This Article Full Text Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Lin, H.-S. Articles by Tainsky, M. A. Search for Related Content PubMed PubMed Citation Articles by Lin, H.-S. Articles by Tainsky, M. A. Related Collections Early Detection Early Detection: Biomarkers: Discovery, Methodology, Validation

Autoantibody Approach for Serum-Based Detection of Head and Neck Cancer

¹ Department of Surgery, John D. Dingell VA Medical Center; ² Department of Otolaryngology-Head and Neck Surgery, Wayne State University and Karmanos Cancer Institute; ³ Bioinformatics Core, Barbara Ann Karmanos Cancer Institute; ⁴ Department of Computer Science, Wayne State University; ⁵ Program in Molecular Biology and Genetics, Karmanos Cancer Institute/Wayne State University; Departments of ⁶ Surgery and ⁷ Pathology, Wayne State University; ⁸ Integrated Biostatistics Core, Barbara Ann Karmanos Cancer Institute and Wayne State University, Detroit, Michigan

Requests for reprints: Ho-Sheng Lin, Department of Otolaryngology/Head and Neck Surgery, Wayne State University, 5 East University Health Center, 4201 St. Antoine, Detroit, MI 48201. Phone: 313-577-0805; Fax: 313-577-8555. E-mail: hlin{at}med.wayne.edu

Currently, no effective tool exists for screening or early diagnosis of head and neck squamous cell carcinoma (HNSCC). Here, we describe an approach for cancer detection based on analysis of patterns of serum immunoreactivity against a panel of biomarkers selected using microarray-based serologic profiling and specialized bioinformatics. We biopanned phage display libraries derived from three different HNSCC tissues to generate 5,133 selectively cloned tumor antigens. Based on their differential immunoreactivity on protein microarrays against serum immunoglobulins from 39 cancer and 41 control patients, we reduced the number of clones to 1,021. The performance of a neural network model (Multilayer Perceptron) for cancer classification on a data set of 80 HNSCC and 78 control samples was assessed using 10-fold cross-validation repeated 100 times. A panel of 130 clones was found to be adequate for building a classifier with sufficient sensitivity and specificity. Using these 130 markers on a completely new and independent set of 80 samples, an accuracy of 84.9% with sensitivity of 79.8% and specificity of 90.1% was achieved. Similar performance was achieved by reshuffling of the data set and by using other classification models. The performance of this classification approach represents a significant improvement over current diagnostic accuracy (sensitivity of 37% to 46% and specificity of 24%) in the primary care setting. The results shown here are promising and show the potential use of this approach toward eventual development of diagnostic assay with sufficient sensitivity and specificity suitable for detection of early-stage HNSCC in high-risk populations. (Cancer Epidemiol Biomarkers Prev 2007;16(11):2396–405)