Abecassis, Michael M.

Background. Long-term outcomes in kidney transplantation remain suboptimal, with subclinical acute rejection (subAR) frequently going undetected by conventional methods. We hypothesized that subAR represents a molecular precursor to clinical acute rejection (cAR). Leveraging a large National Institutes of Health-funded cohort with matched kidney tissue and peripheral blood samples, our primary objective was to determine whether subAR and cAR share molecular patterns, establishing subAR as an early stage of cAR. As a secondary aim, we compared biopsy and blood gene expression profiles to identify unique and shared pathways and to assess their diagnostic utility. Methods. This was a retrospective analysis of gene expression data from an observational clinical trial. We analyzed 578 blood and biopsy samples from 129 kidney transplant recipients enrolled in a multicenter observational study. Gene expression differences were analyzed using ANOVA with false discovery rate correction and molecular pathways analysis. Samples were matched and classified based on histologic findings and blood-based molecular diagnostics. Results. Gene expression and pathway analyses revealed that subAR exhibits a molecular profile consistent with a milder form of clinical rejection in both tissue and blood. Molecular signatures distinguished immune-mediated injury from nonimmune injury. Notably, tissue gene expression profiling in cases that were misclassified by peripheral blood diagnostics were like correctly classified cases, suggesting that histology alone may not reliably define true clinical phenotypes. Conclusions. Molecular profiling of tissue and blood reflects distinct but complementary aspects of transplant biology and confirms that subAR and cAR share overlapping molecular profiles, supporting the hypothesis that subAR represents an early stage in the progression toward clinical rejection. These findings highlight the limitations of relying solely on histological evaluation and support the use of molecular tissue profiling as an adjunct to histology, particularly in diagnostically ambiguous cases. Molecular profiling provides a supporting framework for identifying subclinical rejection and guiding personalized immunosuppression to improve long-term outcomes.

Background and objectives Subclinical acute rejection is associated with poor outcomes in kidney transplant recipients. As an alternative to surveillance biopsies, noninvasive screening has been established with a blood gene expression profile. Donor-derived cellfree DNA (cfDNA) has been used to detect rejection in patients with allograft dysfunction but not tested extensively in stable patients. We hypothesized that we could complement noninvasive diagnostic performance for subclinical rejection by combining a donor-derived cfDNA and a gene expression profile assay. Design, setting, participants, & measurements We performed a post hoc analysis of simultaneous blood gene expression profile and donor-derived cfDNA assays in 428 samples paired with surveillance biopsies from 208 subjects enrolled in an observational clinical trial (Clinical Trials in Organ Transplantation-08). Assay results were analyzed as binary variables, and then, their continuous scores were combined using logistic regression. The performance of each assay alone and in combination was compared. Results For diagnosing subclinical rejection, the gene expression profile demonstrated a negative predictive value of 82%, a positive predictive value of 47%, a balanced accuracy of 64%, and an area under the receiver operating curve of 0.75. The donor-derived cfDNA assay showed similar negative predictive value (84%), positive predictive value (56%), balanced accuracy (68%), and area under the receiver operating curve (0.72). When both assays were negative, negative predictive value increased to 88%. When both assays were positive, positive predictive value increased to 81%. Combining assays using multivariable logistic regression, area under the receiver operating curve was 0.81, significantly higher than the gene expression profile ( P <0.001) or donor-derived cfDNA alone ( P =0.006). Notably, when cases were separated on the basis of rejection type, the gene expression profile was significantly better at detecting cellular rejection (area under the receiver operating curve, 0.80 versus 0.62; P =0.001), whereas the donor-derived cfDNA was significantly better at detecting antibody-mediated rejection (area under the receiver operating curve, 0.84 versus 0.71; P =0.003). Conclusions A combination of blood-based biomarkers can improve detection and provide less invasive monitoring for subclinical rejection. In this study, the gene expression profile detected more cellular rejection, whereas donor-derived cfDNA detected more antibody-mediated rejection.

Background and Aims A high proportion of patients develop chronic kidney disease (CKD) after liver transplantation (LT). We aimed to develop clinical/protein models to predict future glomerular filtration rate (GFR) deterioration in this population. Approach and Results In independent multicenter discovery (CTOT14) and single‐center validation (BUMC) cohorts, we analyzed kidney injury proteins in serum/plasma samples at month 3 after LT in recipients with preserved GFR who demonstrated subsequent GFR deterioration versus preservation by year 1 and year 5 in the BUMC cohort. In CTOT14, we also examined correlations between serial protein levels and GFR over the first year. A month 3 predictive model was constructed from clinical and protein level variables using the CTOT14 cohort (n = 60). Levels of β‐2 microglobulin and CD40 antigen and presence of hepatitis C virus (HCV) infection predicted early (year 1) GFR deterioration (area under the curve [AUC], 0.814). We observed excellent validation of this model (AUC, 0.801) in the BUMC cohort (n = 50) who had both early and late (year 5) GFR deterioration. At an optimal threshold, the model had the following performance characteristics in CTOT14 and BUMC, respectively: accuracy (0.75, 0.8), sensitivity (0.71, 0.67), specificity (0.78, 0.88), positive predictive value (0.74, 0.75), and negative predictive value (0.76, 0.82). In the serial CTOT14 analysis, several proteins, including β‐2 microglobulin and CD40, correlated with GFR changes over the first year. Conclusions We have validated a clinical/protein model (PRESERVE) that early after LT can predict future renal deterioration versus preservation with high accuracy. This model may help select recipients at higher risk for subsequent CKD for early, proactive renal sparing strategies.