Lung cancer-associated auto-antibodies measured using seven amino acid peptides in a diagnostic blood test for lung cancer

Abstract Autoantibody profiling is a developing approach that incorporates immune recognition of myriad aberrant cancer proteins into a single diagnostic assay. We have previously described methodology to screen T7-phage NSCLC-cDNA libraries for phage-expressed proteins recognized by NSCLC-associated antibodies, and developed a multiplex assay that has excellent ability to discriminate NSCLC from control samples. This follow-up report describes the development and testing of a diagnostic autoantibody assay that uses seven amino-acid peptides as capture proteins. A random-peptide M13-phage library was screened for proteins recognized by cancer-associated antibodies. One hundred twenty-one NSCLC case and control samples were divided into two groups for training and validation, or alternately, evaluated sequentially in a leave-one-out analysis. Candidate antibody-markers were ranked by statistical discrimination between cases and controls. Receiver-Operating-Characteristic (ROC-AUC) suggested the predictive potential of various marker combinations. A five-marker combination (AUC=0.982) afforded 90% sensitivity and 73% specificity in a training-and-testing strategy. Leave-one-out validation provided similar class prediction. Data confirm the potential of antibody profiling to provide high levels of cancer prediction. Random peptide libraries offer a universal source of capture proteins for antibody profiling that obviates the need for tumor-specific library construction and abrogates inherent problems with tumor heterogeneity during biomarker discovery.