Global microRNA analysis of the NCI-60 cancer cell …...2011/01/12 · NCI-60 miRNA profiling 1...

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

on June 11, 2020. © 2011 American Association for Cancer Research. mct.aacrjournals.org Downloaded from

Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on January 20, 2011; DOI: 10.1158/1535-7163.MCT-10-0605

http://mct.aacrjournals.org/


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.





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





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.





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





(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.





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





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





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).





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.





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





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).





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





(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.





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





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





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.





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,





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.





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.





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”.





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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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).




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).





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.





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.




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.




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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