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

描述(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，使用纳米量子点(Qds)来帮助准确诊断原发瘤(PT)的淋巴转移(LNM)。适用于癌症检测、诊断和预后的探索性研究(PA-06-299)。有两个问题将在这个项目中解决。首先，我们希望确定与上皮-间充质转化(EMT)相关的基于蛋白质的生物标记物是否可以用于识别PTS中具有高转移风险的肿瘤细胞亚群。第二，我们将探讨利用QD技术同时检测多个生物标志物是否有助于从SCCHN pt准确预测LNM。尽管PTS中预先存在的转移细胞的概念已经提出多年，但这些细胞在人类组织中还没有得到明确的识别。由于蛋白质的表达比基因的表达与肿瘤细胞的生物学行为更相关，利用PTS中表达的多个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，而且有助于了解转移的生物学行为。基于量子点成像的最新发展可以同时检测和定量多个蛋白质，为这一研究提供了一个独特的工具。根据我们的初步发现，我们假设在PTS中存在一种肿瘤细胞亚群，具有转移性特征，如去分化或EMT，这代表了LNM的主要群体。使用基于QD的技术，我们将检测到这一亚群，这将有助于从PT预测LNM和了解转移的生物学特性。(1)利用SCCHN组织标本建立QD相关多抗体(QD-Abs)，用于检测和定量多个生物标记物：根据现有文献以及我们在动物模型和免疫组织化学(IHC)研究中的初步数据，这一特定目标将初步筛选E-钙粘附素、连环素、黏附素、EGFR和整合素等5个与转移相关的生物标记物，并将其抗体与QD偶联。QD-Abs将通过与常规IHC的比较进行验证。初步研究中使用的100个PTSCCHN组织样本(50个LNM阳性和50个LNM阴性)将作为我们的“训练”集。3-5个最具可比性的生物标记物将被确定并选择用于特定目标的进一步研究。(2)验证QD-Abs用于检测和量化人类原发SCCHN组织中选定的生物标记物，并将这些与SCCHN患者的转移和生存相关：另外200个使用与训练集相似的标准选择的样本将被用作我们的“测试”样本，用于验证所建立的QD-Abs系统。我们将尝试识别和量化PT细胞亚群作为转移的高危亚群，并将该亚群与SCCHN患者的淋巴结转移和生存相关联。 公共卫生相关性：可靠地检测LNM对于适当的治疗计划至关重要。该项目旨在开发纳米量子点(Qds)技术来检测多个生物标志物，作为一种预测策略，以帮助从原发肿瘤准确诊断LNM。基于该项目产生的数据，将进一步开发一种新的基于QD的蛋白质检测策略，该策略既可用于临床，也可用于研究实验室。

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.