BACKGROUND Anti‐CD47 (Hu5F9‐G4) is a human monoclonal immunoglobulin G (IgG)4 antibody that is in clinical trials to treat hematologic or solid malignancies. CD47, a glycoprotein expressed on all cells, binds to signal‐regulatory protein α on macrophages and regulates phagocytosis. Blocking CD47 is thought to enhance phagocytosis and promote antitumor responses. Here, we evaluate drug interference in pretransfusion testing, determine mitigation strategies, and compare interference with anti‐CD38 (Daratumumab). STUDY DESIGN AND METHODS Samples from four patients were tested by standard methods. Anti‐IgG (Immucor monoclonal Gamma‐clone and Ortho BioClone) were used, and dithiothreitol and enzyme‐treated RBCs were tested. Allo‐adsorption was performed with papain treated RBCs, pooled platelets, or with commercial human platelet concentrate. Platelet antibody testing was performed according to manufacturer's instructions. RESULTS All plasma samples reacted 3+ to 4+ in all phases with all red blood cells (RBCs) by all methods including immediate spin. Stronger reactivity was observed with D– RBCs with titers as high as 16,384 at indirect antiglobulin testing. Reactivity at indirect antiglobulin testing using Gamma‐clone anti‐IgG (which does not detect IgG4) was only weakly positive and confirmed to be carryover agglutination. Plasma reacted with dithiothreitol, trypsin, papain, α‐chymotrypsin, or warm autoantibody removal medium (W.A.R.M., Immucor) treated RBCs. Direct antiglobulin testing and autocontrol were negative or weak with 3+ reactive eluates. Reactivity was removed by multiple alloadsorptions with papain‐treated cells or pooled platelets. Polyethylene glycol adsorption was invalid due to precipitation of antibody. CONCLUSION Anti‐CD47 (Hu5F9‐G4) interferes with all phases of pretransfusion testing, including ABO reverse typing. To remove interference requires multiple RBC alloadsorptions and/or the use of monoclonal Gamma‐clone anti‐IgG in the indirect antiglobulin testing.

BACKGROUND Mutation in the KLF1 gene is the cause of the In(Lu) (Inhibitor of Lutheran) Lu(a–b–) phenotype and more than 60 alleles have been associated with this phenotype. Here we describe findings from investigation of seven cases: six presenting with a Lu(a–b–) phenotype including the historical index case and one referred from a patient with chronic anemia. STUDY DESIGN AND METHODS Serologic testing was by standard methods. DNA testing included amplification and sequencing of KLF1 and LU coding regions. A StuI polymerase chain reaction–restriction fragment length polymorphism was designed to target c.304T>C in KLF1. RESULTS Five different KLF1 alleles were identified. Three are new: KLF1*90A (p.Trp30Ter), KLF*911A (p.Thr304Lys), and KLF1*304C,318G (p. Ser102Pro, Tyr106Ter) present in two unrelated individuals. Two, including the index case, had c.954dupG (p.Arg319Glufs*34), that is, KLF1*BGM06. The child with unexplained anemia had c.973G>A (p.Glu325Lys), associated with congenital dyserythropoietic anemia. The common c.304T>C was found in two of the seven samples investigated and in 60 of 100 blood donors. CONCLUSION Mutations in KLF1 are pleiotropic and although most are benign, others are associated with hematologic abnormalities. We report three new KLF1 alleles associated with benign In(Lu) and document both the molecular basis of the original In(Lu) phenotype using a frozen sample stored for more than 50 years and the cause of unexplained anemia in a child. We also confirm previous observations that c.304C (p.102Pro) is not, by itself, associated with an In(Lu) phenotype in donors self‐identified as U.S. minorities.

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.

Background Reduced D antigen on red blood cells (RBCs) may be due to “partial” D phenotypes associated with loss of epitope(s) and risk for alloimmunization or “weak” D phenotypes that do not lack major epitopes with absence of clinical complications. Genotyping of samples with weak and discrepant D typing is recommended to guide transfusion and RhIG prophylaxis. The goal was to compare the impact of RHD genotyping on transfusion practice in two centers serving different populations. Study Design and Methods Fifty‐seven samples from Denmark and 353 from the United States with weak or discrepant D typing were genotyped. RBC typing was by multiple methods and reagents. DNA isolated from white blood cells was tested with RBC‐Ready Gene D weak or CDE in Denmark or RHD BeadChip in the United States. RHD was sequenced for those unresolved. Results Of Caucasian samples from Denmark, 90% (n = 51) had weak D types 1, 2, or 3; two had other weak D, two partial D, and two new alleles. In diverse ethnic U.S. samples, 44% (n = 155) had weak D types 1, 2, or 3 and 56% (n = 198) had other alleles: uncommon weak D (n = 13), weak 4.0 (n = 62), partial D (n = 107), no RHD (n = 9), and new alleles (n = 7). Conclusion Most samples with weak or variable D typing from Denmark had alleles without risk for anti‐D. In U.S. samples, 48% could safely be treated as D+, 18% may require consideration if pregnancy possible, and 34% could potentially benefit from being treated as D–. Black and multiracial ethnicities were overrepresented relative to population.

Background and objectives Rh is one of the most diverse and complex blood group systems. Recently, next generation sequencing (NGS) has proven to be a viable option for RH genotyping. We have developed automated software (blood-Typer) for determining alleles encoding RBC antigens from NGS-based whole genome sequencing (WGS). The bloodTyper algorithm has not yet been optimized and evaluated for complex and uncommon RH alleles. Materials and methods Twenty-two samples with previous polymerase chain reaction (PCR) and Sanger sequencing-based RH genotyping underwent WGS. bloodTyper was used to detect RH alleles including those defined by structural variation (SV) using a combination of three independent strategies: sequence read depth of coverage, split reads and paired reads. Results bloodTyper was programmed to identify D negative and positive phenotypes as well as the presence of alleles encoding weak D, partial D and variant RHCE. Sequence read depth of coverage calculation accurately determined RHD zygosity and detected the presence of RHD/RHCE hybrids. RHCE*C was determined by sequence read depth of coverage and by split read methods. RHD hybrid alleles and RHCE*C were confirmed by using a paired read approach. Small SVs present in RHCE*CeRN and RHCE*ceHAR were detected by a combined read depth of coverage and paired read approach. Conclusions The combination of several different interpretive approaches allowed for automated software based-RH genotyping of WGS data including RHD zygosity and complex compound RHD and RHCE heterozygotes. The scalable nature of this automated analysis will enable RH genotyping in large genomic sequencing projects.