Clinical and Molecular Profiling to Develop a Potential Prediction Model for the Response to Alemtuzumab Therapy for Acute Kidney Transplant Rejection

Abstract

Alemtuzumab, a monoclonal antibody that depletes CD52-bearing immune cells, is an effective drug for the treatment of severe or glucocorticoid-resistant acute kidney transplant rejection (AR). Patient-specific predictions on treatment response are, however, urgently needed, given the severe side effects of alemtuzumab. This study developed a multidimensional prediction model with the aim of generating clinically useful prognostic scores for the response to alemtuzumab. Clinical and histological characteristics were collected retrospectively from patients who were treated with alemtuzumab for AR. In addition, targeted gene expression profiling of AR biopsy tissues was performed. Least absolute shrinkage and selection operator (LASSO) logistic regression modeling was used to construct the ALEMtuzumab for Acute Rejection (ALEMAR) prognostic score. Response to alemtuzumab was defined as patient and allograft survival and at least once an estimated glomerular filtration rate (eGFR) > 30 mL/min/1.73 m2 during the first 6 months after treatment. One hundred fifteen patients were included, of which 84 (73%) had a response to alemtuzumab. The ALEMAR-score accurately predicted the chance of response. Gene expression analysis identified 13 differentially expressed genes between responders and nonresponders. The combination of the ALEMAR-score and selected genes resulted in improved predictions of treatment response. The present preliminary prediction model is potentially helpful for the development of stratified alemtuzumab treatment for acute kidney transplant rejection but requires validation.