Anat R Tambur1, Joseph R Leventhal, Jennifer R Zitzner, R Carlin Walsh, John J Friedewald. 1. Transplant Immunology Laboratory, Comprehensive Transplant Center, Feinberg School of Medicine, Northwestern University, 303 E. Chicago Avenue, Tarry Building Suite 11-711, Chicago, IL 60611-3008, USA. a-tambur@northwestern.edu
Abstract
BACKGROUND: The United Network for Organ Sharing algorithm for deceased-donor kidney allocation considers only the human leukocyte antigen (HLA)-A, HLA-B, and HLA-DR loci. Although HLA-DQ serologic specificities can be entered as unacceptable antigens, they are assigned only by the identity of the DQβ chain, disregarding the role of the similarly polymorphic α chain. DQα/β combinations result in unique antigenic epitopes, which serve as targets to different antibodies. Therefore, the presence of HLA antibodies to one DQα/β combination should not preclude negative crossmatch (XM) against another combination. In this retrospective analysis, patients were allowed XM against a particular donor if they had antibodies to some, but not all, DQα/β allele combinations with the donor serologic HLA-DQ antigens. METHODS: HLA antibody signature was obtained using solid-phase Luminex-based antibody analysis. Results were captured at the high-resolution level (as provided by the positive beads). Potential donors were typed to include information on both HLA-DQA and HLA-DQB alleles. RESULTS: Of the 1130 flow XM assays performed, 147 patients had antibodies to donor serologic HLA-DQ antigens. Thirty-five of those patients had antibodies to an allelic DQα/β combination within the donor serologic DQ specificity that were different from the donor's DQα/β, leading to negative flow XM results (24%). Virtual XM, accounting for donor DQα/β combinations, successfully predicts more than 98% of XM outcomes. CONCLUSIONS: In patients with allelic DQα/β antibodies, denying the opportunity for XM based on serologically defined unacceptable antigens can disadvantage the patient. Larger cohort studies are required to substantiate our observation. Introducing DQα/β combination information may increase virtual XM accuracy and organ allocation equity.
BACKGROUND: The United Network for Organ Sharing algorithm for deceased-donor kidney allocation considers only the humanleukocyte antigen (HLA)-A, HLA-B, and HLA-DR loci. Although HLA-DQ serologic specificities can be entered as unacceptable antigens, they are assigned only by the identity of the DQβ chain, disregarding the role of the similarly polymorphic α chain. DQα/β combinations result in unique antigenic epitopes, which serve as targets to different antibodies. Therefore, the presence of HLA antibodies to one DQα/β combination should not preclude negative crossmatch (XM) against another combination. In this retrospective analysis, patients were allowed XM against a particular donor if they had antibodies to some, but not all, DQα/β allele combinations with the donor serologic HLA-DQ antigens. METHODS: HLA antibody signature was obtained using solid-phase Luminex-based antibody analysis. Results were captured at the high-resolution level (as provided by the positive beads). Potential donors were typed to include information on both HLA-DQA and HLA-DQB alleles. RESULTS: Of the 1130 flow XM assays performed, 147 patients had antibodies to donor serologic HLA-DQ antigens. Thirty-five of those patients had antibodies to an allelic DQα/β combination within the donor serologic DQ specificity that were different from the donor's DQα/β, leading to negative flow XM results (24%). Virtual XM, accounting for donor DQα/β combinations, successfully predicts more than 98% of XM outcomes. CONCLUSIONS: In patients with allelic DQα/β antibodies, denying the opportunity for XM based on serologically defined unacceptable antigens can disadvantage the patient. Larger cohort studies are required to substantiate our observation. Introducing DQα/β combination information may increase virtual XM accuracy and organ allocation equity.
Authors: Dulat Bekbolsynov; Beata Mierzejewska; Jadwiga Borucka; Robert S Liwski; Anna L Greenshields; Joshua Breidenbach; Bradley Gehring; Shravan Leonard-Murali; Sadik A Khuder; Michael Rees; Robert C Green; Stanislaw M Stepkowski Journal: Front Immunol Date: 2020-10-29 Impact factor: 7.561
Authors: K J Tinckam; R Liwski; D Pochinco; M Mousseau; A Grattan; P Nickerson; P Campbell Journal: Am J Transplant Date: 2015-06-16 Impact factor: 8.086