 Abstract The diagnosis system for allograft loss lacks accurate individual risk stratification on the basis of donor-specific anti-HLA antibody (anti-HLA DSA) characterization. We investigated whether systematic monitoring of DSA with extensive characterization increases performance in predicting kidney allograft loss. This prospective study included 851 kidney recipients transplanted between 2008 and 2010 who were systematically screened for DSA at transplant, 1 and 2 years post-transplant, and the time of post-transplant clinical events. We assessed DSA characteristics and performed systematic allograft biopsies at the time of post-transplant serum evaluation. At transplant, 110 (12.9%) patients had DSAs; post-transplant screening identified 186 (21.9%) DSA-positive patients. Post-transplant DSA monitoring improved the prediction of allograft loss when added to a model that included traditional determinants of allograft loss (increase in c statistic from 0.67; 95% confidence interval [95% CI], 0.62 to 0.73 to 0.72; 95% CI, 0.67 to 0.77). Addition of DSA IgG3 positivity or C1q binding capacity increased discrimination performance of the traditional model at transplant and post-transplant. Compared with DSA mean fluorescence intensity, DSA IgG3 positivity and C1q binding capacity adequately reclassified patients at lower or higher risk for allograft loss at transplant (category-free net reclassification index, 1.30; 95% CI, 0.94 to 1.67; P<0.001 and 0.93; 95% CI, 0.49 to 1.36; P<0.001, respectively) and post-transplant (category-free net reclassification index, 1.33; 95% CI, 1.03 to 1.62; P<0.001 and 0.95; 95% CI, 0.62 to 1.28; P<0.001, respectively). Thus, pre- and post-transplant DSA monitoring and characterization may improve individual risk stratification for kidney allograft loss. 
Abstract The purpose of the present review is to describe how we improve the model for risk stratification of transplant outcomes in kidney transplantation by incorporating the novel insights of donor-specific anti-HLA antibody (DSA) characteristics. The detection of anti-HLA DSA is widely used for the assessment of pre- and posttransplant risks of rejection and allograft loss; however, not all anti-HLA DSA carry the same risk for transplant outcomes. These antibodies have been shown to cause a wide spectrum of effects on allografts, ranging from the absence of injury to indolent or full-blown acute antibody-mediated rejection. Consequently, the presence of circulating anti-HLA DSA does not provide a sufficient level of accuracy for the risk stratification of allograft outcomes. Enhancing the predictive performance of anti-HLA DSA is currently one of the most pressing unmet needs for facilitating individualized treatment choices that may improve outcomes. Recent advancements in the assessment of anti-HLA DSA properties, including their strength, complement-binding capacity, and IgG subclass composition, significantly improved the risk stratification model to predict allograft injury and failure. Although risk stratification based on anti-HLA DSA properties appears promising, further specific studies that address immunological risk stratification in large and unselected populations are required to define the benefits and cost-effectiveness of such comprehensive assessment prior to clinical implementation. 
Abstract Antibody-mediated rejection (AMR) contributes to heart allograft loss. However, an important knowledge gap remains in terms of the pathophysiology of AMR and how detection of immune activity, injury degree, and stage could be improved by intragraft gene expression profiling. We prospectively monitored 617 heart transplant recipients referred from 4 French transplant centers (January 1, 2006-January 1, 2011) for AMR. We compared patients with AMR (n=55) with a matched control group of 55 patients without AMR. We characterized all patients using histopathology (ISHLT [International Society for Heart and Lung Transplantation] 2013 grades), immunostaining, and circulating anti-HLA donor-specific antibodies at the time of biopsy, together with systematic gene expression assessments of the allograft tissue, using microarrays. Effector cells were evaluated with in vitro human cell cultures. We studied a validation cohort of 98 heart recipients transplanted in Edmonton, AB, Canada, including 27 cases of AMR and 71 controls. A total of 240 heart transplant endomyocardial biopsies were assessed. AMR showed a distinct pattern of injury characterized by endothelial activation with microcirculatory inflammation by monocytes/macrophages and natural killer (NK) cells. We also observed selective changes in endothelial/angiogenesis and NK cell transcripts, including CD16A signaling and interferon-γ-inducible genes. The AMR-selective gene sets accurately discriminated patients with AMR from those without and included NK transcripts (area under the curve=0.87), endothelial activation transcripts (area under the curve=0.80), macrophage transcripts (area under the curve=0.86), and interferon-γ transcripts (area under the curve=0.84; P<0.0001 for all comparisons). These 4 gene sets showed increased expression with increasing pathological AMR (pAMR) International Society for Heart and Lung Transplantation grade (P<0.001) and association with donor-specific antibody levels. The unsupervised principal components analysis demonstrated a high proportion of molecularly inactive pAMR1(I+), and there was significant molecular overlap between pAMR1(H+) and full-blown pAMR2/3 cases. Endothelial activation transcripts, interferon-γ, and NK transcripts showed association with chronic allograft vasculopathy. The molecular architecture and selective AMR transcripts, together with gene set discrimination capacity for AMR identified in the discovery set, were reproduced in the validation cohort. Tissue-based measurements of specific pathogenesis-based transcripts reflecting NK burden, endothelial activation, macrophage burden, and interferon-γ effects accurately classify AMR and correlate with degree of injury and disease activity. This study illustrates the clinical potential of a tissue-based analysis of gene transcripts to refine diagnosis of heart transplant rejection.  |
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