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

描述（由申请人提供）：本项目旨在开发一种基于蛋白质的分子预测策略，使用纳米颗粒量子点（QD）辅助准确诊断原发性肿瘤（PT）的淋巴结转移（LNM）。这是癌症检测、诊断和预后探索性研究的适当应用（PA-06-299）。本项目将解决两个问题。首先，我们想确定是否可以使用与上皮-间质转化（EMT）相关的基于蛋白质的生物标志物来鉴定PT中具有高转移风险的肿瘤细胞亚群。第二，我们将解决是否利用量子点技术同时检测多个生物标志物可以促进准确预测LNM从SCCHN PT。虽然PT中预先存在转移性细胞的概念已提出多年，但这些细胞尚未在人体组织中明确识别。由于蛋白质表达与肿瘤细胞生物学行为的相关性比基因表达更大，因此利用多个蛋白质作为生物标志物预测淋巴结转移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机制。最近发展的基于量子点的成像可以同时检测和定量多蛋白质，为这项研究提供了一个独特的工具。基于我们的初步研究结果，我们假设在携带转移特征的PT中存在肿瘤细胞亚群，例如去分化或EMT，其代表LNM的主要群体。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和理解转移的生物学。两个具体目标将验证这一假设：（1）开发QD连接的多抗体（QD-Abs），用于检测和定量使用SCCHN组织样本的多生物标志物：基于当前文献和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，本具体目标将初步选择5种转移相关的生物标志物，包括E-钙粘蛋白、<$-连环蛋白、dysadherin、EGFR和整合素。1，并将它们的抗体与量子点缀合。QD-Ab将通过与常规IHC进行比较进行验证。与初步研究中使用的相同的100个PT SCCHN组织样本（50个LNM阳性和50个LNM阴性PT）将作为我们的“训练”集。将确定并选择3-5种最佳可比生物标志物用于特定目标2中的进一步研究。(2)为了验证QD-Abs用于检测和定量人原发性SCCHN组织中选定的生物标志物并将其与SCCHN患者的转移和存活相关联：使用与训练集相似的标准选择的另外200个样品将用作我们的“测试”样品，用于验证已建立的QD-Abs系统。我们将尝试识别和量化PT细胞的亚群作为转移的高风险，并将该亚群与LNM和SCCHN患者的生存相关联。 公共卫生相关性：LNM的可靠检测对于制定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，以检测多个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。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，可用于临床和研究实验室。

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.