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Predicting biomarkers for ovarian cancer using gene-expression
microarrays
TR Adib
1,4
, S Henderson
1,4
, C Perrett
2
, D Hewitt
1
, D Bourmpoulia
1
, J Ledermann
3
and C Boshoff*
,1,3
1
Cancer Research UK Viral Oncology Group, Wolfson Institute for Biomedical Research, University College London, Cruciform Building, Gower Street,
London WC1E 6BT, UK;
2
Department of Obstetrics and Gynaecology, University College London, Cruciform Building, Gower Street, London WC1E 6BT,
UK;
3
Department of Oncology, University College London, London WC1E 6BT, UK
Ovarian cancer has the highest mortality rate of gynaecological cancers. This is partly due to the lack of effective screening markers.
Here, we used oligonucleotide microarrays complementary to B12 000 genes to establish a gene-expression microarray (GEM)
profile for normal ovarian tissue, as compared to stage III ovarian serous adenocarcinoma and omental metastases from the same
individuals. We found that the GEM profiles of the primary and secondary tumours from the same individuals were essentially alike,
reflecting the fact that these tumours had already metastasised and acquired the metastatic phenotype. We have identified a novel
biomarker, mammaglobin-2 (MGB2), which is highly expressed specific to ovarian cancer. MGB2, in combination with other putative
markers identified here, could have the potential for screening.
British Journal of Cancer (2004) 90, 686 –692. doi:10.1038/sj.bjc.6601603 www.bjcancer.com
&2004 Cancer Research UK
Keywords: Ovarian cancer; gene expression microarrays; biomakers; MGB2
Ovarian cancer is the leading cause of death from gynaecological
malignancy, with an estimated 24 000 and 6800 new cases in the US
and UK, respectively, during 2001 (Greenlee et al, 2001; Swerdlow
et al, 2001). Early-stage disease is largely asymptomatic, and most
patients are diagnosed when disease has spread beyond the pelvis
with an associated 5-year survival of less than 20% (Ozols, 2002).
This is partly due to the lack of reliable screening strategies. While
around 90% of women with advanced disease have elevated serum
CA125, this marker alone is neither sufficiently sensitive nor
specific for use as a screening tool (Menon and Jacobs, 2001).
Despite new cytotoxic regimens, survival has remained largely
unchanged over the past 20 years. Identification of new molecular
signatures of early disease is a key goal of ovarian cancer research.
Gene-expression microarray (GEM) profiles have previously
been used to compare the expression profile of ovarian cancer with
that of the normal ovary (Ono et al, 2000; Welsh et al, 2001). We
extended this approach by using a more extensive set of probes
(Affymetrix U95Av2), and also characterised metastatic disease in
a search for molecular markers of progression. We investigated the
potential specificity of a number of putative biomarkers by
examining their expression in a panel of other epithelial tissues
and tumours.
MATERIALS AND METHODS
Ovarian tissue samples
Four snap-frozen normal ovarian samples, and six pairs of primary
and omental serous adenocarcinoma (Stage IIIC) from the same
individuals were collected at the time of surgery at the University
College Hospitals NHS Trust. The six paired samples of primary
and secondary ovarian cancer were taken at the time of primary
surgery prior to chemotherapeutic intervention. The normal
ovarian samples were taken at the time of surgery for benign
disease. H&E-stained sections were examined and verified
histopathologically to be stage III serous adenocarcinomas. All
samples comprised at least 70% tumour, except one omental
sample which had 5% tumour content. The normal ovarian
samples were verified to be free of any pathology, including benign
cysts. Ovarian epithelium was macrodissected from the underlying
stroma, and was used for subsequent analysis. For the real-time
quantitative RT–PCR data, we used, in addition, three serous
tumours of low malignant potential (LMP). All patients gave
preoperative informed consent, and the study was approved by the
ethics committee of the Royal Free and University College Medical
School.
RNA sample preparation
Tissue specimens were homogenised in lysis buffer using a rotary
homogeniser. Total RNA was extracted using the Qiagen RNeasy
s
kit (Qiagen, Valencia, CA, USA), according to the manufacturer’s
instructions. The integrity of the RNA was assessed by ethidium
bromide staining after agarose gel electrophoresis. Total RNA
(20 mg) was used to synthesise double-stranded cDNA using the
Superscript
s
Choice System (Life Technologies), with the template
being used for an in vitro transcription reaction to yield biotin-
labelled antisense cRNA (BioArraytHigh Yield RNA Transcript
Labelling Kit, Enzo Diagnostics, Farmingdale, NY, USA). Frag-
mentation, hybridisation and scanning were performed according
to the Affymetrix GeneChip
s
protocol, using the U95Av2
oligonucleotide microarrays containing B12 000 genes (Affyme-
trix, Santa Clara, CA, USA).
Received 6 August 2003; revised 16 November 2003; accepted 27
November 2003
*Correspondence: Professor C Boshoff; E-mail: c.boshoff@ucl.ac.uk
4
These are both first authors.
British Journal of Cancer (2004) 90, 686 – 692
&
2004 Cancer Research UK All rights reserved 0007 – 0920/04
$
25.00
www.bjcancer.com
Molecular and Cellular Pathology
Real-time quantitative RT–PCR
Four genes, shown in the microarray system to be significantly
upregulated, were selected for analysis with real-time quantitative
reverse transcription–polymerase chain reaction (qRT– PCR).
Primer pairs for each gene were designed using the Primer
Express
s
Software (Applied Biosystems) and selected to have the
same annealing temperature (601C). The primer sequences used
were: mammaglobin B2 (MGB2), forward 50-CCGCTGCAGAGGC-
TATGG-30, reverse 50-CATCAGTCCAAAGTTTTTCAGAGTTCT-30,
kallikrein 6 (KLK6), forward 50-GCGGACCCTGCGACAAG-30,
reverse 50-GGATAAGGACCCCACCACAGA-30; serum amyloid A1
(SAA1), forward 50-TTCTCACGGGCCTGGTTTT-30, reverse
50-GCCTCGCCAAGGAACGA-30and hepsin (HPN), forward
50-GGCTCGAGTCCCCATAATCAG-30, reverse 50-GGTAGCCAG-
CACAGAACATCTTG-30. Primers were tested by conventional
PCR and the PCR products were sequenced prior to real-time
quantitation to confirm the specificity (data not shown). Primer
optimisation and efficiencies were performed prior to the relative
quantitation of the expression of the genes (data not shown).
Real-time qRT–PCR was performed on an ABI PRISM
s
7000
SEQUENCE DETECTOR (Applied Biosystems, Applera UK,
Cheshire, UK) using the SYBR
s
Green PCR Master Mix (Applied
Biosystems) in duplicate, with triplicate nontemplate controls
(NTC) in a 25 ml PCR reaction. cDNA (1 ml) was used in a 25 ml
PCR mixture containing 1 SYBR
s
Green PCR mix (Applied
Biosystems) and 0.3 mMof each primer for all genes, apart
from HPN where 0.6 mMforward and reverse were used. The
cDNAs were amplified by denaturation for 10 min at 951C,
followed by 40 cycles of denaturation at 951C for 15 sec and
annealing extension at 601C for 1 min. The threshold cycle (C
T
),
which represents the PCR cycle at which an increase in reporter
fluorescence above a baseline signal can first be detected,
was calculated as previously described (Heid et al, 1996). The
relative expression of each gene was determined on the basis of the
C
T
value. The housekeeping gene GAPDH was used to normalise
the quantity of cDNA used. Average GAPDH C
T
value was
subtracted from that of each target gene to obtain a DC
T
value,
that is, normalised target gene expression relative to GAPDH. An
average DC
T
value was obtained for each of the five groups of 19
cDNA ovarian samples (normal: n¼5, LMP: n¼3, primary: n¼5
and metastasis: n¼2). Each average DC
T
was also subtracted from
that of a calibrator (average DC
T
value of all the normal samples
which provide the physiological expression of each gene target) to
give the DDC
T
value, that is, normalised target gene expression
in the different groups relative to normal. Since C
T
values
are measured when PCR amplification is still in the exponential
phase, the relative quantitative value can be expressed as 2
DDCT
,
as 2 corresponds to the PCR product doubling in each cycle in
the exponential phase.
Immunohistochemistry (IHC)
IHC was performed for hepsin (HPN) on 30 formalin-fixed,
paraffin-embedded tissues histologically characterised into three
distinct tissue groups: normal ovarian, primary ovarian serous
cystadenocarcinoma and metastatic (omentum), to confirm
expression at the protein level. Sections were cut at 4 mm,
deparaffinised and rehydrated in a series of graded alcohols,
before being heated in a microwave in Tris-EDTA (TE) for 25 min.
Endogenous peroxidase activity was blocked by 10 min incubation
with 0.5% hydrogen peroxide (H
2
O
2
) in methanol, prior to the
application of goat polyclonal primary antibody (1 : 50; Santa Cruz
Biotechnology Inc., Insight Biotechnology Ltd, Wembley, UK) for
1 h at 221C. A biotinylated, anti-goat secondary antibody (1 : 400;
DAKO, Cambridgeshire, UK) was applied for 30 min, after which
slides were incubated with the streptavidin-peroxidase complex
(DAKO) for a further 30 min. Sections were visualised by
application of diaminobenzidine (DAB) substrate (DAKO) for
7 min, followed by a wash in running H
2
O and counterstaining for
2 min with Mayer’s haematoxylin (DAKO). All sections were then
dipped in acid alcohol to remove excess haematoxylin, and
immediately placed in running H
2
O. After dehydration in graded
alcohols, slides ended in xylene, and were mounted in DPX.
Data analyses
Background subtraction, normalisation and expression values of
our data were calculated using the rma algorithm (Irizarry et al,
2003), available as part of the Affymetrix package of the
Bioconductor open-source software library for the statistical
language R (http://www.bioconductor.org). The rma algorithm
differs from the standard Affymetrix algorithm in a number of
ways; most importantly, the data are quantile –quantile normalised
at the probe level, prior to calculation of a final expression
summary from the positive match (or PM) probes alone. This
algorithm improves measurement precision, reducing the varia-
tion between replicate data, particularly of low-expressed genes.
Differential expression was calculated using the Benjamini –
Hochberg step-down false-discovery rate (FDR) algorithm set to
0.05, implemented using the Bioconductor multtest package. This
algorithm adjusts P-values upwards to discount the effects of
multiple testing. It is a less-conservative adjustment (admitting
more errors) than the more common, but here impractically
conservative, Bonferroni or Holm algorithms.
Comparative GEM data
Publicly available GEM data from normal epithelia-rich tissues
were obtained from the Genomics Institute of the Novartis
Research Foundation expression atlas (http://expression.gnf.org).
Prostate and lung adenocarcinoma data were obtained from the
Whitehead Institute Centre for Genomic Research (http://www-
genome.wi.mit.edu/cgi-bin/cancer). Both data sets were in the
original Affymetrix CEL format, and were normalised and analysed
using the same methods as our own data described above.
RESULTS
Four normal ovarian samples, plus six paired primary (stage IIIC)
and secondary samples from the same individual were analysed.
The histopathology of adjacent sections showed that 70 –90% of
primary samples and 90% of metastases (except one sample)
constituted tumour cells. The normal ovarian samples were
verified to be free from any benign pathology. Differences in gene
expression discussed below were all tested for significance using a
FDR of 0.05, using the Benjamini–Hochberg step-down algorithm
(Benjamini and Hochberg, 1995) (see Materials and methods). For
clarity, gene names and abbreviations used throughout the text are
summarised in Table 1.
Primary ovarian disease
There were 421 genes more than two-fold and 118 genes more than
three-fold overexpressed in primary compared to normal tissue.
Figure 1 shows significantly overexpressed genes in primary
ovarian cancer sorted into functional groups. These groups include
genes associated with epithelia and cell–cell contact, such as
secreted phosphoprotein 1 (osteopontin, OP), folate receptor 1,
claudins 3 and 4 (CLDN3, 4), keratins 8, 18 and 19 (KRT8, 18, 19),
and agrin (AGRN). These are also shown in Figure 2B, and could
reflect the epithelial origin of these tumours. Genes involved in cell
division and growth include cyclin D1, cellular retinoic acid-
binding protein 2 and lipocalin 2 (oncogene 24p3). Metastasis and
angiogenesis genes include jagged 2, tumour-associated calcium
Predicting biomarkers for ovarian cancer
TR Adib et al
687
British Journal of Cancer (2004) 90(3), 686 – 692&2004 Cancer Research UK
Molecular and Cellular Pathology
signal transducer 2 (TACSTD2), vascular endothelial growth factor
(VEGF), CD24 antigen and neuromedin U.
We compared the consistency of our data with that of another
study (Welsh et al, 2001), where overexpression of tumour genes in
cancer were ranked according to a combined metric, using normal
ovary as a baseline. The four genes CD24, WAP four-disulphide
core domain 2 (HE4), CD9 and Lutheran blood group (LU) were
found to be the most highly expressed by their method, and are
also highly overexpressed in our own data set (Figure 2A). Where
the data sets overlap, they are highly consistent.
We found that a number of kallikreins (KLKs), a family of
trypsin-like serine proteases that include prostate-specific antigen
Table 1 Summary of the names and abbreviations of genes discussed
Symbol Probe-id Acc ID Full name
ADN 40282_2_at M84256 Adipsin
AGRN 33454_at AF016903 Agrin
BF 35822_at L15702 B-factor
CD24 266_at L33930 CD24 antigen
CD9 39389_at L33690 CD9 antigen
CLDN3 33904_at AB000714 Claudin 3
CLDN4 35276_at AB000712 Claudin 4
CP 39008_at M13699 Ceruloplasmin
EZH2 37305_at U61145 Enhancer of zeste homologue 2
FHL2 38422_at U29332 Four-and-a-half LIM domains 2
FOXO1A 40570_at AF032885 Forkhead box O1A
HE4 33933_at X63187 WAP four-disulphide core
domain 2
HPN 37639_at X07732 Hepsin
IFI-15K 1107_at M13755 Interferon, alpha-inducible
protein, 15 kDa
IGL 33273_at X57809 Immunoglobulin lambda locus
KLK10 36838_at AF055481 Kallikrein 10
KLK11 40035_at AB012917 Kallikrein 11
KLK13 36406_at AA401397 Kallikrein 13
KLK2 217_at S39329 Kallikrein 2
KLK3 1804_at X07730 Kallikrein 3
KLK6 37554_at U62801 Kallikrein 6
KLK7 38143_at L33404 Kallikrein 7
KLK8 37131_at AB008390 Kallikrein 8
KLK18 35766_at M26326 Keratin 18
KRT19 40899_at Y00503 Keratin 19
KRT8 33824_at X74929 Keratin 8
LMNB1 37985_at L37747 Lamin B1
LPL 41209_at M15856 Lipoprotein lipase
LU 40093_at X83425 Lutheran blood group
OP 2092_at J04675 Osteopontin
OPCML 41093_at AF070577 Opioid-binding protein/cell
adhesion molecule like
PEG3 39701_at AB006625 Paternally expressed 3
PLIN 37122_at AB005293 Perilipin
PRAME 157_at U65011 Preferentially expressed antigen
of melanoma
PRSS8 634_at L41351 Protease, serine 8
PTTG1 40412_at AF095288 Pituitary tumour transforming 1
SAA1 33272_at AA829286 Serum amyloid A1
SCGB2A1 41066_at NM_002407 Secretoglobin, family 2A,
member 1
SLP1 32275_at X04470 Secretory leucoctye protease
inhibitor
TACSTD2 575_s_at J04152 Tumour-associated calcium
signal transducer 2
VEGF 36100_at AF024710 Vascular endothelial growth
factor
WISP2 35898_at AF100780 WNT1-inducible signalling
pathway protein 2
The symbol here is the most commonly used abbreviation, usually from Online
Mendelian Inheritance in Man (OMIM). Probe is the unique Affymetrix probe ID. The
Accession is the NCBI Refseq, or reference sequence for this gene.
Laminin, beta 2 (laminin S)
Metastasis
Primary
Normal
Epithelial markersCell division and growth
Metastasis and
angiogenesis
Syndecan 4 (amphiglycan, ryudocan)
Cadherin 1, type 1, E-cadherin
Folate receptor 1 (adult)
Claudin 7
Claudin 4
Claudin 3
Keratin 8
Keratin 18
Keratin 18
Aqrin
Troponin T1
Secreted phosphoprotein 1
Secreted phosphoprotein 1
G-protein-coupled receptor
Interleukin 1 receptor, type I
Eyes absent homolog 2 (Drosophila)
Lipocalin 2 (oncogene 24p3)
Spleen tyrosine kinase
Protein kinase C. iota
CD47 antigen
Topoisomerase (DNA) II alpha 170kDa
E2F transcription factor 3
TTK protein kinase
Pituitary tumour-transforming 1
CDC28 protein kinase regulatory subunit 1B
C-myc-binding protein
SRY/sex-determining region Y1-box 9
Cellular retinoic acid binding protein 2
Cellular retinoic acid binding protein 1
High mobility group AT-hook 1
Cyclin D1
Tumour-associated calcium signal transducer 1
Jagged 2
CD24 antigen
Vascular endothelial growth factor
Vascular endothelial growth factor
Tumour-associated calcium signal transducer
2
Neuromedin U
Preferentially expressed antigen in melanoma
Hepsin
Matrix metalloproteinase 12
Figure 1 Heatmap showing genes upregulated in serious ovarian
primary and omental metastatic tumours compared to the normal ovary.
Columns represent individual tissue samples; rows represent individual
genes. Red and green cells represent transcript levels for each gene across
the samples above and below the median, respectively. All differences are
significant at the Po0.05 level after multiple testing adjustment (see
Materials and methods).
ABCDE
Normal
Primary
CD24
LU
CD9
HE4
CLDN3
CLDN4
KRT8
KRT18
AGRN
KLK6
KLK8
KLK10
KLK11
VEGF
OP
PRAME
TACSD2
PRSSB
PEG3
WISP2
FHL2
FOXDIA
OPCML
14
12
10
8
Figure 2 Box and whisker plots show expression of selected genes in
both normal (shaded, n¼4) and primary tissues (unshaded, n¼6). The
selected genes are split into five categories (A–E) from left to right: (A) for
comparison with previous ovarian cancer GEM studies, (B) epithelial
markers, (C) kallikrein serine protease family, (D) a selection of previously
described serious ovarian cancer markers and (E) genes with loss of
expression in primary tumours. Box and whisker plots show a central
median line, an interquartile box. Whiskers 1.5 times the interquartile
range, and outliers of these, are shown as circles.
Predicting biomarkers for ovarian cancer
TR Adib et al
688
British Journal of Cancer (2004) 90(3), 686 – 692 &2004 Cancer Research UK
Molecular and Cellular Pathology
(PSA/KLK3), were overexpressed in ovarian cancer. Kallikreins are
being investigated as potential serum markers for adenocarcino-
mas such as prostate (KLK2), breast (KLK10, 12, 13) and ovary
(KLK6, 8, 10, 11) (Diamandis and Yousef, 2002) (Figure 2C). In
addition, we identified KLK7 as overexpressed in ovarian cancer.
We identified a number of overexpressed genes previously
associated with ovarian and other cancers including VEGF,
osteopontin (OP) (Kim et al, 2002), preferentially expressed
antigen in melanoma (PRAME) (Steinbach et al, 2002), TACSD2
(or GA733-1) (Shetye et al, 1989; Szala et al, 1990) and prostasin
(PRSS8) (Mok et al, 2001) (Figure 2D). These may all play a role in
ovarian carcinogenesis.
We identified 172 genes that were three-fold downregulated in
primary ovarian cancer compared to the normal ovary (Figure 3).
Among these were putative tumour suppressors including the p53
mediator paternally expressed gene-3 (PEG-3) (Relaix et al, 1998;
Deng and Wu, 2000), wnt-inducible signalling protein-2 (WISP-2),
a member of the connective tissue growth factor family (Pennica
et al, 1998), and the Rho-associated transcriptional coactivator
four-and-a-half LIM domains 2 (FHL2) (Muller et al, 2002).
However, the recently reported putative tumour suppressor in
ovarian cancer, opioid-binding protein (OPCML), did not appear
to have significant loss of expression in any of the samples studied
here (Sellar et al, 2003) (Figure 2E).
Omental metastasis
While there were 300 genes with more than three-fold difference
between normal and primary samples, there were only 35 equally
large differences between primary and omental metastases, all
greater in metastases. These genes fell into two main groups. These
included serum amyloid A1 (SAA1), which is a marker of
inflammation and immunoglobulin (Ig) lamda-locus, which may
reflect leucocyte infiltration. We found that many of the gene
differences between primary and paired omental samples reflect
Normal Primary Metastasis
Four-and-a-half LIM domains 2
Ortholog of rat pippin
Paternally expressed 3
Tetraspan 5
Transcription factor 21
WNT1-inducible signaling pathway protein 2
Alutathione S-transferase M5
Steroidogenic acute regulatory protein
37 kDa leucine-rich repeat (LRR) protein
Integral membrane protein 2A
ATP-binding cassette, subfamily A (ABC1)
Monoamine oxidase B
Ribonuclease. RNase A family, 4
Extracellular matrix protein 2
Nuclear receptor subfamily 4,
g
roup A, member 1
Figure 3 Genes downregulated in primary and secondary serous ovarian
cancer compared to the normal ovary. All differences are significant at the
Po0.05 level after multiple testing adjustment (see Materials and methods).
Normal Primary Metastasis
Similar to bK246H3.1
Immunoglobulin lambda joining 3
Immunoglobulin lambda locus
Cytochrome C oxidase subunit VIIa polypeptide 1
Alcohol dehydrogenase IB
Fatty acid binding protein 4, adipocyte
D component of complement (adipsin)
Adipose most abundant gene transcript 1
Putative lymphocyte G0/G1 switch gene
Perilipin
Lipoprotein lipase
Glycocaemia 2
Serum amyloid A1
Figure 4 Genes upregulated in omental metastasis relative to normal
ovary and primary ovarian cancer. The predominance of genes associated
with adipocytes reflects the omental background. All differences are
significant at the Po0.05 level after multiple testing adjustment (see
Materials and methods).
6
4
2
0
_2
Expression difference
SAA1 EZH2 PTTG1 LMNB1 ADN LPL PLIN IGL
Sample 1_6
Figure 5 Expression of genes in metastatic and primary ovarian cancer
samples (n¼12, six-paired). The log difference of selected genes between
the paired metastatic and primary ovarian cancer samples is plotted
(metastatic: primary), so that upwards is higher in metastasis and
downwards is lower. The paired P-values were SAA1-0.03, EZH2-0.82,
PTTG1-0.47, LMNB1-0.41, ADN-0.04, LPL-0.01, PLIN-0.01 and IGL-0.01.
Figure 6 Immunohistochemical staining for hepsin. Hepsin stained
normal and malignant epithelial cells. However, a prominent membrane
staining (arrowheads) was only seen in malignant epithelial cells. Pictures
40; inset 100.
Predicting biomarkers for ovarian cancer
TR Adib et al
689
British Journal of Cancer (2004) 90(3), 686 – 692&2004 Cancer Research UK
Molecular and Cellular Pathology
the high adipocyte content in the omentum, such as adipsin,
lipoprotein lipase and perilipin (Figure 4). We found a number of
putative invasion and metastasis predictive genes including
enhancer of zeste homolog 2 (EZH2) (prostate cancer) (Varamb-
ally et al, 2002), pituitary tumour-transforming 1 interacting
protein (PTTG1) and Lamin B1 (LMNB1) (adenocarcinoma)
(Ramaswamy et al, 2003) to be unchanged in primary and
omental specimens (Figure 5). Essentially, the malignant
primary and epithelial tumour are alike. Hepsin, a prostate cancer
serum biomarker, while marginally overexpressed in primary,
was further overexpressed in secondary ovarian cancer tissue.
Immunohistochemistry for hepsin showed staining of both
the normal ovarian surface epithelium (OSE) and malignant
epithelial cells in primary and omental metastasis. The pattern in
malignant cells was distinct, however, being localised to the
membrane (Figure 6).
Validation of array data with qRT – PCR
In order to validate the gene-expression levels from the micro-
array experiments, we performed real-time qRT–PCR with
GAPDH as a control in five normal ovaries, three LMP ovarian
serous cancers, five primary ovarian serous cystadenocarcinomas
and two omental metastases. Figure 3 shows the corresponding
gene expression patterns of four genes: mammaglobin B2 (MGB2),
serum amyloid A1 (SAA1), kallikrein-6 (KLK6) and hepsin (HPN)
for normal ovary, primary and secondary disease on the
microarrays, compared to that on qRT –PCR. Figure 7 demon-
strates that the differential expression pattern and the quantitative
expression level of each of these four genes, as determined
by qRT–PCR, were comparable to those observed with the
microarrays, confirming the reliability of our array expression
data. Notably, qRT–PCR showed high expression of MGB2 and
KLK6 in the LMP samples.
New biomarkers
We identified a potential new biomarker MGB2 with: (a) higher
expression in both primary and metastatic samples compared to
the normal ovary, (b) high gross expression above the 80th
percentile of all genes in primary and metastatic samples and (c)
with high homology (58% amino-acid identity) to the known
serum marker MGB. Figure 8 shows the GEM profile of MGB2
compared to that of six other proteins that have been suggested as
potential biomarkers: HPN (Tanimoto et al, 1997), IFI-15K, KLK6
(Diamandis and Yousef, 2002), CP (Hough et al, 2001), SLPI
(Shigemasa et al, 2001) and HE4 (Schummer et al, 1999) across a
panel of epithelia-rich tumours and tissues. This panel was
comprised of publicly available Affymetrix data from (see
Materials and methods, Data analysis): (a) prostate adenocarcino-
ma (Singh et al, 2002), (b) lung adenocarcinoma (Bhattacharjee
et al, 2001) and (c) the GNF gene expression atlas containing
various primary epithelial tissues (Su et al, 2002). MGB2 in
particular is specific to ovarian adenocarcinoma.
DISCUSSION
We have used oligonucleotide microarrays representing B12 000
genes to investigate the GEM profiles of epithelial ovarian cancer.
A number of groups have previously investigated gene-expression
profiling of ovarian cancer using microarrays (Wang et al, 1999;
Ismail et al, 2000; Ono et al, 2000; Mok et al, 2001; Shridhar et al,
2001; Welsh et al, 2001). These studies have focused on either the
identification of gene products which can serve as ovarian cancer-
specific markers (Mok et al, 2001), or on the initiation and
progression of ovarian cancer (Ismail et al, 2000; Shridhar et al,
2001). This has been achieved by comparing the normal ovarian
epithelium with ovarian cancer samples, as the majority of ovarian
9
8
7
6
5
4
3
2
1
−1
−2
0
Log2 GEM/qRT−PCR
8
7
6
5
4
3
2
1
−1
−2
0
Log2 qRT−PCR/GEM
6
5
4
3
2
1
−1
−2
−3
−4
0
Log2 qRT−PCR/GEM
6
5
4
3
2
1
−1
−2
0
Log2 qRT−PCR/GEM
Normal Primary OmentalLMP Normal Primary OmentalLMP
Normal Primary OmentalLMP
Normal Primary OmentalLMP
qRT−PCR
GEM
KLK6 HPN
SAA1MGB2
Figure 7 Comparison of qRT–PCR (clear bars, normal (n¼5), primary (n¼5), LMP (n¼3) and metastasis (n¼2)) and GEM data (shaded bars, normal
(n¼4), primary (n¼6) and metastasis (n¼6)) for MGB2, SAA1, KLK6 and HPN in normal, primary and omental metastasis samples. Gene-expression
microarray data are in original Log2 scale, and qRT – PCR is single Log2 unit per round of amplification, error bars show the standard deviation. The normal
level is taken as a 0 baseline reference for both.
Predicting biomarkers for ovarian cancer
TR Adib et al
690
British Journal of Cancer (2004) 90(3), 686 – 692 &2004 Cancer Research UK
Molecular and Cellular Pathology
cancers are thought to arise from the ovarian surface epithelium,
which exists as a single layer of cells covering the ovaries. This
layer of cells easily sloughs off at the time of surgery by manual
handling, and it is a challenge to obtain enough cells for use in any
experimental procedures. Researchers have overcome this problem
by firstly using short-term cell culture to increase the number of
cells available (Ismail et al, 2000), secondly by RNA amplification
(Ono et al, 2000) and thirdly by using commercially available RNA
(Welsh et al, 2001). These approaches, however, have drawbacks:
(i) short-term culture favouring the growth of only a subset of
epithelial cells, (ii) RNA amplification leading to unequal
amplification of all RNA transcripts in the cell population and
(iii) the inclusion of a stromal component in commercially
available RNA.
In this study, we used macrodissected epithelium from the
normal ovarian tissue in addition to matched primary and
secondary metastatic serous ovarian adenocarcinomas. Tumour
specimens were verified histopathologically in five cases to
comprise at least 70% tumour. We confirmed a number of ovarian
cancer genes previously identified by GEM, for example, CD24
(Welsh et al, 2001), HE4 (Schummer et al, 1999), PRAME (Ismail
et al, 2000), B-factor (properdin) (Shridhar et al, 2001), and, where
our studies overlap, the data are highly consistent, despite the
difference in methodology.
A large number of genes overexpressed in primary tumours
were associated with epithelia. This might reflect the epithelial
origin of these tumours or a transformed phenotype. HPN, for
example, was marginally overexpressed in both primary and
secondary ovarian cancer tissue, compared to the normal ovary
(approx. two-fold). HPN is a serine protease that has been shown
to be overexpressed in prostate cancer cells, and significantly
correlates with poor clinical outcome (Dhanasekaran et al, 2001).
We investigated hepsin further by performing IHC, and found the
staining to be localised to the epithelial cells, suggesting that it may
be a marker of epithelia rather than of malignancy (Figure 6).
However, there was a notable difference in the pattern with
malignant cells showing a distinct membranous staining, sugges-
tive of heightened secretion.
We found few differences in the gene signature of stage III
primary serous ovarian adenocarcinomas and their corresponding
omental metastases. Various studies have shown that metastatic
signatures within primary tumours are predictive of subsequent
metastasis. We found that, within the stage III serous ovarian
adenocarcinomas, a number of predictive genes including EZH2
(Varambally et al, 2002), PTTN and Lamin-B (Ramaswamy et al,
2003) are overexpressed in primary, at least as highly as in omental
metastases (Figure 4). This supports the notion that most tumour
cells in advanced primary ovarian lesions have acquired the
genetic signature enabling invasion and metastasis. A GEM study
comparing stage Ia (no ascites) with Ic (ascites, that is, metastatic
spread) might identify genes that infer the propensity of ovarian
tumour cells to metastasise, although it would be challenging to
obtain sufficient material.
We identified a potential new biomarker MGB2, which is
significantly overexpressed in primary and metastatic ovarian
cancer compared to the normal ovarian tissue. This gene is part of
the uteroglobin family, and is also overexpressed in endometrioid
endometrial carcinomas (Moreno-Bueno et al, 2003), and the
axillary lymph nodes of metastatic breast cancers (Ooka et al,
2000). A preliminary qRT– PCR analysis of MGB2 confirmed this
finding and further demonstrated high expression in LMP samples
(n¼3). LMP tumours are a distinct subtype of epithelial ovarian
cancer thought to be as an intermediate stage between clearly
benign and malignant tumours. No biomarker to date is sufficiently
specific for screening and monitoring disease progression in LMP
tumours. MGB2 warrants further investigation in this subgroup.
The only widely used ovarian cancer marker CA125 lacks
specificity (CA125 or MUC16 is not present on the U95Av2 array).
Within the panel of data available to us, MGB2 appears to be a
specific biomarker for ovarian tumours with low expression in
most normal epithelial tissues and prostate and lung tumours. This
survey was far from exhaustive, relying on available published
Serous ovarian Adc
Serous ovarian Adc
Serous ovarian Adc
Serous ovarian Adc
Serous ovarian Adc
Serous ovarian Adc
Omental metastasi
s
Omental metastasi
s
Omental metastasi
s
Omental metastasi
s
Omental metastasi
s
Omental metastasi
s
Normal ovary
Normal ovary
Normal ovary
Normal ovary
Lung Adc
Lung
Lung Adc
Lung Adc
Prostate Adc
Prostate Adc
Prostate Adc
Prostate Adc
Adrenal gland
Kidney
Kidney
Kidney
Liver
Pancreas
Pancreas
Pancreas
Pituitary gland
Pituitary gland
Spleen
Spleen
Thymus
Liver
Thyroid
Thyroid
Trachea
Trachea
Uterus
Spleen
HPN
IFI-15
K
CP
SLPI
HE4
MGB2
KLK6
Figure 8 Gene-expression profile of putative biomarker MGB2 in
ovarian serous adenocarcinoma and a panel of other tissues. Comparison
with six previously described biomarkers HPN, IF1-15K, KLK6, CP, SLPI and
HE4. Serious ovarian AdC ¼primary serous ovarian adenocarcinoma,
omental metastasis ¼serious ovarian omental metastasis, lung AdC ¼lung
adenocarcinoma, prostate AdC ¼prostate adenocarcinoma. Adrenal gland,
kidney, liver, pancreas, pituitary gland, lung, spleen, thyroid, trachea and
uterus, all represent the corresponding normal tissue specimens.
Predicting biomarkers for ovarian cancer
TR Adib et al
691
British Journal of Cancer (2004) 90(3), 686 – 692&2004 Cancer Research UK
Molecular and Cellular Pathology
GEM data. The screening and selection of candidates for further
serological study will benefit from more publicly available data, in
particular breast cancer. The recent development of multiplex
techniques to screen sera for combinations of biomarkers shows
promise for cancer screening (Petricoin et al, 2002). A combina-
tion of biomarkers including MGB2 rather than a single biomarker
alone is more likely to give a specific signature for epithelial
ovarian carcinoma. Our study demonstrates that GEM studies are a
practical and economical prelude to streamline candidate genes for
larger serological studies.
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