Combined IFN-γ and JAK inhibition to treat hemophagocytic lymphohistiocytosis in mice
Bibliographic record
Abstract
BACKGROUND: Familial hemophagocytic lymphohistiocytosis is a life-threatening hyperinflammatory disease caused by genetic defects in the granule-mediated cytotoxic pathway. Success of hematopoietic cell transplantation, the only cure, is correlated with the extent of disease control before transplantation. Unfortunately, disease refractoriness and toxicities to standard chemotherapy-based regimens are fatal in a fraction of patients. Novel targeted immunotherapies, such as IFN-γ blocking antibodies or ruxolitinib, a Janus kinase (JAK) 1/2 inhibitor, are promising but only partially effective at controlling disease. OBJECTIVE: We asked whether combinations of cytokine-targeted therapies, using antibodies or JAK inhibitor, work synergistically to counteract HLH. METHODS: Genetically predisposed mice were infected and treated with distinct combinations of immunotherapies. Disease outcome was monitored and compared to monotherapies. RESULTS: We showed that inhibiting IL-6 or IL-18 signaling in combination with IFN-γ blockade or ruxolitinib did not increase disease control compared to anti-IFN-γ antibodies or ruxolitinib monotherapies. In contrast, clinically relevant doses of ruxolitinib combined with low doses of anti-IFN-γ blocking antibodies corrected cytopenias, prevented overt neutrophilia, limited cytokinemia, and resolved HLH immunopathology and symptomatology. CONCLUSIONS: Our findings demonstrate that IFN-γ blockade and ruxolitinib act synergistically to suppress HLH progression. This supports the use of combined cytokine-targeted therapies as a bridge to hematopoietic cell transplantation in severe familial hemophagocytic lymphohistiocytosis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".