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疾病，全世界约有1.76亿人患病， 并可能导致严重的腹痛和不育症。尽管它很流行，但目前没有广泛的 公认的子宫内膜异位症发病机制理论。此外，目前还没有非传染性疾病的临床生物标志物。 侵入性诊断，因为大多数患者只有在探查性腹腔镜检查时才被诊断出来。因此，应用程序 新的分子技术，以促进对本病的理解、诊断和术中检测 子宫内膜异位症是改善患者护理和管理的关键。环境质谱仪(MS) 技术提供了化学特异性和敏感性，以执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，将MS技术与 用统计学方法识别、表征和验证子宫内膜和子宫内膜的代谢和蛋白质标记物 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技术作为子宫内膜组织快速诊断和活体诊断工具的性能 术中疾病检测。结合我们团队在多发性硬化症、统计学、妇科外科、 在病理和子宫内膜异位症方面，我们是独一无二的，我们准备成功地实现以下拟议目标： 目的1.子宫内膜异位症组织代谢和蛋白质组学标志物的特征 使用MS成像的患者。子宫内膜异位症病变特异性分子标志物的特征 在组织内的微环境可以导致对生物机制的新理解 子宫内膜异位症的发展和精确的潜在治疗靶点。使用一大群组织 预期从手术中收集，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和发现与患者相关的显著分子变化 受控患者亚组中的表型和疾病表现。 目的2.利用子宫内膜异位症和子宫内膜异位症的子宫内膜组织确定子宫内膜异位症的诊断标志物 未受影响的病人。迫切需要新的准确和快速诊断子宫内膜异位症的方法 推进患者护理。我们建议使用MS成像和统计分析来确定诊断标志物 子宫内膜异位症患者在位子宫内膜组织标本的研究 以及来自接受其他良性妇科手术的未受影响的患者的子宫内膜组织， 目的：从组织活检中寻找可用于诊断子宫内膜异位症的分子标记。 目的3.测试MasSpec Pen作为在体子宫内膜异位症的术中检测工具。完成 切除子宫内膜异位症病变和保留健康的邻近组织在子宫内膜异位症的治疗中至关重要 病人的外科治疗。我们建议进行一项临床研究来评估MasSpec的性能 PEN技术用于活体检测子宫内膜异位症病变和在腹腔镜手术中的手术指导。