Characterization of the clinical and immunologic phenotype and management of 157 individuals with 56 distinct heterozygous NFKB1 mutations
Bibliographic record
Abstract
BACKGROUND: An increasing number of NFKB1 variants are being identified in patients with heterogeneous immunologic phenotypes. OBJECTIVE: To characterize the clinical and cellular phenotype as well as the management of patients with heterozygous NFKB1 mutations. METHODS: In a worldwide collaborative effort, we evaluated 231 individuals harboring 105 distinct heterozygous NFKB1 variants. To provide evidence for pathogenicity, each variant was assessed in silico; in addition, 32 variants were assessed by functional in vitro testing of nuclear factor of kappa light polypeptide gene enhancer in B cells (NF-κB) signaling. RESULTS: We classified 56 of the 105 distinct NFKB1 variants in 157 individuals from 68 unrelated families as pathogenic. Incomplete clinical penetrance (70%) and age-dependent severity of NFKB1-related phenotypes were observed. The phenotype included hypogammaglobulinemia (88.9%), reduced switched memory B cells (60.3%), and respiratory (83%) and gastrointestinal (28.6%) infections, thus characterizing the disorder as primary immunodeficiency. However, the high frequency of autoimmunity (57.4%), lymphoproliferation (52.4%), noninfectious enteropathy (23.1%), opportunistic infections (15.7%), autoinflammation (29.6%), and malignancy (16.8%) identified NF-κB1-related disease as an inborn error of immunity with immune dysregulation, rather than a mere primary immunodeficiency. Current treatment includes immunoglobulin replacement and immunosuppressive agents. CONCLUSIONS: We present a comprehensive clinical overview of the NF-κB1-related phenotype, which includes immunodeficiency, autoimmunity, autoinflammation, and cancer. Because of its multisystem involvement, clinicians from each and every medical discipline need to be made aware of this autosomal-dominant disease. Hematopoietic stem cell transplantation and NF-κB1 pathway-targeted therapeutic strategies should be considered 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".