1. health and fitness
2. disease
3. cancer

Vaccine 25S (2007) B110–B121
Review
Clinical proteomics: Discovery of cancer biomarkers
using mass spectrometry and bioinformatics
approaches—A prostate cancer perspective
Balwir Matharoo-Ball, Graham Ball, Robert Rees ∗
Interdisciplinary Biomedical Research Centre, School of Biomedical and Natural Sciences,
Nottingham Trent University, Clifton Lane, Nottingham NG11 8NS, UK
Received 9 May 2007; received in revised form 1 June 2007; accepted 15 June 2007
Abstract
Prostate cancer (PCa) is an intractable disease, where diagnosis and clinical prediction of the disease course and response to treatment is
compromised by the lack of objective and robust biomarker assays. In late stage metastatic disease, treatment options are limited, although it
is recognized that some patients may benefit from immunotherapy and in particular vaccine therapy. However, research into biomarkers that
correlate with the clinical outcome of immunotherapy has lagged behind vaccine development. Thus, proteomic tools are increasingly being
utilized for the discovery of biomarkers which will allow us to make clinical decisions about patient treatment at an earlier stage and should
aid in shortening the development time for vaccines. In this review we will summarize the various proteomic platforms used to investigate
new biomarkers in PCa for better patient diagnosis, prognosis, patient stratification, treatment monitoring and clinical surrogate endpoints.
We will discuss method limitations and highlight the key areas of research required for understanding the etiology of PCa.
© 2007 Elsevier Ltd. All rights reserved.
Keywords: Biomarker; Prostate cancer; Proteomics; Mass Spectrometry; Bioinformatics
Contents
1.
2.
3.
4.
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Clinical proteomics: proteomic methods and their application in prostate cancer research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1. Two-dimensional electrophoresis techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1.1. Technical limitations of two-dimensional electrophoresis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2. MALDI- and SELDI-MS techniques to identify cancer biomarkers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.3. Combination of SELDI and pre-clinical models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.4. Current technology validation challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Complex data management and analysis systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Future applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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1. Introduction
∗
Corresponding author. Tel.: +44 115 848 6342; fax: +44 115 848 6632.
E-mail address: [email protected] (R. Rees).
0264-410X/$ – see front matter © 2007 Elsevier Ltd. All rights reserved.
doi:10.1016/j.vaccine.2007.06.040
In Europe, prostate cancer is the most common noncutaneous cancer in men after lung cancer. There are
approximately 134,000 new cases every year [1] and approx-
B. Matharoo-Ball et al. / Vaccine 25S (2007) B110–B121
imately 56,000 related deaths. The incidence is increasing
rapidly, as the population of males over the age of 50 grows
and through the increased use of prostate specific antigen
(PSA) assessment. Both hereditary and environmental factors influence the pathogenesis of the disease and there is
evidence that inflammation plays a critical role in its aetiology [2]. It is, however, unclear which cells of the immune
system are implicated in the pre-malignant inflammatory
conditions; prostatic intraepithelial neoplasia (PIN) which
is thought to be the precursor lesion of prostate cancer and
benign prostatic hyperplasia (BPH). Adenocarcinoma of the
prostate is the predominant form of prostate cancer and
most localized prostate cancers can be effectively treated by
surgery or radiation therapy. However, every year, 70,000
men [1] require additional treatment due to recurrence and
sometimes dissemination of the cancer to other parts of the
body (metastasis). After the lymph nodes, the skeleton is the
most common site of metastasis, the majority of which are
osteoblastic, or bone forming. Bone metastases are associated with poor prognosis and are the major cause of prostate
cancer-related morbidity (sickness) and mortality (death).
Androgen ablation therapy is a palliative treatment (reduces
pain and tumour burden) and not curative for patients with
metastases [3]; however, eventually the disease will become
androgen independent (hormone refractory) and re-occur,
at which point other treatment options are limited. For this
reason, there is an urgent requirement to develop new treatment options for prostate cancer, where the identification
of new prostate cancer-associated gene/proteins will aid in
the development of a more comprehensive understanding of
the disease and additional “targeted” therapeutic approaches.
PSA is a tumour marker routinely measured in the blood of
prostate cancer patients. Rising levels of PSA in the blood
of patients receiving androgen ablation therapy provide the
earliest indication of tumour progression to androgen independence. Once the disease is androgen independent, the
average survival time is 2 years [3].
PSA, which was discovered almost 25 years ago, is not
prostate cancer specific and PSA is found in the normal
prostate at equal or higher levels than is found in prostate
cancer. BPH, prostatitis, as well as prostate cancer, can result
in higher PSA levels in the serum as a result of cellular
re-organization allowing leakage into the surrounding blood
vessels. Furthermore, in prostate cancer the blood vessels are
more permeable, making it easier for PSA to enter the bloodstream. Despite these limitations, PSA screening has changed
the course of the disease, although 17% of men with “normal”
blood PSA levels have cancer cells within the prostate [4];
78%, of men that have elevated levels of PSA (in the range
of 4–9.9 ng/ml) do not have prostate cancer [5]. Due to the
lack of accurate biomarkers that detect, monitor, and quantify
significant PCa or distinguish it from benign disease, many
early-stage PCa patients are treated as harbouring significant
PCa, involving physical over treatment with associated emotional morbidity [6]. It is therefore difficult to reliably detect
early stage prostate cancer without histological examination.
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Evaluating the Gleason score from a tissue specimen taken
at the time of biopsy is still the most widely used diagnostic and prognostic tool for human prostate cancer [7] and
despite assay improvements, PSA testing does not provide
a prediction of disease outcome in newly diagnosed patients
and cannot, by itself, determine the course of treatment and
increasingly active surveillance is used as an alternative. It
is recognized that some patients with late stage may benefit from immunotherapy and in particular vaccine therapy.
We propose that no single molecular approach will fully
elucidate tumour behaviour, necessitating analysis at multiple levels encompassing integrated data sets to determine
the contributions of genomic alterations, host factors and
environmental factors influencing tumour growth and progression; in addition there is currently no curative treatment
for PCa metastatic disease [8]. For this reason, alternative
therapies are also urgently needed and a growing interest is
focusing on immunotherapeutic approaches. To this extent,
no immunological or other markers currently exist that can
be used as validated surrogate clinical endpoints in trials
for immunotherapy. Potential endpoints include immunological biomarkers (measurement of T-cell and cytokine
responses), autoimmunity as a correlate for treatment outcome, and the possible identification of multi-parametric
(multiple) biomarkers using high-throughput proteomic technologies. High-throughput proteomics technologies should
help in the identification of clinically relevant prostate cancer biomarkers at an earlier stage and should help in the future
development of effective vaccines.
2. Clinical proteomics: proteomic methods and their
application in prostate cancer research
Clinical proteomics can have important applications that
may directly change clinical practice by affecting critical elements of care and management. The ‘proteome’ refers to the
entire repertoire of cellular proteins and because proteins are
involved in almost all biological activities, the proteome is
a rich source of biological information. PCa treatment decisions are complicated by the biologic heterogeneity of the
disease and potential proteomic biomarkers of prostate cancer can benefit not only the early detection of disease, but
can also be used for determining cancer risk, stratifying disease stage and grade, monitoring response to therapy, and
in general assisting in therapeutic decision making. Through
careful sample selection, study design and automation in sample handling and processing, proteomic platforms are fast
becoming powerful tools for deriving protein “signatures”
in a wide range of cancers, including prostate, gastrointestinal, hepatocellular, breast, ovarian, and brain, cancers, where
serum protein patterns can be used to differentiate cancer
from controls [9–14].
Cancer proteomics encompasses the identification and
quantitative analysis of the entire complement of proteins in
a biological sample. The commonly used technologies avail-
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able today include 2D-polyacrylamide gel electrophoresis
(2DE), isotope-coded affinity tags, matrix assisted laser desorption ionization-mass spectrometry (MALDI-MS), liquid
chromatography–MS/MS (LC–MS/MS), imaging MS, protein arrays, and autoantibody expression techniques. Chip
based protein and peptide arrays have been used to study
protein–protein, protein–DNA, and protein–RNA interactions [15,16] whilst autoantibody expression studies deal with
tumour-associated antigens shown to be overexpressed in
neoplastic cells and recognized as foreign by the immune
system [17,18]. This method has been successfully used to
identify and track cancer progression and to identify new
“autoantibody signatures” in PCa.
Proteomic profiles on the other hand, consider all serologic proteins (housekeeping protein, enzymes, antibodies,
etc.), identifying multiparametric biomarkers. Protein profiling, assisted by computational statistical analyses, could
yield a quick and reliable diagnosis of prostate cancer
[9,19–21]. We have conducted research over a number of
years on HLA-peptide expression using ESI-MS/MS (electrospray ionization tandem mass spectrometry) [22,23] as
well as protein expression studies using SEDLI-ToF-MS (surface enhanced desorption laser ionization-time of flight MS)
[24–27] and more recently MALDI-MS in conjunction with
ANNs (artificial neural networks) analysis [28].
2.1. Two-dimensional electrophoresis techniques
The human genome is estimated to contain about 35,000
protein-encoding genes and the goal of clinical proteomics
is to develop technologies for the interrogation of the entire
proteome in a search for proteins that can be used as early
biomarkers of disease, or that may predict response to therapy, or the likelihood of relapse after treatment in blood,
urine, or diseased tissue [29,30]. A number of studies have
attempted to identify and analyze proteins in prostate cancer
from less complex mixtures such as seminal fluids [31,32],
urine [33], laser captured cells from cancer tissues [34–36],
albumin-associated proteins from blood sera [36], glycosylated proteins [37–39] or sub cellular fractions from cancer
cells [40]. However, in order to gain a comprehensive understanding of protein networks, a combinatorial approach is
required.
Protein content has been traditionally measured using
low-throughput techniques such as western blotting, and
immunohistochemical staining [41]. 2DE is by far the most
widely used tool in proteomics approaches for more than 25
years [42]. In 2DE, proteins are separated firstly on the basis
of differences in net charge, through a technique known as
isoelectric focusing (IEF) and secondly on differences in their
molecular mass through polyacrylamide gel electrophoresis.
By use of different staining techniques such as silver staining
[43], Coomasie blue stain, florescent dyes [44], or radiolabels, a few thousand proteins can be visualized in a single
gel. In most proteomic-based work, 2DE gel-matching procedure is used to compare two sets of protein mixtures run
under highly standardized conditions. A minimum of two parallel gels have to be run, to obtain a sound basis for applying
the image technology and to directly compare preparations of
protein mixtures from “disease” and “healthy” states. Ratio
analysis is used to detect quantitative changes in proteins
between two samples. The incorporation of fluorescent dyes
in a process termed 2DE differential in-gel electrophoresis
(DIGE) [45] allows the proteins from different populations
to be labeled and analysed on the same gel. This technique
has made comparative expression analysis between samples
much easier. The major advantage of 2DE is that it enables
the simultaneous separation and visualization of thousands of
unknown proteins. Coupling 2DE, followed by spot protein
digestion using a protease such as trypsin, before mass spectrometric analysis has become a method of choice in recent
years (Fig. 1). This approach allows the identity of the protein through the use of peptide mass fingerprinting (PMF)
and tandem mass spectrometry (Fig.1) to be determined.
PMF is a protein identification technique in which the
mass spectrometer is used to measure the mass of proteolytic peptide fragments. The protein is identified by
matching the experimental peptide mass to the corresponding
PMFs found in the protein or nucleotide sequence databases.
Commonly used databases include SWISSPROT, OWL and
NCBInr databases. However, there remain several limitations
to PMF, including a lack of compete and accurately annotated
genome- and protein-sequence databases for a great number of highly homologous human proteins. Although 2DE
coupled with mass spectrometry has been used to identify
protein changes associated with a variety of human cancers,
until recently, it has had limited applications regarding the
study of early stage prostate cancer. The primary reason for
this limitation is the heterogeneous and infiltrative quality
of prostate cancer that makes it difficult to isolate a pure
population of malignant prostatic epithelium. To overcome
this investigative hurdle, different microdissection techniques
have been developed for procuring pure populations of cells
from human tissue sections.
Laser capture microdissection (LCM) has been used as
a potential tool in proteomic research [46–48]. LCM is a
tool used for the enrichment of homogeneous cells from tissue sections overcoming the problem of tissue heterogeneity.
LCM allows for selection of cells with a precision of 3–5 ␮m
under microscopic visualization. Briefly, a stained slide is
placed under a microscope, and a specific cap with a film
of ethylene vinyl acetate (EVA) film, is placed over the tissue. When the cells of interest are located an infrared laser is
fired, which melts the film in the area of the target. Specific
to prostate cancer studies, LCM has been used to procure
pure populations of patient-matched benign and malignant
prostatic epithelium. Protein expression has been compared
using 2DE and differentially expressed proteins identified by
mass spectrometry [49].
Combinations of the methods described above were used
recently by Lexander et al. [50], who analysed differences
in protein expression in prostate cancer of high and low
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Fig. 1. 2DE-gel based approach to clinical proteomics. There are obvious scientific advantages for determining protein complexes and post-translational
modifications when employing this approach. Intact proteins of interest trypsinised which are then subjected to subsequent MS-based analyses using either
single or tandem mass spectrometry. Throughput is usually indirectly proportional to the amount of information gathered. The peptide mass fingerprint (PMF)
generated can then be searched against databases which will provide protein/peptide identity.
aggressiveness, according Gleason score and DNA ploidy
factors that correlate with prognosis. Cell suspensions from
35 prostatectomy specimens, 29 cancer samples and 10
benign samples were prepared by scraping cells from cut
surfaces which were subsequently examined by 2DE. Protein spots that differed quantitatively between sample groups
were identified by MS fingerprinting of tryptic fragments
and MS/MS sequence analysis. They found 39 protein spots
with expression levels that were raised or lowered in correlation with Gleason score and/or DNA ploidy pattern (31
overexpressed in high-malignant cancer, 8 underexpressed).
Of these, 30 were identified by MS. Among overexpressed
proteins were heat-shock, structural, membrane proteins
and enzymes involved in gene silencing, protein synthesis/degradation, mitochondrial protein import (metaxin 2),
detoxification (GST-pi) and energy metabolism. Stromaassociated proteins were generally underexpressed.
In another study, the proteome patterns correlating with
the three anatomical zones of the prostate: the peripheral
(PZ), the transition (TZ) and the central (CZ) zone were investigated [51]. It is proposed that the CZ may be of mesodermal
origin, whereas the other two are of endodermal origin. Cells
were scraped from macroscopically normal areas of PZ, TZ,
and CZ in radical prostatectomy specimens, prepared and
analysed using 2DE. Ten proteins with significant zonal differential expression were identified, via mass spectrometric
fingerprinting of tryptic fragments and selected tandem mass
spectrometry sequence analysis, eight with underexpression
in the CZ versus the PZ and the TZ (arginase II, ATP synthase, cytokeratin 8, lamin A/C, peroxiredoxin 4, protein
disulfide isomerase A3, tropomyosin, and vimentin), and two
with overexpression in the CZ (peroxiredoxin 2 and creatine
kinase B). The PZ and TZ, have epithelium with highly similar major protein expression profiles, whereas the protein
profile of the CZ differed suggesting functional differences.
Despite the effectiveness of hormone therapy in prostate
cancer, all patients with metastatic disease eventually
progress to an androgen-independent state. Once patients
develop androgen-independent cancer, no effective cure
currently exists. Proteins responsive to androgen and
anti-androgen may be involved in the development and
progression of prostate cancer and the ultimate failure of
androgen-ablation therapy. These proteins represent potential
diagnostic and therapeutic targets for improved management
of prostate cancer. Several studies using prostate cancer cell
lines have investigated different aspects of androgen proteins
using proteomic platforms.
Proteins differently expressed in androgen-sensitive
prostate cancer cell-line LNCaP-FGC and androgen-resistant
line LNCaP-r (a model for development of androgen resistance in prostate cancer) were identified by 2DE and mass
spectrometry [52]. HSP60 was upregulated in LNCaPr, nm23 in LNCaP-FGC, and titin (two isoforms) in
either LNCaP-r or LNCaP-FGC. In non-malignant prostate,
HSP60-staining was in the glandular compartment, particularly basal epithelial cells, whilst in prostate cancer, most
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epithelial cells showed moderate–strong staining without
apparent correlation between staining intensity and Gleason grade. This study highlighted that the identification of
HSP60 correlated with clinical results, indicating that this
model can be used for dissection of mechanisms involved
in transformation to androgen resistance and assignment of
protein markers in prostate cancer. Rowland et al. [53], on the
other hand, investigated the effect of androgen (R1881) and
anti-androgen (bicalutamide) on the androgen-responsive
prostate cancer LNCaP cell line using a quantitative gel-based
proteomic approach analysed by 2DE DIGE. Following
androgen supplementation, 108 spots (68 proteins) were
increased and 57 spots (39 proteins) were decreased. Essentially, no difference was observed between control and
anti-androgen-treated samples, confirming the absence of
“off-target” effects of bicalutamide. Identified proteins were
shown to be involved in diverse processes, including the stress
response and intracellular signalling. This study of androgen responses has provided a number of potential candidates
for development as diagnostic/prognostic markers and drug
targets.
Very few studies recently have been carried out using
serum and 2DE approaches and this is primarily due to
the technical challenge of the serum proteome which has
a few proteins are that are so dominant, such as albumin
and immunoglobulins, that they mask detection of other
proteins. Traditionally, serum-based biomarker studies have
used strategies to deplete albumin and immunoglobulins to
increase the sensitivity for the lower abundance proteins
that may represent biomarkers and drug targets. Qin et al.
[54] used this approach, by fractionating serum samples
from patients with prostate cancer and patients with benign
prostate hyperplasia, using anion displacement liquid chromatofocusing chromatography, which separates proteins by
a pH gradient and a positively charged column. Thereafter,
they determined and identified the serum proteins by IEF
gels and 2D DIGE. Several proteins were shown to be differentially expressed in serum from patients with prostate
cancer, which were identified as squamous cell carcinoma
antigen 1 (SCCA1), calgranulin B, and haptoglobin-related
protein. Follow-up studies to determine the specificities of
these findings are still needed.
2.1.1. Technical limitations of two-dimensional
electrophoresis
Clearly, 2DE has limitations when used in proteomics:
one of the weakest points of 2DE is the difficulty in automating the process and its analysis is limited to low-throughput
means, preventing it from being applied to large numbers of
samples in a limited time frame. In addition, 2DE has much
inter-gel variation. 2DE analysis also only resolves the major
components of a protein mixture and the detection of the low
and high molecular mass, of basic and hydrophobic proteins
is inefficient [55,56]. New technologies are now available that
can overcome these limitations and can be adapted for highthroughput processing. The development of techniques such
as multi-dimensional liquid chromatography, combined with
mass spectrometry, to profile peptides from protein digests
allow hundreds of protein to be identified in lysates [57].
MALDI mass spectrometry has also been used for generating patterns of proteins from clinical samples, such as serum
and plasma, and does not rely on protein identity so can be
used to generate a diagnostic fingerprint. MALDI has also
been used for molecular imaging of tissue sections [58] for
generating disease-related protein mass profiles of normal
and malignant breast cells [59].
2.2. MALDI- and SELDI-MS techniques to identify
cancer biomarkers
In the last 10 years or so, MS has increasingly become
the method of choice for analyses of complex protein samples. Two ionization techniques, MALDI and ESI, have had
a major impact on protein biochemistry because they are able
to produce ions in the gas phase without fragmenting the proteins, a problem with older methods. MALDI produces ions
by sublimating (transforming a solid to a gas) and ionizing
the proteins out of a dry, crystalline stage. MALDI mass spectrometry as a high throughput method involves the analysis of
a protein/peptide molecule which requires only a few microliter of sample; the three process steps are: (i) ionization of the
protein and generation of the gas-phase ions; (ii) separation
of ions according to their mass to charge ratio which is based
upon the time it takes for the launched ion to reach the electrode; small ions travel faster and vice versa; (iii) detection of
the ions. In this way a protein/peptide map is generated with
the sensitivity to detect molecules at a few parts per million
comprising tens-of-thousands of protein ions and requires
pattern-recognition type algorithm for analysis. A variant of
MALDI, where the surface of the MALDI target has been
modified, known as SELDI-MS is widely used in cancer proteomics. Protein fragments in the sample bind to the SELDI
surface because they have an affinity for the substances on
the surface [21]. SELDI is an affinity based MS method in
which proteins are selectively adsorbed to a chemically modified surface, and impurities are removed by washing with
buffer. The end product of analysis is a mass spectrum or
chart with a series of spiked peaks, each representing the
ion or charged protein fragment present in a given sample,
where the height of the peak is related to the abundance of
the protein fragment. The size of the peaks and the distance
between them are a fingerprint of the sample and provide a
clue to its identity [21]. Computational artificial intelligence
type systems that learn, adapt and gain experience over time
are uniquely suited for the analysis of mass spectral proteomic data. SELDI-ToF-MS has successfully been used to
detect several disease-associated proteins in complex biological samples, such as tissue/cell lysates, seminal plasma and
serum [31,32,34–36].
LCM from snap frozen prostate cancer serial sections, to
ensure a homogeneous population of cells, in combination
with SELDI-MS has been used in recent studies of prostate
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cancer cells for the identification of proteomic alterations
associated with the early stages in the development of prostate
cancer [60,61]. These studies identified a 24-kDa peak which
was observed at low or high intensity in profiles of prostate
cancer cells in 19 of 27 lesions and at low intensity in 3
of 8 hPIN lesions but was not detectable in matched normal cells. SDS-PAGE analysis followed by MS identified
the protein as the dimeric form of mature growth differentiation factor 15 (GDF15). However, the increased expression
of mature GDF15 protein in prostate cancer cells could not
be explained on the basis of up-regulation of GDF15 mRNA
because reverse transcription-PCR analysis showed similar
amounts of transcript in normal, hPIN, and prostate cancer
cells in the same set of serial sections from which the protein
profiles were obtained. Zheng et al. [60] on the other hand
identified a peak at m/z 24,782.56 ± 107.27 that was correlated with the presence of prostate carcinoma. Furthermore,
using LCM, the origin of this protein, which the authors designated PCa-24, was derived from the epithelial cells of the
prostate. PCa-24 expression was detected in 16 of 17 (94%)
prostate carcinoma specimens but not in paired normal cells.
In addition, this protein was not expressed in any of the 12
benign prostatic hyperplasia specimens that were assayed.
The biological significance of all these observations awaits
future investigations.
A different strategy was used by Liu et al. [62] who used
collagenase digestion to achieve single cells from matched
cancer and non-cancer specimens, which were subsequently
digested and pelleted to give a cell-free supernatant. A
reversed phase hydrophobic ProteinChip Array was used to
generate SELDI patterns from 43 primary prostate tumours,
including 26 with matched non-cancer specimens. Quantitative proteomics was applied to one tissue sample which
identified metalloproteinase inhibitor-1 as the biomarker
down-regulated in cancer and was shown to be localized to
secretory cells.
From a diagnostic standpoint differences in the composition of secreted protein species are especially relevant. It has
been hypothesized that the circulation could contain a pattern
of expressed proteins that form a disease-specific signature
which can be identified using high-throughput MS proteomic
analysis with high specificity and sensitivity [13]. The discriminatory power of SELDI-ToF has sparked tremendous
excitement after the most notable serum proteomic report
using this technology identified 100% of all ovarian cancer
samples and correctly assigned 95% of healthy and benign
subject correctly [13]. SELDI serum studies in PCa have been
used as diagnostic tools for identifying discriminate serum
peaks capable of distinguishing between normal, BPH and
PCa patients with sensitivities from 63 to 100% and specificities from 38 to 100% [12–14,20,63,64]. An issue plaguing
the SELDI analytical platform has been the identification of
the discriminatory biomarkers. However, a study by Adam
et al. [20] which had reported a discriminatory peak at 8946
m/z was recently followed up with a study using a prospective population [65] that identified the same peak, but more
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importantly was found to retain discriminatory value with an
increased expression in the diseased state. Sequence identification was carried out by liquid chromatography–MS/MS
and subsequent immunoassays verified that an isoform of
apolipoprotein A-II (ApoA-II) was the observed 8946 m/z
SELDI peak. Immunohistochemistry revealed that ApoAII was overexpressed in prostate tumours. SELDI-based
immunoassay also revealed that an 8.9-kDa isoform of ApoAII is specifically overexpressed in serum from individuals
with prostate cancer. ApoA-II was also overexpressed in the
serum of individuals with prostate cancer who have normal prostate-specific antigen (0–4.0 ng/ml). Interestingly; no
other cited studies have reported the same discriminatory
peaks even though the same chromatographic affinity surface
has been used by some groups [14,20]. While these different
groups found different serum proteomic patterns, importantly
all of these patterns showed statistically significant discriminatory power for prostate cancer detection. Clearly, these
studies highlight some of the underlying reproducible and
validation problems associated with SELDI-ToF proteomic
platform.
2.3. Combination of SELDI and pre-clinical models
The application of SELDI-ToF-MS technology is not
only limited to the study of serum for early PCa diagnosis; it has also been used in obtaining proteomic patterns of
the secreted proteins in conditioned medium from LNCAP
cells following stimulation with methyltrienolone (R1881)
an AR-specific agonist, 17beta-estradiol and interleukin-6
[66]. This study reported many peaks which were induced
or repressed following stimulation with R1881 or estradiol.
A peak at 11.8 kDa identified as beta-2-microglobulin (B2M)
was increased upon R1881 stimulation. The investigators further reported an increase of B2M in the serum of mice bearing
human PCa xenograft concluding that this protein is elevated in patients with metastatic and androgen-independent
PCa. This study highlights the potential of preclinical models in biomarker discovery. The human PCa xenograft PC339
was recently used in an immune-incompetent nude mouse
model to identify proteins specifically produced by diseased
cells, rather than as the investigators hypothesized proteins
produced due to secondary body defense mechanisms [67].
The concept of this study was that mouse serum would
contain human proteins which originate from the human
PCa xenograft hence allowing potential biomarkers to be
identified. Using one-dimensional gel electrophoresis, liquid chromatography, and mass spectrometry, tumour-derived
human nm23/nucleoside-diphosphate kinase (NME) in conjunction with six human enzymes involved in glycolysis
(fructose-bisphosphate aldolase A, triose-phosphate isomerase, glyceraldehyde-3-phosphate dehydrogenase, alpha
enolase, and lactate dehydrogenases A and B) were identified in the serum of the tumour-bearing mice. The presence
of human NME and glyceraldehyde-3-phosphate dehydrogenase in the serum of PC339-bearing mice was confirmed
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by Western blotting. Although the putative usefulness of
these proteins in predicting prognosis of prostate cancer
remains to be determined, this study illustrates that human
PCa xenograft approach may be a promising tool for the
focused discovery of new prostate cancer biomarkers.
2.4. Current technology validation challenges
By offering a platform that can quickly acquire proteomic
patterns from complex biological samples, SELDI-ToF-MS
has advantages over other conventional analytical techniques.
Like any emerging technology, however, SELDI-ToF-MS
also faces some challenges that can potentially limit its application. One of these challenges is the reproducibility among
proteomic profiling experiments [68]. The chip-to-chip coefficients of variation of peak intensities can vary from 10 to
40% [69]. In 2005 Semmes et al. [70] reported the only systematic study to assess inter-laboratory reproducibility and
validity across populations of prostate cancer patients. They
reported the reproducible detection of quality-control peaks
and the correct classification of nearly all of the same 14
cancer and 14 control samples at six different sites, based
on discriminating peaks identified in these samples 2 years
earlier [71]. As a result, investigators routinely incorporate a
number of approaches to ensure reproducible results. These
include adding a quality control sample on each chip array,
and normalizing spectral data through commercially available or in-house generated computer programs. Undoubtedly,
the appropriate control and assessment of reproducibility
would be required for any future application of SELDI-ToFMS in disease diagnosis. This encouraging study illustrates
that the proteomic platform does indeed have the potential
capability to be used in widespread cancer diagnosis and
prognosis.
The obvious challenge, as stated by many investigators, is
the identification of the important proteins and peptides that
contribute to the proteomic patterns derived from the SELDIbased studies. The low mass resolution and mass accuracy are
inherent to the design of the SELDI system. Some investigators have attempted direct on-chip identification using SELDI
interface on the QqToF instrument, yet achieved little success.
So far the successful identification of proteins and peptides in
the proteomic patterns still relies on other protein separation
and enrichment methodologies.
Every technology has its limitations. The challenges
that SELDI faces would not diminish the demand for
proteomic research in the post genomics era. Our laboratory and others have been employing a combination
of automated robotic chromatographic ZipTip format and
MALDI-ToF-MS to present a powerful and sensitive analysis of pre-fractionated samples [28]. A whole-protein based
top-down separation strategy (Fig. 2) for the identification
of a stage-specific marker in a group comprising 16 patients
with PCa (metastatic and localized disease) and 15 healthy
individuals was used by Lam et al. [72]. MS-profiling, combined with multivariate analysis, yielded 17 serum proteins
specific to metastatic disease. A single protein detected at
m/z 7771 was found to be significantly decreased in the
sera of all the metastatic PCa patients which was isolated
using a C18 prefractionation step, followed by multidimensional liquid chromatography and, finally, two-dimensional
gel electrophoresis. The separation process was monitored by
UV–vis and MALDI-ToF-MS analysis. This strategy identified the unknown protein as platelet factor 4, a chemokine
with prothrombolytic and antiangiogenic activities. Confirmation was achieved using both Western blot analysis
and enzyme-linked immunosorbent assay. This study highlights some of the drawbacks of whole protein based study
design in that additional separation techniques need to be
employed prior to identification. The novelty of the approach
we have adopted is that we can deconvolute the samples
at the protein level for screening biomarkers, followed by
tryptic digestion (bottom-up, Fig. 2) of the same eluted sample which allows us to obtain ANNs directed signatures
and more importantly protein identification with little or
no additional work-up using MALDI-ToF and ESI-MS/MS.
We have used this strategy in our serum studies to investigate new proteome biomarkers for late stage IV melanoma
versus healthy controls [28]. We have developed a reproducible and standardized integrated sample preparation and
mass spectrometry-based proteomic protocol, combined with
ANNs modelling, for protein and tryptic peptide biomarker
discovery and identification in human samples. Another
group have also recently reported [73] the development of
a streamlined, microscale sample preparation protocol that
used either chromatography-based spin columns or 96-well
filtration plates. The approach is based on affinity capture of
albumin (and other carrier proteins) by a novel dye affinity membrane absorber matrix or beads, with subsequent
elution and concentration of the carrier-protein-bound peptides for direct mass spectrometric analysis. Coupled with
the biomarker enrichment protocol, the authors reported that
the high-resolution MALDI O-ToF mass spectra were reproducible for peptide signatures. The raw mass spectra were
then analysed and used to build discriminant disease models
that were challenged with blinded samples for classification
in ovarian cancer patients.
3. Complex data management and analysis systems
The final challenge MALDI/SELDI faces lies largely
in the application of bioinformatics, i.e. the spectral data
management and analysis. The vast amount of spectral
data generated by MALDI/SELDI technology demands
implementation of advanced data management and analysis strategies. The modelling and analysis of data derived
from clinical studies of PCa based on MALDI/SELDI, poses
a significant challenge due to complexity, redundancy and
high dimensionality within these data types [24,74]. Some of
these problems have been overcome by the use of robust computational algorithms such as ANNs, neuro-fuzzy systems
B. Matharoo-Ball et al. / Vaccine 25S (2007) B110–B121
B117
Fig. 2. Top-down and bottom-up proteomics approaches. The top-down approach illustrates a non-gel based approach in which whole proteins are fractionated
via various methods. The fractionated proteins are then subjected to linear MALDI-MS to generate protein mass spectra which can then be analysed by various
bioinformatic statistical tools. The bottom-up approach utilizes primarily non-gel based fractionation of peptides generated from complex protein mixtures.
The rationale is that the digested peptides will reflect the native proteins and that the peptides behave more uniformly in both fractionation and detection.
Digest-generated peptides are then subjected to mass spectrometry analysis. The approach can involve simple peptide mass profiling as well as quantitative
tandem mass spectrometry to yield protein identification and relative protein concentration.
[75,76], support vector machines [77] independent components analysis [78], Bayesian methodologies [79] or decision
trees [80]. Artificial neural networks (ANNs) are a form of
machine learning capable of accurately modelling biological
systems and identifying biomarkers. They have been applied
to a number of diverse areas for the identification of “biologically relevant” molecules, including mass spectrometry
[24,81,82] and genomic micro-array analysis of tumour tissue [83]. It is well known that ANNs are a powerful tool
for the analysis of complex data [84–86] can cope with data
containing a high level of background noise and can be used
to identify the influence of many interacting factors [87]; all
positives which that makes it highly suitable for the study of
mass spectrometry derived data. A number of studies have
indicated the approach can produce generalized models with
a greater accuracy than conventional statistical techniques
in medical diagnostics [88,89] without relying on predetermined relationships as in other computational modelling
techniques. One commonly used algorithm is the backpropagation algorithm applied to the multi-layer perceptron (MLP)
architecture (Fig. 3) [90,91]. The MLP consists of an input
layer, a hidden layer or layers and an output layer. The input
layer represents the independent variables; the hidden layer
mathematically represents features within the data and the
output layer represents the dependant variables or classes.
Each layer comprises a number of nodes interconnected by
weighted links. The weighted links feed values from the previous layers where they are summed and the resulting value
applied to a transfer function (commonly a sigmoid). Thus
the neural network mathematically modifies input values to
produce predicted output values. The algorithm is applied to
the architecture in order to update the weights in the architecture such that the ANN model is able to make accurate
predictions.
Fig. 3. Structure of a multilayer perceptron showing a 3 layer structure,
interconnected nodes and a sigmoidal transfer function. The input layer represents the independent variables – each node representing a parameter. The
hidden layer represents a mathematical feature detection layer – each node
represents a feature. The output layer represents the dependant variables –
each node represents a variable or class.
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B. Matharoo-Ball et al. / Vaccine 25S (2007) B110–B121
In this field we have made significant advances developing
and applying ANN based approaches based on MLP ANNs
trained with back propagation algorithms, for the analysis
of highly dimensional biomedical data. While others have
applied ANNs to the analysis of complex biomedical data,
the level of cross validation is frequently less robust [75,92]
and these models are likely to suffer from overtraining [93].
We have developed and applied extensive random sample
cross-validation approaches that exceed the standards outlined by Michiels et al. [94]. This procedure randomly splits
the data into training (60%), selection (20%) and test (20%)
data sets. The training set is used to stop training, the selection set is used to prevent overtraining and the test set is used
to assess the generalization of the model, i.e. its performance
on blind data. This process is repeated for 50 different random data splits for the same global data set. These validation
approaches are incorporated within our model development
process and have been shown to produce excellent predictive performance with good sensitivity and specificity for
secondary blind data sets [82] and allow the calculation of
confidence intervals for predictions. Within this cross validation framework, we have incorporated a piece-wise additive
approach to model development and parameterization which
is able to identify the most representative subset of biomarkers associated with the classes being modelled in the system
[28]. In this way using this stepwise approach to model
development we define an optimum set of mass spectrometry
derived markers that may be used to define a clinical question.
Other earlier studies have frequently used sensitivity analysis
[e.g. 82,95,96] to identify features within the trained ANN
model. These however suffer from issues associated with
the analysis of high dimensionality data. We have demonstrated that we can successfully overcome issues associated
with complexity, non-linearity and the high dimensionality
of the data, while providing a clear indication of the relationship between biomarker parameters and clinical class. ANNs
have been criticized by some citing that they have limited
transparency and no insight can be gained into the way they
make predictions [93]. The methods we have developed have
made significant advances in gaining information on biological systems by interrogation of ANN models overcoming this
criticism.
To address issues around the biological variability of samples within given class populations we have developed an
approach for the investigation of structures within the population, based on the median predictions across a number of
models for individuals, ranked by value. By examination of
these values and their resulting distribution we may assign
a probability to an individual falling within a given class
(e.g. cancer or non-cancer) and indicate the likelihood of
that individual belonging to that class. Ranking the predictions of the models using this approach allows us to define
a fuzzy boundary between classes [28,97]. We have successfully applied these approaches (as a part of EU Framework 6
ENACT and other programmes) to the analysis of immunological data [98] for the modelling and prediction of clinical
classes. In this instance immunological parameters have been
used to characterize responders and non-responders following whole tumour cell vaccine therapy. We were able through
the creation of an ANN based predictive model to identify and characterize a subset of predictive cellular immune
markers that were associated with response in hormonerefractory prostate cancer patients; these included expression
of cytokines, such as IFN-␥, TNF-␣ and IL-2, from patient
peripheral blood lymphocytes following in vitro stimulation with calcium ionophore, plus T cell proliferation in
response to vaccine antigen [98]. Through understanding
the mechanisms underlying the response to immunotherapy, the potential exists to predict whether an individual
will respond to vaccination prior to treatment or to monitor
patient’s immune response on therapy and adjust the treatment accordingly to ensure appropriate therapy. In all of the
cases where our ANN approaches have been applied the features identified as predictive have been biologically relevant
and subsequently been validated on new cases [28,82]; the
immunological markers identified in [98] shall be validated
using samples from an ongoing Phase IIb clinical trial in the
same patient population.
The approaches described above have had some successful application in the field of prostate cancer [87,99]. Some
studies have utilized ANN approaches to increase the sensitivity and specificity of PSA tests by inclusion of factors such
as percentage free PSA, age and digital rectal examination
results [100,101]. Others have adopted a similar approach
using PSA, age, stage, bone scan results, and treatment
[102]. Additionally they have been utilized to model recurrence post radical prostatectomy [103], to predict response
to immunotherapy [98] and to predict lymph node spread
[104]. Few of these studies use wide ranging cross platform
“omic” studies from multiple points in the disease progression pathway. Many have relied on existing bimolecular
tests, histopathology or clinical features. The need for integrated, multiplatform multicentre studies was highlighted by
Stephan et al. [99]. We have demonstrated through encouraging preliminary results that we can identify response to
therapy based on immunological and proteomic parameters.
We have also conducted preliminary work using ANNs to
integrate data and predicative models across multiple platforms.
4. Future applications
A number of groups are trying to identify proteins/peptides
and analyze from less complex fluids such as urine [33,105].
Proteomics may hold promise for early detection of disease
using proteomic patterns of body fluid samples, diagnosis as
a complement to histopathology based on proteomic patterns,
individualized selection of therapeutic combinations that best
target the entire disease-specific protein network, real-time
assessment of therapeutic efficacy and toxicity, and change
of therapy dependent upon the diseased protein network asso-
B. Matharoo-Ball et al. / Vaccine 25S (2007) B110–B121
ciated with drug resistance. Continued efforts at improving
the sensitivity of mass-spectrometry analyses will allow this
technology to be used on smaller tissue samples of prostate
cancer. An integrated approach to identifying prostate cancer
biomarkers using “Omic” technologies will prove to be of
value at the key clinical assessment points, strongly associating with disease progression status and treatment response.
In particular, we propose that it is imperative to adopt an integrated and complementary technologies and bioinformatics
analysis to elucidate profiles of stage specific disease markers
that can clearly discriminate patient sub-populations and predict treatment outcome and provide innovative biomarkers for
prostate cancer. This presents a unique opportunity to apply
this methodology to identify prostate cancer biomarkers and
determine their clinical utility.
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