Automated Assay of a Four-Protein Biomarker Panel for Improved Detection of Ovarian Cancer

SIMPLE SUMMARY: The survival of patients diagnosed with ovarian cancer depends largely on the extent of the disease upon diagnosis. When confined to the ovaries, patients’ 10-year survival is more than 70%. This drastically drops to less than 5% when patients are diagnosed with far-advanced disease. Unfortunately, more than 80% of patients are diagnosed at advanced stage due to the lack of test for early detection. We report the development of a blood test measuring four proteins (macrophage migration inhibitory factor, osteopontin, prolactin and cancer antigen 125), which can distinguish ovarian cancer samples, even early-stage disease, from healthy samples in the population tested. This study is another step towards the application of a useful test for early detection of ovarian cancer that is both highly accurate and specific. ABSTRACT: 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.