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Abstract: SA-OR46

Validation of a Urinary Exosome mRNA Signature for the Diagnosis of Human Kidney Transplant Rejection

Session Information

Category: Transplantation

  • 2002 Transplantation: Clinical

Authors

  • El Fekih, Rania, Brigham and Women's Hospital, Boston, Massachusetts, United States
  • Haynes, Brian C., Bio-Techne, Austin, Texas, United States
  • Alghamdi, Areej saud a, Boston Children's Hospital, Boston, Massachusetts, United States
  • Choi, John Yongjoon, Brigham and Women's Hospital, Boston, Massachusetts, United States
  • Hurley, James, Exosome Diagnostics, Waltham, Massachusetts, United States
  • Rennke, Helmut G., Brigham and Women's Hospital, Boston, Massachusetts, United States
  • Weins, Astrid, Brigham and Women's Hospital, Boston, Massachusetts, United States
  • Deban, Christa A., Brigham and Women's Hospital, Boston, Massachusetts, United States
  • Saad, Anis Joseph, Brigham and Women's Hospital, Boston, Massachusetts, United States
  • Halawi, Ahmad, Brigham and Women's Hospital, Boston, Massachusetts, United States
  • Younis, Nour Khaled, Brigham and Women's Hospital, Boston, Massachusetts, United States
  • Solhjou, Zhabiz, Brigham and Women's Hospital, Boston, Massachusetts, United States
  • Macedo, Camila, UPMC, Pittsburgh, Pennsylvania, United States
  • Skog, Johan, Exosome Diagnostics, Waltham, Massachusetts, United States
  • Azzi, Jamil R., Brigham and Women's Hospital, Boston, Massachusetts, United States

Group or Team Name

  • Transplant Research Center, Brigham and Women's Hospital
Background

Traditional biomarkers currently used to monitor kidney allograft rejection are late markers of injury, and lack sensitivity and specificity. Biopsies are invasive and costly. Urinary exosomes are nanoscale extracellular vesicles. They are released from kidney cells, and contain a payload of proteins and nucleic acids that reflect the physiology of the parent cells. Their potential to serve as a liquid biopsy and biomarker for post-transplant rejection has recently been investigated.

Methods

We collected 411 urine samples from 366 patients undergoing for cause or management transplant kidney biopsy. Using a clinically validated platform for exosome isolation and analysis, a transcript of 17 gene targets, previously determined by us to be associated with kidney rejection, were pre-amplified and evaluated by RT-qPCR. Machine learning was applied to the training cohort data to determine an optimal algorithm for detecting kidney rejection.

Results

The prevalence of any-cause rejection among the for-cause and the management biopsy group were 36.3% and 22.2%, respectively. In the for-cause biopsy group, we identified a linear SVM classifier of 3 mRNA features (IL32, B2M, and CXCL11) that distinguishes any-cause rejection from no rejection, achieving an AUC of 0.731, with a sensitivity and an NPV of 93%. This shows the potential to save 43% of unnecessary biopsies. In the management-biopsy group, we identified a classifier that distinguishes any-cause rejection from no rejection, achieving an AUC of 0.781, with a sensitivity of 93% and an NPV of 97%. We are further evaluating the role of significant underlying inflammation determined by biopsy, such as moderate to significant lymphocytic infiltration, interstitial nephritis (BKV nephritis or AIN), glomerulopathy or immune complex deposition, in the classification of samples that are rejection negative by biopsy.

Conclusion

mRNA signatures derived from urinary exosomes represent a powerful and non-invasive tool to assess kidney allograft rejection, detect early renal allograft rejection and support clinicians in therapeutic decisions. This signature holds strong potential for diagnostic use, to be further demonstrated in a prospective clinical setting.

Funding

  • Other NIH Support