Identifying High Risk Cells in Primary SCCHN to Predict Lymph Node Metastasis

识别原发性 SCCHN 中的高风险细胞以预测淋巴结转移

基本信息

批准号：
7473094
负责人：
ZHUO Georgia CHEN
金额：
$ 17.43万
依托单位：
EMORY UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2008
资助国家：
美国
起止时间：
2008-08-04 至 2010-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7473094
关键词：
Address Animal Model Antibodies Antigens Behavior Biological Biological Markers Biology Cancer Detection Cell Line Cell model Cells Clinic Colorectal Cancer Data Detection Development Diagnosis Down-Regulation E-Cadherin Electronics End Point Epidermal Growth Factor Receptor Epithelial Fluorescence Formalin Gene Expression Genotype Goals Head and Neck Squamous Cell Carcinoma Head and neck structure Heterogeneity Human Image Immunohistochemistry Institution Integrins Laboratory Research Lead Link Literature Lymph Node of Head, Face and Neck Malignant Neoplasms Mesenchymal Metastatic Squamous Cell Carcinoma Methodology Methods Molecular NIH Program Announcements Nature Neoplasm Metastasis Optics Paraffin Embedding Patients Phenotype Population Primary Neoplasm Property Proteins Protocols documentation Public Health Publishing Quantum Dots Reporting Research Research Project Grants Risk Sampling Semiconductors Signal Transduction Specimen Squamous cell carcinoma Staging Survival Rate System Technology Testing Time Tissue Sample Tissues Training Validation base cancer cell cancer stem cell concept dysadherin epithelial to mesenchymal transition human tissue improved in vivo insight lymph nodes mouse model nanoparticle nanoscale neoplastic cell novel strategies outcome forecast protein expression size tool treatment planning tumor

项目摘要

DESCRIPTION (provided by applicant): This project aims to develop a protein-based molecular prediction strategy using nanoparticle quantum dots (QDs) to aid in the accurate diagnosis of lymph node metastasis (LNM) from primary tumor (PT). It is an appropriate application for Exploratory Studies in Cancer Detection, Diagnosis, and Prognosis (PA-06-299). There are two questions that will be addressed in this project. First, we would like to determine whether protein-based biomarkers that relate to epithelial-mesenchymal transition (EMT) can be used to identify a subpopulation of tumor cells with high risk for metastasis in PTs. Second, we will address whether the utilization of QD technology for simultaneous detection of multi-biomarkers can facilitate the accurate prediction of LNM from SCCHN PTs. Although the concept of preexisting metastatic cells in PTs has been proposed for many years, these cells have not been clearly identified in human tissues. Since the expression of protein is more relevant than gene expression to the biological behavior of tumor cells, using multi-proteins expressed in PTs as biomarkers to predict LNM may not only achieve more reliable data, but also help in understanding the biology of metastasis. A recent development of QD-based imaging can simultaneously detect and quantify multi-proteins, providing a unique tool for this study. Based on our preliminary findings, we hypothesize that there is a subpopulation of tumor cells in PTs carrying metastatic signatures, such as dedifferentiation or EMT, which represents the major population of LNM. Using QD-based technology, we will detect this sub-population, which will facilitate both prediction of LNM from the PT and understanding the biology of metastasis. Two specific aims will test this hypothesis: (1) To develop QD linked multi-antibodies (QD-Abs) for detection and quantification of multi-biomarkers using SCCHN tissue samples: Based on current literature and our preliminary data from the animal model and immunohistochemistry (IHC) studies, this Specific Aim will initially select 5 metastasis-related biomarkers, including E-cadherin, ¿-catenin, dysadherin, EGFR, and integrin ¿1, and conjugate their antibodies with QDs. The QD-Abs will be validated by comparison with conventional IHC. The same 100 PT SCCHN tissue samples (50 LNM-positive and 50 LNM-negative PTs) as used in the preliminary studies will serve as our "training" set. The 3-5 best-comparable biomarkers will be identified and selected for further studies in Specific Aim 2. (2) To validate QD-Abs for detection and quantification of selected biomarkers in human primary SCCHN tissues and correlate these with metastasis and survival of SCCHN patients: An additional 200 samples selected using similar criteria to the training set will be used as our "testing" samples for validation of the established QD-Abs system. We will try to identify and quantify a subpopulation of the PT cells as high-risk for metastasis and correlate this subpopulation with LNM and survival of the SCCHN patients. PUBLIC HEALTH RELEVANCE: Reliable detection of LNM is paramount for appropriate treatment planning. This project aims to develop nanoparticle quantum dots (QDs) technology to detect multi-biomarkers as a prediction strategy to aid in the accurate diagnosis of LNM from the primary tumor. Based on data generated from this project, a novel strategy of QD-based protein detection will be further developed that can be used in both the clinic and in research laboratories.

描述（由申请人提供）：该项目旨在开发一种基于蛋白质的分子预测策略，使用纳米粒子量子点（QD）来帮助准确诊断原发性肿瘤（PT）的淋巴结转移（LNM），用于探索性研究。在癌症检测、诊断和预后 (PA-06-299) 中，该项目将解决两个问题。与上皮间质转化 (EMT) 相关的基于蛋白质的生物标志物可用于识别 PT 中具有高转移风险的肿瘤细胞亚群。其次，我们将讨论是否利用 QD 技术同时检测多生物标志物。可以促进从 SCCHN PT 中准确预测 LNM 尽管 PT 中预先存在的转移细胞的概念已经提出多年，但自从 PT 的表达以来，这些细胞尚未被明确识别。蛋白质比基因表达与肿瘤细胞的生物学行为更相关，使用PT中表达的多种蛋白质作为生物标志物来预测LNM不仅可以获得更可靠的数据，而且有助于理解转移的生物学。基于图像的成像可以同时检测和量化多种蛋白质，为这项研究提供了独特的工具，根据我们的初步发现，我们发现PT中存在携带转移特征（例如去分化或EMT）的肿瘤细胞亚群。使用基于 QD 的技术，我们将检测该亚群，这将有助于从 PT 预测 LNM 并了解转移的生物学，将检验该假设的两个具体目标：（1）发展。 QD 连接多抗体 (QD-Abs)，用于使用 SCCHN 组织样本检测和定量多生物标志物：根据当前文献以及我们从动物模型和免疫组织化学 (IHC) 研究中获得的初步数据，该特定Aim 最初将选择 5 个与转移相关的生物标志物，包括 E-钙粘蛋白、¿ -连环蛋白、粘着蛋白、EGFR 和整合素 ¿ 1，并将其抗体与 QD 结合，通过与初步研究中使用的相同 100 个 PT SCCHN 组织样本（50 个 LNM 阳性和 50 个 LNM 阴性 PT）进行比较来验证。我们的“训练”集将被识别并选择用于具体目标 2 中的进一步研究。 (2) 验证 QD-Abs检测和量化人类原发性 SCCHN 组织中选定的生物标志物，并将其与 SCCHN 患者的转移和生存相关联：使用与训练集类似的标准选择的另外 200 个样本将用作我们的“测试”样本，以验证已建立的 QD- Abs 系统。我们将尝试识别和量化具有高转移风险的 PT 细胞亚群，并将该亚群与 LNM 和 SCCHN 患者的生存相关联。公共健康相关性：可靠的 LNM 检测对于适当的治疗计划至关重要。该项目旨在开发纳米粒子量子点 (QD) 技术来检测多生物标志物作为预测策略，以帮助准确诊断原发性肿瘤的 LNM。根据该项目生成的数据，将进一步开发一种基于量子点的蛋白质检测新策略，可用于临床和研究实验室。