High throughput proteomics identifies a high-accuracy 11 plasma protein biomarker signature for ovarian cancer

Commun Biol. 2019 Jun 20:2:221. doi: 10.1038/s42003-019-0464-9. eCollection 2019.


Ovarian cancer is usually detected at a late stage and the overall 5-year survival is only 30-40%. Additional means for early detection and improved diagnosis are acutely needed. To search for novel biomarkers, we compared circulating plasma levels of 593 proteins in three cohorts of patients with ovarian cancer and benign tumors, using the proximity extension assay (PEA). A combinatorial strategy was developed for identification of different multivariate biomarker signatures. A final model consisting of 11 biomarkers plus age was developed into a multiplex PEA test reporting in absolute concentrations. The final model was evaluated in a fourth independent cohort and has an AUC = 0.94, PPV = 0.92, sensitivity = 0.85 and specificity = 0.93 for detection of ovarian cancer stages I-IV. The novel plasma protein signature could be used to improve the diagnosis of women with adnexal ovarian mass or in screening to identify women that should be referred to specialized examination.

Keywords: Diagnostic markers; Machine learning; Ovarian cancer.

Publication types

  • Research Support, Non-U.S. Gov't
  • Validation Study

MeSH terms

  • Age Factors
  • Aged
  • Biomarkers, Tumor / blood
  • Cohort Studies
  • Female
  • High-Throughput Screening Assays
  • Humans
  • Machine Learning
  • Middle Aged
  • Neoplasm Staging
  • Ovarian Neoplasms / blood*
  • Ovarian Neoplasms / pathology
  • Proof of Concept Study
  • Proteomics
  • Sensitivity and Specificity


  • Biomarkers, Tumor

Associated data

  • figshare/10.6084/m9.figshare.7642268