Molecular characterisation of cell line models for triple-negative breast cancers
© Grigoriadis et al.; licensee BioMed Central Ltd. 2012
Received: 18 June 2012
Accepted: 31 October 2012
Published: 14 November 2012
Triple-negative breast cancers (BC) represent a heterogeneous subtype of BCs, generally associated with an aggressive clinical course and where targeted therapies are currently limited. Target validation studies for all BC subtypes have largely employed established BC cell lines, which have proven to be effective tools for drug discovery.
Given the lines of evidence suggesting that BC cell lines are effective tools for drug discovery, we assessed the similarities between triple-negative BCs and cell lines, to identify in vitro representatives, modelling the diversity within this BC subtype. 25 BC cell lines, enriched for those lacking ER, PR and HER2 expression, were subjected to transcriptomic, genomic and epigenomic profiling analyses and comparisons were made to existing knowledge of corresponding perturbations in triple-negative BCs. Transcriptional analysis segregated ER-negative BC cell lines into three groups, displaying distinctive abundances for genes involved in epithelial-mesenchymal transition, apocrine and high-grade carcinomas. DNA copy number aberrations of triple-negative BCs were well represented in cell lines and genes with coordinately altered gene expression showed similar patterns in tumours and cell lines. Methylation events in triple-negative BCs were mostly retained in epigenomes of cell lines. Combined methylation and gene expression analyses revealed a subset of genes characteristic of the Claudin-low BC subtype, exhibiting epigenetic-regulated gene expression in BC cell lines and tumours, suggesting that methylation patterns are likely to underpin subtype-specificity.
Here, we provide a comprehensive analysis of triple-negative BC features on several molecular levels in BC cell lines, thereby creating an in-depth resource to access the suitability of individual lines as experimental models for studying BC tumour biology, biomarkers and possible therapeutic targets in the context of preclinical target validation.
KeywordsMicroarray Gene expression profiling Comparative genomic hybridisation Methylation arrays Triple negative Breast cancer
Oestrogen-receptor (ER) negative breast cancer (BC) accounts for approximately 20% of all newly diagnosed breast malignancies [1–3]. Clinically, however, this group of BCs contains different subtypes and can be subdivided into either HER2-positive or triple-negative BCs, defined by very low or absent immunohistochemical expression of ER and progesterone receptor (PR), and low expression and lack of amplification of HER2 . Triple-negative BCs account for 10-15% of all breast tumours and are mostly of high grade, have a high incidence of TP53 mutations, and show proliferative characteristics with a higher propensity to spread to visceral organs . Sharing many of these phenotypic features with triple-negative BCs are breast tumours of the ‘intrinsic’ basal-like subtype. These tumours generally lack ER and HER2 expression and are molecularly characterised by the expression of genes associated with both basal epithelium and myoepithelium of the normal mammary gland (e.g. KRT5/6, KRT14, VIM, CDH3, CRYAB, CAV1 and CAV2, as well as EGFR) [2, 5]. Approximately, 80% of triple-negative BCs show features of basal-like BCs [4, 6, 7]. While most triple-negative BCs show aggressive clinical behaviour and have very limited targeted therapies, they also encompass subgroups of cancers sensitive to chemotherapy and having a good prognosis . Hence, continuous efforts to characterise this BC population have already identified several subgroups. One of the proposed groups comprise “Claudin-low” tumours, which are characterised by gene expression profiles similar to those found in the so-called breast ‘cancer stem cell’ populations , while other subgroups were classified as having higher expression of the interferon-related or apocrine genes [9–12]. BC cell lines are essential tools in BC research and have been widely used to elucidate BC biology and new therapies [13, 14]. Since cell lines are easily propagated and genetically manipulated, extensive information about their transcriptome, genome and to a lesser extent epigenome has been produced [11, 15–19]. Several studies have compared and integrated gene expression profiles and genomic alterations between primary breast tumours and BC cell lines, demonstrating that the heterogeneity found in primary BCs is to a certain extent recapitulated in the panel of commonly used BC cell lines [15, 16, 18]. Given the increasing knowledge of the diversity and complexity among BC subtypes it has also become evident that no individual cell line will recapitulate all aspects of the disease. Here we interrogated genome-wide transcriptional profiles with genomic and epigenetic profiling in a collection of 25 BC cell lines enriched for those of triple-negative phenotype. We have focused on gene signatures, underlying DNA copy number aberrations (CNAs) and epigenetic events specifically associated with triple-negative BCs. By cataloguing these perturbations on a gene-centric basis we have extended the characterisation of these BC cell lines and offer valuable insights on their suitability in modelling certain features of this heterogeneous disease.
BC cell lines segregate into three groups based on their transcriptional profiles
Representation of ER-negative breast tumour-related gene signatures in BC cell lines
Copy number aberrations and associated gene expression changes in BC cell lines represent those observed in triple-negative BCs
Epigenetic influence on sub-type specific genes in ER-negative BC cell lines
Triple-negative BCs represent a heterogeneous group with diverse deregulation of biological pathways. Here, we extended the molecular characterisation of ER-negative BC cell lines based on their genetic, epigenetic and transcriptional profiles, and correlated these with a comprehensive compendium of gene signatures reflecting different features of ER-negative BCs [8, 9, 28, 39–45]. Initial cluster analysis of BC cell lines’ expression profiles resulted in three groups, two clusters encompassing purely ER-negative BC cell lines (“Cluster 3” and “Cluster 2”), while one consisted of three ER-negative and all ER-positive BC cell lines. The first two cell line clusters were in good agreement with recent BC cell line studies [6, 11, 15–18, 31, 46]. While “Cluster 2” encompassed cell lines that were all represented in the Basal “B” cluster of Neve et al. and were assigned to the triple-negative mesenchymal phenotype by Lehmann et al. , most of the “Cluster 3” cell lines were part of Neve’s Basal “A” cluster  and part of the basal-like subtype according to Lehmann et al., . Our “Cluster 3” cell lines exhibited expression patterns found in transcriptional profiles of microdissected grade 3 triple-negative breast tumours  as well as grade 3 versus grade 1 breast carcinomas [9, 47]. HCC1143, an ER-negative/HER2-negative cell line, was the top in vitro representative “Cluster 3” cell line for the triple-negative phenotype of microdissected grade 3 triple-negative breast tumours . The transcriptional profile of HCC1143 also seemed very suitable in modelling the Interferon, IGF1 and MET signalling pathways. BC cell lines with expression patterns most closely associated with the Apocrine.Basal subtype  were not defined to one or the other cluster and HCC1954, an ER-negative/HER2-positive cell line of “Cluster 3” displayed the highest representation. These BCs were originally defined on the basis of their androgen receptor level and many of them harboured ERBB2 amplifications . This is in agreement with our findings, whereby using a recently published BC classifier, named CIT, three ER-negative/HER2-positive cell lines SKBR3, SUM190 and SUM225 were classified to the mApo (molecular Apocrine) breast cancer subtype . In a study, MDAMB453, SUM185, CAL148 and MFM223 showed expression patterns associated with androgen receptor signalling and were more sensitive to androgen receptor antagonist bicalutamide and an Hsp90 inhibitor . While none of those cell lines were part of our study, BT549 and HCC1937, BC cell lines used in our study and good representatives of the Apocrine.Basal subtype showed high sensitivity to Hsp90 inhibitors in Lehmann’s work . The Claudin-low subtype has been described as BC entity [8, 48], which is enriched for ER-negative invasive ductal carcinomas, while displaying low levels of luminal differentiation markers and activation of pathways involved in epithelial-to-mesenchymal transition, stem cell-like features and the immune response . Integration of gene expression with methylation data over BC cell lines revealed a group of CpG islands corresponding to genes within the Claudin-low signature, showing an inverse correlation between their methylation and the genes expression in BC cell lines and BCs . Our findings are in agreement with those from a recent report that led to the identification of a set of genes whose expression was epigenetically regulated and when used as a gene signature identified mesenchymal features in Claudin-Low breast tumours . Furthermore, they postulated that a deviant methylation might reflect cell lineage commitment in agreement with our hypothesis of a contribution of an epigenetic regulation to the Claudin-Low subtype. Aberrant DNA methylation events have initially been thought to accumulate in a random fashion within cells in pre-malignant tissues, however, lately it has also been shown that de novo methylation has a predictable pattern, creating plasticity followed by commitment to alternative cell lineages . Holm and colleagues proposed that BC subtypes might be driven by different epigenetic events and could reflect their different cellular origins . Nevertheless, an alternative hypothesis might also be that the methylation patterns are a result from mutations in genes controlling the epigenetic landscape in breast cancer ; thus further investigation is warranted to determine whether these distinctive methylation patterns are results of genetic aberration in epigenetic regulator genes and/or contribute to delineation of the differentiation hierarchy of Claudin-Low and other BC subtypes.
We and others have recently shown that basal-like BCs are most likely derived from luminal progenitor cells [51, 52]. Identifying in vitro models would enhance our understanding of these cell populations. Interestingly, our cluster and gene signature analysis revealed ER-responsive features for SKBR3, SUM190 and SUM225, three ER-negative/HER2-positive cell lines. SKBR3 cells are well known to have luminal BC characteristics . In contrast, the classification of SUM190 and SUM225 is controversial. While some BC cell line studies assigned them to basal-like cell lines [15, 18], others supported our finding of SUM190 within the ER-positive cluster . SUM225, although not included in this study, was classified as of luminal phenotype in other studies . Common to both is the expression of luminal cytokeratins 8, 18 and 19  as well as genes found in luminal progenitor cell population (data not shown) , more consistent with a luminal classification. Although SUM225 was found to highly express ALDH1, a marker for the so-called BC stem cells , further investigations are necessary to ascertain whether SUM190 and SUM225 represent appropriate in vitro models for luminal intermediate progenitor populations.
High-level amplifications are less likely to represent random aberrations and often encompass genes driving the development or maintenance of tumour growth. Three-quarters of triple-negative BCs harbour at least one amplicon , however, their recurrence rates are lower than those of high-level CNAs found in ER-positive/ HER2-negative and HER2-positive BC subtypes (e.g. ERBB2-amplicon in HER2, and CCND1 and FGFR1 in luminal breast tumours ). Here, we demonstrated that triple-negative BC-specific amplicons are recapitulated in ER-negative BC cell lines and that some of them are associated with higher frequencies either to ”Cluster 2” or “Cluster 3” expression clusters. For example, the region on 5p15.33-p15.1 was found to be recurrently amplified in 5/56 and 10/28 triple-negative BCs [31, 36], was present in six ”Cluster 3” but only in one ”Cluster 2” cell lines. Notably, these genomic sites map to regions of common germline copy number polymorphism and the functional consequences of their increased DNA levels require further validation. Nevertheless, several genes located within these amplified regions were found gained with a higher frequency in basal-like BCs in a recent study investigating 2,000 breast tumours  and expression levels significantly correlated with their DNA copy number in triple-negative BC cell lines and tumours for several of these genes . A recent study investigated genes on 5p15.33-p15.1 in more detail and showed that silencing of the overexpressed and amplified NUNS2, a MYC target gene, reduced cell number in some BC cell lines . NUNS 2 expression has been found significantly increased in malignant tissues whereas it could only be found in testis in normal tissues, furthermore its role in stabilising the mitotic spindle and phosphorylation by Aurora-B make it an interesting target for cancer diagnostics and molecular therapeutics.
Taken together, transcriptional, genomic and epigenetic profiles of 25 BC cell lines, enriched for those representing triple-negative features, help to define cell lines that most closely capture individual examples of the heterogeneous characteristics within triple-negative BCs. By cross-referencing different high-resolution datasets, we provide useful resources to further study transcriptional, as well as genetic and epigenetic modulation and inform the best selection of available in vitro models for the identification and validation of potential novel therapeutic targets relevant to triple-negative BCs.
BC cell lines
BT20, BT474, BT483, BT549, Hs578T, MDAMB157, MDAMB231, MDAMB436, MDAMB468, T47D, SKBR3, ZR75-30, HCC1937, HCC70, HCC1428, HCC1143, HCC38, HCC1187, HCC1569, HCC1954 were obtained from ATCC (Manassas, VA, USA). SUM159, SUM149, SUM1315, SUM225, SUM190 were purchased from Asterand plc (Detroit, MI, USA) (Additional file 1 Table S1). All lines were grown according to the supplier’s recommendation and authenticated by means of Short Tandem Repeat (STR) analysis (PowerPlex® 1.2 System, Promega, WI, US) as previously described . STR profiles were matched to the German Collection of Microorganisms and Cell Cultures (DSMZ)–database (http://www.dsmz.com). BC cell lines were stratified into mesenchymal and epithelial-like morphological groups based on previous studies [11, 16, 18].
RNA and DNA isolation
Cells were grown to ~70% confluence before harvesting nucleic acids. DNA was prepared using the Qiagen DNeasy tissue kit (Qiagen, Valencia, CA) and RNA was isolated using Trizol (Invitrogen, Carlsbad, CA) according to the manufacturers’ protocol. DNA concentration was measured with Picogreen (Invitrogen, Paisley, UK). Integrity of RNA was quantified using the Agilent 2100 Bioanalyser with RNA Nano LabChip Kits (Agilent Biosystems, Foster City, CA).
Analyses of microarray data were performed in the R environment 2.12.0 (http://www.r-project.org/) making use of several Bioconductor packages (http://www.bioconductor.org/). All Microarray probes and external gene signatures were mapped to the Ensembl 55 (human genome build 37) to ensure uniform annotation. Microarray data have been deposited in Array Express (E-TABM-928; http://www.ebi.ac.uk/arrayexpress/). A Sweave document describing the statistical analysis is provided as Supplemental Methods (Addition file 9).
Gene expression profiling
Using the Illumina Totalprep RNA amplification kit (Ambion, UK), 200ng total BC cell line RNA was amplified and hybridised to Illumina HumanWG-6v2.0 arrays gene expression bead-chips at Genizon BioSiences Inc (Quebec, CA). Raw data obtained from Illumina BeadStudio (Illumina, San Diego, CA) were preprocessed using the “lumi” -Bioconductor package . Microarray probes absent in more than 80% of samples based on an Illumina BeadStudio detection P_value >0.01 were removed from further analysis. For unsupervised hierarchical clustering of gene expression, 5,693 unique Ensembl genes with a median absolute deviation (MAD) of ≥0.4 across all BC cell lines were selected. Ward clustering was applied to genes and arrays after median centring using Pearson’s correlation as a distance measurement and 10,000 bootstrap iterations were performed to assess the significance of the observed the stability of the clusters using the pvclust package for R . Resulting clusters were visualised with Java TreeView . Two strategies were applied for gene expression signature analysis: (1) When centroids for specific classes (e.g. BC subtypes or groups of ER-negative breast tumours ) were publicly available, assignment of BC cell lines to these classes was based on their highest Spearman rank correlation. Classification included class centroids defined by Sorlie, Hu , Parker , Prat , CIT256  and Teschendorff . (2) To monitor specific ER-related features, 11 gene signatures were retrieved from publication (see Additional file 3 Table S2 for a detailed description). For the “G3.TN.Tumour” signature, we used our previously published expression data of microdissected breast tumours . Significance Analysis of Microarrays (SAM)  with 1,000 permutations and 0% fdr was used to identify significant genes for triple-negative BCs, using a two-class comparisons between tumours belonging to the triple-negative subtype and all other subtypes. For each BC cell line, a weighted mean expression of genes present in the respective signature was determined, and cell lines were ranked based on their concordance.
Array-based comparative genomic hybridisation (aCGH)
Labelling, hybridisation, image and initial data analysis of the 32k BAC tiling path aCGH platform, produced at the Breakthrough Breast Cancer Research Centre, London, UK  was carried out as previously described . Breakpoint analysis was performed using the circular binary segmentation (cbs) algorithm  and rescaled such that the genome MAD was the same in each sample. Only segments of ≥ 3 BAC clones were used in further analyses. Thresholds for cbs-smoothed data were estimated as described previously . Briefly, cbs-smoothed aCGH Log2 values <−0.08 were classified as losses, >0.08 but ≤0.45 were categorised as gains, and >0.45 were referred to as high-level gains/ amplifications. To determine genomic instability, the fraction of amplified, deleted or total BACs over the whole data set was calculated and presented as a proportion. Gene expression values were compared with median cbs-smoothed aCGH data for all BACs encompassing the genomic position using Pearson’s correlation adjusted for multiple testing . Matched heatmaps between gene expression and genomic data were created as described in  showing the minus log10 Pearson’s P_value of each gene-aCGH pair correlation. The raw and cbs-smoothed aCGH data are deposited at http://rock.icr.ac.uk/collaborations/GrigoriadisA/.
Methylation array analysis
Hybridisation and image analysis of the Illumina GoldenGate methylation beadarrays were performed at the Genome Centre (Barts and the London School of Medicine and Dentistry, London, UK). Methylation profiles of the BC cell lines, obtained through the BeadStudio Methylation Module (Illumina, San Diego, CA), was normalised by dichotomising the un- /methylated CpG islands separately before equalising their median according to the “methylumi” package (http://www.bioconductor.org/). CpG sites located on the X chromosomes were removed, as well as constitutively un-/ methylated probes, resulting in 1,223 CpG sites (data are available at http://rock.icr.ac.uk/collaborations/GrigoriadisA/). The methylation state of CpG islands given as a ß-value  was stratified into three categories: ß-values ≤ 0.25, ≥ 0.75 and between ≥ 0.25 and ≤ 0.75; and interpreted as un-/, methylated and partially methylated CpG sites, respectively. These cut-offs are slightly more stringent than Holm et al. has used them for the analysis of breast carcinomas using the same methylation array platform  to increase the chances of true-positive events. Initial analysis revealed a similar methylation frequency in all BC cell lines, determined as the fraction of methylated CpG sites, affecting on average 31% of all CpG islands. Using a total of 10,000 permutations to obtain reasonable estimates of dependencies, sample labels were permuted and correlation analyses between gene expression and methylation values were carried out on the resampled data set.
Copy Number Aberrations
Median Absolute Deviation
Significance Analysis of Microarrays
Array-based comparative genomic hybridisation
Circular binary segmentation
Bacterial Artificial Chromosome
This study was funded by Breakthrough Breast Cancer. This research was supported by the Experimental Cancer Medicine Centre at King’s College London and also by the National Institute for Health Research (NIHR) Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust and King’s College. RN, JT and JSR-F acknowledge NHS funding for the NIHR Biomedical Research Centre. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. We would like to thank Dr. Alice Gao and Prof. Marketa Zevlebil for providing access to the ROCK database. We are also grateful to Dr Olorunsola Agbaje for advise in statistical analysis.
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