Prediction of Liver Transplant Rejection with a Biologically Relevant Gene Expression Signature.

Background Noninvasive biomarkers distinguishing early immune activation before acute rejection (AR) could more objectively inform immunosuppression management in liver transplant recipients (LTR). We previously reported a genomic profile distinguishing LTR with AR vs. stable graft function.1 This current study includes key phenotypes with other causes of graft dysfunction and utilizes a novel random forest approach to augment the specificity of predicting and diagnosing AR. Methods Gene expression results in LTR with AR vs. non-AR (combination of other causes of graft dysfunction and normal function) were analyzed from single and multicenter cohorts. A 70:30 approach (61 AR; 162 non-AR) was used for training and testing sets. Microarray data was normalized utilizing a liver transplant-specific vector. Results Random forest modeling on the training set generated a 59-probe classifier distinguishing AR vs. non-AR (AUC 0.83; accuracy 0.78, sensitivity 0.70, specificity 0.81, PPV 0.54, NPV 0.89; F-score .61). Using a locked threshold, the classifier performed well on the testing set (accuracy 0.72, sensitivity 0.67, specificity 0.73, PPV 0.48, NPV 0.86; F-score 0.56). Probability scores increased in samples preceding AR vs. non-AR, when liver function tests were normal, and decreased following AR treatment (p<0.001). Ingenuity Pathway Analysis of the genes revealed a high percentage related to immune responses and liver injury. Conclusions We have developed a blood-based biologically relevant biomarker that can be detected prior to AR-associated graft injury distinct from LTR never developing AR. Given its high NPV ("rule out AR"), the biomarker has the potential to inform precision-guided immunosuppression minimization in LTR.