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.

Figure 1

Penalization and shrinkage of predictor variables with LASSO method. LASSO method was used for shrinkage and selection of variables to include in the prediction model for patient specific prognosis on alemtuzumab response. (a) Two tuning parameters were tested corresponding to the minimal cross validated error (lambda.min) and to a value of 1 standard error (SE) above the minimum (lambda.1SE), as shown by the left and right dotted vertical lines respectively. (b) The shrinkage factor (s = 0.44) corresponding to lambda.min resulted in exclusion of 5 variables (grey), the other 10 variables remained in the model. Positive variables (red) give a higher risk of non‐response to alemtuzumab, while negative variables (green) give a lower risk of nonresponse. (c) The shrinkage factor (s = 0.11) corresponding to lambda.1SE resulted in inclusion of 3 variables. Other variables were shrunken to zero (grey). LASSO, least absolute shrinkage and selection operator. [Colour figure can be viewed at wileyonlinelibrary.com]

Figure 2

Gene expression profiling using NanoString Technology – Unsupervised clustering and Differential expression of genes. Gene expression profiling using the Banff‐Human Organ Transplant (B‐HOT) panel of NanoString Technology. (a) Unsupervised hierarchical clustering of the normalized data of the 758 genes measured in biopsy samples collected from alemtuzumab‐treated patients (n = 63). The unsupervised clustering did not separate responders from nonresponders. (b) Volcano plot of differential gene expression (DE) shows multiple genes that were different between responders compared to nonresponders (baseline). The degree of statistical significance according to Benjamini‐Hochberg adjusted P values (adj. P value) is indicated with horizontal lines. [Colour figure can be viewed at wileyonlinelibrary.com]

Figure 3

B‐cell receptor signaling score. NanoString pathway analysis for B‐cell receptor signaling (BCR) related genes calculates a score for each patient based on the overall expression of BCR genes. (a) This BCR score was significantly higher in nonresponders compared with responders (P = 0.006). (b) A significant association was found between this BCR score and the timing of acute rejection (AR) irrespective of response to alemtuzumab (nonresponders: early vs. late P < 0.001, responders: early vs. late P = 0.030). Among the patients with late rejections, nonresponders had a significantly higher BCR score compared with responders (P = 0.033). [Colour figure can be viewed at wileyonlinelibrary.com]

Figure 4

Performance of the mRNA LASSO model. The main model using the reduced cohort had an AUC/c‐index of 0.855. The discrimination of the mRNA LASSO model—that includes mRNA markers and the ALEMAR score as predictors—was excellent (AUC/c‐index = 0.918). The ΔAUC of the mRNA model is 0.063. *AUC, area under the curve. ALEMAR, ALEMtuzumab for Acute Rejection; LASSO, least absolute shrinkage and selection operator. [Colour figure can be viewed at wileyonlinelibrary.com]