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 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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".