Integrative Genome Comparison of Primary and
Omar Kabbarah1., Cristina Nogueira1,2., Bin Feng3, Rosalynn M. Nazarian4, Marcus Bosenberg5, Min
Wu1, Kenneth L. Scott1, Lawrence N. Kwong1, Yonghong Xiao3, Carlos Cordon-Cardo6, Scott R. Granter7,
Sridhar Ramaswamy8, Todd Golub9, Lyn M. Duncan4, Stephan N. Wagner10, Cameron Brennan11*, Lynda
1Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, Massachusetts, United States of America, 2Institute of Molecular
Pathology and Immunology of the University of Porto (IPATIMUP), University of Porto, Porto, Portugal, 3Belfer Institute for Applied Cancer Science, Dana-Farber Cancer
Institute, Boston, Massachusetts, United States of America, 4Dermatopathology Unit, Department of Pathology, Massachusetts General Hospital, Harvard Medical School,
Boston, Massachusetts, United States of America, 5Department of Dermatology, Yale University School of Medicine, New Haven, Connecticut, United States of America,
6Department of Pathology, Columbia University, New York, New York, United States of America, 7Department of Pathology, Brigham and Women’s Hospital, Boston,
Massachusetts, United States of America, 8Massachusetts General Hospital Cancer Center, Boston, Massachusetts, United States of America, 9The Broad Institute of MIT
and Harvard and Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America, 10DIAID, Department of Dermatology, Medical University of Vienna and
Center of Molecular Medicine, Austrian Academy of Sciences, Vienna, Austria, 11HOPP, Department of Neurosurgery, Memorial Sloan-Kettering Cancer Center, New York,
New York, United States of America, 12Department of Dermatology, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, United States of
A cardinal feature of malignant melanoma is its metastatic propensity. An incomplete view of the genetic events driving
metastatic progression has been a major barrier to rational development of effective therapeutics and prognostic
diagnostics for melanoma patients. In this study, we conducted global genomic characterization of primary and metastatic
melanomas to examine the genomic landscape associated with metastatic progression. In addition to uncovering three
genomic subclasses of metastastic melanomas, we delineated 39 focal and recurrent regions of amplification and deletions,
many of which encompassed resident genes that have not been implicated in cancer or metastasis. To identify progression-
associated metastasis gene candidates, we applied a statistical approach, Integrative Genome Comparison (IGC), to define
32 genomic regions of interest that were significantly altered in metastatic relative to primary melanomas, encompassing 30
resident genes with statistically significant expression deregulation. Functional assays on a subset of these candidates,
including MET, ASPM, AKAP9, IMP3, PRKCA, RPA3, and SCAP2, validated their pro-invasion activities in human melanoma cells.
Validity of the IGC approach was further reinforced by tissue microarray analysis of Survivin showing significant increased
protein expression in thick versus thin primary cutaneous melanomas, and a progression correlation with lymph node
metastases. Together, these functional validation results and correlative analysis of human tissues support the thesis that
integrated genomic and pathological analyses of staged melanomas provide a productive entry point for discovery of
melanoma metastases genes.
Citation: Kabbarah O, Nogueira C, Feng B, Nazarian RM, Bosenberg M, et al. (2010) Integrative Genome Comparison of Primary and Metastatic Melanomas. PLoS
ONE 5(5): e10770. doi:10.1371/journal.pone.0010770
Editor: Mikhail V. Blagosklonny, Roswell Park Cancer Institute, United States of America
Received March 9, 2010; Accepted April 30, 2010; Published May 24, 2010
Copyright: ? 2010 Kabbarah et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: National Institutes of Health training grant (T32 AR07098) to O. K. Fellowship from the Foundation for Science and Technology (Fundac ¸a ˜o para a
Cie ˆncia e Tecnologia , PRAXIS/BD/21794/99) to C. N. National Institutes of Health grants UO1 CA84313, RO1 CA93947, and P50 CA93683 to L. C. The funders had
no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: email@example.com (CB); firstname.lastname@example.org (LC)
. These authors contributed equally to this work.
Cutaneous melanoma arises primarily from neural crest-derived
epidermal melanocytes . A reflection of melanoma’s intense
metastatic propensity is the fact that the metastatic risk is
measured on the scale of millimeters, where a tumor thickness of
only 4 mm predicts a high risk of cancer dissemination and death
. When localized to the skin, cutaneous melanoma is largely
curable by surgical excision, whereas metastatic melanoma carries
a median survival of 6–9 months . The recent success of
targeted therapies in melanoma  substantiates the view that a
more comprehensive examination of the genetic events governing
melanoma development, particularly its metastatic potential, may
lead to more effective therapies directed against this disease.
The molecular basis of melanoma genesis and progression has
not been fully elucidated. Several validated genetic mutations (i.e.,
documented DNA structural alterations) responsible for melano-
cytic transformation have been described, including deletion of the
9p21 CDKN2A familial melanoma locus encoding the tumor
suppressors INK4A and ARF, as well as amplification of MITF as
a lineage-specific oncogene . Activation of MAPK signaling is
frequently observed in melanocytic neoplasms through activating
PLoS ONE | www.plosone.org1May 2010 | Volume 5 | Issue 5 | e10770
mutations of BRAF or NRAS in cutaneous melanoma  or
mutations of the heterotrimeric guanine nucleotide-binding
protein GNAQ in uveal melanoma . An integrative cross-
species comparative oncogenomic analysis identified NEDD9, a
member of the p130CAS family, as a target of a recurrent 6p gain;
and functional studies verified its role as a bona fide melanoma
metastasis gene  involved in mesenchymal cell movement .
Recently, Nedd9 expression has also shown to be required for
breast cancer metastasis in vivo . In addition to this handful of
genes, genomic characterization of metastatic melanomas and
melanoma cell lines have uncovered many regions of recurrent,
non-random chromosomal copy number aberrations (CNAs) with
few recognizable or validated cancer-relevant genes, pointing to
the potential existence of many yet-to-be-discovered genetic events
driving melanoma pathogenesis [8,9].
DNA copy number aberrations would be expected to be
retained throughout the life history of a cancer cell. These
aberrations are presumed to include drivers and passengers as well
as events responsible for the initiation and/or progression of
disease. As such, there are significant challenges in the identifi-
cation of metastasis-relevant alterations. In this study, we
examined the genomes of a collection of clinically annotated
primary and metastatic melanomas. Not surprisingly, given the
well-recognized heterogeneous nature of primary melanoma,
many more genomic alterations were definable in metastatic
melanomas, providing an opportunity for comparative analyses to
identify events that are enriched for during metastatic progression.
To this end, using an Integrative Genome Comparison (IGC)
approach, we defined a short list of 30 candidates that showed
increased expression and resided within regions of amplification in
metastatic melanomas. Functional characterization and correlative
analysis of human tissues supported a role for these candidates in
The melanoma genome is highly rearranged and
Using an established oligo-microarray platform offering a
median resolution of 50 kb , we compiled array-CGH profiles
on 25 primary cutaneous and 61 metastatic melanoma specimens.
The clinical and histopathologic characteristics of these samples
are summarized in Supplemental Tables S1 and S2, and the array-
CGH profiles are available online at GEO under super-series
accession #GSE7606. Raw array-CGH profiles were processed by
a modified circular binary segmentation (CBS) algorithm [11,12],
and copy number aberrations (CNAs), represented by genomic
segments bounded by statistically significant copy number
transition points, were defined in each profile (see Methods).
When viewed in skyline recurrence plots (Figure 1A), the overall
patterns of CNAs in metastatic profiles agreed well with major and
frequent events previously reported in melanoma [8,13,14,15],
including gains on 1q, 6p, 7, 8q, 17q, 20, and 22q, as well as losses
on 6q, 8p, 9, 10, and 11q. In contrast, primary melanomas
harbored far fewer genomic alterations detectable by array-CGH.
Indeed, by measuring the breakpoints of the genome with altered
copy number events (see Supplemental Figure S1 legend), one
could demonstrate such statistically significant increase in
discernable genomic events from primary to metastatic melano-
mas (Supplemental Figure S1; p=561025).
In view of the highly rearranged nature of the metastatic
melanoma genome, we next asked whether metastases were
comprised of distinct genomic subclasses by genomic non-negative
matrix factorization (gNMF), an unsupervised classification
algorithm modified for array-CGH data [16,17]. Notably, strong
Cophenetic correlations were observed when gNMF classified
these profiles into 2 or 3 subclasses (e.g. Rank K2 and K3
classification, respectively); whereas Rank K4 showed a sharp drop
in correlation (Figure 1B). Thus, gNMF classification defined three
stable molecular subclasses among the metastatic samples.
Examination of key features of these subclasses revealed that the
K3-1 profile was characterized by gains of chromosomes 1q, 6p, 7,
8q, 13, 20 and 22p, whereas K3-2 showed prominent 1q, 6p, 7 and
8q gains accompanied by loss of 6q, 9p and 11q and K3-3
presented with a general hypoploidy profile (Figure 1C). These
patterns were consistent with the expression heatmap of the
samples grouped according
(Figure 1D). As melanoma metastases have reportedly been
classified into two distinct transcriptional subtypes, and those
subgroups were significantly correlated with clinically-relevant
endpoints, including patient survival , we asked whether this
DNA-based classification was associated with any clinical param-
eters. Notably, the subclass assignments did not correlate with
metastatic site, age or gender (data not shown; Supplemental Table
S1). Instead, when intersected with survival outcome available on a
subset of these samples, K3-3 subclass appeared to have a
significant survival advantage by Kaplan-Meier analysis (Supple-
mental Figure S2), suggesting that these genomic subclasses likely
represent biologically- and clinically-relevant subpopulations.
to their subclass assignment
Defining recurrent regions of amplification and deletion
Further analysis of the focal alterations in the highly rearranged
genomes of primary melanomas and metastases delimited the
boundaries of informative Minimal Common Regions (MCRs)
using a set of heuristically defined rules, including recurrence in 2
or more samples of a CNA spanning regions less than 2Mb in size
with a peak log2 ratio greater than 1.0 (see Methods). In the
primary melanomas, this analysis defined 13 MCRs comprising 6
amplifications with a median size of 1.03 Mb (range 0.075–
1.97 Mb) containing a total of 84 known genes, and 7 deletions
with a median size of 0.32 Mb (range 0.098–0.94 Mb) containing
39 genes (Table 1). In comparison, analysis of the metastasis
profiles defined 39 MCRs comprising 24 amplifications with a
median size of 0.78 Mb (range 0.046–1.59 Mb) containing a total
of 276 known genes, and 15 deletions with a median size of
0.53 Mb (range 0.035–1.7 Mb) encompassing 78 genes (Table 1).
Although the cytological bands of MCRs in primary and
metastatic melanoma do not entirely overlap, these do not
necessarily represent unique events to one or the other melanoma
type since they can be present as regions of larger amplifications or
deletions or lower amplitude changes (and thus can be excluded
from the list of informative MCRs due to the strict criteria used to
define these events). The identification of regions of genomic
alteration enriched in primary or metastatic melanoma is discussed
Of the genes residing within metastases MCRs boundaries
(Table 1), many were linked to networks of relevance to
carcinogenesis and metastasis. For example, a significant number
of genes were involved in G1/S cell cycle transition and in p53-
dependent apoptosis (Table 1; MetaCoreTManalysis, p,0.01),
including p14ARF, p16INK4Aand p15INK4B, which were deleted as
part of the recurrent 9p21 locus deletion, as well as CDK4 and
MDM2, both of which were recurrently amplified in metastatic
melanoma (Supplemental Figure S3). Additionally, MetaCoreTM
analysis identified components of networks governing cell
adhesion, motility and cell matrix assembly that were significantly
represented among genes mapping to the metastases MCRs
(p,0.01). For example, LIPRIN (PPFIA1), a gene known to
Integrative Melanoma Genomics
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Figure 1. Array-CGH characterization of the primary and metastatic melanoma genomes. (A) Summary of genomic profiles of primary
and metastatic melanomas and the recurrence of chromosomal alterations. Recurrence of CNAs across the samples in segmented data (y axis) is
plotted for each probe evenly aligned along the x axis in chromosomal order. The percentage of tumors harboring gains, amplifications, losses and
deletions for each locus is depicted according to the following scheme: dark red (gains with a log2ratio .=0.15) and green (loss with a log2ratio
,=20.15) and are plotted along with bright red (amplifications with a log2ratio $ 0.4) and bright green (deletions with log2ratio #20.4). (B)
Consensus matrices show how often samples are assigned to the same clusters during 100 repetitions of gNMF, computed at K=2–4 for the 61
metastatic melanoma dataset. Each pixel represents how often a particular pair of samples clusters together, colored from 0% (black, samples are
never in the same cluster) to 100% (red, samples are always in the same cluster). Ranks 2 and 3 classification show stable assignments into 2 and 3
blocks, respectively; in contrast, rank 4 assignments are disrupted. Cophenetic correlation coefficients for hierarchically clustered matrices in B. Valid
clustering should show correlation close to 1. (C) gNMF classification with rank K=3 identifies three distinct subgroups. Array-CGH profiles of 61
metastatic melanomas were subjected to gNMF analyses (100 repetitions). Y axis indicates the centroid of three subgroups identified by gNMF. X axis
coordinates represent genomic map order (from chromosome 1 to chromosome 22). The colors denote gained (red) or deleted (green) chromosome
Integrative Melanoma Genomics
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enhance cell matrix interaction, and Contractin (CTTN), a gene
implicated in squamous cell carcinoma migration and metastasis
[19,20], were both recurrently amplified in metastatic melanoma.
Conversely, Fibulin 5 (FBLN5), shown to enhance cell adhesion
, was recurrently deleted in the metastatic samples (Table 1).
IGC analysis of primary and metastastic melanoma
Evolution from primary to metastatic disease is expected to be
accompanied by the acquisition of, or selection for, genomic and
genetic events that confer biologic capabilities necessary for
mestastasis . We thus hypothesized that CNAs observed in
metastasis but not detected in primary disease would be more
likely to represent potential drivers of metastasis. To define such
events, we adopted an integrative genome comparison approach
to define genes that were statistically different between primary
and metastatic samples based on DNA and RNA data (Figure 2A).
First, we employed a statistical test, Fisher-Exact, to delineate
regions that were differentially altered in metastatic versus primary
melanoma. Briefly, we collapsed all CBS-processed array-CGH
profiles of primary or metastatic cohorts down to 2,907 reduced-
segments (hereafter as ‘‘R-segments’’) to generate two R-segment
profiles corresponding to primary and metastatic melanoma
genomes, respectively. For each R-segments above noise threshold
(i.e. Log2 of +/20.15), Fisher’s Exact test p-values were calculated
and corrected for multiple testing (see Methods) to define
statistically significant events that were different between these
two classes. At a false discovery rate (FDR) of 10% (q,=0.1), 300
R-segments spanning 32 contiguous regions of interest (ROIs)
were found to be preferentially gained in metastatic relative to
primary melanomas in a non-random fashion (Figure 2B). Many
of these 32 ROIs clustered predominantly in several chromosomal
regions, including 1q, 6p, 7, 17q and 22, and many of the regions
were gained in poor-prognosis K3-1 and K3-2 subclasses (Figure 1C
and 1D). Of note, no R-segments were found to be preferentially
gained in primary relative to metastatic melanomas and no regions
of loss were significantly different between primary and metastasis.
Next, we sought to determine whether genes resident in regions
of genomic alterations exhibited a pattern of expression reflective
of the underlying copy number aberrations. That is, metastasis
candidate genes resident in regions that are preferentially gained
in metastatic melanomas would be expected to show upregulation
on mRNA level when compared to primary melanomas.
Accordingly, we utilized the well-established SAM algorithm to
identify those genes resident within the 32 Fisher-significant ROIs
that exhibited overexpression patterns in metastastic melanomas
relative to primary disease. Specifically, of the 1090 Affymetrix
probe sets deemed expressed (see Methods) within these 32 ROIs,
SAM analyses identified 676 probe sets that showed significant
overexpression in metastases (FDR,=0.05). These 676 probes
were furthered ranked by the relative fold change of expression to
select the top 34 probes corresponding to 30 unique annotated
genes exhibiting at least 2-fold overexpression in metastases
(Table 2). A number of these genes mapped to chromosome 7,
whose gain has been linked to metastasis and poor prognosis in
patients with non-small cell lung cancer and peripheral nerve
sheath tumors [23,24]. Although a number of these candidates in
Table 2 mapped to known regions of germline CNV, we did not
exclude these from further consideration since well-validated
cancer relevant genes have been known to reside within regions of
germline CNV .
Metastasis candidates promote invasion in vitro
Among the 30 candidate metastasis genes is MET, a receptor
tyrosine kinase (RTK) whose overexpression has been correlated
with progression in multiple cancer types, including melanoma
. Indeed, in a Met-driven transgenic mouse model comprised
of tyrosinase-driven rtTA and tet-Met transgenes on the Ink4a/Arf
‘‘iMet’’), activation of Met signaling in melanocytes engendered
a metastatic melanoma phenotype in vivo (Nogueira C and Chin L,
unpublished). Consistent with such metastatic phenotype in vivo,
derivative iMet melanoma cells showed robust invasion activity in
response to HGF in Boyden chamber invasion assay in vitro
(Supplemental Figure S4). Encouraged by this proof of concept
validation of IGC, we next utilized this in vitro Boyden chamber
invasion assay as a first step to examine the additional metastasis
candidates in Table 2.
To this end, we selected 6 genes from the candidate list (ASPM,
AKAP9, IMP3, PRKCA, RPA3, and SKAP2) based on literature
support (see Discussion) to determine whether their knockdown
would impact on the invasion of a human melanoma cell,
1205LU. As shown in Figure 3A, siRNA-mediated knockdown of
these candidates resulted in a statistically significant inhibition of
invasion in the Boyden Chamber assay compared to a non-
targeting siRNA oligo pool (p,0.05, p,0.05, p,0.01, p,0.05,
p,0.001, p,0.001, respectively). Correspondingly, we also
demonstrated that overexpression of ASPM in WM3211, a weakly
invasive human melanoma cell line, consistently increased
invasion through matrix in the Boyden Chamber assay (p,0.05;
Figure 3B). Similar results were obtained in a second melanoma
cell line, WM115 (data not shown).
Survivin expression is correlated with progression in
It is expected that the putative metastasis genes identified by IGC
would exhibit a progression correlated expression pattern in tumor
tissues. We utilized a validated commercial antibody against
Survivin, an anti-apoptotic protein encoded by BIRC5, to perform
immunostaining on a melanoma progression tissue microarray
(TMA). This TMA contained 480 cores of tumor tissues
representing benign nevi, thin and thick primary cutaneous
melanoma, as well as lymph node and visceral melanoma
metastasis. As shown in Figure 3C, Survivin expression was low
to absent in the majority of the benign nevi but was significantly
elevated in all melanomas (p,0.0001, x2). Importantly, we
observed a significant difference in Survivin expression between
cutaneous and metastastic melanomas when comparing thin (but
not thick) primary melanomas and lymph node metastases
(p=0.0003, x2). Accordingly, a significant difference in Survivin
expression levels was detected between thin and thick primary
cutaneous melanomas (p,0.0001, x2), whereas thick primary
tumors and lymph node metastases did not show statistically
significant differential expression. This pattern of Survivin expres-
sion was consistent with the well-known clinical correlation of
lymph node spread with thickness of the primary cutaneous lesions,
strongly supporting the thesis that the majority of these thick
primary melanomas are likely to already have lymph node spread.
material. (D) Heat-map plot showing discrete CNAs within all samples, with the X axis coordinates represent genomic map position and Y axis
indicates individual samples of the three subgroups identified by gNMF. Red represents chromosomal gain or amplification, and green denotes
chromosomal loss or deletion.
Integrative Melanoma Genomics
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Table 1. High-confidence MCRs in melanoma primary and metastastic samples.
MCR## Cytobands Start EndWidth (bp) Peak## Tumors## Genes Candidates known CNV*?
1 1q21.1142,480,203144,454,5991,974,3961.142 40PDE4DIP, BCL9 yes
2 1q24.1162,608,779163,764,5451,155,766 1.512 13 GPA33 partial
3 2q31.1 175,489,973176,859,5061,369,5331.166 15HOXD11, HOXD13, CHN1 no
4 5p13.3 31,589,91332,485,015895,102 1.1526 yes
5 11q24.2125,577,665 125,652,604 74,9391.1444 no
6 20q13.3359,983,746 60,209,329 225,5831.0336 SS18L1 partial
7 1p21.2 101,168,629101,448,588279,959
8 6q27169,921,072 170,019,43398,361
9 9p24.1 5,899,7346,247,371 347,637
1011q21 93,552,95393,872,148 319,195
11 11q23.3120,465,281 120,683,610218,329
MCR##CytobandsStartEndWidth (bp)Peak## Tumors## GenesCandidatesknown CNV?
1 1p31.267,865,391 68,606,911741,5201.1127GADD45A no
6 5p15.33235,4541,275,5341,040,0801.22721 NKD2 yes
75p13.3 31,589,91332,485,015 895,102 1.8866 partial
85q31.3140,456,938140,607,613150,6751.214 18 yes
9 6p25.3 295,1811,258,151 962,9701.26 107 FOXQ1yes
10 7p15.2 26,923,158 26,995,43272,2741.0298 HOXA9, HOXA11no
11 7q21.3 96,455,43797,489,267 1,033,830 1.619 12ASNS, OCMpartial
12 7q22.199,118,939 99,165,774 46,8351.5351No
14 11q12.156,705,90556,850,264144,3591.2724AGTRL1, TNKS1BP1No
1511q13.3–13.469,334,12970,823,1611,489,0321.01612FGF3, CCND1, CTTNNo
16 12p13.324,332,3024,468,861136,5591.0654FGF23, FGF6No
1812q14.1 56,306,77856,477,913171,1354.414 12CDK4, OS-9, CENTG1, SASNo
1912q15 66,881,51067,951,0611,069,5512.88212 MDM2, RAP1BNo
2013q22.3–31.1 76,477,88078,071,4711,593,5911.2047EDNRB partial
2116q13 55,674,61156,373,793699,1821.032 20GPR56No
22 16q22.1 66,420,08366,520,918 100,8351.3056No
2319p13.2–13.13 11,546,52412,601,1821,054,6581.083 33partial
2422q13.1–13.239,131,32639,950,686 819,3601.172 12MKL1, EP300No
295q33.3–34 159,762,714160,690,553 927,839
306p25.3 295,181430,239 135,058
319p24.1 4,850,8845,547,670 696,786
32 9p21.321,471,141 21,998,963 527,822
22.15 266 CDKN2A, CDKN2Bpartial
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Heterogeneity of primary cutaneous melanoma is well appreci-
melanomas by unsupervised methodologies [27,28,29,30,31]. So-
matic mutation frequencies of BRAF and NRAS, two signature
oncogenes in melanoma, exhibit differential preferences for
primary tumors arising from different anatomic sites associated
with varying UV exposure histories . Through the applica-
tion of a classification algorithm, we now provide the genome-
wide evidence that distinct patterns of copy number aberrations
exist in metastatic melanomas. Moreover, these genomic features
MCR##Cytobands Start EndWidth (bp)Peak## Tumors## GenesCandidates known CNV?
21.0334 GLIPR1 No
35 12q23.195,176,246 95,849,465673,219
36 14q32.12 91,117,10991,405,833 288,724
21.0236 GAS8 yes
3819q13.42 60,820,660 60,855,62534,965
39 22q13.31 44,560,277 44,767,323 207,046
*MCRs were mapped to regions of known copy number varation.
Table 1. Cont.
Figure 2. Integrative genomics identify high-confidence metastasis candidate melanoma genes. (A) Flow chart of integrating copy
number and expression analysis to compare primary and metastastic melanoma genomes. (B) Whole genome q-value profiles based on Fisher’s Exact
Test between primary and metastastic melanomas. X axis coordinates represent genomic map position and Y axis indicates q-value log10 of Fisher’s
Exact Test between primary and metastastic melanomas at each R-segment.
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may potentially stratify patients into cohorts with different clinical
outcome, which is not surprising given that melanoma metastasis
have also been classified transcriptionally into poor and good
outcome subgroups . While we recognize that our sample set
was not sufficiently large to draw conclusion on the prognostic
significance of these genomic subclasses, the provocative data
does suggest that genomics-based prognostic biomarkers can be
defined and, therefore, should encourage comprehensive genome
characterization of large clinically annotated patient cohorts as a
first step toward identification of such DNA-based biomarker(s)
for patient stratification.
The importance of recognizing and accounting for tumor
heterogeneity in molecular studies is highlighted by the observa-
tion that a progression correlated pattern of Survivin expression
was only evident when thin and thick cutaneous melanomas were
stratified in the analyses of Survivin TMA-IHC data. Along this
line, it is intriguing that the Survivin expression difference between
thin primary and lymph node metastases was not preserved
Table 2. The integration of copy number and expression analysis to compare primary and metastastic melanoma genomes
identifies 30 unique genes amplified and overexpressed in metastastic melanoma compared to primary melanoma.
Metastasis by SAMGene
Start (bp)End (bp) Width (bp)Probes Rel Expq value SymbolGene IDDescription
1189,881,478 193,480,0763,598,598 219918_s_at 2.63 0.00 ASPM259266 asp-like, microcephaly
6 29,678,435 30,145,591467,156216229_x_at 2.02 0.00HCG2P780867HLA complex group 2 pseudogene 7 yes
6 31,649,13231,733,853 84,721 212384_at 2.080.00 BAT1 7919 HLA-B associated transcript 1yes
7 6,503,37110,915,7384,412,367 209507_at 2.370.00 RPA36119 replication protein A3, 14kDa No
7 16,413,35117,606,374 1,193,023 217979_at2.75 0.00 TM4SF1327075 transmembrane 4
superfamily member 13
7 21,258,989 24,511,8063,252,817 203820_s_at2.60 0.00IMP-3 10643IGF-II mRNA-binding protein 3No
7 26,009,67326,923,158 913,485 204362_at2.35 0.00SCAP2 8935 src family associated phosphoprotein 2 No
7 26,009,67326,923,158 913,485201091_s_at2.21 0.00CBX3 11335 chromobox homolog 3No
7 31,602,638 38,147,4716,544,833 204051_s_at3.03 0.00 SFRP46424secreted frizzled-related protein 4 No
731,602,638 38,147,4716,544,833 212792_at2.310.00 KIAA0877 23333KIAA0877 proteinNo
7 31,602,63838,147,4716,544,833 202904_s_at2.070.00 LSM523658LSM5 homolog, U6 small nuclear RNA
7 55,852,99455,943,50790,513205194_at 2.360.00 PSPH 5723 phosphoserine phosphataseNo
7 64,310,010 72,299,7067,989,696 213460_x_at2.180.00 WBSCR20C 55695Williams Beuren syndrome
chromosome region 20C
7 73,393,70176,470,379 3,076,678213670_x_at2.25 0.00 WBSCR20B155400 Williams-Beuren Syndrome critical
region protein 20, copy B
7 89,659,035 96,294,9736,635,938 204873_at 2.030.00 PEX15189peroxisome biogenesis factor 1 No
7 89,659,03596,294,973 6,635,938209278_s_at 5.370.00TFPI27980tissue factor pathway inhibitor 2No
7 89,659,035 96,294,9736,635,938204688_at2.010.00 SGCE8910sarcoglycan, epsilonyes
789,659,03596,294,973 6,635,938215483_at2.260.00AKAP9 10142A kinase (PRKA) anchor
protein (yotiao) 9
789,659,03596,294,9736,635,938212094_at 2.440.00 PEG1023089paternally expressed 10 No
796,294,97397,126,111831,138 205047_s_at2.510.00 ASNS 440asparagine synthetaseyes
797,586,13899,133,3311,547,193213479_at 3.27 0.00NPTX2 4885 neuronal pentraxin IINo
7 99,463,59899,609,889 146,291220954_s_at 2.370.00PILRB 29990paired immunoglobin-like type 2
7 100,130,869101,618,306 1,487,437205586_x_at 2.030.01 VGF7425 VGF nerve growth factor inducibleNo
7 105,325,416106,944,473 1,619,057206529_x_at 2.25 0.00SLC26A4 5172 solute carrier family 26, member 4No
7 106,993,757107,808,465 814,708202843_at2.19 0.00 DNAJB94189 DnaJ (Hsp40) homolog,
subfamily B, member 9
7 109,897,338112,000,722 2,103,384202147_s_at 2.050.00IFRD1 3475interferon-related
developmental regulator 1
7115,794,692 116,151,838357,146203510_at3.15 0.00MET4233met proto-oncogene No
17 61,638,66662,311,370672,704 213093_at 2.120.00PRKCA 5578protein kinase C, alphaNo
1773,732,31475,521,0301,788,716202095_s_at 2.200.00 BIRC5 332baculoviral IAP repeat-containing 5
*MCRs were mapped to regions of known copy number varation.
Integrative Melanoma Genomics
PLoS ONE | www.plosone.org7May 2010 | Volume 5 | Issue 5 | e10770
Figure 3. Functional and histopathologic characterization of high-confidence metastasis candidate genes. (A) Knockdown of 6
candidate metastasis genes by siRNA inhibited 1205LU Boyden Chamber cell migration. Data represents the average of three replicates. Statistical
significance was assessed using a Tukey-Kramer Multiple Comparisons Test, in which each target was compared to the effect of a non-targeting siRNA
pool. *=p,0.05; **=p,0.01; ***=p,0.001. The level of target mRNA knockdown is shown in Supplemental Figure S5. (B) Exogenous expression of
ASPM enhanced invasion through Matrigel compared to empty vector control on a modified Boyden Chamber assay. Representative images of
Boyden chamber assays are shown on the right. Data represent three independent experiments. (C) Immunohistochemical survey of Survivin on a
melanoma progression tissue microarray. Survivin expression was scored as 0–3+ (see Methods). Percent of TMA cores scored 0 to 3+ for major
histopathlogical categories (benign nevi, thin and thick primary cutaneous melanomas, lymph node and visceral metastases) are plotted with p
Integrative Melanoma Genomics
PLoS ONE | www.plosone.org8 May 2010 | Volume 5 | Issue 5 | e10770
between thin primary and visceral metastases (x2p=0.0697,
Figure 3C). This is unexpected if one assumes that visceral
metastases progress from lymph node metastases, as suggested by
the traditional linear model of melanoma progression. Instead, this
data raises the possibility that metastatic spread to lymph nodes
and to visceral organs might be driven by distinct molecular
pathways. Interestingly, Survivin and HGF/MET, both repre-
sented in our IGC-derived metastasis list, were found to cooperate
in promoting lymph node and lung metastases in a mouse
transgenic model . Our observation that the expression of
metastases genes, such as Survivin, appears to be significantly
altered when comparing thin and thick primary cutaneous
melanomas also highlights the potential need to sub-stratify
melanomas based on thickness in future IGC analyses, as these
might represent two genetically- and clinically-distinct disease
The integrative approach utilized here where two clinical
subtypes (primary vs. metastases) were compared on both genome-
wide copy number and expression levels is a productive
methodology for identifying metastasis-relevant genes, as reflected
by our ability to define a short list of candidates that included
MET receptor tyrosinase kinase and BIRC5. The veracity of IGC
was further supported by validation of 6 additional candidates
selected from the list based on their cancer-relevant roles in other
tumor types. U3 small nucleolar ribonucleoprotein (IMP3) and
protein kinase C alpha (PRKCA) had been previously linked to
aggressiveness and metastasis in a variety of tumor types, including
breast, colon, renal cell, lung, ovarian, and hepatocellular cancer
(AKAP9), replication protein A3 (RPA3) and SRC kinase
associated phosphoprotein 2 (SKAP2) were enlisted into invasion
assay since, although they had been linked to breast, lung, head
and neck and pancreatic cancer [39,40,41], these genes have not
been previously associated with tumor invasion. By virtue of its
unbiased nature, IGC also identified unexpected candidates, such
as tissue factor pathway inhibitor 2 (TFPI2) and secreted frizzled-
related protein 4 (SFRP4). TFPI2 is a serine protease inhibitor in
the extracellular matrix that is known to be heavily methylated in
an assortment of cancers, including melanoma . While its
expression was low in majority of the samples, TFPI2 was gained
and overexpressed in 3 out of 72 metastases in our dataset.
Similarly, SFRP4, a member of the secreted frizzled-related
protein family and a negative regulator of the Wnt pathway that is
frequently epigenetically silenced in various tumor types [43,44]
was observed to be gained and overexpressed in 4 of 72 of
metastastic melanomas in this study. These patterns suggest
unique subgroups of melanomas in which these two genes might
serve pro-metastasis roles that are presently unrecognized, much
like the example of MITF, a lineage transcription factor that is
commonly downregulated during melanoma progression except in
a specific subset where MITF is amplified .
Although ASPM was part of a signature of 254 genes predictive
of metastasis , a functional role for this gene in metastatic
progression is not obvious given its known role as a spindle protein
that regulates brain size with mutations in the gene being
associated with microcephaly . The report of ASPM
knockdown inhibiting glioblastoma cell growth and neural stem
cell self-renewal  point to proliferative and survival roles for
this gene. Here we uncovered a pro-invasive role for ASPM in
melanoma cells. In this regard, it is worth noting that ASPM maps
to 1q32, a region that is commonly gained in various solid tumors,
including melanoma  and metastatic squamous cell carcino-
mas of the lung . Importantly, 1q gain has been associated
with aggressive disease and metastasis in renal clear cell
carcinomas , hepatocellular carcinoma  and papillary
thyroid carcinoma . In primary gastric adenocarcinoma, 1q32
status has been significantly correlated with lymph node status
, and 1q32 gain has been reported to be a prognostic marker
in a subset of treatment refractory breast cancers . In
summary, these genomic data and preliminary functional
characterization on a short list of metastasis candidates encourage
their enlistment into in-depth functional, clinicopathological and
Materials and Methods
All research involving human participants was approved by the
institutional review boards and granted an exemption. Informed
written patient consent was obtained for all tissues used in this
Melanoma samples and DNA extraction
The primary and metastatic melanoma samples analyzed in this
study were obtained from three centers: The Medical University of
Vienna, Austria (Supplemental Table S1), the Memorial Sloan
Kettering Cancer Center of New York, NY and the Brigham and
Women’s Hospital, Boston, MA (Supplemental Table S2).
Complete sample and clinical annotation can be found in
Supplemental Table S1 and S2. Frozen tissue sections were
prepared and manually macrodissected to obtain an enrichment of
greater than 80% tumor cellularity. Genomic DNA from tissue
and cell lines was extracted using DNeasy Tissue Kit (Qiagen,
Valencia CA). All tumor sample DNA from the Vienna series were
subjected to whole genome amplification (WGA) using the REPLI-
g Kit (Qiagen) to obtain enough material for aCGH hybridization,
while none of the Memorial Sloan Kettering Cancer Center
samples and Brigham and Women’s Hospital samples was
subjected to WGA.
Array CGH profiling on oligonucleotide microarrays
Genomic DNA was fragmented and random-prime labeled as
described previously  and hybridized to oligonucleotide arrays
containing 22,500 elements designed for expression profiling
(Human 1A V2, Agilent Technologies). All data is MIAME
compliant, and the raw data has been deposited in to GEO under
super-series accession #GSE7606. Using NCBI Build 35, 16,097
unique map positions were defined with a median interval
between mapped elements of 54.8 Kb. Fluorescence ratios of
scanned images were calculated as the average of two paired
arrays (dye swap), and the raw profiles were processed to identify
statistically significant transitions in copy number using Circular
Binary Segmentation [11,12]. Each segment was assigned a value
that is the median of the log2 ratios of the spanned probes. The
data were centered by the tallest mode in the distribution of the
segment values. After mode-centering, we defined gains and losses
as log2 ratios $0.15 or #20.15 (66 SD of the middle 75%
quantile of data) and amplification and deletion as a ratio $0.4 or
#20.4 (representing 4 and 94% quantiles), respectively.
values calculated by x2test shown in the table below. Representative cores are shown to demonstrate, from top to bottom, intensity of cytoplasmic
Survivin expression scored as 0 for no staining, 1+ for mild stain intensity, 2+ for moderate stain intensity, and 3+ for intense stain intensity.
Integrative Melanoma Genomics
PLoS ONE | www.plosone.org9May 2010 | Volume 5 | Issue 5 | e10770
High-priority MCRs (see ) were chosen by requiring at least
two samples to show a CNA event and at least one sample to show
an extreme CNA event, defined by thresholds +1 and 21, and size
of the MCRs was less than 2 MB. The MCRs were mapped to
known regions of germline copy number variation (CNV), and
CNV status was noted in Tables 1 and 2. Since well-validated
cancer relevant genes have been known to harbor germline CNVs
 we did not exclude candidates that are resident within these
regions of known CNV.
gNMF and Fisher’s Exact Test
Genomic NMF was applied to the current dataset as previously
described . Briefly, the segmented dataset was first dimension-
reduced by eliminating redundant probe locations and then
transformed to non-negative values. The resultant dataset was a
non-negative matrix, which was subject to gNMF using a custom
software package  and run in MATLAB (The MathWorks,
Inc., Natick, MA). For each factor level two through six, gNMF
was repeated 100 times to build a consensus matrix, and this was
used to assign samples to clusters based on the most common
consensus. The rank K=3 clustering was further tested for
significance by permuting sample labels for secondary samples
independently for each chromosome. One hundred permutations
were subjected to Rank 3 NMF and the consensus matrix was
assessed by cophenetic correlation.
Fisher’s Exact Test was used to identify significantly different
regional gains or losses between primary and metastastic
melanoma. For each aCGH R-segment, each sample was
classified as being copy number normal, gained or lost based on
log2ratio thresholds of +/20.15. Two-by-two contingency tables
tested gained vs. normal and lost vs. normal between primary and
secondary melanoma. Fisher’s Exact Test p-values were corrected
for multiple testing (q-value FDR 10%, ‘‘qvalue’’ package for R,
Survivin immunohistochemistry and tissue microarrays
The melanocytic tumor progression TMA was as described
previously . TMA blocks were sectioned at ,4mm and antigen
was unmasked in retrieval buffer (0.01M citrate buffer, pH6.0)
using a pressure cooker at 125uC. Tissue sections were incubated
with a 1/500 dilution of primary anti-Survivin polyclonoal
antibody NB500-201 (Novus Biologicals, Littleton, CO) for
2 hours at room temperature followed by StreptAvidin- Biotin
labeling. Signal was visualized using Alkaline Phosphatase with
Permanent Red substrate (DAKO, Carpinteria, CA). L.M.D. and
R.M.N. scored each core by visual microscopic inspection as
follows: 0+ for no staining and no background; 1+ for weak blush
of cytoplasmic staining; 2+ for moderately intense granular
cytoplasmic staining; 3+ for markedly intense granular cytoplasmic
staining. Most of the cores showed expression in more than 75% of
the tumor cells; therefore the staining was graded on intensity
rather than % of positive tumor. Statistical comparisons of
Survivin IHC staining were performed using a Chi Square test
corrected for multiple testing.
Invasion assays in Boyden Chamber
For exogenous expression of ASPM in WM3211 and WM115
cells, a GatewayH (Invitrogen, Carlsbad, CA) entry clone
containing the ASPM cDNA variant BC034607 was obtained
from the Center for Cancer Systems Biology (DFCI) and was
recombined into pLenti6 V5/DEST (Invitrogen) for virus
production and cell transduction following the manufacture’s
suggestions. For RNAi experiments, 1205LU cells were transfected
with Dharmacon SMART siRNA oligo pools (Thermo Fisher
Scientific, Lafayette, CO) designed against ASPM, AKAP9, IMP3,
PRKCA, RPA3, or SKAP2, as described previously . Boyden
Chamber assays were utilized to assess the invasiveness of tumor
cells, as one measure of metastatic propensity, following the
manufacture’s suggestions (BD biosciences, San Jose, CA). Briefly,
WM3211, WM115, or 1205LU cells were trypsinized, rinsed twice
with PBS, resuspended in serum-free RPMI 1640 medium. Cells
were then seeded at a density of 2.56104cells/ well in triplicate in
96-well chamber format for ASPM overexpression studies, or at
1.56105cells/well in triplicate in 24-well chamber format for
siRNA experiments, and the cells were placed in the 10% serum-
containing media that served as a chemo-attractant. In parallel,
the same number of cells was plated in a same area in regular cell
culture plates and grown for the same length of time to serve as
input control. Following 20 hrs (ASPM overexpression) or 16 hrs
(siRNA experiments) of incubation, cells that had migrated
through the chamber were fixed in 10% formalin in PBS, stained
with crystal violet and photographed, and cell numbers were
counted using an Adobe Photoshop (Adobe Systems, Inc., San
Jose, CA) add-on computer program. For analyses of Met induced
invasion, boyden chambers were seeded with 56104iMet tumor
cells in serum-free media. Chambers were placed in chemo-
attractant (media containing 10% serum) without and with 50 ng/
ml recombinant HGF and incubated for 24 hrs. Invasive cells
were visualized by staining with crystal violet.
relative to the metastatic melanoma genome. Based on the
number of breakpoints of each sample’s segments exceeding +/
20.15 log2 ratio threshold, the genome instability difference
between two groups was calculated using a t test.
Found at: doi:10.1371/journal.pone.0010770.s001 (1.17 MB TIF)
The primary melanoma genome is less altered
metastasis patients from all three subgroups; K1 and K2 groups
show significantly worse event-free survival than K3 (p=0.0034).
Age and sex are not correlated with the three subgroups, which
was indicated by non-enrichment using Fisher’s Exact Test (data
not shown). The numbers of male patients and female patients
were tested for enrichment in all three subgroups using Fisher’s
Exact Test; similarly, patients were divided into young and old
groups by median age and tested for enrichment in all three
Found at: doi:10.1371/journal.pone.0010770.s002 (1.17 MB TIF)
KM event-free survival curve for 25 melanoma
genes. Genes mapping within metastatic melanoma MCR
boundaries were analyzed in GeneGo software (St Joseph, MI)
and a significant number was represented in the MetaCoreTM
G1/S network(p,0.01). The
p16INK4A and p15INK4B, all of which were deleted in
metastases (blue circles), and CDK4 and MDM2 were both
amplified in metastatic melanoma (red circles). ARHI and GAD45
alpha also mapped to regions of gain/amplification in metastatic
melanoma (red circles). A green line denotes activation and red
and blue lines signify inhibition of activity. For example, p14ARF
inhibits MDM2, which, in turn, activates Ubiquitin and inhibits
Found at: doi:10.1371/journal.pone.0010770.s003 (1.17 MB TIF)
Metastatic Melanoma MCRs were enriched for G1/S
chambers were seeded with 56104 iMet tumor cells in serum-
free media. Chambers were placed in chemo-attractant (media
containing 10% serum) without and with 50 ng/ml recombinant
Met activation promotes cell invasion. Boyden
Integrative Melanoma Genomics
PLoS ONE | www.plosone.org 10May 2010 | Volume 5 | Issue 5 | e10770
HGF and incubated for 24 hrs. Invasive cells were visualized by
staining with crystal violet.
Found at: doi:10.1371/journal.pone.0010770.s004 (1.17 MB TIF)
knockdown of ASPM, AKAP9, IMP3, PRKCA, RPA3 and
SKAP2 in 1205LU cells following transfection of siRNA oligo
pools. % mRNA knockdown is relative to transcript levels after
transfection of a non-targeting siRNA pool (see methods). Ranges
in knockdown levels reflect standard deviations from three
Found at: doi:10.1371/journal.pone.0010770.s005 (1.17 MB TIF)
Quantitative PCR assessment of levels of mRNA
melanoma samples from Medical University of Vienna, Austria.
Found at: doi:10.1371/journal.pone.0010770.s006 (0.04 MB
Sample annotation and clinical Information on
ing and the Brigham and Women’s Hospital.
Found at: doi:10.1371/journal.pone.0010770.s007 (0.03 MB
Annotation of samples from Memorial Sloan Ketter-
We thank Dr. R.A. DePinho for critical reading of the manuscript and
members of the laboratories for helpful discussion. We thank Dr. David
Hill of Human ORFeome at DFCI for the ASPM expression clone.
Conceived and designed the experiments: OK CN CB LC. Performed the
experiments: OK CN BF MW KLS LNK YX CB. Analyzed the data: OK
CN BF RMN MB MW KLS LNK YX CCC SRG LD CB LC.
Contributed reagents/materials/analysis tools: SR TRG SNW. Wrote the
paper: OK CN LC.
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PLoS ONE | www.plosone.org 12 May 2010 | Volume 5 | Issue 5 | e10770