Covalent Chemistry on Nanosubstrates Enables Molecular Analysis of Purified Extracellular Vesicles in Hepatocellular Carcinoma

纳米基质上的共价化学使肝细胞癌中纯化的细胞外囊泡的分子分析成为可能

• 批准号: 10462534
• 负责人: Vatche Agopian
• 金额: $ 58万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-07 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10462534
• 关键词: Address; Adopted; Alcoholic Liver Diseases; Antibodies; Area; Bioinformatics; Biological Assay; Biological Markers; Biology; Biometry; Blood; Blood Circulation; Blood specimen; Cancer Biology; Cancer Etiology; Cells; Cessation of life; Chemistry; Cirrhosis; Clinical; Clinical Oncology; Couples; Coupling; Data; Development; Devices; Diagnosis; Diagnostic; Disease; Disease Progression; Disulfides; Early Diagnosis; Early Intervention; Etiology; Excision; Exhibits; Filtration; Goals; Hepatic; Hepatitis B; Hepatitis C; Heterogeneity; Incidence; Intervention; Joints; Liver; Liver Cirrhosis; Malignant Neoplasms; Mediating; Medical center; Messenger RNA; Methods; Microfabrication; Microfluidics; Minor; Modality; Molecular; Molecular Analysis; Molecular Profiling; Motivation; Nanostructures; Neoplasm Circulating Cells; Oncology; Operative Surgical Procedures; Patients; Performance; Phospholipids; Plasma; Postoperative Period; Precipitation; Primary carcinoma of the liver cells; Prognostic Marker; Quantitative Evaluations; Recovery; Recurrence; Research; Reverse Transcription; Risk; Sampling; Serum; Silicon; Surface; System; Technology; Time; Transcript; Tumor-Derived; Ultracentrifugation; Validation; Yang; alpha-Fetoproteins; anticancer research; assay development; base; cancer diagnosis; curative treatments; diagnostic algorithm; diagnostic biomarker; digital; early detection biomarkers; extracellular vesicles; in-vitro diagnostics; innovation; liquid biopsy; liver transplantation; microchip; molecular pathology; mortality; nano; nanomaterials; nanowire; neoplastic cell; non-alcoholic fatty liver disease; novel marker; particle; radiological imaging; tumor; tumor DNA; vesicular release

PROJECT SUMMARY Extracellular vesicles (EVs) are a heterogeneous group of phospholipid bilayer-enclosed particles with the biomolecular contents mirroring those of their parental cells. Since EVs are present in circulation at a relatively early stage of disease and persist across all disease stages, purification and characterization of tumor-derived EVs are expected to offer an opportunity for early cancer diagnosis. Hepatocellular carcinoma (HCC), the fourth most common cause of cancer-related deaths worldwide, is in dire need of diagnostic and prognostic biomarkers. Current clinical radiographic system and serum biomarkers (e.g., alpha-fetoprotein (AFP)) poorly discriminate early-stage HCC (where potentially curative therapies are available) from at-risk liver cirrhosis (where HCC surveillance is indicated). Moreover, sensitive biomarkers for HCC postoperative recurrence (where timely salvage treatment interventions can suppress disease progression) after curative-intent liver resection and liver transplantation remain a significant challenge for early-stage HCC. Therefore, exploiting the diagnostic potential of HCC EVs and EV cargo profiling for HCC early detection and postoperative recurrence holds great promise to significantly augment the ability of current diagnostic modalities. Conventional methods for isolating EVs, such as ultracentrifugation, filtration, and precipitation, are incapable of discriminating tumor-derived EVs from non-tumor-derived EVs. To address this unmet need, our team developed “EV Click Chips” for HCC EV purification. The innovation of our devices includes i) the covalent chemistry-mediated EV capture/release couples click chemistry-mediated EV capture and disulfide cleavage- driven EV release, ii) an optimized multi-marker cocktail targeting HCC-associated surface markers was adopted to overcome the heterogeneity of HCC EVs; iii) the incorporation of densely packed silicon nanowire substrates (SiNWS) dramatically increases the device surface areas for contacting/interacting with EVs; and iv) the microfluidic chaotic mixer facilitates repeated physical contact between SiNWS and the flow-through EVs, further enhancing the performance of EV purification. The purified HCC EVs can be characterized by quantifying a panel of 10 well-validated HCC-specific mRNA markers by incorporating Droplet Digital PCR (ddPCR) technology. The proposed research will conduct: i) an exploratory development and optimization of EV Click Chips for HCC EV purification, and ii) clinical validations of EV Click Chips for HCC early detection and postoperative recurrence using patient blood samples. Our long-term goal is to develop a new HCC EV purification system (i.e., EV Click Chips) by synergistically integrating four very powerful approaches, including covalent chemistry-mediated EV capture/release, multimarker antibody cocktails, nanostructured substrates, and microfluidic chaotic mixers. The purified HCC EVs will readily allow for quantitative cargo profiling to augment current HCC diagnostic algorithms.

项目概要 细胞外囊泡 (EV) 是一组异质的磷脂双层封闭颗粒，其结构如下： 由于 EV 存在于循环中，其生物分子含量与其亲本细胞的生物分子含量相对较高。 疾病的早​​期阶段并持续存在于所有疾病阶段、肿瘤来源的纯化和表征 EV 有望为肝细胞癌 (HCC) 的早期诊断提供机会。 全球第四大癌症相关死亡原因，迫切需要诊断和预后 目前的临床放射学系统和血清生物标志物（例如甲胎蛋白（AFP））较差。 区分早期 HCC（有潜在治愈性疗法）和高危肝硬化 （其中需要 HCC 监测）此外，HCC 术后复发的敏感生物标志物。 （及时挽救治疗干预可以抑制疾病进展）治疗性肝脏治疗后 因此，切除和肝移植仍然是早期 HCC 的重大挑战。 HCC EV 和 EV 货物分析对于 HCC 早期检测和术后复发的诊断潜力 有望显着增强当前诊断方式的能力。 超速离心、过滤和沉淀等传统方法无法分离 EV 为了解决这一未满足的需求，我们的团队致力于区分肿瘤来源的 EV 和非肿瘤来源的 EV。 开发了用于 HCC EV 纯化的“EV Click 芯片”。我们设备的创新包括 i) 共价键。 化学介导的 EV 捕获/释放对点击化学介导的 EV 捕获和二硫键裂解- 驱动 EV 释放，ii) 针对 HCC 相关表面标记物的优化多标记混合物 用于克服 HCC EV 的异质性；iii) 结合密集硅纳米线 衬底（SiNWS）显着增加了与电动汽车接触/相互作用的器件表面积； iv) 微流体混沌混合器促进 SiNWS 和流通液之间的重复物理接触 EV，进一步增强了 EV 净化的性能。纯化的 HCC EV 的特点是： 通过结合微滴数字 PCR 来量化一组 10 个经过充分验证的 HCC 特异性 mRNA 标记 （ddPCR）技术。拟议的研究将进行：i）探索性开发和优化 用于 HCC EV 纯化的 EV Click 芯片，以及 ii) 用于 HCC 早期检测的 EV Click 芯片的临床验证 我们的长期目标是开发一种新的 HCC EV。 净化系统（即 EV Click Chips）通过协同集成四种非常强大的方法，包括 共价化学介导的 EV 捕获/释放、多标记抗体混合物、纳米结构基质、 和微流体混沌混合器，纯化的 HCC EV 将很容易进行定量货物分析。 增强当前的 HCC 诊断算法。