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.Peer reviewe

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

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

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

HDAC3 (histone deacetylase 3) mRNA and its putative regulatory antisense lncRNA.

<p>(<b>A.</b>) Genomic locus of HDAC3 on chromosome 5 and the antisense transcript downstream of HDAC3 with genomic positions of strand-specific RT-qPCR primers/products. Annotation track DE-TAR corresponds to genomic loci significantly downregulated upon TP53 induction <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106076#pone.0106076-Hackermller1" target="_blank">[43]</a>. Both transcripts appear to be significantly differentially expressed on the custom microarray (), exhibiting a non-synonymous expression pattern (<b>B.</b>). The transcription start site of the annotated antisense RNA overlaps with the transcription start site of DIAPH1. Genome-wide predictions of functional open reading frames (RNAcode, ) correspond mainly to exons of HDAC3 mRNA, while some short putative open reading frames overlap the antisense transcript. (<b>C.</b>) Strand-specific RT-qPCR validations relative to normal sample “RP38” for both, the HDAC3 mRNA and the antisense transcript.</p

Proximal lncRNA – mRNA pairs.

<p>For non-coding DE- probes significantly differentially expressed between normal and tumor samples (FDR) the protein-coding gene (Gencode release v12) with closest genome coordinates was identified, and the pair retained if the protein-coding gene was differentially expressed at the same FDR cutoff. Log2 fold change of the non-coding probe (<i>x</i>-axis) and the maximal log2 fold change of probes located in exons of the protein-coding gene (<i>y</i>-axis) is depicted as a bivariate histogram using hexagonal binning (R package hexbin). Pairs with converse fold changes are shown in the left upper and right lower quadrant. Pairs with consistent fold changes but opposite reading direction are shown in the left lower and right upper quadrant (see also panel describing direction of expression changes for each quadrant). Numbers in quadrant correspond to number of unique genes depicted. (<b>A.</b>) Proximal pairs, where the non-coding probe is intergenic. (<b>B.</b>) Pairs where the non-coding probes is in an intron of the protein-coding gene. (<b>C.</b>) Pairs where the non-coding probe and the protein-coding gene are on opposite strands and overlap at least partially.</p

Differential expression analysis.

<p>The expression patterns of mRNA-probes and non-coding probes of 26 breast tumors and 5 normal breast tissues were investigated using the custom microarray. (<b>A.</b>) Fraction of unique genomic loci significantly differentially expressed () between normal and tumor samples located completely in exons of protein-coding genes (Gencode v12), in exons of known lncRNAs (lincRNAs, Gencode v12 lncRNAs, lncRNAs as annotated in lncRNAdb <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106076#pone.0106076-Amaral1" target="_blank">[51]</a>, and lncRNAs contained in chromatin <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106076#pone.0106076-Mondal1" target="_blank">[27]</a>), in exons of transcripts of uncertain coding potential (TUCPs <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106076#pone.0106076-Cabili1" target="_blank">[23]</a>), in exons of short RNAs (UCSC sno/miRNA track), in genomic loci with conserved secondary structure motifs (Evofold <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106076#pone.0106076-Pedersen1" target="_blank">[59]</a>, RNAz <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106076#pone.0106076-Gruber1" target="_blank">[58]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106076#pone.0106076-Washietl3" target="_blank">[97]</a> and SISSIz <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106076#pone.0106076-Gesell1" target="_blank">[53]</a>), in antisense-direction to known exons (Gencode v12), or in novel genomic regions. (<b>B.</b>) Fraction of unique genomic loci significantly differentially expressed () between Basal-like and Luminal tumors and located in genomic annotations as described for panel A. Numbers beside bars denote absolute number of unique DE-loci.</p

DE-probe overlap with genomic annotation (Basal-like versus Luminal A and B tumors).

<p><b>A.–D.</b>: Number of DE-probes significantly differentially expressed between Basal-like and Luminal A and B tumors () and mapping to different genomic annotations. Log2 transformed odds ratios and their 95% confidence interval for the respective annotation dataset are shown. Odds ratios of observed versus expected probe overlaps were calculated and tested by Fisher's exact test for significant enrichment or depletion, with *** indicating , ** , and * , respectively. Missing error bars denote no DE-probes overlapped with according annotation. Results are shown (<b>A.</b>) for DE-probes located in annotated protein coding genes versus intergenic space based on Gencode release v12, (<b>B.–D.</b>) for intergenic or intronic non-coding DE-probes either located in several classes of known and predicted ncRNAs (B.), in non-coding transcripts regulated during cell cycle (CC), upon TP53 or Stat3 induction <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106076#pone.0106076-Hackermller1" target="_blank">[43]</a> (C.), or in regulatory sites (D.). (<b>E.</b>) Fraction of unique non-coding DE-loci in exons of known short and long ncRNAs, in genomic sites with conserved secondary structures, in antisense-direction to known non-coding exons (Gencode v12), or in novel sites. Numbers denote absolute number of DE-loci located in novel sites. For detailed output of Fisher's exact tests see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106076#pone.0106076.s011" target="_blank">Table S4</a>, and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106076#pone.0106076.s014" target="_blank">Table S7</a> for detailed description of annotation datasets.</p