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NCI-60 miRNA profiling 1
Global microRNA analysis of the NCI-60 cancer cell panel
Rolf Søkilde (1), Bogumil Kaczkowski (2), Agnieszka Podolska (3), Susanna Cirera (3), Jan Gorodkin (4),
Søren Møller (1), Thomas Litman (1,*)
(1) Department of Biomarker Discovery, Exiqon A/S, Bygstubben 9, DK-2950 Vedbæk, Denmark
(2) Bioinformatics Centre, Department of Biology, University of Copenhagen, Ole Maaloes Vej 5, DK-2200
Copenhagen N, Denmark
(3) Department of Animal and Veterinary Basic Sciences, Division of Genetics and Bioinformatics, Faculty of Life
Sciences, University of Copenhagen, Groennegaardsvej 3, DK-1870 Frederiksberg C , Denmark
(4)Center for non-coding RNA in Technology and Health, Division of Genetics and Bioinformatics, University of
Copenhagen, Groennegaardsvej 3,
DK-1870 Frederiksberg C, Denmark
* Corresponding author:
Thomas Litman, Department of Biomarker Discovery, Exiqon A/S, Bygstubben 9, DK-2950 Vedbæk, Denmark,
e-mail: [email protected]
Key Words:
microRNA, microarray expression analysis, NCI-60 cancer cell lines, cluster analysis, drug resistance
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NCI-60 miRNA profiling 2
Abstract
MicroRNAs (miRNAs) are a group of short non-coding RNAs, which regulate gene expression at the
posttranscriptional level. They are involved in many biological processes, including development,
differentiation, apoptosis and carcinogenesis. Because miRNAs may play a role in the initiation and progression
of cancer, they comprise a novel class of promising diagnostic and prognostic molecular markers and potential
drug targets. By applying an LNA-enhanced microarray platform, we have studied the expression profiles of 955
miRNAs in the NCI-60 cancer cell lines, and identified tissue and cell-type specific miRNA patterns by
unsupervised hierarchical clustering and statistical analysis. A comparison of our data to three previously
published miRNA expression studies on the NCI-60 panel showed a remarkably high correlation between the
different technical platforms. Additionally, the current work contributes with expression data for 369 miRNAs
that have not previously been profiled. Finally, by matching drug sensitivity data for the NCI-60 cells to their
miRNA expression profiles, we found numerous drug-miRNAs pairs, for which the miRNA expression and drug
sensitivity profiles were highly correlated and thus, represent potential candidates for further investigation of
drug resistance and sensitivity mechanisms.
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Introduction
Twenty years have passed since the NCI-60 cell panel for anticancer drug screening was established in 1990 (1),
and today, these cell lines represent the most extensively studied and best characterized set of tumor cells
available. The panel comprises 59 human cancer cell lines1 derived from nine different tissues of origin:
melanoma, leukemia, breast, central nervous system (CNS), colon, lung, ovary, prostate, and kidney.
Chemosensitivity profiles of the NCI-60 cells for more than 100,000 compounds have been generated, and
together with genomic, transcriptomic, proteomic, epigenomic, and metabolomic data, this information is
publicly available via the NCI Developmental Therapeutics Program’s website (2) or can be mined directly by
the CellMiner query tool (3). One type of data, which could be important for understanding the link between
genomic and chemosensitivity data, in particular where there appears to be lack of correlation between mRNA
and protein levels, was recently added to the NCI-60 database, namely microRNA (miRNA) expression
information (4-6).
miRNAs constitute a group of short, non-coding RNAs, which repress gene expression at the posttranscriptional
level by binding to complementary sequences in the 3’-UTR of their target mRNAs (7). miRNAs are involved in
the regulation of numerous biological processes, including differentiation, proliferation, and apoptosis.
Moreover, aberrant miRNA expression has been associated with many diseases, including cancer, where
miRNAs can function both as tumor suppressors and as oncogenes (8). Because miRNAs play a role in cancer
pathogenesis, they may have potential as molecular biomarkers, not only for classification purposes, but also
1 one breast cancer cell line, MB-468, has high identity to MDA-MB-231 (http://www.sanger.ac.uk/genetics/CGP/Genotyping/nci60.shtml), and was excluded from the NCI-60 panel that we received for our analysis
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for prediction of treatment response and for discovery of new targets. Today, 1100 miRNAs as well as several
thousand putative miRNA targets have been identified in the human genome and are accessible via miRBase,
the main miRNA database (9). It is the expression of these miRNAs, as well as a number of viral and novel
sequences identified by high-throughput sequencing (10) that we have been studying with the application of an
LNA-enhanced microarray platform.
We present here a comprehensive analysis of the NCI-60 cell line panel’s miRNA landscape, where the
expression of 955 miRNAs has been correlated to the cells’ drug sensitivity, mRNA and protein level, and tissue
of origin. Additionally, our meta-analysis of the four available NCI-60 miRNA data sets, i.e. Gaur et al.’s RT-PCR
study (5), Blower et al.’s custom microarray data (4), Liu et al.’s Agilent array data (6)2 and the current LNA
microarray analysis, cross-validates the individual platforms where they have overlapping and concordant
targets.
2 At the time of submission of this paper, the complete data set was not yet publicly available. Therefore, our analysis is limited to the 365 miRNAs reported to be expressed in at least 10% of the cell lines by Liu et al.
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Materials and Methods
Cell lines
The NCI-60 cell stocks were kindly provided by Dr. Susan Holbeck at the NCI Developmental Therapeutics
Program in May 2006, except for the SR lymphoma cell line which was purchased from ATCC (CRL-2262,
Manassas, VA) in 2007. Supplementary Table S1 lists the origin of the cell lines as well as some of their
characteristics. All cells were grown at 37°C in 5% CO2 in RPMI-1640 medium (Invitrogen), supplemented with 2
mM L-glutamine and 10% fetal bovine serum (Invitrogen). The tissue origin of each cell line is denoted as: BR,
Breast; CNS, glioma; CO, colon; LE, leukemia; LU, lung; ME, melanoma; OV, ovary; PR, prostate; RE, renal. The
cells were grown to ca. 80% confluence and passaged twice before RNA extraction.
RNA extraction and quality control
Total RNA was extracted by Trizol® reagent (Invitrogen) according to the manufacturer’s instructions. RNA
quality was assessed with Bioanalyzer 2100 Nano chips (Agilent) and did not show sign of degradation as
judged by the RNA integrity number (RIN), which was above 8 for most samples. RNA concentrations were
measured on a Nanodrop ND-1000 (Thermo Scientific).
RNA labeling
Our microarray experimental setup utilized a common reference design as described in detail by Søkilde et al.
(11). 1 µg of total RNA from each sample was labeled using the miRCURY™ LNA microRNA Power labeling Kit
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(Exiqon) following the manufacturer’s recommendations. Cell line-specific RNA was labeled with Hy3
fluorophore, while the common reference RNA pool was labeled with Hy5.
Microarray hybridization and scanning
For microRNA profiling, we applied the LNA-enhanced miRCURY Dx 9.2 microarray platform, containing
quadruplicate probes for 955 mature miRNAs (Exiqon). Labeled RNA samples were hybridized to the arrays for
16 hours at 65 °C in a Tecan HS4800 hybridization station. After washing and drying, the microarrays were
scanned in a DNA Microarray Scanner (Agilent) and the resulting images were quantified using Imagene v. 8.0
(BioDiscovery, CA). Both automatic quality control, i.e. flagging of poor spots, and manual, visual inspection
was performed to ensure the highest possible data quality.
Data pre-processing and normalization
All low-level analyses were performed in the R environment, such as importing and pre-processing of the data
using the LIMMA package (12). After excluding flagged spots from the analysis, the “normexp” background
correction method, plus offset=50 was applied, wherafter intensities were log2 transformed and quantile
normalized as implemented in LIMMA. Both log2 intensities (single channel analysis) and log2 ratios (dual
channel analysis) of four intra-slide replicates were averaged. All expression data were deposited in the Rosetta
Resolver (Rosetta Biosoftware, UK) data management system.
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High-level data analysis
To identify miRNAs that were differentially expressed, we applied the empirical Bayes, moderated t-statistics
implemented in LIMMA. Linear models were fitted to the data, and comparisons of interest were extracted as
contrasts. Unsupervised hierarchical clustering with complete linkage, using both Euclidean and (1-Pearson
correlation) as distance metrics, was applied to cluster the NCI-60 cell lines according to their miRNA
expression levels.
Mapping probes from prior datasets
The qPCR data generated by Gaur et al (5) and the array data by Blower et al (4) and by Liu et al (6) were
matched to the current miRBase release 15.0, where annotation was kept at the level of MIMAT names (the
identifier for mature miRNA sequences, see Supplementary Table S2). Probe designs for the Blower array were
downloaded from the ArrayExpress Archive (13), and all probes with a perfect match to a mature microRNA in
miRBase 15.0 were used for the subsequent analysis. Probes with only partial overlap were excluded from the
analysis.
Drug sensitivity correlation analysis
To examine the relationship between drug sensitivity and miRNA expression we took three approaches: First,
we downloaded GI50 data (July 2007 release) for the NCI-60 panel from NCI’s DTP web site (14). The raw data
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consisted of GI50 values for 45,857 drugs, which after applying a variance filter (variance >0.15) were reduced
to 6,081 drugs. After minimum range filtering (GI50_max - GI50_min > 2), 4,442 drugs were left in the data set.
Second, in addition to the raw GI50 data above, we included a pre-processed dataset from the Cellminer
database (3). This data consist of 118 drugs with known mechanism of action, and is described by Bussey et al.
(15).
Correlation between miRNA expression and drug sensitivity was calculated in a pair-wise manner, for each
miRNA-drug combination, where each drug was represented by a vector of GI50 measurements, and each
miRNA was represented by a vector of log2 Hy3 intensities. Both Spearman and Pearson correlation
coefficients were calculated, and the p-values of the correlation significance were adjusted for multiple testing
by Benjamini & Hochberg’s False Discovery Rate (FDR) correction method (16).
Finally, a COMPARE analysis was run to search for correlations with unfiltered GI50 values (October 2009
release), and resulted in 36,553 correlations with p<0.005.
mRNA expression correlation analysis
The NCI-60 cells’ global mRNA expression profiles measured on the Affymetrix HG U133A and U133B
microarray platform were downloaded from GEO (accession number GSE5720) (17) and normalized using
Rosetta Resolver. Correlations between log2 intensities of overlapping miRNA-mRNA pairs (i.e.: miRNAs that
reside within introns of protein coding genes. The pairs are represented by miRCURY (for miRNA) and
Affymetrix (for mRNA) probes) were calculated using both Spearman and Pearson correlation, and the p-values
were adjusted for multiple testing by the Benjamini & Hochberg method with FDR ≤ 0.01. The genomic
coordinates of all known human miRNAs were extracted from the human miRNA annotation file downloaded
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from miRBase release 15.0 (18). Pairs of miRNA and mRNA transcripts were identified by matching the genomic
location: chromosome, start, end and strand.
Molecular Targets correlation analysis
A correlation analysis of our miRNA expression data against the Molecular Targets database (September 2008
release)(19) included 43,981 Pearson correlations with a cut-off value of p<0.005 (Supplementary Table S3).
The Molecular Targets datasets compile information on protein levels, RNA expression, DNA mutation status,
and enzyme activity for the NCI-60 cell lines.
Quantitative real-time PCR
The expression levels of 20 selected miRNAs and 10 reference genes were validated by quantitative RT-PCR
applying the miRCURY LNA™ microRNA PCR system and SYBR™ Green master mix following the manufacturer’s
instructions (Exiqon, Vedbæk, Denmark). The results are shown in Supplementary Figure S3.
Northern blot analysis
To visualize the level and size of 7 candidate miRNAs we performed Northern blot analysis on RNA from the
SNB-19 (glioma), HCT-15 (colon) and SR (leukemia) cell lines. The method and results are described in the
Supplementary (Figure S4).
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Results and Discussion
We studied global miRNA expression in the NCI-60 cell line panel by applying a recently described (20), highly
sensitive and specific miRCURY™ microarray platform, which contains LNA-enhanced and Tm-normalized
capture probes for quantitation of 599 mature human miRNAs annotated in miRBase v.15.0, as well as 345
proprietary miRNAs, 29 viral miRNAs, and 10 snoRNAs.
Cross platform comparison
Three previous studies have reported miRNA expression patterns from the NCI-60 cell lines: Blower et al (4)
and Liu et al. (6) used oligonucleotide microarrays, while Gaur et al (5) applied real-time quantification by
TaqMan stem-loop RT-PCR. A comparison of the different platforms’ coverage and the latest miRBase version
15.0 shows that the current work contributes with expression data for 369 miRNAs that have not previously
been profiled (Fig. 1A), including 345 miRPlus™ sequences not yet annotated in miRBase.
To assess correlation between platforms, the 125 miRNAs common to all four analyzes were identified3 and the
six pair-wise (inter-platform) Pearson correlation coefficients were computed, as summarized in Table 1
(values shaded light gray). Taking into consideration that completely independent RNA preparations and assay
technologies have been applied, the concordance between the four miRNA platforms is remarkably high,
compared to a similar mRNA analysis on the NCI-60 cell line panel (17). The miRNA correlation values (the same
miRNA measured across platforms) ranged from highly correlated (r=0.97) to negative (r=-0.29) and with an
overall average of 0.48 (SD=0.28), which is far better than what has been reported previously in a mRNA
3 Only 365 out of 727 miRs were reported as expressed by Liu et al. Therefore, the number of expressed miRNAs in common between the four studies is 125, and not 158, which is the total number of common miRNAs analyzed.
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correlation study comparing oligonucleotide and cDNA microarray measurements on the NCI-60 panel (the
Pearson correlation coefficient between the two array platforms was close to zero) (21).
Besides obvious methodological and biological inter-study differences (such as differences in growth
conditions, RNA extraction and analysis method), we have noticed that the three previously published studies
apply various types of flooring to the data. While flooring may serve to eliminate negative expression values
due to background subtraction from low-intensity values, such an ad-hoc threshold does not reflect the
experimental measurements and can even lead to artifacts (22). Therefore, we chose to apply the normexp
background correction method for stabilizing the variance of low-intensity probes after log2 transformation.
Although there is a good overall consistency between platforms as assessed by the distribution of miRNA
correlations (Fig.1B), we observe an even higher correlation between the cell line-specific signatures (Table 1,
dark gray shaded values, and Fig. 1C). This suggests that cell line-specific miRNA profiles are robust, and can be
identified regardless of platform. Only two cell lines showed very low correlation between the different
studies, namely the two CNS cell lines SNB-19 and SNB-75,with an average correlation coefficient of 0.18-0.31.
This could indicate that these two cell lines have a phenotype, which is sensitive to culturing conditions.
Interestingly, the LNA microarray was the only platform to correlate the SNB-19 and U251 cell lines closely
together, which accords with their similar genotypes as they are derived from the same individual (23).
For further comparison of the platforms, the best correlating of the 125 common miRNAs were used in an
unsupervised hierarchical cluster analysis, which showed that most of the tissue- and cell-specific expression
patterns are retained between the four assays (Fig. 2). We selected 62 miRNAs with the highest average
correlation (R>0.5) between all combinations of platforms to obtain a core set of “high confidence” miRNAs for
clustering with resampling (Fig. 2A). Strong tissue specific miRNA signatures were identified for cell lines of
colon, melanoma, leukemia and kidney origin (the latter included a single outlier, SN12C). These cell lines
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clustered together independently of platform and cell line origin, suggesting a strong and consistent tissue
specific signature in these tissues, such as miR-211 expression in melanoma, miR-142 expression in leukemia,
miR-192 expression in colon, and miR-138 in kidney. However, we also identified cell lines representing tissue
groups with a more diverse expression pattern, including lung, ovary and breast. Therefore, we chose to cluster
these tissues separately to identify miRNA patterns unique to the individual cell line (Fig. 2B-D).
Again, we found good concordance between the platforms based on the “high confidence” set of miRNAs,
showing that cell lines, though not displaying tissue specific expression, retain their miRNA profile between
different laboratories and studies. This is promising for the research of miRNA function, because related cell
lines studied in different experimental settings will likely display similar miRNA profiles. However, there are
exceptions; for example, the lung cancer cell line HOP-62 on the Gaur platform showed a miRNA profile that
differed markedly from that of the three other platforms (Fig. 2B).
We found that the lung cancer cell lines (Fig. 2B) showed the most intra-tissue diversity among all the cell lines
studied, as these cell lines scattered throughout the full cluster (Fig. 2A). From the clustering of the lung cancer
cell lines, the NSCLC large cell HOP-92, which is extremely slow growing, showed the most distinct miRNA
profile..
Two of the ovarian cancer cell lines, OVCAR-8 and NCI/ADR-RES, have similar karyotypes (24). This is reflected
in Fig. 2C, as all four platforms cluster these cell lines together. Other cell line pairs with high identity and thus,
most likely, a common origin, include the two CNS cell lines SNB-19 and U251, and the melanoma cell lines
M14 and MDA-MB-435.
All platforms agreed on separating the breast cancer cell lines in two distinct clusters, representing miRNA
expression profiles specific for basal and luminal type breast cancer (Fig. 2D).
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In summary, our correlation analysis shows that most miRNA expression profiles are retained across NCI-60
data sets and therefore, the data can potentially be used in combination, for example, in a meta-analysis. The
observed inter-study variation may be due to both technology and biology: Different assay and probe designs
are likely to give different results, besides the expected variation due to differences in growth conditions and
handling of the cell lines. For the rest of the paper we focus on the data generated by use of LNA-enhanced
microarrays.
Tissue and cell line specific miRNA expression
The distribution of tissue specific miRNAs (i.e. those miRNAs that were preferentially expressed in cell lines
originating from one tissue compared to the rest of the cell lines) is summarized in the heatmap below (Fig. 3).
Breast cancer
Because of the heterogeneous nature of the breast cancer cell lines, only two miRNAs were identified as breast
cancer specific compared to the rest of the cell lines: miR-195 and miR-196a. Both these miRNAs are
upregulated in breast cancer patients, and recently, miR-195 has been identified as a serum biomarker
diagnostic for breast cancer (25). When looking at the “breast cancer only” miRNA cluster (Fig. 2D), it appears
that the cell lines fall in two, clinically distinct clusters, representing luminal (MCF7 and T47D), and basal
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(HS578T, BT549 and MDA-MB-231) breast cancer, where the latter, basal triple negative subtype is the
prognostically more unfavorable (26). The luminal cell lines are characterized by hormone receptor expression
(estrogen receptor (ESR1), progesterone receptor (PGR) and HER2) and luminal cytokeratins (e.g. CK7), while
the basal type cancer cell lines are negative for the hormone receptors and have expression of basal
cytokeratins (e.g. CK5 and CK6).
Recently, miR-22 was found to target ESR1, which is in agreement with our data, where low miR-22 expression
is observed when ESR1 levels are high (Fig.3B). A similar inverse relationship between miR-222~miR-221 and
ESR expression was reported by Zhao et al. in both breast cancer cell lines and patients (27), consistent with
our observations.
Among the upregulated miRNAs in ESR positive cell lines are miR-200a~miR-200b~miR-429 (Fig.3B), a cluster
linked to the regulation of E-cadherin via ZEB1/2 (28). miR-193b has been associated with the regulation of
urokinase-type plasminogen activator (uPA), an important prognostic factor in breast cancer development (29).
We found that the E-cadherin mRNA level follows the level of miR-200, while the uPA transcript levels are
negatively correlated with miR-193b, (in support of the reported associations. On the other hand, miR-23b,
which was found to correlate positively with uPA, has been shown to down-regulate uPA and MET in
hepatocellular carcinomas, while this interaction has not been reported in breast cancer.
Scott et al showed that miR-125a/b regulates ERRB2 at both transcript and protein levels (30), which is in line
with our observations: miR-125b is expressed at higher levels in the ERRB2 negative breast cancer cell lines
compared to ERBB2 positive cells (Fig. 3B).
Unexpectedly, we found positive correlation between miR-146 expression and EGFR mRNA and protein levels
(Supplementary Figure S1). This is unlike the canonical model for miRNA action, where high levels of miRNA
repress their corresponding targets protein levels.
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CNS – Glioma
The CNS specific miR-376, miR-377 and miR-654-3p, localize to a large cluster of microRNAs on chromosome
14, often referred to as the miR-379~miR-656 cluster, found in an imprinted region of transcripts. A functional
role of the cluster has been shown in neuronal development (31); interestingly, the three candidates are
expressed at higher levels in the two estrogen receptor positive cell lines, suggesting a role in hormone
regulation (Fig. 3A).
Two microRNA clusters, miR-100~let-7a-2~125b-1 and miR-23b~27b~24-1, are expressed significantly higher in
the brain-derived cells compared to the rest of the cell lines. The miR-100 cluster along with the let-7 family
have been described as brain specific in mammals and conserved expression was also found in zebrafish (32).
Interestingly, no reports have been made on specific expression of the miR-23b cluster in the brain. The brain
specific miR-124 was undetected in all CNS derived cell lines. This microRNA is supposedly important for the
maturation of neurons, and lack of its expression may be explained by the lack of differentiation of the CNS
tumor cell lines. A miRNA reported to be involved in neurogenesis is miR-9; we observed a distinct pattern of
miR-9 and miR-9* expression in three cell lines, SF-295, SNB-19 and U251, while three other CNS cell lines
appear mir-9 and miR-9* negative, namely SF-268, SF-539 and SNB-75, suggesting differences in differentiation
between these two groups.
Colon cancer
Most notably, the cells of colon origin gave rise to many colon specific miRNAs, due to the homogenous
expression pattern observed in these cell lines. Interestingly, SW-620 and COLO-205 do not form tight epithelial
cell clusters, which are characteristic for the other colon cancer cell lines in the panel. Instead, they form round
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bipolar cells with lower cell-to-surface and cell-to-cell adhesion (33). Recently, the miR-200 family was reported
to be connected to epithelial cluster formation (28), which is why we decided to investigate this circuit in the
cell lines. While we did not identify any differences that could account for the lower cell-to-cell adhesion
properties of SW-620 and COLO-205 (Fig. 3C), we found that miR-142 (both 5p and 3p) – a miRNA
characteristic for leukemia and involved in hematopoiesis - was highly expressed in these two cell lines. We
then looked for possible pathways targeted by miR-142 by applying DIANA-mirPath (34) and found that many
adhesion pathways were enriched for target sites for miR-142 (3p and 5p), including TGF-beta signaling,
adherence junction, tight junction, regulation of actin cytoskeleton and ECM-receptor interaction. Many of the
proteins targeted by miR-142 are involved in several of the above pathways and are important regulators of
adhesion (Supplementary Fig. S2).
We also detected differential expression of miRplus sequences, among which we looked closer at hsa-miRPlus-
C1018, a sequence variant of hsa-miR-20a*. We were unable to detect hsa-miR-20a* on the array, and since
the miRplus-C1018 and miR-20a signals appear highly correlated (R2=0.82), this could indicate a mature miRNA-
20a* different from the one annotated in miRBase.
Lung cancer
The lung cancer cell lines displayed very diverse expression profiles. From the clustering of the lung cancer cell
lines alone (Fig. 2B), HOP-92 and HCI-H460 clustered furthest away from the other cell lines, and have
previously been described as large cell undifferentiated carcinomas (35). Another sub-cluster contained NCI-
H322M and EKVX; both are epithelial cell lines with high expression of miR-200 and miR-203. On the other
hand, the squamous miRNA marker miR-205 was only high in NCI-H322M, indicating that this cell line is of
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squamous origin. The other cell line originally described as a squamous cell carcinoma, NCI-H226, had more
resemblance to a mesothelioma in our study, supporting the notion of Nutt et al. (36).
Recently, the metastatic potential of the NCI-60 lung cancer cells was measured in matrigel, and a mRNA
signature was found that correlated to overall survival (37). By applying the same split (into a low and high
metastatic potential group), we identified a short-list of miRNAs (Fig. 3D) which could separate high from low
metastatic potential cell lines. We found miR-452 to be upregulated in the group with high metastatic
potential, which is in agreement with the observation that miR-452 is upregulated in lymph node positive
bladder cancer patients (38).
Leukemia
In general, the miRNA expression pattern in the leukemia cell lines is distinct from the other cell lines, which
are all derived from solid tumors. Thus, we are able to confirm the “hematologic” signature differentiating non-
solid from solid tumors, as suggested by Gaur et al (5). However, we also observe more specific expression
patterns. For example, miR-451 is exclusively expressed in the K562 cell line, which is a bi-potential cell line as
it can differentiate into either erythrocytes or megakaryocytes (39). miR-451 clusters together with miR-144,
which is also highly expressed only in this cell line.
We also noticed high miR-223 expression in a subset of the leukemia cell lines (in particular HL-60 had
extremely high miR-223 levels), which have been shown to be activated by the transcription factor C/EBP
alpha. The myeloma cell line RPMI_8226 and the lymphoma cell line SR are both negative for miR-223, which is
in line with their lineage.
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NCI-60 miRNA profiling 18
Melanoma
The melanoma cell lines displayed a distinct clustering pattern (Fig. 2A), due to their unique expression of miR-
204 and miR-211. These miRNAs have a similar seed sequence but differ in two nucleotides on position 17 and
18. miR-211 is located in an intron of TRPM1 and shows high correlation to the expression of TRPM1 mRNA
(data not shown). TRPM1 is known to be transcriptionally regulated by MITF, but the function of TRPM1 in
melanocytes is still unclear. The role of miR-204 and miR-211 has been investigated in fetal human retinal
pigment epithelium (RPE), where they regulate the epithelial barrier functions and target TGF-betaR2 and
SNAIL2 (40). We also observed high levels of miR-146a and miR-146b in the melanoma cell lines; these miRNAs
are involved in inflammation and psoriasis (41), but their function in melanocytes is still unclear. In addition,
the melanoma cell lines showed high expression of the testis- and ovary-specific miR-509~miR-514 cluster,
which is located in a genomic region (Xq27) known to be highly expressed in melanoma. We investigated (array
CGH data, not shown) whether the high miR-509~miR-514 expression could be explained by a similar increase
in copy number in the melanoma cell lines, but this was not the case.
We found that the LOX-IMVI cell line differed markedly from the other melanoma cell lines in its miRNA profile.
This cell line is amelanotic and thus, does not express typical melanocyte genes. LOX-IMVI is characterized by
low MITF expression and in parallel, diminished expression of melanocyte specific genes and miRNAs, such as
miR-211, which is located within TRPM1.
Ovarian cancer
As the only cell line in the NCI-60 panel, IGROV1 expresses the microRNA cluster miR-302b~miR-302c~miR-
302d~miR-367 (miR-302~miR-367), which is mainly expressed in pluripotent stem cells. Morphological studies
of IGROV-1 cells suggest that they are derived from an ovarian carcinoma with multiple differentiations,
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NCI-60 miRNA profiling 19
endometroid to a large extent, together with some serous clear cells and undifferentiated foci (42). We find it
likely that such compartments of undifferentiated cells posses a pluripotent precursor, which is reflected in the
expression of miR-302~miR-367. In addition, the IGROV1 cell line is the only in the NCI-60 panel carrying the
BRCA1 mutation, which gives a very strong correlation (r=1, p=4E-68) between BRCA1 and the miR-302~miR-
367cluster, but whether this is a causal relationship, or just a coincidence, remains to be determined. The two
closely related NCI-ADR-RES and OVCAR-8 cell lines both have a deletion in 1p36.33 (15), which is reflected in
lack of expression of the miR-200a-200b-429 cluster, situated in this region.
Prostate cancer
The two prostate cancer cell lines both have high expression of miR-31 and two members of the miR-
96~182~183 cluster, all found to be differentially expressed in a recent analysis of prostate cancer and normal
adjacent prostate (43). It has been debated if the two prostate cancer cell lines, PC-3 and DU-145, are
androgen receptor (AR) positive or negative and whether or not they respond to hormone depletion (44). We
therefore surveyed the literature for evidence of miRNAs guided differentiation between androgen dependent
or independent prostate cancer. Lee et al (45) showed that miR-125b was critical in cell proliferation and
expressed at higher levels in PC-3 compared to DU-145, while Shi et al (46) demonstrated that miR-125b could
be upregulated by androgen receptor signaling. We also detect miR-125b at higher levels in PC-3 compared to
DU-145, indicating that PC-3 could have active androgen signaling. The same pattern is seen for miR-100, which
also was found to be upregulated in the study by Shi et al (46). Interestingly, Shi et al speculate that the
expression of miR-125b comes from mir-125b-2, a precursor located on chromosome 21 in the miR-99a~let-
7c~125b-2 cluster. We find that expression from PC-3 originates from another cluster, namely miR-100~let-7a-
2~125b-1 on chromosome 11, as the miR-125b expression data correlates better with this cluster. We only
detect low levels of miR-99a, while miR-100 and let-7a are upregulated in PC-3 compared to DU-145.
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NCI-60 miRNA profiling 20
Renal cancer
In the clustering between platforms (Figure 2A) the renal cancer cell lines all group together except SN12C,
which has a distinct miRNA profile. Stinson et al observed that SN12C is the only renal cell line positive for
neuronal system-specific enolases (NSE) (35), which are expressed in neurons but with cross reactivity to
cancers of other origins. The cell line is negative for many of the renal specific miRNAs, including miR-30a and
miR-886. We detected an interesting pattern in the expression of miR-200 in the renal cell lines: All of the miR-
200 positive renal cell lines were negative for miR-200c~141, which have been shown to be regulated by DNA
methylation (47), suggesting that tissue specific methylation may play a role in kidney development.
Correlation with protein expression and with drug sensitivity
Please see the Supplementary results section, Figure S5 and S6.
Conclusion
Several conclusions can be drawn from this study: First, in spite of major technical differences between the
assays used to study miRNA expression (qRT-PCR, spotted arrays, Agilent arrays, LNA enhanced arrays, total
RNA vs. fractionated RNA), the correlation between the four methods is high, and serves to cross-validate the
assays. Where there is lack of correlation, this can be attributed to the data trimming methods previously
applied, where low values have been flooredwhich renders the correlation analysis imperfect.
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NCI-60 miRNA profiling 21
Second, the biology of the NCI-60 cell lines is reflected in their miRNA landscape, as tissue specific miRNA
signatures are retained across all cell lines. For each histology we also identified a number of characteristic
miRNAs that may explain the regulation of tissue specific markers; this conclusion, however, is in some cases
based on only a few cell lines and should therefore be interpreted with caution.
Third, our miRNA-protein correlation analysis revealed several known (e.g. the miR-200 regulatory pathway) as
well as potentially novel miRNA-protein interactions, most of which are associated with cell adhesion and
motility.
Fourth, the correlation between miRNA expression and drug sensitivity (GI50) data can help in the
identification of new molecular targets and miRNA based biomarkers for personalized medication.
In conclusion, the miRNA landscape of the NCI-60 cell lines assayed by our LNA-enhanced microarray platform
is in good overall agreement with previous miRNA studies. The miRNA expression data are available in GEO
(Accession numberGSE26375). We envisage that future studies on the NCI-60 cell lines will include Chip-seq
data, next-generation sequencing, and functional follow-up studies on associated targets.
Acknowledgments
We would like to thank Dr. Susan Holbeck for providing the NCI-60 cell lines and for the supplementary
correlation analysis data. We are also grateful to Gitte Friis, Jesper Salomon, Hanni Willenbrock, Nana Jacobsen
and Ditte Andreasen for excellent technical assistance.
This research was supported by the EU Sixth Framework Program “OncomiR”.
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NCI-60 miRNA profiling 22
Legends Figure 1. A. 4-way Venn diagram showing the overlap of the miRNAs studied in the four profiling studies (Liu, Blower, Gaur, LNA) and the miRNAs annotated in miRBase version 15.0. B. A comparison of the near-random distribution of all possible combinations of miRNA correlations (left peak around zero) and the matched miRNA correlations (right peak). C. A similar comparison as in C, but for cell line expression profiles: all cell line combinations give a random distribution profile (left peak around zero), while matched cell line specific miRNA profiles correlate well (right peak). Figure 2. Combined hierarchical clustering of the four miRNA datasets, using z-scaled expression data from the common probe set. A. All 59 cell lines (236 expression profiles across 4 platforms). B. The 9 lung cancer cell lines. C. The 7 ovarian cancer cell lines. D. The 6 breast cancer cell lines. The selected set of miRNAs represents the best correlating miRNAs across the four platforms. The clustering is based on resampling analysis of 62 miRs and 59 cell lines per data set. The color code at the very top designates the tissue origin of the cell lines: pink, breast; black, CNS; green, colon; blue, lung; red, leukemia; brown, melanoma; orange, ovary; dark pink, prostate; yellow, renal. Figure 3. A. Tissue specific miRNAs. miRNAs with significantly higher expression level in cell lines from one tissue compare to the rest of the cell lines. The heatmap shows the log2 expression for the miRNAs (mean centered data), which are enriched in each tissue. B. miRNAs significantly different between luminal and basal breast cancer cell lines. C. High expression of miR-142 in cell lines lacking cell-cell adhesion properties. D. miRNAs significantly different between high and low metastatic potential lung cancer cell lines. Table 1. Correlation between studies The table shows pair-wise Pearson correlation coefficients between the different miRNA profiling datasets based on either miRNAs (125 in common, upper right, light gray shaded) or on the 59 cell lines studied (lower left, dark gray shaded).
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NCI-60 miRNA profiling 23
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Søkilde et al. Table 1
Table 1. Correlation between studies
Study Gaur Blower LNA LiuGaur 1 0.43 0.51 0.59Blower 0.58 1 0.47 0.55LNA 0.56 0.55 1 0.47Liu 0.74 0.64 0.69 1
Table 1. Correlation between studiesThe table shows pair-wise Pearson correlation coefficients between the different miRNA profiling datasets based on either miRNAs (125 in common, upper right, in bold) or on the 59 cell lines studied (lower left, in italics).
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Søkilde et al. Figure 1
A BBlower (391)
C
Gaur (181)
LNA (944)
Liu (727)
miRBase 15 (1100)
Figure 1.
A. 4-way Venn diagram showing the overlap of the miRNAs studied in the four profiling studies (Liu, Blower, Gaur, LNA) and the miRNAs annotated in miRBase version 15.0.
B. A comparison of the near-random distribution of all possible combinations of miRNA correlations (left peak around zero) and the matched miRNA correlations (right peak).
C. A similar comparison as in C, but for cell line expression profiles: all cell line combinations give a random distribution profile (left peak around zero), while matched cell line specific miRNA profiles correlate well (right peak).
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Søkilde et al. Figure 2
A. All cell lines
B. Lung C. Ovary D. Breast
Figure 2. Combined hierarchical clustering of the four miRNA datasets, using z-scaledexpression data from the common probe set. A. All 59 cell lines (236 expression profiles across4 platforms). B. The 9 lung cancer cell lines. C. The 7 ovarian cancer cell lines. D. The 6 breastcancer cell lines. The selected set of miRNAs represents the best correlating miRNAs across thefour platforms. The clustering is based on resampling analysis of 62 miRs and 59 cell lines perdata set The color code at the very top designates the tissue origin of the cell lines: pink breast;data set. The color code at the very top designates the tissue origin of the cell lines: pink, breast;black, CNS; green, colon; blue, lung; red, leukemia; brown, melanoma; orange, ovary; dark pink,prostate; yellow, renal.
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Søkilde et al. Figure 3
A B
Breast
CNS
ColonCOLO-205C
Lung
HCT-116
Leukemia
Melanoma
DmicroRNA P-value log2 fold
(high/ low)
miR-342-3p 0.035 -1.3Melanoma
Ovary
Prostate
Renal
pmiR-374b 0.047 -0.7
miR-7 0.009 -0.6
miR-452* 0.031 0.5
miR-452 0.011 1
miRPlus-E1013 0.045 0.6
miR-760 0.007 0.6
miR-31 0.032 1.7
Figure 3. A. Tissue specific miRNAs. miRNAs with significantly higher expression level in cell lines from one tissue compare to the rest of the cell lines. The heatmap shows the log2 expression for the miRNAs (mean centered data), which are enriched in each tissue.B. miRNAs significantly different between luminal and basal breast cancer cell lines.C High expression of miR-142 in cell lines lacking cell-cell adhesion propertiesC. High expression of miR 142 in cell lines lacking cell cell adhesion properties.D. miRNAs significantly different between high and low metastatic potential lung cancer cell lines.
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Søkilde et al. Figure 3C
COLO-205
HCT-116
Figure 3. C. High expression of miR-142 in cell lines lacking cell-cell adhesion properties.
NB! This is Figure 3C enlarged for ease of viewingNB! This is Figure 3C enlarged for ease of viewing.
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Published OnlineFirst January 20, 2011.Mol Cancer Ther Rolf Sokilde, Bogumil Kaczkowski, Agnieszka Podolska, et al. Global microRNA analysis of the NCI-60 cancer cell panel
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