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ASCO Educational Book; 2008
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Identification of High-risk Cohorts for Head and Neck Cancer Prevention Based on Tissue Markers LOH and cyclin D1

William N. William Jr., MD, and Vassiliki A. Papadimitrakopoulou, MD

From the Department of Thoracic/Head and Neck Medical Oncology, The University of Texas M.D. Anderson Cancer Center, Houston, Texas

Authors’ disclosures of potential conflicts of interest are found at the end of this article.

Address reprint requests to William N. William Jr., MD, 1515 Holcombe Blvd., Unit 432, Houston, TX 77030; e-mail: wnwillia{at}mdanderson.org

Overview: To proceed with rational chemoprevention trials with novel agents, head and neck cancer must have a more precise definition. The oncology community has realized that oral carcinogenesis is a multifocal and multiclonal process even within the same lesion, not all intraepithelial neoplasias progress to cancer or can be readily detected and measured, and no specific drug can target all genetic changes present in premalignant cells. Allelotyping of oral premalignant lesions has revealed that alterations in multiple loci occur early during the carcinogenic process. Loss of heterozygosity (LOH) at 3p and 9p loci, which harbor tumor suppressor genes, is not only frequent in premalignant lesions, but also is a predictor of progression to invasive cancer. Additionally, retinoid prevention studies have highlighted the predictive power of polymorphisms of the cyclin D1 gene in the setting of head and neck premalignacies; these polymorphisms often are used as a marker of resistance and a predictor of shorter time to cancer development. Prospective trials are underway to better characterize the prognostic/predictive values of these markers, including one randomized study of erlotinib for the prevention of oral cancer. The data generated from these research protocols will inform the design of future studies, which should incorporate this information in screening/stratification criteria, to allow for more efficient design and conduct of studies and to develop molecularly based risk models.