Profiling Pre-Existing Autoantibodies for Prediction of Primary Graft Dysfunction

Abstract

Purpose Primary Graft Dysfunction (PGD) results in increased mortality and morbidity after lung transplantation (LT). While the role of pre transplant antibodies to self -antigens (AutoAbs) is described, no validated laboratory biomarker panel exists to predict PGD. Our objective was to evaluate effects of pre transplant antibodies to self-antigens (AutoAbs) and examine specific patterns that correlate with PGD. Methods Serum samples collected on the day of transplant prior to surgery and stored in HLA lab were obtained after IRB approval. Pre-transplant AutoAbs were measured using a multiplexed protein array bearing 375 auto antigens developed by Microarray core lab. The score of each AutoAb was calculated based on the signal intensity and signal to noise ratio (SNR) and compared between patients with and without PGD. Kaplan-Meier or Cox proportional hazards model was used to evaluate effects of differential AutoAbs on survival. GLMNET in R package was used for selection and developing a panel for prediction. Results Patients with PGD had lower survival rate, but did not meet statistical significance (p=0.27, figure 1A). 4 AutoAbs, (anti-AchR3, anti-AGTR1, anti-CRP, anti-factor 1) showed significant higher expression in PGD group (fold change > 1.5, p Conclusion Our study reveals a panel of 25 AutoAbs that predict subsequent PGD in LT recipients. While several donor and recipient factors play a role in PGD, prediction model developed with a panel of AutoAbs with high accuracy can make a significant impact in the management of PGD.