Introduction: QSant, a urine-based test quantitates alloimmune injury(AI) and acute rejection (AR) in the allograft via analysis of 6 biomarkers - cfDNA, methylated cfDNA, total protein, CXCL10, clusterin and creatinine. It utilizes a machine learning based algorithm for prognostication of injury and rejection risk. We propose the application of QSant to monitor AI in DGF and its recovery. Also use QSant as a surrogate to biopsy and refine the need for biopsy when the rejection risk is high.

Methods: 65 HLA-DR matched, renal allograft recipients, on TAC/MMF/prednisone, had 431 serial urine samples collected for QSant, over the 3 months post-transplant(MPT). All patients received a single 3mg/kg dose of anti-thymocyte globulin within 24 hours of graft re-vascularization. Clinical DGF was observed in 57% of the cohort. 62/65 (92%) of patients had a biopsy in the first month post-tx, with 80% of biopsies in the first week. Of the clinical DGF patients, 13% received a surveillance biopsy. Only 12% of biopsies had histological AR, others showed AI. QSant was used for quantification of AI (Q-Score >32) or immunequiescence (Q-Score <32) and a time-series analysis was performed. A PCA probed individual biomarker contributions in DGF patients to determine if specific biomarkers could predict recovery from IRI.

Results: The QSant biomarker profile for BPAR is significantly different from pure DGF without AR. A multivariate logistic regression of BPAR - with median Q-Score: 59(95% CI 50 – 69) - established cfDNA to be most significant (p=0.0029). QSant time-series analysis showed a significant(p=5.8e-19) decline from AI (Q-Score:47) to allograft stability (QScore:13) over the 3 MPT. The associated 30% decline in Q-Score indicative of allograft stability was also significant (p=8.66e-10). A significant decline (p=0.004) in rejection risk with Q-Score dropping into immunequiescence was observed after the first 2 weeks. In patients with recovery from DGF, concomitant with Q-Score there was a 3.6x (p=9.4e-6), 3x (p=1.42e-5) and 3x(p=0.000243) decline in clusterin, total protein and CXCL10, respectively.  In the DGF sub-cohort that recovered function, PCA showed Clusterin explained 52% of the variance. For the samples with no histological evidence of rejection (n = 21), 90%(n=19) of the biopsies were done within the first two weeks post-transplantation. 36% of these had a Q-Score > 55: cases where QSant identified potentially evolving rejection missed by biopsy. 26% had Q-Scores in the immunequiescent range: samples where QSant might obviate the need for a biopsy.

Conclusions: Serial monitoring with QSant captures cases of DGF with concomitant AR and the early onset and resolution of DGF-related renal injury. QSant confirms recovery of allograft IRI in 90% of patients at 3 mo post-tx. Persistently elevated Q-Scores in a small subset of DGF patients suggests closer monitoring of these patients for increased risk of AR and chronic allograft injury over time.