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Gynecol Oncol. Author manuscript; available in PMC Sep 1, 2012.
Published in final edited form as:
PMCID: PMC3152615
NIHMSID: NIHMS308080
Comparison of Candidate Serologic Markers for Type I and Type II Ovarian Cancer
Dan Lu,1 Elisabetta Kuhn,1 Robert E. Bristow,2 Robert L. Giuntoli, II,2 Susanne Krüger Kjær,3 Ie-Ming Shih,1,2 and Richard B.S. Roden1,2#
1 Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD
2 Department of Gynecology and Obstetrics, and Oncology, Johns Hopkins University School of Medicine, Baltimore, MD
3 Institute of Cancer Epidemiology, Danish Cancer Society, Copenhagen, Denmark
# Corresponding author: Richard Roden, Johns Hopkins University, Cancer Research Building 2, Room 308, 1550 Orleans St, Baltimore, MD 21231 USA. Tel: 410 502 5161; Fax: 443 287 4295; roden/at/jhmi.edu
Objective
To examine the value of individual and combinations of ovarian cancer associated blood biomarkers for the discrimination between plasma of patients with type I or II ovarian cancer and disease-free volunteers.
Methods
Levels of 14 currently promising ovarian cancer-related biomarkers, including CA125, macrophage inhibitory factor-1 (MIF-1), leptin, prolactin, osteopontin (OPN), insulin-like growth factor-II (IGF-II), autoantibodies (AAbs) to eight proteins: p53, NY-ESO-1, p16, ALPP, CTSD, B23, GRP78, and SSX, were measured in the plasma of 151 ovarian cancer patients, 23 with borderline ovarian tumors, 55 with benign tumors and 75 healthy controls.
Results
When examined individually, seven candidate biomarkers (MIF, Prolactin, CA-125, OPN, Leptin, IGF-II and p53 AAbs) had significantly different plasma levels between type II ovarian cancer patients and healthy controls. Based on the receiver operating characteristic (ROC) curves constructed and area under the curve (AUC) calculated, CA125 exhibited the greatest power to discriminate the plasma samples of type II cancer patients from normal volunteers (AUC 0.9310), followed by IGF-II (AUC 0.8514), OPN (AUC 0.7888), leptin (AUC 0.7571), prolactin (AUC 0.7247), p53 AAbs (AUC 0.7033), and MIF (AUC 0.6992). p53 AAbs levels exhibited the lowest correlation with CA125 levels among the six markers, suggesting the potential of p53 AAbs as a biomarker independent of CA125. Indeed, p53 AAbs increased the AUC of ROC curve to the greatest extent when combining CA125 with one of the other markers. At a fixed specificity of 100%, the addition of p53 AAbs to CA125 increased sensitivity from 73.8% to 85.7% to discriminate type II cancer patients from normal controls. Notably, seropositivity of p53 AAbs is comparable in type II ovarian cancer patients with negative and positive CA125, but has no value for type I ovarian cancer patients.
Conclusions
p53 AAbs might be a useful blood-based biomarker for the detection of type II ovarian cancer, especially when combined with CA125 levels.
Early detection of ovarian cancer is associated with an improved outcome. However there is no sufficiently predictive screening test, and consequently the majority of cases present at an advanced stage. Despite aggressive surgery and chemotherapy regimens, ovarian cancer remains the most lethal gynecologic malignancy. Ovarian cancer is not a single disease, but comprises an amalgam of tumor types with distinct pathogenesis and morphologic features. Screening tests should be developed to detect a single entity, preferably of greatest medical significance, rather than a heterogeneous conglomeration of neoplasms. It was recently proposed that ovarian cancer can be grouped into two broad categories designated type I and type II tumors based upon their distinct pathogenesis [1]. Type I tumors are relatively genetically stable and generally exhibit an indolent behavior. In contrast, type II tumors are highly aggressive and almost always have progressed to advanced stage at diagnosis, when current available therapies are seldom curative [2]. Type II tumors constitute approximately 75% of ovarian cancer but are responsible for 90% of ovarian cancer deaths. Thus a screening test is urgently needed for low volume type II tumors using sensitive and specific biomarkers detectable before the disease is clinically manifest, or more ideally prior to metastasis [1].
High grade serous carcinoma (HGSC) is by far the most common type II ovarian cancer. This subgroup of ovarian cancers exhibits genetic instability and more than 80% [3, 4] of the tumors carry mutation in TP53 gene [5]. Morphologic and recent molecular genetic studies suggest that the majority of the HGSCs arise from the epithelium of fallopian tube rather than the ovarian surface epithelium as previously thought [611]. p53 mutation and subsequent protein accumulation are likely early events in the development of HGSC, since positive p53 immunostaining is observed in serous tubal intraepithelial carcinoma (STIC), the proposed precursor of HGSC [12, 13].
The pathogenesis of cancer from a normal cell to life-threatening metastatic tumor masses is associated with a series of genetic and epigenetic changes that result in mutant or abnormally expressed gene products [1416]. These gene products can potentially be shed into the body fluid as observed for the surface glycoprotein CA125, which is elevated in the blood of most ovarian cancer patients [1719]. In addition, aberrant gene products are potentially antigenic and can be recognized by the humoral immune system to generate specific antibody responses [20, 21], for example, mutated p53 or aberrant expression of NY-ESO-1 triggers autoantibodies (AAbs) in a significant fraction of cancer patients [2225]. Notably, p53 AAbs can appear before the clinical diagnosis of lung cancer [24, 26], suggesting its potential as a biomarker for the early detection of ovarian cancer or even its precursors.
The most extensively studied biomarker of ovarian cancer, CA125, was licensed for monitoring recurrence only. Although an increasing number of novel biomarkers for ovarian cancer have been proposed with advances in the technologies of genomics and proteomics, no single marker has proven sensitive and specific enough for clinical application in screening for ovarian cancer. As compared to a single marker, combining different markers as a panel potentially increases predictive value for detecting ovarian cancer when it is considered as a single entity [27, 28]. Therefore, in this report, we analyzed 14 promising candidate biomarkers for ovarian cancer, each selected based on promising earlier studies, aiming to identify the optimal combination panel for discriminating type II ovarian cancer patients from normal controls and to explore their value for detecting type I disease.
Human plasma samples
Study individuals were recruited at the Johns Hopkins Hospital and included 151 patients with ovarian carcinoma and three groups of controls: 23 with borderline ovarian tumors, 55 with benign tumors, and 75 healthy female blood donors. The characteristics of the recruited patients were summarized in table 1. Blood of tumor patients was collected into heparin-treated tubes prior to surgery, plasma obtained and stored at −80°C. Written informed consent was provided by each participant and this study was approved by the Johns Hopkins Institutional Review Board.
Table 1
Table 1
Characteristics of the patients.
Enzyme-linked immunosorbent assay (ELISA)
The levels of p53 AAbs in the plasma were measured using the commercial p53 ELISAPLUS (Autoantibody) kit from Calbiochem (QIA53) following the manufacturer’s instructions. To measure the levels of AAbs to NY-ESO-1 and p16, hexahistidine (6His)-tagged fusion proteins of NY-ESO-1 and p16 were expressed in Rosetta DE3 cells (Novagen) using pET28a-NY-ESO-1 (NM_139250.1; GI: 21281684) and pET28a-p16 (NM_000077.4; GI: 300863097) constructs followed by purification as described previously [25]. The quality of each protein was examined with SDS-PAGE followed by Coomassie staining and western blot. The protein concentrations were estimated with absorbance at 280 nm (A280) using Nanodrop. Maxisorp immuno plates (Nunc, Rochester, NY) were coated with 0.1μg/well of the purified protein at 4°C overnight (O/N). After being blocked with 1% (w/v) BSA in PBS for 1 hr at room temperature (RT), the plates were incubated with human plasma samples diluted at 1:50 in the blocking buffer for 1 hr at RT. Following extensive wash with PBS/0.01% (v/v) Tween-20, the plates were incubated with HRP-conjugated sheep anti-human IgG (GE healthcare) diluted at 1:5000 in the blocking buffer for 1 hr at RT. The plates were washed again, and then 1-Step ABTS substrate (Pierce) was added for 15 min at 37°C for color development. The absorbance was measured at 405 nm (A405) using a Benchmark Plus ELISA plate reader (Bio-Rad, Hercules, CA). Commercial antibodies were used as positive controls in the assay.
Sandwich ELISA was used to detect AAbs to ALPP (NM_001632.3; GI: 94721245), CTSD (NM_001909.3; GI: 23110949), B23 (NM_002520.4; GI: 262331543), GRP78 (NM_005347.2; GI: 21361242), and SSX (NM_005635.2; GI: 28559010). Myc-DDK-tagged proteins were expressed in 293TT cells using the Human cDNA ORF Clones for each protein (OriGene). The expression was confirmed by western blot using anti-Myc and anti-DDK antibodies. Maxisorp immuno plates (Nunc, Rochester, NY) were coated with 500ng/well of anti-DDK antibody (OriGene) at 4°C O/N. After wash, the plates were blocked with 5% (w/v) milk in PBS for 3 hr at RT. And then 100μg/well of lysate from 293TT cells expressing individual protein was added for incubation at 4°C O/N to capture the protein onto the plates. To eliminate the influences of nonspecific binding due to plasma, the lysate from 293TT cells transfected with the empty vector was added to separate plates serving as background control. The plates were incubated with human plasma samples diluted at 1:100 in the blocking buffer for 1 hr at RT following extensive wash. The bound AAbs from the plasma were detected using HRP-conjugated anti-human IgG (GE healthcare) and 1-Step ABTS substrate (Pierce) as described above. Commercial antibodies (ALPP: ab16695, abcam; CTSD: 219361, Calbiochem; B23: ab24412, abcam; GRP78: sc-1050, Santa Cruz; SSX: sc-28697, Santa Cruz) against each individual protein were used as positive controls in the assay. The relative level of AAbs to each protein in individual plasma was calculated by subtracting the A405 of background control from the A405 of the corresponding protein.
MILLIPLEX MAP assay
The MILLIPLEX MAP Human Cancer Biomarker Panel kit (Millipore) was used to measure the levels of MIF, Leptin, Prolactin, CA-125, OPN, and IGF-II in human plasma according to the manufacturer’s protocol. The plates were washed with Bio-Plex Pro II Wash Station (Bio-Rad, Hercules, CA). The samples were read with Bio-Plex array reader (Bio-Rad, Hercules, CA) and the data was analyzed with Bio-Plex Manager Software 5.0.
Statistical analysis
Comparison between different disease statuses for individual markers was done with the Wilcoxon two-sample test and t test after log transformation, difference is considered significant when passing both tests. The ability of each single marker to discriminate disease from control was assessed with receiver operating characteristic (ROC) curve generated through a univariate logistic regression model, and the area under curve (AUC) was measured based on generalized U-statistic. The discrimination ability of combined markers was assessed with weighted linear combination of markers using a multiple logistic regression model. The adequateness of the multiple logistic regression model was assessed by the Hosmer-Lemeshow goodness-of-fit test. The Akaike information criterion (AIC) and generalized coefficient of determination were used to compare multiple logistic regression model and univariate logistic regression model.
Comparison between different markers and/or combinations for discrimination ability was tested with asymptotic normal approximation [29]. The correlation between two different markers was made with Spearman rank-order correlation test. Statistical Analysis Software 9.2 (SAS Institute, Inc., Cary, North Carolina) was used in all analyses. Statistical significance was set at p value less than 0.05.
Levels of individual markers by disease status
The candidate markers were selected based on previous publications suggesting their promise for detection of ‘ovarian cancer’ [19, 25, 3032]. They include MIF, Leptin, Prolactin, CA-125, OPN, IGF-II, and autoantibodies (AAbs) to eight proteins: p53, NY-ESO-1, p16, ALPP, CTSD, B23, GRP78, and SSX. To simplify the analysis of combination of markers for the detection of type II ovarian cancer, we first examined whether these markers individually exhibited differential levels in the plasma of patients with different disease status. After analyzing the data with different statistical models (Wilcoxon two-sample test and t test after log transformation), we found that among the fourteen markers examined, the levels of six candidate markers (AAbs to p16, ALPP, CTSD, B23, GRP78, and SSX) were similar among different groups of patients (data not shown) and were not analyzed further. In contrast, the levels of five markers (MIF, prolactin, CA-125, OPN, and AAbs to p53) were consistently increased while the levels of two markers (leptin and IGF-II) were consistently decreased in the plasma of patients with type II ovarian cancer as compared to healthy controls (figure 1). Although AAbs to NY-ESO-1 exhibited higher levels in the plasma of patients with type II tumor, the difference reached significance in only one of the two statistical tests. Likewise, NY-ESO-1 AAbs were not observed in the plasma of type I ovarian cancer patients.
Figure 1
Figure 1
Levels of individual candidate biomarkers by disease type
We examined the impact of age on individual biomarkers by comparing their levels in different age groups in type II cancer patients. Using median age 59 as a cut-off, we found that patients of 59 and younger contained similar levels of the individual markers as the older patients (not shown). When we tested the influence of age on the biomarkers in benign tumor group which carry the closest levels of the biomarkers to normal controls, using Spearman rank-order correlation test; we found again that age did not have any effect on the levels of the biomarkers.
Except for the levels of AAbs to p53 and MIF which were comparable in the plasma of patients with type I tumor and healthy controls, the levels of the other five markers (prolactin, CA125, leptin, OPN, and IGF-II) in the plasma of patients with type I tumor showed similar trend as those with type II tumor when compared to the plasma of healthy controls (figure 1). These data suggest that p53 AAbs and MIF are specific for type II ovarian cancer.
When we compared the plasma levels of these seven markers in patients with type I and type II tumors, we found that type II tumor patients carried higher levels of p53 AAbs and CA125 but lower levels of IGF-II. Patients with the two different types of tumors had comparable levels of MIF, prolactin, leptin, and OPN in their plasma (figure 1).
To compare the performance of the seven candidate markers that are potentially associated with type II ovarian cancer, we then plotted a receiver operating characteristic (ROC) curve and calculated the area under curve (AUC) for each marker using healthy blood donors as controls. As shown in figure 2, CA125 exhibited the greatest ability to discriminate type II cancer patients from the healthy controls based on the AUC (0.9310), followed by IGF-II (0.8514), OPN (0.7888), leptin (0.7571), prolactin (0.7247), p53 AAbs (0.7033), MIF (0.6992).
Figure 2
Figure 2
ROC analyses for each marker for discrimination between ovarian cancer patients and healthy volunteers
Analysis of combined markers
We then focused on the seven candidate markers demonstrating significant differences between type II ovarian cancer patients and normal controls because the most optimal combination panel should come from these markers. The best combination panels of multiple (from 2 to 6) markers based on ROC and AUC analyses always include CA125. Interestingly, the combination of CA125 and p53 AAbs showed the best discrimination performance among all the two-marker panels although p53 AAbs had the second-lowest value of AUC as a single marker (table 2). The AUC increased significantly from 0.9310 for CA125 alone to 0.9694 for combining p53AAbs with CA125 (p=0.0122). In contrast, the increase in AUC by the second-best combination CA125 and leptin was not statistically significant (p=0.095). When using a cut-off of 9 U for p53 AAbs and 35 IU/ml for CA125, this two-marker panel was able to yield a sensitivity of 85.7% at 100% specificity with our patient samples, compared to a sensitivity of 73.8% at the same specificity for CA125 alone. The six-marker panel of MIF, prolactin, leptin, OPN, IGF-II and CA125 also significantly improved the performance with an AUC of 0.9719 when compared to CA125 alone. However, this is not significantly different from the two-marker panel combining p53 AAbs and CA125 (p=0.8407), suggesting that combination of p53 AAbs and CA125 might be a similarly effective marker for type II ovarian cancer when compared to the six-marker panel.
Table 2
Table 2
AUC analysis of CA125 in combination with another marker
Independence of AAbs and CA125 in type II ovarian cancer patients
Since p53 AAbs improved the performance of distinguishing type II ovarian cancer patients from normal controls to a greater extent compared to the other markers (MIF, prolactin, leptin, OPN, IGF-II) tested when combined with CA125 despite the fact that p53 AAbs failed to perform as well as the others as a single marker, we hypothesized that the production of p53 AAbs is independent of the detection of CA125 in ovarian cancer patients. When we compared the correlation of CA125 with the other markers of interest in the plasma of the type II ovarian cancer patients, we found that the level of p53 and NY-ESO-1 AAbs have the least correlation with CA125 (table 3).
Table 3
Table 3
Correlation between candidate biomarkers and CA125
To further evaluate the independence of p53 AAbs from CA125, we compared the generation of p53 AAbs in ovarian cancer patients with different levels of CA125 (figure 3). When we limited our analysis to the patients with type II ovarian cancer, the level of p53 AAbs were not statistically different between the plasma of cancer patients with negative and positive CA125 using a cut-off of 35 IU/ml for CA125, although it seems that the absolute levels of p53 AAbs are relatively lower in the CA125 < 35 IU/ml patients. When we used a cut-off of 9 U for p53 AAbs, 15 out of 33 (45.5%) patients with negative CA125 were positive for p53 AAbs and 29 out of 93 (31.2%) patients with positive CA125 were positive for p53 AAbs. Again, there is no statistical difference in the seropositivity of p53 AAbs between type II ovarian cancer patients with different levels of CA125 (p=0.1395).
Figure 3
Figure 3
p53 and NY-ESO-1 AAbs in plasma of type II ovarian cancer patients with CA125 above and below normal values
We also explored whether NY-ESO-1 AAbs in type II ovarian cancer patients were independent of CA125 levels (Figure 3). When using a cut-off of 35 IU/ml for CA125, there was no statistically significant difference in the presence of NY-ESO-1 AAbs; plasma from 5 of 33 (15.2%) type II ovarian cancer patients with CA125 < 35 IU/ml contained detectable NY-ESO-1 antibodies, whereas 17 of 93 (18.3%) type II ovarian cancer patients with CA125 > 35 IU/ml exhibited NY-ESO-1 AAb. However, the detection of AAbs to NY-ESO-1 in the plasma of patients with type II ovarian cancer was independent of AAbs to p53 (figure 3).
In this report, we confirmed that seven previously reported blood biomarkers (CA125, MIF-1, leptin, prolactin, OPN, IGF-II, and p53 AAbs) are associated with the presence of ovarian cancer [19, 25, 30]. We further demonstrated that among them, seropositivity of p53 AAbs and elevated MIF are associated with type II, not type I ovarian cancer; while the other five are not type-specific markers for ovarian cancer, although the blood levels of CA125 and IGF-II exhibit more dramatic change in type II than type I tumor patients when compared to healthy controls (figure 1). The specificity of p53 AAbs for type II tumors supports the notion that ‘ovarian cancer’ is not a single disease, requiring different biomarkers be utilized for each specific disease type. Similarly, NY-ESO-1 AAbs were not apparent in the plasma of type I ovarian cancer patients, but they were detected in a small fraction of type II ovarian cancer patients. Notably, type II tumors are more aggressive and prevalent than type I tumors and need blood biomarkers to aid the early detection [10], therefore it is reasonable to focus on type II tumors for the identification of biomarkers in the future.
In theory, the efficient combination of biomarkers should consist of biomarkers that are not correlated with each other because the combined effect of such markers will be closer to an additive effect. Through the statistical analysis of currently promising candidate markers, our study suggests that seropositivity of p53 AAbs might be a useful blood marker to combine with CA125 for the early detection of type II ovarian cancer. This notion was supported by the low level of correlation between the production of p53 AAbs and CA125 in type II ovarian cancer patients (table 3), and by the comparable seropositivity of p53 AAbs in the patients with negative and positive CA125 (figure 3). These data clearly indicated the additional benefit of combining p53 AAbs with CA125 over CA125 alone in the detection of type II ovarian cancer. Since CA125 positivity correlates with increased ovarian cancer volume measured under laparoscopy/laparotomy [19], it suggests that the production of p53 AAbs by type II ovarian cancer patients is relatively independent of the extent of disease. A similar observation was made for NY-ESO-1 antibodies, suggesting that AAbs can be triggered by low volume type II ovarian cancer. Indeed, p53 AAbs were previously shown to be present in sera of patients with asbestosis several years prior to a diagnosis of cancer [26], therefore it is highly possible that immune system recognizes limited amount of mutant p53 accumulation occurring early in the development of ovarian cancer [12, 13] and mount a well-adapted humoral immune response by generating detectable levels of p53 AAbs early enough when other markers are not detectable [33]. We found that although the magnitude of the p53 AAb responses tended to be lower in CA125 negative patients, the percentage of p53 AAb seropositivity tended to be higher in this group of patients when we used a cut-off providing 100% specificity for p53 AAbs. However, we recognize that this result is only based on our small cohort study; and that much more work is needed in the future to evaluate how useful it is to combine p53 AAbs with CA125 in the early detection of type II ovarian cancer.
We found that recently identified markers MIF, leptin, prolactin, OPN, and IGF-II [30, 34] exhibited differential levels in the plasma of type II ovarian cancer patients compared to disease-free controls (figure 1); however, none of them behaved as well as CA125 as a single marker in our sample set based on AUC of ROC curve (figure 2). We observed a similar trend when we compared all the ovarian cancer patients with disease-free controls (data not shown). Similar to CA125, leptin, prolactin, OPN, and IGF-II also exhibited differential blood levels in patients with type I ovarian cancer and disease-free controls (figure 1), which may partially explain their association with ovarian cancers (i.e. presumably type I and type II), especially low stage disease, reported before [30, 34] since low stage disease is typically skewed more to type I tumors than high stage cases.
Previous studies [32, 35] identified significant seropositivity of IgGs against several other tumor associated antigens including NY-ESO-1, ALPP, CTSD, B23, GRP78, SSX in ovarian cancer patients, but not in normal controls or benign tumor patients. We did not detect a differential antibody response against ALPP, CTSD, B23, GRP78, and SSX with our patient cohort, but this may reflect the antigen source used in our immunoassay. Taylor et al used tumor-derived exosomal proteins in their assays, whereas we used recombinant tagged proteins expressed in 293TT cells and captured from detergent lysates. When Taylor et al compared the exosomal proteins and their recombinant counterparts produced in bacteria, the exosomal proteins exhibited much stronger immunoreactivity with the patient-derived antibodies [32], suggesting the importance of secondary modifications in AAb recognition of these antigens that are apparently not produced by over-expression in 293TT cells. Conversely, we have successfully used p53 and NY-ESO-1 expressed in 293TT cells or in bacteria to detect AAbs in ovarian cancer patients (data not shown).
In summary, our data suggests seropositivity of p53 AAbs as a biomarker relatively independent of CA125 for type II, but not type I, epithelial ovarian cancer. It is particularly useful in CA125 negative patients and combination of p53 AAbs and CA125 effectively improved the discrimination ability to detect type II ovarian cancer. Our results are based on comparison between plasma from women with and without clinically confirmed disease and should be extended to blood specimens from asymptomatic patients. Thus, combining the detection of p53 AAbs and CA125 in screening for type II ovarian cancer among an asymptomatic population warrants further study, but is unlikely to be helpful to identify patients with type I ovarian cancer. Further, the utility of p53 AAbs, CA125 and IGF-II as markers to discriminate between type II and type I in patients with low stage disease also needs further examination in independent and prospective studies.
RESEARCH HIGHLIGHTS
  • We compared 14 biomarkers to discriminate the plasma samples of healthy volunteers and ovarian cancer patients.
  • Autoantibodies to p53 and NY-ESO-1 were present in plasma of type II but not type I ovarian cancer patients.
  • Combining p53 AAbs with CA125 significantly improved discrimination of plasma of type II cancer patients from that of healthy volunteers.
Acknowledgments
Grant support was provided by the US PHS Grants RO1 CA122581 (RBSR, SKK), P50 CA098252 (RBSR, REB and RG), and the HERA foundation (DL). We thank the Tissue bank of the Johns Hopkins SPORE in Cervical Cancer for tissue specimens. We thank the HERA foundation for their encouragement and fund raising activities.
Abbreviations
HGSCHigh grade serous carcinoma
AUCarea under curve
AICAkaike information criterion
AAbsautoantibodies

Footnotes
CONFLICT OF INTEREST STATEMENT
The authors declare that there are no conflicts of interest.
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