Supplementary Material for: Clinical and Emergent Biomarkers and Their Relationship to the Prognosis of Ovarian Cancer

Objective: Ovarian cancer is the most lethal gynecological malignancy, but information relevant to prognosis and outcomes remain unknown. Here, we used statistical methods to focus specifically on interactions between candidate prognostic variables. Methods and Results: Univariate, multivariate, and elastic net modeling of 42 variables were applied to a cohort of 542 ovarian cancer patients with 393 episodes of cancer recurrence/death. In univariate analyses, overexpression of TFF3, MDM2, and p53 were associated with improved recurrence-free survival. In multivariate analyses adjusted for age, histology, stage, grade, ascites, and residual disease, overexpression of PR appeared to provide a protective effect [hazard ratio for >50% of cells positive, 0.64 (95% confidence interval 0.44-0.94) compared to Conclusions: Although no interactions among variables were observed, this study provides important precedent for seeking interactions between clinical and tumor variables in future studies.