Towards personalised medicine for STAT1 gain-of-function primary immunodeficiency
Bibliographic record
Abstract
Germline, monoallelic, gain-of-function (GOF) mutations in signal transducer and activator of transcription 1 (STAT1) cause an ultra-rare form of primary immunodeficiency (PID) through overactivation of the Janus-associated kinase/STAT1 signalling pathway. The clinical phenotype of this disorder is extremely variable and encompasses chronic mucocutaneous candidiasis, combined immunodeficiency and autoimmunity. To date, the functional validation of STAT1 GOF PID remains a significant challenge due to inconsistent access to specialised testing. Genotype: phenotype correlations have also remained elusive, and the molecular mechanism driving the disease is yet to be described. This, in part, means that the clinical management of STAT1 GOF PID at present is mainly limited to supportive care with antimicrobial prophylaxis and prompt treatment of infections. \n \nIn this study, a standardised, flow cytometric-based diagnostic assay panel was designed and optimised to facilitate an accurate yet simple, functional diagnosis of STAT1 GOF PID. Secondly, the collation of clinical data from 428 patients with STAT1 GOF mutations worldwide identified that distinct mutations manifest in unique clinical phenotypes, particularly the T385M GOF mutation which induces a significantly greater disease course severity in comparison to all other mutations. Furthermore, through the development of cell line models and the interrogation of patient primary cells, four distinct molecular mechanisms potentially underpinning STAT1 GOF disease that depend upon the dimeric interface the mutation resides at were outlined. Finally, the design, development, and assessment of three gene editing approaches for STAT1 GOF PID is described. \n \nThis work has opened more avenues for disease interrogation and targeting, has provided proof-of-concept for gene editing as a potentially curative measure for patients with STAT1 GOF PID and could potentially impact clinical decision making and prognostication. But ultimately, it supports the notion of a more personalised approach to treating STAT1 GOF PID.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".