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 crossvalidation 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)
Development of humoral and cellular immunity against self-cellular proteins in cancer patients is a phenomenal observation. The ability of immune system to sense the presence of the disease and to fight of the disease by generating autoantibodies against tumor antigens makes it a natural biosensor. Several screening technologies have been employed for the identification of tumor-specific antibodies in cancer patients. We have developed a multidimensional approach for the identification of diagnostic antigens that utilizes a combination of high-throughput antigen cloning and protein microarray-based serological detection of complex panels of antigens by exploiting the serum autoantibody repertoire directed toward tumor-associated antigens in cancer patients. Furthermore, validation of these antigens by different bioinformatics and biological approaches will reveal the diagnostic/prognostic utility of these antigens for personalized immunotherapy.
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