Molecular Profiling of Premalignant Oral Lesions

描述(由申请人提供)：头颈部鳞状细胞癌(HNSCC)是影响现在和以前的烟草使用者的主要健康问题，每年在美国约有40,000例病例，全球有500,000例。HNSCC的发展是粘膜内发育不良病变的先兆，已知癌前病变的早期诊断与生存率的增加直接相关。然而，目前对癌前疾病的诊断技术，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。由于形态或细胞学改变可能发生在转化过程的晚期，组织学上看起来良性的病变可能具有恶性潜能。相反，即使是严重的口腔发育不良病变也可以自发消退。因此，组织学结果不能清楚地预测恶变。需要一种在早期阶段检测分子癌前病变并预测恶性进展的可能性的方法。 这项提议的目标是确定一组生物标记物，以便识别分子上的癌前病变。因此，将对来自非吸烟者和侵袭性HNSCC的含有正常粘膜组织的活检组织中的微切割角质形成细胞进行基因表达谱分析，以确定潜在的口腔黏膜进展标记(OPM)。对阵列数据的解释和分析将在新成立的生物信息学核心内进行。这一可通过网络访问的注释和编目设施将同时使用无监督和有监督的学习方法。然后，将使用具有不同组织学诊断的定制人体组织阵列样本来验证这些潜在的OPM的有效性，这些组织诊断包括：正常、反应性、过度角化、增生和各种级别的异型增生。这些分析可以确定与异型增生的生物学严重程度和恶性进展的可能性相关的标志物。与病理学家目前使用的细胞模式识别相比，这种分子分析还可以极大地提高诊断预测能力。

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.