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: 10470610
• 负责人: Livia Schiavinato Eberlin
• 金额: $ 23.42万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 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/10470610
• 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，全球约有1.76亿人患病， 会导致严重的腹痛和不孕尽管它的流行，目前没有广泛的 子宫内膜异位症发病机制的公认理论。此外，目前还没有针对非肿瘤的临床生物标志物。 侵入性诊断，因为大多数患者仅在探查性腹腔镜检查时被诊断。因此，应用 新的分子技术，以促进理解，诊断和术中检测， 子宫内膜异位症是迫切需要改善病人的护理和管理。环境质谱法（MS） 技术提供了化学特异性和灵敏度，以进行快速和深入的分子分析， 未经处理的组织样本我们建议进行一项严格的研究，应用质谱技术， 用统计学方法鉴定、表征和验证子宫内膜的代谢和蛋白质标志物， 子宫内膜异位症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技术作为快速诊断子宫内膜组织的工具的性能， 术中疾病检测。结合我们团队在多发性硬化症，统计学，妇科手术， 病理学和子宫内膜异位症，我们是独一无二的准备成功地实现以下拟议的目标： 目标1.表征子宫内膜异位症组织的代谢和蛋白质组学标志物 患者使用MS成像。子宫内膜异位症病变特异性分子标志物的特征 在组织微环境中，可以导致对生物学机制的新理解， 子宫内膜异位症的发展和精确定位潜在的治疗目标。使用大量组织 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 子宫内膜异位症组织的特征，并发现与患者相关的显著分子改变 对照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， 未受影响的患者目前迫切需要新的准确快速诊断子宫内膜异位症的方法， 先进的病人护理。我们建议使用MS成像和统计分析来确定诊断标志物， 子宫内膜异位症使用得自子宫内膜异位症患者的在位子宫内膜的子宫内膜组织样品， 和子宫内膜组织，来自接受其他良性妇科手术的未受影响的患者， 目的是从组织活检中确定可用于诊断子宫内膜异位症的分子标志物。 目标3。测试MasSpec Pen作为体内子宫内膜异位症检测的术中工具。完成 切除子宫内膜异位症病灶和保护健康的邻近组织是最重要的， 手术治疗病人。我们建议进行临床研究，以评估MasSpec的性能 腹腔镜手术中子宫内膜异位症病灶体内检测和手术指导的笔技术。