Molecular Profiling of Premalignant Oral Lesions

描述（由申请人提供）：头颈部鳞状细胞癌（HNSCC）是影响当前和以前烟草使用者的主要健康问题，在美国每年约有40,000例，全世界约有500,000例。恶性鳞状细胞癌的发展是由粘膜内发育不良病变的发展所预示的，并且已知恶性前病变的早期诊断与生存率的增加直接相关。然而，目前对癌前病变的诊断技术，基于对核和细胞异型性的识别，对最终临床结果的预测相对较差。由于形态学或细胞学改变可能发生在转化过程的后期，组织学上良性的病变可能有恶性的潜在。相反，即使是严重发育不良的口腔病变也会自发消退。因此，组织学表现不能明确预测恶性改变。需要一种在早期阶段检测分子癌前病变和预测恶性进展可能性的方法。

DESCRIPTION (provided by applicant): Head and neck squamous cell carcinoma (HNSCC) is a major health problem affecting current and former tobacco users, with approximately 40,000 cases per year in the United States and 500,000 cases worldwide. The development of HNSCC is heralded by the development of dysplastic lesions within the mucosa and early diagnosis of pre-malignant lesions is known to directly correlate with increased survival. However, current diagnostic techniques for premalignant disease, based on recognition of nuclear and cellular atypia, are relatively poor predictors of ultimate clinical outcome. Since morphological or cytological changes may occur late in the process of transformation, histologically benign appearing lesions may have malignant potential. Conversely, even severely dysplastic oral lesions can undergo spontaneous regression. Thus, histological findings cannot clearly predict malignant change. A method for detecting molecularly premalignant lesions at an early stage and for predicting the likelihood of malignant progression is required. The goal of this proposal is to identify a panel of biomarkers that will allow for the identification of molecularly premalignant lesions. Accordingly, gene expression profiling of microdissected keratinocytes from biopsies containing normal mucosal tissue from nonsmokers and invasive HNSCC will be performed in order to identify potential oral mucosa progression markers (OPM). The interpretation and analysis of the array data will be performed within a newly established Bioinformatics Core. This web-accessible annotation, cataloging facility will use both unsupervised and supervised learning methodologies. The utility of these potential OPMs will then be validated using custom human tissue array samples having the different histologic diagnoses including: normal, reactive, hyperkeratosis, hyperplasia and various grades of dysplasia. These analyses may identify markers that correlate with the biologic severity of dysplasia and with the potential for malignant progression. This molecular analysis may also greatly improve diagnostic prediction compared to the cellular pattern recognition currently used by pathologists.