LARGE DELETIONS IN THE F8 GENE PREDICT IMMUNE TOLERANCE INDUCTION FAILURE IN PEOPLE WITH SEVERE HEMOPHILIA A
Bibliographic record
Abstract
Immune Tolerance Induction (ITI) is the only treatment to eradicate inhibitors in people with Severe Hemophilia A (SHA). Successful ITI restores Factor VIII (FVIII) tolerance. ITI is demanding and successful in approximately 70% of people. Therefore, identifying predictors of ITI outcome is essential to guide clinical decision-making. We aimed to identify genetic predictors of ITI success in people with SHA and inhibitors who underwent ITI. This observational multicenter study included people with SHA who underwent ITI, between 2015 and 2023. Clinical and patient data including factor VIII gene (F8) mutation type and DNA samples were collected. Successful ITI was defined by a negative inhibitor titer and an adequate response to FVIII concentrates. The associations between ITI success and F8 genotype and 216 candidate predictors including single nucleotide polymorphisms (SNPs) and human leukocyte antigen (HLA)-variants employing a global screening array (GSA), CA dinucleotide Short Tandem Repeat (STR) polymorphisms in the Interleukin (IL)-10 promoter region, and FCGR2/3 gene locus variations were analyzed. Of 204 participants, 147 (72.1%) achieved ITI success. The majority (52.0%) of participants had F8 intron 22 inversion. None of the candidate SNPs/HLA-variants, IL-10 CA dinucleotide STR, or FCGR2/3 gene locus variations were associated with ITI success. F8 large deletions were negatively associated with ITI success (OR = 0.15, 95% CI 0.04‒0.51, p = 0.002). Our study including 204 people with SHA identified F8 large deletions as a predictor of ITI failure. Pooling cohorts may allow the identification of additional genetic predictors of ITI success in the future.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".