*Purpose: The accuracy and utility of clinically defined and analytically validated predictive biomarkers in kidney transplantation is limited. We leveraged prior feasibility blood-RNA studies to develop a highly sophisticated next-generation sequencing (NGS) platform which interrogates the immunologic profile of kidney transplant patients.

*Methods: The approach has been established in a highly regulated CLIA environment to provide clinical grade assays. By applying algorithm-based clinical decision trees, two discrete gene sets predicting the likelihood of both pre- and post-transplant rejection were developed. The outcome of each assay is the generation of a risk score and a machine learning algorithm-derived cut-off which classifies patients’ risk of early acute or clinical / subclinical rejection. To clinically validate both assays we designed a novel prospective multi-site (14 transplant centers), multi-country (i.e. US, Spain, France, Italy, Australia) observational trial which correlates the 2 unique RNA biomarker signatures with the histopathology and BANFF criteria of either a protocol or for cause kidney biopsy as the gold standard for evidence of transplant rejection (clinical / subclinical acute rejection).

*Results: To date, 322 patients have been enrolled which completes both pre- and post-transplant assay requirements. A target time for acute clinical and subclinical rejection is 6 months; however, all patients will be followed for 24 months to validate a third assay for predictive risk of kidney fibrosis. After 24 months, participants will be monitored through registry data. To harmonize the microscopic attributes of the kidney tissue specimens, a central pathology review of all diagnostic biopsies will be incorporated both independently and in conjunction with site-specific diagnoses.

*Conclusions: The overall approach including an all-comers, multi-center, multi-country design coupled with the rigor of an NGS assay allows for the generation of performance characteristics, including accuracy and precision, which should better inform medical management of kidney transplant patients in a more personalized and predictive manner. This unique design of correlating a quantitative blood-based transcriptomic signature with a rejection phenotype based on histopathology in the kidney biopsy represents a level of evidence which currently does not exist in biomarker transplant biology. We anticipate an interim performance review of both assays in Q2 2022.

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