Protein signatures in exosome-like vesicles of uterine aspirates improve the diagnosis and stratification of endometrial cancer patients

Background: Endometrial cancer (EC) accounts for more than 10,000 deaths per year in the US alone. EC is divided into the more common and less aggressive type 1 and the type 2. There is an urgent need to develop non-invasive tests that can provide early detection of EC and that can discriminate EC subtypes. This study focuses on the identification of protein biomarkers in exosome-like vesicles (ELVs) isolated from uterine aspirates. Uterine aspirates are collected by a minimally invasive procedure and it represents the ideal body fluid since it is the closest to the neoplasic endometrium cells. Methods: Protein extracts from purified ELVs were obtained following ultracentrifugation of UAs from age-matched groups of control, type 1 and type 2 EC patients (10 patients/group). The quality of ELVs was monitored by nanoparticle tracking analysis and immunoblot. To profile protein abundance across different groups, we develop a super-SILAC approach where ELV proteins from three different EC cell lines grown in heavy amino acids were combined with ELV protein extracts of each patient. Proteins were separated by SDS-PAGE and 10 gel-isolated bands per patient were digested with trypsin and analysed by mass spectrometry. From 2138 proteins identified, we generated a list of 54 protein candidates that were further validated by selected reaction monitoring (SRM) in an independent cohort of 107 patients including type 1 EC (n = 45) EC, type 2 EC (n = 21) and controls (n = 41). A total of 86 unique peptides matching the proteins of interest were monitored. Protein quantitation was performed using a QTRAP 5500 Sciex instrument. Results: Our targeted mass spectrometry approach confirmed that ELVs from uterine aspirates contain proteins that can discriminate between cancer patients and healthy individuals, and can classify EC in the different subtypes. A 2-protein signature, composed of Agrin and CD81, achieved an AUC = 0.935 for EC diagnosis. In addition, we also report a new protein signature, combining CLD6, and RAB8A, that can differentiate type 1 versus type 2 EC (AUC = 0.932). This study has important implications in early detection of EC and in patient stratification. Summary/conclusion: A targeted mass spectrometry approach defines protein signatures for endometrial cancer diagnosis and stratification of patients in ELVs isolated from uterine aspirates.