Loss of SHP1 enhances JAK3/STAT3 signaling and decreases proteosome degradation of JAK3 and NPM-ALK in ALK+ anaplastic large-cell lymphoma
Bibliographic record
Abstract
Previous studies showed that most cases of ALK(+) anaplastic large-cell lymphoma (ALK(+)ALCL) do not express SHP1, a tyrosine phosphatase and an important negative regulator for cellular signaling pathways such as that of JAK/STAT. To fully assess the biologic significance of loss of SHP1 in ALK(+)ALCL, we transfected SHP1 plasmids into 2 SHP1(-), ALK(+)ALCL cell lines, Karpas 299 and SU-DHL-1. After 24 hours of transfection, pJAK3 and pSTAT3 were decreased, and these changes correlated with down-regulation of STAT3 downstream targets including cyclin D3, mcl-1, and bcl-2. Expression of SHP1 in these 2 cell lines also resulted in marked decreases in the protein levels of JAK3 and NPM-ALK, and these effects were reversible by proteosome inhibitor MG132. Conversely, when SHP1 expression in SUP-M2 (a SHP1(+) ALK(+)ALCL cell line) was inhibited using siRNA, pSTAT3, pJAK3, JAK3, and NPM-ALK were all up-regulated. Coimmunoprecipitation studies showed that SHP1 was physically associated with JAK3 and NPM-ALK. SHP1 expression in Karpas 299 and SU-DHL-1 led to significant G(1) cell cycle arrest but not apoptosis. To conclude, loss of SHP1 contributes to the pathogenesis of ALK(+)ALCL by 2 mechanisms: (1) it leaves the tyrosine phosphorylation and activation of JAK3/STAT3 unchecked and (2) it decreases proteosome degradation of JAK3 and NPM-ALK.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".