Molecular Analysis of OSCC Tumor Invasion

结节阳性口腔鳞癌患者的5年生存率仅为20%-30% 即使是综合治疗也是如此。不幸的是，目前的分期系统不能预测osee肿瘤。 结节疾病或患者预后，并且没有单一基因被证明具有足够的预后作用。 此外，最先进的临床成像技术可能会漏掉亚临床转移。发现更多 准确而有力地预测淋巴结阳性原发肿瘤和患者预后的方法将 需要对肿瘤微环境中表达的基因进行鉴定和表征。vbl.使用 Affymetrix微阵列和包含肿瘤和间质成分的肿瘤标本， 我们已经发现了一种基因标记，它可以预测哪些肿瘤会转移到 淋巴结。在本方案中，我们将使用该OSEE初步节点正签名进行比较， 检验以下假设：预测OCEE淋巴结病的更准确和可靠的信号 可以通过分别获取OCEE肿瘤和间质的基因签名来识别 原发癌淋巴转移阴性和阳性的成分。在AIM-1中，我们将使用微阵列， 激光捕获显微切割(LEM)和统计以识别OSEE肿瘤细胞成分节点 阳性信号采用OEE淋巴结阳性和阴性原发肿瘤。在AIM-2中，我们将使用 微阵列、LEM和统计学，以定义OSEE间质间隔的节点阳性信号 采用OEE淋巴结阳性和阴性原发肿瘤。两个分子签名都将得到验证 在RNA和蛋白质水平上。此外，每个标记都将被测试其预测结节疾病的能力 使用盲人。骨肉瘤的队列。最后，所有的签名将使用统计平台进行比较， 将使用相同的OSEE队列来选择能够预测节点阳性的最佳基因集 肿瘤。这项研究将涉及具有DNA和组织专业知识的多学科合作者团队 微阵列、激光捕获显微解剖、肿瘤与分子生物学、统计学、医学、头颈部 外科、病理学以及多机构合作。除了确定最适合的签名 OCEE结节病的预测，这项研究的结果将识别基因、生物标志物和信号 肿瘤微环境中可作为诊断、预后和治疗靶点的通路 学习。这项建议中确定的准确和可靠的淋巴转移基因签名将具有 对降低OSEE患者死亡率和发病率具有深远的临床效用。

Patients with node positive oral squamous cell carcinomas (OSee) have a 5-year survival of only 20-30% even with multi-modality treatment. Unfortunately, the current staging system does not predict osee tumor nodal disease or patient outcome, and no single gene has been shown to have sufficient prognostic utility. Moreover, state-of-the-art clinical imaging techniques can miss sub-clinical metastases. Discovering a more accurate and powerful way of predicting lymph node positive primary tumors and patient outcomes will require identification and characterization of the genes expressed within the tumor microenvironment. Using Affymetrix microarrays and tumor specimens comprised of both osee neoplastic and stromal components, we have discovered a gene signature that predicts which osee primary tumors will metastasize to the lymph nodes. In this proposal, using this osee preliminary node positive signature for comparison, we will test the hypothesis that a more accurate and reliable signature for prediction of osee lymph node disease can be identified by separately acquiring gene signatures from the osee neoplastic and the stromal components of lymph node negative and positive osee primary tumors. In Aim-1 we will use microarrays, laser capture microdissection (LeM), and statistics to identify the osee Neoplastic cell component node positive signature using osee lymph node positive and negative primary tumors. In Aim-2 we will use microarrays, LeM and statistics to define a node positive signature for the osee Stromal compartment using osee lymph node positive and negative primary tumors. Both molecular signatures will be validated at the RNA and protein level. Additionally, each signature will be tested for its ability to predict nodal disease using a blinded. cohort of osee tumors. Finally, all signatures will be compared using statistical platforms, and the best set of genes will be selected that can predict node positivity using the same cohort of osee tumors. This study will involve a multi-disciplinary team of collaborators with expertise in DNA and tissue microarrays, laser capture microdissection, tumor and molecular biology, statistics, medicine, head and neck surgery, and pathology as well as multi-institutional collaborations. Besides identifying the best signature for prediction of osee nodal disease, the results from this study will identify genes, biomarkers and signaling pathways in the tumor microenvironment that can be targeted for diagnostic, prognostic and therapeutic studies. The accurate and reliable lymph node metastasis gene signature identified in this proposal will have a profound clinical utility for reducing osee patient mortality and morbidity.