Factor VIII (FVIII)-Haplotype Mismatch Increases Risk of Inhibitor Development in the Treatment of Hemophilia A (HA)
ISTH Academy. Howard T. Jul 10, 2019; 273928; OC 76.5
Tom Howard
Tom Howard
Access to Reserved content is available to attendees of the Congress until the end of the year and always available for full ISTH members.

Click here to join ISTH or renew your membership.

Discussion Forum (0)
Rate & Comment (0)

OC 76.5

Factor VIII (FVIII)-Haplotype Mismatch Increases Risk of Inhibitor Development in the Treatment of Hemophilia A (HA)

V.P. Diego1,2, M. Almeida2,3, K.R. Viel4, B.W. Luu2,3,5, A. Ameri6, M.B. Chitlur7, K. Haack8, J. Curran9, J.S. Powell10, J.M. Peralta9, H. Mead11, R. Rajalingam12, M.A. Escobar13, L.V. Dinh5, S. Williams-Blangero9, C.K. Kasper14, L. Almasy15,16, S.A. Cole17, J. Blangero2,3, T.E. Howard5,9,18
1South Texas Diabetes & Obesity Institute, Department of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Brownsville, United States, 2University of Texas Rio Grande Valley, Department of Human Genetics, School of Medicine, Brownsville, United States, 3South Texas Diabetes & Obesity Institute, Brownsville, United States, 4Histonis, Atlanta, United States, 5Haplomics Biotechnology Corporation, Brownsville, United States, 6Augusta University, Medical College of Georgia, Pediatric Hematology and Oncology, Georgia Cancer Center, Augusta, United States, 7Children's Hospital of Michigan, Detroit Medical Center, and Division of Hematology and Oncology, Carmen and Ann Adams Department of Pediatrics, Wayne State University School of Medicine, Detroit, United States, 8Population Health Program, Texas Biomedical Research Institute, San Antonio, United States, 9South Texas Diabetes & Obesity Institute, and Department of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Brownsville, United States, 10University of California, Division of Hematology, Department of Medicine, School of Medicine, Davis, United States, 11CSL Behring, King of Prussia, United States, 12Immunogenetics and Transplantation Laboratory, Department of Surgery, School of Medicine, University of California, San Francisco, United States, 13Division of Hematology, Department of Medicine, McGovern School of Medicine, University of Texas Health Sciences Center, Houston, United States, 14Division of Hematology, Department of Medicine, Keck School of Medicine, University of Southern California, Los Angeles, United States, 15Lifespan Brain Institute, Children's Hospital of Philadelphia, Department of Biomedical and Health Informatics, Philadelphia, United States, 16University of Pennsylvania School of Medicine, Department of Genetics, Philadelphia, United States, 17Population Health Program, Texas Biomedical Research Institute, San Antonio, Texas, United States, 18VA Valley Coastal Bend Healthcare Center, Department of Pathology & Laboratory Medicine, Harlingen, United States

Main Topic: Coagulation and Anticoagulation
Category: Coagulation Factors & Inhibitors

Background: The FVIII haplotype mismatch hypothesis proposes that HA patients administered therapeutic FVIII proteins (tFVIIIs)—which currently represent the two most common haplotypes designated H1 and H2—mismatched against the FVIII encoded by their mutant FVIII gene (F8) have increased risk for developing neutralizing anti-FVIII-antibodies termed 'inhibitors'. Rigorous evaluation of this hypothesis requires accounting for F8-causal-mutation and heritable genetic effects on inhibitor risk while modeling a mismatch effect.
Aims: We advocate a variance components model for inhibitor status (Inh) that accounts for F8-causal-mutations and heritable genetic variation as random effects while modeling FVIII haplotype mismatch as a regression variable.
Methods: For our study, 442 North American HA patients (237 Whites & 205 Blacks; 88% severely affected) were:
1) Immuno-Chip genotyped at ~167,000 single-nucleotide polymorphisms (SNPs) in genes implicated in autoimmune disease risk;
2) Evaluated by Sanger DNA sequencing and assays for the recurrent intron (I)1- and I22-inversions to identify their F8-causal-mutations; and
3) Tested with the Nijmegen-modified Bethesda assay to determine their Inh status.
Results: The Immuno-Chip genotypes were used to construct a genetic-relationship matrix, and the F8 sequence data along with results from the I1- and I22-inversion assays were used to construct a shared F8-mutation matrix. These matrices were used to estimate the heritable genetic and shared F8-mutation effects. Importantly, modeling a F8-mutation effect has the added advantage of accounting for the mutational heterogeneity in F8-mutations. We found that heritability and F8-mutation effects respectively accounted for 50% and 23% of the phenotypic variance in Inh (both p< 0.0001).
Conclusions: While accounting for age and race effects, we found a significant effect for the FVIII haplotype mismatch variable (p=0.003) such that inhibitor prevalence increased from 29% in the overall sample to 46% in those receiving mismatched tFVIIIs (Figure 1). These results provide further support for a personalized approach to FVIII replacement therapy in patients with HA.

[Figure 1. FVIII-haplotype-mismatch effect on risk of inhibitor development (Inh).]

Code of conduct/disclaimer available in General Terms & Conditions
Anonymous User Privacy Preferences

Strictly Necessary Cookies (Always Active)

MULTILEARNING platforms and tools hereinafter referred as “MLG SOFTWARE” are provided to you as pure educational platforms/services requiring cookies to operate. In the case of the MLG SOFTWARE, cookies are essential for the Platform to function properly for the provision of education. If these cookies are disabled, a large subset of the functionality provided by the Platform will either be unavailable or cease to work as expected. The MLG SOFTWARE do not capture non-essential activities such as menu items and listings you click on or pages viewed.

Performance Cookies

Performance cookies are used to analyse how visitors use a website in order to provide a better user experience.

Google Analytics is used for user behavior tracking/reporting. Google Analytics works in parallel and independently from MLG’s features. Google Analytics relies on cookies and these cookies can be used by Google to track users across different platforms/services.

Save Settings