Langerhans cell histiocytosis: promises and caveats of targeted therapies in high-risk and CNS disease
Bibliographic record
Abstract
Langerhans cell histiocytosis (LCH) is a rare myeloid neoplasm driven by activating mutations in the MAPK pathway, most commonly BRAF-V600E and MAP2K1. It affects children and adults, with a wide spectrum of clinical presentations ranging from self-limited to multisystem (MS) life-threatening forms. LCH is defined by the accumulation of CD1a+/CD207+ cells in different organs, and patients with liver, spleen, or hematopoietic system involvement have a higher risk of mortality. Patients with neurodegeneration (ND) have devastating outcomes and are resistant to systemic therapies. MS-LCH is treated with risk-adapted therapy, but many patients require multiple salvage regimens that are myelosuppressive and expensive. MAPK inhibitors are increasingly being used, but most patients relapse upon discontinuation of therapy. Here, we review the management of central nervous system disease and how novel cerebrospinal fluid biomarkers might predict patients at high risk of ND who could benefit from early MAPK inhibition. Further, we discuss treatment strategies for refractory/relapsed (R/R) LCH, with a focus on MAPK inhibitors' efficacy and challenges (ie, the unknown): long-term toxicity in children, optimal duration, if they are curative, whether it is safe to combine them with chemotherapy, and their high price tag. Lastly, emerging strategies, such as the new panRAF inhibitor (Day 101) in patients with R/R LCH, ERK1/2 or CSF1R inhibition in patients with MEK1/2 inhibitor resistance, and targeting the microenvironment (checkpoint plus MEK inhibition) or senescent cells (mTOR or BCL-XL inhibitors) in R/R patients, are also examined.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| 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".