Browsing by Subject "Protein array"

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Open Access
Development of methods for identification and characterization of autologous antibody responses in Small Cell Lung Cancer and Behcet’s Disease
(2016-08) Poyraz, Alper
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.
Open Access
Identification and utilization of autologous anti-tumor antibodies for the diagnosis and prognosis of cancer
(2015-12) Atakan, Şükrü
Lung cancer is the leading cause of cancer related death worldwide. Current diagnostic methods have limited power and unable to extend patient life significantly. SCLC; the most aggressive subtype of lung cancer is an immunogenic cancer type and able to elicit an immune response of which autologous antibodies are a measurable component. These antibodies are elicited even when the tumor is microscobic and impossible to be diagnosed clinically by the current methods of diagnosis thus antibodies can be utilized for early diagnosis. We aimed to develop a method to identify novel autologous antibodies, identify these antibodies for SCLC, Colorectal, Gastric and Ovarian cancers and validate these antibodies for SCLC diagnosis and prognosis and investigate their utility for autoimmune disease. We have developed and optimized PA screening for novel autologous antibody discovery. We have screened PA with serum pools of cancer patients (SCLC, Colorectal, Gastric and Ovarian), BD and healthy controls since PAs have many advantages compared to other discovery methods like SEREX. We have also performed sensitivity and specificity evaluations by screening custom PAs by individual sera. Image analysis softwares developed by collaboration utilized for evaluation of the screenings. The filtered valuable clones were ordered from the PA manufacturer and HisTagged protein expression and purification was performed with these clones. Pure proteins were screened with 3 independent SCLC and 2 Healthy control cohorts by an iterative ELISA approach for validation of these antibodies as valuable biomarkers. ELISA results were also confirmed by Western blotting. Monte Carlo, SVM and PC were utilized for cut-off determination, panel formation and ROC plotting. AUC was compared for evaluation of diagnostic power. Kaplan-Meier, UCR and MCR analysis was performed for prognostic analysis of the valuable antibodies. Seperately protein expression and autologous antibody presence correlation was evaluated by comparison of IHC and ELISA. The same autologous antibody identification strategy was utilized as a collaborative support for an independent study for identification of NBD specific biomarkers.We have identified 23 distinct autologous antibody biomarkers for SCLC after evaluation of PA and custom PA screenings. For 8 of these antibodies we have completed ELISA screening for all 3 SCLC and 2 healthy control cohorts. 6 of these autologous antibodies were shown to be valuable as a panel for SCLC diagnosis both by MC and SVM. Utilization of 4 of these antibodies; SOX2, p53, POLB and C11orf20, as a panel resulted in superior AUC thus high sensitivity and specificity values (55% sensitivity, 90% specificity). PC method resulted in higher AUC even only by combination of SOX2 and p53 (82% sensitivity, 90% specificity). Although individual correlations were identified, we were unable to show a significant correlation of seropositivity with survival for any of the antibodies which is common to all cohorts. We have identified a significant correlation between SOX2 antigen expression intensity and autologous antibody presence. Mtch1 was identified as a NBD specific autologous antibody by the utilization of our autologous antibody discovery and validation methodology. We were able to identify a panel of 4 antibodies; SOX2, p53, POLB and C11orf20, which resulted in 55% sensitivity at 90% specificity for SCLC. 2 of these antibodies were identified by this study as novel biomarkers; POLB and C11orf20. The panel is capable of exceeding the diagnostic power of the only commercially available diagnostic kit; EarlyCDT-Lung. PC method is very promising since a sensitivity value of 82% was reached at 90% specificity which is a diagnostic power comparable that of low-dose CT. As a future perspective we are planning to apply PC method to all the PA data and develop a kit based on this method to be utilized for SCLC diagnosis.