GlycoKnow Ovarian: a Glycoprotein-based, Serum Diagnostic to Distinguish Ovarian Cancers from Benign Pelvic Masses

Objective Blood-based biomarkers offer an unprecedented opportunity to realize the promise of precision medicine in improving diagnostic workflows. Previous academic studies have established the association of the circulating glycoproteome with ovarian cancer. Here a glycoproteomic classifier was built, tested, and applied to both internal and external validation cohorts to distinguish malignant from benign pelvic masses. Method Serum samples from patients with pelvic masses were collected from retrospective biobanks and the prospective VOCAL trial. In total, 38 peptides and glycopeptides were quantified by an AI-enabled, targeted mass spectrometry platform. A classifier was built, locked, and evaluated in a hold-out test set. The locked diagnostic was then evaluated in an internal validation cohort as well as two external validation cohorts from UT MD Anderson Cancer Center. Results LASSO-regularized logistic regression in the training cohort resulted in an optimal classifier with 16 features that was evaluated on a hold-out test set, with strong performance in ovarian cancer and benign pelvic masses (AUC=0.909; sensitivity=86.7%; specificity=89.7%). Comparable performance was observed in internal (sensitivity=72.8%; specificity=82.7%) and two external (early-stage sensitivity=63.6% and 62.7%; specificity=90.5% and 83.3%) validation cohorts with varying per-stage prevalence. Conclusions A novel, CA-125-independent glycoprotein panel was developed to help distinguish benign conditions from ovarian cancer. These circulating biomarkers have great potential to detect ovarian cancer while retaining high specificity and could open new avenues for an improved ovarian cancer diagnostic.