to extract the tumors metabolic profiles further used for hierarchical cluster analysis resulting in three significantly different metabolic clusters (Mc1, Mc2, and Mc3). The clusters were further combined with gene and protein expression data. RESULTS: Our result revealed distinct differences in the metabolic profile of the three metabolic clusters. Among the most interesting differences, Mc1 had the highest levels of glycerophosphocholine (GPC) and phosphocholine (PCho), Mc2 had the highest levels of glucose, and Mc3 had the highest levels of lactate and alanine. Integrated pathway analysis of metabolite and gene expression data uncovered differences in glycolysis/gluconeogenesis and glycerophospholipid metabolism between the clusters. All three clusters had significant differences in the distribution of protein subtypes classified by the expression of breast cancer-related proteins. Genes related to collagens and extracellular matrix were downregulated in Mc1 and consequently upregulated in Mc2 and Mc3, underpinning the differences in protein subtypes within the metabolic clusters. Genetic subtypes were evenly distributed among the three metabolic clusters and could therefore contribute to additional explanation of breast cancer heterogeneity. CONCLUSIONS: Three naturally occurring metabolic clusters of breast cancer were detected among primary tumors from non-treated breast cancer patients. The clusters expressed differences in breast cancer-related protein as well as genes related to extracellular matrix and metabolic pathways known to be aberrant in cancer. Analyses of metabolic activity combined with gene and protein expression provide new information about the heterogeneity of breast tumors and, importantly, the metabolic differences infer that the clusters may be susceptible to different metabolically targeted drugs.

Integrated analysis reveals microRNA networks coordinately expressed with key proteins in breast cancer.

BACKGROUND: The role played by microRNAs in the deregulation of protein expression in breast cancer is only partly understood. To gain insight, the combined effect of microRNA and mRNA expression on protein expression was investigated in three independent data sets. METHODS: Protein expression was modeled as a multilinear function of powers of mRNA and microRNA expression. The model was first applied to mRNA and protein expression for 105 selected cancer-associated genes and to genome-wide microRNA expression from 283 breast tumors. The model considered both the effect of one microRNA at a time and all microRNAs combined. In the latter case the Lasso penalized regression method was applied to detect the simultaneous effect of multiple microRNAs. RESULTS: An interactome map for breast cancer representing all direct and indirect associations between the expression of microRNAs and proteins was derived. A pattern of extensive coordination between microRNA and protein expression in breast cancer emerges, with multiple clusters of microRNAs being associated with multiple clusters of proteins. Results were subsequently validated in two independent breast cancer data sets. A number of the microRNA-protein associations were functionally validated in a breast cancer cell line. CONCLUSIONS: A comprehensive map is derived for the co-expression in breast cancer of microRNAs and 105 proteins with known roles in cancer, after filtering out the in-cis effect of mRNA expression. The analysis suggests that group action by several microRNAs to deregulate the expression of proteins is a common modus operandi in breast cancer.

Long non-coding RNAs differentially expressed between normal versus primary breast tumor tissues disclose converse changes to breast cancer-related protein-coding genes.

Breast cancer, the second leading cause of cancer death in women, is a highly heterogeneous disease, characterized by distinct genomic and transcriptomic profiles. Transcriptome analyses prevalently assessed protein-coding genes; however, the majority of the mammalian genome is expressed in numerous non-coding transcripts. Emerging evidence supports that many of these non-coding RNAs are specifically expressed during development, tumorigenesis, and metastasis. The focus of this study was to investigate the expression features and molecular characteristics of long non-coding RNAs (lncRNAs) in breast cancer. We investigated 26 breast tumor and 5 normal tissue samples utilizing a custom expression microarray enclosing probes for mRNAs as well as novel and previously identified lncRNAs. We identified more than 19,000 unique regions significantly differentially expressed between normal versus breast tumor tissue, half of these regions were non-coding without any evidence for functional open reading frames or sequence similarity to known proteins. The identified non-coding regions were primarily located in introns (53%) or in the intergenic space (33%), frequently orientated in antisense-direction of protein-coding genes (14%), and commonly distributed at promoter-, transcription factor binding-, or enhancer-sites. Analyzing the most diverse mRNA breast cancer subtypes Basal-like versus Luminal A and B resulted in 3,025 significantly differentially expressed unique loci, including 682 (23%) for non-coding transcripts. A notable number of differentially expressed protein-coding genes displayed non-synonymous expression changes compared to their nearest differentially expressed lncRNA, including an antisense lncRNA strongly anticorrelated to the mRNA coding for histone deacetylase 3 (HDAC3), which was investigated in more detail. Previously identified chromatin-associated lncRNAs (CARs) were predominantly downregulated in breast tumor samples, including CARs located in the protein-coding genes for CALD1, FTX, and HNRNPH1. In conclusion, a number of differentially expressed lncRNAs have been identified with relation to cancer-related protein-coding genes.

The HER2 amplicon includes several genes required for the growth and survival of HER2 positive breast cancer cells.

About 20% of breast cancers are characterized by amplification and overexpression of the HER2 oncogene. Although significant progress has been achieved for treating such patients with HER2 inhibitor trastuzumab, more than half of the patients respond poorly or become resistant to the treatment. Since the HER2 amplicon at 17q12 contains multiple genes, we have systematically explored the role of the HER2 co-amplified genes in breast cancer cell growth and their relation to trastuzumab resistance. We integrated aCGH data of the HER2 amplicon from 71 HER2 positive breast tumors and 10 cell lines with systematic functional RNA interference analysis of 23 core amplicon genes with several phenotypic endpoints in a panel of trastuzumab responding and non-responding HER2 positive breast cancer cells. Silencing of HER2 caused a greater growth arrest and apoptosis in the responding compared to the non-responding cell lines, indicating that the resistant cells are inherently less dependent on the HER2 pathway. Several other genes in the amplicon also showed a more pronounced effect when silenced; indicating that expression of HER2 co-amplified genes may be needed to sustain the growth of breast cancer cells. Importantly, co-silencing of STARD3, GRB7, PSMD3 and PERLD1 together with HER2 led to an additive inhibition of cell viability as well as induced apoptosis. These studies indicate that breast cancer cells may become addicted to the amplification of several genes that reside in the HER2 amplicon. The simultaneous targeting of these genes may increase the efficacy of the anti-HER2 therapies and possibly also counteract trastuzumab resistance. The observed additive effects seem to culminate to both apoptosis and cell proliferation pathways indicating that these pathways may be interesting targets for combinatorial treatment of HER2+ breast cancers.

Full sequencing of TP53 identifies identical mutations within in situ and invasive components in breast cancer suggesting clonal evolution.

In breast cancer, previous studies have suggested that somatic TP53 mutations are likely to be an early event. However, there are controversies regarding the cellular origin and linear course of breast cancer. The purpose of this study was to investigate tumor evolution in breast cancer by analyzing TP53 mutation status in tumors from various stages of the disease. The entire coding sequence of TP53 was sequenced in a cohort of pure ductal carcinoma in situ (DCIS), pure invasive cancer (≤15mm) and mixed lesions (i.e. invasive cancer with an in situ component). Of 118 tumor samples, 19 were found to harbor a TP53 mutation; 5 (15.6%) of the pure DCIS, 4 (10.5%) of the pure invasive cancers and 10 (20.8%) of the mixed lesions. In the mixed lesions, both the invasive and the DCIS components showed the same mutation in all 5 cases where the two components were successfully microdissected. Presence of the same mutation in both DCIS and invasive components from the same tumor indicates same cellular origin. The role of mutant TP53 in the progression of breast cancer is less clear and may vary between subtypes.