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Front Immunol. 2017 Dec 5;8:1687. doi: 10.3389/fimmu.2017.01687. eCollection 2017.

Novel Non-Histocompatibility Antigen Mismatched Variants Improve the Ability to Predict Antibody-Mediated Rejection Risk in Kidney Transplant.

Author information

1
Division of Transplant Surgery, Department of Surgery, University of California, San Francisco (UCSF), San Francisco, CA, United States.
2
Institute for Computational Health Sciences, University of California, San Francisco (UCSF), San Francisco, CA, United States.
3
Department of Medicine, Division of Immunogenetics and Transplantation Immunology, The Johns Hopkins University School of Medicine, Baltimore, MD, United States.
4
Department of Pediatrics, University of California, San Francisco (UCSF), San Francisco, CA, United States.

Abstract

Transplant rejection is the critical clinical end-point limiting indefinite survival after histocompatibility antigen (HLA) mismatched organ transplantation. The predominant cause of late graft loss is antibody-mediated rejection (AMR), a process whereby injury to the organ is caused by donor-specific antibodies, which bind to HLA and non-HLA (nHLA) antigens. AMR is incompletely diagnosed as donor/recipient (D/R) matching is only limited to the HLA locus and critical nHLA immunogenic antigens remain to be identified. We have developed an integrative computational approach leveraging D/R exome sequencing and gene expression to predict clinical post-transplant outcome. We performed a rigorous statistical analysis of 28 highly annotated D/R kidney transplant pairs with biopsy-confirmed clinical outcomes of rejection [either AMR or T-cell-mediated rejection (CMR)] and no-rejection (NoRej), identifying a significantly higher number of mismatched nHLA variants in AMR (ANOVA-p-value = 0.02). Using Fisher's exact test, we identified 123 variants associated mainly with risk of AMR (p-value < 0.001). In addition, we applied a machine-learning technique to circumvent the issue of statistical power and we found a subset of 65 variants using random forest, that are predictive of post-tx AMR showing a very low error rate. These variants are functionally relevant to the rejection process in the kidney and AMR as they relate to genes and/or expression quantitative trait loci (eQTLs) that are enriched in genes expressed in kidney and vascular endothelium and underlie the immunobiology of graft rejection. In addition to current D/R HLA mismatch evaluation, additional mismatch nHLA D/R variants will enhance the stratification of post-tx AMR risk even before engraftment of the organ. This innovative study design is applicable in all solid organ transplants, where the impact of mitigating AMR on graft survival may be greater, with considerable benefits on improving human morbidity and mortality and opens the door to precision immunosuppression and extended tx survival.

KEYWORDS:

antibody-mediated rejection; exome sequencing; gene expression; kidney organ transplant; machine learning; non-histocompatibility antigen

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