Mass Spectrometry-based Global Molecular Approaches and Computational Tools to Determine Phenotypic and Environmental Signatures of Endometriosis

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ABSTRACT – PROJECT 2 Approximately 10% of reproductive-aged women are diagnosed with endometriosis, an inflammatory, estrogen- dependent disorder characterized by endometrial tissue outside the uterus. This is likely an underestimate of the frequency. In the absence of molecular biomarkers of this disease, the “gold standard” for diagnosis is histologic confirmation of the lesions via invasive surgical procedures (laparoscopy or laparotomy), which delays diagnosis. Accordingly, Project 2 will use mass spectrometry (MS)-based, global approaches to compare the proteomes of endometriotic lesions with eutopic endometrium from patients or women without disease with the goal of identifying protein biomarkers that enable better stratification of the disease phenotypes. Additionally, we will apply innovative computational methods to correlate the results with multiple molecular profiles (proteins, environmental chemicals [ECs] and metabolites) in patient and control sera, which could enable novel diagnostic strategies. This experimental strategy reflects the fact that endometriosis significantly alters the proteome of the affected cells. Also, ECs have been associated with the disease. For example, Drs. Giudice and Fisher reported alterations in the tissue proteome (fat) of women with endometriosis that correlate with EC exposures. With regard to other small molecules, recent studies suggest shifts in tissue metabolites may manifest in the blood of endometriosis patients. As such they may be linked to the disease process. Our overall strategy derives from the fact that MS-based analyses at a global level are transforming investigators' ability to explicate complex disease phenotypes. In this context, Specific Aim 1 will identify differentially expressed (DE) proteins in lesions, eutopic samples and sera that are associated with endometriosis using a MS-based approach for relative quantification. Specific Aim 2 will identify metabolomic and exposomic features in banked serum samples from endometriosis patients vs. control individuals. Specific Aim 3 will apply machine learning-based approaches to the -omic datasets generated in this project to define phenotypic molecular signatures of endometriosis that could aid in disease classification and diagnosis. The major significance of the proposed experiments is that we are redefining the landscape of endometriosis, using a precision medicine approach. Moreover, our data will reveal effectors with roles in the heterogeneous clinical manifestations of endometriosis that can be targets for diagnostic modalities and therapeutic interventions. As shown by the preliminary data, the members of the Project 2 team have extensive experience with the proposed technologies and computational strategies in other contexts. Regarding innovation, to our knowledge this is the first time that a multi-dimensional, multi-disciplinary approach to endometriosis will be pursued by using advanced computational, machine learning-based unsupervised methods to coalesce high order data sets. We believe the results will enable a predictive, non- invasive approach to detect endometriosis and reveal potential biomarkers, contributing factors underlying disease, and therapeutic targets.