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Evidence for the alloimmune basis and prognostic significance of Borderline T cell–mediated rejection
Author(s) -
Wiebe Chris,
Rush David N.,
Gibson Ian W.,
Pochinco Denise,
Birk Patricia E.,
Goldberg Aviva,
BlydtHansen Tom,
Karpinski Martin,
Shaw Jamie,
Ho Julie,
Nickerson Peter W.
Publication year - 2020
Publication title -
american journal of transplantation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.89
H-Index - 188
eISSN - 1600-6143
pISSN - 1600-6135
DOI - 10.1111/ajt.15860
Subject(s) - medicine , immunosuppression , hazard ratio , tacrolimus , human leukocyte antigen , immunology , hla dq , hla dr , oncology , transplantation , antigen , confidence interval , allele , haplotype , biology , biochemistry , gene
Prognostic biomarkers of T cell–mediated rejection (TCMR) have not been adequately studied in the modern era. We evaluated 803 renal transplant recipients and correlated HLA‐DR/DQ molecular mismatch alloimmune risk categories (low, intermediate, high) with the severity, frequency, and persistence of TCMR. Allograft survival was reduced in recipients with Banff Borderline (hazard ratio [HR] 2.4, P  = .003) and Banff ≥ IA TCMR (HR 4.3, P  < .0001) including a subset who never developed de novo donor‐specific antibodies ( P  = .002). HLA‐DR/DQ molecular mismatch alloimmune risk categories were multivariate correlates of Banff Borderline and Banff ≥ IA TCMR and correlated with the severity and frequency of rejection episodes. Recipient age, HLA‐DR/DQ molecular mismatch category, and cyclosporin vs tacrolimus immunosuppression were independent correlates of Banff Borderline and Banff ≥ IA TCMR. In the subset treated with tacrolimus (720/803) recipient age, HLA‐DR/DQ molecular mismatch category, and tacrolimus coefficient of variation were independent correlates of TCMR. The correlation of HLA‐DR/DQ molecular mismatch category with TCMR, including Borderline, provides evidence for their alloimmune basis. HLA‐DR/DQ molecular mismatch may represent a precise prognostic biomarker that can be applied to tailor immunosuppression or design clinical trials based on individual patient risk.

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