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

> **NIH NIH P01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2022 · $420,763

## Abstract

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
...

## Key facts

- **NIH application ID:** 10458759
- **Project number:** 5P01HD106414-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** SUSAN J. FISHER
- **Activity code:** P01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $420,763
- **Award type:** 5
- **Project period:** 2021-08-01 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10458759

## Citation

> US National Institutes of Health, RePORTER application 10458759, Mass Spectrometry-based Global Molecular Approaches and Computational Tools to Determine Phenotypic and Environmental Signatures of Endometriosis (5P01HD106414-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10458759. Licensed CC0.

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