Abstract B69: Epigenomics and single-cell sequencing define a developmental hierarchy in Langerhans cell histiocytosis
Bibliographic record
Abstract
Abstract Langerhans cell histiocytosis (LCH) is a rare neoplasm predominantly affecting children. It occupies a characteristic hybrid position between cancer and inflammatory disease, which makes it an attractive model for studying cancer development. Constitutive activation of the ERK signaling pathway is a common feature of LCH, but its pathogenesis remains unclear. To explore the molecular mechanisms underlying the pathophysiology of LCH and the mechanisms that might cause the characteristic clinical heterogeneity of this disease, we investigated cellular heterogeneity in primary LCH lesions. We used a multilayered approach employing immunohistochemistry, flow cytometry, single-cell RNA-sequencing, and ATAC-sequencing of biopsies obtained from different patients with LCH. Based on the single-cell RNA expression data, we identified different LCH cell populations within LCH lesions that display distinct pathway signatures, including a stem cell-like, proliferative state and different subsets with a more differentiated phenotype, including subsets similar to maturing antigen-presenting cells and a subset that could contribute to tissue destruction in LCH. Computational analysis discovered a hierarchy between these cells that suggests a directed developmental program in each tumor. We confirmed the presence of these distinct subsets using immunohistochemical analysis of different biopsies. Furthermore, using chromatin accessibility profiling in prospectively purified LCH cell subsets in combination with single-cell RNA-seq data and integrative bioinformatic analysis, we analyzed the transcription factors and gene regulatory networks that may underlie the observed developmental hierarchy in LCH lesions. This analysis reveals an intricate interplay of immune-regulatory (including JAK-STAT, AP-1, and NF-κB signaling) and developmental regulators (including epigenetic modifiers such as EP300 or KDMs) that may shape LCH cell development. Together, this study sketches a molecular portrait of LCH lesions and enables new, unprecedented insight into this disease. Moreover, it demonstrates the power of combining single-cell RNA-sequencing and epigenome profiling for dissecting complex developmental hierarchies and their regulatory underpinnings, and thus can serve as a template for the analysis of tumorigenesis beyond LCH. Citation Format: Florian Halbritter, Matthias Farlik, Raphaela Schwentner, Gunhild Jug, Nikolaus Fortelny, Ingrid Simonitsch-Klupp, Wolfgang Bauer, Christoph Bock, Caroline Hutter. Epigenomics and single-cell sequencing define a developmental hierarchy in Langerhans cell histiocytosis [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr B69.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".