A combined biomarker panel shows improved sensitivity and specificity for detection of ovarian cancer

Background: Mortality from ovarian cancer remains high due to the lack of methods for early detection. The difficulty lies in the low prevalence of the disease necessitating a significantly high specificity and positive-predictive value (PPV) to avoid unneeded and invasive intervention. Currently, cancer antigen- 125 (CA-125) is the most commonly used biomarker for the early detection of ovarian cancer. In this study we determine the value of combining macrophage migration inhibitory factor (MIF), osteopontin (OPN), and prolactin (PROL) with CA-125 in the detection of ovarian cancer serum samples from healthy controls. Materials and Methods: A total of 432 serum samples were included in this study. 153 samples were from ovarian cancer patients and 279 samples were from age-matched healthy controls. The four proteins were quantified using a fully automated, multi-analyte immunoassay. The serum samples were divided into training and testing datasets and analyzed using four classification models to calculate accuracy, sensitivity, specificity, PPV, negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC). Results: The four-protein biomarker panel yielded an average accuracy of 91% compared to 85% using CA-125 alone across four classification models (p = 3.224 × 10−9). Further, in our cohort, the four-protein biomarker panel demonstrated a higher sensitivity (median of 76%), specificity (median of 98%), PPV (median of 91.5%), and NPV (median of 92%), compared to CA-125 alone. The performance of the four-protein biomarker remained better than CA-125 alone even in experiments comparing early stage (Stage I and Stage II) ovarian cancer to healthy controls. Conclusions: Combining MIF, OPN, PROL, and CA-125 can better differentiate ovarian cancer from healthy controls compared to CA-125 alone.

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Identification of an epigenetic biomarker panel with high sensitivity and specificity for colorectal cancer and adenomas

Molecular Cancer ◽

10.1186/1476-4598-10-85 ◽

2011 ◽

Vol 10 (1) ◽

pp. 85 ◽

Cited By ~ 77

Author(s):

Guro E Lind ◽

Stine A Danielsen ◽

Terje Ahlquist ◽

Marianne A Merok ◽

Kim Andresen ◽

...

Keyword(s):

Colorectal Cancer ◽

Sensitivity And Specificity ◽

High Sensitivity ◽

Biomarker Panel ◽

Epigenetic Biomarker

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An Improved Prediction Model for Ovarian Cancer Using Urinary Biomarkers and a Novel Validation Strategy

International Journal of Molecular Sciences ◽

10.3390/ijms20194938 ◽

2019 ◽

Vol 20 (19) ◽

pp. 4938 ◽

Cited By ~ 1

Author(s):

Shin-Wha Lee ◽

Ha-Young Lee ◽

Hyo Joo Bang ◽

Hye-Jeong Song ◽

Sek Won Kong ◽

...

Keyword(s):

Ovarian Cancer ◽

Cell Adhesion ◽

Adhesion Molecule ◽

Cell Adhesion Molecule ◽

Urinary Biomarkers ◽

Benign Tumors ◽

Urinary Biomarker ◽

Biomarker Panel ◽

Pelvic Masses ◽

Validation Strategy

This study was designed to analyze urinary proteins associated with ovarian cancer (OC) and investigate the potential urinary biomarker panel to predict malignancy in women with pelvic masses. We analyzed 23 biomarkers in urine samples obtained from 295 patients with pelvic masses scheduled for surgery. The concentration of urinary biomarkers was quantitatively assessed by the xMAP bead-based multiplexed immunoassay. To identify the performance of each biomarker in predicting cancer over benign tumors, we used a repeated leave-group-out cross-validation strategy. The prediction models using multimarkers were evaluated to develop a urinary ovarian cancer panel. After the exclusion of 12 borderline tumors, the urinary concentration of 17 biomarkers exhibited significant differences between 158 OCs and 125 benign tumors. Human epididymis protein 4 (HE4), vascular cell adhesion molecule (VCAM), and transthyretin (TTR) were the top three biomarkers representing a higher concentration in OC. HE4 demonstrated the highest performance in all samples withOC(mean area under the receiver operating characteristic curve (AUC) 0.822, 95% CI: 0.772–0.869), whereas TTR showed the highest efficacy in early-stage OC (AUC 0.789, 95% CI: 0.714–0.856). Overall, HE4 was the most informative biomarker, followed by creatinine, carcinoembryonic antigen (CEA), neural cell adhesion molecule (NCAM), and TTR using the least absolute shrinkage and selection operator (LASSO) regression models. A multimarker panel consisting of HE4, creatinine, CEA, and TTR presented the best performance with 93.7% sensitivity (SN) at 70.6% specificity (SP) to predict OC over the benign tumor. This panel performed well regardless of disease status and demonstrated an improved performance by including menopausal status. In conclusion, the urinary biomarker panel with HE4, creatinine, CEA, and TTR provided promising efficacy in predicting OC over benign tumors in women with pelvic masses. It was also a non-invasive and easily available diagnostic tool.

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Development of a Multimarker Assay for Early Detection of Ovarian Cancer

Journal of Clinical Oncology ◽

10.1200/jco.2008.19.2484 ◽

2010 ◽

Vol 28 (13) ◽

pp. 2159-2166 ◽

Cited By ~ 178

Author(s):

Zoya Yurkovetsky ◽

Steven Skates ◽

Aleksey Lomakin ◽

Brian Nolen ◽

Trenton Pulsipher ◽

...

Keyword(s):

Ovarian Cancer ◽

Early Detection ◽

Late Stage ◽

Early Stage ◽

Screening Strategy ◽

Ca 125 ◽

Great Promise ◽

Biomarker Panel ◽

Healthy Women ◽

Early Stage Ovarian Cancer

PurposeEarly detection of ovarian cancer has great promise to improve clinical outcome.Patients and MethodsNinety-six serum biomarkers were analyzed in sera from healthy women and from patients with ovarian cancer, benign pelvic tumors, and breast, colorectal, and lung cancers, using multiplex xMAP bead-based immunoassays. A Metropolis algorithm with Monte Carlo simulation (MMC) was used for analysis of the data.ResultsA training set, including sera from 139 patients with early-stage ovarian cancer, 149 patients with late-stage ovarian cancer, and 1,102 healthy women, was analyzed with MMC algorithm and cross validation to identify an optimal biomarker panel discriminating early-stage cancer from healthy controls. The four-biomarker panel providing the highest diagnostic power of 86% sensitivity (SN) for early-stage and 93% SN for late-stage ovarian cancer at 98% specificity (SP) was comprised of CA-125, HE4, CEA, and VCAM-1. This model was applied to an independent blinded validation set consisting of sera from 44 patients with early-stage ovarian cancer, 124 patients with late-stage ovarian cancer, and 929 healthy women, providing unbiased estimates of 86% SN for stage I and II and 95% SN for stage III and IV disease at 98% SP. This panel was selective for ovarian cancer showing SN of 33% for benign pelvic disease, SN of 6% for breast cancer, SN of 0% for colorectal cancer, and SN of 36% for lung cancer.ConclusionA panel of CA-125, HE4, CEA, and VCAM-1, after additional validation, could serve as an initial stage in a screening strategy for epithelial ovarian cancer.

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Osteopontin, Macrophage Migration Inhibitory Factor and Anti-Interleukin-8 Autoantibodies Complement CA125 for Detection of Early Stage Ovarian Cancer

Cancers ◽

10.3390/cancers11050596 ◽

2019 ◽

Vol 11 (5) ◽

pp. 596 ◽

Cited By ~ 3

Author(s):

Jing Guo ◽

Wei-Lei Yang ◽

Daewoo Pak ◽

Joseph Celestino ◽

Karen H. Lu ◽

...

Keyword(s):

Ovarian Cancer ◽

Cancer Patients ◽

Late Stage ◽

Early Stage ◽

Characteristic Curve ◽

Stage I ◽

Inhibitory Factor ◽

Early Stage Disease ◽

Biomarker Panel ◽

Early Stage Ovarian Cancer

Early detection of ovarian cancer promises to reduce mortality. While serum CA125 can detect more than 60% of patients with early stage (I–II) disease, greater sensitivity might be observed with a panel of biomarkers. Ten protein antigens and 12 autoantibody biomarkers were measured in sera from 76 patients with early stage (I–II), 44 patients with late stage (III–IV) ovarian cancer and 200 healthy participants in the normal risk ovarian cancer screening study. A four-biomarker panel (CA125, osteopontin (OPN), macrophage inhibitory factor (MIF), and anti-IL-8 autoantibodies) detected 82% of early stage cancers compared to 65% with CA125 alone. In early stage subjects the area under the receiver operating characteristic curve (AUC) for the panel (0.985) was significantly greater (p < 0.001) than the AUC for CA125 alone (0.885). Assaying an independent validation set of sera from 71 early stage ovarian cancer patients, 45 late stage patients and 131 healthy women, AUC in early stage disease was improved from 0.947 with CA125 alone to 0.974 with the four-biomarker panel (p = 0.015). Consequently, OPN, MIF and IL-8 autoantibodies can be used in combination with CA125 to distinguish ovarian cancer patients from healthy controls with high sensitivity. Osteopontin appears to be a robust biomarker that deserves further evaluation in combination with CA125.

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Preoperative Measurement of Serum C-Reactive Protein: Is it Useful in the Differential Diagnosis of Adnexal Masses?

The International Journal of Biological Markers ◽

10.5301/jbm.5000226 ◽

2017 ◽

Vol 32 (1) ◽

pp. 83-89 ◽

Cited By ~ 2

Author(s):

Adriana Yoshida ◽

Sophie F. Derchain ◽

Denise R. Pitta ◽

Nathália Crozatti ◽

Liliana A.L.A. Andrade ◽

...

Keyword(s):

Ovarian Cancer ◽

Sensitivity And Specificity ◽

Menopausal Status ◽

Ovarian Tumors ◽

Cancer Center ◽

Benign Tumors ◽

C Reactive Protein ◽

Cross Sectional ◽

Adnexal Masses ◽

Reactive Protein

Background Serum biomarkers may help to discriminate malignant from benign adnexal masses with equivocal features on imaging. Adequate discrimination of such tumors is crucial for referring patients to either a specialized cancer center or a nonspecialized gynecology service. Aim We aimed to investigate whether the preoperative level of serum C-reactive protein (CRP), alone or combined with CA125 and menopausal status in the Ovarian Score (OVS), is useful in the prediction of malignancy in women with ovarian tumors. Methods This cross-sectional study included 293 patients who underwent surgery in a tertiary cancer center. Receiver operating characteristic (ROC) areas under the curves (AUC) for CRP, CA125 and OVS were calculated in different scenarios, as well as their sensitivity and specificity, using standard cutoff points (for CRP, 10 mg/L; for CA125, 35 U/mL). Results CA125 and the OVS performed significantly better than CRP alone in the differentiation of benign disease from epithelial ovarian cancer (EOC) (AUC = 0.86 for CA125, 0.79 for OVS, and 0.73 for CRP). OVS and CRP alone were superior to CA125 only in the differentiation of borderline ovarian tumors from advanced stages of EOC and non-EOC. Sensitivity and specificity were 52.5% and 83%, respectively, for CRP, 77.9% and 66.7% for CA125, and 71.3% and 67.8% for OVS. Conclusions OVS is as good as CA125 in the differentiation of benign tumors from ovarian cancer. The addition of CA125 and menopausal status to CRP enhanced the relatively low discriminatory power of isolated CRP.

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Diagnosis of epithelial ovarian cancer using a combined protein biomarker panel

British Journal of Cancer ◽

10.1038/s41416-019-0544-0 ◽

2019 ◽

Vol 121 (6) ◽

pp. 483-489 ◽

Cited By ~ 4

Author(s):

Matthew R. Russell ◽

Ciaren Graham ◽

Alfonsina D’Amato ◽

Aleksandra Gentry-Maharaj ◽

Andy Ryan ◽

...

Keyword(s):

Ovarian Cancer ◽

Epithelial Ovarian Cancer ◽

Protein Biomarker ◽

Biomarker Panel

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High-resolution serum proteomic features for ovarian cancer detection.

Endocrine Related Cancer ◽

10.1677/erc.0.0110163 ◽

2004 ◽

Vol 11 (2) ◽

pp. 163-178 ◽

Cited By ~ 161

Author(s):

T P Conrads ◽

V A Fusaro ◽

S Ross ◽

D Johann ◽

V Rajapakse ◽

...

Keyword(s):

Ovarian Cancer ◽

High Resolution ◽

Sensitivity And Specificity ◽

Mass Spectra ◽

Spectral Feature ◽

Low Resolution ◽

Mass Spectral ◽

High Resolution Mass ◽

Process Error ◽

Resolution Mass

Serum proteomic pattern diagnostics is an emerging paradigm employing low-resolution mass spectrometry (MS) to generate a set of biomarker classifiers. In the present study, we utilized a well-controlled ovarian cancer serum study set to compare the sensitivity and specificity of serum proteomic diagnostic patterns acquired using a high-resolution versus a low-resolution MS platform. In blinded testing sets, the high-resolution mass spectral data contained multiple diagnostic signatures that were superior to the low-resolution spectra in terms of sensitivity and specificity (P<0.00001) throughout the range of modeling conditions. Four mass spectral feature set patterns acquired from data obtained exclusively with the high-resolution mass spectrometer were 100% specific and sensitive in their diagnosis of serum samples as being acquired from either unaffected patients or those suffering from ovarian cancer. Important to the future of proteomic pattern diagnostics is the ability to recognize inferior spectra statistically, so that those resulting from a specific process error are recognized prior to their potentially incorrect (and damaging) diagnosis. To meet this need, we have developed a series of quality-assurance and in-process control procedures to (a) globally evaluate sources of sample variability, (b) identify outlying mass spectra, and (c) develop quality-control release specifications. From these quality-assurance and control (QA/QC) specifications, we identified 32 mass spectra out of the total 248 that showed statistically significant differences from the norm. Hence, 216 of the initial 248 high-resolution mass spectra were determined to be of high quality and were remodeled by pattern-recognition analysis. Again, we obtained four mass spectral feature set patterns that also exhibited 100% sensitivity and specificity in blinded validation tests (68/68 cancer: including 18/18 stage I, and 43/43 healthy). We conclude that (a) the use of high-resolution MS yields superior classification patterns as compared with those obtained with lower resolution instrumentation; (b) although the process error that we discovered did not have a deleterious impact on the present results obtained from proteomic pattern analysis, the major source of spectral variability emanated from mass spectral acquisition, and not bias at the clinical collection site; (c) this variability can be reduced and monitored through the use of QA/QC statistical procedures; (d) multiple and distinct proteomic patterns, comprising low molecular weight biomarkers, detected by high-resolution MS achieve accuracies surpassing individual biomarkers, warranting validation in a large clinical study.

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