Enver Akalin1, Matthew R Weir2, Suphamai Bunnapradist3, Daniel C Brennan4, Rowena Delos Santos5, Anthony Langone6, Arjang Djamali7, Hua Xu8, Xia Jin8, Sham Dholakia9, Robert N Woodward8, Jonathan S Bromberg10. 1. Division of Nephrology, Kidney Transplant Program, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, New York. 2. Division of Nephrology, Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland. 3. Department of Medicine, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, California. 4. Comprehensive Transplant Center, The Johns Hopkins University School of Medicine, Baltimore, Maryland. 5. Division of Nephrology, Washington University School of Medicine, St. Louis, Missouri. 6. Vanderbilt University Medical Center, Medical Specialties Clinic, Veteran Affairs Hospital Renal Transplant Program, Nashville, Tennessee. 7. Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin. 8. Research and Development, CareDx, Brisbane, California. 9. Medical Affairs, CareDx, South San Francisco, California. 10. Department of Surgery, University of Maryland School of Medicine, Baltimore, Maryland.
Abstract
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.
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.
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