Poggio, Emilio

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.