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

• 批准号: 10219741
• 负责人: Livia Schiavinato Eberlin
• 金额: --
• 依托单位: UNIVERSITY OF TEXAS AT AUSTIN
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-18 至 2021-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10219741
• 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 Surgical Procedures; Image; In Situ; Incidence; Knowledge; Laparoscopy; Lead; Lesion; Manuals; Mass Spectrum Analysis; Metabolic; Metabolic Marker; Methods; Molecular; Molecular Analysis; Molecular Diagnosis; Molecular Profiling; Operative Surgical Procedures; Pathogenesis; Pathologic; Pathology; Patient Care; Patient Care Management; Patients; Performance; Phenotype; Prevalence; Procedures; Proteomics; Quality of life; Recurrence; 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; base; 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; 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

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性疾病，影响全球约 1.76 亿人。 并可导致严重腹痛和生育能力低下，尽管其普遍存在，但目前尚未广泛传播。 此外，目前还没有非子宫内膜异位症的临床生物标志物。 侵入性诊断，因为大多数患者仅在探查性腹腔镜检查时才被诊断出来。 新分子技术促进对疾病的理解、诊断和术中检测 子宫内膜异位症迫切需要改善患者护理和管理。 技术提供化学特异性和灵敏度来进行快速和深入的分子分析 我们建议结合 MS 技术进行严格的研究。 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记物 此外，我们建议评估与患者症状和疾病表现相关的子宫内膜异位组织。 环境 MS 技术作为子宫内膜组织快速诊断工具的性能，以及体内 结合我们团队在多发性硬化症、统计学、妇科手术方面的专业知识， 病理学和子宫内膜异位症，我们独特地准备成功实现以下拟议目标： 目标 1. 表征子宫内膜异位症组织的代谢和蛋白质组标记物 使用 MS 成像对子宫内膜异位症病变特有的分子标记进行表征。 组织微环境中的生物机制可以带来对生物机制的新认识 子宫内膜异位症的发展并利用大量组织确定潜在的治疗目标。 通过手术前瞻性收集，我们将使用 MS 成像和统计分析来识别分子 子宫内膜异位症组织的特征并揭示与患者相关的显着分子改变 受控患者亚组内的表型和疾病表现。 目标 2. 使用子宫内膜异位症和子宫内膜异位症的子宫内膜组织定义子宫内膜异位症的诊断标志物 未受影响的患者迫切需要准确、快速诊断子宫内膜异位症的新方法。 我们建议推进 MS 成像和统计分析，以确定诊断标志物。 子宫内膜异位症 使用从子宫内膜异位症患者的在位子宫内膜获得的子宫内膜组织样本， 以及来自接受其他良性妇科手术的未受影响患者的子宫内膜组织， 目标是确定可用于从组织活检中诊断子宫内膜异位症的分子标记。 目标 3. 测试 MasSpec Pen 作为体内子宫内膜异位症完整检测的术中工具。 切除子宫内膜异位病灶并保留健康的邻近组织对于子宫内膜异位症至关重要 我们建议进行一项临床研究来评估 MasSpec 的性能。 用于子宫内膜异位病灶体内检测和腹腔镜手术中手术指导的笔技术。