Supplementary information for Søkilde et al. NCI-60 microRNA profiling

List of supplementary tables:

Supplementary Table S1: Characteristics of the NCI-60 cell lines employed in the study.

Supplementary Table S2: Mapping of detection assays to miRBase version 15.0 for the four platforms.

Supplementary Table S3: Molecular target correlation analysis.

Supplementary Table S4: correlation coefficients for all 36,553 drug GI50-miRNA expression pairs

Supplementary Table S5: correlation with drug sensitivity

List of supplementary figures:

Supplementary Figure S1: breast cancer luminal and basal cell line network.

Supplementary Figure S2: Summary of the pathways of predicted targets for miR-142-3p and miR-142-5p.

Supplementary Figure S3: PCR to array correlations

Supplementary Figure S4: Northern blots

Supplementary Figure S5: Correlation with protein expression

Supplementary methods

Northern blot

Total RNA was purified and separated by electrophoresis in a denaturing 15% polyacrylamide Criterion gel

(Bio-Rad) at 30mA for 45 min, followed by electro-blotting to a Hybond-N nylon membrane (Amersham

Biosciences) at 30mA for 2 hrs. After UV cross linking for 2 min, the membrane was pre-hybridized for 2

hours at 60 °C in a 15ml standard Northern buffer (6 x SSC, 0.2 % SDS, 10 x Denhardt’s solution).

Membranes were hybridized overnight at 60 °C in the presence of 32P- end-labeled, LNA spiked probes, in

10 ml of 6 x SSC, 0.2 % SDS, 10 x Denhardt’s solution. Increasing stringency washes were carried out at

room temperature, with a final wash at 40 °C. The membranes were wrapped in saran-wrap, exposed from

24-72 hours and visualized in a STORM840 PhosphorImagerTM system (Molecular Dynamics) and

subsequently exposed from 24-120 hours to X-ray films, which were then developed. The results are shown

in Supplementary Figure S4.

Supplementary results

Breast cancer luminal and basal cell line network.

miR-142 targets and pathways.

Supplementary Figure S2: Summary of the pathways of the predicted targets for miR-142-3p and miR-142-5p. A. Diana target patways. B. Example from regulation of cytoskeleton. C. Example of target site in viniculin (VCL).

Quantitative real-time RT-PCR validation To validate our miRNA microarray based approach we compared miRNA expression data for four of the

NCI-60 cell lines with an independent miRNA expression profiling study using quantitative real-time RT-PCR

(qPCR). A subset of significantly differentially expressed miRNAs was analyzed together with stably

expressed microRNAs and small nucleic/nucleolar RNAs (snRNA/snoRNA) as endogenous controls.

The experimental setup of the quantitative RT-PCR was run according to the miRCURY LNA™ miRNA PCR

System protocol on a Lightcycler 480 system from Roche Applied Science. For each combination of the

miRCURY LNA™ miRNA PCR, primer set and total RNA sample replicates of the first strand cDNA synthesis

as well as the quantitative PCR amplification were made corresponding to four technical replicates. In

total, 38 different primer sets including the three reference genes were run in parallel. The reference assays

hsa-miR-103, 5S rRNA and SNORD49A were selected from a panel of nine potential stably expressed

reference genes using the software program GeNorm (1). For data quality control, each real-time PCR

amplification and dissociation curve was checked for amplifying one amplicon, only. Single missing data

point for the technical quadruplicates was removed before averaging. The PCR efficiencies were calculated

for each assay by averaging single curve efficiencies measured by the LinReg software (2) and the PCR

efficiencies were applied individually to the qPCR assays. Also, if one of the cell lines either for the qPCR

assays or the array data reached the limit of detection the full data set were discharged. Fifteen miRNAs

reached the background level. Finally, twenty microRNAs and four cell lines were included in the data

analysis. Depicted in figure 1 is the correlation between array log2-transformed mean-centered values and

PCR efficiency-corrected mean-centered cycle numbers. These two values ought to be directly comparable

to each other for every two fold changes of the array signal the cycle number in qPCR changes with 1. In

conclusion for every 2-fold changes the array and qPCR measurements are equivalent and the plot display a

linear correlation of R=-1 with a slope of -1 (3). The overall correlation is R=-0.922 and the slope =-0.64

indicating higher dynamic range for the qPCR technique compared to the microarray. The data show

excellent correlation between the two methods and demonstrate the use of array to screen a large number

of miRNAs and afterwards use the qPCR technique for validation (4).

Supplementary Figure S3. Correlation of qPCR results with array results. Array data is mean-centred log2 transformed data and qPCR data is mean-centred Cp-values. The data points represent qPCR and array data for 20 miRNA on both platforms, providing 80 pairs of points. The Pearson correlation coefficient is calculated to 0.922, while the slope is -0.65.

Northern blots

Supplementary Figure S4. Northern blot validation of miRplus sequences. Two microRNAs known to be expressed in the cell lines, miR-9 and miR-142, were used as controls. The two candidates, Cand-1972 and Cand-1993, gave a specific signal around 60-70 nucleotides, indicating a potential pre-mir. They also overlap other known non-coding transcripts, a SNORNA and a pre-miRNA with a single mismatch, which matches the tRNA-Gly-GGY.

Correlation with protein expression A recent protein array profiling study of the NCI-60 cell lines (5) enabled us to perform a correlation

analysis to our miRNA expression data, where we identified a number of known miRNA-protein correlations

as well as several potentially novel interactions. The former is exemplified by the well described interaction

between the miR-200 family and E-cadherin: miR-200 targets ZEB1 and ZEB2, which are transcriptional

repressors of E-cadherin. Therefore, high miR-200 expression leads to high E-cadherin (positive correlation,

Supplementary Figure S5A). miR-200 is a key modulator of E-cadherin, which is involved in cell-cell

adhesion and its expression is lost during epithelial to mesenchymal transition (EMT). It has been shown

that RAB25 and PRSS8 are members of a module of genes, tightly co-regulated by miR-200

and ZEB1 (6). Therefore, not surprisingly, these two proteins are also co-regulated in the NCI-60 data set

and strongly correlate to miR-200 expression. RAB25 is a member of the RAS oncogene family and has been

implicated in tumor progression by directing the localization of integrin-recycling vesicles (7). PRSS8

(protease, serine, 8) has been shown to be involved in EMT in bladder cancer (8). Other proteins that

correlate to miR-200 expression are keratin-8, keratin-18 and β-catenin (Supplementary Figure S5B).

TYR (tyrosinase) is expressed in melanoma cell lines and correlates with expression of miR-211. TYR

catalyses the first step in the pathway to melanin and correlates also with MITF expression in the

melanoma cell lines. LOXIMVI totally lacks expression of TYR and miR-211, underscoring that this

melanoma cell line is amelanotic.

The breast cancer cell line HS578T expresses FN1 (Fibronectin 1) at very high levels compared to the rest of

the cell lines, and it also expresses the muscle specific miR-145~miR-143. An interaction between the

extracellular matrix protein FN1 and these microRNAs has not yet been investigated. In addition, HS578T

cells are known to express high levels of the CTGF (connective tissue growth factor), which in response

leads to increased expression of extracellular matrix genes.

We found an inverse correlation between MSN (moesin, a protein involved in cell-cell recognition, signaling

and cell motility) and the miR-200 family, miR-194-2~192 and miR-194-1~215; all of these miRNAs have

target sites in moesin 3’ UTR, which therefore, appears as a direct target.

Finally, we find that PAI1 (Plasminogen activator inhibitor, inhibits the tPA and uPA proteases and thus

fibrinolysis, but it has also been associated to various types of cancer) also known as SERPINE1 correlates

positively to miR-100 and miR-125b.

Supplementary Figure S5. A. Part of the miR-200 regulatory network. The association labeled 1 is found by multiple studies, e.g. Gregory et al 2008

B

Gene symbol Gene name Correlating miRNA

RAB25 member RAS oncogene family miR-145~200c, miR-200a~200b~429, miR-203

CDH1 E-cadherin

MSN Moesin miR-192~215, miR-194~192, miR-141~200c, miR-200a~200b~429, miR-203

VIL1 Villin 1 miR-192~215, miR-194~192

KRT8 Keratin 8 miR-203, miR-200c, miR-141

PRSS8 Protase, serine, 8

TYR Tyrosinase miR-211

NCAM1 Neural cell adhesion molecule 1

FN1 Fibronectin 1 miR-145, miR-143

ANXA2 Annexin A2 miR-22, miR-24, miR-23a, miR-23b, miR-222

HNRNPA2B1 Heterogeneous nuclear ribonucleoprotein A2/B1

miR-223, miR-142-5p

HSPD1 Heat shock 60 kDa protein 1 miR-203, miR-148b

SERPINE1 Plasminogen activator inhibitor type 1

miR-100, miR-125b, miR-378

ESR1 Estrogen receptor 1 miR-195, miR-371-3p

RNASEH2A Ribonuclease H2, subunit A miR-142-5p, miR-142-3p

STMN1 Stathmin 1 miR-142-5p, miR-22

PCNA Proliferating cell nuclear antigen miR-22

PRKCB Protein kinase C, beta miR-223, miR-29a

CTNNB1 Catenin (cadherin-associated protein), beta 1

miR-27b

STAT5 Signal transducer and activator of transcription 5

miR-24

Supplementary Figure S5. . B. Some of the top identified protein-miRNA correlations. The miRNAs that correlate positively with protein expression are in black, while the miRNAs with negative correlation to protein expression are printed in red.

Correlation with drug sensitivity

Several studies have already associated miRNA expression with drug resistance. In particular, miR-21 has

been reported to confer taxol resistance in breast cancer (9), VM-26 resistance in glioblastoma (10), and

gemcitabine resistance in cholangiocarcinoma, and up-regulation of miR-21 combined with down-

regulation of one of its targets, PDCD4, has very recently been associated with poor prognosis in gastric

cancer (11). Other miRNAs have been implied in multidrug resistance in small cell lung cancer, where miR-

134 was associated with Cisplatin resistance, miR-379 with Etoposide resistance, and miR-495 with

Doxorubicin resistance (12). Another miRNA, miR-326 was found to repress MRP1 (ABCC1) expression in

VP-16 resistant MCF-7 cell lines (13), and miR-451 supposedly targets the MDR1 gene, suggesting a

regulatory role of miRNAs in ABC transporter mediated multidrug resistance. This notion is supported by

our own studies on the overexpression of the multidrug transporter ABCG2 in mitoxantrone resistant

S1MI80 colon cancer cells, where high ABCG2 levels may be due to loss of epigenetic miRNA regulation by a

truncated 3’UTR (14).

To examine the relationship between miRNA expression and drug sensitivity, both Pearson and Spearman

correlation coefficients were calculated for all 36,553 drug GI50-miRNA expression pairs, and can be found

in Supplementary Table S4. Some of the highest and most significant correlations are listed in

Supplementary Table S5A.

Several interesting observations follow from the table: First, the top-scoring correlations, such as those

between miR-449a~449b and Cyclophosphamide1, and miR-518a-3p and Uromitexan, can all be ascribed to

a single cell line’s hyper-sensitivity (or in a few cases: resistance) to the drug in question, and a

corresponding high (rarer: low) expression of a given miRNA. Such unique “singular” relationships were not

1 In the case of cyclophosphamide, which is a prodrug that needs to be activated by hepatic microsomal enzymes to obtain chemotherapeutic activity, one would expect that none of the cell lines were sensitive towards treatment with the native drug. That the breast cancer cell line T47D shows sensitivity is peculiar, and probably an experimental artifact. One could speculate that perhaps T47D cells are able to oxidize the prodrug to its active metabolites, but mining the available NCI-60 expression data did not indicate that this particular cell line had high CYP450 levels compared to the other NCI-60 cell lines.

picked up by our initial, conservative data filtering approach, which required a minimum amount of

variation in the data for inclusion in the correlation analysis. The question that remains to be answered is,

whether we by counting all potential relations, including the “single” associations, are minimizing the

number of false negatives, at the cost of generating many false positive results? It can be argued, that the

association between a single cell line’s hyper-sensitivity to a drug and its miRNA expression are

epiphenomena with no causal relationship. But at the same time, it can be argued, that a potential

mechanism of action could be overlooked, if one ignores these very dramatic “on/off” responses, which

could point towards potential new treatments and diagnostics. Therefore, it is necessary to validate the

putative association between miRNA expression and drug sensitivity by functional studies, for example by

applying antagomiRs to knock down the miRNA of interest and study the consequent phenotype. Such

studies have already been carried out for the miRNAs mentioned earlier, and all have demonstrated a

direct effect on drug response. From our table, the identified relationship between Imatinib sensitivity and

miR-451 (and miR-144, which clusters together with miR-451) expression in the leukemia K562 cell line, is

indirectly supported by a recent report, showing that miR-451 and Imatinib both inhibit growth of

glioblastoma stem cells (15). Therefore, we do not consider the observed “single” associations as artifacts,

but as potentially important indicators of regulatory mechanisms.

However, one has to be careful in the interpretation of such association data, even in the case where there

is a whole series of cells that point in the same direction. A clear example of this is the leukemia cell panel,

which shows a very strong correlation between miR-142-5p/3p expression and a number of drugs to which

the leukemia cells are hypersensitive compared to the other NCI-60 cell lines. The problem with these

apparently obvious associations is that the leukemia cells in general are more sensitive to drugs than the

other cell lines (16), a phenomenon which may be attributed to their growth characteristics (in suspension

rather than attached) or to other features inherent for leukemia. Therefore, one cannot conclude, based on

the overall intrinsic sensitivity of the leukemia cell lines, that there is a causative relationship between

miRNA expression and mechanism of action of the drug to which they are hypersensitive. On the other

hand, if a correlation between drug sensitivity and miRNA expression is observed within a series of cells

belonging to the same tissue type, then there is very strong evidence that this relationship is mechanistic.

Therefore, we performed an “intra-tissue” correlation analysis of each of the eight tissue classes (excepting

the prostate cell lines, which only counted two, and therefore, were non-informative for this purpose)

separately. The results are summarized in Supplementary Table S5B, and the rather few candidate pairs,

which appear from this analysis, are currently under investigation. It should be noted, however, that none

of the previously suggested associations between drug resistance and expression of specific miRNAs, such

as the miR-21 associated resistance to taxol, VM-26, or gemcitabine, could be confirmed in this study,

suggesting that drug resistance is a complex phenomenon and therefore, in general, no simple relationship

can be expected between miRNA expression and sensitivity or resistance to a particular drug.

Supplementary Table S5A: miRNA correlation with GI-50, single cell line associations. A positive Pearson correlation coefficient (Corr.) indicates that high expression of the particular miRNA is associated with hypersensitivity (S) to the drug. A negative Corr. suggests that expression of the correlating miRNA is associated with resistance (R) to the drug. miRNA NSC Corr. Signif P Drug name Molecular target Cell line(s) involved miR-580 688097 0.97 (S) 3.04E-35 rhuMAb-Her2 Memb receptor LU HOP-92

miR-888 676836 -0.94 (R) 6.33E-29 Peninsularinone Protein synthesis

OV NCI/ADR-RES 368672 -0.83 (R) 3.52E-13 SUN 2071 Protein synthesis

147340 0.77 (S) 1.70E-12 Anisomycin-HCl Protein synthesis

miR-449b 26271

0.86 (S) 3.38E-18 Cyclophosphamide DNA damage BR T47D

miR-449a 0.80 (S) 2.86E-14 miR-518a-3p 113891 0.86 (S) 5.25E-17 Uromitexan Redox CNS SNB-75

miR-653 645019 0.84 (S) 1.21E-14 Jayama RT LE SR, 8226, MOLT-4

miR-22 660634 -0.82 (R) 2.76E-14 2-Chloro-3-(2-chloro-

ethoxy)-naphthazarin PKC; Prolyl isomerase

LE CEM, HL-60, MOLT-4 8226 680072 -0.77 (R) 9.92E-13 Ochraceolide A FTI/Prenyl

miR-142-5p

680072 0.80 (S) 5.53E-14 Ochraceolide A FTI/Prenyl LE CEM, HL-60, K562, MOLT-4 8226

29228 0.75 (S) 6.26E-12 Diallyldisulfide Redox LE CEM, HL-60, K562,

MOLT-4 8226 SR 292206 0.75 (S) 9.34E-12 Rustaiana Redox

miR-145 113891 0.81 (S) 1.23E-13 Uromitexan Redox CNS SNB-75

miR-801 680072 0.78 (S) 2.08E-13 Ochraceolide A FTI/Prenyl LE CEM, HL-60, K562, MOLT-4 8226

miR-504 719344 0.80 (S) 2.49E-13 Anastrozole Aromatase ME SK-MEL-2

miR-451 743414

0.78 (S) 4.24E-13 Imatinib Kinase LE K562

miR-144 0.76 (S) 3.03E-12 miR-100* 632307 -0.79 (R) 2.12E-12 Erbitux Kinase RE ACHN

Supplementary Table S5B: miRNA correlation with GI-50, tissue specific associations miRNA Drug Pearson Corr Adjusted p Tissue

miR-206 5-FU -0.9939 (R) 0.1076 colon

miR-99a Camptothecin, 10-OH* 0.9722 (S) 0.2244 lung

miR-100 Camptothecin, 10-OH 0.9849 (S) 0.2458 colon

miR-206 Taxol analogue -0.9838 (R) 0.2458 colon

miR-148b Camptothecin, 7-Cl -0.9830 (R) 0.2791 ovary

miR-363* 5-FU -0.9839 (R) 0.2791 ovary

miR-148b Taxol analogue -0.9856 (R) 0.2791 ovary

miR-186.1 Taxol analogue -0.9830 (R) 0.2791 ovary

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