Usefulness of serum mass spectrometry to identify women diagnosed with higher grades of cervical intraepithelial neoplasia may differ by race

Article (PDF Available)inInternational Journal of Women's Health 3(1):185-92 · July 2011with14 Reads
DOI: 10.2147/IJWH.S20685 · Source: PubMed
Abstract
An early detection of precursor lesions of cervical cancer will help to eliminate the worldwide burden of cervical cancer. This exploratory study aimed to identify, by matrix-assisted laser desorption/ionization (MALDI) time-of-flight (TOF) mass spectrometry (MS), serum protein profiles that distinguish cervical intraepithelial neoplasia grades CIN 1 or lower (≤CIN 1) from CIN 2+ among 127 women infected with human papillomavirus (HPV) 16. Of these 127 women, 25 and 23 were diagnosed with CIN 2 or CIN 3, respectively (cases), and 79 were diagnosed with ≤CIN 1 (non-cases). Serum protein profiles were generated by MALDI-TOF-MS. A total of 95 m/z peaks were tested for association with case status by two racial groups, African American (AAs) and Caucasian American (CAs). Overall, 2 protein peaks identified by our study demonstrated higher specificity for identifying CIN 2+ than previously published studies. An increasing intensity of [m/z 4459] was associated with a higher risk of being a case, regardless of race with a specificity of 58% for CIN 2 and a specificity of 75% for CIN 3. An increasing intensity of [m/z 4154] was not only associated with a higher risk of being a case only among CAs, but also had an opposite effect among AAs. Identification of specific proteins associated with the peaks detected in serum and development of antibody-based tests such as ELISA should lead to the development of race-specific, non-invasive and cost effective screening tests with higher specificity for identifying HPV 16 associated CIN 2+.
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ORIGINAL RESEARCH
open access to scientific and medical research
Open Access Full Text Article
http://dx.doi.org/10.2147/IJWH.S20685
Usefulness of serum mass spectrometry to identify
women diagnosed with higher grades of cervical
intraepithelial neoplasia may differ by race
Roland Matthews
1
Andres Azuero
2
Senait Asmellash
3
Earl Brewster
1
Edward E Partridge
4
Chandrika J Piyathilake
5
1
Department of Obstetrics and
Gynecology, The Morehouse School
of Medicine, Atlanta, Georgia, USA;
2
School of Nursing,
3
Departments of
Surgery,
4
Obstetrics and Gynecology,
5
Nutrition Sciences, University of
Alabama at Birmingham, Birmingham,
Alabama, USA
Correspondence: Chandrika J Piyathilake
Department of Nutrition Sciences,
University of Alabama at Birmingham
(UAB), 1675 University Blvd, Webb 326,
Birmingham, AL 35294, USA
Tel +1 205 975 5398
Fax +1 205 934 7049
Email piyathic@uab.edu
Background: An early detection of precursor lesions of cervical cancer will help to eliminate
the worldwide burden of cervical cancer.
Methods: This exploratory study aimed to identify, by matrix-assisted laser desorption/
ionization (MALDI) time-of-flight (TOF) mass spectrometry (MS), serum protein profiles that
distinguish cervical intraepithelial neoplasia grades CIN 1 or lower (#CIN 1) from CIN 2+
among 127 women infected with human papillomavirus (HPV) 16. Of these 127 women,
25 and 23 were diagnosed with CIN 2 or CIN 3, respectively (cases), and 79 were diagnosed
with #CIN 1 (non-cases). Serum protein profiles were generated by MALDI-TOF-MS. A total
of 95 m/z peaks were tested for association with case status by two racial groups, African
American (AAs) and Caucasian American (CAs).
Results: Overall, 2 protein peaks identified by our study demonstrated higher specificity for
identifying CIN 2+ than previously published studies. An increasing intensity of [m/z 4459]
was associated with a higher risk of being a case, regardless of race with a specificity of 58%
for CIN 2 and a specificity of 75% for CIN 3. An increasing intensity of [m/z 4154] was not
only associated with a higher risk of being a case only among CAs, but also had an opposite
effect among AAs.
Conclusion: Identification of specific proteins associated with the peaks detected in serum
and development of antibody-based tests such as ELISA should lead to the development of race-
specific, non-invasive and cost effective screening tests with higher specificity for identifying
HPV 16 associated CIN 2+.
Keywords: serum mass spectrometry, cervical intraepithelial neoplasia, race
Introduction
Worldwide, cervical cancer (CC), which is caused mainly by 13 high-risk or
carcinogenic genotypes of human papillomaviruses (HPVs), is the third most
prevalent type of cancer in women.
1,2
HPV is the most common sexually transmitted
virus.
3
In addition, HPV infections and CC risks are compounded by infections with
human immunodeficiency virus (HIV) and the resulting acquired immune deficiency
syndrome.
4,5
Although CC is a priority global health condition affecting millions of
women, it is preventable by use of organized screening programs and regular follow-up
of at-risk women. A cytology-based screening test for CC (Pap test) has been used
in high-income countries for the last 50 years. Compared with cytology, testing for
HPVs is more sensitive in detecting cervical intraepithelial neoplasia grades CIN 2+,
but with lower specificity.
6
For much of the world, screening by the Pap test or by
HPV tests may not however, be a viable option because of the need for specialized
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practitioners or lack of laboratory infrastructure to perform
these tests. Therefore, development of improved screening
tools that can be applied worldwide, and without the need
for such infrastructure, will greatly aid in the prevention and
control of CC.
We have previously conducted an exploratory study
to identify candidate surface-enhanced laser desorption/
ionization (SELDI) time of flight (TOF) mass spectrometry
(MS) protein profiles in plasma that may distinguish cervical
intraepithelial neoplasia 3 (CIN 3) from CIN 1 among women
infected with high-risk HPVs. The results of this study sug-
gested the possibility of using plasma SELDI protein profiles
to identify women who are likely to have CIN 3 lesions.
7
The
current report describes an exploratory study to identify, by
matrix-assisted laser desorption/ ionization (MALDI) TOF
MS, protein profiles in serum that may distinguish CIN 1 or
lower (#CIN 1) from CIN 2+ among AA and CA women
infected with HPV 16, one of the most carcinogenic types
of HPVs.
Materials and methods
Patient population
The study is based on the analysis of serum samples from
127 HPV 16 positive women who were enrolled in a pro-
spective follow-up study funded by the National Cancer
Institute (R01 CA105448, Prognostic Significance of DNA
and Histone Methylation). The study has been described in
a previous publication.
8
All women were diagnosed with
abnormal cervical cells in clinics of the Health Departments
in Jefferson County and surrounding counties in Alabama and
were referred to the University of Alabama at Birmingham
(UAB) for further examination by colposcopy and biopsy.
The women were 19–50 years old, had no history of cervical
cancer or other cancers of the lower genital tract, no history
of hysterectomy or destructive therapy of the cervix, were
not pregnant, and were not using antifolate medications
such as methotrexate, sulfasalazine, or phenytoin. Of these
127 women, 25 and 23 were diagnosed with CIN 2 or CIN 3,
respectively (cases), and 79 were diagnosed with #CIN 1
(normal cervical epithelium, n = 3, HPV cytopathic effect,
n = 6, reactive nuclear enlargement, n = 15 or CIN 1, n = 55,
non-cases). All women tested positive for HPV 16 in exfo-
liated cervical cells. All women included in this analysis
participated in an interview that assessed sociodemographic
variables and lifestyle risk factors. Height and weight mea-
surements were obtained by use of standard protocols. The
BMI was calculated using the height and weight measure-
ments (weight kg/[height m]
2
). Pelvic examinations and
collection of cervical cells and biopsies were accomplished
following the protocols of the colposcopy clinic. Fasting
blood samples were collected from all women and processed
immediately to isolate serum. Several serum sample aliquots
were stored at 80°C. Serum samples which were not sub-
jected to freeze-thaw cycles were used to generate serum
protein profiles. The study protocol and procedures were
approved by the UAB Institutional Review Board.
Testing for HPVs
DNA was extracted from cervical cells using the QIAamp
MiniElute Media Kit (Qiagen, Inc, Valencia, CA) following
the manufacturers instruction for HPV genotyping test.
HPV genotyping test (Linear Array, Roche Diagnostics,
Indianapolis, IN) was performed according to the manu-
facturer’s instructions by a research associate trained by
personnel from Roche Diagnostics. Briefly, target DNA
amplified by Polymerase Chain Reaction (PCR) utilized
the PGMY09/11 L1 consensus primer system and included
co-amplification of a human cellular target, β-globin, as
an internal control. Detection and HPV genotyping were
achieved using a linear array HPV genotyping test and this
test included probes to genotype for 37 anogenital HPV types
(6, 11, 16, 18, 26, 31, 33, 35, 39, 40, 42, 45, 51, 52, 53, 54, 55,
56, 58, 59, 61, 62, 64, 66, 67, 68, 69, 70, 71, 72, 73 (MM9),
81, 82 (MM4), 83 (MM7), 84 (MM8), IS39, and CP6108).
HPV 16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, 68 were
considered to be high-risk (HR) HPV types and all other
types were considered to be low-risk (LR) HPVs.
Generation of serum protein proles
A high-affinity, solid-core lipophilic extraction resin was
used to enrich the low-molecular-weight protein fraction of
the samples. Bondapak C18 125A, 37–55 µm resin (Waters,
Milford, MA, USA) was packed into 96-well, 0.45-µm
Unifilter plates (Whatman, Florham Park, NJ, USA) and the
packed resins were activated with 80% acetonitrile (aque-
ous). Serum samples were thawed, diluted (1:50) in distilled
water, acidified by adding trifluoroacetic acid (TFA) to a
final concentration of 1% v/v (475 µL of sample plus 25 µL
of 20% TFA), and mixed with the activated C18 resins. The
unbound serum proteins were removed by centrifugation
of the 96-well plate for 5 minutes at 1500 g. The resin was
washed twice with 200 µL of 1% TFA per well, and the
bound peptides and low-molecular-weight proteins were
eluted with 100 µl of 70% CH
3
CN:0.1% TFA (aqueous).
Eluants were mixed with an equal volume of matrix consist-
ing of 20 mg/mL sinapinic acid (Fluka, St Louis, MO, USA)
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Serum mass spectrometry and cervical intraepithelial neoplasia
in 50:50 CH
3
CN:0.1% TFA) and spotted onto a MALDI
target plate for MALDI-TOF analysis. Profiles of peptides
and low-molecular-weight proteins were obtained using a
200 Hz MALDI-TOF/TOF MS (Ultraflex III, Bruker Dal-
tonics). Spectra were acquired in a linear positive ion mode
with the mass window set to acquire from 2–20 kDa. Mass
calibration was accomplished externally by use of a mixture
of standards consisting of insulin, cytochrome c, myoglobin,
and ubiquitin (Bruker Daltonics, Bremen, Germany).
Data obtained from the mass spectrometry analysis of the
serum samples were exported as text files for preprocessing
and further analysis. In-house spectra analysis tools built with
MATLAB were used to pre-process the mass spectrometry
data. The spectral preprocessing carried out included baseline
and noise estimation followed by subtraction of background
noise using a local (in m/z) noise estimator, normalization
using total ion current, peak detection with a signal-to-noise
ratio (S/N) cutoff of 4, and finally peak alignment based on a
common set of peaks that appear in at least two-thirds of all
spectra. The preprocessing step resulted in 95 peaks in the
mass-to-charge (m/z) range of 2–20 kDa on which statistical
analysis as described below was carried out.
Statistical methods
Two sets of analyses were conducted in this study. In the
first set of analyses, the 95 peaks were tested for association
with case status. The case group included the 48 women
with CIN 2 or CIN 3 and the non-case group included the
79 women with diagnosis #CIN 1. In the second set of analy-
ses, the 95 peaks were tested for association with extreme
diagnoses of cervical lesions, ie, cases diagnosed with CIN 3
(n = 23) vs non-cases with diagnosis ,CIN 1 (n = 24).
Within each set of analyses, logistic regression models
were used to test the association of peak intensities and
participant characteristics with case status. Using each indi-
vidual peak, 5 initial models were fitted: a bivariate model
with peak intensity as a predictor of case status, and 4 models
for interactions between peak intensity and 4 participant
characteristics (age, BMI, race, and infection with mul-
tiple HR-HPV types), respectively. Each interaction model
included the main effects for peak intensity and participant
characteristic, and an interaction term. A model with only the
peak intensity as predictor assumed that the effects of inten-
sity on case status are similar for all participants. A model
with a peak-by-characteristic interaction allowed separating
effects of intensity on case status, according to the levels or
categories of the characteristic. The statistical significance
was held at the traditional 0.05 level.
The peaks, characteristics, and peak-by-characteristic
interactions that individually showed significant association
with case status were then used to construct a multivariable
logistic model that predicted the case status. The redundant
predictors were dropped from the multivariable model using
a backward-selection algorithm.
Alternative peak selection procedures used to construct
the multivariable models included least absolute shrinkage
and selection (LASSO) regression, and least angle regres-
sion (LAR).
9
In these procedures, the initial set of predictors
included all 95 peaks, the 4 participant characteristics, and
the 380 peak by characteristic interactions.
Measures of sensitivity and specificity were calculated for
the final multivariable models, as well as for the individual
predictors used in them. Each logistic regression model
estimated the probabilities of case status for the range of
values of the predictors. In order to use the model-predicted
probabilities to classify individuals into cases or non-cases,
a cutoff probability value was required. A natural cutoff
point is 0.5, however, this value might not be optimal. For
each logistic model, the optimal cutoff probability value was
determined using a receiver operating characteristic (ROC)
curve. For each predicted probability taken as cutoff value,
a ROC curve plots the resulting sensitivity (on the vertical
axis) vs 1 – specificity (on the horizontal axis). The optimal
cutoff value is that for which sensitivity and 1 specificity are
closest to the ideal values of 1 and 0, respectively. In addition,
the area under the ROC curve is a measure of the predictive
ability of the model.
10
Areas from 0.7 to 0.8 indicate fair
predictive ability; areas from 0.8 to 0.9 indicate acceptable
predictive ability; and areas greater than 0.9 indicate excel-
lent predictive ability.
Leave-one-out cross-validation was conducted for the final
multivariable logistic models (values for each individual were
removed from the dataset; then a logistic model was calculated
with the remaining individuals and used to predict the status
of the removed individual). For the two peaks that showed the
strongest association with case status, cross- validated mea-
sures of sensitivity and specificity were computed separately
by race. All statistical analyses were conducted using SAS v.
9.2 software (SAS Institute, Cary, NC; 2008).
Results
Sample characteristics
Average ages in years for cases and non-cases were 23.6
(SD = 3.7) and 23.4 (SD = 4.8), respectively (difference
in mean age P = 0.85). Average BMI measures in kg/m
2
for cases and non-cases were 25.9 (SD = 6.8) and 26.9
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Matthews et al
(SD = 9.2), respectively (difference in mean BMI P = 0.47).
The proportions of AAs for cases and non-cases were 39.6%
(n = 19) and 49.4% (n = 39), respectively (difference in
proportions, P = 0.28). Among cases, the proportion of
participants with infections with multiple HPV types was
47.9% (n = 23); among non-cases, this proportion was 44.3%
(n = 35, difference in proportions, P = 0.69).
Association of peaks with case status
In the initial set of analyses, which compared 48 cases (CIN 2+)
with 79 non-cases (#CIN 1), significant bivariate associations
were detected between case status and the following: 8 peaks,
1 peak by race interaction, and 1 peak by age interaction. Thus,
the initial multivariable model included the main effects for
10 peaks, 2 interaction terms, and main effects for age and
race. Thenal multivariable model, however, included only 2
components: (1) a main effect for peak [m/z = 4459], and (2) a
race by [m/z = 4154] interaction, which required the inclusion
of main effects for race and peak [m/z = 4154].
The alternative LASSO and LAR regression procedures
for peak selection retained only [m/z = 4459] as a predictor.
Because in the final multivariable logistic model the rela-
tionship between case status and the race by [m/z = 4154]
interaction was statistically significant in the presence of
[m/z = 4459], the larger model with both peaks and interac-
tion was preferred and remained as the final model.
At the observed median [m/z = 4459] intensity, the
model-predicted odds of being CIN 2+ was estimated at 0.58
(probability of being a case = 0.37). Using the odds of being
a case at the median intensity as reference, Figure 1 shows
the model-predicted odds ratios for the observed range of
intensity values. According to the model, increasing intensity
of [m/z = 4459] was associated with a higher risk of being
CIN 2+, regardless of race.
At the observed median [m/z = 4154] intensity, the model-
predicted odds of being CIN 2+ among CAs was estimated
at 0.75 (probability of being a case = 0.43); among AAs, the
model predicted odds of being a case was estimated at 0.56
(probability of being a case = 0.36). Using the odds of being
CIN 2+ at the median intensity as reference, Figure 2 shows that
the model-predicted odds ratios for the observed [m/z = 4154]
range of intensity values differed by race. According to the
model, an increasing intensity of [m/z = 4154] was associated
with a higher risk of being CIN 2+ only among CAs, but had
an opposite effect among AAs. The interaction term was used
to model this differential effect by race.
After determining the optimal model-predicted cut-
off probability using a ROC curve (Figure 3), the final
multivariate logistic model accurately classified 35 cases
and 52 non-cases. However, 13 cases were misclassified as
non-cases, and 27 non-cases were misclassified as cases.
Concordance with case status was 68.5%; concordance
beyond chance, as measured by the Kappa statistic, was
estimated at 0.37 (95% CI = 0.21, 0.53). The area under the
ROC curve of 0.72 indicated that the final multivariate model
had fair predictive ability.
0
0 500 1000
Reference odds
at median intensity
1500
Intensity
Model-predicted odds ratio
2000 2500 3000 3500
1
2
3
4
5
6
7
8
9
10
11
12
13
Figure 1 Association between the intensity of peak m/z = 4459 and odds ratio for
CIN 2+
a
.
Note:
a
Reference value for odds ratio shown is 0.58 (logistic model predicted odds
for CIN 2+ at median intensity of the peak).
Abbreviation: CIN, cervical intraepithelial neoplasia.
0
05000 10000
African American Caucasian
Reference odds
at median intensity
15000
Intensity
Model-predicted odds ratio
20000 25000 30000
0.5
1
1.5
2
2.5
3
3.5
4
4.5
Figure 2 Association between the intensity of peak m/z = 4154 and odds ratio for
CIN 2+ for Caucasian Americans
a
and African Americans.
b
Notes:
a
Reference value for odds ratio shown is 0.75 for Caucasian Americans
(logistic model predicted odds for CIN 2+ among Caucasian Americans at median
intensity of the peak);
b
Reference value for odds ratio shown is 0.56 for African
Americans (logistic model predicted odds for CIN 2+ among African Americans at
median intensity of the peak).
Abbreviation: CIN, cervical intraepithelial neoplasia.
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Serum mass spectrometry and cervical intraepithelial neoplasia
There was a small expected decrease in the precision
of the prediction after applying the leave-one-out cross-
validation algorithm. For the final multivariate model, the
cross-validation algorithm resulted in accurate classification
of 38 cases and 41 non-cases. Ten cases were misclassified
as non-cases, and 38 non-cases were misclassified as cases.
Concordance with case status was 62.2%; concordance
beyond chance, as measured by the Kappa statistic, was
estimated at 0.28 (95% CI = 0.13, 0.43). Cross-validated
sensitivity and specificity for the final multivariate model as
well as for each of its two components are shown in Table 1.
As can be seen in this table, the final model resulted in better
sensitivity compared with each of its individual components
alone; however, the models with individual components
resulted in better specificity compared to the final model.
This observed variability in the sensitivity and specificity
measures, when comparing the final model vs the component-
only models, suggests some instability in the prediction,
caused by variability in the probability of being CIN 2+ that
remained unexplained by the logistic regression models.
In the subsample of 24 non-cases with diagnosis ,CIN 1
and 23 cases with CIN 3, the average ages in years for cases
and non-cases were 23.2 (SD = 3.6) and 22.8 (SD = 3.9),
respectively (difference in mean age P = 0.73). Average
BMI measures in kg/m
2
for cases and non-cases were 25.0
(SD = 5.8) and 29.4 (SD = 9.2), respectively (difference in
mean BMI P = 0.06). The proportions of AAs for cases and
non-cases were 26% (n = 6) and 70% (n = 17), respectively
(difference in proportions, P = 0.0034). Among the cases,
the proportion of participants with infections with multiple
HR-HPV types was 47.8% (n = 11); among the non-cases,
this proportion was 45.8% (n = 11, difference in proportions
P = 0.89).
In the second set of analyses, which utilized the sub-
sample of 24 non-cases with diagnosis ,CIN one and
23 cases diagnosed with CIN 3, significant univariate
associations were detected between case status and the
following: race, 9 peaks, and 2 peak by BMI interactions.
Thus, the initial multivariable model included 11 peaks,
2 interactions, and main effects for BMI and race. The final
multivariable model, however, included only 2 components:
(1) a main effect for peak [m/z = 4459], and (2) a main effect
for race. After determining the optimal cutoff point with a
ROC curve (Figure 4), the final multivariate logistic model
accurately classified 21 non-cases and 16 cases. However,
7 cases were misclassified as non-cases, and 3 non-cases
were misclassified as cases. Concordance with case status
was 78.7%; concordance beyond chance, as measured by the
Kappa statistic, was estimated at 0.57 (95% CI = 0.34, 0.80).
The area under the ROC curve of 0.8152 indicated that the
model had acceptable predictive ability.
The leave-one-out cross-validation algorithm produced
similar prediction results compared to those from the original
model. Measures of cross-validated sensitivity and specific-
ity for the final multivariate model as well as for each of its
two components are shown in Table 2. As can be seen in
this table, the final model provided more accurate specific-
ity compared with each of its individual components alone,
but the sensitivity decreased compared to the race-only
model. Again, this observed variability in the sensitivity
and specificity measures, when comparing the final model
vs the component-only models, suggests some instability
0 0.25 0.5
1-Specificity
Sensitivity
0.75 1
0
0.25
0.5
0.75
1
Area under the curve = 0.7271
Cutoff at predicted probability = 0.32
Figure 3 Receiver operating characteristic curve for all cases (CIN 2+) and all
controls (#CIN 1).
Abbreviation: CIN, cervical intraepithelial neoplasia.
Table 1 Cross-validated screening test measures for the nal multivariate model and individual models with the two components of
the nal model predicting cervical intraepithelial neoplasia (CIN) 2+ vs #CIN 1 (n = 127)
Model: Predictor(s) Sensitivity Specicity Cutoff prob.
a
Final model: [m/z = 4459], Race*[m/z = 4154], Race, [m/z 4154]
79% 52% 0.32
Model with Component 1 only: [m/z = 4459]
69% 58% 0.34
Model with Component 2 only: Race*[m/z = 4154], Race, [m/z = 4154]
58% 58% 0.38
Note:
a
Optimal cutoff probabilities were determined by receiver operating characteristic curves.
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Matthews et al
in the prediction, caused by variability in the probability
of being a case that remained unexplained by the logistic
regression models.
Prediction results by race
After determining that peaks [m/z = 4459] and [m/z = 4154]
showed the strongest association with case status, the predic-
tion results using these two peaks were compared by race.
Table 3 shows cross-validated sensitivity and specificity
measures tabulated by race, for a model predicting CIN 2+
using peak [m/z = 4459] as the only predictor. Because the
effect of increasing [m/z = 4154] intensity on CIN 2+ was
opposite by race (Figure 2), two models predicting CIN 2+,
using [m/z = 4154] intensity as the only predictor, were
fitted for each race, respectively. Cross-validated sensitiv-
ity and specificity for these models are shown in Table 4.
Table 5 shows cross-validated sensitivity and specificity
measures tabulated by race, for a model predicting CIN 3
using peak [m/z = 4459] as the only predictor, among the
subsample of 24 non-cases with diagnosis ,CIN 1 and
23 cases diagnosed with CIN 3. As can be seen in this table,
sensitivity was higher for CAs, but specificity was higher
for AAs, suggesting again that the predictive ability of the
[m/z = 4459] intensities on extreme diagnoses might vary
by race.
Discussion
An important application of MALDI-TOF MS is the simulta-
neous analysis of multiple proteins to establish “fingerprint”
profiles that discriminate disease from non-disease. This is
an important approach, since no single biomarker or protein
alone will improve the early detection/diagnosis of diseases,
including cancer or pre-cancers. Body fluids, such as serum,
are a source of putative protein biomarkers with the potential
to elucidate organ-specific carcinogenic events. Because of
its high sensitivity for proteins in the low molecular weight
range and because of its capability of high throughput screen-
ing, MALDI-TOF MS has been used to distinguish healthy
controls from patients diagnosed with several cancers includ-
ing the colon, lung, ovary, breast and esophagus.
11–15
Previous
studies have also documented differences in serum protein
profiles between cervical cancer and healthy controls.
16
Three differentially expressed potential biomarkers with
relative molecular weights of 3974 Da, 4175 Da and 5906 Da
identified in this study demonstrated 90% sensitivity and
specificity in disguising cervical cancer from controls. To
our knowledge, the current study is the first to document
the usage of MALDI-TOF-MS technology in the analysis
of serum protein profiles of patients diagnosed with cervi-
cal pre-cancer and evaluated differences in sensitivity and
specificity of protein peaks by race.
We focused on women infected with HPV 16 as this virus
is the most frequent causative agent for developing cervical
cancer world-wide.
17
Even though only a fraction of women
infected with HPV 16 develops CIN 2+, these lesions have
the highest rate of progression to CC.
18
Further, the recur-
rence rate of CIN 2+ after a loop electrosurgical excision
procedure was shown to be significantly higher among
those who were tested positive for HPV 16 before and after
the procedure.
19
Therefore, identification of this fraction of
women and treatment of their lesions and closer follow-up
after treatment are important unmet medical needs in the
current management protocols. Currently available tests do
0 0.25 0.5
1-Specificity
Sensitivity
0.75 1
0
0.25
0.5
0.75
1
Area under the curve = 0.8152
Cutoff at predicted probability = 0.60
Figure 4 Receiver operating characteristic curve for CIN 3 and controls diagnosed
with ,CIN 1.
Abbreviation: CIN, cervical intraepithelial neoplasia.
Table 2 Cross-validated screening test measures for the nal multivariate model and individual models with the two components of
the nal model predicting cervical intraepithelial neoplasia (CIN) 3 vs ,CIN 1 (n = 47)
Model: Predictor(s) Sensitivity Specicity Cutoff prob.
a
Final model: [m/z 4459], Race 70% 87% 0.60
Model with Component 1 only: [m/z 4459] 61% 75% 0.50
Model with Component 2 only: Race 74% 71% 0.72
Note:
a
Optimal cutoff probabilities were determined by receiver operating characteristic curves.
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Serum mass spectrometry and cervical intraepithelial neoplasia
not have adequate specificity for identifying women with
HPV 16-associated CIN 2+.
In our population, 40% of women infected with HPV 16
were diagnosed with CIN grades higher than 2 (CIN 2+).
Identification, treatment, and closer follow-up of these women
would offer a cost-effective strategy to reduce the cervical
cancer burden. A meta-analysis showed that detecting any
HR-HPV by the Hybrid Capture 2 test among women with
abnormal pap demonstrated 97.2% sensitivity for detecting
CIN 2+ and 97.1% sensitivity for detecting CIN 3+. This
analysis also demonstrated a pooled specificity of 30.6% and
26.1% when the outcome was CIN 2+ and CIN 3+ respec-
tively.
20
A recent study demonstrated that the sensitivity
of the HPV 16/18 genotyping test for detection of CIN 2+
was .93% while the specificity of the test for detection of
CIN 2+ and CIN 3+ was 44.2% and 43%, respectively.
21
Two protein peaks identified by our study demonstrated
higher specificity for identifying CIN 2+ than these pub-
lished studies. An increasing intensity of [m/z = 4459] was
associated with a higher risk of being a case, regardless of race
with a specificity of 58% for CIN 2 and a specificity of 75%
for CIN 3. Further, to our knowledge for the first time, we
also document interesting racial differences in the associations
between peak intensities and higher risk of being diagnosed
with CIN 2+. An increasing intensity of [m/z = 4154] was not
only associated with a higher risk of being a case only among
CAs, but also had an opposite effect among African AAs. With
[m/z = 4459], the specificity was higher for AAs, but the sensitiv-
ity was higher for CAs suggesting that the predictive ability of the
[m/z = 4459] peak intensities varies by race. With [m/z = 4154],
on the other hand, the sensitivity was similar for the two races,
but the specificity was higher for AAs, indicating that the peak
had slightly better predictive ability among AA women.
This report suggests a capacity of serum protein profiles
to differentiate between HPV 16 positive women free of true
pre-neoplastic lesions (#CIN 1) and women diagnosed with
higher grades of CIN, especially CIN 3, in our population
of women infected with HPV 16. In the statistical analyses,
infection with other types of HR-HPVs was used as a predic-
tor of case status, by itself and as an interaction with the m/z
peaks, but no significant association was found. Therefore,
these results suggest that it is unlikely that co-infections with
other HPVs interfere with identifying CIN lesions in women
infected with HPV 16. Further, the results also suggested that
specific serum profiles might be useful for differentiating
CIN cases from non-cases in AAs and CAs.
Identification of specific proteins associated with peaks
that are significantly different between #CIN 1 and CIN 2+
by race may potentially lead to the development of new
screening tests which are suitable in different racial groups.
Identification of these serum proteins and development
of antibody-based tests, such as ELISA, may lead to the
development of cost-effective, non-invasive, sensitive, and
simple to use tests. Further, serum based screening tests are
likely to be more acceptable than cervical cell based tests in
populations where the prevalence of obesity is high, as studies
indicate lower rates of cervical cancer screening among obese
compared with non-obese women due to embarrassment and
perceived weight stigma.
22
Also, lack of appropriately sized
equipment for examination of obese women in some clinical
settings may lead to poor quality cervical cell samples that
result in unreliable or invalid test results. Therefore, serum
based screening biomarkers are likely to be extremely useful
in this group of women. Collectively, these results suggest the
need for developing race-specific markers to maximize the
usefulness of serum protein based tests as effective screening
tools. Despite intense screening in the past decades, higher
rates of cervical cancer still persist among some sub-groups
of women, including AA women.
23
Race-specific screening
tests are likely to reduce these disparities.
Although these prediction results using serum biomark-
ers are promising, there was still a considerable amount of
variability in the probabilities of case status that remained
Table 3 Cross-validated screening test measures by race for
a model predicting cervical intraepithelial neoplasia (CIN) 2+
vs #CIN 1, using peak [m/z = 4459] as the only predictor
a
Race n Sensitivity Specicity
African American 58 58% 64%
Caucasian 69 76% 53%
Combined 127 69% 58%
Note:
a
Cutoff probability was 0.34.
Table 4 Cross-validated screening test measures by race for
a model predicting cervical intraepithelial neoplasia (CIN) 2+
vs #CIN1, using peak [m/z = 4154] as the only predictor
Race n Sensitivity Specicity Cutoff prob.
African American 58 63% 64% 0.32
Caucasian 69 62% 53% 0.40
Table 5 Cross-validated screening test measures by race for a
model predicting cervical intraepithelial neoplasia (CIN) 3 vs ,CIN 1,
using peak [m/z = 4459] as the only predictor (n = 47)
a
Race n Sensitivity Specicity
African American 23 50% 76%
Caucasian 24 65% 71%
Combined 47 61% 75%
Note:
a
Cutoff probability was 0.5.
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Matthews et al
unexplained by the statistical models used in this study.
A classical screening approach used in this study permitted
the detection of the two individual peaks associated with
case status. Only interactions between individual peaks and
4 participant characteristics (age, BMI, race, and infection
with multiple HR-HPV types) were considered. However,
peak by peak interactions (or peak by peak profiles) were not
considered in either the classical screening approach or in the
alternative LASSO and LAR peak selection procedures, due
to the overwhelming number of possibilities. With 95 peaks,
restricting the interactions or profiles to only a maximum of
four peaks at a time, there are 4465 possible 2-way interac-
tions, 138,415 possible 3-way interactions, and 3,183,545
possible 4-way interactions. Because interactions (or peak
by peak profiles) cannot be ruled out from being associated
with case status, further examination of these peak by peak
profiles using larger sample sizes and data mining techniques
is warranted in future studies.
Acknowledgment
This publication was supported by Grant Number (U54
CA118948-01) from the National Cancer Institute.
Disclosure
The authors have no conflicts of interest that are directly
relevant to the content of this study.
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  • Article · Apr 2013
  • [Show abstract] [Hide abstract] ABSTRACT: The present study aimed to analyze sera proteins in females with cervical intraepithelial neoplasia, grade III (CIN III) and in healthy control females, in order to identify a potential biomarker which detects lesions that have a greater probability of cervical transformation. The present study investigated five sera samples from females who were Human Papilloma Virus (HPV) 16(+) and who had been histopathologically diagnosed with CIN III, as well as five sera samples from healthy control females who were HPV-negative. Protein separation was performed using two-dimensional (2D) gel electrophoresis and the proteins were stained with Colloidal Coommassie Blue. Quantitative analysis was performed using ImageMaster 2D Platinum 6.0 software. Peptide sequence identification was performed using a nano-LC ESIMS/MS system. The proteins with the highest Mascot score were validated using western blot analysis in an additional 55 sera samples from the control and CIN III groups. The eight highest score spots that were found to be overexpressed in the CIN III sera group were identified as α-1-B glycoprotein (A1BG), complement component 3 (C3), a pro-apolipoprotein, two apolipoproteins and three haptoglobins. Only A1BG and C3 were validated using western blot analysis, and the bands were compared between the two groups using densitometry analysis. The relative density of the bands of A1BG and C3 was found to be greater in all of the serum samples from the females with CIN III, compared with those of the individuals in the control group. In summary, the present study identified two proteins whose expression was elevated in females with CIN III, suggesting that they could be used as biomarkers for CIN III. However, further investigations are required in order to assess the expression of A1BG and C3 in different pre-malignant lesions.
    Full-text · Article · Aug 2014