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状况，在全球范围内影响约1.76亿并可能引起严重的腹痛和差。尽管率很高，但目前却没有可接受的子宫内膜异位发病理论。此外，目前尚无非临床生物标志物用于非 - 作为侵入性诊断，大多数患者仅在探索性腹腔镜检查时才被诊断。那，应用新的分子技术，以提高对术中的理解，诊断和术中检测要改善患者护理和管理，需要注重子宫内膜异位症。环境质谱（MS）技术提供了化学特异性和敏感性，以进行快速和深度分子分析未加工的组织样品。我们建议进行一项严格的研究，应用MS技术结合使用统计方法来识别，表征和验证子宫内膜和子宫内膜的代谢和蛋白质标志物子宫内膜异位组织与患者症状和疾病表现相关。此外，我们建议评估环境MS技术作为子宫内膜组织快速诊断和体内快速诊断的工具的性能术中疾病检测。结合我们团队在MS，统计，妇科手术方面的专业知识，病理学和子宫内膜异位症，我们被毒死了，可以成功执行以下提出的目标：目标1。子宫内膜异位症的子宫内膜异位组织的代谢和蛋白质组学标记使用MS成像的患者。特定于子宫内膜异位病变的分子标记的表征在组织微环境中，可以使人们对统治的生物学机制有了新的了解子宫内膜异位症的发展和查明治疗的潜在靶标。使用大量的组织从手术中前瞻性收集，我们将使用MS成像和统计分析来识别分子子宫内膜异位组织的特征和发现与患者相关的显着分子改变受控患者亚组中的表型和疾病表现。目标2。使用子宫内膜异位症和子宫内膜组织定义子宫内膜异位症的诊断标记未受影响的患者。准确和快速诊断性子宫内膜异位症的新方法至关重要预先患者护理。我们建议使用MS成像和统计分析来确定使用子宫内膜异位症患者子宫内膜的子宫内膜组织样品子宫内膜异位症，以及来自未受影响的患者的子宫内膜组织，接受其他良性妇科程序，鉴定可用于诊断组织活检子宫内膜异位症的分子标记的目标。 AIM 3。测试MassPec笔作为体内子宫内膜异位症检测的术中工具。完全的检查子宫内膜异位病变和保存健康的邻近组织至关重要患者的手术治疗。我们建议进行一项临床研究，以评估MassPec的性能用于体内检测子宫内膜异位病变和腹腔镜手术指导的PEN技术。