HLA-DRB1–factor VIII binding is a risk factor for inhibitor development in nonsevere hemophilia: a case-control study

Kempton, Christine L.; Payne, Amanda B.

doi:10.1182/bloodadvances.2018019323

Cited by 11 publications

(15 citation statements)

References 14 publications

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“…To further optimize predictive accuracy in future models other variables could be included, that were not present in our current dataset. Studies in Pompe disease and hemophilia describe a potential influence of certain gene polymorphisms, such as the HLA haplotype [35][36][37]. In hemophilia, the presence of so-called danger signals before or during the first infusions (e.g., recent surgery, bleeds, vaccinations and active infections) were associated with an increased risk for iADA development [38].…”

Section: Discussionmentioning

confidence: 99%

Predicting the Development of Anti-Drug Antibodies against Recombinant alpha-Galactosidase A in Male Patients with Classical Fabry Disease

Veen

Vlietstra²,

Dussen

et al. 2020

IJMS

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Fabry Disease (FD) is a rare, X-linked, lysosomal storage disease that mainly causes renal, cardiac and cerebral complications. Enzyme replacement therapy (ERT) with recombinant alpha-galactosidase A is available, but approximately 50% of male patients with classical FD develop inhibiting anti-drug antibodies (iADAs) that lead to reduced biochemical responses and an accelerated loss of renal function. Once immunization has occurred, iADAs tend to persist and tolerization is hard to achieve. Here we developed a pre-treatment prediction model for iADA development in FD using existing data from 120 classical male FD patients from three European centers, treated with ERT. We found that nonsense and frameshift mutations in the α-galactosidase A gene (p = 0.05), higher plasma lysoGb3 at baseline (p < 0.001) and agalsidase beta as first treatment (p = 0.006) were significantly associated with iADA development. Prediction performance of a Random Forest model, using multiple variables (AUC-ROC: 0.77) was compared to a logistic regression (LR) model using the three significantly associated variables (AUC-ROC: 0.77). The LR model can be used to determine iADA risk in individual FD patients prior to treatment initiation. This helps to determine in which patients adjusted treatment and/or immunomodulatory regimes may be considered to minimize iADA development risk.

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Section: Discussionmentioning

confidence: 99%

Predicting the Development of Anti-Drug Antibodies against Recombinant alpha-Galactosidase A in Male Patients with Classical Fabry Disease

Veen

Vlietstra²,

Dussen

et al. 2020

IJMS

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“…Additionally, aligning with Pashov's predictions (32), there is an apparent burden of novel peptide presentation that is required to provoke a clinically detectable inhibitor. Kempton applied a predicted >10 novel peptide-MHC surfaces per patient to be a meaningful threshold resulting in an overall risk (OR) increase of 4.4 (95% CI 1.1-15.0), adjusted for intensive FVIII treatment (34). Additionally, their data suggests higher levels of HLA-DRB1 binding and resultant novel pMHC surfaces for some F8 genotypes identified previously as "higher risk" (e.g., R593C).…”

Section: In Silico Proof Of Principle Predicting Complexity Of Inhibimentioning

confidence: 97%

“…They extend their predictions to 25 common HLA DR, DP, DQ isoforms with estimated worldwide population coverage of >70, >90, and >80%, respectively and 956 distinct F8 missense mutations at 605 different loci from 3,243 individuals, a total of 160 (4.9%) of whom were identified as having an inhibitor. The experimental design is based on their previous work described by Shepherd et al and also Kempton and Payne (29,34), identifying HLA-II binding, t-FVIII-derived peptides that form a predicted upward-facing, novel p-MHC surface to interact with helper TCRs. Layered on top of this extended repertoire of HLA and F8 genotypes is a comprehensive cross referencing of all putative FVIII-derived HLA-II core binding 9 mer peptide sequences with the primary sequences of the 20,000 proteins constituting our human proteome (www.uniprot.org/).…”

Section: In Silico Proof Of Principle Predicting Complexity Of Inhibimentioning

confidence: 99%

“…Competition from the multitude of other intracellular self and non-self peptides vying for presentation position within the HLAII repertoire has not been accounted for, although may be an explanation, in part, for the limited retrievable repertoire. Given the Kempton proposed requirement of multiple presented FVIII-derived peptides to drive a clinically relevant response (34), the van Haren data demonstrating a more limited repertoire of actually available peptides contributes further to explain why clinically observed inhibitor responses are lower than might be predicted (40), and goes some way to answering the question, not "why has this individual generated an antibody response, " but rather "why has this individual not generated an anti-FVIII antibody response? "…”

Section: In Silico Proof Of Principle Predicting Complexity Of Inhibimentioning

confidence: 99%

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FVIII Immunogenicity—Bioinformatic Approaches to Evaluate Inhibitor Risk in Non-severe Hemophilia A

Hart

2020

Front. Immunol.

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The lifelong inhibitor risk in non-severe hemophilia A has been an important clinical and research focus in recent years. Non-severe hemophilia A is most commonly caused by point mutation, missense F8 genotypes, of which over 500 variants are described. The immunogenic potential of just a single amino acid change within a complex 2,332 amino acid protein is an important reminder of the challenges of protein replacement therapies in diverse, global populations. Although some F8 genotypes have been identified as "high risk" mutations in non-severe hemophilia A (e.g., R593C), this is likely, in part at least, a reporting bias and oversimplification of the underlying immunological mechanism. Bioinformatic approaches offer a strategy to dissect the contribution of F8 genotype in the context of the wider HLA diversity through which antigenic peptides will necessarily be presented. Extensive modeling of all permutations of FVIII-derived fifteen-mer peptides straddling all reported F8 genotype positions demonstrate the likely heterogeneity of peptide binding affinity to different HLA II grooves. For the majority of F8 genotypes it is evident that inhibitor risk prediction is dependent on the combination of F8 genotype and available HLA II. Only a minority of FVIII-derived peptides are predicted to bind to all candidate HLA molecules. In silico predictions still over call the risk of inhibitor occurrence, suggestive of mechanisms of "protection" against clinically meaningful inhibitor events. The structural homology between FVIII and FV provides an attractive mechanism by which some F8 genotypes may be afforded co-incidental tolerance through homology of FV and FVIII primary amino sequence. In silico strategies enable the extension of this hypothesis to analyse the extent to which co-incidental cross-matching exists between FVIII-derived primary peptide sequences and any other protein in the entire human proteome and thus potential central tolerance. This review of complimentary in vitro, in silico, and clinical epidemiology data documents incremental insights into immunological mechanism of inhibitor occurrence in non-severe hemophilia A over the last decade. However, complex questions remain about antigenic processing and presentation to truly understand and predict an individual person with hemophilia risk of inhibitor occurrence.

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“…A cohort of 57 HLA typed subjects with Hemophilia A was used in a study of neutralizing antidrug antibodies to FVIII (19). As FIGURE 3 | The range of Jensen-Shannon distances.…”

Section: Assessing a Cohort Used In A Clinical Study For Association mentioning

confidence: 99%

SampPick: Selection of a Cohort of Subjects Matching a Population HLA Distribution

et al. 2019

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Immune responses to therapeutic proteins and peptides can adversely affect their safety and efficacy; consequently, immunogenicity risk-assessments are part of the development, licensure and clinical use of these products. In most cases the development of anti-drug antibodies is mediated by T cells which requires antigen presentation by Major Histocompatibility Complex Class II (MHCII) molecules (also called Human Leucocyte Antigen, HLA in humans). Immune responses to many protein therapeutics are thus HLA-restricted and it is important that the distribution of HLA variants used in the immunogenicity assessments provides adequate coverage of the target population. Due to biases inherent to the collection of samples in a blood bank or donor pool, simple random sampling will not achieve a truly representative sample of the population of interest. To help select a donor cohort we introduce SampPick, an implementation of simulated annealing which optimizes cohort selection to closely match the frequency distribution of a target population or subpopulation. With inputs of a target background frequency distribution for a population and a set of available, HLA-typed donors, the algorithm will iteratively create a cohort of donors of a user selected size that will closely match the target population rather than a random sample. In addition to optimizing the HLA types of donor cohorts, the software presented can be used to optimize donor cohorts for any other biallelic or monoallelic trait.

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HLA-DRB1–factor VIII binding is a risk factor for inhibitor development in nonsevere hemophilia: a case-control study

Cited by 11 publications

References 14 publications

Predicting the Development of Anti-Drug Antibodies against Recombinant alpha-Galactosidase A in Male Patients with Classical Fabry Disease

Predicting the Development of Anti-Drug Antibodies against Recombinant alpha-Galactosidase A in Male Patients with Classical Fabry Disease

FVIII Immunogenicity—Bioinformatic Approaches to Evaluate Inhibitor Risk in Non-severe Hemophilia A

SampPick: Selection of a Cohort of Subjects Matching a Population HLA Distribution

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