Use Of Tcrm To Aid Diagnosis Of Allograft Rejection In Kidney Transplantation

Introduction: Long-term kidney transplant (KT) allograft outcomes have not improved as expected despite a better understanding of rejection and improved immunosuppression. Here we discuss our previously validated computed rejection score, the tissue common rejection module (tCRM), measured by amplification-based assessment of 11 genes from formalin-fixed paraffin-embedded (FFPE) biopsy specimens, which allows for quantitative, unbiased assessment of immune injury. Methods: We applied tCRM in a prospective trial of 124 KT recipients, and contrasted assessment by tCRM and histology reads from 2 independent pathologists on protocol biopsies at 3, 6, 12, and 24 months and cause biopsies. Three 4 μm shaves from FFPE biopsy specimens were used for RNA extraction and amplification performed by Fluidigm qPCR of the 11 tCRM genes. The tCRM score was then calculated using the geometric mean of the 11 genes. Biopsy diagnoses of either rejection or borderline were considered to have inflammation present, while negative biopsies had no inflammation. Results: The tCRM score was significantly elevated in histological categories that aligned between both pathologists diagnosing inflammation (mean 3.7 ± 2.8) vs. no inflammation (mean 1.5 ± 1.0) on biopsies (Figure 1). Unsupervised clustering of tCRM gene expression across biopsies where pathologists had discrepant scores is shown in Figure 2, and approximately 45% of the time there was discordance between pathologists. This discordance was most notable in the cases diagnosed as borderline by one pathologist, but stable by the other. Samples where both pathologists agreed (either both inflammation or both no inflammation) tended to cluster by high and low tCRM gene expression, respectively. Conclusion: Accurate, quantitative, and unbiased assessment of rejection on the clinical sample is critical. Given the discrepant diagnoses between pathologists on the same samples, individuals could utilize the tCRM score as a tiebreaker in unclear situations. We propose that the tCRM quantitative score can provide this information rapidly, with a short turnaround time, and drive clinical decision making.