link between p14ARF and retinoic acid receptor β signaling · 7/12/2013 · Significant progress...

Mutual exclusivity analysis of genetic and epigenetic drivers in melanoma identifies a

link between p14ARF and retinoic acid receptor β signaling

Christina Dahl1, Claus Christensen1, Göran Jönsson2, Anders Lorentzen1*, Mette Louise

Skjødt1#, Åke Borg2, Graham Pawelec3, and Per Guldberg1

1Danish Cancer Society Research Center, Copenhagen, Denmark; 2Department of

Oncology, University Hospital, Lund, Sweden; 3Department of Internal Medicine II, University

of Tübingen, Tübingen, Germany

Running title: p14ARF as a target of retinoic acid signaling

Key words: Melanoma, melanocytes, cancer drivers, retinoic acid, DNA methylation

Conflicts of Interest: none to declare

Present address: *Eucaryotic Cell Biology Research Group, Department of Science, Roskilde

University, Roskilde, Denmark; # Department of Mammalian Cell Technology,

Biopharmaceutical Research Unit, Novo Nordisk A/S, Måløv, Denmark

Correspondence: Per Guldberg, Danish Cancer Society Research Center,

Strandboulevarden 49, DK-2100 Copenhagen, Denmark.

Phone: +45 3525 7500; Fax: +45 3525 7721; E-mail: [email protected]

on October 5, 2020. © 2013 American Association for Cancer Research. mcr.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 July 12, 2013; DOI: 10.1158/1541-7786.MCR-13-0006

http://mcr.aacrjournals.org/

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ABSTRACT

Melanoma genomes contain thousands of alterations, including mutations, copy number

alterations, structural aberrations and methylation changes. The bulk of this variation is

stochastic and functionally neutral, with only a small minority representing “drivers” that

contribute to the genesis and maintenance of tumors. Drivers are often directly or inversely

correlated across tumors, reflecting the molecular and regulatory signaling pathways in which

they operate. Here, we generated a profile of genetic and epigenetic drivers in 110 human

melanoma cell lines and searched for non-random distribution patterns. We found statistically

significant mutual exclusivity among components of each of the p16INK4A-CDK4-RB, RAS-

RAF-MEK-ERK and PI3K-AKT pathways. In addition, we found an inverse correlation

between promoter hypermethylation of RARB (encoding retinoic acid receptor β (RARβ)) and

CDKN2A alterations affecting p14ARF (P < 0.0001), suggesting a functional link between

RARβ signaling and the melanoma-suppressive activities of p14ARF. We show that all-trans

retinoic acid (ATRA) can increase the expression of p14ARF in primary human melanocytes,

and that the steady-state levels of p14ARF in these cells are regulated via RARβ.

Furthermore, we show that the ability of ATRA to induce senescence is reduced in p14ARF-

depleted melanocytes, and we provide proof-of-concept that ATRA can induce irreversible

growth arrest in melanoma cells with an intact RARβ-p14ARF axis, independent of p16INK4A

and p53 status. These data identify a previously unrecognized crosstalk between RARβ and

p14ARF with possible implications for melanoma treatment, and highlight the power of mutual

exclusivity analysis of cancer drivers to unravel molecular pathways.




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INTRODUCTION

Melanoma develops from pigment-producing melanocytic cells and is the most aggressive

form of skin cancer, causing nearly 50,000 deaths worldwide each year. The incidence of

melanoma has risen markedly over the past 50 years and continues to rise in many Western

populations. Early detection and surgical excision of the primary tumor can be curative for

the majority of patients, but advanced, disseminated forms of the disease are associated with

a dismal prognosis (1). Until recently, treatment options for metastatic melanoma were

limited due to the intrinsic resistance of melanoma cells to conventional forms of anti-cancer

therapy. Significant progress has come from different lines of treatment, including the

combination of dacarbazine with ipilimumab (a monoclonal antibody against CTLA-4), the

combination of a gp100 peptide vaccine with interleukin-2, monotherapy with trametinib (a

MEK inhibitor), and monotherapy with the BRAF inhibitors vemurafenib (also known as

PLX4032) and dabrafenib (reviewed in Ref. 2). In phase III clinical trials, these treatments

have all been shown to induce complete or partial tumor regression and increase survival in

patients with metastatic melanoma. However, intrinsic or acquired resistance to these

treatment options remains a major concern.

A milestone in melanoma research was the discovery of BRAF mutation as a frequent and

early event in melanoma development (3). Extensive work has shown that BRAF mutations

(predominantly p.V600E) occurring in >50% of melanomas lead to constitutive activation of

the RAS-RAF-MEK-ERK signaling pathway (3) and confer a state of “oncogene addiction” on

melanoma cells (4;5). These findings provided important insights into the biology of

melanoma and paved the way for the development of therapeutic small-molecule drugs

directed against the BRAF oncogene (reviewed in Ref. 2). The work leading to the discovery

of BRAF mutations had been preceded by decades-long candidate gene approaches, which

identified other genes recurrently mutated in melanoma, including NRAS, the CDKN2A locus

(also known as INK4A/ARF) and PTEN as some of the most frequent targets (reviewed in

Ref. 6). In addition to these mutational events, other types of molecular alteration have been

shown to contribute to melanoma development and progression, including gains of the KIT,

MITF, CCND1 and MYC proto-oncogenes and transcriptional silencing of tumor suppressors

by promoter hypermethylation (reviewed in Ref. 6).




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Data from recent whole-genome or -exome sequencing of melanomas (7-12), together with

genome-wide studies of copy number variations (13-15) and DNA methylation changes (16),

have added new layers of complexity to our understanding of the melanoma genome. An

important challenge arising from these studies is to identify “driver” events that contribute to

melanoma genesis and maintenance, and to distinguish them from functionally neutral

“passenger” events. Another challenge is to identify the molecular signaling pathways that

are perturbed by driver events. One increasingly used approach to address these challenges

is mutual exclusivity analysis. The rationale for this approach is that, although the patterns of

genomic alterations in individual tumors are exceedingly diverse and complex, drivers will

tend to cluster within a limited number of biological pathways that are essential for the

tumorigenic process (17). Once a component of a pathway becomes altered through a

specific genomic alteration and confers a selective advantage on the tumor cell, second hits

in the same pathway will provide no additional advantage and thus will be less likely to

accumulate. The consequences of such selection processes are that two hits in the same

pathway rarely occur in the same tumor, and that different events within a pathway are

inversely correlated across tumors.

Genomic analyses of melanoma have demonstrated mutual exclusivity between BRAFV600E

and NRAS mutations, both leading to activation of the RAS-RAF-MEK-ERK pathway (3),

between NRAS mutations and inactivating events in PTEN, both leading to activation of the

PI3K-AKT pathway (18), and among mutations in genes encoding components of the

p16INK4A-CDK4-RB senescence barrier (19). Less is known about the functional significance

of other, less frequent alterations. Moreover, integrated analyses of the various types of

genome alterations, including copy number variations and DNA-methylation changes, have

not been conducted. Here, we have examined the status of known genetic and epigenetic

drivers in 110 human melanoma cell lines and assessed their pair-wise relationships. This

approach confirmed the examples of mutually exclusivity described above and further

uncovered an inverse correlation between epigenetic silencing of the retinoic acid receptor β

(RARβ) and genetic inactivation of the melanoma suppressor, p14ARF. Functional studies in

melanocyte and melanoma models suggest that p14ARF is a downstream target of RARβ

signaling, and that the status of RARβ and p14ARF in individual melanomas may predict the

response to treatment with retinoic acid, the biologically active form of vitamin A.




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MATERIALS AND METHODS

Cells and reagents

Melanoma cell lines were obtained from The European Searchable Tumour Line Database

(ESTDAB) (http://www.ebi.ac.uk/ipd/estdab) and maintained in RPMI 1640 medium

containing 10% fetal bovine serum and antibiotics at 37°C and 5% CO2. The 110 cell lines

used in this study were previously characterized for surface markers and tumor-associated

antigens, and were DNA fingerprinted to control for duplication and cross-contamination (20).

Primary human melanocytes were obtained from Invitrogen and maintained in Medium 254

containing Human Melanocyte Growth Supplement (Invitrogen) at 37°C and 5% CO2. ATRA

and 5-aza-2’-deoxycytidine were purchased from Sigma-Aldrich. LE135 was purchased from

Santa Cruz.

Mutations and homozygous deletions

Genomic DNA from melanoma cell lines was isolated using standard procedures. Sequence

analysis of CDKN2A was performed as described (21). Pyrosequencing of the BRAF

c.1799T>A mutation was performed using the PyroMark Q24 platform (Qiagen) and

previously described primers (22). Mutation analysis of BRAF (exons 11 and 15), NRAS

(exons 2 and 3), KIT (exons 9, 11, 13 and 17), PIK3CA (exons 9 and 20), PIK3R1 (exon 9,

10 and 12), IDH1 (exon 4), FGFR1 (exon 7), GNAQ (exon 5), TP53 (exons 4-9), CTNNB1

(exon 3), CDK4 (exon 2), RB1 (exons 1-27) and PTEN (exons 1-9) was performed using a

combination of PCR and denaturing gradient gel electrophoresis followed by direct sequence

analysis. Primers are listed in Supplementary Table 5. Analysis of RB1 mutations was

restricted to cell lines with wildtype CDKN2A and CDK4. Homozygous CDKN2A and PTEN

deletions were determined as described (23;24).

Copy number assessment

Copy numbers of MITF, MYC, AKT3, CDK4 and CCND1 were determined by qPCR using

the LightCycler 2.0 instrument (Roche) and the FastStart DNA MasterPLUS SYBR Green I Kit

(Roche). The LINE-1 retrotransposon was used as a reference gene for all copy number

analyses. A mixture of DNA from six human healthy donors was used as a diploid control.

Copy number gain was defined as a relative copy number of ≥ 4. Primer sequences and

assay conditions are listed in Supplementary Table 5.




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Bisulfite treatment and methylation analysis

Genomic DNA (500 ng) was bisulfite converted according to standard procedures (25) or

using the EZ DNA Methylation-Gold™ Kit (Zymo Research). The methylation status of the

INK4A, APC, RARB, PYCARD, RASSF1A and IGFBP7 promoter regions was determined by

mehylation-specific melting curve analysis (MS-MCA) (26) using the LightCycler 1.1 and 2.0

instruments (Roche) and the FastStart DNA Master SYBR Green I Kit (Roche). Primer

sequences and PCR conditions are listed in Supplementary Table 5. Enzymatically

methylated DNA (CpGenome™ Universal Methylated DNA; Millipore) was used as a

methylation-positive control.

Real-time quantitative PCR

Total RNA was isolated from cells using the RNeasy kit (Qiagen, Valencia, CA, USA) and

quantified using a Nanodrop spectrophotometer (Nanodrop Technology, Inc., Wilmington,

DE, USA). Only RNA samples with an A260/A280 ratio of 1.8 or higher were used. Reverse

transcription was performed in reactions containing 5 µg of total RNA, oligo(dT) and random

hexamers as primers and SuperScript III reverse transcriptase (Invitrogen). Real-time

quantitative PCR was performed using the Roche LightCycler 2.0 and the FastStart DNA

MasterPLUS SYBR Green I Kit (Roche). Primer sequences and assay conditions are listed in

Supplementary Table 5.

Immunoblotting

Cytoplasmic and nuclear extracts were fractionated using the NE-PERTM Nuclear and

Cytoplasmic Extraction Reagent Kit (Pierce) and analyzed independently. Protein

concentration was measured using the Coomassie Plus Protein Assay Reagent (Pierce).

p14ARF expression was analyzed by 12% NuPAGE gels using MES running buffer

(Invitrogen). Membranes were incubated overnight with 2 µg/ml anti-p14ARF antibody (Clone

14P03; Labvision) followed by 2 h of incubation at room temperature.

Transfection and RNA interference

Normal human melanocytes were transfected using the Amaxa nucleofector program U-024

as recommended by the manufacturer. To obtain effective transient RNA interference

melanocytes were transfected twice using 1 µM ON-TARGETplus SMARTpool siRNA to




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RARB (Dharmacon). ON-TARGETplus siCONTROL Non-targeting si-RNA (Dharmacon) was

used as control.

p14ARF-depleted melanocytes

Human melanocytes stably expressing shRNA targeting CDKN2A will be described in detail

elsewhere (Christensen and Guldberg, manuscript in preparation). In brief, melanocytes

were transfected with pSuper plasmids containing resistance to G418 (5), and two

independent cultures of stable transfectants were generated: HEM-1, which expresses

shRNA to a target sequence within CDKN2A exon 2, and HEM-2, which expresses shRNA to

a target within CDKN2A exon 1α and has spontaneously lost p14ARF. Both cultures were

maintained in Medium 254 containing Human Melanocyte Growth Supplement (Invitrogen),

stem cell factor (100 ng/ml, Stratmann Biotech) and endothelin-1 (10 ng/ml, Sigma-Aldrich).

Senescence-associated β-Galactosidase assay

Cellular senescence was assessed by using the Senescence β-Galactosidase Staining Kit

(Cell Signaling, Beverly, MA, USA) according to the manufacturer’s instructions. SA-β-Gal-

positive cells were identified by their blue-green cytoplamatic staining under 200×

magnification and a total of at least 70 cells were counted from 10 random fields in each

experiment. All microscopic fields were scored independently by two persons for

determination of the mean percentage of positively stained cells.

Growth rates

Cells were seeded at low density in 25 cm2 flasks and allowed to attach overnight. The next

day the cells were incubated with 10 µM ATRA or vehicle (DMSO). Media was replaced

every second day for 16 days when control population treated with vehicle reached

confluence. Cells were counted with trypan blue exclusion at day 0, 4, 8, 12 and 16.

Growth inhibition assays

Cells were seeded into 24-well plates (1.5-2.5 × 103 cells per well) and allowed to attach

overnight. The medium was then replaced with 10 µM ATRA (Sigma) or vehicle (DMSO) and

grown in the presence or absence of the drug for 14-19 days. Each cell line were fixed at the

same time and stained with crystal violet. Crystal violet was subsequently extracted and the




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absorbance was read at 595 nm in a microtiter plate spectrophotometer. Relative

absorbance was normalized to the control cells treated with vehicle.

Cell proliferation

BrdU incorporation was determined using the BrdU Cell Proliferation Assay Kit (Cell

Signaling). In brief, cells were seeded in a 96-well format (2 × 103 cells/well) and cultured for

24 h in the presence of 10 µM BrdU. Cells were fixed, denaturated and reacted with

detection antibody solution for 1 h and HRP-conjugated secondary antibody for 30 min. TMB

substrate was added for 25 min, and absorbance was measured at 450 nm.

Cell cycle analysis

Cell cycle analysis was performed by flow cytometry (FACSVerse BD and FACSdiva BD).

Briefly, 106 cells were trypsinized, fixed in 70% ethanol, washed and resuspended in PBS

containing 10 µg/ml propidium iodide (Sigma) and 1 µg/ml RNase (Sigma). A total of 20,000

events were collected per sample and cell cycle profiles were obtained using the FlowJo V10

software. When examining the cell cycle distribution, sub-G1 and polyploid cells were

excluded from the analysis. G1, S and G2 phase gates were applied to histograms by

manually defined distribution peaks.

Statistical analysis

All calculations were carried out in R (http://www.r-project.org). Mutation, deletion,

amplification and promoter hypermethylation events were transformed into dichotomous

variables and analyzed for correlations using the Pearson correlation coefficient. The

significance level of these correlations was determined using the Fisher's exact test.




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RESULTS

Genetic and epigenetic drivers in human melanoma cell lines

DNA from 110 unrelated melanoma cell lines was systematically examined for specific

mutation, deletion, amplification and promoter hypermethylation events. A list of genes and

the frequencies and types of alterations are shown in Figure 1, and detailed information is

provided in Supplementary Tables 1-4. A total of 646 alterations were identified in this series

of melanoma cell lines, with an average of 5.9 (range 0-10) alterations per cell line. One cell

line (ESTDAB-097) carried none of the drivers investigated, and ESTDAB-098 cells carried

low-copy-number gains of CCND1 and MITF as the only changes.

Mutations were identified in known hot-spot regions of 10 different proto-oncogenes (Figure

1; Supplementary Tables 1 and 2). The most frequent target was BRAF, which harbored

mutations in 75 of the cell lines (68%), with p.V600E accounting for 66 of these events

(88%). Mutations in NRAS were found in 18 of the cell lines (16%), and 16 of these

mutations affected residue Q61. Mutations in the remaining proto-oncogenes were less

frequent, affecting residues K22 or R24 of CDK4 (N = 8), residues D32 or S45 of CTNNB1 (N

= 4), residue R132 of IDH1 (N = 3), residues E545 or Q546 of PIK3CA (N = 2), residue Q209

of GNAQ (N = 2), residue L576 of KIT (N = 1), residue P252 of FGFR1 (N = 1) and residues

464-468 of PIK3R1 (N = 1). The two cell lines harboring GNAQ mutations were established

from uveal melanomas, consistent with previous reports showing that GNAQ mutations occur

at high rates in this melanoma subtype (27).

Mutations and/or deletions of the tumor suppressors CDKN2A, PTEN and TP53 were

identified in 93 (85%), 33 (30%) and 26 (24%) of the cell lines, respectively (Figure 1;

Supplementary Table 1). The majority of CDKN2A alterations were predicted to affect both

p16INK4A and p14ARF; however, subsets of alterations were specific for either protein, including

deletion (N = 2) or mutation (N = 1) of exon 1α (specific for p16INK4A), deletion of exon 1β

(specific for p14ARF; N = 1), and missense mutations in exon 2 potentially affecting either

p16INK4A (N = 3) or p14ARF (N = 1). One specific CDKN2A mutation affecting both p16INK4A and

p14ARF (p.R80X/p.P94L) was found in five of the cell lines. In the following, the gene symbols

INK4A and ARF will be used to specify, when appropriate, whether CDKN2A alterations have

a predicted effect on p16INK4A and p14ARF, respectively.




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A total of 73 copy number gains were identified, with frequencies of 25% for CCND1, 19% for

MITF, 15% for MYC, 5% for AKT3 and 3% for CDK4 (Figure 1; Supplementary Table 3).

None of the AKT3 gains was greater than 5.

A total of 255 promoter hypermethylation events were identified, with frequencies of 56% for

IGFBP7, 55% for RASSF1A, 53% for PYCARD, 45% for RARB, 13% for APC and 11% for

INK4A (Figure 1; Supplementary Table 4). Overall, 99 of the cell lines (90%) were

hypermethylated in one or more of the six promoter regions, 77 (70%) had two or more

hypermethylated genes, and 50 (45%) had three or more hypermethylated genes.

Pathway analysis and pair-wise associations

Table 1 lists the genomic alterations grouped according to known molecular pathways.

Genes encoding components of the RAS-RAF-MEK-ERK pathway were altered in 86% of

the cell lines, the majority of which (96%) carried mutations in BRAF and/or NRAS.

Consistent with previous evidence showing that activating mutations in KIT, FGFR1 and

GNAQ can activate the RAS-RAF-MEK-ERK pathway, cell lines carrying mutations in either

of these genes carried wildtype copies of BRAF and NRAS. The p16INK4A-CDK4-RB

senescence barrier was affected in 91% of the cell lines, either through inactivating INK4A

events, RB1 mutations and/or amplification or mutation of CDK4; genetic events affecting the

PI3K-AKT signaling pathway were found in 53% of the cell lines, with alterations in PTEN

and NRAS accounting for 86% these events; and the p53-p14ARF axis was affected in 79% of

the cell lines, either through TP53 mutation, ARF mutation/deletion, or both.

For genes that were altered in at least four of the melanoma cell lines, we performed pair-

wise comparisons using the Pearson correlation coefficient (Table 2). Negative associations

were found between BRAF and NRAS mutations, which only coexisted in two cell lines

harboring non-p.V600E BRAF mutations (P = 5.8 ×10-8), CDK4 and INK4A alterations (P =

2.2 × 10-5), and NRAS and PTEN alterations (P = 0.012). PTEN alterations correlated

positively with BRAF mutations (P = 9.6 ×10-5).

Among other statistically significant direct and inverse correlations (see Table 2), one

particularly interesting finding was an inverse correlation between CDNK2A alterations and




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hypermethylation of the RARB promoter. Notably, this association was much stronger for

ARF alterations (P = 2.7 × 10-5) than for INK4A alterations (P = 0.025). Of the 110 cell lines,

52 harbored mutation or deletion of ARF and wildtype RARB (47%), 26 had RARB promoter

hypermethylation and wildtype ARF (24%), and 23 harbored alteration of both genes (21%).

Only 9 cell lines carried both wildtype ARF and RARB (8%). Notably, while RARB

hypermethylation was underrepresented in cell lines with ARF alterations, it was

overrepresented in cell lines carrying CDKN2A alterations that specifically target the

expression or function of p16INK4A, e.g., INK4A promoter hypermethylation events and

mutations and deletions affecting exon 1α (14 out of 18; chi-square test, P < 0.05).

Retinoic acid induces p14ARF expression in human melanocytes via RARβ

Previous studies have shown that human epidermal melanocytes are growth inhibited by all-

trans retinoic acid (ATRA) (28;29) and that the expression of the RARβ2 isoform (the

predominant receptor mediating the proliferation inhibitory effects of ATRA (30)) is often lost

in melanomas due to promoter hypermethylation. As our genomic data suggested a link

between RARB and ARF in melanoma, we conducted a series of experiments to address

whether p14ARF is a downstream target gene of the RARβ-mediated signaling cascade. We

first asked whether the expression of p14ARF in melanocytes is altered upon short-term

exposure to ATRA. In two independent cultures of primary human melanocytes, treatment

with 10 µM ATRA for 48 h led to upregulation of RARβ2, a known direct target of retinoic acid

(Ref. 31; and data not shown). Interestingly, in the same cells, ATRA also increased the

expression of p14ARF at both the mRNA and protein levels in a dose-dependent manner

(Figure 2A). To examine whether this ability of ATRA to induce p14ARF expression is

mediated via RARβ, we cultured melanocytes in the presence or absence of the RARβ

antagonist LE135 (32). As shown in Figure 2B, LE135 effectively blocked the ATRA-induced

expression of p14ARF. As LE135 may also act as an antagonist of RARα, we extended our

analysis by transiently transfecting normal human melanocytes with either siRNAs targeting

RARβ or control siRNAs. As shown in Figure 2C, knockdown of RARβ resulted in a >60%

reduction in the steady-state expression levels of p14ARF levels 48 h post-transfection.

Collectively, these data suggest that RARβ signaling contributes to the regulation of p14ARF

expression in melanocytes.




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Cellular responses to ATRA in normal and p14ARF-depleted melanocytes

To characterize in greater detail the cellular reponse of normal melanocytes to ATRA, we first

assessed the effects on cell proliferation by measuring the rate of DNA synthesis. As shown

in Figure 3A, BrdU incorporation was reduced by 50% after 10 days of growth in the

presence of ATRA. Furthermore, this effect could be rescued by LE135, suggesting that the

growth-inhibitory effects of ATRA are mediated via RARβ. To determine the effect on cell

cycle distribution, melanocytes were treated with ATRA for 10 days and analyzed by FACS

analysis. These experiments showed an accumulation of cells in G1 phase of the cell cycle

and a corresponding decrease in the fraction of cells in S and G2 phases (Figure 3B).

We next measured the activity of senescence-associated β-galactosidase (SA-β-Gal), a

hallmark of senescence (33). Treatment of two independent cultures of melanocytes with

ATRA for two weeks increased the fraction of SA-β-Gal-positive cells by 300-600% (Figure

3C). As senescence induced by oncogenic stimuli has been shown to be accompanied by

higher p14ARF expression in some cell types (34) and as ATRA increased the expression of

p14ARF in normal human melanocytes (Figure 2A), we investigated the role of p14ARF in

mediating ATRA-induced growth arrest. To this end, we used two independent lines of

melanocytes that express shRNAs against CDKN2A sequences and express low levels of

p14ARF (HEM-1 and HEM-2; see Materials and Methods). When SA-β-Gal was used as a

surrogate marker of senescence, the response to ATRA was significantly reduced in p14ARF-

depleted cells compared to normal counterpart cells (Figures 3C and 3D). In conclusion,

treatment of normal human melanocytes with ATRA is associated with loss of proliferative

activity, G1 arrest and increased activity of SA-β-Gal, and this response may at least in part

depend on p14ARF.

ATRA induces irreversible G1 arrest in melanoma cells with an intact RARβ-p14ARF axis

The majority of human melanomas are inherently resistant or weakly sensitive to ATRA (35).

Part of this resistance can be explained by epigenetic silencing of RARβ2, which is found in

20-70% of melanoma cell lines (reviewed in Ref. 6; and this study). However, resistance

mechanisms in melanoma cells expressing RARβ2 have not been elucidated. Given the

strong genetic and functional links between RARβ and p14ARF, we addressed whether the

status of RARB and ARF could predict the sensitivity of melanoma to ATRA. Specifically, we

asked if melanoma cells with an intact RARβ-p14ARF axis are sensitive to ATRA. From the




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nine melanoma cell lines identified with an unmethylated RARB promoter and wildtype ARF,

we excluded ESTDAB-097 and ESTDAB-098 as they grew slowly in culture and carried no

melanoma-specific genome alterations, and ESTDAB-179, which did not express p14ARF

even though no CDKN2A alterations were identified. The remaining six cell lines all

expressed p14ARF and RARβ2 mRNA (data not shown). Consistent with the data obtained in

melanocytes (Figure 2A), treatment with ATRA increased the expression levels of p14ARF in

these cell lines by 1.5-2.5 fold (data not shown). Interestingly, all six cell lines responded to

treatment with ATRA (Figure 4). Two of these lines harbored INK4A alterations, one

harbored a TP53 mutation, and one harbored defects in both genes, suggesting that p16INK4A

and p53 are dispensable for execution of ATRA-induced growth inhibition. Cell lines

harboring RARB promoter hypermethylation (with p14ARF expression) or ARF deletion (with

RARβ2 expression) had a poor response to ATRA (Figures 4A and 4C). Collectively, these

results suggest that melanomas with an intact RARβ-p14ARF axis would potentially be

susceptible to treatment with ATRA.

In the context of our findings, a pertinent question is whether restoration of RARβ expression

is able to restore ATRA sensitivity in insensitive melanoma cells. To address this question,

we first treated two melanoma cell lines with RARB promoter hypermethylation and wildtype

ARF with the DNA-hypomethylating agent 5-aza-2’-deoxycytidine (5-aza-CdR). As shown in

Figure 4D, treatment with 1 µM 5-aza-CdR for 4 days reactivated RARβ2 mRNA expression

in both cell lines. Furthermore, in contrast to untreated cells, the growth of 5-aza-CdR-treated

cells was significantly inhibited by ATRA, suggesting that derepression of RARβ2 can restore

ATRA sensitivity (Figure 4E).

We made several attempts to stably deplete RARβ- and p14ARF-expressing melanoma cell

lines of p14ARF to be able to study more directly the role of this protein in long-term cellular

repsonses to ATRA. However, although we used the same shRNA vectors that efficiently

knocked down p14ARF in normal melanocytes (Figure 2; and data not shown) and stable

clones were readily generated with the empty vector, we were unable to generate clones

expressing shRNA vectors targeting p14ARF in any of these six cell lines (data not shown).

Cell cycle analysis of RARβ- and p14ARF-expressing melanoma cells treated with ATRA

showed a decrease in the fraction of cells in S and G2 phases of the cell cycle and an




14

increase in the fraction of cells in G1 phase (Figure 5A). Analysis of the sub-G1 fraction did

not show an increase in the number of apoptotic cells in ATRA-treated cultures (data not

shown). After 3 weeks of growth in the presence of ATRA, BrdU incorporation was reduced

by 50% (Figure 5A), similar to what was observed in primary melanocytes (Figure 2A). Cells

were then released and cultured for one week in medium without exogenously added ATRA.

As shown in Figure 5B, BrdU incorporation was not increased after the release. Together,

these results suggest that ATRA induces irreversible G1 arrest in melanoma lines with an

intact RARβ-p14ARF axis.




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DISCUSSION

We have generated a profile of genetic and epigenetic drivers in human melanoma-derived

cell lines and searched for pair-wise associations across all genes to identify possible

functional interplays. The use of established cell lines for this type of approach has both

advantages and disadvantages, which should be taken into account when interpreting our

data. A major advantage of using cell line DNA as a template is that some genomic changes,

particularly homozygous deletions and copy number gains, can be determined with greater

accuracy as they will not be masked by contaminating normal tissue. Although cell lines may

contain genomic artifacts introduced as a result of cell culturing, previous studies have

shown that cell lines usually are representative of the tumors from which they originate (36).

The frequencies of genome alterations found in our study are generally higher than, but still

compatible with the frequencies observed in melanoma biopsies (6). A potential limitation of

studies based on cancer cell lines, including ours, is that they are confounded by a bias in

favor of tumors that are able to grow under standard culture conditions and thus may have

specific cellular properties and genomic changes. Accordingly, the collection of melanoma

cell lines used in our study, although relative large in number, may not be clinically

representative.

Previous work on smaller collections of melanoma cell lines (37;38) and uncultured

melanomas have found a number of driver associations, which we confirmed in this study,

including negative associations between BRAF and NRAS mutations, CDK4 and INK4A

alterations, and NRAS and PTEN alterations, and a positive correlation between PTEN and

BRAF alterations. The functional implications of these associations have all been

corroborated in various experimental models (3;19;39). In addition to the above associations,

a previous study (38) suggested a negative association between ARF (exon 1β) and TP53

alterations, consistent with the known role of p14ARF as a positive regulator of p53 (34) and

with the consensus view that the relatively low frequency of TP53 mutations in melanoma

can be explained by a high frequency of inactivating ARF events. An association between

ARF and TP53 alterations did not reach statistical significance in our study, and thus it

remains unclear whether p14ARF and p53 operate in the same or different molecular

pathways in melanoma. In support of the latter, previous work has shown that p19Arf (the

mouse homolog of p14ARF) can function as a melanoma suppressor by inducing p53-

independent senescence (40).




16

As the most important novel finding in our study we uncovered a strong inverse correlation

between hypermethylation of the RARB promoter and ARF alterations. This association was

particularly relevant as little is known about the melanoma-suppressive properties of RARβ,

and as genomic alterations of other core components of the retinoic acid signaling pathway

have not been described in melanoma. To corroborate the link between RARβ and p14ARF

suggested by the genomic data, we first investigated whether the regulation of p14ARF

expression was influenced by changes in RARβ expression or ligand concentrations. In

cultures of normal human melanocytes, steady-state levels of p14ARF were increased at both

the mRNA and protein level upon short-term exposure to ATRA, while they were reduced in

response to depletion or chemical inhibition of RARβ. These findings are consistent with an

important role of RARβ signaling in the regulation of p14ARF expression in melanocytes and

would, to our knowledge, identify retinoic acid as the first naturally occurring inducer of

p14ARF. Previous studies have shown that p14ARF expression can be induced by

hyperproliferative stimuli such as those elicited by the Myc and E1A oncogenes (34),

providing an explanation for the tumor-specific properties and expression patterns of p14ARF.

Several reports have suggested that p14ARF and p19Arf are not expressed in most normal

cells, including cultured human melanocytes (41). However, our data show that ATRA can

induce the expression of p14ARF in normal melanocytes in the nanomolar range, i.e., at

concentrations similar to the concentrations of retinoids in normal epidermis (42).

Considering the important role of retinoids and retinoic acid receptors in embryonic

development and cellular differentiation (43), p14ARF expression in melanocytes may depend

on the differentiation state of these cells, which is, at least in part, controlled by retinoic acid

(44). A functional role of p14ARF in normal differentiated melanocytes remains unknown.

The implication of p14ARF as a melanoma suppressor has been controversial and notoriously

difficult to study, primarily due to the unusual structure of the CDKN2A locus, which encodes

p14ARF and p16INK4A in different reading frames (34). While a critical role for p16INK4A in

replicative and oncogene-induced melanocyte senescence has been well documented both

in vitro and in vivo (33;45;46), these studies have provided little insight into a possible role of

p14ARF. Studies of cancer-associated CDKN2A mutation patterns have suggested that exon

2 mutations preferentially target p16INK4A function (38) and thus that p14ARF is coincidentally

inactivated in a high proportion of melanomas due to the strong selection pressure against




17

p16INK4A. In order to better understand the association between RARβ signaling and the

melanoma-suppressive properties of the CDKN2A locus, we conducted a series of

experiments in the settings of melanocytes and melanoma cells. Consistent with previous

work (28;29), we showed that ATRA could induce senescence in normal human

melanocytes. This response was dependent on CDKN2A, as senescence was less

pronounced in cells depleted of p14ARF and p16INK4A. Furthermore, while melanoma cell lines

are generally resistant to retinoids (35), cell lines with wildtype copies of RARB and ARF

responded to treatment with ATRA. Importantly, some of the ATRA-responsive cell lines

harbored INK4A and/or TP53 alterations, suggesting that the response to ATRA is

independent of the functions of p16INK4A and p53.

p14ARF is lost in a great proportion of melanomas and is considered as a bona fide tumor

suppressor. In this context it was unexpected that stable depletion of 14ARF was not

compatible with continued growth of melanoma cells with an intact RARβ-p14ARF axis. This

finding may suggest that p14ARF is essential for cell survival in some cases of melanoma and

thus represents an example of non-oncogene addiction, as has been described for a number

of genes required for cancer cells to tolerate cellular stresses (47). In spite of this unresolved

issue, the collected data from our genetic and functional studies suggest that RARβ and

p14ARF operate in the same molecular pathway, and that the melanoma-suppressive function

of p14ARF may be related to its role in this pathway.

Our findings may have clinical implications. Topical treatment with retinoic acid has been

found to be effective in dysplastic nevi (48) and resulted in tumor regression in two patients

with cutaneous metastatic melanoma (49). However, the clinical activity of retinoids for

systemic treatment of melanoma is limited with only few patients showing a positive

response (50-52). Currently, no predictive markers of ATRA responsiveness are available for

clinical use. Our data showing that melanoma cell lines with wildtype copies of RARB and

ARF respond to treatment with ATRA suggest that the status of these genes identifies a

group of patients who may benefit from therapies including retinoids.

In conclusion, our data suggest that RARβ and p14ARF may be linked in a signaling pathway

that is operating in melanocytic cells and is lost in the majority of melanomas. This is an

example of how integrated genomic analysis of cancer may be used to map individual drivers




18

to specific molecular pathways and to reveal previously unrecognized molecular crosstalk.

The set of melanoma drivers examined here is not exhaustive. Indeed, recent sequencing

studies have identified several additional putative melanoma drivers, albeit at low frequency

(<10%) (7-12). Moreover, our understanding of the importance of the multiple methylation

changes present in melanoma genomes is still at an early stage. The collection of melanoma

cell lines characterized here may be useful for further unraveling the hierarchy of driver

genes and molecular pathways in melanoma. As exemplified by the identification of a subset

of melanomas that respond to retinoic acid, these cell lines may also be of value for studying

the genomic basis of therapeutic sensitivity and resistance in melanoma and for identifying

new targets for intervention. With the realisation that intrinsic and acquired resistance is an

inherent problem of current melanoma treatment options (2), the need for developing new

rational treatment regimens that can effectively control this malignancy is urgent.

ACKNOWLEDGEMENTS

We thank Vibeke Ahrenkiel for technical assistance, and Ulrik Ralfkiær and Birgitte Grum-

Schwensen for help with cell-cycle analysis. This study was supported by grants from the

Danish Cancer Society, the Neye Foundation and the Danish Cancer Research Foundation.

The cell lines used here are from the EU Research Infrastructure Support program (contract

QLRI-CT-2001-01325).




19

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FIGURE LEGENDS

Figure 1. Profile of genetic and epigenetic alterations in melanoma cell lines (N = 110).

Figure 2. ATRA induces p14ARF expression in human melanocytes via RARβ. (A)

Expression levels of p14ARF in normal human melanocytes exposed to ATRA for 48 h. Left,

immunoblotting of a representative melanocyte culture. β-actin was used as control for

protein loading. Right, qRT-PCR. The p14ARF expression levels were normalized to PBGD

expression. The data represent the mean +SD of two independent experiments. (B) Inhibition

of ATRA-induced p14ARF expression by the RARβ antagonist LE135. Cells were grown for 48

h in the presence or absence of ATRA (10 µM) and in the presence or absence of LE135 (1

µM). The mRNA expression levels of p14ARF were measured by qPCR and normalized to

PBGD expression. The data represent the mean +SD of two independent experiments. (C)

Expression levels of RARβ2 and p14ARF in normal human melanocytes transiently

transfected with siRNAs targeting RARβ. The RARβ2 and p14ARF expression levels were

determined by qRT-PCR 48 h post transfection and normalized to PBGD expression.

Expression in cells transfected with control siRNAs was set at 100%. The data represent the

mean +SD of two independent experiments.

Figure 3. Cellular responses to ATRA in normal and p14ARF-depleted melanocytes. (A)

BrdU incorporation in primary human melanocytes grown for 48 h in the presence or

absence of ATRA (10 µM) and in the presence or absence of LE135 (1 µM). (B) FACS

analysis showing representative cell cycle profiles of normal human melanocytes cultured for

10 days in the presence or absence of ATRA (10 µM). (C) ATRA-induced senescence in two

cultures of normal human melanocytes (WT-1 and WT-2) and two independent pools of

melanocytes expressing shRNAs against CDKN2A sequences (HEM-1 and HEM-2). Cells

were treated with ATRA (10 µM) for two weeks, and the number of senescent cells was

determined by SA-β-Gal positivity. Columns represent percentage of SA-β-Gal positive cells.

(D) The same data as in D, but with the increase in senescence in normal melanocytes set at

100%. Statistical analysis was performed using a paired t-test. *, P<0.05; ns, non-significant.

Figure 4. ATRA-mediated growth inhibition of melanoma cells with an intact RARβ-

p14ARF axis. (A) Dose-dependent ATRA sensitivity of melanoma cells with different ARF and




24

RARB status. Cells were treated with increasing concentrations of ATRA for 14-19 days. For

each cell line, all dishes were fixed at the same time, stained with crystal violet and

photographed. (B) Time-dependent effect of ATRA treatment on growth rate in ESTDAB-196

cells. Cells were seeded at low density in 25-cm2 flasks and fresh medium with ATRA or

vehicle was added every 48 h for 16 days. Cells were counted with trypan blue exclusion.

Mean cell number +SD of duplicate samples are shown. The growth curves shown are

representative of three independent experiments. (C) Growth inhibition assays of melanoma

cells. Cells were grown in the presence or absence of 10 µM ATRA for 14-19 days and were

then fixed and stained with crystal violet. The absorbance was read at 595 nm in a microtiter

plate spectrophotometer. The absorbance values of cells treated with vehicle were set at

100%. Shown is the average +SD of two independent experiments performed in triplicate.

Mut, mutated; Del, deleted; Met, methylated; WT, wildtype. (D) Expression levels of RARβ2

in two melanoma cell lines (ESTDAB-013 and ESTDAB-079) with RARB promoter

hypermethylation after treatment with 1 µM 5-aza-CdR for 4 days. (E) ATRA-induced growth

inhibition of 5-aza-CdR-treated melanoma cells. Cells were grown in the presence or

absence of ATRA (10 µM) and in the presence or absence of 5-aza-CdR (1 µM) for 14-17

days. Fixation, staining and absorbance measurement were done as described in C. The

absorbance values of cells treated with vehicle were set at 100%. Shown is the average +SD

of two independent experiments performed in triplicate. Statistical analysis was performed

using a paired t-test. *, P<0.05; ns, non-significant.

Figure 5. Cellular responses to ATRA in melanoma cells lines expressing RARβ and

p14ARF. (A) Representative FACS analyses showing the proportions of cells in the different

stages of the cell cycle after 3 of weeks of growth in the presence or absence of ATRA (10

µM). (B) DNA synthesis measured by BrdU incorporation. Cells were grown in the presence

or absence of ATRA (10 µM) for 3 weeks and then for 1 week without ATRA (release).




Table 1. Molecular pathway distribution of genome alterations in melanoma Pathway Gene(s) No. of cell lines (%)

RAS-RAF-MEK-ERK

BRAF 73 (66)

NRAS 16 (15)

KIT 1 (1)

FGFR1 1 (1)

GNAQ 2 (2)

BRAF + NRAS 2 (2)

WT 15 (14)

p16INK4A-CDK4-RB

INK4A 88 (80)

CDK4 8 (7)

RB1 1 (1)

INK4A + CDK4 3 (3)

WT 10 (9)

PI3K-AKT

PTEN 30 (27)

NRAS 16 (15)

KIT 1 (1)

FGFR1 1 (1)

PIK3CA 1 (1)

PIK3R1 1 (1)

AKT3 4 (4)

NRAS + PTEN 1 (1)

NRAS + PIK3CA 1 (1)

PTEN + AKT3 2 (2)

WT 52 (47)

p53-p14ARF

TP53 12 (11)

ARF 61 (55)

TP53 + ARF 14 (13)

WT 23 (21)




Table 2. Associations among genetic and epigenetic drivers in melanoma cell lines (N=110). Correlations shown are phi coefficients for pairs of dichotomous variables. Correlations for which P < 0.05, P < 0.01 or P < 0.001 are shown with a single, double or triple asterisk, respectively.

AKT3 APC ARF BRAF CCND1 CDK4 MYC CTNNB1 IGFBP7 INK4A MITF NRAS PTEN PYCARD RARB RASSF1A TP53

AKT3 0.03 -0.01 0.08 -0.04 -0.08 0.01 -0.05 -0.11 0.00 -0.01 -0.11 0.02 -0.01 0.03 -0.02 0.05 APC -0.21 -0.03 -0.03 -0.13 0.07 -0.07 0.12 0.03 -0.12 0.20 -0.19 0.09 0.26** 0.02 -0.02

ARF 0.20* 0.03 -0.23* 0.23* 0.03 0.03 0.57*** -0.12 -0.12 0.19* 0.10 -0.41*** 0.08 -0.17

BRAF 0.21* 0.03 0.06 0.13 -0.09 0.05 0.18 -0.54*** 0.36*** -0.14 -0.02 -0.04 -0.17 CCND1 0.16 0.06 0.23* 0.12 -0.07 0.05 -0.20 0.09 -0.05 0.00 0.01 0.08

CDK4 -0.14 0.10 0.05 -0.49*** 0.15 -0.15 -0.02 0.01 0.19 0.06 -0.04 MYC 0.06 0.16 0.12 -0.07 -0.18 0.12 0.13 0.05 -0.04 0.07 CTNNB1 -0.02 -0.04 0.03 -0.09 0.08 -0.11 -0.08 -0.12 0.01

IGFBP7 0.13 0.10 -0.01 0.10 0.34*** 0.09 0.08 0.06 INK4A -0.21* 0.01 0.09 0.10 -0.22* -0.03 -0.09

MITF -0.15 -0.02 -0.14 0.08 -0.02 0.06

NRAS -0.24* 0.07 0.05 0.11 0.10 PTEN 0.06 -0.31** 0.08 -0.08

PYCARD -0.03 0.23* 0.01

RARB -0.17 0.10 RASSF1A -0.09

TP53

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