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§
§
From the Departments of Microbiology and Molecular Cell Biology,*
Urology,
and
Pathology,
Eastern Virginia Medical School,
Norfolk; and the Virginia Prostate Center,§
Norfolk, Virginia
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Introduction |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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5% of all newly diagnosed cancers in the United
States.1
More than 90% are of the transitional cell
carcinoma (TCC) histology.2
At present, the most reliable
way of diagnosis and surveillance of TCC is by cystoscopic examination
and bladder biopsy for histological confirmation. The invasive and
labor-intensive nature of this procedure presents a challenge to
develop better, less costly, and noninvasive diagnostic tools. Urine
cytology has for many years been the gold standard of the noninvasive
approaches. It has high specificity and provides the advantage over
biopsy of screening the entire urothelium.2,3
However, its
high false-negative rate, particularly for low-grade tumors, has
limited its use as an adjunct to cystoscopy. Many noninvasive molecular diagnostic tests have been developed based on an ever-increasing knowledge about the molecular alterations associated with bladder cancer pathogenesis. The bladder tumor antigen,4 the bladder tumor antigen stat,5 the fibrinogen/fibrin degradation products,6 and the nuclear matrix protein-22 tests,3,7 have been approved by the Food and Drug Administration to be used in conjunction with cystoscopy. Additional molecular assays currently being evaluated for their diagnostic/prognostic utility2,3,8,9 are the Telomerase,10 Immunocyt,11 and hyaluronic acid/hyaluronidase12,13 tests, microsatellite analysis,14 as well as assays detecting blood group antigens,15 carcinoembryonic antigen,16 p53 and retinoblastoma proteins,3 E cadherin,17,18 and various growth factors.9 Because of the molecular heterogeneity of these tumors, it is likely that there will be no single molecular assay that will replace cystoscopy. The identification and simultaneous analysis of a panel of biomarkers, representative of the various biological characteristics of the cancer, has greater potential for improving the early detection/diagnosis of TCC.
For many years, two-dimensional (2D) gel electrophoresis has been the principal tool for the separation and analysis of multiple proteins.19 This methodology, which is able to resolve thousands of proteins in one experiment, provides the highest resolution in protein separation. However, it is labor intensive, requires large quantities of starting material, lacks interlab reproducibility, and is not practical for clinical application. Although development of image analysis software for the comparison of 2D gel-protein maps and automation of protein spot excision20 have facilitated the analysis of the separated proteins, most of the major technical difficulties of 2D gel electrophoresis remain.
Significant technological advances in protein chemistry in the last 2 decades have established mass spectrometry as an indispensable tool for protein study.21-23 Although the resolving power of 2D gels remains unchallenged, the high sensitivity, speed, and reproducibility of mass spectrometry have boosted its application in all aspects of protein analysis, including discovery, identification (ie, peptide mapping, sequencing), and structural characterization. Analogous to the DNA chip technologies that allow the study of gene expression profiles, Ciphergen Biosystems, Inc. (Fremont, CA) has recently developed the ProteinChip technology coupled with SELDI-TOF-MS (surface-enhanced laser desorption/ionization time of flight mass spectrometry) to facilitate protein profiling of complex biological mixtures.24,25 This technology utilizes patented chip arrays to capture individual proteins from complex mixtures that are subsequently resolved by mass spectrometry. This innovative technology has numerous advantages over 2D-polyacrylamide gel electrophoresis: it is much faster, has a high-throughput capability, requires orders of magnitude lower amounts of the protein sample, has a sensitivity for detecting proteins in the picomole to attamole range, can effectively resolve low mass proteins (2,000 to 20,000 Da), and is directly applicable for clinical assay development.
The efficacy of the SELDI technology for discovery of prostate cancer protein markers in serum, seminal plasma, and cell extracts, as well as the development of immunoassays for the detection of known prostate cancer markers has recently been demonstrated by our laboratory.26,27 This report describes our initial evaluation using the ProteinChip SELDI-MS system to detect potential TCC biomarkers in urine, and to assess these biomarkers for the diagnosis of TCC. Multiple protein changes were reproducibly found in the urine of TCC patients, including five potentially novel urinary TCC biomarkers, and seven protein cluster regions consisting of different numbers of proteins observed in the cancer versus the control groups. One of these potential urinary TCC-associated protein biomarkers was identified as belonging to the defensin family of peptides.
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Materials and Methods |
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Urine samples were collected throughout a period of several months
from patients seen in the department of Urology, Eastern Virginia
Medical School. The urine samples were immediately aliquoted and stored
at -80°C in the Tissue and Body Fluid Bank of the Virginia Prostate
Center, until assayed. A total of 94 urine specimens were collected.
The demographics of the TCC patient and control groups are provided in
Table 1
. Healthy controls
(n = 34) included volunteers with no evidence of
disease, and healthy individuals (ie, no history or evidence of
urological cancer) participating in the prostate cancer screening
program at Eastern Virginia Medical School. TCC
(n = 30 patients) was histologically or
cytologically confirmed at the time of specimen collection. In the case
of recurrences none of the patients had received chemo- or
immunotherapy within 3 months before specimen collection. Grading was
assessed using the World Health Organization system. Tumor stage and
grade of patients with TCC are shown in Table 2
. Other urogenital diseases
(n = 30 patients) included clinical or
pathologically confirmed prostatitis (n = 6),
prostatism (n = 9), urinary tract infections
(n = 1), benign prostatic hyperplasia
(n = 12), amyloidosis (n
= 1), inflammation of prostate and bladder (n =
1), bladder outlet obstruction (n = 1), and
prostate cancer (n = 1). One patient with
benign prostatic hyperplasia and one with prostatism had concomitant
prostatitis.
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Urine samples were thawed and briefly centrifuged (1 minute,
10,000 rpm) for the removal of cellular material. Protein concentration
of the supernatants was estimated using the bicinchoninic acid kit
(Pierce, Rockford, IL). Samples were diluted with binding buffer (20
mmol/L Tris, pH 9, 0.4 mol/L NaCl, 0.1% Triton X 100) to equal protein
concentration (2 mg/ml) and subjected to protein size fractionation
using a K30 microspin column (Ciphergen Biosystems, Inc.). After a
30-minute incubation on ice, diluted urine samples were applied to the
spin columns and centrifuged for 3 minutes at 720 x g.
The ProteinChip SELDI analysis was performed similar to that described
in an earlier report.26
Briefly, 5-µl aliquots of the
flow-through (fraction) and the unfractionated sample diluted in 20
mmol/L Tris, pH 9.0, 0.1% Triton X-100, were directly applied onto
different arrays of a SAX2 chip that consists of a strong anion
exchanger chemistry. After a brief wash with H2O,
0.5 µl of saturated matrix solution (
-cyano-4-hydroxycinnamic acid
in 50% acetonitrile and 0.5% trifluoroacetic acid) was applied on the
array and allowed to air dry. The chips were then placed in the PBS-I
mass reader, where nanosecond laser pulses are generated from a
nitrogen laser (337 nm). Spectra were generated using an average of 60
laser shots at each of the following laser intensities (L): 15 (filter
in), 30 (filter in), and 55 (filter out) and manually compared for the
detection of protein differences between the various groups. A protein
or protein cluster was considered to be differentially expressed in the
TCC group, if statistically significant differences in its frequency,
compared to the normal and/or other diseases group, were observed. For
the calculation of protein peak numbers resolved at low laser
intensities, spectra collected at L15 and L30 were combined using the
SELDI software (0.5% variation). External calibration was performed
using bovine insulin (5,733.6 Da), bovine cytochrome C (12,230.9 Da),
and bovine serum albumin (66,410 Da) as standards (Ciphergen
Biosystems, Inc.).
Processing of Bladder Barbotage
Bladder washings were centrifuged at 1,500 rpm for 5 minutes for the collection of cellular material. Supernatants were discarded with the exception of 1 to 2 ml that were used for resuspending the cell pellet. Cytospin preparations of 50 to 100 µl of the resuspended cell pellet were then made, the slides immediately placed in 100% EtOH, and stained with hematoxylin and eosin. The stained slides were examined by a pathologist (SN) to identify the cancer cells, and the individual cancer cells or clusters were procured using the Pixcell 100 Laser Capture Microdissection Microscope (Arcturus Engineering, Mountain View, CA), as previously described.26,28
ProteinChip SELDI Analysis of Cell Lysates
Protein extracts were prepared from 500 to 1,000 microdissected
cells by resuspending the cells in 3 to 5 µl of 20 mmol/L Hepes
containing 0.1% Nonidet P-40, vortexing for 5 minutes, and then
centrifugation at 14,000 rpm for 1 minute. The entire lysate was
applied onto a nickel IMAC3 (Immobilized Metal Affinity) chip array,
and incubated for 1 hour. The chips were washed with 20 mmol/L Tris, pH
7.5, 0.1% Triton X-100, 0.5 mol/L NaCl (5 times), and
HPLC-H20 (5 times). Mass analysis was performed
as described for urine, using either -cyano-4-hydroxycinnamic acid
or sinapinic acid as the energy absorbing molecules.
Statistical Analysis
Sensitivity is defined as the ratio of the TCC patients that contained the biomarker to the total number of TCC patients included in the study. Specificity is defined as the ratio of the individuals that do not have the protein peak and do not have TCC, to the total number of individuals without TCC. Positive predictive value is defined as the probability that an individual with the biomarker has TCC. Negative predictive value is defined as the probability that an individual without the biomarker does not have TCC. Statistics were performed using the chi-square test, after organizing the data in two-dimensional contingency tables and testing for independence of variables. Comparison of peak numbers between the various groups was performed using Student’s t-test. In all cases, P < 0.05 was considered statistically significant.
Immunoassay
The SELDI immunoassay was performed similar to that described in a
previous report.26
Briefly, the arrays of a preactivated
chip (PS1, Ciphergen Biosystems, Inc.), were coated with 4 µl of
Protein G (0.5 mg/ml in 50 mmol/L sodium bicarbonate, pH.8: Sigma
Chemical Co., St. Louis, MO) for 2 to 4 hours at room temperature with
shaking. Residual active sites were subsequently blocked with 1 mol/L
ethanolamine (30 minutes, room temperature), followed by sequential
washes in 15-ml conical tubes with phosphate-buffered saline (PBS) and
0.5% Triton X (3x) and PBS (4x). Two µl of defensins-1, -2, and -3
(HNP-1, -2, and -3) monoclonal antibody (mAb) (IgG1, 0.2 mg/ml;
Serotec), prostate-specific membrane antigen (PSMA) 7E11C5.3 mAb (IgG1,
0.2 mg/ml; kindly provided by Cytogen Corporation, Princeton, NJ) or
mouse IgG1 (30 µg/ml) were applied on the chip and allowed to bind at
4°C, overnight with shaking. Unbound Abs were removed by sequential
washes in 15-ml conical tubes with PBS and 0.5% Triton X (1x), PBS
and 0.1% Triton X (3x), and PBS (4 x). Urine samples were diluted in
100 µl of PBS-0.1% CETAB29
(Sigma Chemical Co., St.
Louis, MO) at a total protein concentration of 0.055 mg/ml, and after a
20-minute incubation in ice, were applied onto the arrays using a
bioprocessor (Ciphergen). After a 3-hour incubation at 4°C, the
unbound urinary proteins were washed away by five washes with PBS-0.1%
CETAB (5 minutes each, room temperature) followed by five washes with
HPLC-H20, -cyano-4-hydroxycinnamic acid added,
and the chip subjected to mass analysis. The spectra were generated
using signal averaging of 90 laser shots.
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Results |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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Ninety-four urine samples were assayed by SELDI mass spectrometry.
Processing on a strong anion exchanger chip surface resolved up to 70
protein peaks. Figure 1a
is a
representative protein spectrum showing the protein masses between
2,000 to 150,000 Da of a single urine specimen. Generation of spectra
was performed at laser intensities 15, 30, and 55, so as to better
resolve low- and high-molecular mass proteins, respectively. As shown,
the SELDI technology was particularly effective in resolving the low
molecular weight (<10 kd) proteins and polypeptides. Interestingly,
urine samples from TCC patients appeared to contain higher numbers of
protein peaks. Collection of data at laser intensities 15 and 30,
generated an average of 33 protein peaks from the TCC urine samples
versus an average of 21 and 22 for the normal and other
urogenital diseases, respectively (P < 0.001).
Similarly, at higher laser intensities (ie, 55-filter out), TCC samples
had an average of 34 protein peaks, versus 27 and 20 in the
normal and other urogenital diseases groups (P
< 0.001 for the normals and P = 0.008 for the other
diseases).
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shows that reproducibility was quite acceptable. The mean, standard
deviation (SD) and coefficient of variation (CV) were determined for
four prominent peaks, designated as proteins 1 to 4. The intraassay
reproducibility, ie, the mean mass, SD (%CV) for protein 1 was
6,440.6 ± 0.92 Da (0.014%); for protein 2, 7,914 ± 3.32 Da
(0.042%); for protein 3, 13,262 ± 0.78 Da (0.006%); and for
protein 4, 66,288 ± 69.3 Da (0.1%) (Figure 1, b and c
; spectra 1
and 2). The interassay reproducibility was determined to be
6,443.3 ± 3.85 Da (0.06%) for protein 1, 7,918.3 ± 6.12 Da
(0.08%) for protein 2, 13,267 ± 8.42 Da (0.06%) for protein 3,
and 66,277 ± 15.56 Da (0.023%) for protein 4 (Figure 1, b and c
;
spectra 1 and 2 versus 3).
Analysis of urine specimens from patients with TCC, patients with other
diseases of the urogenital tract, and normal individuals, revealed that
five prominent protein peaks were preferentially expressed in TCC.
Representative mass spectra and gel views of these proteins are shown
in Figure 2
. One of the proteins was
observed as a doublet or occasionally as a triplet protein peak (Figure 2a)
having an average mass of 3.353 (SD: 21 Da), 3.432 (SD: 24.4 Da),
and 3.470 kd (SD: 6.32 Da), respectively. This protein will be referred
to as marker urinary bladder cancer 1 or UBC1. The average SELDI mass
associated with the other four TCC-associated proteins are UBC2: 9.495
kd (SD: 46.5 Da); UBC3: 44.6 kd (SD: 372.8 Da); UBC4: 100.120 kd (SD:
866.8 Da); and UBC5: 133.190 kd (SD: 772.9 Da) (Figure 2
; b, c, and d).
Of the TCC patient urine samples evaluated, 47% (14 of 30) were
positive for UBC1, 53% (16 of 30) for UBC2, 70% (21 of 30) for UBC3,
43% (13 of 30) for UBC4, and 63% (19 of 30) for UBC5 (Table 3A)
. Frequency of almost all markers was
observed to increase with progression from low-grade (I to II) to
high-grade (III) and low-stage (Ta) to higher stage (T1–3) carcinomas
(data not shown). Nevertheless, larger numbers of samples will need to
be analyzed to confirm these initial observations.
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. The frequency of the markers in this control
group is significantly different from their frequency in the TCC urine
samples (Table 3A)
, and does not appear to change significantly
when aged-matched normal individuals (ie, older than 50 years old) are
considered (Table 3B)
.
Biomarkers UBC1 and UBC4 were found to be present in urine specimens
from patients with other urogenital diseases at a frequency (4 of 30 or
13%) nearly equal to the normal group. Markers UBC2, -3, and -5,
however, were found at relatively higher frequencies: 30% (9 of 30)
for UBC2 and UBC3, and 40% (12 of 30) for UBC5. The difference in the
frequency of the markers between this control (ie, other diseases) and
the TCC cancer group remains statistically significant for markers
UBC1, -3, and -4, but was not significant for markers UBC2 and UBC5
(Table 3A)
.
Based on these results, the overall specificity of the individual
markers for TCC detection range from 70 to 86% (Table 3A)
. Similarly,
the negative predictive values varied from 76 to 85%, and the positive
predictive values from 50 to 62% (Table 3A)
.
Detection of the 3.3/3.4-kd UBC1 Marker in Microdissected Bladder Cancer Cells
To test the cellular expression of the TCC-associated proteins in
urine, bladder cancer cells were microdissected from a bladder
barbotage, cell lysates prepared, and the lysates subjected to SELDI
analysis. A total of six matched (ie, from the same TCC patient)
bladder-washing and urine specimen sets were analyzed. Bladder cancer
cells from all six patients expressed the 3.3/3.4-kd protein that was
also present in 4 of 6 matched urine samples. Figure 3
shows three of the matched sets that
were positive for the marker in both cell lysate and urine. It is
notable, that the doublet peak pattern for this protein found in urine
is maintained in the spectra of the cell lysates. Bladder epithelial
cells from two different bladder barbotage specimens, characterized by
the pathologist as benign, were also found to contain the 3.3/3.4-kd
protein (data not shown). In contrast to the UBC1 protein marker, the
9.5-kd (UBC2), 44-kd (UBC3), 100-kd (UBC4), and 133-kd (UBC5) urinary
proteins were not detected in the bladder cell lysates.
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Searching through protein databases (SWISS-PRO;
www.expasy.ch/tools/tagident.html) for proteins with similar
molecular weight to the five TCC-associated markers, suggested that the
doublet 3.3/3.4-kd marker corresponds to human defensins-2 and
-129
with reported molecular masses of 3.38 and 3.45
kd, respectively. To test this hypothesis, a SELDI-based immunoassay
was performed using a commercially available antibody against human
defensins-1, -2, and -3. A total of three positive and three negative
urine specimens for this marker were analyzed. As shown in Figure 4A
, marker UBC1 was readily captured when
the defensin- Ab was prebound on the chip. In contrast, in the
absence of the defensin Ab (Figure 4B)
or in the presence of an
unrelated Ab, no specific binding above the background levels was
detected (Figure 4, C and D)
. Urine specimens that were UBC1-negative
by SELDI direct binding remained UBC1-negative by the SELDI immunoassay
(Figure 4E)
.
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In addition to the detection of differences in the frequency of
individual protein peaks between the TCC and the control groups,
regional differences in the mass spectra were also observed. Table 4
shows the number and percent positive,
and P values for the seven protein cluster regions that
demonstrated differences between the TCC group and control groups and
Figure 5
shows the spectra of these
regions. The protein pattern displayed by five of these clusters,
including 4,950 to 5,150 Da (Figure 5a)
, 5,710 to 6,000 Da (Figure 5a)
,
6,758 to 7,750 Da (Figure 5b)
, 15,000 to 16,000 Da (Figure 5c)
, and
85,000 to 92,000 Da (Figure 5f)
, was found to be
significantly different in urine samples from TCC patients than the
patterns found in the healthy and other disease controls. The only
exception was the 37,500 to 40,000 Da (Figure 5d)
region that was found
not to be statistically (0.75 < P < 0.9)
different between the TCC and the other diseases group. Interestingly,
a protein cluster with masses ranging from 79,500 to 82,000 Da (Figure 5e)
was found in 65% of the healthy control group and in 80% of urine
samples from the non-TCC disease group but in only 33% of the TCC
group. The difference in frequency of this cluster between the control
and the TCC groups was statistically significant.
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Combination of the TCC Markers Increases Sensitivity in TCC Detection
The SELDI technology provides the advantage of analyzing
multiple markers simultaneously. Therefore, to maximize the diagnostic
utility of the TCC-associated biomarkers, the individual proteins UBC1
to UBC5 and seven protein clusters were placed in various combinations
to form a biomarker panel, and the urine spectra for all groups
re-analyzed. A biomarker combination was classified as positive if any
marker of the combination set was present in a sample, and negative if
none of the markers were detected in the specimen. Using these
biomarkerpanels, the sensitivity for detecting TCC increased from 43 to
70%, using individual biomarkers (Table 3)
to 83 to 87%. (Table 5)
. However, as expected, there was a
compromise in the overall specificity of the assay, from an average of
81% for single markers to 67% using a combination of biomarkers
(Table 5)
. There was a notable increase in the negative predictive
values of the assay to 90% versus an average of 79% for a
single marker, and the positive predictive values of 54% (Table 5)
was
similar to the average positive predictive values of 58% for a single
assay (Table 3)
.
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All three of the combination sets shown on Table 5
, were capable of
detecting low-grade and low-stage carcinomas with relatively high
sensitivity. As shown in Table 6
, the
3.3/3.4-, 44-, and 85- to 92-kd combination set detects 67% of grade I
and II and 71% of stage Ta carcinomas. The 3.3/3.4-, 9.5-, 100-kd, and
3.3/3.4-, 9.5- and 85- to 92-kd combination sets provided a slightly
superior sensitivity of 78% for grades I and II and 79% for Ta
carcinomas. Most notable was that the detection rate of the SELDI urine
assay was markedly superior to the 33% rate obtained by either voided
urine or bladder washing cytology for these same patients. All
combination biomarker panels, provided higher sensitivities (86 to
91%) in detecting grade III carcinomas and with the exception of the
3.3/3.4-, 9.5-, and 100-kd set, stage T1 to T3 tumors (93%).
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Discussion |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
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Two-dimensional gel electrophoresis has been the classical proteomic tool for protein separation and analysis. It has vastly contributed to our current knowledge of the proteomics of bladder cancer by generating disease-associated protein databases,30,31 leading to the identification of potential TCC-associated biomarkers.32,33 Although the ability of 2D to resolve thousands of proteins remains unchallenged, the complexity of the experimental procedure involved and the very high amounts of starting material, makes it impractical for direct clinical application.
Wright and colleagues,26 and Paweletz and colleagues,27 have used the Protein Chip mass spectrometry technology to detect potentially novel biomarkers of prostate cancer in serum, seminal plasma,26 and cell extracts.26,27 Furthermore, chip-based multiplex immunoassays for the simultaneous detection of known prostate cancer markers are currently under development.26,34 Our results expand these initial findings, and further support the applicability of this technology for protein profiling of urine samples as a method of high diagnostic sensitivity for TCC.
With the exception of the 79.5- to 82-kd protein cluster that appeared more frequently in the normal compared to the TCC group, the rest of individual markers and clusters were TCC-associated. This may be considered as a reflection of increased protein excretion in urine of bladder cancer patients detected herein and reported earlier18,35 and attributed either to leakage of serum proteins from the tumor neovasculature, or to increased turnover of bladder cancer cells.18 If this holds true however, the specificity of the assay may be affected by the presence of renal disease, and this will have to be addressed in future studies.
In the current study quantitative differences of proteins between the various groups have not been addressed, which may provide an additional explanation for the lack of detection of additional normal-associated protein peaks. This is because of the fact that detection and confirmation of quantitative differences by mass spectrometry is not yet standardized and, although feasible, is technically very challenging.22 The development of a reliable method of protein quantification as well as the application of different types of chip chemistries that promises to increase the resolving power of the assay, are ongoing efforts to detect additional normal- as well as TCC- associated urinary proteins.
Searching the protein databases suggested that the 3.3/3.4-kd
TCC-associated protein (biomarker UBC1) might be a member of the
defensin family of peptides. The identity was confirmed to be
defensins-1 and -2 using a SELDI immunoassay. Defensins form a family
of small peptides with antimicrobial, cytotoxic, and anti-tumor
activities.36
Based on their primary structure, two
families, the - and ß-defensins have been characterized in
humans.37
ß-defensins have been found to be primarily
expressed in epithelial cells of the kidneys, skin, and respiratory
system38,39
whereas -defensins in neutrophils and
intestinal Paneth cells.40
Recent data further demonstrate
the immunolocalization of -defensins in Langerhans cells and duct
cells of submandibular glands of oral carcinoma
patients41,42
as well as endothelial and smooth muscle
cells of coronary vessels.43
The presence of defensin
peptides in bladder cancer cells has not been reported before. This
finding may be secondary to release of these peptides from tumor
activated neutrophils. Alternatively, expression of these peptides by
the bladder cells cannot be ruled out and will have to be tested by
studies at the mRNA level.
The presence of the Paneth cell-specific defensin in urine from ileal neobladder has been demonstrated,44 nevertheless, the presence of that type of defensin in urine samples from the same patients before cystectomy could not be shown. The Ab used in our study recognizes the neutrophil-specific defensins HNP1, 2, and 3, providing an explanation for the different results obtained in the two studies.
The presence of the defensin polypeptides in benign bladder cells suggests that, in contrast to urine, the presence of this marker is not tumor-specific at the cellular level. However, changes in its amount during tumorigenesis are expected to occur, resulting in the detection of higher levels in the urine from TCC patients. Alternatively, the presence of these polypeptides may also be indicative of the initial phases of tumorigenesis, not yet detected by the pathologist. In support of this hypothesis is the fact that one patient was found with TCC stage T1, grade II 3 months after the collection of the bladder barbotage. In any case, development of a sensitive immunoassay to monitor quantitative changes of this peptide may provide useful information with regard to tumor development and progression.
The mass of the UBC2 to UBC5 TCC-associated urinary proteins matches a variety of proteins, such that their identity cannot be made with any certainty. Therefore, studies are ongoing to purify and identify these proteins by tryptic peptide mapping20 and amino acid sequencing.23
With the exception of the defensins, peaks of similar mass to the UBC2 to UBC5 urinary biomarkers were not detected in cancer cells procured from cytology specimens. Although utilization of suboptimal cell lysis conditions cannot be ruled out, there are several additional possible explanations for this result, including identification of these markers as extracellular proteins, or alternatively, as proteolytic fragments of intracellular proteins.
The sensitivity of each individual marker (UBC1 to UBC5) or each of the seven protein clusters for detecting TCC was found to be relatively low. However, combining the individual markers and protein clusters increased the overall TCC detection rate and the rate for low-grade and low-stage carcinomas. Larger scale studies addressing the efficacy of these and other markers, either used individually or in combination, for detecting the different stages/grades of TCC will be essential. Nevertheless, based on the exploratory study described in this report, the SELDI combinatorial approach provided a sensitivity of 78% in detecting grade I and II carcinomas, compared to sensitivities of 20 to 30% by voided urine cytology.3 Although these results are preliminary, this observation coupled with the prospective for further marker addition, suggests the potential of the SELDI proteomic approach for detecting early TCC.
The combinatorial biomarker analysis approach increased the sensitivity, but decreased the specificity of the assay. However, it should be noted that this approach relies on simple conventional statistical methods. To reliably process the enormous amount of SELDI data, and increase the overall accuracy of the assay, some type of artificial intelligence program, such as fuzzy logic, cluster analysis, or neural network (ANN) will be most likely required. ANNs previously developed to predict outcome in prostate45 or bladder cancers46 based on clinicopathological and molecular markers have provided promising results. Artificial intelligence programs for the ProteinChip SELDI system are currently under development. Further improvements in the diagnostic accuracy of the SELDI assay will have to take into consideration the reproducibility of repeat testing of urine from the same individual, as well as possible diurnal variations.
In conclusion, the ability to simultaneously test for multiple protein changes by the Protein Chip SELDI system, increases the diagnostic sensitivity, and with appropriate statistical methodology, has the potential to improve the urinary diagnosis of TCC. Larger scale studies to establish the potential of these findings and correlate the SELDI diagnostic approach with known TCC urinary markers are in progress.
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Acknowledgements |
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Footnotes |
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Supported in part by grants from the American Cancer Society (IRG-93-036-06), the National Cancer Institute Early Detection Research Network (CA85067), and the Virginia Prostate Center. A. V. is recipient of a fellowship from the American Foundation of Urologic Disease and Hoechst Marion Roussel Inc.
Current address of S. Mendrinos: Department of Pathology, Emory University, Atlanta, GA.
Accepted for publication January 9, 2001.
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 C. I. Balog, P. J. Hensbergen, R. Derks, J. J. Verweij, G. J. van Dam, B. J. Vennervald, A. M. Deelder, and O. A. Mayboroda Novel Automated Biomarker Discovery Work Flow for Urinary Peptidomics Clin. Chem., January 1, 2009; 55(1): 117 - 125. [Abstract] [Full Text] [PDF]
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 A. Navas-Acien and E. Guallar Measuring Arsenic Exposure, Metabolism, and Biological Effects: The Role of Urine Proteomics Toxicol. Sci., November 1, 2008; 106(1): 1 - 4. [Full Text] [PDF]
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D. McLerran, W. E. Grizzle, Z. Feng, W. L. Bigbee, L. L. Banez, L. H. Cazares, D. W. Chan, J. Diaz, E. Izbicka, J. Kagan, et al. Analytical Validation of Serum Proteomic Profiling for Diagnosis of Prostate Cancer: Sources of Sample Bias Clin. Chem., January 1, 2008; 54(1): 44 - 52. [Abstract] [Full Text] [PDF]
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 D. Fliser, J. Novak, V. Thongboonkerd, A. Argiles, V. Jankowski, M. A. Girolami, J. Jankowski, and H. Mischak Advances in Urinary Proteome Analysis and Biomarker Discovery J. Am. Soc. Nephrol., April 1, 2007; 18(4): 1057 - 1071. [Abstract] [Full Text] [PDF]
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 K. Mosley, F. W. K. Tam, R. J. Edwards, J. Crozier, C. D. Pusey, and L. Lightstone Urinary proteomic profiles distinguish between active and inactive lupus nephritis Rheumatology, December 1, 2006; 45(12): 1497 - 1504. [Abstract] [Full Text] [PDF]
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T. Jahnukainen, D. Malehorn, M. Sun, J. Lyons-Weiler, W. Bigbee, G. Gupta, R. Shapiro, P. S. Randhawa, R. Pelikan, M. Hauskrecht, et al. Proteomic Analysis of Urine in Kidney Transplant Patients with BK Virus Nephropathy J. Am. Soc. Nephrol., November 1, 2006; 17(11): 3248 - 3256. [Abstract] [Full Text] [PDF]
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 N. S. Azad, N. Rasool, C. M. Annunziata, L. Minasian, G. Whiteley, and E. C. Kohn Proteomics in Clinical Trials and Practice: Present Uses and Future Promise Mol. Cell. Proteomics, October 1, 2006; 5(10): 1819 - 1829. [Abstract] [Full Text] [PDF]
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T. Pisitkun, R. Johnstone, and M. A. Knepper Discovery of Urinary Biomarkers Mol. Cell. Proteomics, October 1, 2006; 5(10): 1760 - 1771. [Abstract] [Full Text] [PDF]
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 M. Bellew, M. Coram, M. Fitzgibbon, M. Igra, T. Randolph, P. Wang, D. May, J. Eng, R. Fang, C. Lin, et al. A suite of algorithms for the comprehensive analysis of complex protein mixtures using high-resolution LC-MS Bioinformatics, August 1, 2006; 22(15): 1902 - 1909. [Abstract] [Full Text] [PDF]
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U. Muller, G. Ernst, C. Melle, R. Guthke, and F. von Eggeling Convergence of the proteomic pattern in cancer Bioinformatics, June 1, 2006; 22(11): 1293 - 1296. [Abstract] [Full Text] [PDF]
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 G. Ernst, C. Melle, B. Schimmel, A. Bleul, and F. von Eggeling Proteohistography--Direct Analysis of Tissue with High Sensitivity and High Spatial Resolution Using ProteinChip Technology J. Histochem. Cytochem., January 1, 2006; 54(1): 13 - 17. [Abstract] [Full Text] [PDF]
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J. Li, J. Zhao, X. Yu, J. Lange, H. Kuerer, S. Krishnamurthy, E. Schilling, S. A. Khan, S. Sukumar, and D. W. Chan Identification of Biomarkers for Breast Cancer in Nipple Aspiration and Ductal Lavage Fluid Clin. Cancer Res., December 1, 2005; 11(23): 8312 - 8320. [Abstract] [Full Text] [PDF]
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J. Li, R. Orlandi, C. N. White, J. Rosenzweig, J. Zhao, E. Seregni, D. Morelli, Y. Yu, X.-Y. Meng, Z. Zhang, et al. Independent Validation of Candidate Breast Cancer Serum Biomarkers Identified by Mass Spectrometry Clin. Chem., December 1, 2005; 51(12): 2229 - 2235. [Abstract] [Full Text] [PDF]
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G. L. Hortin and B. Meilinger Cross-Reactivity of Amino Acids and Other Compounds in the Biuret Reaction: Interference with Urinary Peptide Measurements Clin. Chem., August 1, 2005; 51(8): 1411 - 1419. [Abstract] [Full Text] [PDF]
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G. Malik, M. D. Ward, S. K. Gupta, M. W. Trosset, W. E. Grizzle, B.-L. Adam, J. I. Diaz, and O. J. Semmes Serum Levels of an Isoform of Apolipoprotein A-II as a Potential Marker for Prostate Cancer Clin. Cancer Res., February 1, 2005; 11(3): 1073 - 1085. [Abstract] [Full Text] [PDF]
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J. M. Koomen, L. N. Shih, K. R. Coombes, D. Li, L.-c. Xiao, I. J. Fidler, J. L. Abbruzzese, and R. Kobayashi Plasma Protein Profiling for Diagnosis of Pancreatic Cancer Reveals the Presence of Host Response Proteins Clin. Cancer Res., February 1, 2005; 11(3): 1110 - 1118. [Abstract] [Full Text] [PDF]
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O. J. Semmes, Z. Feng, B.-L. Adam, L. L. Banez, W. L. Bigbee, D. Campos, L. H. Cazares, D. W. Chan, W. E. Grizzle, E. Izbicka, et al. Evaluation of Serum Protein Profiling by Surface-Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry for the Detection of Prostate Cancer: I. Assessment of Platform Reproducibility Clin. Chem., January 1, 2005; 51(1): 102 - 112. [Abstract] [Full Text] [PDF]
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E. O'Riordan, T. N. Orlova, J. Mei J, K. Butt, P. M. Chander, S. Rahman, M. Mya, R. Hu, J. Momin, E. W. Eng, et al. Bioinformatic Analysis of the Urine Proteome of Acute Allograft Rejection J. Am. Soc. Nephrol., December 1, 2004; 15(12): 3240 - 3248. [Abstract] [Full Text] [PDF]
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 Z. Zhang, R. C. Bast Jr., Y. Yu, J. Li, L. J. Sokoll, A. J. Rai, J. M. Rosenzweig, B. Cameron, Y. Y. Wang, X.-Y. Meng, et al. Three Biomarkers Identified from Serum Proteomic Analysis for the Detection of Early Stage Ovarian Cancer Cancer Res., August 15, 2004; 64(16): 5882 - 5890. [Abstract] [Full Text] [PDF]
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A. Vlahou, A. Giannopoulos, B. W. Gregory, T. Manousakas, F. I. Kondylis, L. L. Wilson, P. F. Schellhammer, G. L. Wright Jr, and O. J. Semmes Protein Profiling in Urine for the Diagnosis of Bladder Cancer Clin. Chem., August 1, 2004; 50(8): 1438 - 1441. [Full Text] [PDF]
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S. G. Soltys, Q.-T. Le, G. Shi, R. Tibshirani, A. J. Giaccia, and A. C. Koong The Use of Plasma Surface-Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry Proteomic Patterns for Detection of Head and Neck Squamous Cell Cancers Clin. Cancer Res., July 15, 2004; 10(14): 4806 - 4812. [Abstract] [Full Text] [PDF]
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C. Melle, G. Ernst, B. Schimmel, A. Bleul, S. Koscielny, A. Wiesner, R. Bogumil, U. Moller, D. Osterloh, K.-J. Halbhuber, et al. A Technical Triade for Proteomic Identification and Characterization of Cancer Biomarkers Cancer Res., June 15, 2004; 64(12): 4099 - 4104. [Abstract] [Full Text] [PDF]
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E. P. Diamandis How Are We Going to Discover New Cancer Biomarkers? A Proteomic Approach for Bladder Cancer Clin. Chem., May 1, 2004; 50(5): 793 - 795. [Full Text] [PDF]
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J. T. Wadsworth, K. D. Somers, L. H. Cazares, G. Malik, B.-L. Adam, B. C. Stack Jr., G. L. Wright Jr., and O. J. Semmes Serum Protein Profiles to Identify Head and Neck Cancer Clin. Cancer Res., March 1, 2004; 10(5): 1625 - 1632. [Abstract] [Full Text] [PDF]
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J. Koopmann, Z. Zhang, N. White, J. Rosenzweig, N. Fedarko, S. Jagannath, M. I. Canto, C. J. Yeo, D. W. Chan, and M. Goggins Serum Diagnosis of Pancreatic Adenocarcinoma Using Surface-Enhanced Laser Desorption and Ionization Mass Spectrometry Clin. Cancer Res., February 1, 2004; 10(3): 860 - 868. [Abstract] [Full Text] [PDF]
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 J. T. Wadsworth, K. D. Somers, B. C. Stack Jr, L. Cazares, G. Malik, B.-L. Adam, G. L. Wright Jr, and O. J. Semmes Identification of Patients With Head and Neck Cancer Using Serum Protein Profiles Arch Otolaryngol Head Neck Surg, January 1, 2004; 130(1): 98 - 104. [Abstract] [Full Text] [PDF]
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M. A. Rogers, P. Clarke, J. Noble, N. P. Munro, A. Paul, P. J. Selby, and R. E. Banks Proteomic Profiling of Urinary Proteins in Renal Cancer by Surface Enhanced Laser Desorption Ionization and Neural-Network Analysis: Identification of Key Issues Affecting Potential Clinical Utility Cancer Res., October 15, 2003; 63(20): 6971 - 6983. [Abstract] [Full Text] [PDF]
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 K. R. Kozak, M. W. Amneus, S. M. Pusey, F. Su, M. N. Luong, S. A. Luong, S. T. Reddy, and R. Farias-Eisner Identification of biomarkers for ovarian cancer using strong anion-exchange ProteinChips: Potential use in diagnosis and prognosis PNAS, October 14, 2003; 100(21): 12343 - 12348. [Abstract] [Full Text] [PDF]
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B. Ye, D. W. Cramer, S. J. Skates, S. P. Gygi, V. Pratomo, L. Fu, N. K. Horick, L. J. Licklider, J. O. Schorge, R. S. Berkowitz, et al. Haptoglobin-{alpha} Subunit As Potential Serum Biomarker in Ovarian Cancer: Identification and Characterization Using Proteomic Profiling and Mass Spectrometry Clin. Cancer Res., August 1, 2003; 9(8): 2904 - 2911. [Abstract] [Full Text] [PDF]
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C. Melle, G. Ernst, B. Schimmel, A. Bleul, S. Koscielny, A. Wiesner, R. Bogumil, U. Moller, D. Osterloh, K.-J. Halbhuber, et al. Biomarker Discovery and Identification in Laser Microdissected Head and Neck Squamous Cell Carcinoma with ProteinChip(R) Technology, Two-dimensional Gel Electrophoresis, Tandem Mass Spectrometry, and Immunohistochemistry Mol. Cell. Proteomics, July 1, 2003; 2(7): 443 - 452. [Abstract] [Full Text] [PDF]
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B. J. Duggan, J. J. McKnight, K. E. Williamson, M. Loughrey, D. O'Rourke, P. W. Hamilton, S. R. Johnston, C. C. Schulman, and A. R. Zlotta The Need to Embrace Molecular Profiling of Tumor Cells in Prostate and Bladder Cancer Clin. Cancer Res., April 1, 2003; 9(4): 1240 - 1247. [Abstract] [Full Text] [PDF]
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Y. Qu, B.-L. Adam, Y. Yasui, M. D. Ward, L. H. Cazares, P. F. Schellhammer, Z. Feng, O. J. Semmes, and G. L. Wright Jr. Boosted Decision Tree Analysis of Surface-enhanced Laser Desorption/Ionization Mass Spectral Serum Profiles Discriminates Prostate Cancer from Noncancer Patients Clin. Chem., October 1, 2002; 48(10): 1835 - 1843. [Abstract] [Full Text] [PDF]
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J. Li, Z. Zhang, J. Rosenzweig, Y. Y. Wang, and D. W. Chan Proteomics and Bioinformatics Approaches for Identification of Serum Biomarkers to Detect Breast Cancer Clin. Chem., August 1, 2002; 48(8): 1296 - 1304. [Abstract] [Full Text] [PDF]
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L. H. Cazares, B.-L. Adam, M. D. Ward, S. Nasim, P. F. Schellhammer, O. J. Semmes, and G. L. Wright Jr. Normal, Benign, Preneoplastic, and Malignant Prostate Cells Have Distinct Protein Expression Profiles Resolved by Surface Enhanced Laser Desorption/Ionization Mass Spectrometry Clin. Cancer Res., August 1, 2002; 8(8): 2541 - 2552. [Abstract] [Full Text] [PDF]
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 T. K. Bane, J. F. LeBlanc, T. D. Lee, and A. D. Riggs DNA affinity capture and protein profiling by SELDI-TOF mass spectrometry: effect of DNA methylation Nucleic Acids Res., July 15, 2002; 30(14): e69 - e69. [Abstract] [Full Text] [PDF]
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B.-L. Adam, Y. Qu, J. W. Davis, M. D. Ward, M. A. Clements, L. H. Cazares, O. J. Semmes, P. F. Schellhammer, Y. Yasui, Z. Feng, et al. Serum Protein Fingerprinting Coupled with a Pattern-matching Algorithm Distinguishes Prostate Cancer from Benign Prostate Hyperplasia and Healthy Men Cancer Res., July 1, 2002; 62(13): 3609 - 3614. [Abstract] [Full Text] [PDF]
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C. Rosty, L. Christa, S. Kuzdzal, W. M. Baldwin, M. L. Zahurak, F. Carnot, D. W. Chan, M. Canto, K. D. Lillemoe, J. L. Cameron, et al. Identification of Hepatocarcinoma-Intestine-Pancreas/Pancreatitis-associated Protein I as a Biomarker for Pancreatic Ductal Adenocarcinoma by Protein Biochip Technology Cancer Res., March 1, 2002; 62(6): 1868 - 1875. [Abstract] [Full Text] [PDF]
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P. R. Srinivas, S. Srivastava, S. Hanash, and G. L. Wright Jr Proteomics in Early Detection of Cancer Clin. Chem., October 1, 2001; 47(10): 1901 - 1911. [Abstract] [Full Text] [PDF]
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