1. health and fitness
2. disease
3. cancer

Current Molecular Medicine 2010, 10, 249-257
249
Tumor Markers: The Potential of “Omics” Approach
M. Sikaroodi1, Y. Galachiantz2 and A. Baranova*,1,3
1
Molecular & Microbiology Department, College of Science, George Mason University, Fairfax, VA 22030
2
3
USA; Biomedical Center, St. Petersburg, Russian Federation; Research Center for Medical Genetics,
RAMS, Moskvorechie Str., 1, Moscow, Russian Federation
Abstract: Tumor markers are the molecules that indicate the presence or prognosis of malignancy. Most often,
tumor markers are produced by the cancer tissue itself. Many of them could be secreted into the body fluids in
small quantities. Thus, tumor markers could be useful for early diagnostics of primary tumors and relapsed
disease, as well as for determining tumor prognosis and predicting likely response of the tumor to therapy.
Tumor markers are part of the clinical routine. Nevertheless, lack of sensitivity and specificity precludes routine
usage of single tumor markers in population-based screening. Shortcomings of single tumor markers could be
solved by parallel evaluation of multiple tumor markers that can perform with required certainty. Genome and
proteome-wide approaches currently lead to identification and initial characterization of hundreds new tumor
marker candidates. Most prominent of such methods are serological analyses of recombinant cDNA
expression libraries (SEREX), 2-dimensional polyacrylamide gel electrophoresis, mass spectrometry, as well
as protein and DNA microarrays. Last but not the least is a computational approach allowing high-throughput
detection of tumor marker candidate genes in publicly available datasets. Listed approaches are critically
discussed in this review as well as the most crucial tumor-related findings. Finally, a perspective on the future
of tumor markers in the tailored medicine is given.
Keywords: Tumor markers, marker panels, mass spectrometry, 2D–PAGE, SEREX, bioinformatics, differential
display.
INTRODUCTION
Early detection of malignancy is the key to effective
treatment of cancer. With the recent advances in
cellular and molecular biology, it seems possible to
identify markers that will permit diagnosis of cancer in
its early, non-symptomatic stages when complete
eradication of malignancy is still possible [1]. Patients
with advanced stages of cancer also benefit from the
introduction of tumor markers into the standard clinical
routine as such markers allowing direct monitoring of
disease progression and therefore a more effective
treatment and individualized therapy [2]. Although,
available tumor markers are not sensitive or specific
enough for population-based screenings for the
presence of malignancy, they still hold the promise of
early detection and potential aid for the treatment.
Consequently, the need for discovery of more specific
biomarkers, and/or the creation of tumor marker panels
to improve the detection and clinical management of
the disease is an important avenue in cancer research.
WHAT ARE TUMOR MARKERS?
Tumor markers refer to substances, usually proteins
or parts of proteins, which are produced by the body in
response to cancer growth or by the tumor itself, and
are detectable in serum, urine or other body fluids and
tissues. The best way to detect a tumor in its early
*Address correspondence to this author at the Molecular Biology and
Microbiology, David King Hall, MSN 3E1, George Mason University,
Fairfax, VA, 22030, USA; Tel: 703-993-42-93;
E-mail: [email protected]
1566-5240/10 $55.00+.00
stages is by the application of a simple, minimally
invasive test, avoiding unnecessary biopsies, as is
common in current practices of oncology. Table 1
shows examples of tumor markers currently being used
in clinical practice.
Table 1.
Some Tumor Markers Currently Being Used in
Clinical Routine (Combined According to
www.cancer.org)
Tumor
Marker
Description
Cancer Type
PSA
Prostate-Specific Antigen
Prostate
PAP
Prostatic Acid Phosphatase
Prostate
CA 125
Cancer Antigen
Ovarian
CEA
Carcinoembryonic Antigen
Colorectal
AFP
Alpha-Fetoprotein
Liver/germ cell
HCG
Human Chorionic
Gonadotropin
Uterine
CA 19-9
Cancer Antigen
Colorectal/Pancreatic
CA 15-3
Cancer Antigen
Breast/ovary/lung/prostate
CA 27-29
Cancer Antigen
Breast
LDH
Lactate Dehydrogenase
Most tumor types
NSE
Neuron Specific Enolase
Neuroblastoma/lung (SC)
The majority of these markers are not specific to a
particular type of cancer, with the exception of PSA.
This marker allows for the detection of early stages of
prostatic carcinoma, but is also elevated in benign
inflammatory diseases of the prostate. This example
© 2010 Bentham Science Publishers Ltd.
250
Current Molecular Medicine, 2010, Vol. 10, No. 2
Sikaroodi et al.
illustrates that tumor markers can be produced by
normal tissue, thus malignancy detection is usually
based on the concentration of the marker exceeding a
defined threshold. Unfortunately, the process of
transition between normal and malignant states is not
clear as tissue exposure to an inflammatory
environment may serve as a promoter of proliferation
for dormant pre-malignant cells [3] as well as for de
novo “field cancerization” [4]. Consequently, tumor
markers’ elevation often accompanies common
inflammatory processes such as prostatitis [5],
pancreatitis [6], urogenital infections [7], and
bronchopulmonary injury [8] are common in normal
pregnancy [9]. The relative magnitude in marker
elevation cannot serve as solid ground for differential
diagnosis, as “normal” levels of tumor marker
expression are subject to polymorphism. For example,
serum PSA levels are associated with a G/A
polymorphism at androgen responsive element 1
(ARE1) of PSA and/or the CAG repeats in exon 1 of
the androgen receptor (AR) gene [10, 11]. Thus, it is
unlikely that sensitivity and specificity of tumor
detection could be improved by the finding of a solitary
“ideal” marker, or by collecting more data about
existing ones. Most likely, the future is in tumor
detection panels encompassing from tens to hundreds
of marker molecules. In the recent “proof-of-theprinciple” studies, the multimarker panels comprised of
25 and 31 analytes offered the highest power for the
prognostic discrimination between patients with
squamous cell carcinoma of the head and neck and
diagnostic discrimination between pancreatic cancer
and chronic pancreatitis, respectively [12, 13].
as well. Difficulties in cross-platform and crosstechnology proteomic data transfers and analyses rival
those common for gene expression microarray
experiments.
As a switch to the marker panels is anticipated,
high-throughput attempts to the discovery of new tumor
markers en mass are under way in many labs involved
in translational research. One group of methods is
aimed at revealing protein markers such as, often as
molecules that have undergone post-translational
modifications (a proteomics/immunomics approach),
and another group focuses on revealing mRNA
sequences overrepresented or exclusive to tumor
samples (a genomics/transcriptomics approach). The
most pronounced methods of the proteomics/
immunomics group are serological identification of
antigens by recombinant expression cloning (SEREX),
protein microarrays, two-dimensional polyacrylamide
gel electrophoresis (2D-PAGE) and variety of mass
spectrometry based approaches. The advantage of the
proteomics/immunomics approach is its relative
straightforwardness: profiling is performed directly on
the serum or urine samples and leads to isolation of
potentially immunogenic epitopes. These epitopes
could be useful not only for easy detection of tumors,
but also for cancer immunotherapy. Drawbacks of
proteomics/immunomics include the difficulties in postscreening
phases
of
peak/spot
identification.
Additionally, serum profiles of oncology patients are
usually compared only to serum profiles of healthy
subjects, while should be compared to profiles
characteristic for multiple chronic inflammatory states
Serological identification of antigens by recombinant
expression cloning (SEREX) is a strategy for
identification of tumor antigens based on an existing
antibody repertoire of cancer patients [17]. SEREX was
developed by implementing molecular cloning
techniques into the original strategy of autologous
typing [18]. In this approach, a cDNA library is
constructed by using mRNA from fresh tumor
specimens as a template, then packaged into lambdaphage vectors and recombinant clones are expressed
in E. coli [19]. Recombinant proteins produced by
bacterial cells are transferred onto nitrocellulose
membranes, and identified as antigens by their
reactivity with high titer IgG antibodies of patient’s
serum. Nucleotide sequences of cDNA inserts in the
plasmids contained in positive clones are determined in
order to identify corresponding human antigens.
Genomics/transcriptomics
methods
identify
molecules of interest without hesitation, but in this case
the molecule is usually mRNA. Nucleic acids are
attractive as molecular markers easily detectable by
PCR, including quantitative PCR. On the other hand,
differential or even exclusive tumor–specific expression
of mRNA does not guarantee that corresponding
protein product could be useful as a serum tumor
marker. The most widely employed methods of
genomics/transcriptomics are expression microarrays
[14, 15], and SAGE [16]. Both methods are very well
established, and will not be reviewed here, except a
relatively new approach of direct mRNA profiling in the
cell-free fractions of the body fluids. Recently, a
bioinformatics approach demonstrated unexpected
relevance to tumor marker discovery: computer-based
methods are attractively cheap, but require extensive
follow-up studies. The latter is true for any other highthroughput method of marker discovery as well. In this
review, several methods of measuring tumor markers
will be discussed, as well as advantages and limitations
associated with each technique.
PROTEOMICS/IMMUNOMICS APPROACH TO
TUMOR MARKER DISCOVERY
Serological
Identification
of
Antigens
Recombinant Expression Cloning (SEREX)
by
In SEREX researchers test potential antigens by
serum of the cancer patients, thus the experimental
outcome is limited to the antigens present in the tumor
in vivo, and cell culture related artifacts are avoided.
Although, SEREX allows an unbiased search for
antigenic proteins, all epitopes that fail to undergo
proper post-translational or conformational changes
when expressed in bacteria will escape the detection
[17]. There have been several modifications to the
originally described method to improve identification of
tumor specific antigens. After initial identification of
Tumor Markers
prospective biomarker with autologous (or pooled) sera
tumor specificity of antigen needs to be proved by
allogenic screening. At least 3 modification of allogenic
screening were suggested: SADA [20, 21], SMARTA
[22] and SeroGRID [23].
In the original study that introduced SEREX, five
different antigens were identified including NY-ESO-1
[24]. Lately, this list was expanded to more than 100
well-described different antigens, and many more
contained in the publicly available Cancer Immunome
Database [www2.licr.org/CancerImmunomeDB]. These
studies revealed that human tumors express multiple
antigens causing immune responses in the host. Some
of them are known and common for many individual
tumors, and some are less obvious and have not been
known to cause immune responses in humans
previously. The spectrum of tumor antigen specificities
supports the hypothesis that the immunogenicity of a
given molecule depends more on the context in which it
is presented than on its more or less restricted
expression in certain tissues [17].
The clinical significance of anti-tumoral antibodies in
cancer patients is not known, although the presence of
some antibodies is associated with a poor prognosis in
various cancers [17]. The expression of a particular
antigen is not a clear indication of the production of
corresponding antibodies, and there is no correlation
between the antibody production and the clinical stage
of the tumor. There is only a limited diagnostic use for
any single serum antibody assay, as most of these
antibodies are present in only a small percentage of the
patients. The development of the autoantibodies-based
diagnostic panels is a clear way to go. For example, in
one of the diagnostic attempts of this kind, a panel of
22 peptides expressed by prostate cancer tissue
allowed detection of the tumor with a specificity of
88.2% and a sensitivity of 81.6%. These results were
significantly better than PSA screening, especially
among men with a PSA between 4 and 10 ng/ml [25].
TWO-DIMENSIONAL POLYACRYLAMIDE GEL
ELECTROPHORESIS
Two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) followed by Mass Spectrometry
(MS) for protein analysis and identification is a powerful
tool and a primary technique for discovering disease
biomarkers. Expression patterns revealed by 2D-PAGE
gels are compared after simultaneous separation of
proteins in immobilized pH gradient by their charge (pI)
and by gel mobility that depends on the molecular
weight of the protein [26, 27]. Because a protein’s
charge and molecular weight are unrelated, protein
spots are distributed across the gel in a relatively
uniform way. Spots that correspond to individual
proteins can be detected by staining with Coomassie
blue or with fluorescent dyes, such as SYPRO RUBY
[28]. Identification of proteins of interest is performed
after spot excision, followed by proteolytic or chemical
digestion and MS analyses.
Current Molecular Medicine, 2010, Vol. 10, No. 2
251
Generally speaking, 2D-PAGE is a reliable
separation technique that provides excellent resolution
of complex protein mixtures, even allowing
identification of the proteins changed by individual
missense mutations [26]. However, there are several
disadvantages of the method: a large amount of
starting material is required, it is time-consuming, has
low sensitivity with conventional stains and relatively
poor resolution [26, 29]. In the past, tumor markers
revealed by 2D-PAGE usually were not very specific,
but that is explained by heterogeneity of the starting
material. The invention of laser capture microdissection
(LCM) [30] improved specificity of 2D-PAGE
biomarkers as well as markers revealed by other
methods.
MASS SPECTROMETRIC METHODS
Mass spectrometry (MS) has become one of the
most important tools for disease marker discovery. This
is especially due to the introduction of electrospray
ionization (ESI) [31] and matrix-assisted laser
desorption ionization (MALDI) [32,33] to the field. MSbased methods of peptide fragment separations
include quadruple ion-trap-MS (e.g., LCQ-MS), matrixassisted laser desorption ionization (MALDI), surfaceenhanced laser desorption/ionization (SELDI), and
Fourier transform ion cyclotron resonance (FTICR)-MS.
MALDI-MS is a laser-based soft ionization method, first
introduced by Karas and Hillenkamp in 1988 [32-34]. A
matrix is used in MALDI –MS to protect the sample
from being destroyed by a direct laser beam. The laser
radiates this matrix-analyte mixture and results in
vaporization of the matrix along with the sample. This
directed energy transfer event provides high ion yields
of the intact analyte and allows for the measurement of
compounds with high accuracy and sensitivity, which
can be very useful for protein identification and
characterization [34], especially for protein spots
extracted from 2D gels.
A common type of MALDI spectrometer is the
MALDI-TOF (time-of-flight) [34, 35]. TOF analysis is
based on accelerating a set of protein-derived ions to a
detector. As all of the ions are given the same amount
of energy, but have a different mass, the ions reach the
detector at different times. An ion’s mass is determined
by its time of flight. The arrival time at the detector is
dependent upon the mass, charge, and kinetic energy
(KE) of the ion. Although, MALDI mass spectrometry is
a powerful tool for accurate mass determination of the
peptide mixtures, it is highly dependent on the sample
and the sample preparation. Obtaining meaningful
MALDI data, depends on the choice of suitable
matrices and solvents, the functional and structural
properties of the analyte, sample purity, and sample
preparation [35]. This is a major limitation of MALDITOF, as the presence of buffer components, lipids and
carbohydrates prevent efficient ionization of the
proteins. Because of this, samples of body fluids must
be pre-separated prior to MS analyses by other
techniques, for example by 2D-PAGE.
252
Current Molecular Medicine, 2010, Vol. 10, No. 2
Surface-enhanced
laser
desorption/ionization
(SELDI) is an affinity-based mass spectrometric
method in which proteins of interest are selectively
absorbed in a chemically modified surface on a biochip.
This approach was introduced in 1993 by Hutchens
and Yip [36, 37]. SELDI-TOF MS combines retention
with mass spectrometry, unlike other methods based
on elution, such as HPLC/MS. The principle of this
approach is that proteins are captured by adsorption,
partition,
electrostatic
interaction,
or
affinity
chromatography on a solid-phase protein chip surface.
Chromatographic surfaces of SELDI protein chips are
uniquely designed to retain proteins from complex
mixtures according to their specific properties [38].
Because proteins are retained on the surface,
contaminants can easily be washed away prior to MS
analyses, thus eliminating the need for preparation of
techniques [39]. Another advantage of SELDI is the
small sample requirements (typically 1–2 l per
analysis), which is ideal for small biopsies or
microdissected tissue samples common in cancer
research. Apart from the sample preparation, SELDI
technology is similar to MALDI MS. After adding a
matrix solution proteins are ionized with a nitrogen
laser and their molecular masses measured by TOF
MS [40].
SELDI-MS can be used in a high-throughput setting
that allows processing of hundreds of low volume
specimens. This explains its growing popularity as a
primary tool for differentiation of disease from normal
states, thereby illuminating possible protein markers.
SELDI-TOF MS is most effective at profiling lowmolecular-weight (<20-kDa) proteins and peptides. In
this range, SELDI-TOF detection sensitivity exceeds
the Western blot technique, while at higher molecular
weights (>30 kDa) the two methods are comparable
[41]. On the other hand, SELDI does not allow direct
identification of resolved proteins. Analyses of complex
protein mixes, e.g. specimens of the serum, urine,
needle aspirates or cell lysates generate mass spectra
represented by peak profiles that in some cases may
be diagnostically useful. For example, SELDI-based
serum proteomic patterns discriminated men with
elevated PSA levels due to benign processes from men
with carcinoma within the diagnostic gray zone of PSA
levels [42]. Nevertheless, “classical” SELDI based TOF
approach for detection cannot separate ions that are
close in mass/charge, so some discrete ions can
coalesce into single peak. To enhance the resolution of
SELDI technology, it could be interfaced to a SELDI
hybrid quadrupole TOF (QqTOF) mass spectrometer
[43, 44]. Some isobaric fragment ions would remain
unresolved even with the QqTOF instrument and would
require Fourier transform ion cyclotron resonance
(FTICR) MS. The very high accuracy measurements
offered by FTICR-MS are used to generate ‘accurate
mass tags’ (AMTs) suitable for protein identification
[45, 46].
The vast majority of SELDI-MS studies of human
tumors still use spectral signatures from sera to
differentiate normal and diseased states without actual
Sikaroodi et al.
protein identification. Reproducibility of the proteomic
profiling approach was recently questioned, as two
independent studies have reported the same pattern to
date [47]. The most disturbing observation comes from
the study of Hu and co-authors, who demonstrated that
changes in the profiling protocol result in the
reproducible shift in protein profiles masking more
subtle changes corresponding to different histological
subtypes of cancer [48]. Potential solutions to these
problems, including adherence to the minimal
requirements for marker confirmation and validation are
reviewed elsewhere [47, 49, 50].
The confirmation of the diagnostic value of a protein
spectrum ultimately relies on the biological validation
and identification of the underlying proteins. Therefore,
the most valuable analytical methods are those that
allow profiling in parallel with protein identification.
Labeling proteins with isotope-coded affinity tags
(ICAT) or isobaric tags for relative and absolute
quantitation
(iTRAQ)
followed
by
liquid
chromatography/tandem mass spectrometry (LCMS/MS) are promising analytical methods for just this
reason [51, 52]. Recently, iTRAQ approach was
employed for searches of tumor markers of endometrial
carcinoma and lead to the development of the panel of
markers including pyruvate kinase, chaperonin 10, and
alpha(1)-antitrypsin discriminating endometrial cancer
from a number of types of benign endometrium with
both sensitivity and specificity of 0.95 [53]. ICAD
allowed identification and quantification of proteins in
the pancreatic juice samples as compared to normal
controls, revealing insulin-like growth factor binding
protein-2 as novel tumor marker [54]. Similarly, ICAT
revealed overexpresion of vitamin D-binding protein in
nipple aspirate fluid of early stage breast tumor-bearing
breasts [55].
In classical profiling studies, e.g. SELDI-based
projects, relatively few have reported the identities of
proteins allowing for discrimination of tumor-bearing
and healthy signatures. One such study reported the
alpha subunit of haptoglobin, an acute-phase protein
produced by liver cells as a potential marker for ovarian
cancer [56]. Another study of the same tumor type
revealed a diagnostic value of down-regulation of
apolipoprotein A1 and a truncated form of transthyretin
in combination with up-regulation of a cleavage
fragment of inter-alpha-trypsin inhibitor heavy chain H4
[57]. The latter study is especially prominent, as a
combination of all three new markers with CA125
greatly improved specificity and sensitivity of the
diagnostic assay. SELDI analysis led to the
identification of 8.9-kDa isoform of ApoA-II as a tumor
marker superior to PSA in diagnosis of indolent
prostatic carcinoma [58]. Guo and co-authors
established calgranulin A and chaperonin 10 as protein
markers for endometrial carcinoma [44]. Unfortunately,
both markers are non-exclusive to tumor cells,
suggesting that although these proteins could not be
used for diagnostic purposes independently, they may
prove useful when combined with other tumor markers
[44, 59]. Potential cancer-related biomarkers often
appear to be fragments or isoforms of major serum
Tumor Markers
components and house-keeping proteins. For example,
one of the studies identified down-regulation of - and
- subunits of hemoglobin as a serum indicator of
ovarian carcinoma [60]. Other examples include a
serum amyloid A that could serve as a biomarker to
distinguish prostate cancer patients with bone lesions
[61] and the C-terminal part of the V10 fragment of
vitronectin discriminating hepatocellular carcinoma
from chronic liver disease [62].
Actual tumor-derived molecules may escape
detection in serum due to their low molecular mass or
low concentration. Recent observations suggest that
many potential serum biomarkers are bound to carrier
proteins, such as albumin and usually become
depleted during sample preparation [63]. As the
accumulation of biomarkers on circulating carrier
proteins greatly amplifies the total serum/plasma
concentration
of
the
measurable
biomarker,
examination of the carrier-bound molecules represents
an important future avenue in tumor marker discovery.
Recently, the harvesting of biomarkers bound to the
carrier proteins followed by high-resolution MALDI
orthogonal TOF (OTOF) MS was successfully
employed for discriminant analysis of the ovarian
cancer serum samples [64]. Importantly, a number of
discriminating
protein
fragments,
particularly
transthyretin, has been reported as putative ovarian
carcinoma biomarkers earlier [61].
A number of recent publications reported
proteomics signatures that classify cancer patients
according to their likely outcomes rather than
differentiate tumor-bearing and non-tumor-bearing
individuals. For example, Taguchi and colleagues [64]
developed a MALDI-based profiling approach for
preselection of the patients with advanced non–smallcell lung cancer (NSCLC), who will be most likely
beneficial from expensive targeted therapy with smallmolecule inhibitors of the EGF receptor gefitinib and
erlotinib. SELDI-TOF based profiles were shown to
capture the signature of biochemical relapse in
prostatic adenocarcinomas, independent of their
clinical PSA status [65]. Unfortunately, in both of these
studies diagnostic peaks were not tracked down to the
particular proteins or protein fragments.
Another interesting field for application of MS-based
techniques to a search for cancer biomarkers is the
shift of the discovery focus from the proteins, such as,
to their posttranslationally modified components. For
example, glycome profiles acquired by using MALDIMS of permethylated N-glycans released from serum
samples showed that fucosylation of glycan structures
is generally higher in prostate cancer and revealed a
number of cancer-specific glycans [66]. Whether tumor
glycomics will develop into specific area of biomarker
research and rival more traditional protein biomarkers
remains to be seen.
PROTEIN MICROARRAYS
Unlike DNA microarrays, which quantify mRNA
messages possibly correlated in functionality with
Current Molecular Medicine, 2010, Vol. 10, No. 2
253
proteins encoded by them, protein microarrays
measure activity and/or abundance directly reflecting
the functional aspects of these molecules. In brief,
protein microarrays are composed of immobilized
homogeneous or heterogeneous spots representing
antibodies, DNA or RNA fragments, small molecular
compounds, peptides, recombinant proteins or total cell
lysates. The spot array could be probed by tagged
antibodies, ligands, total serum, or cell lysates to
analyze the signal intensity, which is proportional to the
quantity of the tagged molecules [67, 68]. The protein
microarrays offer high-throughput utilization, and also
require a low volume of sample material.
Experiments by using protein microarrays are either
function-based or abundance-based [67, 68]. Functionbased microarrays are useful for the measurement of
enzyme activity or substrate specificity of arrayed
proteins, and are used mostly in pharmacological
research. Abundance-based arrays allow for more
focused proteomics research and are supported by
forward or reverse phase spotting. In forward phase
arrays, labeled bait molecules, which are typically
antibodies, are immobilized on the surface. The arrays
are then incubated with the test sample that may
contain various analytes [67]. In reverse-phase arrays,
which are more applicable in tumor marker
identification, multiple analytes represented by tested
samples are immobilized within the same spot, but the
spot array is probed with a single detection molecule
[67, 68]. Therefore this method allows for detection of a
single analyte in a number of samples under the same
conditions [67]. This is useful in the detection of a
specific protein or a specific (e.g. phosphorylated) state
of the protein in the process of marker validation.
Reverse protein arrays detect analytes in very small
volume of sample, e.g. in 30-60 μl of whole cell lysate
or in core needle biopsies obtained with a 16 or 18
gauge needle [67, 68]. Protein microarray printers use
the same technology as DNA microarray printers, but
the layout is different, as protein array surfaces should
not change the protein structure [67]. Silanized silica,
nylon and nitrocellulose may be used as substratum,
and the latter is more common [69]. Printing different
sample dilutions on the same array allows quantitative
matching of the antibody to the analyte concentration,
which helps to analyze each analyte in a linear fashion,
thereby improving the sensitivity and specificity of the
technique.
Like other techniques in their infancy, protein
microarray technology has its own limitations, including
its unavailability of specific and high affinity antibodies
for a range of potential diagnostic targets [67, 69].
Unlike DNA probes that allow for affinity customization,
probes for protein arrays usually represent a “black
box”, so multiple probes needs to be pre-tested in order
to find the most reliable molecule that allow high fidelity
quantification of analytes. Normalization techniques for
antibody microarrays still have room for improvement,
with
IgM-ELISA-based
normalization
currently
established as the best performing procedure [69].
254
Current Molecular Medicine, 2010, Vol. 10, No. 2
PCR-like amplification methods are unavailable for
proteins, so low abundance molecules may not be
detected. Pre-fractionation of the proteins has been
suggested to solve this problem [70]. Current methods
of protein extraction and purification cannot guarantee
the integrity of the proteins, thus introducing an extra
level of inter-experimental variability [71]. These limiting
factors must be overcome in order to enhance the
performance of protein microarray technology.
Currently, this technology has been applied
infrequently to tumor marker studies. Sreekumar and
co-authors monitored levels for 146 human proteins in
LoVo colon carcinoma cells treated with ionizing
radiation with forward phase arrays of corresponding
antibodies, and demonstrated radiation-induced downregulation of carcinoembryonic antigen [72]. Forward
phase attempts to evaluate the proteome in clinical
samples were performed for malignant and adjacent
normal tissue in a primary breast cancer specimen [73]
and for serum samples obtained from 33 prostate
carcinoma patients and 20 controls [74]. Reverse
phase protein microarrays were used in the analysis of
the lysates of follicle center cells isolated by laser
capture microdissection from three different types of
follicular lymphomas [75] and in NCI-60 cancer cell
lines [76]. Reverse phase proteomic mapping of
phosphorylation end points is also under development
and has been tested in prostate, breast and ovarian
carcinoma studies [71-80]. Recently, phosphoproteomic network analyses of microdissected tumor cells
from childhood rhabdomyosarcoma patients showed
that this approach can be used as a mean to select
patients to receive mTOR/IRS pathway inhibitors
before administration of chemotherapy [81]. Currently,
it is unclear whether this type of analysis is applicable
to serum samples.
An interesting approach has been developed by Qui
and co-authors, who explored the use of microarrays
spotted with tumor proteins as an alternative to
Western blots for tumor antigen profiling. In this study,
lung tumor cell line protein lysate was separated into
1840 fractions and spotted on nitrocellulose slides,
then probed by sera from lung carcinoma patients and
healthy controls. Less than four percent of arrayed
fractions demonstrated increased reactivity in cancer
patients, indicative in the presence of potentially
immunogenic tumor marker yet to be purified from
given tumor lysate [82]. In contrast to other protein
arrays primarily aimed for marker validation studies,
this method allows for the discovery of new tumor
markers.
GENOMICS/TRANSCRIPTOMICS AND BIOINFORMATICS APPROACH TO TUMOR MARKER DISCOVERY
Direct mRNA Profiling in the Body Fluids
PCR-based quantification of tumor-specific mRNA
in the blood is a valuable tool for registering circulating
tumor cells representing seeds for micrometastases
Sikaroodi et al.
[83, 84]. For example, molecular detection of
cytokeratin and AFP mRNAs in the nuclear cell
component of peripheral blood was employed for the
assessment of microscopic lymphatic spread of nonsmall cell lung carcinoma [84] and hepatocellular
carcinoma [85]. Unfortunately, cell-containing fractions
of body fluids are unsuitable for tumor marker
discovery by gene expression profiling, as any cancer
specific molecular signature will be buried under
overwhelming amount of lymphocytic transcripts.
Luckily, the analyses of cell-free fraction of body fluids
indicate the substantial presence of free circulating
RNA that could be extracted and amplified for
diagnostic purposes. The origin of this RNA is unclear;
most probably it is derived for apoptotic bodies [86] and
stays protected from degradation via its association
with proteins or lipid vesicles [87]. Consistent with this
hypothesis, serum RNA concentration in cancer
patients is higher than healthy individuals [88]. Profiling
of these circulating RNA molecules by expression
microarrays or by Real-Time PCR may lead to the
developments of new classes of tumor markers
possibly suitable for diagnostics of primary tumors. The
feasibility of this approach has been proven recently by
fetal mRNA profiling in amniotic fluid [89] and in sera
samples collected from patients with gastric cancer [90]
and oral squamous cell carcinoma [91].
Bioinformatics
Discovery
Approach
to
Tumor
Marker
The completion of the human genome sequence
and the impressive repertoire of the Expressed
Sequence Tags (ESTs) representing a wide spectrum
of tissue types both provide a powerful source of data
to identify molecular targets and diagnostic markers for
a broad range of diseases, including cancer. Modern
bioinformatics approaches allow automation of
sequence annotation and provide tools for highthroughput analyses of accumulated sequences. Every
newly sequenced cDNA clone is routinely evaluated for
similarity to known genes and other ESTs, mapped to
the corresponding completed genome and placed in
UniGene and other databases. These databases and
tools are extremely useful for mining functional
information about human transcripts and thus helping
uncover candidates for subsequent experimental
studies. However, even if the function of mRNA or its
fragment remains unknown, this mRNA may still have
medical utility as potential tumor marker could rely
solely on its expression pattern as a diagnostic test.
Moreover, as in this case the diagnostic molecule is an
mRNA itself, there is no restriction on its proteinencoding ability. Some recent studies point at
expression levels of human antisense RNAs,
particularly of intronic antisenses, as a novel source for
tumor-specific molecules [92, 93]. One of the possible
explanations of the overall cancer-specific increase in
the production of non-coding RNA is a well-known
epigenetic phenomenon of the chromatin phenotype
disruption commonly found in human tumors.
Tumor Markers
As every EST represents a random sample, multiple
copies of the same sequence reflect the relative
abundance of the corresponding transcript, and thus
provide assessment of expression profiles for particular
tissues in normal and malignant states. Automated
mining of potential tumor markers in EST databases
seeks to identify UniGene clusters predominantly
containing ESTs obtained by sequencing of tumorderived libraries. Mining could be performed either as
pairwise comparisons of normal tissues with
corresponding tumors [94-96], or as global subtraction
procedures discriminating tumor-specific clusters
without any regard to particular tissue of origin [97].
The latter approach is better designed for tumor marker
discovery, as the resulting candidate RNAs are
characterized by minimal presence in normal cells
could be useful in serum-based diagnostics. Both
methods are also known as CDD (Computer-based
Differential Display) and are employed by on-line
expression visualization tools, e.g. Virtual Northern
(vNorthern) and SAGE digital gene expression
displayer (DGED). Currently, there is no public EST
database interface capable of sorting cDNA libraries
according to their preparation techniques or to perform
data threshold adjustments. Therefore, many
researchers use in-house computational tools that
allow variation of identification criteria.
Experimental
evaluation
of
computationally
predicted tumor markers often confirms their tumorspecific or tumor-prevalent expression patterns [98,
99]. In one of the recent studies, CDD technique led to
identification of novel tumor marker, Brachyury, and
isolation of its HLA-A0201 Brachyury epitope that
promotes expansion of T lymphocytes with the ability to
lyse Brachyury-expressing tumor cells [100]. It is
interesting that tumor markers previously characterized
by traditional methods (e.g. PSA and CEA) are
independently identified by many CDD algorithms [9799]. Many tumor-specific mRNAs appear to represent
new isoforms of known human genes, thus indicating
that tumor-specific alternative splicing is a widespread
phenomenon [99, 101, 102]. However, sets of
candidate molecules identified in silico usually contain
a larger proportion of false positives than
experimentally obtained ones, therefore requiring more
extensive marker validation procedures.
Current Molecular Medicine, 2010, Vol. 10, No. 2
Regardless
of
acknowledged
pitfalls,
it
is
unquestionable that the immense opportunities of
“omics” will surely advance our understanding of
human tumorigenesis and lead to early and efficient
diagnoses of primary malignancies.
ACKNOWLEDGEMENTS
The authors are extremely grateful to Prof. A.P.
Kozlov for sharing a new look on tumor markers and for
valuable discussions, to Dr. Yury Shebzukhov for
sharing a point of view on SEREX approaches and to
Prof. A. Christensen and Dr. K. Schlauch for their help
with MS proofreading and helpful suggestions. This
work was partially supported by NIH grant 1 R15
CA113331-01 (AB).
REFERENCES
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
CONCLUSION
High-throughput “omics” approaches to identify
tumor markers are widely employed for the creation of
“treasure troves” of candidate molecules. No initial
methods of candidate selection remain free of false
positives, thus indicating extreme importance for
validation of reported discoveries. To reduce the lists of
available targets, in addition to traditional comparisons
of tumor and normal specimens, extra controls
representing common chronic inflammatory and premalignant states must be included in the initial study.
Integrative and cross-platform approaches may also be
necessary, to identify the most reliable candidates.
255
[15]
[16]
[17]
[18]
[19]
[20]
[21]
Wulfkuhle, J.D., Liotta, L.A. and Petricoin, E.F. (2003) Nat.
Rev. Cancer, 3(4), 267-275.
Schmid, H.P., Adolfsson, J. and Aus, G. (2001) Eur. Urol.,
40(5), 488-494.
Okada, F. (2002) Redox Rep., 7(6), 357-368.
Braakhuis, B.J., Tabor, M.P., Kummer, J.A., Leemans, C.R.
and Brakenhoff, R.H. (2003) Cancer Res., 63(8), 1727-1730.
Gumus, B.H., Nese, N., Gunduz, M.I., Kandiloglu, A.R.,
Ceylan, Y. and Buyuksu, C. (2004) Int. Urol. Nephrol., 36(4),
549-553.
Chen, R., Brentnall, T.A., Pan, S., Cooke, K., Moyes, W.K.,
Crispin, D.A., Goodlett, D.R., Aebersold, R. And Bronner,
M.P. (2007) Mol. Cell Proteomics, 6(8), 1331-1342.
Konety, B.R. and Getzenberg, R.H. (2001) J. Urol., 165(2),
600-611.
Cremades, M.J., Menendez, R., Pastor, A., Llopis, R. and
Aznar, J. (1998) Respir. Med., 92(5), 766-771.
Sarandakou, A., Protonotariou, E. and Rizos, D. (2007) Crit.
Rev. Clin. Lab. Sci., 44(2), 151-178.
Xue, W., Irvine, R.A., Yu, M.C., Ross, R.K., Coetzee, G.A.
and Ingles, S.A. (2000) Cancer Res., 60(4), 839-841.
Xu, J., Meyers, D.A., Sterling, D.A., Zheng, S.L., Catalona,
W.J., Cramer, S.D., Bleecker, E.R. and Ohar, J. (2002)
Cancer Epidemiol. Biomarkers Prev., 11(7), 664-669.
Linkov, F., Lisovich, A., Yurkovetsky, Z., Marrangoni, A.,
Velikokhatnaya, L., Nolen, B., Winans, M., Bigbee, W.,
Siegfried, J., Lokshin, A., Ferris, R.L. (2007) Cancer
Epidemiol. Biomarkers Prev., 16(1), 102-107.
Zeh, H.J., Winikoff, S., Landsittel, D.P., Gorelik, E.,
Marrangoni, A.M., Velikokhatnaya, L., Winans, M.T., Lee, K.,
Moser, A., Bartlett, D., Lotze, M.T., Siegfried, J.M.,
Whitcomb, D., Papacristou, G., Slivka, A., Bigbee, W.L. and
Lokshin A.E. (2005) Cancer Biomark., 1(6), 259-269.
Michiels, S, Koscielny, S. and Hill, C. (2007) Br. J. Cancer,
96(8), 1155-1158.
Pollack, JR. (2007) Am. J. Pathol., 171(2), 375-385.
Porter, D., Yao, J. and Polyak, K. (2006) Drug Discov.
Today, 11(3-4), 110-118.
Sahin, U., Tureci, O. and Pfreundschuh, M. (1997) Curr.
Opin. Immunol., 9, 709-716.
Old, L.J. (1981) Cancer Res., 41(2), 361-375.
Sahin, U., Tureci, O., Schmitt, H., Cochlovius, B., Johannes,
T., Schmits, R., Stenner, F., Luo, G., Schobert, I. and
Pfreundschuh, M. (1995) Proc. Natl. Acad. Sci. USA, 92,
11810-11813.
Scanlan, M.J., Gout, I., Gordon, C.M., Williamson, B.,
Stockert, E., Gure, A.O., Jager, D., Chen, Y.T., Mackay, A.,
O'Hare, M.J. and Old, L.J. (2001) Cancer Immun., 1(4), 1-17.
Scanlan, M.J., Welt, S., Gordon, C.M., Chen , Y.T., Gure,
A.O., Stockert, E., Jungbluth, A.A., Ritter, G., Jager, D.,
256
[22]
[23]
[24]
[25]
[26]
[27]
[28]
[29]
[30]
[31]
[32]
[33]
[34]
[35]
[36]
[37]
[38]
[39]
[40]
[41]
[42]
[43]
[44]
[45]
[46]
[47]
[48]
[49]
[50]
[51]
Current Molecular Medicine, 2010, Vol. 10, No. 2
Jager, E., Knuth, A. and Old, L.J. (2002) Cancer Res.,
62(14), 4041-4047.
Lagarkova, M.A., Koroleva, E.P., Kuprash, D.V., Boitchenko,
V.E., Kashkarova, U.A., Nedospasov, S.A. and Shebzukhov,
Y.V. (2003) Immunol. Lett., 85(1), 71-74.
Krause, P., Tureci, O., Micke, P., Buhl, R., Huber, C. and
Sahin, U. (2003). J. Immunol. Methods, 283(1-2), 261-267.
Chen, Y.T., Scanlan, M.J., Sahin, U., Tureci, O., Gure, A.O.,
Tsang, S., Williamson, B., Stockert, E., Pfreundschuh, M.
and Old, L.J. (1997) Proc. Natl. Acad. Sci. USA, 94(5), 19141918.
Bradford, T.J., Wang X. and Chinnaiyan A.M. (2006) Urol.
Oncol., 24(3), 237-242.
O’Farrells, P.H. (1975) J. Biol. Chem., 250(10), 4007-4021.
Craven, R.A., Selby, P.J. and Banks, R.A. (2003) in
Principles of Molecular Oncology, Humana Press, Totowa,
NJ, pp. 247-264.
Berggren, K., Chernokalskaya, E., Steinberg, T.H., Kemper,
C., Lopez, M.F., Diwu, Z., Haugland, R.P. and Patton, W.F.
(2000) Electrophoresis, 21, 2509-2521.
Monteoliva, L. and Albar, J.P. (2004) Brief Funct. Genomic
Proteomic, 3(3), 220-239.
Espina, V., Milia, J., Wu, G., Cowherd, S. and Liotta, L.A.
(2006) Methods Mol. Biol., 319, 213-229.
Fenn, J.B., Mann, M., Mann, M., Meng, C.K., Wong, S.F. and
Whitehouse, C.M. (1989) Science, 246(4926), 64-71.
Karas, M. and Hillenkamp, F. (1988) Anal. Chem., 60(20),
2299-2301.
Hillenkamp, F. and Karas, M. (2000) Int. J. Mass Spectrom.,
200, 71-77.
Lewis, J.K. and Wei, J. (2000) in Encyclopedia of Analytical
Chemistry. Meyers, R. A., Ed. Chichester, John Wiley &
Sons Ltd, pp. 5880-5894.
Siuzdak, G. (1996) Mass Spectrometry for Biotechnology,
Academic Press, New York.
Hutchens, T.W. and Yip, T.T. (1993) Rapid Commun. Mass
Spectrom., 7, 576-580.
Yip, T.T. and Hutchens, T.W. (1994) Mol. Biotechnol., 1(2),
151-164.
Issaq, H.J., Conrads, T.P., Prieto, D.A., Tirumalai. R. and
Veenstra, TD. (2003) Anal. Chem., 75(7), 148A-155A.
von Eggeling, F., Junker, K., Fiedle, W., Wollscheid, V.,
Durst, M., Claussen, U. and Ernst, G. (2001) Electrophoresis,
22(14), 2898-2902.
Issaq, H.J., Veenstra, T.D., Conrads, T.P. and Felschow, D.
(2002) Biochem. Biophys. Res. Commun., 292(3), 587-592.
Grus, F.H., Joachim, S.C. and Pfeiffer, N. (2003) Proteomics,
3(6), 957-961.
Ornstein, D.K., Rayford, W., Fusaro, V.A., Conrads, T.P.,
Ross, S.J., Hitt, B.A., Wiggins, W.W., Veenstra, T.D., Liotta,
L.A. and Petricoin, E.F. 3rd. (2004) J. Urol., 172(4), 13021305.
Petricoin, E.F. and Liotta, L.A. (2004) Curr. Opin. Biotechnol.,
15(1), 24-30.
Guo, J., Yang, E.C., Desouza, L., Diehl, G., Rodrigues, M.J.,
Romaschin, A.D., Colgan, T.J. and Siu, K.W. (2005)
Proteomics, 3(6), 957-961.
Bogdanov, B. and Smith, R.D. (2005) Mass Spectrom. Rev.,
24(2), 168-200.
Qian W.J., Monroe M.E., Liu, T., Jacobs, J.M., Anderson,
G.A., Shen, Y., Moore, R.J., Anderson, D.J., Zhang, R.,
Calvano, S.E., Lowry, S.F., Xiao, W., Moldawer, L.L., Davis,
R.W., Tompkins, R.G., Camp, D.G. 2nd and Smith, R.D.
(2005) Mol. Cell Proteomics, 4(5), 700-709.
Coombes, K.R., Morris, J.C., Hu, J., Edmonson, S.R. and
Baggerly, K.A. (2005) Nat. Biotechnol., 23, 291-292.
Hu, J., Coombes, K.R., Morris, J.S. and Baggerly, K.A.
(2005) Brief. Funct. Genomics Proteomics, 3(4), 322-331.
Liggett, W.S., Barker, P.E., Semmes, O.J., and Cazares,
L.H. (2004) Dis. Markers, 20(6), 295-307.
Diamandis, E.P. (2006) Cancer Res., 66, 5540-5541.
Shiio, Y. and Aebersold, R. (2006) Nat. Protoc., 1(1), 139145.
Sikaroodi et al.
[52]
[53]
[54]
[55]
[56]
[57]
[58]
[59]
[60]
[61]
[62]
[63]
[64]
[65]
[66]
[67]
[68]
[69]
[70]
[71]
[72]
[73]
Wiese, S., Reidegeld, K.A., Meyer, H.E. and Warscheid, B.
(2007) Proteomics, 7(3), 340-350.
Desouza, L.V., Grigull, J., Ghanny, S., Dube, V., Romaschin,
A.D., Colgan, T.J. and Siu, K.W. (2007) Mol. Cell
Proteomics, 6(7), 1170-1182.
Chen, R., Pan, S., Yi, E.C., Donohoe, S., Bronner, M.P.,
Potter, J.D., Goodlett, D.R., Aebersold, R. and Brentnall, T.A.
(2006) Proteomics, 6(13), 3871-3879.
Pawlik, T.M., Hawke, D.H., Liu, Y., Krishnamurthy, S.,
Fritsche, H., Hunt, K.K. and Kuerer, H.M. (2006) BMC
Cancer, 6, 68.
Ye, B., Cramer, D.W., Skates, S.J., Gygi, S.P., Pratomo, V.,
Fu, L., Horick, N.K., Licklider, L.J., Schorge, J.O., Berkowitz,
R.S. and Mok, S.C. (2003) Clin. Cancer Res., 9(8), 29042911.
Zhang, Z., Bast, R.C. Jr., Yu, Y., Li, J., Sokoll, L.J., Rai, A.J.,
Rosenzweig, J.M., Cameron, B., Wang, Y.Y., Meng, X.Y.,
Berchuck, A., Van Haaften-Day, C., Hacker, N.F., de Bruijn,
H.W., van der Zee, A.G., Jacobs, I.J., Fung, E.T. and Chan,
D.W. (2004) Cancer Res., 64(16), 5882-5890.
Malik, G., Ward, M.D., Gupta, S.K., Trosset, M.W., Grizzle,
W.E., Adam, B.L., Diaz, J.I. and Semmes, O.J. (2005) Clin.
Cancer Res., 11(3), 1073-1085.
Yang, E.C., Guo, J., Diehl, G., DeSouza, L., Rodrigues, M.J.,
Romaschin, A.D., Colgan, T.J. and Siu, K.W. (2004) J.
Proteome Res., 3(3), 636-643.
Woong-Shick, A., Sung-Pil, P., Su-M, B., Joon-Mo, L., SungEun, N., Gye-Hyun, N., Young-Lae, C., Ho-Sun, C., HeungJae, J., Chong-Kook, K., Young-Wan, K., Byoung-Don, H.
and Hyun-Sun J. (2005) Cancer Sci., 96(3), 197-201.
Le, L., Chi, K., Tyldesley, S., Flibotte, S., Diamond, D.L.,
Kuzyk, M.A. and Sadar, M.D. (2005) Clin. Chem., 51(4), 695707.
Paradis, V., Degos, F., Dargere, D., Pham, N., Belghiti, J.,
Degott, C., Janeau, J.L., Bezeaud, A., Delforge, D.,
Cubizolles, M., Laurendeau, I. and Bedossa, P. (2005)
Hepatology, 41(1), 40-47.
Lopez, M.F., Patton, W.F., Whiteley, G.R., Rosenblatt, K.,
Gurnani, P., Nandi, A., Neill, S., Cullen, S., O'Gorman, M.,
Sarracino, D., Lynch, C., Johnson, A., Mckenzie, W. and
Fishman, D. (2007) Clin. Chem., 53(6), 1067-1074.
Taguchi, F., Solomon, B., Gregorc, V., Roder, H., Gray, R.,
Kasahara, K., Nishio, M., Brahmer, J., Spreafico, A.,
Ludovini, V., Massion, P.P., Dziadziuszko, R., Schiller, J.,
Grigorieva, J., Tsypin, M., Hunsucker, S.W., Caprioli, R.,
Duncan, M.W., Hirsch, F.R., Bunn, P.A. Jr. and Carbone,
D.P. (2007) J. Natl. Cancer Inst., 99, 838-846.
Kohli, M., Siegel, E., Bhattacharya, S., Khan, M.A., Shah, R.
and Suva, L.J. (2006) Cancer Biomark., 2(6), 249-258.
Kyselova, Z., Mechref, Y., Al Bataineh, M.M., Dobrolecki,
L.E., Hickey, R.J., Vinson, J., Sweeney, C.J. and Novotny,
M.V. (2007) J. Proteome Res. 6(5), 1822-1832.
Espina, V., Mehta, A.I., Winters, M.E., Calvert, V., Wulfkuhle,
J., Petricoin, E.F. 3rd and Liotta, L.A. (2003) Proteomics,
3(11), 2091-2100.
rd
Liotta, L. and Petricoin, E.F. 3 (2000) Nat. Rev. Genet.,
1(1), 48-56.
Rapkiewicz, A., Espina, V., Zujewski, J.A., Lebowitz, P.F.,
rd
Filie, A., Wulfkuhle, J., Camphausen, K., Petricoin, E.F. 3 ,
Liotta, L.A. and Abati, A. (2007) Cancer, 111(3),173-184.
Poetz, O., Schwenk, J.M., Kramer, S., Stoll, D., Templin,
M.F. and Joos, T.O. (2005) Mech. Ageing Dev., 126, 161170.
Hamelinck, D., Zhou, H., Li, L., Verweij, C., Dillon, D., Feng,
Z., Costa, J. and Haab, B.B. (2005) Mol. Cell Proteomics,
4(6), 773-784.
Sreekumar, A., Nyati, M.K., Varambally, S., Barrette, T.R.,
Ghosh, D., Lawrence, T.S. and Chinnaiyan, A.M. (2001)
Cancer Res., 61(20), 7585-7593.
Hudelist, G., Pacher-Zavisin, M., Singer, C.F., Holper, T.,
Kubista, E., Schreiber, M., Manavi, M., Bilban, M. and
Czerwenka, K. (2004) Breast Cancer Res. Treat., 86(3), 281291.
Tumor Markers
[74]
[75]
[76]
[77]
[78]
[79]
[80]
[81]
[82]
[83]
[84]
[85]
[86]
[87]
[88]
Miller, J.C., Zhou,H., Kwekel, J., Cavallo, R., Burke, J.,
Butler, E.B., Teh, B.S. and Haab, B.B. (2003) Proteomics,
3(1), 56-63.
Zha, H., Raffeld, M., Charboneau, L., Pittaluga, S., Kwak,
rd
L.W., Petricoin, E. 3 , Liotta, L.A. and Jaffe, E.S. (2004) Lab.
Invest. 84(2), 235-244.
Shankavaram, U.T., Reinhold, W.C., Nishizuka, S., Major, S.,
Morita, D., Chary, K.K., Reimers, M.A., Scherf, U., Kahn, A.,
Dolginow, D., Cossman, J., Kaldjian, E.P., Scudiero, D.A.,
Petricoin, E., Liotta, L., Lee, J.K. and Weinstein, JN. (2007)
Mol. Cancer Ther., 6(3), 820-832.
Wulfkuhle, J.D., Aquino, J.A., Calvert, V.S., Fishman, D.A.,
Coukos, G., Liotta, L.A. and Petricoin, E.F. 3rd. (2003)
Proteomics, 3(11), 2085-2090.
Sheehan, K.M., Calvert, V.S., Kay, E.W., Lu, Y., Fishman,
D., Espina, V., Aquino, J., Speer, R., Araujo, R., Mills, G.B.,
Liotta, L.A., Petricoin, E.F. 3rd and Wulfkuhle, J.D. (2005)
Mol. Cell Proteomics, 4(4), 346-355.
Grubb, R.L., Calvert, V.S., Wulkuhle, J.D., Paweletz, C.P.,
Linehan, W.M., Phillips, J.L., Chuaqui, R., Valasco, A.,
Gillespie, J., Emmert-Buck, M., Liotta, L.A. and Petricoin,
E.F. (2003) Proteomics, 3(11), 2142-2126.
Cowherd, S.M., Espina, V.A., Petricoin, E. 3rd and Liotta,
L.A. (2004) Clin. Breast Cancer, 5(5), 385-392.
Petricoin, E.F. 3rd, Espina, V., Araujo, R.P., Midura, B.,
Yeung, C., Wan, X., Eichler, G.S., Johann, D.J. Jr, Qualman,
S., Tsokos, M., Krishnan, K., Helman, L.J. and Liotta,
LA.(2007) Cancer Res. 67(7), 3431-3440.
Qiu, J., Madoz-Gurpide, J., Misek, D.E., Kuick, R., Brenner,
D.E., Michailidis, G., Haab, B.B., Omenn, G.S. and Hanash,
S. (2004) J. Proteome Res., 3(2), 261-267.
Miura, M., Ichikawa, Y., Tanaka, K., Kamiyama, M.,
Hamaguchi, Y., Ishikawa, T., Yamaguchi, S., Togo, S., Ike,
H., Ooki, S. and Shimada, H. (2003) Anticancer Res.,
23(2B), 1271-1276.
Saintigny, P., Coulon, S., Kambouchner, M., Ricci, S.,
Martinot, E., Danel, C., Breau, J.L. and Bernaudin, J.F.
(2005) Int. J. Cancer, 115(5), 777-782.
Liu, Y., Wu, M.C., Qian, G.X. and Zhang, B.H. (2002)
Hepatobiliary Pancreat. Dis. Int., 1(1), 72-76.
Biggiogera, M., Bottone M.G. and Pellicciari C. (1998) J.
Histochem. Cytochem., 46(9), 999-1005.
El-Hefnawy, T., Raja, S., Kelly, L., Bigbee, W.L., Kirkwood,
J.M., Luketich, J.D. and Godfrey T.E. (2004) Clin. Chem.,
50(3), 564-573.
Ng, E.K., Tsui, N.B., Lam, N.Y., Chiu, R.W., Yu, S.C., Wong,
S.C., Lo, E.S., Rainer, T.H., Johnson, P.J. and Lo, Y.M.
(2002). Clin. Chem., 48(8), 1212-1217.
Received: June 05, 2009
Current Molecular Medicine, 2010, Vol. 10, No. 2
Revised: June 19, 2009
Accepted: June 19, 2009
[89]
[90]
[91]
[92]
[93]
[94]
[95]
[96]
[97]
[98]
[99]
[100]
[101]
[102]
257
Larrabee, P.B., Johnson, K.L., Lai, C., Ordovas, J., Cowan,
J.M., Tantravahi, U. and Bianchi, D.W. (2005) JAMA, 293(7),
836-842.
Tani, N., Ichikawa, D., Ikoma, D., Tomita, H., Sai, S., Ikoma,
H., Okamoto, K., Ochiai, T., Ueda, Y., Otsuji, E., Yamagishi,
H., Miura, N. and Shiota, G. (2007) Anticancer Res., 27(2),
1207-1212.
Li, Y., Elashoff, D., Oh, M., Sinha, U., St John, M.A., Zhou,
X., Abemayor E. and Wong D.T. (2006) J. Clin. Oncol.,
24(11), 1754-1760.
Reis, E.M., Nakaya, H.I., Louro, R., Canavez, F.C.,
Flatschart, A.V., Almeida, G.T., Egidio, C.M., Paquola, A.C.,
Machado, A.A., Festa, F., Yamamoto, D., Alvarenga, R., da
Silva, C.C., Brito, G.C., Simon, S.D., Moreira-Filho, C.A.,
Leite, K.R., Camara-Lopes, L.H., Campos, F.S., Gimba, E.,
Vignal, G.M., El-Dorry, H., Sogayar, M.C., Barcinski, M.A., da
Silva, A.M., Verjovski-Almeida, S. (2004) Oncogene, 23(39),
6684-6692.
Klimov, D., Skoblov, M., Ryazantzev, A., Tyazhelova, T. and
Baranova, A. (2006) J. Bioinform. Comput. Biol. 4(2), 515521.
Schmitt, A.O., Specht, T., Beckmann, G., Dahl, E., Pilarsky,
C.P., Hinzmann, B. and Rosenthal, A. (1999) Nucleic Acids
Res., 27(21), 4251-4260.
Brett, D., Kemmner, W., Koch, G., Roefzaad, C., Gross, S.
and Schlag, P.M. (2001) Oncogene, 20(33), 4581-4585.
Jin, L., Wang, H., Narita, T., Kikuno, R., Ohara, O., Shihara,
N., Nishigori, T., Horikawa, Y. and Takeda, J. (2003) J. Mol.
Endocrinol., 31(3), 519-528.
Baranova, A.V., Lobashev, A.V., Ivanov, D.V., Krukovskaya,
L.L., Yankovsky, N.K. and Kozlov, A.P. (2001) FEBS Lett.,
508(1), 143-148.
Krukovskaja, L.L., Baranova, A., Tyezelova, T., Polev, D. and
Kozlov, A.P. (2005) Tumour Biol., 26(1), 17-24.
Shen, D., He, J. and Chang H.R. (2005) Int. J. Mol. Med.,
15(2), 205-212.
Palena, C., Polev, D.E., Tsang, K.Y., Fernando, R.I.,
Litzinger, M., Krukovskaya, L.L., Baranova, A.V., Kozlov,
A.P. and Schlom, J. (2007) Clin. Cancer Res. 13(8), 24712478.
Hui, L., Zhang, X., Wu, X., Lin, Z., Wang, Q., Li, Y. and Hu,
G. (2004) Oncogene, 23(17), 3013-3023.
Xu, Q. and Lee, C. (2003) Nucleic Acids Res., 31(19), 56355643.