Overcoming T-cell exhaustion in LCH: PD-1 blockade and targeted MAPK inhibition are synergistic in a mouse model of LCH
Bibliographic record
Abstract
Langerhans cell histiocytosis (LCH) is an inflammatory myeloid neoplasia characterized by granulomatous lesions containing pathological CD207+ dendritic cells (DCs) with persistent MAPK pathway activation. Standard-of-care chemotherapies are inadequate for most patients with multisystem disease, and optimal strategies for relapsed and refractory disease are not defined. The mechanisms underlying development of inflammation in LCH lesions, the role of inflammation in pathogenesis, and the potential for immunotherapy are unknown. Analysis of the immune infiltrate in LCH lesions identified the most prominent immune cells as T lymphocytes. Both CD8+ and CD4+ T cells exhibited "exhausted" phenotypes with high expression of the immune checkpoint receptors. LCH DCs showed robust expression of ligands to checkpoint receptors. Intralesional CD8+ T cells showed blunted expression of Tc1/Tc2 cytokines and impaired effector function. In contrast, intralesional regulatory T cells demonstrated intact suppressive activity. Treatment of BRAFV600ECD11c LCH mice with anti-PD-1 or MAPK inhibitor reduced lesion size, but with distinct responses. Whereas MAPK inhibitor treatment resulted in reduction of the myeloid compartment, anti-PD-1 treatment was associated with reduction in the lymphoid compartment. Notably, combined treatment with MAPK inhibitor and anti-PD-1 significantly decreased both CD8+ T cells and myeloid LCH cells in a synergistic fashion. These results are consistent with a model that MAPK hyperactivation in myeloid LCH cells drives recruitment of functionally exhausted T cells within the LCH microenvironment, and they highlight combined MAPK and checkpoint inhibition as a potential therapeutic strategy.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".