Autocrine release of interleukin-9 promotes Jak3-dependent survival of ALK+ anaplastic large-cell lymphoma cells
Bibliographic record
Abstract
The aberrant fusion protein NPM-ALK plays an important pathogenetic role in ALK+ anaplastic large-cell lymphoma (ALCL). We previously demonstrated that Jak3 potentiates the activity of NPM-ALK. Jak3 activation is restricted to interleukins that recruit the common gamma chain (gammac) receptor, including IL-9. NPM-ALK was previously shown to promote widespread lymphomas in IL-9 transgenic mice by unknown mechanisms. We hypothesized that IL-9 plays an important role in ALK+ ALCL via Jak3 activation. Our studies demonstrate the expression of IL-9Ralpha and IL-9 in 3 ALK+ ALCL-cell lines and 75% and 83% of primary tumors, respectively. IL-9 was detected in serum-free culture medium harvested from ALK+ ALCL-cell lines, supporting autocrine release of IL-9. Treatment of these cells with an anti-IL-9-neutralizing antibody decreased pJak3 and its kinase activity, along with pStat3 and ALK kinase activity. These effects were associated with decreased cell proliferation and colony formation in soft agar and cell-cycle arrest. Evidence suggests that cell-cycle arrest can be attributed to up-regulation of p21 and down-regulation of Pim-1. Our results illustrate that IL-9/Jak3 signaling plays a significant role in the pathogenesis of ALK+ ALCL and that it represents a potential therapeutic target for treating patients with ALK+ ALCL.
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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) | 0.001 | 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".