Integrative analysis of breast cancer profiles in TCGA by TNBC subgrouping reveals novel microRNA-specific clusters, including miR-17-92a, distinguishing basal-like 1 and basal-like 2 TNBC subtypes

Human breast cancer expression data and their demographic information were obtained from NIH NCI Genomic Data Commons public database [9], originally acquired in the scope of TCGA-BRCA program and processed using the same pipeline. Only samples with both mRNA and miRNA expression profiling were considered. Selection of TNBC cases and their classification into TNBC subtypes was adopted from results of 4-subtype schema of Lehmann et al. [4].

All analyses were based on raw expression counts downloaded from Genomic Data Commons database. First, mRNAs/miRNAs entries that were not expressed in at least half of samples of any of the TNBC subtypes were filtered out. Next, the default processing pipeline from R package DESeq2 (v.1.20) [10] was applied to normalize the counts and correct outlying values. This includes a size factor estimation using the standard median ratio method, a dispersion estimation using parametric fitting, expression data fitting using negative binomial generalized linear model with the minimum of 7 replicates for outlier replacement and the lower bound of 0.5 on estimated counts.

Selected TNBC subtypes were compared using DESeq2 differential expression pipeline, performing two-tailed Wald test of the fitted models using the normal distribution as null distribution. For multiple group comparison, one-way ANOVA test with Tukey’s HSD correction was applied over log2-transformed data. FDR was controlled with Benjamini–Hochberg procedure and comparisons with adjusted p-value ≤0.05 were considered as statistically significant. The differences in expression between groups of interest were quantified with log2 fold change. Note that DESeq2 reports shrunken log2 fold change to avoid possible bias in low-expressed entries. Tables with complete results are attached. The most significant differences – with respect to their adjusted p-values – are illustrated with heatmaps conveniently exported via MetaboAnalyst (v4.0) [11], using an appropriate size of top RNAs and Ward’s method for hierarchical clustering. Up-regulated and down-regulated mRNAs are shown separately, since a vast majority of all top mRNAs falls into only one of these directions.

Differentially expressed miRNAs were analyzed with mirPATH (v3.0) [12], miTALOS (v2) [13], and miRNet (v2.0) [14] for target gene pathway enrichment. These multiple tools were used for their application of multiple pathway databases (e.g. KEGG, Gene Ontology, and Reactome) and different target databases (including TarBase, microT-CDS, and TargetScan) encompassing both experimentally validated and computationally predicted targets. Some of these tools allow only a limited number of miRNAs on input, in which case the top miRNAs were selected with respect to their statistical significance. Up-regulated and down-regulated miRNAs were analyzed separately in attempt to distinguish which functional results are a subject of up-regulation and down-regulation. All produced results with p-value ≤0.05 are attached.

Top 1000 up-regulated and top 1000 down-regulated mRNAs with respect to their adjusted p-value were analyzed with DAVID functional annotation tool (v6.8) [15] to produce clusters of functional annotations. The default parameters with medium stringency were used, computing over the background of the whole human genome. Again, up-regulated and down-regulated mRNAs were analyzed separately. Clusters with enrichment score ≥ 1 containing at least one annotation with adjusted p-value ≤0.05 are listed.

miRNet was further utilized to construct core networks of differentially expressed miRNAs and their targets with highest connectivity, setting up the degree threshold appropriately to obtain a network of a reasonable size.

MicroRNA-mRNA pairs identified during correlation analysis as significantly correlated were filtered for those with correlation coefficient < − 0.5 and with RNAs differentially expressed between BL1 and BL2 with abs (log2 fold change) > 0.5. Next, candidate pairs checked against microT-CDS (v5.0) [16] and TargetScan (v7.2) [17] target prediction databases with the default parameter settings, selecting pairs present in either database directly or indirectly with a closely related paralogous mRNA. Furthermore, candidate pairs were also narrowed to RNAs, the expression profiles of which showed a possible effect on survival rate of TNBC cases in METABRIC cohort based on visualization by Kaplan-Maier Plotter web tool [18] with trichotomization of samples. Since the low number of TNBC cases is not sufficient to achieve a high statistical power in survival analysis, the RNAs with the largest impact on survival outcome were selected even though the difference might not be statistically significant.

Cells were obtained from ATCC and cultured according to the provided recommendations: RPMI with 10% fetal bovine serum and 1% penicillin/streptomycin (HCC70) or DMEM with 10% fetal bovine serum and 1% penicillin/streptomycin (MDA MB 468).

RNA was extracted from cultured cells using Trizol (Invitrogen) according to the manufacturer’s protocol. For detection of microRNA species, purified RNA (250 ng) was subjected to microRNA-specific RT-PCR using the Taqman primer/probe system (Applied Biosystems) and the High-capacity reverse transcription kits (Applied Biosystems) followed by qPCR on the QuantStudio 5 (Applied Biosystems). For detection of mRNA, purified RNA (500 ng) was subjected to reverse transcription using random primers (Applied Biosystems), followed by qPCR using mRNA-specific primers and SYBR Green Universal Master Mix (Applied Biosystems). Expression was quantified using the delta-delta Ct method, normalized to either small nucleolar U6 (microRNAs) or GAPDH (mRNAs) and plotted in reference to the average of all control samples using Prism version 6 (GraphPad Software). Students t-test was used for comparing expression values between two samples.

The NIH NCI Genomic Data Commons (GDC) database [9] contains mRNA expression profiles of 1098 cases of human breast cancer from TCGA-BRCA project [19]. Lehmann et al. [4] analyzed expression data of 1059 of these cases, identified 180 TNBC cases and 176 of them assigned among the subtypes BL1, BL2, M, and LAR. Adopting this subtyping, we next selected cases for which microRNA expression data were also available, resulting in 173 cases (Fig. 1a; list of case IDs and corresponding subtypes are in Additional file 1) with 60,483 quantified mRNAs and 1881 quantified microRNAs using RNA-Seq and miRNA-Seq technologies. The distribution of individual subtypes is shown in Fig. 1b. These groups are approximately balanced and each of them contains more than 30 samples.

Demographic details for individuals with TNBC grouped by the subtypes are listed in Table 1. All persons are female, approximately one-third black or African American, and predominantly diagnosed with ductal or lobular neoplasms. The most frequent age at diagnosis is in the 40’s although this trend is shifted to the 50’s for BL2 subtype, whereas M and LAR subtypes have a notable proportion of cases diagnosed in the 20’s and 30’s. Based on the monitored vital status, reported mortality for the LAR subtype is almost double the rate for other subtypes.

Exploration of the expression landscape of all TNBC subtypes reveals over 200 microRNAs as differentially expressed with statistical significance. Hierarchical clustering reveals several clusters of 10 or more microRNAs, often with a strong co-expression pattern, that are distinct among the subtypes (Fig. 2). These data support the idea that microRNA expression is tightly linked to intrinsic subtypes within TNBC.

Given the disparity in patient outcomes between BL1 and BL2 [4], we further focused on gene expression signature differences between these subtypes. Differential analysis of gene expression identified over 8000 differentially expressed mRNAs, as shown on a selected example in Fig. 3 (complete list in Additional file 2). Gene ontology analysis of the top mRNAs revealed multiple functional areas relevant to cancer pathology (Table 2, complete list in Additional file 3). Transcripts up-regulated in BL1 are connected with mRNA synthesis and processing, nuclear export, cell division as well as DNA repair and viral processing, whereas transcripts up-regulated in BL2 are related to extracellular matrix, collagen, cell junctions, and cellular membrane components. These differences suggest a role for gene expression in altering interactions with the extracellular environment in BL2, possibly facilitating spread of tumor cells, which would be consistent with more frequent distant relapses clinically observed for the BL2 TNBC subtype [4]. Given the critical nature of these cellular functions, we sought to identify microRNAs with a strong likelihood to regulate mRNA expression differences between BL1 and BL2 subtypes.

Differential expression analysis identified 159 microRNAs expressed with statistical significance. Top 50 microRNAs are presented in Fig. 4 (complete list in Additional file 4). Subsequent functional analysis of targets of these microRNAs was performed over various gene annotation databases and microRNA target databases, encompassing databases for experimentally validated targets as well as algorithmically predicted targets. In general, many biological functions, each with hundreds of mRNAs differentially expressed, were predicted to be targeted by several dozen microRNAs (Additional file 5). The detected functions are often cancer-related, but also extend to many other biological processes, and frequently are linked to both up-regulated and down-regulated microRNAs, illustrating the regulatory complexity of microRNAs. Although these results do not identify any particular microRNA-mRNA pairs relevant for BL1 and BL2 subtype distinction, it affirms the role of microRNAs in the etiology of the subtypes. Separate network analysis of differentially expressed up-regulated and down-regulated microRNAs and their targets confirms that mRNA targets in the network interaction core are strongly linked to cancer biology, including functions such as cell growth and cell cycle, apoptosis regulation, vasodilation, glucose metabolism, and inflammation (Fig. 5).

In order to identify nodes likely to underlie biological differences between BL1 and BL2 tumors we conducted network analysis, combining predicted miRNA-mRNA pairs with BL1-BL2 differential expression data. Further, we sought to find suitable pairs of microRNAs and their targets for experimental validation of their expression and regulation in BL1 and BL2 TNBC cell lines. Expression patterns of microRNAs should exhibit significant anti-correlational tendency with the expression levels of their targeted mRNAs. Therefore, we compared expression profiles of all differentially expressed RNAs and all significantly non-zero correlations were selected as outlined in Fig. 6 (complete table with values in Additional file 6).

To identify mRNA-miRNA pairs likely to exhibit a biological relationship, we considered only pairs with correlation coefficient below − 0.5, consisting of RNAs with absolute log2 fold change above 0.5. As a result, 280 candidate pairs remained, consisting of 27 unique microRNAs and 168 unique mRNAs. To refine our selection, we chose only pairs identified by target prediction databases and further, only considered mRNAs with a possible impact on survival outcomes, resulting in 10 candidate pairs of 3 unique microRNAs and 8 unique mRNAs (Table 3). Their correlations and a heatmap of expression within BL1 and BL2 TNBC subgroups are shown in Fig. 7, as well as an example of survival charts.

We next sought to validate the predicted expression differences of microRNAs and their targets that were shown to be distinct between the BL1, BL2, and M subtypes of TNBC, as recapitulated in breast cancer cell lines. For this, we chose cells lines previously identified as corresponding to specific TNBC subtypes (HCC70 = basal-like 1; MDA-MB-468 = basal-like2; and MDA-MB-231, SUM159 and Hs578t = M) [3]. We focused on the network of miRNAs and mRNAs identified as distinct between BL1 and BL2 tumors (Fig. 5b, Table 3). Expression of miR-17 and miR-19a was elevated in MDA-MB-468 (BL1) cells as compared to HCC70 (BL2) cells while miR-18a was not statistically significant (Fig. 8a). miR-17, miR-18a, and miR-19a are co-expressed from the MIR17–92a cluster of microRNAs and are predicted to target mRNAs regulating cell cycle, apoptosis, and signal transduction (Fig. 5 and Table 3). We examined the expression of these predicted targets in HCC70 and MDA-MB-468 cells as representative of the BL1 and BL2 TNBC subtypes. Intriguingly, of the fourteen miR-17-, miR-18a-, and miR-19a- targets tested, only four showed elevated expression in HCC70 (BL2) cells compared to MDA-MB-468 (BL1) cells. Remarkably however, predicted targets of miR-17 and miR-19a, IL1R1 and INPP4B (Table 3), were expressed more strongly in HCC70 (BL2) cells, while the predicted targets of miR-18a were not differentially expressed (Fig. 8b). Thus, TNBC cell lines showed similar anti-correlation between miRNA (miR-17, miR-19a) and mRNA target (IL1R1, INPP4B) as the TCGA-based segregation of TNBC tumors into BL1 and BL2 subtypes (Table 3). In addition, CDKN1A (miR-17 target that did not anti-correlate in the TCGA data) and FAM214A (miR-18a target) also showed elevated expression in the HCC70 (BL2) cells (Fig. 8b).