Poggio, Emilio D.

Background: Although donor and recipient characteristics are used to estimate graft failure risk, they remain limited in predicting early acute rejection. We developed and validated a next-generation sequencing (NGS) assay targeting the pre-transplant immunologic profile to predict early acute rejection following kidney transplantation. Methods: This prospective, international study enrolled 321 kidney transplant participants across 13 sites, forming discovery and validation cohorts. Peripheral blood was collected prior to transplant and at 1, 3, 6, 12, and 24 months post-transplant, with protocol biopsies performed at 3 and 12 months, as well as any indication biopsies. Biopsies were assessed by a blinded central pathologist according to the 2019 Banff criteria. The pre-transplant risk assessment (PTRA™) test to evaluate RNA expression of a 29-gene signature algorithm designed to predict early acute rejection risk was clinically validated utilizing 122 deceased donor kidney transplant recipients. Results: PTRA classified 31 of 122 participants (25%) as high-risk and 91 (75%) as low-risk. There were 9 early acute rejection events in the first 60 days post-transplant: 6/31 (19%) in the high-risk group and 3/91 (3%) in the low-risk group. PTRA discrimination for early acute rejection at 60 days post-transplant yielded an AUC of 0.78 (95% confidence interval [CI]: 64.4 to 91.5), p<0.001. A cut-off of ≤45 was defined as low-risk and >45 as high-risk for early acute rejection within 60 days post-transplant. Sensitivity 0.67 (95% CI: 0.36 to 0.97), specificity 0.78 (95% CI: 0.70 to 0.86), positive predictive value 0.19 (95% CI: 0.05 to 0.33), and negative predictive value 0.97 (95% CI: 0.93 to 1.00). The odds ratio comparing patients identified as high- vs low-risk by PTRA was 7.04 (95% CI: 1.64 to 30.2), p=0.009. Conclusions: This study validates the ability of PTRA to stratify kidney transplant recipients as high- or low-risk for early acute rejection using a pre-transplant transcriptomic profile, with implications for graft health and personalized treatment management.

Background The primary objective of this analysis was to assess the association between non-invasive biomarker results within the first 2-years post-kidney transplant and 10-year All Cause Graft Loss (ACGL). Methods CTOT-08 was linked with the Scientific Registry of Transplant Recipients (SRTR) to assess 10-year ACGL. Cox Regression was used to determine the association between gene expression profile (GEP) and donor-derived cell-free DNA (dd-cfDNA) results and ACGL. A Cox Regression with time-varying covariates was used to test associations with ACGL with simultaneous and repeated biomarker results. Results The final landmarked Cox model included clinical acute rejection (aHR 2.58 (1,30,5.13) for 1 rejection, aHR 3.41 (1.07,10.85) for 2 rejections), diabetes (aHR 1.71 (0.99,2.96)), recipient age (aHR 1.02 (1.00,1.04)), the highest quartile of positive GEP (aHR 2.44 (1.45,4.10)) and the highest quartile of positive dd-cfDNA (aHR 2.91 (1.77,4.78)). In the time-varying model, having both biomarkers positive at the same time carried the greatest association with ACGL (aHR 4.29 (2.10,8.76)). Conclusions In this multicenter, prospective analysis of GEP and dd-cfDNA on patient and graft outcomes, an independent association with long-term ACGL was demonstrated. Further, a time-varying analysis demonstrated that two biomarkers with independent methodologies tested at the same time can provide more information than either one alone.

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.

Nephron number may be an important determinant of kidney health but has been difficult to study in living humans. We evaluated 1638 living kidney donors at Mayo Clinic (MN and AZ sites) and Cleveland Clinic. We obtained cortical volumes of both kidneys from predonation computed tomography scans. At the time of kidney transplant, we obtained and analyzed the sections of a biopsy specimen of the cortex to determine the density of both nonsclerotic and globally sclerotic glomeruli; the total number of glomeruli was estimated from cortical volume×glomerular density. Donors 18–29 years old had a mean 990,661 nonsclerotic glomeruli and 16,614 globally sclerotic glomeruli per kidney, which progressively decreased to 520,410 nonsclerotic glomeruli per kidney and increased to 141,714 globally sclerotic glomeruli per kidney in donors 70–75 years old. Between the youngest and oldest age groups, the number of nonsclerotic glomeruli decreased by 48%, whereas cortical volume decreased by only 16% and the proportion of globally sclerotic glomeruli on biopsy increased by only 15%. Clinical characteristics that independently associated with fewer nonsclerotic glomeruli were older age, shorter height, family history of ESRD, higher serum uric acid level, and lower measured GFR. The incomplete representation of nephron loss with aging by either increased glomerulosclerosis or by cortical volume decline is consistent with atrophy and reabsorption of globally sclerotic glomeruli and hypertrophy of remaining nephrons. In conclusion, lower nephron number in healthy adults associates with characteristics reflective of both lower nephron endowment at birth and subsequent loss of nephrons.