Kristina R Dahlstrom1, Karen S Anderson2, Matthew S Field2, Diego Chowell2,3, Jing Ning4, Nan Li4, Qingyi Wei5,6, Guojun Li1,7, Erich M Sturgis1,7. 1. Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, Texas. 2. Center for Personalized Diagnostics, Biodesign Institute, Arizona State University, Tempe, Arizona. 3. Simon A. Levin Mathematical, Computational, and Modeling Sciences Center, Arizona State University, Tempe, Arizona. 4. Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas. 5. Duke Cancer Institute, Duke University Medical Center, Durham, North Carolina. 6. Department of Medicine, Duke University School of Medicine, Durham, North Carolina. 7. Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
Abstract
BACKGROUND: Because of the current epidemic of human papillomavirus (HPV)-related oropharyngeal cancer (OPC), a screening strategy is urgently needed. The presence of serum antibodies to HPV-16 early (E) antigens is associated with an increased risk for OPC. The purpose of this study was to evaluate the diagnostic accuracy of antibodies to a panel of HPV-16 E antigens in screening for OPC. METHODS: This case-control study included 378 patients with OPC, 153 patients with nonoropharyngeal head and neck cancer (non-OPC), and 782 healthy control subjects. The tumor HPV status was determined with p16 immunohistochemistry and HPV in situ hybridization. HPV-16 E antibody levels in serum were identified with an enzyme-linked immunosorbent assay. A trained binary logistic regression model based on the combination of all E antigens was predefined and applied to the data set. The sensitivity and specificity of the assay for distinguishing HPV-related OPC from controls were calculated. Logistic regression analysis was used to calculate odds ratios with 95% confidence intervals for the association of head and neck cancer with the antibody status. RESULTS: Of the 378 patients with OPC, 348 had p16-positive OPC. HPV-16 E antibody levels were significantly higher among patients with p16-positive OPC but not among patients with non-OPC or among controls. Serology showed high sensitivity and specificity for HPV-related OPC (binary classifier: 83% sensitivity and 99% specificity for p16-positive OPC). CONCLUSIONS: A trained binary classification algorithm that incorporates information about multiple E antibodies has high sensitivity and specificity and may be advantageous for risk stratification in future screening trials. Cancer 2017;123:4886-94.
BACKGROUND: Because of the current epidemic of human papillomavirus (HPV)-related oropharyngeal cancer (OPC), a screening strategy is urgently needed. The presence of serum antibodies to HPV-16 early (E) antigens is associated with an increased risk for OPC. The purpose of this study was to evaluate the diagnostic accuracy of antibodies to a panel of HPV-16 E antigens in screening for OPC. METHODS: This case-control study included 378 patients with OPC, 153 patients with nonoropharyngeal head and neck cancer (non-OPC), and 782 healthy control subjects. The tumor HPV status was determined with p16 immunohistochemistry and HPV in situ hybridization. HPV-16 E antibody levels in serum were identified with an enzyme-linked immunosorbent assay. A trained binary logistic regression model based on the combination of all E antigens was predefined and applied to the data set. The sensitivity and specificity of the assay for distinguishing HPV-related OPC from controls were calculated. Logistic regression analysis was used to calculate odds ratios with 95% confidence intervals for the association of head and neck cancer with the antibody status. RESULTS: Of the 378 patients with OPC, 348 had p16-positive OPC. HPV-16 E antibody levels were significantly higher among patients with p16-positive OPC but not among patients with non-OPC or among controls. Serology showed high sensitivity and specificity for HPV-related OPC (binary classifier: 83% sensitivity and 99% specificity for p16-positive OPC). CONCLUSIONS: A trained binary classification algorithm that incorporates information about multiple E antibodies has high sensitivity and specificity and may be advantageous for risk stratification in future screening trials. Cancer 2017;123:4886-94.
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