Impact of allele-specific anti-human leukocyte antigen class I antibodies on organ allocation.

Technological advances in the field of histocompatibility have allowed us to define anti-human leukocyte antigen (HLA) antibody specificity at the allelic level. However, how allele-specific antibodies affect organ allocation is poorly studied. We examined allelic specificities of class I HLA antibodies in 6726 consecutive serum samples from 2953 transplant candidates and evaluated their impact on the corresponding crossmatch and organ allocation. Out of 17 class I HLA antigens represented by >1 allele in the LABScreen single antigen bead assay, 12 had potential allele-specific reactivity. Taking advantage of our unbiased cohort of deceased donor-candidate testing (123,135 complement-dependent cytotoxicity crossmatches between 2014 and 2017), we estimated that the presence of allele-specific antibody detected using a single antigen bead assay (median fluorescence intensity, >3000) against only the rare allele was a poor predictor of a positive complement-dependent cytotoxicity crossmatch, with a positive predictive value of 0% to 7%, compared with 52.5% in allele-concordant class I HLA antibodies against A or B locus antigens. Further, we confirmed allele-specific reactivity using flow crossmatch in 3 scenarios: A11:01/A11:02, A68:01/A68:02, and B44:02/B44:03. Our results suggest that allele-specific antibodies may unnecessarily exclude transplant candidates (up to 10%) from organ offers by overcalling unacceptable antigens; incorporation of selective reactivity pattern in allocation may promote precision matching and more equitable allocation.

Development of a Calculated Panel Reactive Antibody Web Service with Local Frequencies for Platelet Transfusion Refractoriness Risk Stratification.

BACKGROUND: Calculated panel reactive antibody (cPRA) scoring is used to assess whether platelet refractoriness is mediated by human leukocyte antigen (HLA) antibodies in the recipient. cPRA testing uses a national sample of US kidney donors to estimate the population frequency of HLA antigens, which may be different than HLA frequencies within local platelet inventories. We aimed to determine the impact on patient cPRA scores of using HLA frequencies derived from typing local platelet donations rather than national HLA frequencies. METHODS: We built an open-source web service to calculate cPRA scores based on national frequencies or custom-derived frequencies. We calculated cPRA scores for every hematopoietic stem cell transplantation (HSCT) patient at our institution based on the United Network for Organ Sharing (UNOS) frequencies and local frequencies. We compared frequencies and correlations between the calculators, segmented by gender. Finally, we put all scores into three buckets (mild, moderate, and high sensitizations) and looked at intergroup movement. RESULTS: 2531 patients that underwent HSCT at our institution had at least 1 antibody and were included in the analysis. Overall, the difference in medians between each group's UNOS cPRA and local cPRA was statistically significant, but highly correlated (UNOS vs. local total: 0.249 and 0.243, ρ = 0.994; UNOS vs. local female: 0.474 and 0.463, ρ = 0.987, UNOS vs. local male: 0.165 and 0.141, ρ = 0.996; P < 0.001 for all comparisons). The median difference between UNOS and cPRA scores for all patients was low (male: 0.014, interquartile range [IQR]: 0.004-0.029; female: 0.0013, IQR: 0.003-0.028). Placement of patients into three groups revealed little intergroup movement, with 2.96% (75/2531) of patients differentially classified. CONCLUSIONS: cPRA scores using local frequencies were modestly but significantly different than those obtained using national HLA frequencies. We released our software as open source, so other groups can calculate cPRA scores from national or custom-derived frequencies. Further investigation is needed to determine whether a local-HLA frequency approach can improve outcomes in patients who are immune-refractory to platelets.

Recurrent genetic HLA loss in AML relapsed after matched unrelated allogeneic hematopoietic cell transplantation.

Immune evasion is a hallmark of cancer and a central mechanism underlying acquired resistance to immune therapy. In allogeneic hematopoietic cell transplantation (alloHCT), late relapses can arise after prolonged alloreactive T-cell control, but the molecular mechanisms of immune escape remain unclear. To identify mechanisms of immune evasion, we performed a genetic analysis of serial samples from 25 patients with myeloid malignancies who relapsed ≥1 year after alloHCT. Using targeted sequencing and microarray analysis to determine HLA allele-specific copy number, we identified copy-neutral loss of heterozygosity events and focal deletions spanning class 1 HLA genes in 2 of 12 recipients of matched unrelated-donor HCT and in 1 of 4 recipients of mismatched unrelated-donor HCT. Relapsed clones, although highly related to their antecedent pretransplantation malignancies, frequently acquired additional mutations in transcription factors and mitogenic signaling genes. Previously, the study of relapse after haploidentical HCT established the paradigm of immune evasion via loss of mismatched HLA. Here, in the context of matched unrelated-donor HCT, HLA loss provides genetic evidence that allogeneic immune recognition may be mediated by minor histocompatibility antigens and suggests opportunities for novel immunologic approaches for relapse prevention.

A FHIR Human Leukocyte Antigen (HLA) Interface for Platelet Transfusion Support.

Platelet transfusions are a cornerstone of therapy for patients who develop thrombocytopenia while undergoing Hematopoietic Stem Cell Transplantation (HSCT). Many patients who develop Platelet Transfusion Refractoriness (PTR) require HLA-matched platelets. Identifying these patients early could lead to better utilization of platelets as well as increased platelet counts. We built a SMART on FHIR visualization tool to aid the oncology, blood bank, and blood donor center teams in identifying these patients by showing trends in thrombocytopenia along with a computer generated calculated Panel Reactive Antibody (cPRA) level. To do this, we required a FHIR interface to our HLA database. We describe our methods and outcome for constructing this FHIR interface, as well as the architecture and data flow of HLA data from its proprietary database to the SMART on FHIR environment and application database along with RESTful cPRA web service calculator. Future work will evaluate the clinical impact of this platelet visualization tool and overall success of our FHIR implementation.