Evaluation of biomarker panels for early stage ovarian cancer detection and monitoring for disease recurrence

Abstract

Objective

To determine the utility of novel combinations of biomarkers, using both a one-step and two-step assay format, to distinguish serum of early ovarian cancer patients from that of healthy controls and to discern the utility of these biomarkers in a monitoring capacity.

Methods

For ovarian cancer detection, HE4, Glycodelin, MMP7, SLPI, Plau-R, MUC1, Inhibin A, PAI-1, and CA125 were evaluated in a cohort of 200 women with ovarian cancer and 396 healthy age-matched controls. Each biomarker was assessed by serum-based immunoassays utilizing novel monoclonal antibody pairs or commercial kits. For detection of disease recurrence, HE4, Glycodelin, MMP7 and CA125 were evaluated in 260 samples from 30 patients with OC monitored longitudinally after diagnosis.

Results

Based upon ROC curve analysis, the sensitivity/specificity of specific biomarker combination algorithms ranged from 59.0%/99.7% to 80.5%/96.5% for detection of early stage ovarian cancer and 76.9%/99.7% to 89.2%/97.2% for detection of late stage cancer. In monitoring evaluation of 27 patients who experienced recurrence of OC, sensitivity for predicting recurrence was 100% for the biomarker panel and 96% for CA125. At least one of the panel biomarkers was elevated earlier (range 6–69 weeks) than CA125 and prior to clinical evidence of recurrence in 14/27 (52%) patients.

Conclusions

We have developed and demonstrated the utility of several one- and two-step multi-marker combinations with acceptable test characteristics for possible use in an ovarian cancer screening population. A subset of this panel may also provide adjunctive information to rising CA125 levels in disease monitoring.

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.