Squamous cell carcinoma of the head and neck (SCCHN) frequently metastasizes to the regional lymphatics and this is the best predictor of disease prognosis and outcome. Accurate staging of lymph nodes in the neck is essential to optimal patient management, but current clinical methods are inadequate, and misdiagnose the presence or absence of cervical nodal metastasis in many patients. Consequently, the current standard of care, elective neck dissection, may represent overtreatment of over 50% of clinically tumor-negative patients. Sentinel lymph node (SLN) biopsy may obviate this problem, and is currently undergoing a multi-center validation trial. One problem with this approach is the current lack of a highly accurate, rapid (intraoperative) assay to assess the SLN. Frozen section analysis is only 60-80% sensitive, and false negatives due to frozen section analysis would frequently result in second procedures when final histology detects metastatic disease. Re-operation on such SLN biopsied necks is likely to be poorly received by both patients and surgeons, due to increased technical difficulty, morbidity of a second surgery and anesthetic, anxiety due to diagnostic uncertainty and increased costs. In addition, 7-10% of pathologically tumor negative patients manifest tumor recurrence, possibly indicating the presence of pathologically occult disease. We hypothesize that quantitative RT-PCR (qRT-PCR) can overcome these problems and can provide accurate, definitive staging of the neck in an intraoperative time frame. In preliminary studies, we have identified outstanding markers for qRT-PCR in SCCHN. We have also developed methods to facilitate rapid, internally controlled and automated qRT-PCR in a clinical setting. In the current proposal, we will demonstrate that our fully automated qRT-PCR assays can be optimized for use in an intraoperative time frame, and that they can be performed in a standardized and reproducible manner. These assays should prove to be useful tools for staging of lymph nodes from SCCHN patients.
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