Long-life immunosuppression is an important factor that contributes to shorten the long-term survival of kidney transplant recipients. Identifying patients in whom donor-specific tolerance has developed would constitute a major advance in their care. Since this ability would allow the minimization or even withdrawal of immunosuppressive therapy in selected patients.

We and others have previously identified a unique set of biomarkers of transplantation tolerance (JCI 2010) measured in peripheral blood.

Aims: The GAMBIT study aims to translate these defined biomarkers of tolerance into clinically useful identities.

Methods: To simplify the test for clinical use we performed best subset selection using microarray data from the two independent kidney transplant recipient cohorts in the published studies. The optimal model was selected through cross-validation in one patient cohort and validated in the other.

Platform transition from microarray expression to RT-PCR has been performed in the GAMBIT study. Housekeeping gene was HPRT. Two-sample Wilcoxon test was used for univariate statistics. Elastic net and GLM models were used for prediction of tolerance.

Results: In the original cohort best-subset selection suggested that the expression of 3 genes (PNOC, SH2DB1 and TLR5) provided the best and most stable performance. In the validation cohort the 3 gene-signature provided a classification specificity and sensitivity of 0.82 and 0.84 respectively.

In the GAMBIT cohort 8 of the candidate genes were differentially expressed in tolerant recipients (n=12) when compared to stable (n=177) and chronic rejectors (n=29) (BH-adjusted p value <0.01).

In the GAMBIT cohort, an elastic net model selects 7 features with an Area Under the Curve (AUC) of 0.77 for optimal prediction. Using the 3 gene-signature provided similar performance with an AUC of 0.75.

Conclusions: Tolerant recipients can be identified using blood gene expression of just 3 genes measured by RT-PCR with high predictive accuracy. Validation for routine clinical use of these biomarkers would bring to the fore the possibility of personalized medicine.

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