Activated Conventional T-Cells Are Present in Langerhans Cell Histiocytosis Lesions Despite the Presence of Immune Suppressive Cytokines
Bibliographic record
Abstract
Langerhans cell histiocytosis (LCH) lesions are characterized by neoplastic CD1a(+)/Langerin(+) histiocytes (LCH-cells) and display many features of chronic inflammation. Cancer cells can escape immune-surveillance through intra-tumoral secretion of immune-suppressive cytokines. We therefore studied by immunohistochemistry the local cytokine milieu and phenotypic characteristics of T-cells and LCH-cells present in LCH lesions collected from 25 therapy naïve patients. LCH biopsies predominantly expressed interleukin-10 (IL-10) (10/25), transforming growth factor-beta (TGF-β) (9/25), or both cytokines (6/25). The absolute number of CD3(+)T-cells and the CD3(+)FOXP3(-) conventional cell (T-CONV) versus the CD3(+)FOXP3(+) regulatory T-cell (T-REG) was comparable for each suppressive cytokine profile (5:1). IL-10-expressing lesions contained, however, a higher proportion of T-CONV expressing the activation markers CD25 98% (38%-100%) and inducible costimulatory molecule (ICOS) 86% (47%-100%) than lesions wherein solely TGF-β was detected (CD25(+) 20% (6%-54%); ICOS(+) 29% (7%-51%)). Virtually all T-REG expressed CD25 and ICOS in IL-10 lesions, whereas TGF-β(+) lesions contained a lower proportion of ICOS(+) T-REG (P=0.05). IL-10(+) lesions contained more LCH-cells expressing high intensity of ICOS ligand (ICOSL) compared with TGF-β(+) lesions (P=0.03). ICOS expression by lesion-infiltrating T-CONV and T-REG positively correlated to the extent of ICOSL expression by LCH-cells (P=0.004). Our study points out that the combined detection of interlesional IL-10 and ICOSL expression by LCH-cells is associated with the highest prevalence of activated T-CONV. Immune profiling of LCH-affected tissues obtained at the time of diagnosis may set the stage for the development of new types of therapies, which aim at local boosting of immune cells that recognize and eliminate neoplastic LCH-cells.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".