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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.

SummaryPlatelet 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. Citation: Gordon WJ, Baronas J, Lane WJ. A FHIR human leukocyte antigen (HLA) interface for platelet transfusion Support. Appl Clin Inform 2017; 8: 603–611 https://doi.org/10.4338/ACI-2017-01-CR-0010

Aim Currently, there are two different approaches to calculating cPRA. The first, used in USA since 2009, is the expectation maximization (EM) algorithm. The second, utilized in Europe/Canada, is to simply observe the percentage of donors with a particular antigen (“count method”). Recently, the UNOS/OPTN Histocompatibility Committee has been re-evaluating the optimal method for determining cPRA. This has been driven by the need to consider incorporating DP (and other) locus antibodies. The benefit of the EM method, based on haplotype frequencies, is to estimate population frequencies that would include unobserved, rare phenotypes. However, because DP is not in linkage disequilibrium, it may no longer be feasible to use. Here, we evaluate the use of the count method in comparison with the EM approach for determining HLA antigen frequencies in the U.S. donor population. Methods Using the UNOS/OPTN data, we evaluated HLA typings from 39,568 deceased donors from 1/2015–1/2018. We then compared the observed antigen frequencies (count method) with those derived from the UNet cPRA calculator (EM method). Results See table. Download : Download high-res image (81KB) Download : Download full-size image Conclusions There was high concordance between the two methods of calculating cPRA in antigen frequencies within the A, B, and DR loci. Instances where there were large discrepancies were primarily due to parent antigens that were subsequently split; the UNet cPRA was based upon a pool of donors from 2007–9. This suggests, at the very least, that updates to the cPRA calculator are needed. Since DPB is not in linkage disequilibrium with the other loci, thereby negating our ability to use the EM algorithm, we suggest a new updated cPRA calculator be based upon the count method of calculating antigen frequencies.

Aim Platelet transfusions are a cornerstone of therapy for patients who develop thrombocytopenia while undergoing hematopoietic stem cell transplantation (HSCT). Many of these patients demonstrate anti-HLA alloantibody mediated platelet transfusion refractoriness (PTR) and require HLA-matched platelets. The calculated panel reactive antibody (cPRA) is a useful tool for identifying patients at risk for PTR, but it requires manual intervention to calculate and so it is only calculated when requested. However, routine use of cPRA before every HSCT, using the existing pre-HSCT alloantibody results, could identify PTR earlier and more efficiently manage its complex transfusion needs for improved patient care. To accomplish this goal, we created an electronic medical record (EMR) web app for use by clinicians, blood bank/donor center, and tissue typing lab to better identify and manage PTR patients. Methods An online cPRA web-service was created to electronically receive alloantibody data and return cPRA values for several different ethnicities. Additionally, to increase flexibility, the cPRA web-service calculation can be customized with user defined antigen frequencies and epitopes. To utilize the cPRA web-service, a PTR web app was created with connections to several clinical data sources including: Tissue Typing Lab, Blood Bank, Donor Center, and EMR. The PTR web app was embedded into the EMR so that it could be easily viewed while caring for patients undergoing HSCT. Platelet utilization and transfusion effectiveness were compared before and after implementation. Results The cPRA web-service allows for automated computer generated cPRAs, making it feasible to calculate up-to-date cPRAs for every HSCT patient. The web app displays platelet counts along with platelet transfusion events, platelet units in inventory, and has a section for the clinical care teams and lab staff to communicate. We plan on making the cPRA web-service publicly available for others to use and customize. Conclusions We built a customizable cPRA web-service and platelet transfusion visualization web app to provide better point-of-care access to real time cPRA values to enable PTR risk stratification of HSCT patients. The web app also summarizes all of the key clinical data to efficiently manage PRT and improve platelet usage.