Abstract
Cystic fibrosis (CF) is an autosomal recessive disorder affecting the cystic fibrosis transmembrane conductance regulator (CFTR). CF is characterized by repeated lung infections leading to respiratory failure. Using a high-throughput method, we developed a T7 phage display cDNA library derived from mRNA isolated from bronchoalveolar lavage (BAL) cells and leukocytes of sarcoidosis patients. This library was biopanned to obtain 1070 potential antigens. A microarray platform was constructed and immunoscreened with sera from healthy (n = 49), lung cancer (LC) (n = 31) and CF (n = 31) subjects. We built 1,000 naïve Bayes models on the training sets. We selected the top 20 frequently significant clones ranked with student t-test discriminating CF antigens from healthy controls and LC at a False Discovery Rate (FDR) < 0.01. The performances of the models were validated on an independent validation set. The mean of the area under the receiver operating characteristic (ROC) curve for the classifiers was 0.973 with a sensitivity of 0.999 and specificity of 0.959. Finally, we identified CF specific clones that correlate highly with sweat chloride test, BMI, and FEV1% predicted values. For the first time, we show that CF specific serological biomarkers can be identified through immunocreenings of a T7 phage display library with high accuracy, which may have utility in development of molecular therapy.