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Identification of a Gene Signature for Rapid Screening of Oral Squamous Cell Carcinoma

Authors' Affiliations: Departments of ¹ Otorhinolaryngology-Head and Neck Surgery, ² Pathology and Laboratory Medicine, ³ Department of Pathology, School of Dental Medicine, and ⁴ Department of Biostatistics and Epidemiology, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania Health System, Philadelphia, Pennsylvania; ⁵ Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania; and ⁶ Department of Head and Neck Surgery, University of Texas M.D. Anderson Cancer Center, Houston, Texas

Requests for reprints: Barry L. Ziober, Molecular Tumor Biology Laboratory, Department of Otorhinolaryngology-Head and Neck Surgery, University of Pennsylvania Health System, 5 Silverstein/Ravdin, 3400 Spruce Street, Philadelphia, PA 19104. Phone: 215-898-0075; E-mail: bziober{at}mail.med.upenn.edu.

Purpose: Oral cancer is a major health problem worldwide and in the U.S. The 5-year survival rate for oral cancer has not improved significantly over the past 20 years and remains at 50%. Patients diagnosed at an early stage of the disease typically have an 80% chance for cure and functional outcome, however, most patients are identified when the cancer is advanced. Thus, a convenient and an accurate way to detect oral cancer early will decrease patient morbidity and mortality. The ability to noninvasively monitor oral cancer onset, progression, and treatment outcomes requires two prerequisites: identification of specific biomarkers for oral cancers as well as noninvasive access to and monitoring of these biomarkers that could be conducted at the point of care (i.e., practitioner's or dentist's office) by minimally trained personnel.

Experimental Design: Here, we show that DNA microarray gene expression profiling of matched tumor and normal specimens can identify distinct anatomic site expression patterns and a highly significant gene signature distinguishing normal from oral squamous cell carcinoma (OSCC) tissue.

Results: Using a supervised learning algorithm, we generated a 25-gene signature for OSCC that can classify normal and OSCC specimens. This 25-gene molecular predictor was 96% accurate on cross-validation, averaging 87% accuracy using three independent validation test sets and failing to predict non–oral tumors.

Conclusion: Identification and validation of this tissue-specific 25-gene molecular predictor in this report is our first step towards developing a new, noninvasive, microfluidic-based diagnostic technology for mass screening, diagnosis, and treatment of pre-OSCC and OSCC.

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