Objectives Longitudinal multimarker combinations have the potential to improve sensitivity while maintaining the high specificity required for the early detection of ovarian cancer. The use of multiple markers to improve sensitivity over cancer antigen 125 (CA125) in longitudinal algorithms for early ovarian cancer detection requires the selection of markers with optimal discriminatory power and low longitudinal variance relative to disease-initiated changes. Our objective was to identify a multimarker panel suitable for ovarian cancer, where each individual marker has its own baseline, permitting longitudinal algorithm development.

Materials and Methods In this retrospective study, we measured CA125, human epididymis protein 4 (HE4), matrix metalloproteinase-7 (MMP-7), CA72-4, CA19-9, CA15-3, carcinoembryonic antigen, and soluble vascular cell adhesion molecule (sVCAM) concentrations using immunoassays in pretreatment sera from 142 stage I ovarian cancer cases and 5 annual samples each from 217 healthy controls. After random division into training and validation sets, all possible biomarker combinations were explored exhaustively using linear classifiers to identify the panel with the greatest sensitivity for stage I disease at a high specificity of 98%. To evaluate longitudinal performance of the individual markers, the within-person over time and the between-person coefficient of variation (CV) were estimated. Hierarchical modeling across women of log-concentrations enabled the borrowing of information across subjects to moderate variance estimates given the small number of observations per subject.

Results The 4-marker panel comprising CA125, HE4, MMP-7, and CA72-4 performed with the highest sensitivity (83.2%) at 98% specificity. The within-person CVs were lower for CA125, HE4, MMP-7, and CA72-4 (15%, 25%, 25%, and 21%, respectively) compared with their corresponding between-person CV (49%, 20%, 35%, and 84%, respectively) indicating baselines in healthy volunteers. After simple log-transformations, the within-volunteer variation across volunteers was modeled with a normal distribution permitting parsimonious hierarchical modeling.

Conclusions The multiplex panel chosen is suitable for the early detection of ovarian cancer and the individual markers have their own baseline permitting longitudinal algorithm development.