Abstract
In the setting of hematopoietic stem cell transplantation, donor-patient HLA matching is the prime donor selection criterion. Matching algorithms provide ordered lists of donors where the probability of a donor to be an HLA match is calculated in cases where either donor or patient HLA typing information is ambiguous or incomplete. While providing important information for the selection of suitable donors, these algorithms are computationally demanding and often need several minutes up to hours to generate search results. Here, we present a new search kernel implementation for Hap-E Search, the haplotype frequency-based matching algorithm of DKMS. The updated search kernel uses pre-calculated information on donor genotypes to speed up the search process. The new algorithm reliably provides search results in <1 min for a large donor database (>9 Mio donors) including matching and mismatching donors, even for frequent or incomplete patient HLA data where the matching list contains several thousand donors. In these cases, the search process is accelerated by factors of 10 and more compared to the old Hap-E Search implementation. The predicted matching probabilities of the new algorithm were validated with data from verification typing requests of 67,550 donor-patient pairs.
Highlights
Matching of donor and patient human leukocyte antigens (HLA) is a primary factor for patient outcome after hematopoietic stem cell transplantation [1,2,3]
Newly registered potential stem cell donors have been typed with varying scope—from only two HLA loci (HLA-A and -B) to 5 or 6 loci [HLA-A, -B, -C, -DRB1, -DQB1]—by applying different typing technologies from serology via SSO/SSP and Sanger sequencing to NGS
Our basic approach to improve the performance of Hap-E Search is to reduce the computational load during each donor search: the workload is transferred to a preparation step that has to be performed once before the search kernel gets operational
Summary
Matching of donor and patient human leukocyte antigens (HLA) is a primary factor for patient outcome after hematopoietic stem cell transplantation [1,2,3]. In order to overcome this problem of missing or ambiguous data, several prediction tools— Haplo Stats at NMDP [4] and EasyMatch [5] at the French registry—as well as probabilistic matching algorithms—HapLogic at NMDP [6, 7], OptiMatch at ZKRD [8, 9], and Hap-E Search at DKMS [10]—have been developed These matching algorithms use population-specific HLA haplotype frequencies [11,12,13,14,15] to determine the probability that an incompletely HLA-typed donor will be a 10/10 (9/10, 8/8, 7/8) match for a defined patient Important information for donor searches is provided
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