Development of methods for identification and characterization of autologous antibody responses in Small Cell Lung Cancer and Behcet’s Disease

Abstract

Autologous antibodies are known to be elicited in Behçet’s Disease (BD) and Small Cell Lung Cancer (SCLC). SCLC consists 15-20% of all lung cancer cases. It is follows a most aggressive course and generally patients are diagnosed at later stages. The median survival of patients is 9-12 months. Diagnostic methods such as CT and PET are somewhat useful in the diagnosis of lung cancer but not so much for SCLC as the doubling time of this tumor is very rapid. Therefore, new diagnostic tools are needed for early diagnosis and to increase median survival of patients. Behçet’s Disease is autoimmune disease and the prevalence of BD in Turkey is the highest in the world. Also autologous antibodies against various antigens associated with BD have been discovered in BD. BD has vascular, oral, cutaneous and neuronal subtypes and autologous antibodies correlating with each subtype have been reported. However, for BD, there does not exist a diagnostic or prognostic test as none have been developed yet. However autoantibodies can be utilized for the diagnosis and follow-up of SCLC and BD because it is known that autoantibodies are expressed well in advance of disease symptoms. The first aim of this study was to determine a correlation between antigen expression levels in tumor tissues and the presence of autologous antibodies. The second aim of this study was to extend earlier experiments related to the characterization of autologous antibodies against known and novel antigens in SCLC and BD, utilizing high-density protein arrays (PA). The third and major aim of this study was to develop a reliable and sensitive method that could be used to evaluate protein array screening results and lastly, to validate these results by performing optimized ELISA and Western Blot experiments. Previously, PAs were screened with 50 SCLC, 50 BD and 50 healthy serums in our laboratory, and evaluated visually utilizing no automation. Sensitivity and specificity values were calculated using custom-generated antigen panels which included 180 antigens. ELISA experiments were performed to validate antigens thus discovered. However, largely discrepant PA and ELISA results, together with inconsistent ELISA results required us to optimize ELISA conditions, as well as to generate an automated PA evaluation method that would generate numeric data. We modified ELISA by altering various parameters until we were able to obtain consistent results. We also generated a reliable method by which we could produce numeric data corresponding to antibody presence as determined from PA screening results. The method is based on the calculation of pixel intensities of sero-reactive clones on the array which are converted to numeric data, and the subsequent determination of proper cut-offs by which sensitivity and specificity of antibody responses can be generated by comparing values obtained from healthy to those obtained from diseased serum. We call this the “Digital Spot Evaluation” (DSE) tool. DSE was performed utilizing Adobe Photoshop CS6 and parameters of the test were optimized using five replicate screens of a given serum. Pearson’s r correlation values of repeated experiments after optimization were close to 1. Also, when protein arrays are screened using DSE on different days by different researchers, results are highly concordant. We evaluated protein array screening data obtained for SCLC and healthy sera by DSE. In particular, antibody intensities against SOX2, p53 and POLB proteins were calculated and sensitivity/specificity values were determined. With DSE based evaluation of protein arrays, we reached 44%, 6% and 20% sensitivity at 100% specificity for SOX2, p53 and POLB proteins respectively. On the other hand if we evaluate 3 proteins together as a panel, our sensitivity increases to 56% at 100% specificity, and 66% sensitivity at 96% specificity. However, even after optimization, ELISA results showed 32%, 4% and 4% sensitivity at 100% specificity for SOX2, p53 and POLB proteins respectively, demonstrating that DSE is significantly more sensitive than ELISA. We are planning to use DSE to evaluate PA data generated from many other types of tumors in the future and to and possibly to develop a kit based on this method to be utilized for the diagnosis and follow-up of SCLC and BD.