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

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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.