STAT3/5-Dependent IL9 Overexpression Contributes to Neoplastic Cell Survival in Mycosis Fungoides
Bibliographic record
Abstract
PURPOSE: Sustained inflammation is a key feature of mycosis fungoides (MF), the most common form of cutaneous T-cell lymphoma (CTCL). Resident IL9-producing T cells have been found in skin infections and certain inflammatory skin diseases, but their role in MF is currently unknown. EXPERIMENTAL DESIGN: We analyzed lesional skin from patients with MF for the expression of IL9 and its regulators. To determine which cells were producing IL9, high-throughput sequencing was used to identify malignant clones and Vb-specific antibodies were employed to visualize malignant cells in histologic preparations. To explore the mechanism of IL9 secretion, we knocked down STAT3/5 and IRF4 by siRNA transfection in CTCL cell lines receiving psoralen+UVA (PUVA) ± anti-IL9 antibody. To further examine the role of IL9 in tumor development, the EL-4 T-cell lymphoma model was used in C57BL/6 mice. RESULTS: Malignant and reactive T cells produce IL9 in lesional skin. Expression of the Th9 transcription factor IRF4 in malignant cells was heterogeneous, whereas reactive T cells expressed it uniformly. PUVA or UVB phototherapy diminished the frequencies of IL9- and IL9r-positive cells, as well as STAT3/5a and IRF4 expression in lesional skin. IL9 production was regulated by STAT3/5 and silencing of STAT5 or blockade of IL9 with neutralizing antibodies potentiated cell death after PUVA treatment in vitro IL9-depleted mice exhibited a reduction of tumor growth, higher frequencies of regulatory T cells, and activated CD4 and CD8 T lymphocytes. CONCLUSIONS: Our results suggest that IL9 and its regulators are promising new targets for therapy development in mycosis fungoides. Clin Cancer Res; 22(13); 3328-39. ©2016 AACR.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".