Targeted Proteomics and Support Vector Classification Reveal Potential Biomarkers for the Early Detection of High-grade Serous Ovarian Cancer

Abstract

AbstractThe 5-year prognosis of late-stage epithelial ovarian cancer (EOC) remains poor, thus the discovery of early-stage EOC biomarkers is of paramount importance. Extracellular vesicles (EVs) circulating in blood are thought to contain proteomic cargo originating from an EOC microenvironment and are thus amenable for clinical biomarker discovery. We profiled the proteome of EVs purified from patient blood plasma, ascites and cell lines using strong cation exchange peptide fractionation and Orbitrap-based tandem mass spectrometry. To further increase sensitivity and specificity of the method, CD9-affinity purification and ultracentrifugation were used to purify EVs. Using parallel reaction monitoring we identified a compendum of 240 proteins that were differentially enrirched in EVs derived from EOC (n=10) patients versus women with non-cancerous gynecological conditions (n=9). Support vector machines were optimized using leave-one-out cross-validation and this methodology was implemented on a test set of malignant (n=4) and control (n=3) donors. Using the relative levels of >450 EV-associated peptides in a cohort of plasma-derived EVs, we identified several combinatorial peptides capable of discriminating high-grade serous EOC with up to 100% accuracy in Stage I, II, and III donors. This study demonstrates an adaptable biomarker discovery pipeline and provides pinoeering evidence of EV-associated biomarkers for the detection of early-stage EOC.