Molecular Profiling of Breast Cancer Cell Lines Defines Relevant Tumor Models and Provides a Resource for Cancer Gene Discovery (original) (raw)

Abstract

Background

Breast cancer cell lines have been used widely to investigate breast cancer pathobiology and new therapies. Breast cancer is a molecularly heterogeneous disease, and it is important to understand how well and which cell lines best model that diversity. In particular, microarray studies have identified molecular subtypes–luminal A, luminal B, ERBB2-associated, basal-like and normal-like–with characteristic gene-expression patterns and underlying DNA copy number alterations (CNAs). Here, we studied a collection of breast cancer cell lines to catalog molecular profiles and to assess their relation to breast cancer subtypes.

Methods

Whole-genome DNA microarrays were used to profile gene expression and CNAs in a collection of 52 widely-used breast cancer cell lines, and comparisons were made to existing profiles of primary breast tumors. Hierarchical clustering was used to identify gene-expression subtypes, and Gene Set Enrichment Analysis (GSEA) to discover biological features of those subtypes. Genomic and transcriptional profiles were integrated to discover within high-amplitude CNAs candidate cancer genes with coordinately altered gene copy number and expression.

Findings

Transcriptional profiling of breast cancer cell lines identified one luminal and two basal-like (A and B) subtypes. Luminal lines displayed an estrogen receptor (ER) signature and resembled luminal-A/B tumors, basal-A lines were associated with ETS-pathway and BRCA1 signatures and resembled basal-like tumors, and basal-B lines displayed mesenchymal and stem/progenitor-cell characteristics. Compared to tumors, cell lines exhibited similar patterns of CNA, but an overall higher complexity of CNA (genetically simple luminal-A tumors were not represented), and only partial conservation of subtype-specific CNAs. We identified 80 high-level DNA amplifications and 13 multi-copy deletions, and the resident genes with concomitantly altered gene-expression, highlighting known and novel candidate breast cancer genes.

Conclusions

Overall, breast cancer cell lines were genetically more complex than tumors, but retained expression patterns with relevance to the luminal-basal subtype distinction. The compendium of molecular profiles defines cell lines suitable for investigations of subtype-specific pathobiology, cancer stem cell biology, biomarkers and therapies, and provides a resource for discovery of new breast cancer genes.

Introduction

Breast cancer, a leading cause of cancer death in women, is recognized to be a molecularly heterogeneous disease. Markers such as estrogen receptor (ER), progesterone receptor (PR) and ERBB2/HER2 are used for prognostication, and to stratify patients for appropriately targeted therapies [1].

More recently, DNA microarray studies have suggested a refined classification of breast cancer, distinguishing five major subtypes based on different patterns of gene expression, underlying DNA copy number alterations (CNAs), and associated clinical outcomes [2][5]. Luminal subtypes A and B are ER positive and share expression markers with the luminal epithelial layer of cells lining normal breast ducts. Luminal-A tumors are genetically simple (1q/16p gain) and are associated with favorable outcome, while luminal-B tumors exhibit high proliferation rates, frequent DNA amplification (e.g. 8q24/MYC), and less favorable prognosis. Basal-like tumors share expression markers with the underlying basal (myoepithelial) layer of normal breast ducts, are ER negative, exhibit frequent chromosome segmental gains/losses, and are associated with poor outcome in most studies. The ERBB2 subtype is associated with expression of genes co-amplified with ERBB2 (encoding HER2) on chromosome cytoband 17q12, and the normal-like subtype shares expression patterns with normal breast tissue.

Breast cancer cell lines have been used widely to investigate breast cancer pathobiology, and to screen and characterize new therapeutics [6], [7]. Advantages of cell lines include the relative ease of pharmacologic and genetic manipulation, the variety of available functional assays, and, for some studies, the purity of the cancerous epithelial population (and absence of stromal cell contamination). However, while some investigators choose particular cell lines based on the known ER or HER2 status, many others rely on standard “workhorses” like MCF7 without regard to the particular tumor subtypes being modeled. The recent recognition of microarray molecular subtypes points to the need for additional consideration in cell line selection.

The goal of our study was to profile gene expression and CNAs genome-wide in a collection of 52 publicly-available and commonly-used breast cancer cell lines, in order to assess the relation of these cell lines to the recognized molecular subtypes of breast cancer, and to discover new candidate breast cancer genes and pathways.

Materials and Methods

Breast Cancer Cell Lines

184A1, BT20, BT474, BT483, BT549, Hs578T, hTERT-HME1, MCF7, MCF10A, MDA-MB134, MDA-MB157, MDA-MB175, MDA-MB231, MDA-MB361, MDA-MB436, MDA-MB453, MDA-MB468, SKBR3, T47D, UACC812, UACC893, ZR75-1 and ZR75-30 were obtained from ATCC (Manassas, VA, USA). EFM19 and EFM192A were obtained from DSMZ (Braunschweig, Germany). HCC38, HCC70, HCC202, HCC712, HCC1007, HCC1143, HCC1395, HCC1419, HCC1428, HCC1500, HCC1569, HCC1599, HCC1806, HCC1937, HCC1954, HCC2157, HCC2185, HCC2218, HCC2688 and HCC3153 were obtained from the cell repository of the Hamon Center for Therapeutic Oncology Research, UT Southwestern Medical Center (many are now available from ATCC). CAL51 was a kind gift from J. Gioanni from the Centre Antoine-Lacassagne, Nice, France. SUM44PE, SUM52PE, SUM102PT, SUM149PT and SUM190PT were kind gifts from Dr. Stephen P. Ethier (now available from Asterand, Detroit, MI). MCF10A was grown in MEGM media (Cambrex, East Rutherford, NJ). SUM52PE and SUM149PT were grown in Ham's F12 media with 5% FBS, supplemented with 5 µg/ml insulin and 1 µg/ml hydrocortisone. SUM44PE, SUM102PT and SUM190PT were grown in Ham's F12 with 0.1% BSA, supplemented with 5 µg/ml insulin, 1 µg/ml of hydrocortisone, 5 mM ethanolamine, 10 mM HEPES, 5 µg/ml transferrin, 10 nM of Triiodo Thyronin (T3) and 50 nM sodium selenite (10 ng/ml EGF was also included for SUM102PT). All other cell lines were grown in RPMI-1640 with 10% FBS and 1% Pen/Strep. Clinicopathological characteristics of cell lines are summarized in Table 1. A subset of cell lines (focused on the HCC series) was subjected to a more detailed molecular pathological characterization of ESR1, PGR, ERBB2, EGFR and BRCA1, as summarized in Table 2.

Table 1. Clinicopathological features of breast cancer cell lines.

Cell line Subtype# ER* PR* ERBB2/HER2* Source Tumor type
184A1 B NA RM NA
BT20 A PT AC
BT474 L + + + PT IDC
BT483 L + + PT IDC
BT549 B PT IDC
CAL51 B NA PE AC
EFM19 L + + PE IDC
EFM192A L + + + PE AC
HCC38 B PT DC
HCC70 A PT DC
HCC202 L + PT DC
HCC712 L + PT DC
HCC1007 L + + PT DC
HCC1143 A PT DC
HCC1187 A PT DC
HCC1395 B PT DC
HCC1419 L + PT DC
HCC1428 L + + PE Met AC
HCC1500 L + + PT DC
HCC1569 A + PT Met C
HCC1599 A PT DC
HCC1806 NA PT Sq C
HCC1937 A PT DC
HCC1954 A + PT DC
HCC2157 A PT NA
HCC2185 L PE Met LC
HCC2218 L + PT DC
HCC2688 L NA PT DC
HCC3153 A PT DC
HS578T B PT C Sar
hTERT-HME1 B NA RM NA
MCF7 L + + PE Met AC
MCF10A B RM F
MDA134 L + PE IDC
MDA157 B PE Med C
MDA175 L + PE IDC
MDA231 B PE Met AC
MDA361 L + + + BR Met AC
MDA436 B PE AC
MDA453 L + PE Met C
MDA468 A PE Met AC
SKBR3 L + PE AC
SUM44 NA + + + PE ILC
SUM52 L + + PE Met C
SUM102 B PE IDC, apocrine
SUM149 B PE Inf
SUM190 L + PT Inf
T47D L + + PE IDC
UACC812 L + + PT IDC
UACC893 L + PT IDC
ZR75-1 L + AF IDC
ZR75-30 L + + AF IDC

Table 2. Molecular pathological analysis of breast cancer cell line subset.

Cell line Phenotype BRCA1 Q-PCR# ERBB2 Q-RT-PCR* IHC Western
ESR1 PGR ERBB2 EGFR ESR1 PGR ERBB2 ESR1 PGR ERBB2 EGFR
HCC38 Triple neg 1.18
HCC70 Triple neg 0.37 + +
HCC202 ERBB2 amp 28.88 + + + + +
HCC712 Hormone+ 0.95 + + + +
HCC1143 Triple neg 1.08 + +
HCC1187 Triple neg 0.42 +
HCC1395 Triple neg 0.36
HCC1419 ERBB2 amp 8.39 + + +
HCC1428 Hormone+ 0.20 + + + + + +
HCC1500 Hormone+ 0.38 + + + + +
HCC1569 ERBB2 amp 33.75 + + + + +
HCC1806 Triple neg 0.08 + +
HCC1937 Triple neg INS C 5382 0.33 + +
HCC1954 ERBB2 amp 45.01 + + + + +
HCC2185 Triple neg 0.63 + +
HCC3153 Triple neg 943 ins 10 0.64 + +
MCF7 Hormone+ 0.56 + +
BT483 Hormone+ 0.19 + + + +
BT549 Triple neg 0.63 + +
MDA157 Triple neg 0.76 +
MDA231 Triple neg 0.90 + +
MDA453 Triple neg 3.88 +
MDA134 Hormone+ 0.76 + +
MDA175 Triple neg 0.57
HMEC1585 Control 0.54 + +
CALU3 Control 12.59 + + + +
NC11 Control 1.75
DNA20 Control 2.00

RNA and DNA isolation

Cells were grown to 70–80% confluence, then harvested for total RNA and genomic DNA. For HCC lines, RNA was prepared using the Qiagen RNeasy Midi Kit (Qiagen, Valencia, CA) and DNA by phenol/chloroform extraction. For all other lines, RNA was isolated using Trizol (Invitrogen, Carlsbad, CA) according to the manufacturer's protocol, and DNA using the Blood Cell Maxi Kit (Qiagen).

ERBB2 copy number assessment by quantitative PCR

ERBB2 copy number was quantified by real-time quantitative PCR (Q-OCR), using the Chromo4 PCR System (Bio-Rad Laboratories, Hercules, CA). GAST, located at 17q21 (on the same chromosomal arm as ERBB2) was used as a reference control. PCR primer sequences for ERBB2 and GAST are as follows (forward and reverse, respectively): ERBB2( 5′-TTGGGAGCCTGGCATTTCT-3′ and 5′-AGGTCATCGTGCCCACTCTT-3′); GAST (5′-GTAGGCATCCTTCCCCCATT-3′ and 5′-AGCCATGGTCCCTGCTTCTT-3′), with PCR product lengths of 59 and 70 base pairs, respectively. Primers were chosen by TaqMan Primer Express™ 1.5 (Applied Biosystem, Foster City, CA) and purchased from Invitrogen. PCR reactions were carried out in a final volume of 20 µl containing 20 ng genomic DNA, 300 nM each primer (for both ERBB2 and GAST, in independent reactions) and 1× Power SYBR Green PCR Master Mix (Applied Biosystems, Foster City, CA). PCR conditions were as follows: one cycle at 95°C for 10 minutes, followed by 40 cycles each at 95°C for 15 seconds and 60°C for 1 minute. Samples were analyzed in triplicate. Each amplification reaction was checked for the absence of nonspecific PCR products by melting curve analysis. ERBB2 copy number calculation was carried out using the comparative Ct method [8] after validating that the efficiencies of PCR reactions of both ERBB2 and GAST were equal. Human Genomic DNA (DNA20) (EMD Biosciences, Darmstadt, Germany), a mixture of pooled human whole blood from 6–8 individual male and female donors, was run in every assay as a calibrator sample. ERBB2 gene copy number in normal human genomic DNA was set as 2 and copy number more than 4 in cell lines was considered to be increased.

mRNA levels of ESR1, PGR, ERBB2 and EGFR

Transcript levels of ESR1, PGR, ERBB2 and EGFR were analyzed as a part of RT2 Profiler Custom PCR Array (SuperArray Bioscience, Frederick, MD). After making cDNA from 1.0 µg total RNA using RT2 PCR Array First Strand Kit (SuperArray Bioscience), quantitative PCR was performed with the Chromo4 PCR System (Bio-Rad Laboratories) using RT2 Real-Time SYBR Green PCR Master Mix (SuperArray Bioscience) according to the manufacturer's protocol. We chose two different housekeeping genes, β-actin (ACTB) and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) as internal controls, using the average of their Ct values. Primers were chosen by Taqman Primer Express™ 1.5 and purchased from Invitrogen, as follows: (forward and reverse, respectively): ESR1 (5′-ATCTCGGTTCCGCATGATGAATCTGC-3′ and 5′-TGCTGGACAGAAATGTGTACACTCCAGA-3′); PGR (5′-CCTGTGGGAGCTGTAAGGTCTT-3′ and 5′-GCAGTCATTTCTTCCAGCACATA-3′), ERBB2 (5′-TGACCTGCTGGAAAAGGGGGAGCG-3′ and 5′-TCCCTGGCCATGCGGGAGAATTCAG-3′); EGFR (5′-ATAGTCGCCCAAAGTTCCGTGAGT-3′ and 5′-ACCACGTCGTCCATGTCTTCTTCA-3′); ACTB (5′ GGCTGTGCTGTGGAAGCTAAG-3′ and 5′-ATGATGGAGTTGAAGGTAGTTTCGT-3′) [9]. We also analyzed the values of NC11 (normal lymphocyte) cell line for ESR1, PGR, ERBB2 and EGFR mRNA expression, and the tumor cell values were reported relative to NC11. For data analysis, the comparative Ct method [8] was used.

Western blot analysis and immunohistochemistry (IHC)

Preparation of total cell lysates and Western blotting were done as described previously [10]. Primary antibodies used were mouse monoclonal anti-ER-α (Cell Signaling, Beverly, MA), mouse monoclonal PR (6A1) (Cell Signaling), mouse monoclonal anti-HER2 (Cell Signaling), rabbit monoclonal anti-EGFR (Cell Signaling) and mouse monoclonal anti-actin (Sigma-Aldrich). Actin levels were used as a control for protein loading. Peroxidase-labeled anti-mouse or anti-rabbit antibodies (Amersham Pharmacia, Piscataway, NJ) were used as secondary antibody. IHC on breast cancer cell lines was described previously [11].

BRCA1 mutation analysis

DNA sequence analysis was performed on the entire BRCA1 gene in available lymphocyte DNA matched to breast cancer cell lines. In the lymphocyte DNA matching HCC3153, a heterozygous duplication of 10 base pairs was detected at position 943 in exon 11 of BRCA1 (943ins10). The region of BRCA1 exon 11 containing the 943ins10 mutation was amplified from genomic DNA in the tumor cell line (HCC3153) using standard PCR conditions. Sequence analysis revealed only the mutant sequence. Absence of the normal allele was also confirmed by single strand conformation analysis as well as gel electrophoresis of the amplified fragment on 5% acrylamide denaturing gels.

Gene expression profiling

Gene expression profiling was performed on Human Exonic Evidence Based oligonucleotide (HEEBO) arrays obtained from the Stanford Functional Genomics Facility and containing 36,192 oligonucleotides representing 18,141 mapped human genes. 40 µg of sample RNA and 40 µg of “universal” reference RNA (derived from 11 different established human cell lines) were differentially labeled with Cy5 and Cy3, respectively, using an amino-allyl coupling protocol, then cohybridized onto the microarray in a high volume mixing hybridization at 65°C for 40 hrs. Details of the array processing and sample labeling/hybridization methods have been described [12]. Following hybridization, arrays were washed and scanned using a GenePix 4000B Axon scanner (Axon Instruments, Union City, CA). Fluorescence ratios were extracted using Spot Reader software (Niles Scientific, Portola Valley, CA) and uploaded to the Stanford Microarray Database [13] for storage, retrieval, and analysis. For two lines, HCC1806 and SUM44PE, expression profiling array hybridizations did not meet quality-control inspection and were excluded from analysis. The complete microarray expression data are available at the Stanford Microarray Database (SMD) (http://smd.stanford.edu) and at the Gene Expression Omnibus (GEO) (accession GSE15376); all microarray data reported in the manuscript are described in accordance with MIAME guidelines.

Gene expression profiling analysis

Background-subtracted fluorescence log2 ratios were globally normalized for each array, and then mean-centered for each gene (i.e. reporting relative to the average log ratio across all samples). Unless otherwise specified, we included for subsequent analysis only well-measured genes defined as those with fluorescence intensities in the Cy5 or Cy3 channel at least 1.5-fold above background in at least 60% of samples. For unsupervised hierarchical clustering, we included only the 8,750 well-measured genes whose expression varied at least 3-fold from the mean in at least 5 samples (Table S1). Hierarchical clustering was performed and displayed using Cluster and TreeView software (http://rana.lbl.gov/EisenSoftware.htm). Enrichment for functionally related genes was tested across a collection of 1,687 curated gene sets (C2) using Gene Set Enrichment analysis (GSEA; Release 2.0) [14]. Cell lines were classified according to breast tumor subtype (luminal-A, luminal-B, ERBB2, basal-like and normal-like) using the nearest centroid method applied to the set of “intrinsic genes” (i.e. genes with small within-specimen compared to between-specimen expression variance), as done previously [15], here using Euclidean distance. To classify breast tumors (from the Sorlie et al. dataset [3]) according to cell line subtype (luminal, basal A, or basal B), we first built a classifier by combining the top 100 genes positively and negatively correlating with each of the three “one vs. others” cell line subtype distinctions, using Significance Analysis of Microarrays (SAM) [16]. The cell line subtype classifier, comprising 484 genes, was then applied to classify primary tumors using the nearest centroid method (with Euclidean distance). We also classified each cell line as being associated with a good or bad prognosis signature (70-gene prognostic signature [17]), the presence or absence of a wound healing signature (512-gene wound signature [18]), and the presence or absence of an hypoxia signature (123-gene hypoxia signature [19]). For each signature, we calculated the gene expression centroid of the two groups of breast tumors (as determined in the original publications), and then correlated each centroid with cell line expression of the respective signature genes. Membership was assigned to the group with the highest correlation (Pearson correlation).

Array-based comparative genomic hybridization (aCGH)

Arrays for CGH were obtained from the Stanford Functional Genomics Facility. aCGH was performed using cDNA arrays containing 39,632 cDNAs, representing 22,279 mapped human genes (18,049 UniGene clusters [20], together with 4,230 additional mapped ESTs not assigned to UniGene IDs), according to previously published protocols [21], [22]. Briefly, 4 µg of genomic DNA from cell lines was random-primer labeled with Cy5 and co-hybridized onto a microarray along with 4 µg of Cy3 labeled normal leukocyte female reference DNA. Following overnight hybridization, the arrays were washed and scanned as above. The complete aCGH data are available at SMD and at GEO (accession GSE15376).

aCGH analysis

Background-subtracted log2 fluorescence ratios were normalized for each array by mean centering. Well-measured genes used for subsequent analysis were those with fluorescence intensities in the Cy3 reference channel at least 1.4 fold above background. Map positions for arrayed cDNA clones were assigned using the NCBI genome assembly, accessed through the UCSC genome browser database (NCBI Build 36.1). For genes represented by multiple arrayed cDNAs, the average log2 ratio was used. The complete processed aCGH dataset is available as Table S2. DNA gains and losses were identified using the cghFLasso (R package for Fused Lasso) method [23], which controls the false discovery rate (FDR) by using normal-normal hybridization arrays to approximate the null distribution of the test statistics (see [23] for more details). A FDR<1% was used to call gains and losses. The fraction of the genome altered was determined by calculating the fraction of genes with fluorescence ratios ≥3 (for amplifications) or with significant non-zero fused lasso calls (for gains and losses). Some analyses (where indicated) were carried out on cytobands (boundaries defined by NCBI Build 36.1) rather than individual genes. For each cell line, cytobands exhibiting CNA were defined as those with at least two genes called by cghFLasso, and the magnitude of the CNA defined as the average log2 ratio of genes within the cytoband. We defined high-level DNA amplifications and multi-copy deletions as continuous regions identified by cghFLasso with at least 50% of genes having fluorescence ratios ≥3 or ≤0.25 respectively. These sites were also checked against known copy number variants (CNVs) reported in the Database of Genomic Variants (http://projects.tcag.ca/variation). Significant associations between cytobands and gene-expression subtypes were identified using SAM with a FDR<5%.

Integrating genomic and transcriptional profiles

To integrate DNA copy number data (generated using cDNA microarrays) and gene-expression data (HEEBO oligonucleotide arrays), each gene expression measurement was first assigned a DNA copy number from either a probe interrogating the same named gene, or the average copy number of the nearest 5′ and 3′ probes (NCBI Build 36.1). Identification of genes with correlated copy number and expression was carried out using the DR-Correlate application of DR-Integrator (K. Salari, manuscript in preparation). Briefly, for each gene a modified Student's _t_-test was performed comparing gene expression levels in cell lines from the lowest and the highest deciles of all cell lines' copy number for the same gene; random permutations of sample labels were used to estimate a FDR.

Results

Transcriptional profiling identifies three breast cancer cell line subtypes

To catalog molecular variation in a collection of 52 widely-used breast cancer cell lines, we first profiled gene expression using whole genome oligonucleotide microarrays. Unsupervised hierarchical clustering of the 8,750 most variably expressed genes stratified cell lines into two main groups (see dendrogram, Fig. 1B). One group, designated “luminal” (blue dendrogram branches), contained all the ER-positive cell lines (Fig. 2A), and was characterized by the expression of ERα-regulated genes (e.g. MYB, RET, EGR3, TFF1; Fig. 1H, and not shown) [24][27], as well as genes associated with luminal epithelial differentiation (e.g. GATA3 and FOXA1, Fig. 1I) [28].

Figure 1. Clustering of expression profiles defines breast cancer cell line subtypes.

Figure 1

(A) Thumbnail “heatmap” of two-way hierarchical clustering of 50 breast cancer cell lines (columns) and 8,750 variably expressed genes (rows) (data available as Table S1). Gene expression ratios are depicted by log2 pseudocolor scale shown; gray represents poorly measured data. (B) Enlarged view of the sample dendrogram. Clustering stratifies cell lines into two main groups, luminal (blue dendrogram branches) and basal, the latter further subdivided into two subgroups, basal A (red) and basal B (orange). (C–I) Selected gene expression patterns extracted from the cluster; corresponding locations in the thumbnail are indicated by the vertical colored bars. (C) Basal-B; (D) Basal cytokeratins; (E) Basal; (F) Basal-A; (G) Luminal cytokeratins; (H) ER-associated; (I) Luminal differentiation.

Figure 2. Subtype-specific expression and molecular characteristics.

Figure 2

(A) Clinical, pathological and molecular characteristics of cell line expression subtypes. Black boxes indicate metastasis derivation, ER-positivity, TP53 mutation, ERBB2/HER2 positivity, PTEN mutation, PIK3CA mutation. Mutation data compiled from the Sanger (http://www.sanger.ac.uk) and IARC (http://www-p53.iarc.fr) websites, and from refs. [94], [95]. White cross-hatched boxes indicate missing data. (B) Classification of cell lines by nearest resemblance to tumor gene-expression subtype: luminal A (dark blue), luminal B (light blue), ERBB2-associated (purple), basal-like (red) or normal-like (green); and by positivity (black boxes) for 70-gene, wound and hypoxia signature. (C) Expression levels of selected stem/progenitor cell relevant markers; log2 ratios are depicted by pseudocolor scale shown (gray represents poorly measured data). (D) Relation of tumor subtypes to cell line subtypes. Subtype of 86 tumors [3] is shown color-coded as above. Resemblance to each cell line subtype is depicted by Euclidian distance, indicated by blue intensity (representing shorter distances); best match is bracketed in black.

The other group, designated “basal”, contained only ER-negative cell lines (Fig. 2A) and was characterized by the expression of basal epithelial gene markers including MSN, ETS1, CAV1 and EGFR (Fig. 1E, and not shown) [29][32]. Basal cell lines were further stratified into two subgroups, designated A and B (in line with Neve et al. [33], discussed further below). The basal-A subtype (red dendrogram branches) contained many of the “HCC” lines established at UT Southwestern, including two known BRCA1 mutant lines (HCC1937, HCC3153) ([34], and this study). Basal-A lines were characterized by expression of PROM1 (aka CD133), a marker of various cancer stem cells [35], as well as other genes like GABRP and VTCN1 (Fig. 1F and 2C). Some of the basal-A lines also shared expression of luminal epithelial markers like KRT8 and KRT18 (Fig. 1G).

The basal-B subtype (orange dendrogram branches) included non-tumorigenic lines (MCF10A, hTERT-HME1, 184A1) as well as several highly invasive lines exhibiting features of epithelial-mesenchymal transition (EMT) (MDA-MB231, MDA-MB436, MDA-MB157, Hs578t) [36]. Basal-B lines were characterized by markers associated with aggressive tumor features, including PLAT (plasminogen activator) [37] and TGFB1 [38] (Fig. 1C), as well as marker phenotypes associated with normal breast and breast cancer progenitor/stem cells (MUC−/CALLA+; CD44+/CD24−/low; and ITGB3(CD61)+) (Fig. 2C) [39][41]. In contrast to other basal lines, the subset of mesenchymal-like basal-B lines lacked expression of basal cytokeratin markers KRT5 and KRT17 (Fig. 1D, and not shown).

Subtype-specific differences in gene expression could also be identified by pathway analysis, using Gene Set Enrichment Analysis (GSEA) [14]. Included among the top signature associations (Table 3), the luminal cell line subtype was characterized by enriched expression of ER and good prognosis signatures, basal-A by ETS pathway and BRCA1 signatures, and basal-B by EMT and epidermal growth factor (EGF) signatures.

Table 3. GSEA of breast cancer cell line subtypes.

Subtype Gene Set Description Source FDR*
Luminal BRCA_ER_POS Correlated with ER+ in breast cancer [17] 0.017
BRCA_PROGNOSIS_POS Correlated with good prognosis in breast cancer 0.094
Basal-A ETSPATHWAY ETS transcription factor pathway BioCarta 0.063
BRCA_BRCA1_POS Correlated with BRCA1 (germline) in breast cancer [17] 0.063
IFN_ALL_UP Upregulated with interferon-α,β,γ treatment [96] 0.071
IFNALPHA_HCC_UP Upregulated with interferon-α treatment [97] 0.076
GLYCOGEN Glycogen processing Broad Institute 0.078
Basal-B JECHLINGER_EMT_UP Upregulated in EMT [98] 0.040
EGF_HDMEC_UP Upregulated with EGF treatment [99] 0.042
DORSEY_DOXYCYCLINE_UP Upregulated with GAB2 expression [100] 0.047
HTERT_DN Downregulated with hTERT-immortalization [101] 0.048
HINATA_NFKB_UP Upregulated by NF-κB [102] 0.049

In regard to molecular markers and gene mutations (Fig. 2A), the luminal subtype included all the ER-positive cancer lines (P<0.001, 2-tailed Fisher's exact test), and all but two of the ERBB2-positive lines (P = 0.002), half of which were also ER-positive. PTEN inactivating mutations and PIK3CA activating mutations, functioning on the same pathway, were mutually exclusive in all but one sample. Interestingly, PTEN mutations were more common in the combined basal-like cell lines (P = 0.020), while PIK3CA mutations were more frequent in luminal lines (P = 0.022). TP53 mutations occurred more often in basal-like lines (P = 0.038).

Relationship of breast cancer cell line and tumor subtypes

To determine the relation between breast cancer cell line subtypes (luminal, basal-A, basal-B) and breast tumor subtypes (luminal-A, luminal-B, ERBB2, basal-like, and normal-like), we first classified cell lines according to tumor subtype using a nearest centroid approach applied to the set of “intrinsic genes” used originally to define the tumor subtypes [2], [3] (see Methods) (Fig. 2B). By expression patterns, most of the luminal lines most closely resembled either luminal-A or luminal-B tumors. Most basal-A lines resembled basal-like tumors, and most basal-B lines resembled either basal-like or ERBB2 tumors (despite that none were ERBB2-positive).

We also carried out the reverse analysis, building a cell line subtype classifier to classify 86 breast tumors (from the original Stanford/Norway study defining the five tumor subtypes [3]) according to cell line subtype (see Methods) (Fig. 2D). Notably, all basal-like tumors most resembled basal-A cell lines. Luminal-A and -B tumors most resembled luminal cell lines, while ERBB2 subgroup tumors most resembled either luminal or basal-A cell lines. A similar analysis of breast tumors arising in carriers of BRCA1 mutation, analyzed from a different dataset (The Netherlands Cancer Institute) [17], revealed highest resemblance in 17 of 18 cases to basal-A lines (not shown), while two BRCA2 mutation associated cases most resembled luminal cell lines.

In addition to the above cluster-derived luminal/basal tumor subtypes, alternative breast tumor subtype classifiers have been proposed, including a 70-gene prognostic signature supervised on the metastatic/non-metastatic distinction [17], a “wound” signature trained on the serum response of cultured fibroblasts [18], and a hypoxia signature derived from the hypoxic response of cultured mammary and renal tubular epithelial cells [19]. Each of the three signatures predicts unfavorable clinical outcome. Interestingly, the basal-like lines (considered together) were those predominantly expressing the 70-gene (P = 0.001, Fisher's exact test) wound (P = 0.004), and hypoxia (P<0.001) signatures (Fig. 2B).

Genomic profiles of breast cancer cell lines

To survey DNA copy number alterations in the panel of 52 breast cancer cell lines, we carried out CGH on cDNA microarrays with validated performance characteristics [21] and covering 22,000 genes with an average mapping resolution (inter-probe distance) of <70 Kb. Across the sample set, the most frequent CNAs (called by cghFLasso–see Methods) were gains on 1q, 3q, 5p, 7p, 8q, 11q, 17q, and 20q, and losses on 3p, 4, 8p, 9p, 11q, 13q, 18p, and Xq.

Overall, the spectrum of cytoband gains and losses was similar in the cell lines compared to primary tumors (Fig. 3A), though the frequency of those CNAs was generally higher with the cell lines. Cell line subtype-specific CNAs could be identified by SAM analysis (Fig. 3B). Luminal cell lines were characterized by more frequent gains on 1q, 8q, 11q, 12q, 14q, 17q and 20q, and losses on 8p, 9p, 11q, 13q, and 18p. Of these, gains on 1q, 8q, and 20q, and losses on 1p, 8p and 13q (asterisked in Fig. 3B) also characterize luminal-B breast tumors, while 17q gain characterizes ERBB2-associated tumors [4], [5]. Notably, simple patterns characteristic of luminal-A tumors (1q+, 16p+, 16q−) were not well-represented among the luminal cell lines. Basal-A and basal-B cell lines also exhibited characteristic gains/losses (Fig. 2B), but none also selectively characteristic of basal-like tumors.

Figure 3. Genomic profiles define spectra of CNAs in cell line subtypes.

Figure 3

(A) Spectra of gains (red) and losses (green) across the genome, plotted as average log2 ratio, for 89 breast tumors [4], above, compared to the set of 50 cell lines (profiled for both expression and CNAs), below. (B) Spectra of gains and losses for the cell line subtypes: luminal (above), basal A (middle) and basal B (below). Statistically significant subtype-specific CNAs, called by SAM (FDR<5%), are marked by a black bar. The subset of those loci that also characterize the corresponding primary breast tumor subtype is marked by an asterisk.

Luminal cell lines displayed overall higher frequencies of high-level DNA amplification (i.e. fluorescence ratios ≥3, corresponding to at least 5-fold amplification [21]) (Fig. 4A), a characteristic shared with luminal-B tumors [4]. Luminal and basal-A lines both exhibited overall higher frequencies of gain/loss (a characteristic feature of basal-like tumors [4]), compared to basal-B lines (Fig. 4B).

Figure 4. Cell line subtypes exhibit distinct genomic instabilities.

Figure 4

Fraction of genome comprising (A) high-level DNA amplification; or (B) low-level gain/loss, stratified by cell line subtype (luminal, basal-A, basal-B). Box plots show 25th, 50th and 75th percentiles; _P_-values (Students _t_-test) for pairwise comparisons are shown.

Integrated analysis for cancer gene discovery

The molecular profiles generated provide opportunities to identify breast cancer cell lines with an altered copy number and expression of known cancer genes, useful to model pathogenesis and therapy, and to discovery new breast cancer genes. For the latter, high-amplitude CNAs, i.e. high-level DNA amplifications and homozygous deletions, are particularly informative in pinpointing new cancer genes. Within the aCGH dataset we identified 80 loci of high-level amplification in 35 different cell lines, each spanning 49–49,014 Kb (median 1,115 Kb). We also identified 13 multi-copy (possibly homozygous) deletions (fluorescence ratios ≤0.25) in 8 cell lines spanning 132–7,825 Kb (median 1,477 Kb). The boundaries of amplicons/deletions did not correspond to known germline CNVs (reported in the Database of Genomic Variants), and, for the subset of recurrent alterations, finding distinct boundaries in different cell lines was more consistent with somatic alteration. Several regions of high-level amplification contained known oncogenes, like 8q24 (MYC), 11q13 (CCND1) and 17q12 (ERBB2). Other amplicons did not correspond to known oncogenes and presumably harbor novel breast cancer genes.

Gains and losses contribute to breast cancer by the increased and decreased expression of oncogenes and tumor suppressors, respectively. Using DR-Correlate (see Methods), we identified 3,511 genes (∼18% of all well-measured genes) whose altered expression correlated significantly (FDR<0.05) with altered gene copy number (Table S3). Of these, 487 resided within loci of high-amplitude CNA (Table 4). This subset included known breast cancer genes, like EGFR (7p11), FGFR1 (8p12), ERBB2 (17q12), PPM1D (17q23) and ZNF217 (20q13). This subset is likely also enriched for novel breast cancer genes, and as such represents a rich source for cancer gene discovery. Notably, among the larger group of amplified/overexpressed genes are several with known functions relevant to oncogenesis, like cell proliferation (e.g. EIF3H, HEY1, MELK, GAB2, CDC6, GRB2) [42][47], survival (e.g. HIPK1, MCL1, MAPKAPK2, VCP, VDAC2, APIP, MAP3K3) [48][54], migration/invasion (e.g. MUC1, ADAM9, SH3PXD2A, CD44, PAK1, GIT1, PTPN1 ) [55][61], ER-signaling (e.g. BCAS2, MUC1, NCOA3, TFAP2C ) [62][65], and maintenance of genome integrity (e.g. NBN, RAD21, FANCG, BUB3, RAD9A, TAOK1, RAD51C, RAE1) [66][73]. Also represented are several “druggable” classes [74], like kinases (e.g. HIPK1, MAPKAPK2, MELK, RPS6KB2, PAK1, TAOK1, PIP4K2B, RPS6KB1, TLK2, MAP3K3), phosphatases (e.g. PTPN1), proteases (e.g. ADAM9), G protein-coupled receptors (e.g. GPRC5C) and ion channels (e.g. VDAC2).

Table 4. High-amplitude amplifications and deletions.

Cytoband P-Border (nt) Q-Border (nt) Size (kB) Cell Lines Significant DNA-RNA Correlations# Other notable genes
AMPLIFICATION
1p32.2 56946690 57156366 210 EFM192A
1p22.1-1p21.3 93549298 97052934 3504 SUM44* DR1, FNBP1L, ARHGAP29, ALG14
1p13.3 107738670 109306637 1568 HCC2688 C1orf59, PRPF38B, STXBP3, GPSM2, CLCC1 VAV3
1p13.2 114220960 115183599 963 MCF7, UACC812 AP4B1, DCLRE1B, HIPK1, TRIM33, BCAS2, CSDE1, NRAS
1q21.2 148738080 148885763 148 HCC1143 TARS2, MCL1, ENSA, GOLPH3L
1q21.2-q21.3 149460307 150130540 670 HCC712, UACC812 PIP5K1A, PSMD4, ZNF687, PI4KB, PSMB4, POGZ, SNX27, MRPL9
1q21.3 151000411 151885402 885 HCC712
1q22 153424958 153999982 575 UACC812 MUC1, C1orf2, CLK2, HCN3, PKLR, C1orf104, RUSC1, ASH1L, YY1AP1
1q23.3 159283361 159357995 75 SUM190 KLHDC9
1q32.1 204736293 205144756 408 UACC812 MAPKAPK2 IKBKE
3p14.2-p14.1 61765808 64574645 2809 MCF7
3q26.32 178223920 180535525 2312 HCC2185 TBL1XR1, ZNF639 PIK3CA
3q29 194971434 195513283 542 HCC1937
3q29 196883266 196931777 49 HCC1937
4q12 53304442 54084198 780 HCC1007 SCFD2, FIP1L1
5p15.33 712977 2811691 2099 HCC1954 ZDHHC11, PDCD6, MRPL36, NDUFS6 TERT
6p12.1 55358212 57236103 1878 HCC1007 KIAA11586, ZNF451, BAG2
6q16.3-q21 104858272 109112665 4254 HCC2185 HACE1, ATG5, C6orf203, PDSS2, SEC63, OSTM1, SNX3, FOXO3A
6q21-q22.31 111961945 123089199 11127 HCC2185 C6orf225, HDAC2, DSE, GOPC, NUS1, ASF1A, HSF2, SERINC1
7p15.2 26557965 27107611 550 HCC1007
7p11.2 54595526 55931398 1336 BT20, MDA468 EGFR
7q21.13-q21.2 90779687 91868629 1089 SUM52 MTERF, AKAP9, CYP51A1, KRIT1, ANKIB1
7q21.3 95239813 96489919 1250 SUM52 SLC25A13, SHFM1
7q22.1 100294293 100421513 127 SUM52 SLC12A9
8p21.3 21593811 21966432 373 MDA134 XPO7
8p12-p11.21 32328805 41907423 9579 BT483, HCC1500, HCC1599, MDA134, SUM44*, SUM52 FUT10, C8orf41, MAK16, ZNF703, ERLIN2, PROSC, BRF2, RAB11FIP1, EIF4EBP1, ASH2L, LSM1, BAG4, DDHD2, WHSC1L1, LETM2, FGFR1, TACC1, PLEKHA2, TM2D2, ADAM9, GOLGA7, AGPAT6 IKBKB
8q12.2-q12.3 61817956 62960675 1143 SUM190 CHD7
8q13.3 71707355 72999610 1292 SKBR3
8q21.11-q21.13 79781799 85260376 5479 EFM192A, HCC1419, HCC1599, SKBR3 HEY1, TPD52, ZBTB10
8q21.3-q22.1 89113344 95233478 6120 EFM192A, HCC1419, SKBR3 OSGIN2, NBN, DECR1, OTUD6B, RBM12B, TMEM67
8q22.2-q22.3 100879473 101995283 1116 HCC1419, HCC2185 COX6C, POLR2K
8q22.3 104311423 104550566 239 HCC1419 FZD6
8q23.1-q24.21 108267427 131134620 22867 EFM192A, HCC1419, HCC1599, HCC2185, SKBR3, ZR75-30 EIF3E, TRPS1, EIF3H, C8orf53, RAD21, TAF2, DSCC1, MRPL13, MTBP, DERL1, WDR67, C8orf76, ZHX1, ATAD2, C8orf32, FAM91A1, TMEM65, TRMT12, RNF139, TATDN1, NDUFB9, SQLE, KIAA0196, NSMCE2, FAM84B MYC
8q24.22 133917771 134337653 420 ZR75-30 PHF20L1
8q24.3 141658961 143348731 1690 HCC1419, MDA436, ZR75-30 GPR20, FLJ43860
8q24.3 144310706 144753628 443 MDA436, ZR75-30 ZFP41, GLI4, ZNF696, C8orf51, RHPN1, MAFA
8q24.3 145137850 146252219 1114 BT483, HCC1419, MDA436, ZR75-30 GRINA, OPLAH, SHARPIN, KIAA1833, FBXL6, CPSF1, VPS28, KIFC2, ZNF252
9p13.3-p13.2 33876876 38058023 4181 HCC2185 UBE2R2, UBAP2, WDR40A, KIF24, KIAA1161, DCTN3, GALT, IL11RA, VCP, FANCG, PIGO, STOML2, RUSC2, TESK1, CD72, C9orf100, TLN1, CREB3, RGP1, HINT2, CLTA, RNF38, MELK, ZCCHC7, GRHPR, ZBTB5, POLR1E, FBXO10, RG9MTD3, WDR32, MCART1
9q33.3 128307884 129195638 888 SUM44* RALGPS1
10q21.1-q21.2 72507196 73797267 1290 HCC2157 DNAJB12
10q22.2-q22.3 76461776 82106491 5645 EFM19, HCC2157 SAMD8, VDAC2, DLG5, POLR3A, RPS24, LOC283050, ZMIZ1, PPIF, SFTPA1, FAM22E, C10orf57, ANXA11
10q24.33-q25.1 105307581 106054698 747 EFM19 SH3PXD2A
10q26.13 124598599 124962466 364 SUM52 IKZF5, BUB3
11p13 33062705 35600197 2537 HCC1806* HIPK3, FBXO3, CAPRIN1, NAT10, ABTB2, CAT, APIP, PDHX, CD44
11q13.2 66874536 67198753 324 MDA134, ZR75-1 RAD9A, RPS6KB2, CORO1B, TMEM134
11q13.3-q13.4 68427956 70812048 2384 HCC1143, HCC1500, HCC1954, MDA134, MDA175, MDA361, SUM44*, SUM190, IGHMBP2, FADD, PPFIA1, CTTN, SHANK2 CCND1
11q13.4 73316198 73649077 333 BT474, MDA134, SUM190 UCP2, C2CD3, PPME1
11q13.4-q14.1 74648813 77963474 3315 MDA134, SUM44*, SUM52, SUM190 ARRB1, PRKRIR, EMSY, PHCA, PAK1, AQP11, CLNS1A, C11orf67, INTS4, NDUFC2, ALG8, GAB2, NARS2
12p12.3 18727378 19246201 519 HCC1500
12q21.31-q21.33 88265969 88443930 178 SUM52 WDR51B, GALNT4
13q22.2-q31.1 74756931 78096263 3339 UACC812 UCHL3
13q31.3-q32.1 90798074 93942902 3145 UACC812
16q12.2 51800892 53524601 1724 EFM19, SUM44* CHD9, FTO
17p12 12611513 13636592 1025 EFM192A ELAC2
17q11.2 23686912 24013273 326 ZR75-30 POLDIP2, TREM199, SLC46A1, PIGS, SPAG5, FLJ25006, KIAA0100, SDF2
17q11.2 24894649 25818484 924 HCC202 TAOK1, LOC116236, GIT1, ANKRD13B, CPD
17q11.2 27727543 28293356 566 SUM190 ZNF207
17q12 31206068 31649844 444 MDA361 FLJ12120
17q12-q21.2 32627885 36209712 3582 BT474, EFM192A, HCC202, HCC1419, HCC1569, HCC1954, HCC2218, MDA361, SKBR3, SUM190, UACC812, UACC893, ZR75-30 ACACA, TADA2L, DDX52, SOCS7, MLLT6, CISD3, PCGF2, PSMB3, PIP4K2B, CCDC49, RPL23, LASP1, CACNB1, FAM153C, RPL19, LOC90110, FBXL20, MED1, PPP1R1B, STARD3, TCAP, PERLD1, ERBB2, C17orf37, GRB7, IKZF3, GSDML, ORMDL3, PSMD3, MED24, MSL-1, CASC3, CDC6, RARA, SMARCE1
17q21.31 38419019 38738864 320 SUM190 RND2
17q21.32-q25.1 43329972 50826668 7497 BT474, EFM192A, HCC202, HCC712, HCC1419, HCC2218, ZR75-30 SP2, PNPO, CDK5RAP3, SNX11, HOXB13, CALCOCO2, ATP5G1, UBE2Z, SNF8, ZNF652, PHB, SPOP, SLC35B1, FAM117A, MYST2, PDK2, XYLT2, MRPL27, LRRC59, EME1, ACSF2, RSAD1, EPN3, SPATA20, ABCC3, ANKRD40, CROP, TOB1, NME1, TOM1L1, COX11, STXBP4
17q23.2-q24.2 53282667 63106134 9823 BT474, HCC712, HCC2218, MCF7, MDA361, ZR75-30 SFRS1, DYNLL2, MKS1, SUPT4H1, MTMR4, RAD51C, TRIM37, FAM33A, C17orf71, YPEL2, DHX40, CLTC, PTRH2, TMEM49, TUBD1, RPS6KB1, RNFT1, HEATR6, USP32, APPBP2, PPM1D, BRIP1, INTS2, MED13, METTL2A, TLK2, TANC2, CYB561, WDR68, CCDC44, MAP3K3, LYK5, CCDC47, DDX42, PSMC5, SMARCD2, DDX5, CCDC45, SMURF2, GNA13, HELZ
17q25.1 69755691 71418122 1662 HCC2218, MDA361, MDA453, UACC893 GPRC5C, SLC9A3R1, NAT9, TMEM104, FDXR, C17orf28, CDR2L, ICT1, KCTD2, SUMO2, NUP85, GGA3, MRPS7, MIF4GD, SLC25A19, GRB2, CASKIN2, TSEN54, MYO15B, SAP30BP, H3F3B, UNK, WBP2
18q21.32-q21.33 55178911 57628085 2449 HCC1500
19p13.2 14932742 15602448 670 HCC1143 ILVBL, BRD4, AKAP8L
19q12-q13.11 33966349 38052482 4086 HCC1569, HCC1599 UQCRFS1, POP4, PLEKHF1, C19orf2, DPY19L3, ANKRD27
19q13.11 39866832 40146793 280 HCC1599
19q13.42 60551045 60898029 347 EFM19 FIZ1, ZNF784, CCDC106
19q13.43 63208125 63774724 567 HCC1806* ZNF329, ZNF274, ZNF8, ZSCAN22, ZNF324, TRIM28, CHMP2A, UBE2M
20p12.2 10224083 10433564 209 HCC2185 MKKS
20q11.22 32363269 33563203 1200 BT474 DYNLRB1, NCOA6, UQCC
20q13.12 42493067 43286511 793 BT474, SUM52 SERINC3
20q13.12-q13.13 45234836 48636574 3402 BT474, HCC1419, MCF7 NCOA3, PREX1, ARFGEF2, STAU1, DDX27, ZNFX1, SLC9A8, SPATA2, PTPN1
20q13.13-q13.32 49139330 57334442 8195 BT474, HCC1419, MCF7, SKBR3 ZFP64, ZNF217, BCAS1, PFDN4, C20orf108, CSTF1, C20orf43, TFAP2C, BMP7, RAE1, RBM38, RAB22A, VAPB, STX16, NPEPL1, GNAS, TH1L, ATP5E, SLMO2 AURKA
20q13.33 61801252 62370522 569 HCC1419 PRR17, OPRL1
22q11.21 18256420 19686015 1430 SUM190 COMT, HTF9C, PI4KA
22q12.1 24895479 25885840 990 HCC202 HPS4
Xp11.23-p11.22 48635684 51225253 2590 HCC712
Xp11.22 52255712 54236019 1980 HCC202 TMEM29, PHF8
Xq28 148368959 149592006 1223 HCC202
DELETION
6q16.3-q21 102493055 105832848 3340 HCC1395 HACE1
7q11.23-q21.11 77246720 77484743 238 HCC1806* TMEM60, PHTF2
8p23.3 604200 2080787 1477 HCC2688 ERICH1
9p24.3-p24.2 958704 3213008 2254 HCC2185 VLDLR, KIAA0020
9p21.2-p21.1 26894518 29207861 2313 BT474, EFM19 PLAA, IFT74 CDKN2A
13q14.3-13q21.2 52175620 60001053 7825 HCC1395
15q24.3 74984799 75116728 132 HCC1806* RCN2
17p12 11405197 11987872 583 EFM19 MAP2K4
17q21.31 38252285 38419019 167 HCC1806* BRCA1
18q11.2-q12.1 22256956 23913060 1656 HCC2185
21q21.1 18342236 21590772 3249 ZR75-30
Xp11.3 46208136 46345060 137 HCC2157
Xq25 122657657 123338533 681 HCC1806*

Discussion

Using whole-genome DNA microarrays, we collected transcriptional and genomic profiles across a set of 52 widely used breast cancer cell lines, with the primary goals to establish their suitability in modeling known breast tumor heterogeneity, and to create a resource for cancer gene discovery. Cluster analysis of transcriptional profiles defined three cell line subtypes, one luminal and two basal (A and B), consistent with other recent studies of breast cancer cell lines [31], [33], [75]. The luminal subtype included all ER-positive cell lines, and associated gene expression patterns reflected both ER and luminal differentiation pathways, the latter including GATA3 and FOXA1, key transcriptional mediators of luminal differentiation [28], [76]. The basal-like cell lines were ER-negative and exhibited more frequent mutations of TP53 and PTEN, consistent with findings in basal-like tumors [3], [77]. The basal-A subtype exhibited enriched expression of ETS pathway genes, a pathway linked to diverse tumor phenotypes including invasion and metastasis [78]. The basal-B subtype, which included the three non-tumorigenic lines (consistent with prior studies [75]), as well as five highly invasive/metastatic lines with features of EMT, exhibited enriched expression of EMT and EGF regulated genes, the latter pathway also previously linked to basal-like tumors [79].

Recently, Neve et al. [33] profiled 51 breast cancer cell lines (though using a lower-resolution (∼1 Mb) CGH platform), 38 of which (∼3/4th) overlapped with the 52 we profiled. All the overlapping lines except for one clustered into the same corresponding gene-expression subtype in both their and our study. The exception was HCC1500, which we classified as luminal while Neve et al. labeled it as basal B. The discrepancy may reflect a cell line identification error. We note that ATCC describes the line as ER-positive, more consistent with a luminal classification.

Our comparisons of expression profiles between breast cancer cell line subtypes and breast tumor subtypes provided valuable information relevant to the suitability of cell lines in modeling known breast tumor heterogeneity. Luminal-A/B tumors best matched luminal cell lines. Notably, basal-like tumors most corresponded to basal-A cell lines. Consistent with this finding, two breast cancer cell lines from BRCA1 mutation carriers also clustered in basal-A (and basal-A lines exhibited enrichment of a BRCA1 signature), where it has been established that _BRCA1_-associated tumors share many features with sporadic basal-like tumors [80]. Interestingly, ERBB2-associated tumors matched both luminal and basal-A lines. While ERBB2 represents a distinct expression tumor subtype in multiple independent cohorts [3], [15], [81], it is noteworthy that most ERBB2 (HER2+) cell lines clustered in the luminal subtype. The basis for the discrepant ERBB2 grouping in cell lines and tumors is unclear but warrants further investigation.

It has been suggested that the origin of the luminal vs. basal breast cancer distinction reflects the transformation of different breast epithelial progenitor cell compartments [82], [83]. Breast epithelial stem/progenitor cells support mammary gland development during puberty and subsequent growth and remodeling during pregnancy [84]. A prevailing view is that breast epithelial stem cells give rise to bipotent basal/luminal progenitors, which then give rise to basal and luminal restricted progenitors, and from there to differentiated basal/myoepithelial and luminal epithelial cells [84], [85]. Bipotent human breast epithelial stem/progenitors have been characterized with the cell surface phenotype MUC−/low/CALLAlow/+ [39]. Separately, breast cancer stem cells, identified prospectively as tumor initiating cells when transplanted into immunodeficient mice, have been characterized by the surface expression phenotype CD44+/CD24−/low [40], also a presumed phenotype of normal breast epithelial stem or early progenitor cells [84].

Our transcriptional profiles of breast cancer cell lines are consistent with an origin in (or at least a likeness of the bulk cell population to) the various stem/progenitor cell compartments. Basal-B lines predominantly express CD44+/CD24−/low and MUC−/CALLA+ phenotypes characteristic of stem or bipotent progenitor cells, as well as ITGB3 (CD61), also recently characterized as a cancer stem cell marker in MMTV-wnt-1 induced murine breast cancer [41]. In contrast, basal-A lines appear mainly CD44+/CD24+, but express PROM1 (aka CD133), a marker of luminal progenitors in mice [86] also more recently characterized as a stem cell marker in BRCA1-associated breast cancer [87], while luminal lines express markers of luminal lineage restriction like GATA3 and FOXA1 [28]. Conspicuously absent from our analysis is a breast tumor subtype corresponding to the stem-cell like (and sometimes mesenchymal-like) basal-B lines. Whether basal-B lines reflect an uncommon tumor subtype not yet characterized, or else a stem/progenitor subpopulation of tumor cells enriched in culture, or even an artifact of cell culture, remains to be determined. Regardless, breast cancer cell lines are likely to prove useful for discovering new stem cell markers, and for studying stem/progenitor cell biology.

Our genomic profiles of breast cancer cell lines indicate that overall the spectra of CNAs is reflective of breast tumors, consistent with prior findings from loss of heterozygosity (LOH) analysis [11]. Overall, however, cell lines exhibited higher frequencies and greater complexities of CNAs, and seemingly more than might be explained by a higher sensitivity of detecting CNAs in stromal-free tumor cell populations. Notably absent among the luminal subtype were the “simple” karyotypes characteristic of luminal-A tumors (i.e. 1q+, 16p+/16q−). By genomic profiles, luminal cell lines shared features characteristic of luminal-B tumors, including certain subtype-specific CNAs and overall higher levels of DNA amplification. Likewise, basal-A cell lines and basal-like tumors shared the feature of high levels of chromosome segment gain/loss. However, overall only a subset of subtype-specific CNAs was preserved. Therefore, at the genomic level it is uncertain how well cell line subtypes faithfully represent tumor subtype counterparts.

Taken together, the transcriptional and genomic profiles support the conclusion that luminal and basal-A cell lines are the most appropriate cell line models of luminal-B and basal-like tumors, respectively. Further, the basal lines are likely useful models for biological studies of the 70-gene, wound and hypoxia signatures. Despite incongruent expression results, luminal lines with amplification/overexpression of ERBB2 are likely appropriate models of ERBB2-associated tumors. Our findings indicate that new cell lines are needed to more faithfully model luminal-A tumors. Currently available cell lines likely reflect certain biases in the specimen source of cell line, and/or in the culturing methods, as suggested by the predominance of HCC lines (from UT Southwestern) among the basal-A group. Different culturing methods (e.g. ref. [88]) might support the establishment of cell lines from luminal-A tumors.

Our genomic profiles also identified numerous high-level DNA amplifications and multi-copy deletions, pinpointing known and novel cancer genes. Further, by integrating the genomic and transcriptional datasets, we could define a set of candidate cancer genes residing at these loci and exhibiting both altered copy number and expression. The larger set of amplified/overexpressed genes included several known breast cancer oncogenes, as well as many plausible candidates including genes with known functions relevant to carcinogenesis, like cell proliferation, survival and motility/invasion, and genome integrity (e.g. DNA damage response). Though genes maintaining genome integrity are more typically considered candidate tumor suppressors, the overexpression of such genes has been linked to genome instability [67], [89]. The set of amplified/overexpressed genes also included many druggable targets [74], most notably several kinases. Importantly, the same cell lines used for discovery can also be used to functionally examine cancer gene candidates, for example using RNA interference to knockdown the expression of amplified oncogene candidates, and then assaying loss of tumorigenic phenotypes in cultured cells or in vivo (e.g. refs.[90], [91]). Indeed, high-throughput RNA interference approaches [92], [93] might be used to evaluate many or all of the candidate cancer genes simultaneously.

In summary, transcriptional and genomic profiling of 52 commonly used breast cancer cell lines identifies cell line subtypes, and defines the cell line subtypes that most faithfully capture the known heterogeneity of breast tumors. Specifically, luminal and basal-A lines appear to best model the features of luminal-B and basal-like tumors, while basal-B lines might inform stem cell biology. In addition, our integrated analysis of genomic and transcriptional profiles pinpoints loci and genes with altered copy number and expression, providing a rich source for discovery and future characterization of new breast cancer genes.

Supporting Information

Table S1

8,750 variably expressed genes (log2 ratios)

(1.11 MB ZIP)

Table S2

Processed aCGH data (log2 ratios)

(2.82 MB ZIP)

Table S3

Genes with significantly correlated copy number and expression

(0.13 MB TXT)

Acknowledgments

We wish to thank the SFGF for microarray manufacture, SMD for database support, and members of the Pollack lab for helpful discussion.

Footnotes

Competing Interests: The authors have declared that no competing interests exist.

Funding: This work was supported by grants from the NIH (CA97139; J.R.P.), the California Breast Cancer Research Program, (8KB-0135; J.R.P.), and the Longenbaugh Foundation (J.D.M.). K.S. is a Paul & Daisy Soros Fellow and fellow of the Medical Scientist Training Program. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Table S1

8,750 variably expressed genes (log2 ratios)

(1.11 MB ZIP)

Table S2

Processed aCGH data (log2 ratios)

(2.82 MB ZIP)

Table S3

Genes with significantly correlated copy number and expression

(0.13 MB TXT)