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.
In this proposal a gene signature that can predict of osee lymph node disease will be identified by ?eparately acquiring gene signatures from the osee neoplastic and the stromal components of lymph node negative and positive osee p~imary tumors.
