Lipidomic analysis of extracellular vesicles and its potential for cancer diagnosis

Abstract

Breast and ovarian cancer are among the deadliest types of cancer in women. The development of diagnostic methods for early diagnosis of cancer can improve patient survival rates. Cancer cells produce extracellular vesicles (EVs), including exosomes and microvesicles, which can be found in cancer patients’ body fluids. Cancer-derived EVs play an important role in cancer development, progression and metastasis. In addition, they have a great potential as a source of biomarkers for the development of body fluid-based methods for cancer diagnosis. The aim of this PhD thesis was to study the lipid composition of cancer-derived EVs and its potential for the identification of lipid biomarkers for breast and ovarian cancer diagnosis. The lipid composition of EVs secreted by cancerous and non-cancerous mammary and ovarian epithelial cells was studied by untargeted lipidomics. Cancer-derived EVs were found to be enriched in sphingolipids and glycerophospholipids compared to their parental cells. It was found that EVs produced by cancerous cells can be distinguished from those produced by non-cancerous cells based on their lipid content. Furthermore, EVs’ lipid composition differs between breast cancer subtypes, as well as between ovarian cancer subtypes. EVs found in blood plasma from breast cancer patients and healthy individuals were also analysed by untargeted lipidomics and a combination of five specific lipid species was found to have a diagnostic value for breast cancer diagnosis. Importantly, the same combination of lipids was also able to differentiate between EVs found in blood plasma from primary breast cancer patients and EVs found in blood plasma from healthy individuals. In conclusion, this study demonstrates the potential of the EVs’ lipid content for the identification of lipid biomarkers for the early detection of breast and ovarian cancer and their use for the development of diagnostic methods, based on blood plasma analysis of cancer patients.