High throughput proteomics identifies 484 high-accuracy plasma protein biomarker signatures for ovarian cancer

Abstract

Ovarian cancer is usually detected at a late stage with the 5-year survival at 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 981 proteins in patients with ovarian cancer and benign tumours, using the proximity extension assay. A novel combinatorial strategy was developed for identification of multivariate biomarker signatures, resulting in 484 mutually exclusive models out of which 448 did not contain the present biomarker MUCIN-16. The top-ranking model consisted of 14 proteins and had a AUC=0.95, PPV=1.0, sensitivity=0.99 and specificity=1.0 for detection of stage III-IV ovarian cancer in the discovery data, and an AUC=0.89, PPV=0.93, sensitivity=0.89 and specificity=0.95 in the replication data. 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.

Discovery and preliminary validation of a new panel of personalized ovarian cancer biomarkers for individualized detection of recurrence

“…20 There have been 10 studies employing PEA technology to screen for over 92 proteins in OvCa serum or plasma to identify key players and biomarkers. [21][22][23][24][25][26][27][28][29][30] Ours is the first reported study to use all 12 available PEA panels for profiling 1,104 proteins in OvCa. Moreover, the focus so far has been on identifying diagnostic biomarkers of early detection for OvCa.…”

“…Moreover, the focus so far has been on identifying diagnostic biomarkers of early detection for OvCa. 22,[23][24][25][27][28][29][30] Our study takes a different angle of identifying personalized markers that are highly sensitive for detecting tumor burden but in the form of recurrent, instead of primary disease. Amongst our top 23 candidate proteins, KLK11, TFPI2, TNFSF14, KLK6, PVRL4, and PDGFA were selected as potential diagnostic biomarkers of OvCa in previous studies employing PEA analysis.…”