The T385M STAT1 gain-of-function mutation confers the most severe disease outcomes
Bibliographic record
Abstract
Background Gain-of-function (GOF) mutations in STAT1 cause a combined immunodeficiency characterized by chronic mucocutaneous candidiasis (CMC), recurrent infections, and autoimmunity. Mutations in the DNA-binding domain (DBD) have previously been associated with poor outcomes, but the contributions of specific variants to clinical phenotype remain unexplored. Methods We performed a systematic literature review to identify patients with confirmed STAT1 GOF mutations, integrating new cases with a previously reported international cohort. Clinical and genetic data were analyzed at both domain and mutation level to define genotype-phenotype correlations. Results A total of 533 unique patients from 36 countries were identified, harboring 135 distinct mutations. As previously reported, DBD mutations were associated with increased risk of systemic infections, bronchiectasis, autoimmunity, and reduced survival. However, mutation-level stratification revealed that the T385M variant accounted for much of this effect. Compared with both other DBD mutations and mutations elsewhere in STAT1 , T385M conferred significantly higher rates of infection, bronchiectasis, autoimmunity, and premature death (p < 0.001). Conversely, certain coiled-coil (CC) domain mutations, such as R274Q, were associated with milder disease and improved survival. Conclusion Our findings demonstrate that the adverse prognosis previously ascribed to DBD mutations in STAT1 GOF is predominantly driven by the T385M variant. Mutation-specific, rather than domain-level, stratification is therefore essential for accurate risk assessment and clinical management. In particular, patients predicted to have severe disease, such as those with the T385M mutation should be considered early for curative interventional therapies such as stem cell transplant or gene therapy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".