How do I manage the platelet transfusion-refractory patient?

BACKGROUND: Platelet transfusion-refractoriness is a challenging and expensive clinical scenario seen most often in patients with hematologic malignancies. Although the majority of platelet transfusion-refractory cases are due to nonimmune causes, a significant minority are caused by alloimmunization against Class I human leukocyte antigens (HLAs) or human platelet antigens (HPAs). Such platelet transfusion-refractory patients can be effectively managed with appropriate antigen-negative products. STUDY DESIGN AND METHODS: Our institution has developed a diagnostic and management algorithm for the platelet transfusion-refractory patient with an early focus on identifying those cases caused by immune-mediated factors. Using physical platelet cross-matches to initially classify platelet transfusion-refractory patients as immune-mediated or not, cross-match-compatible inventory is then provided to immune-mediated patients, whereas subsequent HLA (with or without HPA) testing is performed. RESULTS: Our blood donor program performs Class I HLA typing of all repeat platelet donors to facilitate the identification of antigen-negative platelet units (virtual cross-matching) as well as the recruitment of HLA-matched donors. The platelet transfusion-refractoriness algorithm realizes an initial net cost savings once two apheresis platelets are saved from use for each newly identified, immune-mediated platelet transfusion-refractory patient. CONCLUSION: An algorithm utilizing physical platelet cross-matches, Class I HLA and HPA antibody testing, and upfront Class I HLA typing of platelet donors leads to overall resource savings and improved clinical management for platelet transfusion-refractory patients.

Concordance between predicted HLA type using next generation sequencing data generated for non-HLA purposes and clinical HLA type.

We explored the feasibility of obtaining accurate HLA type using pre-existing NGS data not generated for HLA purposes. 83 exomes and 500 targeted NGS pharmacogenomic panels were analyzed using Omixon HLA Explore, OptiType, and/or HLA-Genotyper software. Results were compared against clinical HLA genotyping. 765 (94.2%) Omixon and 769 (94.7%) HLA-Genotyper of 812 germline allele calls across class I/II loci and 402 (99.5%) of 404 OptiType class I calls were concordant to the second field (i.e. HLA-A*02:01). An additional 19 (2.3%) Omixon, 39 (4.8%) HLA-Genotyper, and 2 (0.5%) OptiType allele calls were first field concordant (i.e. HLA-A*02). Using Omixon, four alleles (0.4%) were discordant and 24 (3.0%) failed to call, while 4 alleles (0.4%) were discordant using HLA-Genotyper. Tumor exomes were also evaluated and were 85.4%, 91.6%, and 100% concordant (Omixon and HLA-Genotyper with 96 alleles tested, and Optitype with 48 class I alleles, respectively). The 15 exomes and 500 pharmacogenomic panels were 100% concordant for each pharmacogenomic allele tested. This work has broad implications spanning future clinical care (pharmacogenomics, tumor response to immunotherapy, autoimmunity, etc.) and research applications.