ABO Genotyping finds more A2 to B kidney transplant opportunities than lectin-based subtyping.

ABO compatibility is important for kidney transplantation, with longer waitlist times for blood group B kidney transplant candidates. However, kidneys from non-A1 (eg, A2) subtype donors, which express less A antigen, can be safely transplanted into group B recipients. ABO subtyping is routinely performed using anti-A1 lectin, but DNA-based genotyping is also possible. Here, we compare lectin and genotyping testing. Lectin and genotype subtyping was performed on 554 group A deceased donor samples at 2 transplant laboratories. The findings were supported by 2 additional data sets of 210 group A living kidney donors and 124 samples with unclear lectin testing sent to a reference laboratory. In deceased donors, genotyping found 65% more A2 donors than lectin testing, most with weak lectin reactivity, a finding supported in living donors and samples sent for reference testing. DNA sequencing and flow cytometry showed that the discordances were because of several factors, including transfusion, small variability in A antigen levels, and rare ABO∗A2.06 and ABO∗A2.16 sequences. Although lectin testing is the current standard for transplantation subtyping, genotyping is accurate and could increase A2 kidney transplant opportunities for group B candidates, a difference that should reduce group B wait times and improve transplant equity.

Multiple GYPB gene deletions associated with the U- phenotype in those of African ancestry.

BACKGROUND: The MNS blood group system is defined by three homologous genes: GYPA, GYPB, and GYPE. GYPB encodes for glycophorin B (GPB) carrying S/s and the "universal" antigen U. RBCs of approximately 1% of individuals of African ancestry are U- due to absence of GPB. The U- phenotype has long been attributed to a deletion encompassing GYPB exons 2 to 5 and GYPE exon 1 (GYPB*01N). STUDY DESIGN AND METHODS: Samples from two U-individuals underwent Illumina short read whole genome sequencing (WGS) and Nanopore long read WGS. In addition, two existing WGS datasets, MedSeq (n = 110) and 1000 Genomes (1000G, n = 2535), were analyzed for GYPB deletions. Deletions were confirmed by Sanger sequencing. Twenty known U- donor samples were tested by a PCR assay to determine the specific deletion alleles present in African Americans. RESULTS: Two large GYPB deletions in U- samples of African ancestry were identified: a 110 kb deletion extending left of GYPB (DEL_B_LEFT) and a 103 kb deletion extending right (DEL_B_RIGHT). DEL_B_LEFT and DEL_B_RIGHT were the most common GYPB deletions in the 1000 Genomes Project 669 African genomes (allele frequencies 0.04 and 0.02). Seven additional deletions involving GYPB were seen in African, Admixed American, and South Asian samples. No samples analyzed had GYPB*01N. CONCLUSIONS: The U- phenotype in those of African ancestry is primarily associated with two different complete deletions of GYPB (with intact GYPE). Seven additional less common GYPB deletion backgrounds were found. GYPB*01N, long assumed to be the allele commonly encoding U- phenotypes, appears to be rare.

Automated typing of red blood cell and platelet antigens from whole exome sequences.

BACKGROUND: Genotyping has expanded the number red blood cell (RBC) and platelet (PLT) antigens that can readily be typed, but often represents an additional testing cost. The analysis of existing genomic data offers a cost-effective approach. We recently developed automated software (bloodTyper) for determination of RBC and PLT antigens from whole genome sequencing. Here we extend the algorithm to whole exome sequencing (WES). STUDY DESIGN AND METHODS: Whole exome sequencing was performed on samples from 75 individuals. WES-based bloodTyper RBC and PLT typing was compared to conventional polymerase chain reaction (PCR) RHD zygosity testing and serologic and single-nucleotide polymorphism (SNP) typing for 38 RBC antigens in 12 systems (17 serologic and 35 SNPs) and 22 PLT antigens (22 SNPs). Samples from the first 20 individuals were used to modify bloodTyper to interpret WES followed by blinded typing of 55 samples. RESULTS: Over the first 20 samples, discordances were noted for C, M, and N antigens, which were due to WES-specific biases. After modification, bloodTyper was 100% accurate on blinded evaluation of the last 55 samples and outperformed both serologic (99.67% accurate) and SNP typing (99.97% accurate) reflected by two Fyb and one N serologic typing errors and one undetected SNP encoding a Jknull phenotype. RHD zygosity testing by bloodTyper was 100% concordant with a combination of hybrid Rhesus box PCR and PCR-restriction fragment length polymorphism for all samples. CONCLUSION: The automated bloodTyper software was modified for WES biases to allow for accurate RBC and PLT antigen typing. Such analysis could become a routing part of future WES efforts.

A whole genome approach for discovering the genetic basis of blood group antigens: independent confirmation for P1 and Xga.

BACKGROUND: Although P1 and Xga are known to be associated with the A4GALT and XG genes, respectively, the genetic basis of antigen expression has been elusive. Recent reports link both P1 and Xga expression with nucleotide changes in the promotor regions and with antigen-negative phenotypes due to disruption of transcription factor binding. STUDY DESIGN AND METHODS: Whole genome sequencing was performed on 113 individuals as part of the MedSeq Project with serologic RBC antigen typing for P1 (n = 77) and Xga (n = 15). Genomic data were analyzed by two approaches, nucleotide frequency correlation and serologic correlation, to find A4GALT and XG changes associated with P1 and Xga expression. RESULTS: For P1, the frequency approach identified 29 possible associated nucleotide changes, and the serologic approach revealed four among them correlating with the P1+/P1- phenotype: chr22:43,115,523_43,115,520AAAG/delAAAG (rs66781836); chr 22:43,114,551C/T (rs8138197); chr22:43,114,020 T/G (rs2143918); and chr22:43,113,793G/T (rs5751348). For Xga , the frequency approach identified 82 possible associated nucleotide changes, and among these the serologic approach revealed one correlating with the Xg(a+)/Xg(a-) phenotype: chrX:2,666,384G/C (rs311103). CONCLUSION: A bioinformatics analysis pipeline was created to identify genetic changes responsible for RBC antigen expression. This study, in progress before the recently published reports, independently confirms the basis for P1 and Xga . Although this enabled molecular typing of these antigens, the Y chromosome PAR1 region interfered with Xga typing in males. This approach could be used to identify and confirm the genetic basis of antigens, potentially replacing the historical approach using family pedigrees as genomic sequencing becomes commonplace.

Automated typing of red blood cell and platelet antigens: a whole-genome sequencing study.

BACKGROUND: There are more than 300 known red blood cell (RBC) antigens and 33 platelet antigens that differ between individuals. Sensitisation to antigens is a serious complication that can occur in prenatal medicine and after blood transfusion, particularly for patients who require multiple transfusions. Although pre-transfusion compatibility testing largely relies on serological methods, reagents are not available for many antigens. Methods based on single-nucleotide polymorphism (SNP) arrays have been used, but typing for ABO and Rh-the most important blood groups-cannot be done with SNP typing alone. We aimed to develop a novel method based on whole-genome sequencing to identify RBC and platelet antigens. METHODS: This whole-genome sequencing study is a subanalysis of data from patients in the whole-genome sequencing arm of the MedSeq Project randomised controlled trial (NCT01736566) with no measured patient outcomes. We created a database of molecular changes in RBC and platelet antigens and developed an automated antigen-typing algorithm based on whole-genome sequencing (bloodTyper). This algorithm was iteratively improved to address cis-trans haplotype ambiguities and homologous gene alignments. Whole-genome sequencing data from 110 MedSeq participants (30 × depth) were used to initially validate bloodTyper through comparison with conventional serology and SNP methods for typing of 38 RBC antigens in 12 blood-group systems and 22 human platelet antigens. bloodTyper was further validated with whole-genome sequencing data from 200 INTERVAL trial participants (15 × depth) with serological comparisons. FINDINGS: We iteratively improved bloodTyper by comparing its typing results with conventional serological and SNP typing in three rounds of testing. The initial whole-genome sequencing typing algorithm was 99·5% concordant across the first 20 MedSeq genomes. Addressing discordances led to development of an improved algorithm that was 99·8% concordant for the remaining 90 MedSeq genomes. Additional modifications led to the final algorithm, which was 99·2% concordant across 200 INTERVAL genomes (or 99·9% after adjustment for the lower depth of coverage). INTERPRETATION: By enabling more precise antigen-matching of patients with blood donors, antigen typing based on whole-genome sequencing provides a novel approach to improve transfusion outcomes with the potential to transform the practice of transfusion medicine. FUNDING: National Human Genome Research Institute, Doris Duke Charitable Foundation, National Health Service Blood and Transplant, National Institute for Health Research, and Wellcome Trust.

International validation of a dithiothreitol (DTT)-based method to resolve the daratumumab interference with blood compatibility testing.

BACKGROUND: Daratumumab (DARA) consistently interferes with routine blood bank serologic testing by directly binding to CD38 expressed on reagent red blood cells (RBCs). Treating RBCs with dithiothreitol (DTT) eliminates the DARA interference. We conducted an international, multicenter, blinded study aimed at validating the DTT method for use by blood bank laboratories worldwide. STUDY DESIGN AND METHODS: Paired plasma sample unknowns were sent to 25 participating blood bank laboratories. Sample 1 was spiked with DARA only (10 µg/mL), and Sample 2 with DARA plus a clinically significant RBC antibody (anti-D [n = 6], anti-Fya [n = 9], or anti-s [n = 10]). Sites were instructed to perform an antibody screen with and without DTT-treated RBCs and to use a DTT-treated RBC panel for antibody identification. Qualitative data about the DTT method were collected by online survey. The primary outcome was the proportion of study sites able to identify the antibody unknown in the presence of DARA. RESULTS: All sites observed the DARA interference with the antibody screen. The DARA interference was seen with all testing methods (gel, tube, or solid phase). Using the DTT method, 25 of 25 sites (100%) successfully identified the antibody unknown in the presence of DARA. Feedback on the DTT method was positive, with 17 of 19 (90%) sites responding to the survey indicating that they planned to use the DTT method to test clinical samples from DARA-treated patients. CONCLUSION: The DTT method is robust and reproducible and can be implemented by transfusion services worldwide to help provide safe blood products to patients treated with DARA.

Comprehensive red blood cell and platelet antigen prediction from whole genome sequencing: proof of principle.

BACKGROUND: There are 346 serologically defined red blood cell (RBC) antigens and 33 serologically defined platelet (PLT) antigens, most of which have known genetic changes in 45 RBC or six PLT genes that correlate with antigen expression. Polymorphic sites associated with antigen expression in the primary literature and reference databases are annotated according to nucleotide positions in cDNA. This makes antigen prediction from next-generation sequencing data challenging, since it uses genomic coordinates. STUDY DESIGN AND METHODS: The conventional cDNA reference sequences for all known RBC and PLT genes that correlate with antigen expression were aligned to the human reference genome. The alignments allowed conversion of conventional cDNA nucleotide positions to the corresponding genomic coordinates. RBC and PLT antigen prediction was then performed using the human reference genome and whole genome sequencing (WGS) data with serologic confirmation. RESULTS: Some major differences and alignment issues were found when attempting to convert the conventional cDNA to human reference genome sequences for the following genes: ABO, A4GALT, RHD, RHCE, FUT3, ACKR1 (previously DARC), ACHE, FUT2, CR1, GCNT2, and RHAG. However, it was possible to create usable alignments, which facilitated the prediction of all RBC and PLT antigens with a known molecular basis from WGS data. Traditional serologic typing for 18 RBC antigens were in agreement with the WGS-based antigen predictions, providing proof of principle for this approach. CONCLUSION: Detailed mapping of conventional cDNA annotated RBC and PLT alleles can enable accurate prediction of RBC and PLT antigens from whole genomic sequencing data.

Resolving the daratumumab interference with blood compatibility testing.

BACKGROUND: Daratumumab (DARA), a promising novel therapy for multiple myeloma, is an IgG1κ monoclonal antibody that recognizes CD38 on myeloma cells. During routine compatibility testing, we observed that the plasma of five of five DARA-treated patients demonstrated a positive antibody screen and panreactivity on red blood cell (RBC) panel testing. We hypothesized that the observed panreactivity reflected DARA binding to CD38 on reagent RBCs, and we investigated methods to prevent this binding. STUDY DESIGN AND METHODS: DARA binding to CD38+ or CD38- HL60 cells was assessed by flow cytometry. To remove cell surface CD38, cells were incubated with dithiothreitol (DTT) or trypsin. Soluble CD38 or anti-DARA was used to neutralize DARA in solution. Routine blood bank serologic methods were used to test samples from DARA-treated patients and normal plasma samples spiked with DARA and/or alloantibodies. RESULTS: Normal plasma samples spiked with DARA (0.1-10 µg/mL) and incubated with reagent RBCs recapitulated the interference observed with samples from DARA-treated patients. Flow cytometry experiments confirmed DARA binding to CD38+ HL60 cells, but not to CD38- controls. DTT treatment of CD38+ HL60 cells reduced DARA binding by 92% by denaturing cell surface CD38. Treating DARA-containing plasma with soluble CD38 or anti-DARA idiotype also inhibited DARA binding. CONCLUSION: DARA causes panreactivity in vitro by binding to CD38 on reagent RBCs. Treating reagent RBCs with DTT is a robust method to negate the DARA interference, enabling the safe provision of blood to DARA-treated patients. Because DTT denatures Kell antigens, K- units are provided to these patients.