Abstract / Summary
Introduction Timely diagnosis of kidney allograft rejection is critical for post-transplant management, yet treatment decisions may need to be made empirically while awaiting the final pathology report. We previously demonstrated that biopsy transport medium (BTM) contains molecular signals reflective of allograft rejection. Building on these findings, we developed a rapid BTM qPCR workflow using a targeted gene panel for early molecular assessment of rejection. Methods Kidney transplant biopsies assessed at Leiden University Medical Center from April 2023 to November 2024 were eligible. A predefined gene panel was quantified in BTM by qPCR, while matched formalin-fixed paraffin-embedded tissues were profiled using Tempo-Seq. Elastic Net, Random Forest, and support vector machine models were developed to classify rejection and predict continuous histological indices directly from the BTM. Results A total of 134 paired BTM samples and biopsy tissues were analyzed. The best-performing BTM gene-panel models demonstrated good discrimination for rejection, with a median AUC of 0.80, and good calibration (median slope, 0.92; median intercept, 0.08). For continuous histological indices, the best-performing BTM gene-panel models achieved R square values of 0.33 for Activity, 0.31 for TCMR/TI, and 0.27 for AMR/MVI. Corresponding biopsy gene-panel models showed similar patterns across diagnostic outcomes. Conclusions The BTM qPCR workflow captures molecular features of kidney allograft rejection and enables prediction of rejection status along the spectrum of continuous activity indices. By providing same-day molecular information while definitive biopsy interpretation is pending, this rapid approach has the potential to complement conventional histopathological diagnostics and support early clinical decision-making.