Targeted Proteomics of Plasma Extracellular Vesicles Uncovers MUC1 as Combinatorial Biomarker for the Early Detection of High-grade Serous Ovarian Cancer
Tyler Cooper1,2, Dylan Z Dieters-Castator3, Jiahui Liu5, Gabrielle M Siegers5, Lorena Veliz4, Desmond Pink5, John Lewis5, Francois Lagugne-Labarthet4, Yangxin Fu5, Helen Steed6, Gilles A Lajoie1, Lynne M Postovit2,5,6.
1Biochemistry, Western University, London, ON, Canada; 2Biomedical and Molecular Sciences, Queen's University , Kingston, ON, Canada; 3Anatomy and Cell Biology, Western University, London, ON, Canada; 4Chemistry, Western University, London, ON, Canada; 5Oncology, University of Alberta, Edmonton, AB, Canada; 6Obstretrics and Gynecology, University of Alberta, Edmonton, AB, Canada
Introduction: The five-year prognosis for patients with late-stage high-grade serous carcinoma (HGSC) remains dismal, underscoring the critical need for identifying early-stage biomarkers. This study explores the potential of extracellular vesicles (EVs) circulating in biofluids, which are believed to harbor proteomic cargo reflective of the HGSC microenvironment, as a source for biomarker discovery.
Methods: We conducted a comprehensive proteomic profiling of EVs isolated from ascites, plasma, and primary cell lines using ultra-performance liquid chromatography with tandem mass spectrometry (UPLC-MS/MS). CD9-affinity immunoprecipitation, ultracentrifugation, and size exclusion chromatography were used in parallel as EV purification strategies to increase the diversity of identified proteins. Likewise, we employed employing both data-dependent (DDA) and data-independent acquisition (DIA) methods which allowed us to identify >2000 proteins in plasma for targeted proteomics using parallel reaction monitoring. We focused our comparisons on the proteomic signatures of women with early-stage HGSC (FIGO I/II) to those with benign gynecological conditions. The initial cohort, comprising 9-10 donors, utilized DDA proteomics for targeted proteomics. The subsequent cohort, involving 30 HGSC patients and 30 control subjects, employed DIA proteomics for a similar purpose. Support vector machine (SVM) classification was applied in both cohorts and identified prospective combinatorial biomarkers with high specificity and sensitivity (ROC-AUC > 0.90).
Results: Mucin-1 (MUC1) emerged as a significant combinational biomarker in both cohorts. Validation through an ELISA assay on benign (n=18), Stage I (n=9), and Stage II (n=9) donors corroborated the diagnostic utility of MUC1 in the early-stage detection of HGSC and positive correlation to tumour burden.
Conclusions: This study highlights the value of EV-based proteomic analysis in the discovery of combinatorial biomarkers for early HGSC detection.