title: Autoantibody approach for serum-based detection of head and neck cancer.
abstract: 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.
authors.affiliation: Department of Surgery, John D. Dingell VA Medical Center, Wayne State University, Detroit, Michigan 48201, USA. hlin@med.wayne.edu
authors.forename: Ho-Sheng
authors.initials: HS
authors.lastname: Lin
authors.forename: Harvinder S
authors.initials: HS
authors.lastname: Talwar
authors.forename: Adi L
authors.initials: AL
authors.lastname: Tarca
authors.forename: Alexei
authors.initials: A
authors.lastname: Ionan
authors.forename: Madhumita
authors.initials: M
authors.lastname: Chatterjee
authors.forename: Bin
authors.initials: B
authors.lastname: Ye
authors.forename: Jerzy
authors.initials: J
authors.lastname: Wojciechowski
authors.forename: Saroj
authors.initials: S
authors.lastname: Mohapatra
authors.forename: Marc D
authors.initials: MD
authors.lastname: Basson
authors.forename: George H
authors.initials: GH
authors.lastname: Yoo
authors.forename: Brian
authors.initials: B
authors.lastname: Peshek
authors.forename: Fulvio
authors.initials: F
authors.lastname: Lonardo
authors.forename: Chuan-Ju G
authors.initials: CJ
authors.lastname: Pan
authors.forename: Adam J
authors.initials: AJ
authors.lastname: Folbe
authors.forename: Sorin
authors.initials: S
authors.lastname: Draghici
authors.forename: Judith
authors.initials: J
authors.lastname: Abrams
authors.forename: Michael A
authors.initials: MA
authors.lastname: Tainsky
mesh_terms: D001323:Autoantibodies / Q000097:blood* / Q000276:immunology; D017123:Bacteriophage T7 / Q000235:genetics; D014408:Biomarkers, Tumor / Q000097:blood* / Q000276:immunology; D002294:Carcinoma, Squamous Cell / Q000097:blood / Q000175:diagnosis* / Q000276:immunology; D000939:Epitopes / Q000276:immunology; D005260:Female; D015723:Gene Library; D006258:Head and Neck Neoplasms / Q000097:blood / Q000175:diagnosis* / Q000276:immunology; D006801:Humans; D007074:Immunoglobulin G / Q000276:immunology; D008297:Male; D016571:Neural Networks, Computer; D040081:Protein Array Analysis / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D001323:Autoantibodies; D014408:Biomarkers, Tumor; D000939:Epitopes; D007074:Immunoglobulin G