Novel Panel Of Autoantibodies For The Detection Of Malignant Mesothelioma

BackgroundMalignant mesothelioma (MM) is a rare but extremely aggressive cancer that originates from mesothelial cells of the pleural membranes and peritoneal tissues. Since symptoms are vague, and diagnostic tools are not sensitive and specific enough to detect, the disease is delayed until it reaches advanced stages. The survival rate has been hampered by the lack of efficient and accurate early detection methods with only9-12months survival time. However, the5year survival rate can be increased to40%when it can be diagnosed in early stage. According to the survey by WHO,80%of the disease relates with asbestos and the onset of the disease is delayed for longer than30years beyond asbestos exposure. Since the immune system may detect the early changes of tumor progression by responding with tumor-associated autoantibody production, in this study, we translated the humoral immune response to cancer proteins into a valuable blood test for MM making solid foundation for MM diagnosis, therapy and prognosis.MethodsA T7phage MM cDNA library was constructed using MM tumor tissues and biopanned for tumor-associated antigens (TAAs) using pooled MM patient and normal serum samples. About1000individual phage TAA clones from the biopanned library were subjected to protein microarray construction and tested with53MM and52control serum samples as a training group using Cy5/Cy3double fluorescence system. The classifiers developed from the training group were further validated with50patient and50normal serum samples as an independent blind validation. The phage clones that selected for the classifier development were identified through sequencing and BLASTN.ResultsThe original titer for mesothelioma T7phage cDNA library is5.6X106pfu/mL. PCR results for100random choosing phages showed that95%phages were recombinated with the size of 100-500bp. The titer of the library after biopanning was1.3X103pfu/mL and1008phages were piched for microarray construction. Nine candidate autoantibody markers were selected from the training group using Tclass system and logistic regression statistical analysis, which achieved94.3%sensitivity and90.4%specificity with an AUC value of0.89in Receiver Operating Characteristic analysis. The classifier was further evaluated in the validation tests, and the sensitivity of86.0%and the specificity of86.0%were obtained with an AUC of0.82. Sequencing analysis revealed that five of these nine candidate markers were found to be strongly homology to cancer related proteins (PDIA6, MEG3, SDCCAG3, IGHG3, IGHG1).ConclusionsOur results indicated that the selected classifier of9autoantibody candidate markers achieved much greater accuracies than the current serological biomarkers for MM detection and hence could be a useful tool for early detection, cancer therapy and prognosis.