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 (LTRs). We previously reported a genomic profile distinguishing LTR with AR versus stable graft function. This current study includes key phenotypes with other causes of graft dysfunction and uses a novel random forest approach to augment the specificity of predicting and diagnosing AR.

Methods: Gene expression results in LTRs with AR versus non-AR (combination of other causes of graft dysfunction and normal function) were analyzed from single and multicenter cohorts. A 70:30 approach (61 ARs; 162 non-ARs) was used for training and testing sets. Microarray data were normalized using a LT-specific vector.

Results: Random forest modeling on the training set generated a 59-probe classifier distinguishing AR versus non-AR (area under the curve 0.83; accuracy 0.78, sensitivity 0.70, specificity 0.81, positive predictive value 0.54, negative predictive value [NPV] 0.89; F-score 0.61). Using a locked threshold, the classifier performed well on the testing set (accuracy 0.72, sensitivity 0.67, specificity 0.73, positive predictive value 0.48, NPV 0.86; F-score 0.56). Probability scores increased in samples preceding AR versus 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 before 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 LTRs.