DOI: 10.1055/a-2994-8048 ISSN: 0340-6245

Association of Mutation Location with Immune Tolerance Induction Outcomes in Children with Non-Inversion F8 Null Variants

Zhengping Li, Jialu Zhang, Zekun Li, Guoqing Liu, Wanru Yao, Gang Li, Xiaoling Cheng, di Ai, Zhenping Chen, Wenman Wu, Runhui Wu

Background Children with hemophilia A and F8 null mutations represent a refractory phenotype in immune tolerance induction (ITI). However, clinical heterogeneity remains poorly understood; thus, identifying driving factors is crucial for developing effective solutions. Objective To investigate the genetic factors underlying ITI outcome heterogeneity in pediatric F8 null mutations. Methods We retrospectively analyzed 109 patients with null mutations across three levels: (i) mutation type (nonsense, large deletions, frameshifts); (ii) mutation location (heavy- vs. light-chain); and (iii) specific domain and amino acid (aa) boundaries. Results The overall ITI success was 64.2%. Mutation location remained associated with ITI success after multivariable adjustment (odds ratio = 4.815, P = 0.013). Heavy-chain mutations demonstrated a significant advantage, with an estimated time to 50% success of 9.0 versus 27.4 months for light-chain mutations (P = 0.013). This location-dependent pattern was prominent in nonsense and large deletion but absent in frameshift mutations. Further refinement identified the A1-domain as the most favorable zone (highest success rate in nonsense subgroup: 100% [7/7]). In an exploratory analysis of nonsense mutations, mutations proximal to aa 446 showed higher observed success than those distal to it. Conversely, A3-C1 domains represented high-risk regions for failure. Conclusions Mutation location was associated with ITI outcome heterogeneity among patients with non-inversion F8 null variants. Heavy-chain mutations were associated with 4.8-fold higher odds of ITI success than light-chain mutations, mainly in nonsense mutations and large deletions. The A1-domain findings and exploratory aa 446 boundary may refine genetic risk stratification, but require external validation.

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