Quantitative polymerase chain reaction profiling of immunomarkers in rejecting kidney allografts for predicting response to steroid treatment.
Transplantation 2012;
94:596-602. [PMID:
22902790 DOI:
10.1097/tp.0b013e31825db651]
[Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
BACKGROUND
Steroid-resistant acute rejection is a risk factor for inferior renal allograft outcome.
METHODS
From 873 kidney transplant recipients (1995-2005), 108 patients with a first rejection episode were selected for study using strict inclusion criteria and clinical endpoint definition. We aimed to predict response to corticosteroid treatment using gene expression of 65 transcripts. These reflect cytokines, chemokines, and surface and activation markers of various cell types including T cells, macrophages, B cells, and granulocytes. Steroid resistance (40% of the patients) was defined as requirement for antithymocyte globulin treatment within 2 weeks after corticosteroid treatment.
RESULTS
None of the clinical and histomorphologic parameters showed a significant association with response to treatment. Univariate logistic regression analysis resulted in 11 messenger RNA markers, including T-cell-related transcripts CD25, lymphocyte activation gene-3, Granzyme B, and interleukin-10, and macrophage-specific transcripts mannose receptor and S100 calcium-binding protein A9, which significantly discriminated steroid resistant from steroid-responsive rejections (P<0.05). In multivariate logistic regression, the combination of T-cell activation markers CD25:CD3e ratio (odds ratio, 8.7; confidence interval, 2.4-31.2) and lymphocyte activation gene-3 (odds ratio, 3.3; confidence interval, 1.4-7.7) represented the best predictive model for steroid response (P<0.0001). Specificity and sensitivity were 78% and 60%, respectively. After internal stratified 10-fold cross-validation, the model remained significant. Inclusion of clinical variables into the model with molecular variables did not enhance prediction.
CONCLUSIONS
Differences in intragraft expression profiles reflect variability in the response to antirejection treatment. In acute rejection, molecular markers, particularly those reflecting T-cell activation, offer superior prognostic value compared with conventional parameters.
Collapse