Lymphocyte Crossmatch Testing or Donor HLA-DP and -DQ Allele Typing Effectiveness in Single Cord Blood Transplantation for Patients With Anti-HLA Antibodies Other Than Against HLA-A, -B, -C, and -DRB1

Anti-human leukocyte antigen (HLA) antibodies other than those against HLA-A, -B, -C, and DRB1 are a risk factor for engraftment delay and failure, especially in cord blood transplantation (CBT). The primary objective of this study was to assess the impact of the presence of anti-HLA antibodies on CBT and to evaluate the utility of lymphocyte crossmatch testing or additional HLA-DP and -DQ typing of CB units in improving transplant outcomes. We retrospectively assessed the engraftment rates and transplant outcomes of 772 patients who underwent their first CBT at our hospital between 2012 and 2021. Donors were routinely typed for HLA-A, -B, -C, and-DRB1 alleles, and the anti-HLA antibodies of recipients were screened before donor selection in all cases. Among patients who had antibodies against other than HLA-A, -B, -C, and DRB1 (n = 58), lymphocyte crossmatch testing (n = 32) or additional HLA-DP/-DQ alleles typing of CB (n = 15) was performed to avoid the use of units with corresponding alleles. The median patient age was 57 years (16 to 77). Overall, 75.7% had a high-risk disease status at transplantation, 83.5% received myeloablative conditioning regimens, and >80% were heavily transfused. Two hundred twenty-nine of the 772 recipients (29.6%) were positive for anti-HLA antibodies. There were no statistical differences in the number of infused CD34-positive cells between the anti-HLA antibody-positive and the anti-HLA antibody-negative patients. Of the 229 patients with anti-HLA antibodies, 168 (73.3%) had antibodies against HLA-A, -B, -C, and-DRB1 (Group A), whereas 58 (25.3%) had antibodies against HLA-DP, HLA-DQ, or -DRB3/4/5 with or without antibodies against HLA-A, -B, -C, and -DRB1 (Group B). No patients in both Groups A and B exhibited donor-specific anti-HLA antibodies against HLA-A, -B, -C, and -DRB1. The neutrophil engraftment rate was lower in patients with anti-HLA antibodies than in those without antibodies (89.9% versus 94.1%), whereas nonrelapse mortality (NRM) before engraftment was higher in antibody-positive patients (9.6% versus 4.9%). In patients who received 2 or more HLA allele-mismatched CB in the host-versus-graft (HVG) direction (n = 685), the neutrophil engraftment rate was lower in the anti-HLA antibody-positive recipients than in the antibody-negative recipients with significant differences (88.8% versus 93.8%) (P = .049). Similarly, transplant outcomes were worse in the antibody-positive patients with respect to 2-year overall survival (OS) (43.1% versus 52.3%) and NRM (44.0% versus 30.7%) than in the antibody-negative patients. In contrast, the results of Group B were comparable to those of the antibody-negative patients, while those of Group A were statistically worse than the antibody-negative patients in terms of all engraftment rate (88.6%), OS (34.2%), and NRM (49.0%). The presence of anti-HLA antibodies negatively impacts engraftment, NRM, and OS in CBT. However, HLA-DP/-DQ allele typing of CB units or lymphocyte crossmatch testing could be a useful strategy to overcome poor engraftment rates and transplant outcomes, especially in patients with anti-HLA antibodies against HLA-DP, HLA-DQ, or -DRB3/4/5.

BacTag - a pipeline for fast and accurate gene and allele typing in bacterial sequencing data based on database preprocessing

BACKGROUND

Bacteria carry a wide array of genes, some of which have multiple alleles. These different alleles are often responsible for distinct types of virulence and can determine the classification at the subspecies levels (e.g., housekeeping genes for Multi Locus Sequence Typing, MLST). Therefore, it is important to rapidly detect not only the gene of interest, but also the relevant allele. Current sequencing-based methods are limited to mapping reads to each of the known allele reference, which is a time-consuming procedure.

RESULTS

To address this limitation, we developed BacTag - a pipeline that rapidly and accurately detects which genes are present in a sequencing dataset and reports the allele of each of the identified genes. We exploit the fact that different alleles of the same gene have a high similarity. Instead of mapping the reads to each of the allele reference sequences, we preprocess the database prior to the analysis, which makes the subsequent gene and allele identification efficient. During the preprocessing, we determine a representative reference sequence for each gene and store the differences between all alleles and this chosen reference. Throughout the analysis we estimate whether the gene is present in the sequencing data by mapping the reads to this reference sequence; if the gene is found, we compare the variants to those in the preprocessed database. This allows to detect which specific allele is present in the sequencing data. Our pipeline was successfully tested on artificial WGS E. coli, S. pseudintermedius, P. gingivalis, M. bovis, Borrelia spp. and Streptomyces spp. data and real WGS E. coli and K. pneumoniae data in order to report alleles of MLST house-keeping genes.

CONCLUSIONS

We developed a new pipeline for fast and accurate gene and allele recognition based on database preprocessing and parallel computing and performed better or comparable to the current popular tools. We believe that our approach can be useful for a wide range of projects, including bacterial subspecies classification, clinical diagnostics of bacterial infections, and epidemiological studies.