Clinical and Emergent Biomarkers and Their Relationship to the Prognosis of Ovarian Cancer

Aminah Jatoi, Robert A. Vierkant, Kieran M. Hawthorne, Matthew S. Block, Susan J. Ramus, Nicholas B. Larson, Brooke L. Fridley, Ellen L. Goode

Research output: Contribution to journalReview article

6 Scopus citations

Abstract

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 <1%], and TFF3 showed a nonlinear association. Importantly, we observed no interactions among variables. However, patients with tumors with moderate TFF3 expression were at a marginally increased risk of recurrence, and patients with tumors with high expression were at a similar to slightly lower risk, compared to those with tumors with no TFF3 expression. Conclusions: Although no interactions among variables were observed, this study provides important precedent for seeking interactions between clinical and tumor variables in future studies.

Original languageEnglish (US)
Pages (from-to)59-68
Number of pages10
JournalOncology (Switzerland)
Volume90
Issue number2
DOIs
StatePublished - Feb 1 2016

Keywords

  • Interactions
  • Modeling
  • Ovarian cancer
  • Prognosis

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

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