Characterisation of the novel HLA-B*58:02:04 allele by next-generation sequencing.

HLA-B*58:02:04 differs from HLA-B*58:02:01 by one synonymous nucleotide in codon 215 in exon 4.

Platelet transfusions in haploidentical haematopoietic stem cell allograft candidates: Protecting HLA-A and HLA-B antigens through eplet analysis.

In patients awaiting an allogeneic haematopoietic stem cell transplantation, platelet transfusion is a risk factor for anti-HLA class I immunization because the resulting donor-specific antibodies complicate the allograft process. The objective of the present study was to determine the feasibility of a novel eplet-based strategy for identifying HLA class I mismatches between potential donors and the recipient when pre-allograft platelet transfusions were required. We included 114 recipient/haploidentical relative pairs. For each pair, we entered HLA-class I typing data into the HLA Eplet Mismatch calculator, defined the list of mismatched eplets (for the recipient versus donor direction) and thus identified the shared HLAs to be avoided. Using this list of HLAs, we defined the theoretical availability of platelet components (PCs) by calculating the virtual panel-reactive antibody (vPRA). We also determined the number of PCs actually available in France by querying the regional transfusion centre's database. The mean ± standard deviation number of highly/moderately exposed eplets to be avoided in platelet transfusions was 5.8 ± 3.3, which led to the prohibition of 38.5 ± 2 HLAs-A and -B. Taking into account the mismatched antigens and the eplet load, the mean ± standard deviation theoretical availability of PCs (according to the vPRA) was respectively 34.49% ± 1.95% for HLA-A and 80% ± 2.3% for HLA-B. A vPRA value below 94.9% for highly or moderately exposed eplets would predict that 10 PCs were actually available nationally. Although epitope protection of HLA molecules is feasible, it significantly restricts the choice of PCs.

Characterization of the novel HLA-B*07:491 allele by next generation sequencing.

HLA-B*07:491 differs from HLA-B*07:02:01 by one nucleotide substitution in codon 218 in exon 4.

Characterization of the novel HLA-C*05:282Q allele by next generation sequencing.

HLA-C*05:282Q differs from HLA-C*05:01:01 by one nucleotide substitution in codon -24 in exon 1.

Characterization of the novel HLA-DRB1*14:255 allele by next generation sequencing.

HLA-DRB1*14:255 differs from HLA-DRB1*14:54:01 by one nucleotide substitution in codon 205 in exon 4.

Characterization of the novel HLA-DQB1*06:03:47 allele by next generation sequencing.

HLA-DQB1*06:03:47 differs from HLA-DQB1*06:03:01 by one synonymous nucleotide substitution in codon 158 in exon 3.

Characterization of the novel HLA-DPB1*1359:01 allele by next generation sequencing.

HLA-DPB1*1359:01 differs from HLA-DPB1*03:01:01 by one nucleotide substitution in codon 77 in exon 2.

Characterization of the novel HLA-C*03:613 allele by next generation sequencing.

HLA-C*03:613 differs from HLA-C*03:02:02 by one nucleotide substitution in codon 190 in exon 4.

HLA graph, a free and ready-to-use bioinformatics tool to explore anti-HLA eplets reactivity pattern.

INTRODUCTION: HLA antigens are highly polymorphic, and their immunogenicity is dependent on the configurations of polymorphic amino acids. Monitoring anti-HLA immunization is essential in organ transplantation, as antibodies directed against HLA molecules are a major cause of rejection. Anti-HLA antibodies are not specific for HLA antigens, but recognize B-cell epitopes present on HLA molecules. METHODS: To better understand antibody reactivity patterns, we calculated the Spearman correlation of the mean fluorescence intensity (MFI) of anti-HLA antibodies identified by a single-antigen assay performed using a Luminex® immunobeads assay on a large number of samples. Then, we built a computer tool analyzing antibody reactivity patterns with an accessibility by a web browser linked to the International Epitope Registry. We also extended our model to Onelambda® and Lifecodes® single-antigen class I and class II assays. RESULTS AND DISCUSSION: The resulting MFI correlations reflect HLA antibody cross-reactivity and eplets similarity. We built HLA Graph, a computer tool that analyzes the eplets involved in antibody reactivity profiles. HLA Graph is usable with Onelambda® and Lifecodes® single-antigen class I and class II assays. The interpretation of reactivity against alleles not tested by the antibody assays and against the alpha and beta chains of HLA-DQ and HLA-DP loci were also developed. CONCLUSION: HLA Graph is a free and ready-to-use bioinformatics tool that can be used by all laboratories performing anti-HLA antibody identification by immunobead assay.

Utility of assessing CD3+ cell chimerism within the first months after allogeneic hematopoietic stem-cell transplantation for acute myeloid leukemia.

After allogeneic hematopoietic stem-cell transplantation (alloHSCT), the chimerism assay is used to monitor cell engraftment and quantify the respective proportions of donor/recipient cells in blood or bone-marrow samples. Here, we aimed to better assess the utility of determining CD3+ cell chimerism within the first 6 months post alloHSCT. One hundred and thirty five patients diagnosed with acute myeloid leukemia were enrolled in this study. We observed significantly lower overall survival and relapse free survival for patients without full donor chimerism (<95%, <98%, <99%) in whole blood at Day 30, as well as at Day 90 after alloHSCT, than for patients with full donor chimerism. This outcome was not observed when assessing selected CD3+ cells. However, at Day 90, patients with discordant whole blood versus selected CD3+ cell chimerism showed both significantly lower overall survival and relapse free survival, giving an interest to assess selected cells chimerism.

Characterization of the novel HLA-C*04:438 allele by next generation sequencing.

HLA-C*04:438 differs from HLA-C*04:01:01 by one nucleotide substitution in codon 237 in exon 4.

Characterization of the novel HLA-DQA1*01:80 allele by next generation sequencing.

HLA-DQA1*01:80 differs from HLA-DQA1*01:04:01 by one nucleotide substitution in codon -18 in exon 1.

Characterization of the novel HLA-C*04:450 allele by next-generation sequencing.

HLA-C*04:450 differs from HLA-C*04:01:01 by one nucleotide substitution in codon 328 in exon 7.

Characterization of the novel HLA-DRB1*04:326 allele by next generation sequencing.

HLA-DRB1*04:326 differs from HLA-DRB1*04:01:01 by one nucleotide substitution in codon 23 in exon 2.

Characterization of the novel HLA-C*01:214 allele by sequencing-based typing.

HLA-C*01:214 differs from HLA-C*01:02:01:01 by one nucleotide substitution in codon -10 in exon 1.

Antibodies against HLA cross-reactivity groups: From single antigen bead assay to immunoinformatics interpretation of epitopes.

Identification of anti-human leukocyte antigen (HLA) antibodies (Abs) is based on Luminex™ technology. We used bioinformatics to (i) study the correlations of mean fluorescence intensities (MFIs) for all the possible allele pairs, and (ii) determine the degree of epitope homology between HLA antigens. Using MFI data on anti-HLA Abs from 6000 Luminex™ assays, we provide an updated overview of class I and II HLA antigen cross-reactivity in which each node corresponded to an allele and each link corresponded to a strong correlation between two alleles (Spearman's ρ > 0.8). We compared these correlations with the serological groups and the results of an epitope analysis. The strongest correlations concerned allele-specific Abs directed against the same antigen. For the HLA-A locus, the highest values of Spearman's ρ reflected broad specificity. For the HLA-B locus, graphs defined the HLA-Bw4 public epitope, and correlations between HLA-A and -B alleles were only present for beads with the same Bw4 public epitope. For the HLA-C locus, we identified two groups that differed with regard to their KIR ligand subclassification. Lastly, the HLA-DRB1 subgroups were part of a network. In the epitope analysis, Spearman's ρ was related to the number of matched epitopes within pairs of alleles. The combination of Spearman's ρ with simple, undirected graphing constitutes an effective tool for understanding routinely encountered cross-reactivity profiles. Based on this model, we have implemented an online data visualization tool available at http://cusureau.pythonanywhere.com/.

A one-step assay for sorted CD3+ cell purity and chimerism after hematopoietic stem cell transplantation.

A hematopoietic chimerism assay is the laboratory test for monitoring engraftment and quantifying the proportions of donor and recipient cells after hematopoietic stem cell transplantation recipients. Flow cytometry is the reference method for determining the purity of CD3+ cells on the chimerism of selected CD3+ cells. In the present study, we developed a single-step procedure that combines the CD3+ purity assay (using the PCR-based Non-T Genomic Detection Kit from Accumol, Calgary, Canada) and the qPCR chimerism monitoring assay (the QTRACE qPCR assay from Jeta Molecular, Utrecht, the Netherlands). First, for the CD3+ purity assay, we used a PCR-friendly protocol by changing the composition of the ready-to-use reaction tubes (buffer and taq polymerase) and obtained a satisfactory calibration plot (R2 = 0.8924) with a DNA reference scale of 2 ng/µl. Next, 29 samples (before and after CD3 positive selection) were analyzed, the mean cell purity was, respectively, 19.6% ± 6.45 and 98.9% ± 1.07 in the flow cytometry assay; 26.8% ± 7.63 and 98.5% ± 1.79 in the PCR-based non-T genomic detection assay. Our results showed that the CD3+ purity assay using a qPCR kit is a robust alternative to the flow cytometry assay and is associated with time savings when combined with a qPCR chimerism assay.

[Relevance of antibodies in hematopoietic stem cell transplantation: Antibodies anti-HLA, anti-platelets, anti-granulocytes, anti-erythrocytes and anti-MICA. Guidelines from the Francophone Society of Bone Marrow Transplantation and Cellular Therapy (SFGM-TC)]. / Importance des anticorps dans la prise en charge de la greffe de cellules souches hématopoïétiques : anticorps anti-HLA, anti-plaquettes, anti-granuleux, anti-érythrocytes et anti-MICA. Recommandations de la Société francophone de greffe de moelle et de thérapie cellulaire (SFGM-TC).

The presence of allo-antibodies in the serum of a recipient awaiting hematopoietic stem cell transplantation (HSCT) may have an impact on transfusion efficiency and/or donor choice, especially in the absence of an identical sibling donor. Prior to transplantation, donor specific anti-HLA (Human Leukocyte Antigen) antibodies (DSA) have a recognized effect on transplant outcome, correlated with the increasing MFI value and with the ability of such antibody to fix the complement fraction. Anti-platelet antibodies (anti-HLA class I and anti-HPA [Human Platelet Antigen]) are better involved in transfusion inefficiency and can be responsible for refractory status. ABO incompatibilities require a specific treatment of the graft in presence of high titer to avoid hemolytic adverse effects. Investigations of these antibodies should be carried out on a regular basis in order to establish appropriate transfusion recommendation, select an alternative donor when possible or adapt the source of cells. After transplantation, in case of delayed recovery or graft rejection, long term aplasia, persistent mixed chimerism or late release, and after elimination of the main clinical causes, a biological assessment targeted on the different type of antibodies will have to be performed in order to orient towards the cause or the appropriate therapy. Further studies should be carried out to determine the impact of anti-MICA antibodies and recipient specific anti-HLA antibodies, on the outcome of the transplantation.

Flow cytometry crossmatching to investigate kidney-biopsy-proven, antibody-mediated rejection in patients who develop de novo donor-specific antibodies.

INTRODUCTION: The appearance of de novo donor-specific anti-human leukocyte antigen antibodies (dnDSAs) after kidney transplantation is independently associated with poor long-term allograft outcomes. The objective of the present study was to evaluate the predictive value of a flow cytometry crossmatching (FC-XM) assay after the appearance of dnDSAs related to antibody-mediated allograft rejection (ABMR) after kidney transplantation. MATERIALS AND METHODS: A total of 89 recipients with dnDSAs after transplantation were included. The crossmatching results were compared with the dnDSA profile (the mean fluorescence intensity (MFI), the complement-binding activity, and the IgG subclass profile) and the biopsy's morphological features. RESULTS: Of the 89 patients, 59 (66%) were positive in an FC-XM assay, 17 (19%) had complement-binding DSAs, 55 (62%) were positive for IgG1 and/or IgG3 in a solid phase assay, and 45 (51%) had morphological biopsy features linked to ABMR. CONCLUSION: An FC-XM assay was unable to discriminate between cases with or without ABMR on biopsy findings; it had a low positive predictive value (<70%) and a low negative positive predictive value (<42.9%), taking into account the sensitivity of our assay (limit of detection: DSAs with an MFI >3000). In this context, the height of the MFI of the dnDSAs might be enough for a high positive predictive value for ABMR and additional testing for complement binding activity can remain optional.

HLA-B*15:47:01 allele with undefined serological equivalent considered as B Blank.

We report a discordance between complement-dependent cytotoxicity and next-generation sequencing molecular typing revealing HLA-B*15:47:01 allele with undefined serological equivalent confirmed by high-level immunization against the B15 serotype. Due to the high-level immunization against HLA-B15 and B70 antigens, we considered the HLA-B*15:47:01 allele to be B Blank and not as B15 or B70 serological specificity.