MSACL 2023 Abstract

INTRODUCTION:
Cancer cells produce extracellular vesicles (EVs) which can be found in cancer patients’ body fluids, making these nanovesicles a promising target for liquid biopsies. Breast cancer is the most common cancer among women, and ovarian cancer the most lethal gynaecological cancer. Identification of body fluid-based biomarkers could contribute to a much earlier diagnosis of both breast and ovarian cancer, and improve patient survival rates.

OBJECTIVES:
The main objective was to study whether there is potential in the EVs’ lipid composition for the identification of biomarkers for breast and ovarian cancer diagnosis.

METHODS:
Untargeted lipidomic analyses of cancerous and non-cancerous mammary and ovarian epithelial cells and their secreted EVs, as well as EVs found in human blood plasma, were carried out. The cell panel of cancerous mammary cells studied included nine breast cancer cell lines representative of the ER+/PR+, HER2+ and triple negative breast cancer subtypes. The ovarian epithelial cells included two high-grade serous and two endometrioid ovarian cancer cell lines. The EVs produced in vitro by epithelial cells were isolated and purified by ultrafiltration and size-exclusion chromatography (SEC). The EVs found in blood plasma were isolated by a bespoke method based on the combination of density gradient ultracentrifugation and SEC for lipidomic analysis. The blood plasma was collected from women with breast cancer (n=19, 10 patients had primary breast cancer) and healthy women (n=10) who participated voluntarily in the study. The untargeted lipidomic analysis was carried out by reversed-phase liquid chromatography mass spectrometry. The data acquisition was performed in both positive and negative electrospray ionisation modes. Univariate and multivariate analyses were carried out, including logistic regression classification with recursive feature elimination (LR-RFE) analysis.

RESULTS:
Breast and ovarian cancer-derived EVs were enriched in sphingolipids (Cer, SM and HexCer) and glycerophospholipids (LPC, LPE, PC, PE, PI, PA, ether PE and ether PC) when compared to their parental cells, which were mainly enriched in triglycerides. We found that EVs and their parental cells can be perfectly classified (100% accuracy) into their respective breast cancer subtype (ER+/PR+, HER2+ and triple negative breast cancer subtypes) based on their lipid composition. We also found that the high-grade serous and the endometrioid ovarian cancer cells and their secreted EVs can also be classified with 100% accuracy. Our in vitro study also showed that EVs and their parental cells can be classified into cancerous or non-cancerous based on specific lipid species, this applied for both breast and ovarian cancers. Equally, our analysis of EVs found in blood plasma showed that specific PE and PC species allowed us to distinguish between EVs isolated from blood plasma from breast cancer patients (n=19, 90% accuracy) and healthy volunteers (n=10, 100% accuracy). The same result was obtained for the analysis of EVs found in blood plasma from patients with primary breast cancer (n=10, 90% accuracy), which demonstrates the relevance of these findings in the early detection of breast cancer.

CONCLUSION
EVs are an important source of lipid biomarkers for EV-based liquid biopsy for early detection of cancer.