Development of a cross-platform biomarker signature to detect renal transplant tolerance in humans
Pervinder Sagoo, … , Maria P. Hernandez-Fuentes, Robert I. Lechler
Pervinder Sagoo, … , Maria P. Hernandez-Fuentes, Robert I. Lechler
Published May 24, 2010
Citation Information: J Clin Invest. 2010;120(6):1848-1861. https://doi.org/10.1172/JCI39922.
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Development of a cross-platform biomarker signature to detect renal transplant tolerance in humans

Abstract

Identifying transplant recipients in whom immunological tolerance is established or is developing would allow an individually tailored approach to their posttransplantation management. In this study, we aimed to develop reliable and reproducible in vitro assays capable of detecting tolerance in renal transplant recipients. Several biomarkers and bioassays were screened on a training set that included 11 operationally tolerant renal transplant recipients, recipient groups following different immunosuppressive regimes, recipients undergoing chronic rejection, and healthy controls. Highly predictive assays were repeated on an independent test set that included 24 tolerant renal transplant recipients. Tolerant patients displayed an expansion of peripheral blood B and NK lymphocytes, fewer activated CD4+ T cells, a lack of donor-specific antibodies, donor-specific hyporesponsiveness of CD4+ T cells, and a high ratio of forkhead box P3 to α-1,2-mannosidase gene expression. Microarray analysis further revealed in tolerant recipients a bias toward differential expression of B cell–related genes and their associated molecular pathways. By combining these indices of tolerance as a cross-platform biomarker signature, we were able to identify tolerant recipients in both the training set and the test set. This study provides an immunological profile of the tolerant state that, with further validation, should inform and shape drug-weaning protocols in renal transplant recipients.

Authors

Pervinder Sagoo, Esperanza Perucha, Birgit Sawitzki, Stefan Tomiuk, David A. Stephens, Patrick Miqueu, Stephanie Chapman, Ligia Craciun, Ruhena Sergeant, Sophie Brouard, Flavia Rovis, Elvira Jimenez, Amany Ballow, Magali Giral, Irene Rebollo-Mesa, Alain Le Moine, Cecile Braudeau, Rachel Hilton, Bernhard Gerstmayer, Katarzyna Bourcier, Adnan Sharif, Magdalena Krajewska, Graham M. Lord, Ian Roberts, Michel Goldman, Kathryn J. Wood, Kenneth Newell, Vicki Seyfert-Margolis, Anthony N. Warrens, Uwe Janssen, Hans-Dieter Volk, Jean-Paul Soulillou, Maria P. Hernandez-Fuentes, Robert I. Lechler

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Figure 6

Algorithm for microarray gene expression analysis.

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Ratio of FOXP3 to α-1,2-mannosidase (MAN1A2) expression assessed by qRT-...
Four-class analysis of microarray gene expression data identified probes significantly differentially expressed between all patient groups of the training and test sets using a Kruskal-Wallis nonparametric test. Probes were ranked within the training set based on their P values with adjustment for 1% FDR. The top 10 ranked probes that overlapped with genes identified in the test set were subsequently used for ROC analysis.