Causes of Renal Allograft Injury in Recipients With Normal Donor-derived Cell-free DNA

Predicting Cellular Rejection of Renal Allograft Based on the Serum Proteomic Fingerprint

Kidney transplantation is an essential medical procedure that significantly enhances the survival rates and quality of life for patients with end-stage kidney disease. However, despite advancements in immunosuppressive therapies, allograft rejection remains a leading cause of organ loss. Notably, predictions of cellular rejection processes primarily rely on biopsy analysis, which is not routinely performed due to its invasive nature. The present work evaluates if the serum proteomic fingerprint, as acquired by Fourier Transform Infrared (FTIR) spectroscopy, can predict cellular rejection processes. We analyzed 28 serum samples, corresponding to 17 without cellular rejection processes and 11 associated with cellular rejection processes, as based on biopsy analyses. The leave-one-out-cross validation procedure of a Naïve Bayes model enabled the prediction of cellular rejection processes with high sensitivity and specificity (AUC > 0.984). The serum proteomic profile was obtained in a high-throughput mode and based on a simple, rapid, and economical procedure, making it suitable for routine analyses and large-scale studies. Consequently, the current method presents a high potential to predict cellular rejection processes translatable to clinical scenarios, and that should continue to be explored.

Molecular immune monitoring in kidney transplant rejection: a state-of-the-art review

Although current regimens of immunosuppressive drugs are effective in renal transplant recipients, long-term renal allograft outcomes remain suboptimal. For many years, the diagnosis of renal allograft rejection and of several causes of renal allograft dysfunction, such as chronic subclinical inflammation and infection, was mostly based on renal allograft biopsy, which is not only invasive but also possibly performed too late for proper management. In addition, certain allograft dysfunctions are difficult to differentiate from renal histology due to their similar pathogenesis and immune responses. As such, non-invasive assays and biomarkers may be more beneficial than conventional renal biopsy for enhancing graft survival and optimizing immunosuppressive drug regimens during long-term care. This paper discusses recent biomarker candidates, including donor-derived cell-free DNA, transcriptomics, microRNAs, exosomes (or other extracellular vesicles), urine chemokines, and nucleosomes, that show high potential for clinical use in determining the prognosis of long-term outcomes of kidney transplantation, along with their limitations.

Kidney Allograft Monitoring by Combining Donor-Derived Cell-Free DNA and Molecular Gene Expression: A Clinical Management Perspective

Donor-derived cell-free DNA (dd-cfDNA) may safely assess kidney allograft rejection. Molecular Microscope (MMDx®) gene expression may offer increased precision to histology. This single-center retrospective study monitored kidney transplant recipients for rejection at specified time intervals by utilizing creatinine (SCr), proteinuria, donor-specific antibodies (DSAs), and dd-cfDNA. A clinically indicated biopsy sample was sent for histopathology and MMDx®. Patients were categorized into rejection (Rej) and non-rejection (NRej) groups, and further grouped according to antibody-mediated rejection (ABMR) subtypes. Rej and NRej groups included 52 and 37 biopsies, respectively. Median follow-up duration was 506 days. DSAs were positive in 53% and 22% of patients in both groups, respectively (p = 0.01). Among these groups, pre- and post-intervention median SCr, proteinuria, and dd-cfDNA at 1 month, 2 months, and at the last follow-up revealed significant difference for dd-cfDNA (all p = 0.01), however, no difference was found for SCr and proteinuria (p > 0.05). The AUC was 0.80 (95% CI: 0.69-0.91), with an optimal dd-cfDNA criterion of 2.2%. Compared to histology, MMDx® was more likely to diagnose ABMR (79% vs. 100%) with either C4d positivity or negativity and/or DSA positivity or negativity. Hence, a pre- and post-intervention allograft monitoring protocol in combination with dd-cfDNA, MMDx®, and histology has aided in early diagnosis and timely individualized intervention.

An Assessment of the Value of Donor-derived Cell-free DNA Surveillance in Patients With Preserved Kidney Allograft Function

BACKGROUND

Donor-derived cell-free DNA (dd-cfDNA) is a biomarker validated to detect rejection when measured to assess kidney allograft dysfunction. However, it remains unclear whether routine surveillance with dd-cfDNA provides additional information over standard monitoring of kidney allografts with creatinine and donor-specific antibodies (DSAs), particularly among those with little suspicion of rejection or injury. We investigated the value of measuring dd-cfDNA in patients with preserved allograft function and describe its association with future events.

METHODS

Three-hundred seventeen kidney transplant recipients with a creatinine ≤1.5 mg/dL, no current DSA, and no prior rejection were assessed with dd-cfDNA and categorized into low (dd-cfDNA <0.5%; n = 239), moderate (dd-cfDNA 0.5% to <1.0%; n = 43), and high (dd-cfDNA ≥1.0%; n = 35) groups. The occurrence of rejection, DSA, graft loss, and change in estimated glomerular filtration rate over time after dd-cfDNA assessment was compared.

RESULTS

Over follow-up, rejections were more commonly found among patients with high vs low dd-cfDNA (17% versus 5%; P = 0.01); a similar nonsignificant trend was observed among patients with moderate compared to low dd-cfDNA (12% versus 5%; P = 0.13). DSA development was uncommon and not different between groups (low: 4%; moderate: 3%; high: 0%; P = 0.52). There was only 1 graft loss in a patient with low dd-cfDNA, and dd-cfDNA was not associated with graft dysfunction over time.

CONCLUSIONS

Most patients with elevated dd-cfDNA in conjunction with preserved allograft function remained stable over follow-up without deterioration in function or graft loss. Studies are needed to differentiate patients with elevated dd-cfDNA who will develop adverse outcomes from those who will remain clinically stable.

Diagnostic Potential of Minimally Invasive Biomarkers: A Biopsy-centered Viewpoint From the Banff Minimally Invasive Diagnostics Working Group

With recent advances and commercial implementation of minimally invasive biomarkers in kidney transplantation, new strategies for the surveillance of allograft health are emerging. Blood and urine-based biomarkers can be used to detect the presence of rejection, but their applicability as diagnostic tests has not been studied. A Banff working group was recently formed to consider the potential of minimally invasive biomarkers for integration into the Banff classification for kidney allograft pathology. We review the existing data on donor-derived cell-free DNA, blood and urine transcriptomics, urinary protein chemokines, and next-generation diagnostics and conclude that the available data do not support their use as stand-alone diagnostic tests at this point. Future studies assessing their ability to distinguish complex phenotypes, differentiate T cell-mediated rejection from antibody-mediated rejection, and function as an adjunct to histology are needed to elevate these minimally invasive biomarkers from surveillance tests to diagnostic tests.

Detection of BK polyomavirus-associated nephropathy using plasma graft-derived cell-free DNA: Development of a novel algorithm from programmed monitoring

Graft-derived cell-free DNA (GcfDNA) is a promising non-invasive biomarker for detecting allograft injury. In this study, we aimed to evaluate the efficacy of programmed monitoring of GcfDNA for identifying BK polyomavirus-associated nephropathy (BKPyVAN) in kidney transplant recipients. We recruited 158 kidney transplant recipients between November 2020 and December 2021. Plasma GcfDNA was collected on the tenth day, first month, third month, and sixth month for programmed monitoring and one day before biopsy. ΔGcfDNA (cp/mL) was obtained by subtracting the baseline GcfDNA (cp/mL) from GcfDNA (cp/mL) of the latest programmed monitoring before biopsy. The receiver operating characteristic curve showed the diagnostic performance of GcfDNA (cp/mL) at biopsy time and an optimal area under the curve (AUC) of 0.68 in distinguishing pathologically proven BKPyVAN from pathologically unconfirmed BKPyVAN. In contrast, ΔGcfDNA (cp/mL) had a sensitivity and specificity of 80% and 84.6%, respectively, and an AUC of 0.83. When distinguishing clinically diagnosed BKPyVAN from clinical excluded BKPyVAN, the AUC of GcfDNA (cp/mL) was 0.59 at biopsy time, and ΔGcfDNA (cp/mL) had a sensitivity and specificity of 81.0% and 76.5%, respectively, and an AUC of 0.81. Plasma ΔGcfDNA (cp/mL) was not significantly different between TCMR [0.15 (0.08, 0.24) cp/mL] and pathologically proven BKPyVAN[0.34 (0.20, 0.49) cp/mL]. In conclusion, we recommend programmed monitoring of plasma GcfDNA levels after a kidney transplant. Based on our findings from the programmed monitoring, we have developed a novel algorithm that shows promising results in identifying and predicting BKPyVAN.

Clinical Validation of an Immune Quiescence Gene Expression Signature in Kidney Transplantation

Background

Despite advances in immune suppression, kidney allograft rejection and other injuries remain a significant clinical concern, particularly with regards to long-term allograft survival. Evaluation of immune activity can provide information about rejection status and help guide interventions to extend allograft life. Here, we describe the validation of a blood gene expression classifier developed to differentiate immune quiescence from both T cell-mediated rejection (TCMR) and antibody-mediated rejection (ABMR).

Methods

A five-gene classifier (DCAF12, MARCH8, FLT3, IL1R2, and PDCD1) was developed on 56 peripheral blood samples and validated on two sample sets independent of the training cohort. The primary validation set comprised 98 quiescence samples and 18 rejection samples: seven TCMR, ten ABMR, and one mixed rejection. The second validation set included eight quiescence and 11 rejection samples: seven TCMR, two ABMR, and two mixed rejection. AlloSure donor-derived cell-free DNA (dd-cfDNA) was also evaluated.

Results

AlloMap Kidney classifier scores in the primary validation set differed significantly between quiescence (median, 9.49; IQR, 7.68-11.53) and rejection (median, 13.09; IQR, 11.25-15.28), with P<0.001. In the second validation set, the cohorts were statistically different (P=0.03) and the medians were similar to the primary validation set. The AUC for discriminating rejection from quiescence was 0.786 for the primary validation and 0.800 for the second validation. AlloMap Kidney results were not significantly correlated with AlloSure, although both were elevated in rejection. The ability to discriminate rejection from quiescence was improved when AlloSure and AlloMap Kidney were used together (AUC, 0.894).

Conclusion

Validation of AlloMap Kidney demonstrated the ability to differentiate between rejection and immune quiescence using a range of scores. The diagnostic performance suggests that assessment of the mechanisms of immunologic activity is complementary to allograft injury information derived from AlloSure dd-cfDNA. Together, these biomarkers offer a more comprehensive assessment of allograft health and immune quiescence.