ARTICLE
Auteur(s) : Faina
Linkov1, Yian Gu4, Alan A
Arslan2,3,4, Mengling Liu4, Roy E
Shore2,5, Lyudmila Velikokhatnaya1, Karen L
Koenig2,4, Paolo Toniolo3, Adele
Marrangoni1, Zoya Yurkovetsky1, Anne
Zeleniuch-Jacquotte2,4, Anna E
Lokshin1,6,7
1Department of Medicine, University
of Pittsburgh Cancer Institute, Division of Cancer
Prevention and Population Science, University
of Pittsburgh, Pittsburgh, USA
2New York University Cancer Institute, New York
University School of Medicine, New York, USA
3Department of Obstetrics and Gynecology, New
York University School of Medicine, New York, USA
4Department of Environmental Medicine, New York
University School of Medicine, New York, USA
5Radiation Effects Research Foundation, Minami-ku
Hiroshima-shi, Japan
6Department of Pathology, University
of Pittsburgh, Pittsburgh, USA
7Department of Ob/Gyn Reproductive Sciences,
University of Pittsburgh, Pittsburgh, USA
accepté le 6 Février 2009
Biomarkers are cellular or soluble indicators of health status,
making them very important for monitoring people who are both
healthy, and those who have an established disease. As a newly
discovered biomarker assay makes the transition from a research
setting to the clinical diagnostic laboratory, it should progress
through defined stages of assay evaluation [1]. Reliability is one
of the key issues in biomarker validation, and several reliability
studies have already been conducted by our research group [2, 3].
Biomarker validation studies can detect the various components of
variability of a biomarker and indicate directions for assay
improvement, along with possible use in epidemiological or clinical
settings [4].
Although a number of studies have measured the reliability of
hormones and growth factors, very few studies have evaluated the
temporal reliability of multiple, serum-based potential biomarkers
of cancer [2-4]. In this study, we assessed the reliability of 55
biomarkers including cancer antigens (AFP, CA 125, CA 15-3, CA
19-9, CA 72-4, CEA, MICA, SAA, SCC, S100, TTR, ULBP1, 2, 3); growth
factors/related molecules: (ErbB2, IGFBP1); cell adhesion
molecules, proteases, and protease inhibitors (sE-selectin,
sICAM-1, sVCAM-1, PAI-1 (total and active) matrix metallopeptidases
(MMP 1, 2, 3, 7, 8, 9, 12, 13), kallikreins (KLK8, 10); chemokines
(eotaxin, fractalkine, GRO-α, IP-10, MCP-1, MCP-2, MCP-3, MIF, MIG,
MIP-1α, MIP-1β, RANTES); adipokines (leptin, resistin), apoptotic
factors (sFas, sFasL, DR5, Cyfra 21-1); angiogenesis inhibitors
(angiostatin, endostatin, thrombospondin); and other markers
(mesothelin, HSP 27, MPO). This study is one of the first
evaluating the reliability of multiple biomarkers in serum samples
from an existing prospective cohort, using the multiplexing Luminex
technology.
In our previous studies, we compared the biomarker expression
levels between individuals with established tumors and healthy
controls. Specifically, we found differential expression of
cytokines, chemokines, cancer antigens and other serum markers in
patients with ovarian cancer, melanoma, head and neck cancer,
endometrial cancer, and several other malignancies [5-8].
Additionally, in our previous studies we explored the differences
in hormone and cytokine expression between postmenopausal and
premenopausal women [3, 9, 10]. Thus, since we have already
established that there are difference in biomarker expression
between individuals with various diseases and healthy controls, the
goal of this study was to evaluate longitudinal differences in
biomarker expression in individuals free of malignancies. The null
hypothesis of this study was that there is no difference in
biomarker expression in healthy individuals over time.
Study population and methods
Between March 1985 and June 1991, the New York University Women’s
Health Study (NYUWHS) enrolled a cohort of 14 274 women aged 34-65
years at the Guttman Breast Diagnostic Institute, a breast
screening clinic based in New York City. At the time of enrollment
and at annual screening visits thereafter, subjects were asked to
complete questionnaires and to provide 30 ml of peripheral venous
blood. Fifty one percent of cohort members donated blood on more
than one occasion, usually at one-year intervals. Characteristics
of the study participants have been described previously.
Blood samples were collected before breast examination between
9:00 a.m. and 3:00 p.m. Fasting was not required for study
enrollment. After collection, blood specimens were kept at room
temperature for approximately 1 h and at 4oC for
30 min. Samples were then centrifuged at 3 500 rpm for
15 min, and then serum was partitioned into 1ml aliquots in
airtight plastic vials and frozen at - 80oC for
long-term storage.
The repeat samples collected at yearly intervals from
approximately half of the NYUWHS participants, were used to conduct
a reliability (temporal stability) study. Subjects were selected at
random among the NYUWHS participants who fulfilled the following
criteria: a) large number of aliquots still in storage (> 11 at
each visit), b) no diagnosis of cancer (except non-melanoma skin
cancer), c) neither a case nor a control in any of the ongoing,
nested, case-control studies, d) no use of exogenous hormones (such
as oral contraceptive or hormone replacement therapy) at the time
of any of the blood donations. For postmenopausal women, two yearly
samples were retrieved from the serum bank for 35 women, and were
included on the same well-plate in a random order. The average days
between collection of the two samples was 367 (± 5). Since serum
levels of some cytokines are influenced by sex hormones [11],
separate groups of post- and premenopausal women were selected.
Women were classified as postmenopausal if they reported: the
absence of menstrual cycles in the previous six months, a total,
bilateral oophorectomy or a hysterectomy without total oophorectomy
if their age was 52 years or older. Women were classified as
premenopausal if they reported at least one menstrual cycle during
the six months prior to enrollment. For premenopausal women,
three-yearly samples were retrieved from the serum bank for 30
women. The “yearly” samples were on average 442 ± 176 days apart
for the 1st and 2nd samples, and 463±139 days
apart for the 2nd and 3rd samples. For
quality control, a random sample of five premenopausal and five
postmenopausal women was selected and their blinded sample
duplicates were analyzed to assess intra- and inter-batch
coefficients of variation (CVs). All samples were re-labeled prior
to being sent to the laboratory in order to ensure blinding of the
laboratory personnel.
Multiplex analysis
Never-thawed, 1mL serum samples were sent on dry ice to the
University of Pittsburgh Cancer Institute, where they were stored
at - 80oC until they were assayed. Serum levels of
biomarkers were analyzed using xMAPTM technology. The
xMAPTM technology (Luminex) combines the principle of a
sandwich immunoassay with the fluorescent-bead-based technology
allowing individual and multiplex analysis of up to 100 different
analytes in a single microtiter well. CA 15-3, ErbB2, CEA, KLK8,
hKLK10, CA 125, Cyfra 21-1, CA 19-9, ULBP1-3, MICA, SCC, SAA, TTR,
thrombospondin, mesothelin, angiostatin, endostatin, AFP, CA 72-4,
IGFBP1, S100 and HSP27 were measured using the assay developed and
were optimized in the Luminex Core Facility of the University of
Pittsburgh Cancer Institute
(http://www.upci.upmc.edu/facilities/luminex/index. html); MMP9,
resistin, fractalkine, sE-selectin, sVCAM1, MPO, tPAI-1, PAI-1
active, leptin, sFas, sFasL, MIF, sICAM-1, were measured using
Linco/Millipore (St. Lois, MO, USA) kits; all MMPs other than MMP9
were measured using R&D kits, MIP-1a, MIP-1β, IP-10, eotaxin,
RANTES, MCP-1, MCP-2, MCP-3, DR5, MIG, and GRO-α were measured
using Invitrogen (Camarillo, CA, USA) kits. The xMAPTM
serum assays were performed with a 96-well microplate format as
previously described [7].
Statistical analysis
All analyses were performed on the natural logarithm-transformed
values as previously described [11]. The temporal reliability was
estimated by the intraclass correlation coefficient (ICC) [12, 13].
The variance components were estimated with a random effects,
one-way analysis of variance model, using the SAS procedure MIXED.
Exact 95% confidence intervals (CIs) for the ICCs were calculated
as described by McGraw & Wong [14]. We determined a priori that
serum markers worthy of future consideration should be detectable
in at least 60% of the samples and should have an ICC of at least
0.55 based on our previous experience [3], and recommendations in
the literature. The bootstrap method was used in calculating the
Spearman correlation coefficient (r) between continuous variables
as previously described [10]. Differences in the median biomarker
expression level between premenopausal and postmenopausal women
were evaluated with the Wilcoxon-Mann-Whitney test. All analyses
were performed using SAS 9.1 (SAS Institute, Cary, NC). All p
values are two-sided.
Results
ICC and its 95% CI for the Biomarkers
Table 1 lists biomarkers for which more
than 60% of the analyzed samples had values above the lower limit
of detection (LLD); the ICC was ≥ 0.55.The highest ICC was observed
for AFP, which was 0.97. The results demonstrate that 34 of the 55
markers under investigation had ICCs ≥ 0.55, indicating that a
single measurement of these biomarkers can represent the long-term
average level, for up to two or three years. The 34 biomarkers that
were found to be stable include; cancer antigens AFP, CA 15-3, CEA,
CA-125, SCC, SAA; growth factors/related molecules: ErbB2, IGFBP-1;
proteases and adhesion molecules: MMP-1, 8, 9, sE-selectin,
KLK8,10, sICAM-1, sVCAM-1; chemokines: fractalkine, MCP-2,
RANTES, MCP-1, MIP-1α, Eotaxin, GRO-α, MIP-1β, IP-10; angiogenesis
inhibitors: angiostatin and endostatin; adipokines: leptin and
resistin; apoptotic factor: sFas; and other molecules: mesothelin,
MPO, and PAI-1 (total and active). A detailed description is
provided in table 1. The rest of the
biomarkers under investigation either had ICCs less than 0.55 or
had low levels of detection (< 60%). These included cancer
antigens: CA 19-9, CA 72-4, MICA, S100, TTR, ULBP1, ULBP2, ULBP3;
cell adhesion molecules: MMP 2, 3, 7, 12, 13; chemokines: MCP-3,
MIF, MIG; adipokines: leptin and resistin; apoptotic factors:
sFasL, DR5, Cyfra 21-1; angiogenic inhibitors and other markers:
thrombospondin and HSP 27. Eight of the 55 biomarkers had ICCs less
than 0.55, including MMP7, thrombospondin, MMP3, MIG, HSP 27, MIF,
TTR, and MMP2 (listed in the order of decreasing reliability). The
remaining biomarkers were detectable in less than 60% of the
samples. ICCs for these 21 markers have not been calculated because
of the very small percentage of samples above the detection limit
and are not included in the table.
Differences in the median biomarker expression level between
premenopausal and postmenopausal women. The Wilcoxon-Mann-Whitney
test was used to evaluate the differences in the medians between
the pre- and postmenopausal women selected for this study. Based on
this test, 15 markers out of 55 were differentially expressed
between the two groups using p < 0.05 as the significance level.
These biomarkers included TTR, eotaxin, GRO- α, PAI-1(active),
fractalkine, sICAM-1, sE-Selectin, tPAI-1, SCC, thrombospondin,
MMP-2, MCP-1, CA-125, SAA, and resistin.
Table 1 Percentage of samples above detection limit,
intra batch CVs, intraclass correlations (95% CIs), and medians
(25th and 75th percentiles) of serum
biomarkers measured by the Luminex xMap™ method*
|
Biomarker (unit)
|
% of samples above detection limit
|
CV (%)
|
ICC (95%CI)
|
Median (25th-75th percentiles)
|
|
Cancer Antigens AFP (ng/mL)
|
100%
|
0.9
|
0.97 (0.95-0.98)
|
1.7 (1.3-2.1)
|
|
CA 15-3 (pg/mL)
|
99%
|
0.7
|
0.95 (0.92-0.97)
|
3.5 (2.6-4.7)
|
|
ErbB2 (ng/mL)
|
99%
|
0.7
|
0.85 (0.77-0.90)
|
2.1 (1.9-2.4)
|
|
IGFBP-1 (ng/mL)
|
100%
|
0.8
|
0.82 (0.73-0.88)
|
2.8 (1.5-4.9)
|
|
CEA (ng/mL)
|
86%
|
16.3
|
0.68 (0.54-0.79)
|
1.3 (1.0-1.8)
|
|
CA 125 (pg/mL)
|
84%
|
5.8
|
0.68 (0.55-0.79)
|
4.5 (1.7-9.1)
|
|
SCC (pg/mL)
|
99%
|
2.0
|
0.67 (0.54-0.78)
|
272 (191-428)
|
|
SAA (ug/mL)
|
98%
|
5.2
|
0.67 (0.54-0.78)
|
6.4 (3.0-13)
|
|
Adhesion molecules
|
|
|
|
|
|
MMP1 (ng/mL)
|
99%
|
1.5
|
0.88 (0.82-0.92)
|
1.8 (1.0-3.2)
|
|
sE-selectin (ng/mL)
|
98%
|
11.0
|
0.86 (0.80-0.91)
|
21 (13-27)
|
|
KLK10 (ng/mL)
|
99%
|
2.6
|
0.81 (0.72-0.87)
|
2.5 (2.2-3.1)
|
|
KLK8 (ng/mL)
|
99%
|
3.2
|
0.80 (0.70-0.87)
|
2.9 (2.4-3.5)
|
|
Mesothelin (ng/mL)
|
100%
|
1.5
|
0.78 (0.68-0.85)
|
8.4 (7.1-11)
|
|
sICAM-1 (ng/ml)
|
100%
|
0.8
|
0.64 (0.50-0.75)
|
177 (134-204)
|
|
MMP9 (ng/mL)
|
100%
|
0.6
|
0.63 (0.48-0.74)
|
185 (137-261)
|
|
sVCAM-1 (ug/mL)
|
100%
|
0.8
|
0.62 (0.48-0.74)
|
1.2 (0.99-1.37)
|
|
PAI-1(active) (ng/mL)
|
100%
|
4.1
|
0.58 (0.43-0.71)
|
9.7 (7.6-14)
|
|
MMP8 (ng/mL)
|
100%
|
1.7
|
0.56 (0.40-0.69)
|
9.9 (6.1-15)
|
|
tPAI-1 (ng/mL)
|
100%
|
1.1
|
0.69 (0.57-0.79)
|
35 (29-43)
|
|
Chemokines
|
|
|
|
|
|
Fractalkine (pg/mL)
|
71%
|
4.8
|
0.85 (0.76-0.91)
|
78 (< LLD -746)
|
|
MCP-2 (pg/mL)
|
94%
|
2.9
|
0.78 (0.68-0.86)
|
23 (14-29)
|
|
RANTES (ng/mL)
|
91%
|
0.3
|
0.76 (0.66-0.84)
|
4.6 (3.0-7.0)
|
|
MCP-1 (pg/mL)
|
100%
|
3.4
|
0.75 (0.65-0.84)
|
224 (177-271)
|
|
MIP-1α (pg/mL)
|
100%
|
3.0
|
0.74 (0.63-0.83)
|
97 (68-166.)
|
|
Eotaxin (pg/mL)
|
100%
|
1.8
|
0.70 (0.58-0.80)
|
73 (58-94)
|
|
GRO-α (pg/mL)
|
63%
|
7.4
|
0.70 (0.54-0.82)
|
18 (< LLD-112)
|
|
MIP-1β (pg/mL)
|
96%
|
11.4
|
0.62 (0.47-0.74)
|
97 (59-153)
|
|
IP-10 (pg/mL)
|
100%
|
2.0
|
0.60 (0.46-0.73)
|
15 (11-19)
|
|
Angiogenic inhibitors
|
|
|
|
|
|
Endostatin (ng/mL)
|
100%
|
3.1
|
0.79 (0.70-0.86)
|
57 (47-68)
|
|
Angiostatin (ug/mL)
|
100%
|
11.3
|
0.66 (0.52-0.76)
|
21 (18-25)
|
|
Adipokines
|
|
|
|
|
|
Leptin (ng/mL)
|
100%
|
2.2
|
0.82 (0.74-0.88)
|
8.8 (5.1-16)
|
|
Resistin (ng/mL)
|
100%
|
5.1
|
0.63 (0.49-0.75)
|
12 (8.3-16)
|
|
Apoptotic Factors
|
|
|
|
|
|
sFas (ng/mL)
|
100%
|
3.5
|
0.92 (0.88-0.95)
|
4.4 (3.8-5.2)
|
Discussion
To date, few studies have addressed the temporal reliability of
chemokines, cancer antigens, growth factors, apoptotic factors, and
adipokines in healthy subjects. Our results are consistent with
previous studies of CA 15-3 [15], MCP-1 [16], and RANTES [17]. To
our knowledge, our group is the first to evaluate the temporal
reliability of the majority of these biomarkers in healthy
individuals, as, other than CA 15-3, CA 125, MCP-1, and RANTES, few
markers have been explored in previous research of temporal
reliability.
CA 125 has been used extensively for the diagnosis and follow-up
of ovarian cancer patients [18]. A previous study evaluating
CA 125 in healthy, menopausal women, using radioimmunoassay
suggested that single, low CA 125 values are reliable indicators of
a woman’s true CA 125 value [19]. Using the multiplexing method, we
confirmed that CA 125 is a reliable marker.
Resistin, a recently discovered adipokine, is purportedly
involved in metabolic and inflammatory processes in humans and may
be an important marker with which to assess disease risk in
large-scale epidemiological studies. In our study, resistin was one
of the most reliable markers, which confirmed the results of a
recent study using ELISA. In that study, individual blood resistin
concentrations did not significantly change over a period of one
year, and showed a high degree of reliability [20].
In general, very limited number of studies have evaluated
longitudinal changes of these biological markers in healthy
individuals [9, 10]. The majority of existing studies relied on
correlations and did not report the variance components or ICCs,
which provide superior assessment of reliability. Additionally,
most of the existing studies on biomarker reliability to date, do
not assess markers in healthy participants, evaluating only
biomarker changes in patients with various benign and malignant
conditions.
In this study, serum levels of most of the biomarkers were
similar to those measured by the same xMAPTM method in
other studies [5], and those measured by ELISA in healthy
populations. The differences in population characteristics (age,
gender, etc.), assay sensitivity and specificity, standards used in
the assays [21], or sample collection, processing, storage and
assay performance [22], may contribute to the observed differences
in biomarker concentrations in healthy subjects. Therefore,
standardization of procedures needs to be done before there can be
any direct comparison between studies.
Previous studies have suggested that the circulating levels of
serum biomarkers can be affected by a wide range of factors,
including age, gender, race, blood pressure, serum cholesterol,
BMI, percentage body fat, visceral fat, cigarette smoking, the use
of hormone replacement therapy, menopausal status, and physical
exercise [23]. Reliability studies in the area of biomarkers are
complicated by the fact that “normal” levels of biomarkers may
differ with age. One of the previous studies found elevated cancer
antigen levels in elderly individuals without any confirmed
malignancies [24], suggesting that biomarkers may change over time
due to the aging process rather than to occult pathology. Despite
these factors affecting biomarker reliability, our study has
demonstrated that a substantial number of biomarkers were stable
over a one-two year period.
Our study had some limitations. The samples were not assayed in
duplicate. However, this is common in assays using the Luminex
method, which provides an average value based on 100 bead
measurements. Our study population included women only, so the
results may not be extrapolated to males. Future studies need to
look into the reliability of biomarkers in premenopausal versus
postmenopausal women in more detail, and compare the reliability of
biomarkers in males and females. Despite these limitations, this is
one of the first and the largest studies assessing the reliability
of multiple serum markers using Luminex methodology.
In addition to addressing these limitations, in our future
studies it would be important to evaluate more carefully the
presence of various biological markers implicated in cancer
development in the serum of healthy individuals. The presence of
these biomarkers in the serum of healthy individuals is still not
well understood. A good example of this concept is ErbB2, a
member of the epidermal growth factor receptor family, implicated
in the development of many human cancers. At this point, the
presence of ErbB2 in serum samples from healthy individuals has not
been explored by large epidemiological studies. Since the presence
of overexpression of ErbB2 in serum of healthy individuals could be
symptomatic of the development of breast cancer or cancer in
general, the detection of high and reliable-over-time levels of
ErbB2 in the blood of healthy individuals could be an indication
for the continuation of more frequent tests to unveil the
cause.
Additionally, this study resulted in novel data on the
differences between biomarker expression levels in healthy,
premenopausal and healthy, postmenopausal women over time. Due to a
relatively small size of this cohort, only very large differences
between pre- and postmenopausal women would be detectable in our
study. This study detected differences between premenopausal versus
postmenopausal women in 15 out 55 markers, including TTR, eotaxin,
GRO-α, PAI-1(active), fractalkine, sICAM-1, sE-Selectin, tPAI-1,
SCC, thrombospondin, MMP-2, MCP-1, CA-125, SAA, and resistin. These
results were consistent with previous research of healthy women
followed up through menopausal transition, which suggested that
SAA, tPAI, and MCP-1 differ between premenopausal and
postmenopausal status [25]. Additionally, previous evidence
suggests that there are differences in PAI-1, MMP-1, and MMP-2
levels between healthy, premenopausal versus postmenopausal women
[26, 27]. Consistently with our study, Grover et al. found that
both hysterectomy and menopausal status have a clear effect on
serum CA 125 levels and must be considered if serum CA 125 is to be
used as a screening test [28]. Other than SAA, MCP-1, PAI-1, MMP-1,
and MMP-2, the rest of the markers in our study that were
differentially expressed between postmenopausal and premenopausal
women have been only rarely investigated in healthy women in
relation to menopausal status.
In conclusion, using the xMAP™ method we found that serum
concentrations of cancer antigens: AFP, CA 15-3, CEA, CA-125, SCC,
SAA; growth factors/related molecules: ErbB2, IGFBP-1; proteases
and adhesion molecules: MMP-1,8,9, sE-selectin, KLK8,10, sICAM-1,
sVCAM-1; chemokines: fractalkine, MCP-1,2, RANTES, MIP-1α, MIP-1β,
eotaxin, GRO-α, IP-10; angiogenesis inhibitors: angiostatin and
endostatin; adipokines: leptin and resistin; apoptotic factor:
sFas; and other proteins: mesothelin, MPO, and PAI-1, are
detectable and remain stable for up to two years in stored serum
samples, suggesting that a single measurement of this markers may
be sufficient for utilization in clinical and epidemiological
studies.
Acknowledgments
This research was supported by the National Institutes of Health
(grants R01 CA98661 and R03 CA96428), and by a Cancer Center grant
CA16087 from the National Cancer Institute and a grant ES00260 from
the NIEHS.
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