Monogenic Immune Diseases Provide Insights Into the Mechanisms and Treatment of Chronic Graft-Versus-Host Disease
Bibliographic record
Abstract
Chronic graft-versus-host disease (GvHD) has become a leading cause of morbidity and mortality following allogeneic hematopoietic stem cell transplantation (HSCT) and can burden patients with devastating and lifelong health effects. Our understanding of the pathogenic mechanisms underlying chronic GvHD remains incomplete and this lack of understanding is reflected by lack of clear therapeutic approaches to steroid refractory disease. Observations predominantly from mouse models and human correlative studies currently support a three phase model for the initiation and development of chronic GvHD: 1) early inflammation and tissue damage triggers the innate immune system. This leads to inflammatory cytokine/chemokine patterns that recruit effector immune cell populations; 2) chronic inflammation causes the loss of central and peripheral tolerance mechanisms leading to emergence of pathogenic B and T cell populations that promote autoimmune and alloimmune reactions; 3) the dysregulated immunity causes altered macrophage polarization, aberrant tissue repair leading to scarring and end organ fibrosis. This model has led to the evaluation of many new therapies aimed at limiting inflammation, targeting dysregulated signaling pathways and restoring tolerance mechanisms. However, chronic GvHD is a multisystem disease with complex clinical phenotypes and it remains unclear as to which cluster of patients will respond best to specific therapeutic strategies. However, it is possible to gain novel insights from immune-related monogenic diseases. These diseases either share common clinical manifestations, replicate steps from the three phase chronic GvHD model or serve as surrogates for perfectly targeted drugs being investigated in chronic GvHD therapy. In this review, we will summarize the evidence from these monogenic immune related diseases that provide insight into pathogenic pathways in chronic GvHD, rationales for current therapies and novel directions for future drug discovery.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".