Quantitative proteomic profiling of extracellular vesicles from menstrualeffluent for the discovery of non-invasive diagnostic biomarkers of endometriosis

Abstract: Endometriosis is a disease caused by the extension of endometrial glands and stroma outside the uterine cavity. Approximately 10% of reproductive age women, more than 4 million in the United States alone, are affected by endometriosis; the true prevalence is expected to be much higher due to the high rate of undiagnosed cases. Significant physical, psychological, and economic suffering has been documented in patients suffering from endometriosis. In spite of the tremendous interest and unmet need, there is no FDA-approved non-invasive diagnostic tool or biomarker for endometriosis. One of the key reasons is the lack of understanding of the correlation between clinical symptoms, histological manifestations, and underlying dysfunction resulting in disease. In addition, endometriosis is often associated with a range of other disorders, creating a high biomarker noise. Extracellular vesicles (EV) are secreted by all cells and are thought to mediate intercellular as well as inter-organ communication and are known to communicate the specific disease state. EVs have been hypothesized to play a role in the pathogenesis and diagnosis of endometriosis. In disease, EV content is known to be altered to reflect the disease type in a highly specific way. We seek to overcome the low signal, high noise hurdle of endometriosis biomarker discovery by 1) focusing on EVs, the cellular messengers of the disease, present in menstrual effluent 2) using state of the art, highly sensitive, quantitative proteomic technique, and 3) using cohort/panel of biomarkers. Our improvement in proteomic technique alone results in 10-fold more significant quantitative differences. In combination, they present a very significant conceptual, technical, and instrumentation leap over past studies. We will perform the multiplexed quantitative proteomics using isobaric tandem mass tags (TMT) on menstrual effluent EVs from surgically confirmed endometriosis patients and compare them to controls with endometriosis like symptoms including chronic pelvic pain, but with no endometriosis being detected at the surgery. It will provide us a patient specific, high-quality database of proteomic content of exosomes secreted in menstrual effluent and identify functional signaling networks on them, to lay down the foundation for the exploration of EVs from menstrual effluent. The goal of this grant is to identify the cohort/panel of biomarkers of endometriosis on menstrual effluent EVs, that can be taken to assay development and clinical validation in phase II.