Online Raman Diagnostics of Oncometabolites

肿瘤代谢物的在线拉曼诊断

• 批准号: 9147682
• 负责人: Zachary Schultz
• 金额: $ 36.39万
• 依托单位: UNIVERSITY OF NOTRE DAME
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9147682
• 关键词: Algorithms; Biological Assay; Biological Markers; Capillary Electrophoresis; Cells; Chemical Structure; Chemicals; Chromatography; Citric Acid Cycle; Comb animal structure; Complex; Coupled; Data; Detection; Development; Developmental Process; Diagnosis; Diagnostic; Disease; Exhibits; Explosion; Fingerprint; Gases; Gene Expression; Gene Proteins; Glycolysis; Goals; Gold; Investigation; Investments; Isomerism; Label; Laboratories; Link; Liquid substance; Machine Learning; Malignant Neoplasms; Mass Spectrum Analysis; Metabolic; Metabolism; Methodology; Methods; Modeling; Monitor; Normal Cell; Nuclear Magnetic Resonance; Nutrient; Oncogenes; Oxygen; Patients; Pharmaceutical Preparations; Play; Production; Prognostic Marker; Public Health; Raman Spectrum Analysis; Regulation; Reproducibility; Research; Resistance development; Resolution; Sampling; Surface; Techniques; Technology; Time; abstracting; aerobic glycolysis; base; cancer biomarkers; cancer cell; cancer diagnosis; cancer therapy; chemical property; clinically relevant; cost; detector; diagnostic assay; effective therapy; glucose uptake; improved; innovation; malignant breast neoplasm; mass spectrometer; metabolome; metabolomics; neoplastic cell; new technology; novel; novel diagnostics; patient subsets; personalized medicine; prognostic; protein profiling; success; therapeutic target; therapy resistant; tool; treatment strategy; trend; tumor; tumor metabolism; tumor progression

Project Abstract Cancer cells utilize normal metabolic processes out of context to promote tumor survival. For example, Otto Warburg and others discovered that tumors have increased glucose uptake, glycolysis, and lactate production, often with a reduction in citric acid cycle. While “aerobic glycolysis” at first glance is energetically expensive for tumor cells because it circumvents high ATP production from the citric acid cycle, it allows cancer cells to survive under low nutrient or low oxygen conditions and to instead use glycolytic intermediates for the synthesis of essential cellular building blocks without further energy investment. This change in metabolite regulation suggests a powerful method for monitoring and diagnosing cancer. This project seeks to develop surface enhanced Raman scattering (SERS) as online detection method for the characterization of metabolites from breast cancer tumor models. Using the SERS results from tumor lysates, diagnostic algorithms will be constructed to improve treatment for cancer. Results show that fluid dynamics can be used to increase the reproducibility and sensitivity of SERS detection in flowing liquids. We propose to develop methodology to enable the use this innovation to investigate metabolites in cancer cell lysates using capillary electrophoresis coupled to a SERS flow detector. We will investigate known metabolites that have been linked to cancer, as well as examine key metabolites associated with oncogenes. The SERS data collected will be used to formulate diagnostic algorithms that can provide a yes/no indicator of cancer. The specific aims of this project are as follows: · AIM 1. Demonstrate the utility of the novel flow detector to assess changes in key metabolites from tumor cell lysates. The tumor cell lysates will be compared with non-cancerous cell lysates to identify trends in these metabolites relevant to breast cancer. · AIM 2. Compare the identification and quantification capabilities with the current gold standard, LC- MS. This aim will assess how SERS characterization both compares with existing technology but also increases coverage of the metabolome. · AIM 3. We will use the metabolites to develop statistical machine learning algorithms to predict the sample label (cancer or not). The predictor obtained will be used as a diagnostic tool of cancer. The development of new technologies that provide unique chemical specific information will enable improved diagnostic assays for the treatment of cancer.

项目摘要 癌细胞利用正常的代谢过程，以促进肿瘤存活。例如， 奥托·沃堡（Otto Warburg）等人发现肿瘤增加了葡萄糖摄取，糖酵解和乳酸 生产，通常会减少柠檬酸周期。乍一看，“有氧糖酵解”是精力充沛的 对于肿瘤细胞来说，昂贵，因为它可以从柠檬酸周期中绕过高的ATP产生，因此可以 癌细胞在低营养或低氧条件下生存，而是使用糖酵解中间体 为了综合基本的细胞构建块，而无需进一步的能源投资。这种变化 代谢产物调节提出了一种监测和诊断癌症的有力方法。 该项目旨在开发表面增强的拉曼散射（SER）作为在线检测方法 来自乳腺癌肿瘤模型的代谢产物的表征。使用肿瘤的SERS结果 裂解物，将构建诊断算法以改善癌症的治疗方法。结果表明流体 动力学可用于增加流动液体中SERS检测的可重复性和灵敏度。我们 提出开发方法，以使这种创新能够研究癌细胞中的代谢产物 使用毛细管电泳耦合到SERS流动检测器的裂解物。我们将研究已知的代谢产物 与癌症有关，并检查与癌基因相关的关键代谢产物。 Sers 收集的数据将用于制定可以提供癌症指标的诊断算法。 该项目的具体目的如下： 目的1。演示新的流量检测器的实用性，以评估来自关键代谢产物的变化 肿瘤细胞裂解物。将肿瘤细胞裂解物与非癌细胞裂解物进行比较 确定与乳腺癌相关的这些代谢产物的趋势。 目标2。将识别和定量功能与当前的金标准LC-进行比较 多发性硬化症。该目标将评估SERS表征如何与现有技术进行比较，但是 还增加了代谢组的覆盖范围。 目标3。我们将使用代谢物来开发统计机器学习算法以预测 样品标签（癌症与否）。获得的预测因子将用作癌症的诊断工具。 提供独特化学特定信息的新技术的开发将改善 诊断刺客治疗癌症。