Surface Proteome of Extracellular Vesicles and Correlation Analysis for Identification of Breast Cancer Biomarkers

Abstract

Breast cancer (BC) is the second leading cause of death in Canadian women. Detection of the disease at an early stage greatly increases the average 5-year survival rate, however non-invasive early detection methods are not available to-date. Cells release various types of extracellular vesicles (EVs) to mediate intercellular communication by transferring signals in the form of bioactive molecules such as proteins, metabolites, and nucleic acids. Understanding the composition of these biomolecules may shed light on the physiological state of the cell of origin. Therefore, EVs are a promising source of biomarkers for non-invasive detection of BC. However, the surface proteome of EVs is not yet understood well enough to propose BC biomarkers that could be detected directly from biofluids. In this study, small EVs (sEVs) and medium EVs (mEVs) were isolated by differential ultracentrifugation from breast cancer MDA-MB-231 and MCF7, and non-cancerous breast epithelial MCF10A cell lines and analyzed by nano-liquid chromatography coupled to tandem mass spectrometry. EV proteins were analyzed by two approaches: (1) global proteomic analysis and (2) enrichment of EV surface proteins by labelling surface-accessible proteins with a Sulfo-NHS-SS-Biotin reagent. Potential BC biomarkers were obtained from the first approach (1) by identifying the presence of cell line specific sEV proteins, filtering for membrane/surface proteins using UniProt annotations, and predicting the co-localization of proteins on sEVs with known EV marker proteins (CD63, CD9, CD81) by correlation analysis. This resulted in 11 potential BC sEV biomarkers (C8A, AXL, ST14, FAM20B, PROM2, CLDN3, ITGA7, MEGF10, SHISA2, GJC1, IFNGR1); the presence of ST14, CLDN3 and ITGA7 was validated by Western blot analysis. The surface labelling approach (2) enriched proteins previously not identified using the first approach (1). Potential general BC biomarkers were selected from surface proteins commonly identified from MDA-MB-231 and MCF7, but not identified in MCF10A EVs. Annotation with known BC disease associations from DisGeNET yielded 9 and 2 potential surface proteins on sEVs and mEVs, respectively. This study demonstrates the emerging role of EVs as a rich source of known and novel biomarkers which may be used for non-invasive detection of BC.