Download Assessment of 54 Biomarkers for Biopsy

1966
Assessment of 54 Biomarkers for Biopsy-Detectable
Prostate Cancer
Dipen J. Parekh,1 Donna Pauler Ankerst,1 Jacques Baillargeon,2 Betsy Higgins,1
Elizabeth A. Platz,3 Dean Troyer,5 Javier Hernandez,6 Robin J. Leach,1,4
Anna Lokshin,7 and Ian M. Thompson1
Departments of 1Urology, 2Pathology, and 3Cellular and Structural Biology, University of Texas Health Science Center,
and 4Brooke Army Medical Center, San Antonio, Texas; 5Department of Preventive Medicine and Community Health,
University of Texas Medical Branch, Galveston, Texas; 6Department of Epidemiology, Johns Hopkins Bloomberg
School of Public Health, Baltimore, Maryland; and 7University of Pittsburgh Cancer Center, Pittsburgh, Pennsylvania
Abstract
Objective: We analyzed the association of 54 biomarkers
from seven classes including adipokines, immune
response metalloproteinases, adhesion molecules, and
growth factors with prostate cancer risk adjusting for
the Prostate Cancer Prevention Trial (PCPT) risk score.
Methods: A total of 123 incident prostate cancer cases
and 127 age-matched controls were selected from
subjects in the San Antonio Center for Biomarkers of
Risk of Prostate Cancer cohort study. Prediagnostic
serum concentrations were measured in the sample
collected at baseline using LabMAP technology. The
odds ratios (OR) of prostate cancer risk associated with
serum concentrations of 54 markers were estimated
using univariate conditional logistic regression before
and after adjustment for the PCPT risk score. Two-way
hierarchical unsupervised clustering techniques were
used to evaluate whether the 54-marker panel distinguished cases from controls.
Results: Vascular endothelial growth factor, resistin,
interleukin 1Ra (IL-1Ra), granulocyte colony-stimulating
factor, matrix metalloproteinase-3, plasminogen activator inhibitor, and kallikrein-8 were statistically significantly (P < 0.05) underexpressed in prostate cancer
cases, and A-fetoprotein was statistically significantly
overexpressed in prostate cancer cases, but all had area
underneath the receiver-operating characteristic curve
<60%; none were statistically significant adjusting for
multiple comparisons (P < 0.0008) or after adjustment
for the PCPT risk score. Statistical clustering of patients
by the marker panel did not distinguish a separate
group of cases from controls.
Conclusions: This age-matched case-control study did
not support findings of increased diagnostic potential
from a 54-marker panel when compared with the
conventional risk factors incorporated in the PCPT
risk calculator. Future discovery of new biomarkers
should always be tested and compared against
conventional risk factors before applying them in
clinical practice. (Cancer Epidemiol Biomarkers Prev
2007;16(10):1966 – 72)
Introduction
The pathogenesis of prostate cancer is complex and likely
multifactorial. Several etiologic factors have been proposed involving a broad range of molecular systems,
pathways, and mechanisms. Adipokines, markers of
immune response, metalloproteinases, adhesion molecules, hormones, and several growth factors have been
implicated in the process of both cancer initiation and
progression in the prostate as well as in other organ
systems (1-10). Several from these groups have been
proposed as biomarkers for cancer detection as well as
for prognosis and follow-up in prostate cancer based
on their detection in different biospecimens (10-13). The
rapid evaluation of panels of biologically motivated
markers for prostate cancer is critical for the development of promising markers for early detection. Prostatespecific antigen (PSA) is currently the most widely used
marker for the early detection of prostate cancer. It is
Received 4/4/07; revised 6/2/07; accepted 7/11/07.
Requests for reprints: Dipen Parekh, Department of Urology, University of Texas
Health Sciences Center at San Antonio, 7703 Floyd Curl Drive, San Antonio,
TX 78284-7802. Phone: 210-567-5640; Fax: 210-567-6868. E-mail: [email protected]
Copyright D 2007 American Association for Cancer Research.
doi:10.1158/1055-9965.EPI-07-0302
important in the initial phases of biomarker development
to compare against this current gold standard. It has been
recently shown that other risk factors contribute to PSA
for early detection of prostate cancer of high-grade
disease, including digital rectal exam (DRE), family
history (positive if brother, father, or son had prostate
cancer), race (African Americans versus other), age, and
history of a prior negative prostate biopsy. These
variables are integrated in the form of a risk score that
acts as a weighted average of the most prominent risk
factors and is more efficient than adding several risk
factors (14). For early evaluation of biomarkers, it is
also worthwhile to ensure adjustment for these effects in
the analysis and as well as to compare the effect of the
new markers against these as a benchmark comparison.
Case-control studies provide the most efficient mechanism for evaluating large panels of markers as they
ensure representative numbers of cases.
The purpose of this investigation was to examine the
association of a panel of 54 markers with prostate cancer
risk in a nested age-matched case control study of 123
cases and 127 controls in the San Antonio Center for
Biomarkers of Risk of Prostate Cancer (SABOR) study
cohort. The panel includes adipokine markers of immune
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Cancer Epidemiology, Biomarkers & Prevention
response, metalloproteinases, adhesion molecules, hormones, growth factors, tumor markers, and other independent molecules not classified as yet under any specific
group. This cohort has the advantage that measures of
PSA (measured within 1 year of the serum draw for this
analysis), family history, age, race, DRE finding, and
history of a prior biopsy are available on these men for
diagnostic comparison of the new markers.
Materials and Methods
Study Population. We identified incident prostate
cancer cases and controls from members of the SABOR
study cohort. SABOR is a prospective screening cohort of
f3,500 men from San Antonio and South Texas. This
cohort is an effort of the San Antonio Clinical and
Epidemiologic Center of the Early Detection Research
Network and is supported by the National Cancer
Institute. Recruitment of a multiethnic population-based
sample was achieved using outreach clinics throughout
South Texas. Healthy men, without a history of prostate
cancer, were eligible for participation. Participant enrollment began in March 2001 with annual follow-up
examinations. A concerted effort was made to oversample ethnic minorities and medically underserved
populations. After informed consent, men completed an
extensive series of instruments (demographics, diet,
quality of life, family history, ethnicity/race, American
Urological Association symptom score), provided biological samples, and underwent a directed physical
examination including DRE, height, weight, and anthropometric measures. Serum was collected and stored at
the University of Texas Health Sciences Center at San
Antonio on all SABOR participants at baseline. Blood
sample tubes for serum micronutrient and lipid analyses
were collected. Clotted blood was processed in a refrigerated centrifuge, and serum was stored at 70jC.
At each annual visit, a brief survey is taken of medical
problems, and whether prostate cancer had been
diagnosed since the time of the last visit. Thereafter,
participants underwent phlebotomy and a DRE. If DRE
was abnormal or PSA exceeded 2.5 ng/mL, a prostate
biopsy was recommended. After 2004, subjects were
provided with information related to prostate cancer risk
by level of PSA (15) All prostate cancer cases were
reviewed by a central pathologist. A total of 123 prostate
cancer cases were diagnosed subsequent to enrollment
in the cohort. For each case, one control was sampled
from the cohort using incidence density sampling (16) to
ensure that age-matched controls had accrued at least the
same amount of follow-up time as the matched cases at
their time of diagnosis.
Marker Assessment. All serum samples were assessed
using the LabMAP technology (Luminex), which combines the principal of sandwich immunoassay with the
fluorescent-bead – based technology allowing individual
and multiplex analysis of up to 100 different analytes in a
single microtiter well as reported previously (17).
Statistical Methods. Conditional logistic regression
was used to estimate the odds ratio (OR) of prostate
cancer risk by 2-fold change of each biomarker (biomarker
on log2 scale), as well as for the conventional risk factors
PSA (log2 scale), DRE (abnormal versus normal), family
history of prostate cancer (positive versus negative),
effect of a prior biopsy (ever versus never), race (African
American versus not), and the Prostate Cancer Prevention Trial (PCPT) risk score (log2 scale; ref. 14). Areas
underneath the receiver-operating characteristic curve
(AUCs) of individual markers, SDs, and tests of the
null hypothesis of no diagnostic potential (H0/AUC =
50%) were calculated using U statistics (18). Individual
markers were evaluated for independent diagnostic
information after adjustment for the PCPT risk score by
multivariable logistic regression including the marker
and PCPT risk score and similarly adjusting for PSA.
In multivariable models where the individual marker
retained statistical significance at the 0.05 level after
adjusting for PSA or the PCPT risk score, the expected
cross-validation predictive capability of the multivariable
model was compared with the model containing only
PSA or the PCPT risk score using the Bayesian information criterion (BIC). The BIC equals 2 log likelihood +
the number of parameters in the model logarithm
of the sample size, and models with smaller BIC have
better expected predictive performance than models with
larger BIC. Two-way unsupervised hierarchical clustering of patients and markers was done to assess whether
Table 1. Participant characteristics
Controls (n = 127)
Mean age (SD)
Race/ethnicity
Caucasian
African American
Hispanic
Other
Median PSA (range)
Mean PSA (95% CI)
Family history of PCA
No
Yes
Digital rectal exam
Normal
Abnormal
Not done
Prior biopsy done
Never
One or more
61.4 (7.4)
88
13
25
1
1.1
1.1
(69.3%)
(10.2%)
(19.7%)
(0.8%)
(0.2, 8.3)
(0.2, 5.7)
P
Cases (n = 123)
63.0 (7.4)
71
21
31
0
3.1
3.0
(57.7%)
(17.1%)
(25.2%)
(0.0%)
(0.2, 766.0)
(0.5, 19.6)
0.10
0.15
<0.0001
0.66
94 (74.0%)
33 (26.0%)
87 (70.7%)
36 (29.3%)
120 (94.5%)
6 (4.7%)
1 (0.0%)
100 (81.3%)
21 (17.1%)
2 (1.6%)
98 (77.2%)
29 (22.8%)
106 (86.2%)
17 (13.8%)
0.003
0.09
Cancer Epidemiol Biomarkers Prev 2007;16(10). October 2007
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Evaluation of Markers for Prostate Cancer
subgroups of patients had similar marker profiles as
well as whether subgroups of markers expressed similar
expression levels (19). Clustering was done using
agglomerative complete linkage after rank transformation using 50 markers that were expressed above the
detection limit in 75% or more of participants. Clusters
of patients were matched against case/control status to
determine whether cancer cases clustered together, and
clusters of markers were matched against predefined
biological subgroups to determine whether observed
statistical correlations among markers corresponded to
biologically hypothesized ones. Correlations between
pairs of individual markers and tests of whether the
correlations were significantly different from 0 (no correlation) were based on the Spearman rank test statistic.
All statistical analyses were done in the R freeware
statistical package [R 2.2.1 A Language and Environment, Copyright 2005]. For the AUC and univariate
logistic regression tests done on each of the 54 biomarkers and six established risk factors, Bonferroniadjusted two-sided a levels = 0.0008 (0.05/60) were used
to account for multiple comparisons. Statistical comparisons of patient characteristics were done using twosample t tests (age, logarithm of PSA) and by m2 tests
(race/ethnicity, family history, DRE, prior biopsy) and
were done at the two-sided 0.05 level.
Results
Characteristics of the 123 prostate cancer cases and 127
age-matched controls are shown in Table 1. As to be
expected, cases had a statistically significantly higher
PSA value on average [mean = 3.0 ng/mL; 95%
confidence interval (95% CI)= 0.5 to 19.6 ng/mL] than
controls (mean = 1.1 ng/mL; 95% CI = 0.2 to 5.7 ng/mL)
and were more likely to have an abnormal DRE. Fewer
cases (13.8%) than controls (22.8%) reported having a
prior prostate biopsy, although the result was not
statistically significant.
Results from the logistic regressions and AUCs for the
54 markers in comparison to the standard risk factors
are shown in Table 2. Vascular endothelial growth
factor (VEGF), resistin, granulocyte colony-stimulating
factor (G-CSF), interleukin 1Ra (IL-1Ra), matrix metalloproteinase-3 (MMP-3), plasminogen activator inhibitor-1
(PAI-1), and kallikrein-8 were underexpressed in prostate cancer cases (P < 0.05), and a-fetoprotein (AFP)
was overexpressed in prostate cancer cases (P < 0.05).
However, none of these markers had a significant effect
on prostate cancer in terms of the ORs or AUCs adjusted
for the high number of multiple comparisons (all
P values >0.0008). None of the AUCs exceeded 60%. In
contrast, the conventional risk factors PSA and DRE, and
the composite PCPT risk score were highly statistically
significant (P < 0.0001), and PSA (AUC = 80.4%) and the
PCPT risk score (AUC = 84.9%) had AUCs 20% higher
than any of the markers. None of the individual markers
were statistically significant (all P values >0.05) after
adjusting for the PCPT risk score in a multivariable
logistic regression, whereas the PCPT risk score retained
its statistical significance (P > 0.0001), and the magnitude
of its OR for a doubling of risk in each of the adjusted
models (all ORs >6.30 per doubling of risk except for
one at 5.58 when adjusting for G-CSF). Similar results
were obtained when performing multivariable logistic
regression of individual markers adjusting for PSA. PSA
remained highly statistically significant in each of these
models (all P values <0.0001). Only adiponectin was
statistically significant (P = 0.04) at the 0.05 level after
adjusting for PSA, a marker that had not been statistically
significant at the 0.05 level alone. However, the BIC
measure of predictive performance for the combined
model of adiponectin and PSA (BIC = 112.04) indicated
worse predictive performance (higher BIC) than the
model containing PSA alone (BIC = 110.94).
An exploratory analysis comprising unsupervised
clustering of patients and markers (Fig. 1) revealed that
the 54-marker panel could not distinguish cases from
controls. Cases and controls did not cluster together as
seen on the left side of the graph, where each hierarchical
cluster of patients comprised both cases and controls.
There was a hotspot of primarily controls with overexpression of a subgroup of markers on the far right, but
these markers fell across a range of the classes. Finally
there was no to only weak evidence that unsupervised
clustering separated markers of different classes. A
group of the tumor markers seemed to cluster together
(middle of row of marker classes at top).
The combined evidence shows that this panel of 54
markers provides only weak diagnostic information for
prostate cancer that is highly outweighed by the conventional prognostic factors of PSA and DRE and the PCPT
risk score. With this in mind, we sought to characterize the
correlations between markers of the same biological class
collapsed over cases and controls, along with their
correlations with PSA.
Adipokines. As indicated by the unsupervised
clustering, this group of biomarkers did not correlate
well with each other. Of all pairs of the seven markers in
this group, only correlations between tumor necrosis
factor-RI (TNF-RI) and TNF-RII (correlation r = 0.43)
and TNFa and VEGF (r = 0.39) were statistically
significant (both P < 0.0001). All other correlations were
<0.23, and only five other pairs were statistically
significant at the 0.05 level. None of these markers
showed strong correlation with PSA (median r = 0.03;
range, 1.00 to 0.09).
Immune Response. The 12 markers in the immune
response group clustered in groups of two or three
(Fig. 1), and several pairs exhibited correlations >30%
(all P values < 0.0001). IL-1Ra correlated with IL-6
(r = 0.47), IL-2R (0.37), G-CSF (0.52), and macrophage
inflammatory protein-1 (MIP-1; 0.47). In addition to
IL-1Ra, IL-6 correlated with IL-2R (0.42), G-CSF (0.58),
and MIP-1 (0.59). In addition to IL-1Ra and IL-6, IL-2R
correlated with MIF (0.42), MPO (0.55), and MIP-1 (0.60).
In addition to IL-1Ra and IL6, G-CSF correlated with
MIP-1 (0.45) and IP-10 (0.32). Finally, in addition, MIF
and MPO correlated (0.36). None of these markers
showed strong correlation with PSA (median r = 0.005;
range, 0.16 to 0.12).
Metalloproteinases. The five markers in the metalloproteinases group all fell within a larger subcluster
(right-hand side of dendogram in Fig. 1), but only
two pairs exhibited statistically significant correlation
(P < 0.0001): MMP and MMP-1 (0.33) and tPAI 1 and
PAI-1 (0.68). All other pairs of markers in this group had
Cancer Epidemiol Biomarkers Prev 2007;16(10). October 2007
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Cancer Epidemiology, Biomarkers & Prevention
Table 2. Marker associations with prostate cancer in comparison to the standard risk factors, including the
composite PCPT risk score
Marker
OR (P)
(1) Adipokines
TNF-RI
TNF-RII
Leptin
TNFa
VEGF
Adiponectin
Resistin
(2) Immune response
IL-2R
sIL-6R
MIF
IL
IL-6
G-CSF
IL-1Ra
MPO
MIP-1
MCP-1a
sE-Selectin
IP-10
(3) Metalloproteinases
MMP-2
MMP-3
MMP-9
PAI-1 (active)
tPAI-1
(4) Adhesion
SVCAM-1
SICAM-1
(5) Hormones, growth factors
FSH
LH
Prolactin
TSH
Adrenocorticorticotropic hormone
GH
IGFBP-1
(6) Tumor markers
HCG
AFP
aKallikrein_10
Carcinoembryonic antigen
CA-125
CA 15-3
CA 19-9
CA 72_4
Kallikrein_8
Mesothelin
(7) Growth and tumor markers
EGFR
EGF
TGFa
HGF
NGF
ErbB2
Other
FAS_L
Fractalkine
EOTAXIN
Cytokeratin_19
Fas
Standard risk factors
PSA
Abnormal DRE
Positive family history
Previous biopsy
African American
PCPT risk
AUC, % (P)
0.84
0.81
0.88
1.02
0.89
0.76
0.82
(0.56)
(0.36)
(0.16)
(0.83)
(0.15)
(0.12)
(0.10)
55.0
52.6
55.5
50.2
57.9
52.5
56.3
(0.09)
(0.24)
(0.07)
(0.48)
(0.01)
(0.25)
(0.04)
0.95
0.92
1.03
1.12
0.94
0.94
0.91
0.95
0.95
0.86
1.12
0.90
(0.26)
(0.63)
(0.78)
(0.34)
(0.03)
(0.02)
(0.16)
(0.58)
(0.34)
(0.40)
(0.53)
(0.49)
53.3
53.4
52.3
52.3
55.7
59.4
56.9
53.9
54.6
50.1
53.0
52.5
(0.18)
(0.17)
(0.26)
(0.26)
(0.06)
(0.005)
(0.03)
(0.15)
(0.10)
(0.49)
(0.21)
(0.25)
0.93
0.68
1.21
0.81
0.60
(0.65)
(0.37)
(0.37)
(0.03)
(0.09)
53.9
58.3
51.0
58.1
54.5
(0.15)
(0.01)
(0.39)
(0.01)
(0.11)
0.59 (0.16)
1.19 (0.50)
54.9 (0.09)
53.6 (0.16)
1.17
1.02
1.08
0.81
0.99
1.05
1.04
53.6
51.4
54.6
55.5
51.7
54.2
52.1
(0.31)
(0.89)
(0.55)
(0.18)
(0.67)
(0.30)
(0.59)
(0.16)
(0.35)
(0.10)
(0.07)
(0.32)
(0.12)
(0.28)
NA
1.55 (0.04)
1.08 (0.35)
0.90 (0.12)
0.95 (0.21)
1.00 (1.00)
1.07 (0.71)
NA
0.57 (0.02)
0.98 (0.81)
NA
57.5 (0.02)
52.5 (0.25)
54.9 (0.09)
51.6 (0.33)
53.2 (0.19)
54.6 (0.10)
NA
57.3 (0.02)
51.8 (0.31)
0.65
1.00
0.98
0.94
1.00
0.97
54.9
52.5
53.3
52.1
50.5
52.8
(0.34)
(0.97)
(0.36)
(0.60)
(0.86)
(0.92)
(0.09)
(0.25)
(0.18)
(0.28)
(0.45)
(0.22)
+/
+
+
+
+
+
+
+
+
+
+
+
NA
+
+
+
NA
+
NA
0.98 (0.40)
0.88 (0.22)
NA
1.14 (0.41)
NA
52.7 (0.19)
54.3 (0.12)
NA
50.4 (0.46)
2.74
5.0
1.24
0.55
1.58
6.51
80.4 (<0.0001)
+
84.9 (<0.0001)
+
(<0.0001)
(0.003)
(0.48)
(0.07)
(0.21)
(<0.0001)
NA
+
NA
+
NOTE: OR is odds ratio for cancer for marker greater than the median value of controls versus not; + means marker overexpressed in cancer cases, and
means underexpressed according to the AUC; P value for AUC is for a test of the null hypothesis AUC = 50% (random) versus AUC > 50%.
Cancer Epidemiol Biomarkers Prev 2007;16(10). October 2007
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Evaluation of Markers for Prostate Cancer
correlations <0.14. None of these markers showed strong
correlation with PSA (median r = 0.09; range, 0.12
to 0.04).
Adhesion. There were only two adhesion markers,
and they clustered together on the heat map (Fig. 1) with
correlation 0.39 (P < 0.0001). None of these markers
showed strong correlation with PSA (median r = 0.03;
range, 0.07 to 0.01).
Hormones and Growth Factors. Although a group of
four of the seven markers in the hormones and growth
factors group clustered together (Fig. 1), only folliclestimulating hormone (FSH) and luteinizing hormone
correlated substantially (r = 0.59, P < 0.0001). None of the
correlations for any of the other pairs exceeded 0.18.
None of these markers showed strong correlation with
PSA (median r, 0.00; range, 0.11 to 0.12).
Tumor Markers. Of the 10 tumor markers, 5 clustered
tightly together, and 2 others close to these (Fig. 1).
The correlation matrix for this group of markers is
shown in Table 3. Human chorionic gonadotropin
(HCG) and CA125 correlated with several other
markers in the group, and CA15-3 did not correlate
with any other markers. None of these markers showed
strong correlation with PSA (median r = 0.06; range,
0.19 to 0.01).
Growth and Tumor Markers. The six markers in the
growth and tumor markers group did not cluster
together (Fig. 1). However, ErbB2 statistically significantly correlated with hepatocyte growth factor (HGF;
r = 0.37) and epidermal growth factor receptor (EGFR;
r = 0.46), as did transforming growth factor (TGF) with
nerve growth factor (NGF; r = 0.43) and EGF (r = 0.32;
all P values <0.0001). None of these markers showed
strong correlation with PSA (median r = 0.04; range,
0.07 to 0.00).
Discussion
The use of PSA as a biomarker has significantly
impacted the detection and management of prostate
cancer during the past decade. Despite its widespread
use, PSA testing is associated with several limitations as
a screening tool that are well recognized. Several
individual biomarkers have been tested in different
biospecimens such as serum, plasma, prostate tissue,
seminal fluid, and urine to either replace or augment the
existing use of PSA in cancer detection and prognosis.
Previous studies have evaluated either individual
biomarkers or a group of biomarkers belonging to a
single family based on structure or function in the
diagnosis of prostate cancer as well as for risk
stratification and prognosis. The current study is the
first, to our knowledge, to evaluate a large number (54)
of biomarkers covering a broad spectrum of biological
activity in a case control fashion using a uniform,
standardized technology for prostate cancer. VEGF
underexpression was associated with a higher risk of
prostate cancer in our study. This is in contrast to earlier
reports that reported a direct association between VEGF
overexpression and risk of developing biochemical
recurrence, lymph node metastases and distant metastases in patients with prostate cancer (8, 20). This
variability can be explained by the significant difference
in the patient populations in the two studies. Cases of
prostate cancer in the current study were from within a
screening cohort with a preoperative median PSA levels
of 3.1, whereas previously reported VEGF overexpression was found in unscreened patient population with
median PSA levels of 7.3 and more advanced disease
(8). It is conceivable that a range of expression (both
under and overexpression) of VEGF may be involved as
the disease progresses from the precancerous to the
locally confined to the more advanced stages. Human
Figure 1. Heat map clustering
of several classes of biomarkers
between cases and controls.
Cancer Epidemiol Biomarkers Prev 2007;16(10). October 2007
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Cancer Epidemiology, Biomarkers & Prevention
Table 3. Correlation between each pair of markers in the tumor markers group (group 6 of Table 1)
HCG
aCa_72
aAFP
aKallikrein_10
aMesothelin
CEA
CA_19-9
Kallikrein_8
CA125
CA15-3
HCG
aCa_72
aAFP
Kall10
Mes
CEA
CA199
Kall8
CA125
CA15-3
1
0.08
1
0.06
0.06
1
0.38
0.12
0.15
1
0.11
0.05
0.04
0.11
1
0.37
0.16
0.07
0.24
0.09
1
0.25
0.16
0.01
0.18
0.16
0.35
1
0.38
0.18
0.04
0.44
0.13
0.42
0.18
1
0.25
0.16
0.06
0.34
0.02
0.36
0.27
0.31
1
0.02
0.10
0.01
0.00
0.02
0.01
0.01
0.03
0.02
1
NOTE: Correlations in bold are statistically significant at the 0.05 level, and of these, all correlations z0.25 were also significant at 0.0001.
kallikrein-8 underexpression was also associated with
increased risk of prostate cancer in the present study.
The role of other members of the kallikrein family such
as PSA and human kallikrein-2 have been extensively
studied in different stages of prostate cancer in the past
(21). Although kallikrein-8 has recently been advocated
as a promising biomarker in ovarian cancer, it has not
been evaluated, to our knowledge, in prostate cancer
(22). Further studies need to be done evaluating
kallikrein-8 expression in biospecimens in patients with
different stages of prostate cancer to validate the
results of our study. Although a limitation of our
study compared with others could be the small sample
size, the strong statistical significance maintained for
the established risk factors, PSA and DRE, lends
evidence that a small sample size was not the
underlying reason for the low or lack of statistical
significance of this panel of 54 markers on univariate
or multivariate analyses adjusting for the PCPT risk
score.
Several studies have evaluated different classes of
biomarkers individually in prostate cancer (2, 6, 8). The
current study is unique in that multiple classes of
biomarkers were tested using a uniform, standardized
technique, facilitating comparisons among different
classes of biomarkers, thus reducing technical or
temporal errors. One of the major limitations in accepting new prostate cancer biomarkers is the lack of direct
comparison to the conventional risk factors for prostate
cancer. Although some studies have compared newly
tested biomarkers only to PSA for prostate cancer
detection, there have been no head-to-head comparison
between new biomarkers and a panel of conventional
risk factors, which, besides PSA, include other variables
such as DRE, family history of prostate cancer, prior
negative prostate biopsy, age, and race/ethnicity. The
PCPT risk calculator, which incorporates the above risk
factors, was originally developed from subjects in the
PCPT and has since been validated in the SABOR
cohort, which forms the cohort for the current study
(23). When compared with the PCPT risk calculator as
well as PSA, all biomarkers found to be marginally
significant on univariate analysis underperformed. The
findings of this study suggest that future endeavors in
the discovery of new biomarkers for prostate cancer
should involve a rigorous comparison to the wellknown conventional risk factors for cancer detection.
The finding that none of the individual biomarkers
within the same family or group significantly clustered
with each other between cases and controls reinforces
the fact that the conventional risk factors continue to be
the most accurate way of predicting risk of prostate
cancer today.
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2519
Correction
In an article (1) in the October 2007 issue, an author’s
affiliation was incorrect and grant information was
omitted. The following corrects the error and the
omission.
Elizabeth Platz6 Department of Epidemiology, Johns
Hopkins Bloomberg School of Public Health, Baltimore,
Maryland.
The authors gratefully acknowledge the Clinical and
Epidemiologic and Validation Center of the Early
Detection Research Network (NIH grant number
U01CA086402) and the San Antonio Cancer Institute,
whose generous funding made this work possible.
Dipen J. Parekh is supported by UTHSCSA Institute for
Integration of Medicine and Science (IIMS) Mentored
Career Development Award.
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Copyright D 2007 American Association for Cancer Research.
doi:10.1158/1055-9965.EPI-16-11-COR1
Cancer Epidemiol Biomarkers Prev 2007;16(11). November 2007
Assessment of 54 Biomarkers for Biopsy-Detectable
Prostate Cancer
Dipen J. Parekh, Donna Pauler Ankerst, Jacques Baillargeon, et al.
Cancer Epidemiol Biomarkers Prev 2007;16:1966-1972.
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