Evaluation of biomarker panels for early stage ovarian cancer detection and monitoring for disease recurrence
Introduction
Ovarian cancer is the fourth leading cause of cancer deaths among women in the United States [1]. Early stage ovarian cancer has an excellent prognosis if treated, but advanced stage ovarian cancer, which is diagnosed in approximately 70% of patients, is associated with a poor survival rate of only 10–30% [2]. Given the limitations of treatment for advanced ovarian cancer and the success of treatment for early stage disease, a screening test is intuitively appealing. The ability to accurately detect early stage disease would potentially improve ovarian cancer survival dramatically. However, the low prevalence of ovarian cancer (30–50 cases/100,000 women) limits the achievable sensitivity and specificity of any screening test.
Prior attempts to establish population-based screening protocols for ovarian cancer have employed CA125, ultrasound, and novel molecular and statistical approaches. CA125 is useful for discriminating benign from malignant pelvic masses and can be used to assess response to treatment, but is not sensitive or specific enough to justify screening[3], [4], [5]. Ultrasound-based screening has resulted in a positive predictive value (PPV) of 9.4% [6], while a screening algorithm employing both longitudinal CA125 patterns and ultrasound imaging has achieved a PPV of 19% in clinical trials [7].
The use of marker panels to improve sensitivity and specificity has been extensively investigated with some of the most promising reported markers including CA72-4, M-CSF, OVX1, LPA, Prostacin, Osteopontin, Inhibin and Kallikrein[7], [8], [9], [10], [11], [12], [13], [14], [15]. Zhang et al. recently reported combining multi-panel analysis with artificial neural network (ANN) modeling and attained a sensitivity of 71% with a specificity of 98% for the detection of early stage ovarian cancer [16]. No marker panel reported to date, however, has achieved adequate performance characteristics to use as an ovarian cancer screening test. There remains a need for validated biomarkers to improve the ability to diagnose ovarian cancer at an early stage in a format amenable to routine clinical immunoassay laboratories.
The objective of this study was to evaluate a panel of candidate biomarkers and to determine whether novel combination(s) of these biomarkers, used either as a standard panel or as part of a two-step algorithm, could distinguish serum of early ovarian cancer patients from that of healthy controls without the use of specialized instrumentation, such as mass spectrometry, or complex computational algorithms, such as neural networks. We further wished to discern the utility of these biomarkers in a monitoring capacity to detect disease persistence or recurrence.
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Study population
A total of 700 serum samples were collected for the ovarian cancer detection study, comprising three separate cohorts (Table 1): (1) Ovarian cancer: equally distributed between stages I, II, and III (n = 200). (2) Normals: Healthy women (n = 500), further subdivided into: (a) Normal (n = 396), median age 55; comparison group for calculation of test characteristics; (b) Reference (n = 104, mean age 63), 98.1% from women > 55 years, used to establish baseline levels of each marker and calculate
Screening study
Demographics and clinical characteristics of individuals providing serum are listed in Table 1. ROC curves were constructed for each individual marker; curves for HE4, Glycodelin, CA125 and Plau-R are depicted in Fig. 1. The cutoff for each individual marker was applied to both early (I/II) and late (III) stage samples and the data presented in Table 2. Individual marker performance for the detection of early ovarian cancer versus normal using the 2SD cutoff was: sensitivity 6.8–62.4%;
Discussion
Current limitations of biomarkers for ovarian cancer screening relate to the relatively poor sensitivity and specificity for detection of early stage disease. The influx of data from genomic and proteomic-based profiling studies of ovarian cancer may increase the likelihood that new biomarker combinations capable of detecting early stage disease will be identified[31], [32], [33], [34], [35]. The use of transcriptional profiling and proteomics has identified genes over-expressed in ovarian
Conflict of interest statement
This study was sponsored by BD-TriPath Oncology. The following authors are employees of BD-TriPath: CW, RC, JG, QH, DM, and TF. AB has performed consultant services for BD-TriPath for which he received honoraria. LH has served on the BD-TriPath gynecologic scientific advisory board without compensation and is the PI for a clinical trial sponsored by BD-TriPath.
Acknowledgments
The authors would like to acknowledge Malena Sansbury for her technical assistance and Charlotte Brown for her editing contributions.
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