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Molecular Imaging and Diagnosis of Endometriosis using Mass Spectrometry Technologies

使用质谱技术进行子宫内膜异位症的分子成像和诊断

基本信息

批准号：
10665085
负责人：
Livia Schiavinato Eberlin
金额：
$ 17.72万
依托单位：
BAYLOR COLLEGE OF MEDICINE
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-01 至 2025-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10665085
关键词：
Abdominal Pain Advanced Development Affect Age Benign Biological Biopsy Caring Chemicals Chronic Clinical Clinical Research Data Detection Development Devices Diagnosis Diagnostic Procedure Disease Electrospray Ionization Endometrial Endometrium Excision Functional disorder Goals Gynecologic Gynecologic Surgical Procedures In Situ Incidence Knowledge Laparoscopy Lesion Manuals Mass Spectrum Analysis Metabolic Metabolic Marker Methods Molecular Molecular Analysis Molecular Diagnosis Molecular Profiling Operative Surgical Procedures Pathogenesis Pathologic Pathology Patient Care Patients Performance Phenotype Prevalence Procedures Proteomics Quality of life Recurrent disease Reporting Robotics Sampling Sensitivity and Specificity Specificity Spectrometry, Mass, Electrospray Ionization Speed Statistical Data Interpretation Statistical Methods Symptoms Techniques Technology Testing Time Tissue Sample Tissue imaging Tissues Training Translating Validation Woman Work accurate diagnosis clinical biomarkers clinical decision-making clinical phenotype cohort diagnostic biomarker disease diagnosis endometriosis eutopic endometrium expectation experimental study improved in situ imaging in vivo innovation insight ionization machine learning algorithm mass spectrometric imaging minimally invasive molecular imaging molecular marker new technology noninvasive diagnosis patient subsets predictive signature preservation prospective protein biomarkers rapid diagnosis rapid technique reproductive specific biomarkers statistics subfertility theories tool treatment strategy

项目摘要

ABSTRACT: Endometriosis is a chronic pathologic condition that affects approximately 176 million worldwide, and can cause severe abdominal pain and subfertility. Despite its prevalence, there is currently no widely accepted theory of pathogenesis for endometriosis. Further, there are currently no clinical biomarkers for non- invasive diagnosis as most patients are only diagnosed at the time of exploratory laparoscopy. Thus, application of new molecular technologies to advance the understanding, diagnosis, and intraoperative detection of endometriosis is critically needed to improve patient care and management. Ambient mass spectrometry (MS) techniques offer the chemical specificity and sensitivity to perform rapid and in-depth molecular analysis of unprocessed tissue samples. We propose to conduct a rigorous study applying MS techniques in conjunction with statistical methods to identify, characterize, and validate metabolic and protein markers of endometrial and endometriosis tissues correlated to patient symptoms and disease presentation. Further, we propose to evaluate the performance of ambient MS techniques as tools for rapid diagnosis of endometrial tissues, and in vivo intraoperative disease detection. Combining our team’s expertise in MS, statistics, gynecological surgery, pathology and endometriosis, we are unique poised to successfully carry out the following proposed aims: Aim 1. Characterize metabolic and proteomic markers of endometriosis tissues from endometriosis patients using MS imaging. Characterization of molecular markers that are specific to endometriosis lesions within tissue microenvironment can lead to new understanding of the biological mechanisms governing endometriosis development and pinpoint potential targets for treatment. Using a large cohort of tissues prospectively collected from surgeries, we will use MS imaging and statistical analysis to identify molecular signatures of endometriosis tissues and uncover significant molecular alterations correlated to patient phenotypes and disease presentation within controlled patient subgroups. Aim 2. Define diagnostic markers of endometriosis using endometrial tissues from endometriosis and unaffected patients. New methods for accurate and rapid diagnosis endometriosis are critically needed to advance patient care. We propose to use MS imaging and statistical analyses to identify diagnostic markers of endometriosis using endometrial tissue samples obtained from eutopic endometrium of endometriosis patients, and endometrium tissues from unaffected patients undergoing other benign gynecological procedures, with the goal of identifying molecular markers that could use for diagnosis of endometriosis from tissue biopsies. Aim 3. Test the MasSpec Pen as an intraoperative tool for in vivo endometriosis detection. Complete excision of endometriosis lesions and preservation of healthy adjacent tissues is of utmost importance in the surgical treatment of patients. We propose to conduct a clinical study to evaluate the performance of MasSpec Pen technology for in vivo detection of endometriosis lesions and surgical guidance in laparoscopic procedures.

摘要：子宫内膜异位症是一种慢性病理疾病，全球约有1.76亿患者。